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10.1371/journal.pcbi.1002768 | A Bayesian Inference Framework to Reconstruct Transmission Trees Using Epidemiological and Genetic Data | The accurate identification of the route of transmission taken by an infectious agent through a host population is critical to understanding its epidemiology and informing measures for its control. However, reconstruction of transmission routes during an epidemic is often an underdetermined problem: data about the location and timings of infections can be incomplete, inaccurate, and compatible with a large number of different transmission scenarios. For fast-evolving pathogens like RNA viruses, inference can be strengthened by using genetic data, nowadays easily and affordably generated. However, significant statistical challenges remain to be overcome in the full integration of these different data types if transmission trees are to be reliably estimated. We present here a framework leading to a bayesian inference scheme that combines genetic and epidemiological data, able to reconstruct most likely transmission patterns and infection dates. After testing our approach with simulated data, we apply the method to two UK epidemics of Foot-and-Mouth Disease Virus (FMDV): the 2007 outbreak, and a subset of the large 2001 epidemic. In the first case, we are able to confirm the role of a specific premise as the link between the two phases of the epidemics, while transmissions more densely clustered in space and time remain harder to resolve. When we consider data collected from the 2001 epidemic during a time of national emergency, our inference scheme robustly infers transmission chains, and uncovers the presence of undetected premises, thus providing a useful tool for epidemiological studies in real time. The generation of genetic data is becoming routine in epidemiological investigations, but the development of analytical tools maximizing the value of these data remains a priority. Our method, while applied here in the context of FMDV, is general and with slight modification can be used in any situation where both spatiotemporal and genetic data are available.
| In order to most effectively control the spread of an infectious disease, we need to better understand how pathogens spread within a host population, yet this is something we know remarkably little about. Cases close together in their locations and timing are often thought to be linked, but timings and locations alone are usually consistent with many different scenarios of who-infected-who. The genome of many pathogens evolves so quickly relative to the rate that they are transmitted, that even over single short epidemics we can identify which hosts contain pathogens that are most closely related to each other. This information is valuable because when combined with the spatial and timing data it should help us infer more reliably who-transmitted-to-who over the course of a disease outbreak. However, doing this so that these three different lines of evidence are appropriately weighted and interpreted remains a major statistical challenge. In our paper we present a new statistical method for combining these different types of data and estimating trees that show how infection was most likely transmitted between individuals in a host population. Because sequencing genetic material has become so affordable, we think methods like ours will become very important for future epidemiology.
| Predicting the most likely transmission routes of a pathogen through a population during an epidemic outbreak provides valuable information, which can be used to inform intervention strategies and design control policies [1], [2]. In principle, studying transmission routes during past epidemics is likely to be broadly informative of how the same pathogens spread through similar populations in future outbreaks. Estimating a set of connected transmission routes from a single case is synonymous with estimating the transmission tree corresponding to the outbreak. Uncovering the transmission routes between individual hosts or other relevant infectious units (for example farms or premises) can provide valuable epidemiological information, such as the factors associated with source and target individuals, dissemination kernels and transmission modes. Unfortunately, reconstructing these transmission trees with available data can be an exceptionally hard task, as the problem is typically underdetermined: the precise number of cases is often unknown, and dates and times of infections are rarely known with precision, making it difficult to distinguish between a large number of alternative scenarios [3].
With knowledge of location and timing of disease incidence it is possible to sample transmission trees that are consistent with the space-time data, and when these samples of trees share emergent statistical or structural properties, they can lead to epidemiological insights. For example, Haydon et al. [4] generated transmission trees corresponding to the 2001 Foot-and-Mouth Disease Virus (FMDV) epidemics in the UK, and used these trees to estimate the reproductive number during different weeks of the epidemic. These trees could be pruned to investigate the consequences of different or earlier interventions on the final size of the epidemics. However, the data were consistent with very large numbers of different trees and so the approach was not suited to identifying with confidence “who infected who”.
For pathogens with high mutation rates that fix mutations across their genome during the course of a single outbreak, genetic data can provide critical additional information regarding the relationships between isolates. The last few years have witnessed a revolution in our ability to generate genomic data relatively cheaply and in an automatised fashion [5]. Pathogen genome sequences collected during epidemics, if sufficiently diverse, can then be used to discriminate between alternative transmission routes.
Several attempts to reconstruct transmission pathways have tried to combine genetic and other epidemiological data, many by adding spatial or temporal information to the process of phylogenetic reconstruction [6]–[11]. However, Jombart et al. point out that a “phylogenetic” approach attempts to infer hypothetical common ancestors among the sampled genomes, and may not be appropriate for a set of genomes containing both ancestors and their descendants [12]. Cottam et al. [13] identified a large set of transmission trees that were consistent with available genetic data, and ranked the likelihood of these trees using data on their relative timings, to find the most likely transmission tree. Ypma et al. [14] moved this approach forward by constructing an inference scheme that uses spatial, temporal and genetic data simultaneously, but assumed these data are independent of each other. Genetic and epidemiological data are evidently correlated, and a rigorous inference scheme should estimate the likelihood of a transmission tree accounting for these correlations.
In this work, we present a novel framework, based on a bayesian inference scheme, able to reconstruct transmission trees and infection dates of susceptible premises, integrating coherently genetic and spatiotemporal data with a single model and likelihood function. Our scheme uses epidemiological data (times of reporting and removal from the susceptible population of infected, spatially-confined hosts, their locations, and estimates of the age of an infection based on clinical signs) together with pathogen sequences obtained from infected hosts to estimate transmission trees and infection dates during outbreaks. The genetic information is incorporated considering the probability distribution of the number of substitutions between sequences during the time durations separating them, and computing the likelihood of observing these sequences for a given transmission tree and the estimated infection dates. Each host generates an isotropic infectious potential responsible for transmission between hosts, whose strength is estimated from the data; the dynamical progression of the disease, from latency to infectiousness is part of the estimation scheme (for a visual representation see Fig. 1).
As an illustration of the method, we concentrate on the case of FMDV, an infectious disease affecting cloven-hoofed animals, which has severely affected the UK in 2001 and, on a smaller scale but still contentiously, in 2007. The infectious agent is single-stranded, positive-sense RNA virus, belonging to the genus Aphthovirus in the Picornaviridae family, and its small genome (8.2 kb) is easily sequenced. Its high substitution rate ( per nt per day as measured over part of the 2001 UK epidemic [13]), implies that the number of mutations accumulate during infection of host individuals on a single premise is sufficient to be reasonably confident of distinguishing between infected premises. Upon infection by FMDV, a host individual first experiences a non-infectious latent period with lesions appearing on peripheral epithelia subsequently. The virus can spread through aerosol dispersal, on fomites, or through direct contact. Importantly, a visual exam of the clinical state of the lesions on infected hosts can provide valuable information about the age of the infection. For this application, premises comprising populations of spatially-confined hosts will be considered as the unit of infection (the centroids of premises will be used as geographical coordinates), and complete FMDV genomes sampled from each premise will be used for the inference; the removal of a premise from the population corresponds to its culling. As the time course of FMDV infection within an individual host follows empirically characterised distributions [13], when transmission events are inferred between premises infected at very different times and therefore with correspondingly long and unrealistic apparent latency durations, we interpret these as an indication of the presence of one or more unsampled infected premises, that epidemiologically linked the observed premises.
After testing our method on simulated data, we considered two real datasets from two different FMDV epidemics: the 2007 UK epidemic (8 premises) [15] and the Darlington cluster within the 2001 UK epidemic (15 premises) [13]. For the former case, we confirmed the role of IP5 as the link between the two phases of the epidemics, whereas for the latter, our scheme highlights the presence of premises outside our sample that were part of the transmission process. While in this paper we discuss results related to FMDV, our method is in principle general and can be applied to epidemics generated by other pathogens, for which genetic and epidemiological data are both available.
Prior to applying our method to real data, we first used our model to simulate data for an outbreak infecting 20 premises whose locations are known in a 22×11 km area. The model was fitted to the observable data, that is, for each premise , the time at which the virus was detected, a 8000 bp DNA sequence sampled at , an assessment of the lesion age , and the time at which the premise was culled (see Fig. 1 for a visualisation). More information on this dataset can be found in Text S1.
In Fig. 2 (top left), the size of the dots corresponds to the posterior probabilities of pairwise transmissions, while the circles represent the true transmissions as they occurred in the simulation. Fig. 2 (top right) shows the tree with highest posterior probability. We note that only one true transmission () is not reconstructed accurately, the algorithm instead identifying . However, the transmission has a high posterior probability and is included in the tree with the second highest posterior probability (see Fig. S2). The posterior probabilities for the mean latency duration and the mean transmission distance include the true values in the 95%-posterior intervals (bottom panels of Fig. 2). Posterior distributions for other model parameters and latent variables are provided in the Figs. S3, S4.
In order to test our method for a large dataset, we considered an upscaled simulation of an outbreak infecting 100 premises. Results are described in Text S1.
Having established the validity of the inference scheme, we applied it to a dataset corresponding to the 2007 outbreak of FMDV in the UK, which infected 8 premises in Surrey and Berkshire [15]. Genetic sequences and epidemiological collected on each premise are available in the Dataset S1 and S2, respectively. The most likely reconstructed scenario (Fig. 3, top right) comprises two phases: IP1b was infected by an external source, and transmitted the virus to the neighbouring premise IP2b and to IP5 further away; the virus remained contained and undetected on IP5 until it spread to a closeby premise IP4b; finally the virus spread from IP4b to the other premises. While the link made by IP5 between the two phases is highly supported, the estimation of the other transmissions was more uncertain: within the two clusters (IP1b, IP2b, IP5) and (IP5, IP4b, IP3b, IP3c, IP6b, IP7, IP8) several other transmission scenarios have non-negligible posterior probabilities (Fig. 3, top left and Fig. S5). The mean estimated latency duration has a posterior median of 14 days and a 95%-credible interval of (6, 49) (as shown in Fig. 3, bottom left); the long delay between the infection of IP5 and the subsequent transmissions is responsible for this result (posterior distributions of latency durations of every premises are shown in Fig. S7). The long distance between IP5 and its source (IP5 is 18.2 km away from IP1b) explains the large mean transmission distance (Fig. 3, bottom right), whose posterior median is 17 km and 95%-posterior interval is (5,58). Posterior distributions of other model parameters and latent variables are provided in Figs. S6, S7, while a phylogenetic tree, based on statistical parsimony tree, implemented in the software package TCS [16] is represented in Fig. S14.
For a more complex scenario, we considered the FMDV epidemic that occurred in the UK in 2001, and in particular a group of 12 premises within the so-called “Darlington cluster” (Durham county), for which one virus sequence per premise is available [13]. This spatial cluster comprises 3 additional premises that were not epidemiologically linked to the rest of the cluster and which we exclude (we discuss the choice of the subgroup of premises in the Text S1). Genetic sequences and epidemiological data for this cluster can be found in the Datasets S3 and S4, respectively.
Our method allowed us to reconstruct a transmission scenario with little ambiguity, accounting for over 99% of the posterior probability, where premise K plays the role of a hub and only two chains of transmissions of length greater than two are found (Fig. 4, top panels). When premises become infectious approximately at the same time, they have a very low probability of mutual infection, even if the collected genomes are very close and share substitutions (premises M and D, or L and E, for example). Premise K, on the other hand, became infectious very early on and is then estimated to have seeded the infection to the many premises that were observed at later times.
Interestingly, some premises infected by the hub share mutations that are not found on the other premises, suggesting that different unsampled strains evolved on the hub and went on to infect distinct clusters of farms (see the statistical parsimony network in Fig. S14). However, another hypothesis can be formulated: the virus fixed the common substitutions while replicating on an unsampled premise, which constitutes a missing node in the transmission tree. This “ghost premise” went on to infect the premises we observed. The missing node scenario is supported by the distribution of the mean latency duration estimated for this dataset, which has a median of 24 days, and a 95%-posterior interval of (17, 35) (Fig. 4, bottom left). These values are inconsistent with a typical latency period of FMDV of 5 days (95% confidence interval of 1–12) [17]–[19]. In particular, the premises infected by the hub all display high mean latency values (Fig. S11). We propose that these unrealistically long latency periods indicate the existence of missing premises intermediate in the chain of infection and so in our model, latency should be considered as an aggregated parameter, corresponding to the the sum of the real latent period and the time the virus spent on the unsampled premise. We will return to this point in the Discussion. The comparison of our results with those found by Cottam et al. on the same dataset [13] highlights that our method strengthens the role of infecting hubs in the network (premise K), and therefore infers a lower number of long transmission chains. Details about the individual differences between the most likely trees inferred by the two methods can be found in Text S1, while transmission trees with higher posterior probabilities and posterior probabilities of other paramteres can be found in Figs. S9, S10.
The estimates of the transmission kernel for the two real data sets are similar: the 95%-posterior intervals of the mean transmission distance (defined as ) overlap, ranging from 5 to 58 km for the 2007 outbreak and ranging from 9 to 72 km for the 2001 epidemic (Figs. 3 and 4, bottom right panels). On the other hand, the posterior distributions we obtained are related to the range of distances covered in the data sets (up to about 24 km for 2007 and 16 km for 2001), and cannot be used to extrapolate long distance transmission events: despite the large values of the mean transmission distance, the lengths of the average inferred transmission in the trees with the highest posterior probabilities are 4.3 km for the 2007 outbreak and 5.8 km for the 2001 epidemic.
In the inference scheme, we used vague priors for model parameters. When we estimated the interval from the end of latency to detection, however, we used a more informative prior, centered over the estimated lesion age (Eq. (8) in Materials and Methods). We investigated the effect on the most likely transmission tree of (i) using a flatter prior (thus believing less than we did previously in the veterinarian assessment) and (ii) using a more peaked prior (thus believing in it more). The trees are illustrated in Fig. S12, and the priors in the Fig. S13. For the 2007 outbreak, the tree differed only by one transmission in case (i), and by three transmissions in case (ii). Remarkably, in all cases, the identification of the link between the two phases in IP5 maintained a posterior probability of one. For the 2001 epidemic, the star-like shape (with K as a hub) of the tree was strengthened in case (i), where premise K now infected 9 premises, while more chains of length greater than two were inferred in case (ii). Constraining the inference less around the estimates of the lesion ages relaxes the timing constraints and increases the weight accorded to genetic similarity in the transmission inference. As a result, transmissions mirror more closely the phylogenetic structure of the dataset, leading to a reduced hub role of premise K. In conclusion, we remark that the tree structure is robust and does not crucially depend on the specific choice of the prior for the values of the time intervals between the end of latency and detection (lesion ages).
Our method relies on one approximation: we do not reconstruct the genomes transmitted at the times of infection, and therefore we obtain a pseudo-posterior probability for the genetic data, where the similarity between isolates only depends on the Hamming distance between the sequences, and not on the full genetic network (see Materials and Methods for details). We checked whether the use of a pseudo-posterior distribution led to appropriate inference by applying the estimation algorithm to three series of 100 simulations (one for the test outbreak and two for the FMDV datasets) generated using our model. For the first series, we used the parameter values that were used in the test simulation. For the two other series, we used the posterior medians of the parameters estimated previously. We were especially interested in the fraction of correctly predicted pairwise transmissions: for each premise, between 79% and 93% of the simulations reproduced the source with the highest posterior probability in the original inference (Table 1). Given the challenging nature of the data sets (closely spaced premises becoming infectious almost simultaneously in the test data, and an abnormally long period of time between infection and transmission between two waves of infection in the 2007 data), these results suggest the approximation is performing well. Moreover, the mean of the posterior probability of each true transmission (the proportion of iterations in the chain at which a premise is infected by the estimated source) is also reproduced in about 80% of the cases. Performances vary slightly across datasets depending on the characteristics of the epidemics (e.g. number of premises and parameter values), but are broadly compatible. For example, in the second phase of the 2007 outbreak, several scenarios have high posterior probabilities, lowering the fraction of correctly estimated transmissions. Further performance estimators are listed in Table S1.
We propose here a new bayesian inference scheme, with which we estimate transmission trees and infection dates for an epidemic outbreak using genetic and epidemiological data. Our scheme is general, and with slight modification can be applied to rapidly evolving pathogens affecting spatially-confined hosts. To illustrate how this approach can be used to generate new insights and deliver statistically formal measures of confidence (in particular transmission links), we applied it to the case of an RNA virus (FMDV) infecting premises whose spatial location is known. The knowledge of complete viral sequences, timing of reporting and culling of premises and estimates of the age of an infection made this case an ideal benchmark. After testing our method on simulated data (20 premises), we applied it to two pre-existing datasets: the still disputed 2007 FMDV outbreak in the UK (8 premises) [15] and the Darlington cluster within the larger 2001 epidemic (12 premises) [13]. The method proved successful in reconstructing the transmission network on the test dataset, and highlighted the role of IP5 as a relay between the two phases of the 2007 outbreak. The results for the Darlington cluster are intriguing, as they highlight the likely incompleteness of the dataset, and suggest the presence of unobserved premises in the transmission tree. The performance of the algorithm was evaluated through simulations, which showed the inference scheme to be consistent and accurate and able to deal successfully with clusters of infections.
The power of this inference platform relies on a number of simplifying assumptions. In this application we have made two in particular that require further consideration. The first postulates that the epidemics are generated by a single introduction of the pathogen to a single premise. While this may often be adequate for small or early stage outbreaks, it is likely to be inadequate for more complex cases. For example, the Darlington dataset is a small subset of the 2001 epidemic, in which it was first considered to be an isolated cluster of infected premises. Previous analysis on the whole cluster [13] demonstrated two independent introductions. Trying to estimate “polyphyletic” transmission trees assuming only a single root would strain this formulation of the model and lead to unrealistic results. In order to solve this problem, the MCMC should be able to explore a parameter space where independent introductions range from one to the number of the premises (each of them being independently infected by an external source) and compute their likelihood. Moreover, the genetic data can be used to discriminate between a situation where a single external source infects several spatially-confined hosts in a cluster, and the presence of multiple external sources, characterised by distinct genomes. In practice, we could proceed by (i) describing the external source(s) as a set of genetic sequences varying in time (and possibly in space), (ii) specifying the probability of transmission of the infection from the external source(s) to any of the premises and (iii) updating the transmission tree at each iteration of the MCMC by comparing this probability with the probability of transmission from one of the infectious premises in the cluster considered.
The second assumption is that the epidemic has been completely observed and that there are no missing nodes in the transmission tree. When this assumption is likely to be violated, as in the case of the Darlington cluster, our method inferred unrealistically long latency times for some premises, an indication that a missing intermediate infected premise, where virus might have replicated extensively, may have been involved in the transmission chain. This situation is particularly likely in large epidemics, where perfect knowledge of every case is unlikely, or in epidemics arising in areas or countries where host or premise identification is ambiguous and comprehensive collection of data not feasible. In the 2007 outbreak, where no infected premises were missing, the premise linking the two phases showed a mean latency duration of over 25 days. In this case, the observation results from the real time the virus spent on the farm prior to its detection and reporting: by the time it was observed, the animals had started to heal and dating the lesions was more difficult. The long latency times could also account for the time virus spent in a non-replicative state (e.g. on fomites): this case would be indicated by a slow rate of evolution on the premise where the virus is observed. In conclusion, extended latency times are valuable “alarm bells”, as they suggest a discrepancy between the observations and the actual course of the disease. A substantial improvement to the scheme would be to include in the inference additional sources of data, such as the locations of premises that may have maintained infections that were not detected, or premises that were infected but were removed prior to being confirmed as infected. We leave this development for future work. We only mention here that the solution given in the paragraph above to deal with multiple introductions could be adapted to deal with missing premises: any infectious premise could generate a set of genetic sequences describing possible missing premises. This set of sequences could then be used to compute a new probability of transmission from missing premises, to be compared with the probabilities of transmission from internal and external sources. We leave this for future work.
Other minor assumptions in our model can be readily eased. We hypothesized that all premises have the same infection potential; however, it would be straightforward to make the infectiousness parameter in the model a function of the specific characteristic of the premise, like size or composition (for example, for FMDV sheep are considered to be less infectious than cows, which are in turn less infectious than pigs [17]). Moreover, we note that the infectious potential felt by a premise at time is the sum of the contributions deriving from all the other premises that are infectious at that particular time. As unsampled premises could also contribute to this potential, the temporal dynamics of infection could be modeled in a more complex manner than the step function adopted here. The estimation of the age of an infection from clinical signs is used as a prior distribution in our scheme: an accurate knowledge of this quantity makes the inference computationally more efficient, but it is not essential, and the method can be applied to cases where this quantity is not available. The model used for the mutations of the virus is very simple and does not account for the specific characteristiscs of the FMDV genome, or for some well-known mutation biases (like the transition/transversion bias observed in [20]: we decided once more to go for the simplest and more general assumption, while more detailed and pathogen-specific mutation models could easily be incorporated in our framework.
Our “hosts” do not necessarily correspond to single animals/humans but were interpreted in a wider sense as “infectious units”. These units do not constitute a limitation to our method: even in the case of an infection where the units are individuals, the genetic divergence between sequencing results from an unknown number of viral replications in the donor individual post sampling (but prior to transmission) and in the recipient prior to sampling. In the case of a higher-order unit of infection, the genetic divergence between sequences from sequential samples will be just the result of a larger unknown number of generations.
It is conceivable that multiple pathogen strains circulated on a single premise remained unsampled and went on to infect other premises. For example, FMDV is known to generate independent populations within single animals [20] and different genomes could circulate on a premise. Ideally, several sequences from each premise should be obtained and these data incorporated into the model. Finally, for the specific pathogen considered here, we have used a fixed substitution rate for both the Darlington cluster and the 2007 outbreak. Independent estimates obtained for the whole 2001 epidemic [21] and for 2007 outbreak yield very similar values, which do not change substantially the likelihoods of observing the sequenced genomes. In other applications, the substitution rate may be poorly known. In these cases, it could be viewed as an unknown parameter and estimated in the MCMC simulation.
Computation time is a key element for a method that is expected to be useful in real-time during an outbreak. The computation time was strongly reduced by using a conditional pseudo-distribution of observed sequences instead of the exact conditional distribution. Clearly, it would be ideal to run the Bayesian estimation using the exact conditional distribution of observed sequences . To do so, one could incorporate in the MCMC the unknown transmitted genetic sequences as augmented data (see Eq. (3) below), initialize using for example statistical parsimony [16] and determine a proposal distribution for based on a stochastic algorithm estimating genetic networks [22]. Unfortunately, this strategy is at present unfeasible on standard computing resources. However, despite the use of a pseudo-distribution, the running time of our inference algorithm strongly increases with the number of premises. We stress that the main focus of this work was to combine epidemiological and genetic data in a coherent framework, rather than producing an optimised code. Basic optimization procedures should dramatically increase the efficiency of the code. In particular, we suggest three directions worth pursuing: (i) use a conditional pseudo-distribution of the genetic sequences which can be computed faster, but still yielding a good approximation of the posterior distribution of the unknowns; (ii) parallelize the MCMC [23] and code it in a lower-level language; (iii) use alternative algorithms, such as sequential Monte Carlo [24].
Our bayesian inference scheme is a rigorous general platform on which different models can be implemented and tested. It is a useful tool that could be used in real time to detect the presence of missing links in inferred chains of transmission, and to assign confidence values to each inferred transmission event. The specific model we chose for FMDV contains a representation of the dynamics of FMD infections. Different models could be implemented to describe the dynamics of different pathogens, or the specific characteristics of a particular outbreak, while still maintaining rigorous estimation based on genetic and epidemiologic data. Previous work was initiated by Cottam et al. [13], and significantly extended by Jombart et al. [12] and Ypma et al. [14]: all these studies considered the likelihood of the transmission tree given temporal, spatial and genetic data (here denoted by the generic vectors , and ) as a product of three independent likelihoods: . Cottam et al. assumed a binary () and a uniform (their estimation does not depend on the location of the premises); Jombart et al. designed a less “ad hoc” approach by introducing a maximum parsimony strategy to weight genetic similarity, while spatial and temporal information were considered only when several possible ancestors were genetically indistinguishable; finally Ypma et al. had more complicated forms for these likelihood functions. Our method can be considered as the “next step” on this road, as we relax the assumption of independence between the information sources, and we estimate the likelihood of transmission trees given all the sources of information simultaneously. Although some specific aspects of our inference scheme can be refined, expressing the likelihood of a transmission tree as a joint likelihood, depending on both epidemiological and genetic data, significantly advances this form of analysis.
The test data sets analyzed in the Results section were simulated under the model presented below and in Text S1. In these data sets, the outbreak spread over 20 premises (F1, …, F20), randomly and uniformly located in a rectangular 20×10 km region. Values of transmission and latency parameters were and . Observed sequences had length and substitution rate . In Text S1, we analyzed an upscaled test data set with 100 premises, with the same premise density as above, and same values for parameters , , and .
The data corresponding to the 2007 FMDV outbreak in the UK and to the Darlington cluster within the 2001 epidemic can be found in Refs. [15] and [13], respectively, and are incudedin the Datasets S1, S2, S3, S4. In particular, FMDV sequence length was and the substitution rate per nt per day [13].
Consider a cluster of infected hosts (in this case premises) whose centroids are located at Longitude-Latitude coordinates . Let be the function defining the transmission tree: a given premise is infected by a source , which consists of either another premise , , or an external source denoted by 0. For each premise, we consider four timing variables as illustrated by Fig. 1: premise is infected by at time , is infectious at time , where is the latency duration for premise , is detected as infected at time and is removed from the infectious population at time . The duration from infectiousness to detection, , is assessed by experts on the base of clinical signs: let denote this assessment. At time , the pathogen is sampled on premise and the genomes are collected for sequencing: let denote the observed consensus sequence.
Among these variables, only , , , and are observed. The others are latent variables to be reconstructed with the bayesian inference scheme.
In this section we briefly describe the essence of the model. The complete specification of the model is provided in the following sections. For a full description of the symbols, we refer to Table 2.
Our model for the dynamics of an infection takes into account the dependence between timing, space and genetics. It includes (i) the delays between infection and observation of infection and (ii) the difference between transmitted and observed genetic sequences of the pathogen. The direct acyclic graph (DAG) in Fig. 5 shows the structure of the model. Upper case letters are used for latent and observed variables, while Greek letters denote unknown parameters. Lower case letters are used for fixed parameters. Observation times and observed consensus sequences are viewed as response variables. They depend on the transmission tree and on the temporal dynamics (infection times, latency durations and detection durations).
The model assumes that the epidemic starts with the infection of a single premise from an external source. Then, transmissions and infection times depend on the infection potential generated by previously infected premises. The infection potential depends on the transmission parameters , the spatial location of premises and the times at which infected premises exit from latency and are removed from the infectious population: an infected premise is infectious between and , and the probability of infecting premise decreases exponentially with the distance . The parameter appears in the transmission kernel and quantifies the decrease with distance of the infection potential of each infectious premise, while quantifies the infection strength of each infectious premise. The mean transmission length, defined here as , is a function of the distances between farms and of the transmission kernel we used. Latency durations and durations from infectiousness to the time that virus is sampled are assumed to be independent. The distribution of is parametrised by its expectation and its variance ; is the vector of latency parameters. The distribution of is centered around the empirical estimate but has a variance increasing with , equal to , where . The premise index is sorted with respect to increasing infection times .
We aim to assess the joint posterior distribution of the transmission tree , infection times , latency durations , durations from infectiousness to detection , and parameters , given the data. Data are observed sequences , pathogen observation times , observed durations from infectiousness to detection , removal times and premise locations :(1)where means “proportional to” (the multiplicative constant does not depend on the unknowns ). In this decomposition, are viewed as response variables (or model output), as latent variables and as explanatory variables. The term is the complete likelihood of the model and the term is the conditional complete likelihood of the model given observation times . In the following sections, we specify the terms appearing in the last two lines of Equation (1).
Assumptions: (a) there is only one sequence per infected premise; (b) sequences in all the premises evolve at a constant rate ( is the substitution rate per day per nucleotide).
The model for is based on the probability distribution of the number of substitutions between two sequences during the evolutionary durations separating the sequences. Let denote the number of substitutions and the evolutionary duration ( is the sum of time intervals computed along the transmission tree). The conditional distribution of given is a Binomial distribution taking into account the Jukes-Cantor's correction (see Text S1):and the probability of given is:(2)
Therefore, does not depend on :and can be written as a multiple sum of products of binomial probabilities. The sum is computed over the unknown transmitted genetic sequences, say , at time (the initial sequence of the root of the tree is not needed):(3)In Equation (3), is the set of all possible sequences (the size of is , where is the length of the sequence); is the number of substitutions between and ; is the probability given by Equation (2) with and . The subscript denotes the premise whose node of infection belongs to the tree path from the root of the tree to the observation of (at time ) and whose infection is just preceding the observation of . The node of infection of a given premise is defined as the point on the tree at which “the branch leading to the observation of ” and “the branch leading to the observation of the infecting premise ” diverged. The tree path from one point of the tree to another is defined as the most direct path on the graph conncting the two points. If did not infect any other premise, then is itself. In the particular case where was infected after the observation of the infecting farm and did not infect any other premise between and ”, the subscript coincides with , and . In the most frequent other cases, denotes the premise whose node of infection belongs to the tree path from the root of the tree to the infection of (at time ) and whose infection is just preceding the infection of ; in these cases, and . In other words, the first series of factors in Equation (3) accounts for the probabilities of the number of substitutions between an observed sequence and the immediately preceding unobserved, transmitted sequence, while the second series of factors accounts for the probabilities of the number of substitutions between each transmitted sequence and the transmitted or observed sequences immediately preceding in time. Equation (3) is written in the Supporting Text S1 (Equation (2)) for the simple transmission tree drawn in Supporting Fig. S1.
The conditional distribution for (Eq. (3)) was written as a distribution depending solely on the genetic distances for pairs of sequences. However, in each pair, there is at least one unobserved transmitted sequence. Therefore, exploiting Equation (3) would lead us to consider extra latent variables (or augmented data), namely the unobserved sequences . In order to reduce the complexity of the posterior, we preferred not to include these extra latent variables, but rather to use a conditional pseudo-distribution of , . In our method, replaces which is the conditional complete likelihood of the model given observation times . Thus, is a conditional complete pseudo-likelihood given observation times and we refer to it as a conditional pseudo-distribution. It follows that the posterior distribution that we assess is actually a pseudo-posterior distribution.
With index being sorted with respect to increasing infection times , can be written:(4)where is the set of observed sequences of premise . We considered the sequence of the first infected premise as arbitrary. Thus, was discarded in the pseudo-distribution. Moreover, to compute exactly appearing in Equation (4), we should write this probability as a sum over the unknown transmitted genetic sequences (as done in Equation (3)). In order to avoid the inclusion of unknown transmitted sequences as augmented data, we replaced, for , the conditional probability of given past sequences () by the product of the conditional probabilities of given each past sequence ():where denotes the infection time at which the chain of infection leading to and the chain of infection leading to diverged ( is one of the latent variables in , also called “augmented data”) and is the evolutionary duration separating the observation of and . Thus, the conditional pseudo-distribution of satisfies:(5)The right hand side of Equation (5) replaces in Equation (1). Equation (5) is written in Equation (3) in Text S1 for the simple transmission tree drawn in Fig. S1.
We tested another form for , described in Text S1. The form given by Equation (5) above led to the best reconstruction of the transmission tree .
satisfies the relation . Therefore, the conditional distribution of is simply:(6)where 1 is the indicator function (1 if event occurs, zero otherwise).
Assumptions: (a) Only one premise is infected by an external source, while the others premises in the dataset are infected by previously-infected premises within the dataset; (b) any premise may infect other premises after the latency period and before the culling time ; (c) infectious premises have same infection strength , considered constant; (d) the infection risk of a susceptible premise by an infectious premise decreases exponentially with the distance separating both premises, this distance being measured by the distance between the centroids of the premises; (e) the presence of unsampled premises in the area (premises for which genetic or epidemiological data is not available) is ignored.
With the index being sorted with respect to increasing infection times , the probability can be written:(7)where and .
Each premise has the same chance () to be infected first (by an external source ), and its infection time is assumed to be greater or equal than a minimum infection time (in this work we used ), and less than or equal to the minimum removal time :Subsequent infections occur with the following probabilities:where the term is the probability that premise has not been infected until time by the previously infected premises , and the term is the probability density that premise has been infected by at time . The function is an exponential transmission kernel, defined for all distance asFor transmissions modelled using the exponential transmission kernel, the mean transmission distance (mean length of transmissions) is : this measure depends on the distances between farms as well as on the transmission kernel we used. Other transmission kernels, such as those presented in [25], [26] could be tested. The selection of the best transmission kernel will be crucial for datasets with large number of premises and large spatial extent. In our applications, where the number of premises is limited and the spatial extent is much smaller than the dispersal capacity of the pathogen, there are enough data to infer the transmission parameters, but not enough to carry out a significant model selection about the transmission kernel.
Assumptions: (a) a priori, latencies and durations from infectiousness to detection are independent; (b) characteristics of the latency distribution (expectation and variance) do not depend on time and premise; (c) the expectation (resp. variance) of the duration from infectiousness to observation is equal to (resp. is proportional to) the estimate provided.
We chose gamma distributions for latency durations , with shape and scale parameters and , respectively, so that and . We refer to as mean latency duration. We chose gamma distributions for detection durations with shape and scale parameters and , respectively, so that and . Thus, the joint distribution of the vectors of latent variables and satisfies:(8)where is the gamma function.
The four components of have independent exponential priors with mean parameters :(9)We have used the values .
We built a Monte Carlo Markov Chain (MCMC) algorithm to assess the posterior distribution of , coded in the R language [27]. Details of this algorithm are provided in Text S1. We recall that, in order to reduce the complexity of the algorithm, we replaced the conditional distribution of observed consensus sequences appearing in the posterior distribution by a pseudo-distribution. This replacement allowed us to remove some of the latent variables, namely the unobserved pathogen sequences transmitted at the infection times. Therefore, the MCMC algorithm assesses a pseudo-posterior distribution of . Vague priors were used for parameters and (see above). In the cases considered in this study, iterations of the MCMC algorithm were enough to assess the posterior distributions of the unknowns. Running iterations took about two days for the simulation with 20 premises and one month for the simulation with 100 premises on an Intel Xeon Quad Core processor with clock speed 2.93 GHz and 48 Gb of RAM memory. The components of the algorithm which are especially computationally costly are (i) the search of the most recent ancestral premises appearing in the pseudo-distribution of the observed genetic sequences given in Equation (5), (ii) the computation of the joint distribution of and in Equation (7) which is based on a convolution between the transmission kernel and the sources of infection, and (iii) the verification that timing constraints are satisfied when infection times are updated (see proposal distributions in Text S1).
We generated data sets using the model described above and the location of the premises. The spread of the disease was first simulated using the conditional distributions of , , , , and , with previously inferred parameters, thus obtaining the complete dynamics of the infection and a transmission tree. Subsequently, genetic distances between the observed sequences were generated using the binomial distributions described in Equation (2). We note that in this case we generated the unobserved transmitted sequences as well.
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10.1371/journal.ppat.1000909 | Combining ChIP-chip and Expression Profiling to Model the MoCRZ1 Mediated Circuit for Ca2+/Calcineurin Signaling in the Rice Blast Fungus | Significant progress has been made in defining the central signaling networks in many organisms, but collectively we know little about the downstream targets of these networks and the genes they regulate. To reconstruct the regulatory circuit of calcineurin signal transduction via MoCRZ1, a Magnaporthe oryzae C2H2 transcription factor activated by calcineurin dephosphorylation, we used a combined approach of chromatin immunoprecipitation - chip (ChIP-chip), coupled with microarray expression studies. One hundred forty genes were identified as being both a direct target of MoCRZ1 and having expression concurrently differentially regulated in a calcium/calcineurin/MoCRZ1 dependent manner. Highly represented were genes involved in calcium signaling, small molecule transport, ion homeostasis, cell wall synthesis/maintenance, and fungal virulence. Of particular note, genes involved in vesicle mediated secretion necessary for establishing host associations, were also found. MoCRZ1 itself was a target, suggesting a previously unreported autoregulation control point. The data also implicated a previously unreported feedback regulation mechanism of calcineurin activity. We propose that calcium/calcineurin regulated signal transduction circuits controlling development and pathogenicity manifest through multiple layers of regulation. We present results from the ChIP-chip and expression analysis along with a refined model of calcium/calcineurin signaling in this important plant pathogen.
| All organisms have the innate ability to perceive their environment and respond to it, largely through controlling gene expression. Tailored specificity of a response is primarily achieved through signal cascades involving unique receptors, downstream transcription factors (proteins that bind to DNA to regulate gene expression), and the genes these transcription factors regulate. For fungal plant pathogens, signal transduction cascades are involved in perception of hosts, transgression of physical barriers, suppression or elicitation of host defenses, in vivo nutrient acquisition, and completion of their life cycle. We know that the Ca2+/calcineurin signaling pathway is a central conduit regulating these aspects of the life cycle for fungal pathogens of plants and animals. In this study, we used advanced ChIP-chip and microarray gene expression technologies to identify the genes that the Ca2+/calcineurin responsive transcription factor MoCRZ1 directly binds to and regulates the expression of. Our findings show conservations and divergence in this pathway within the fungal kingdom. It also identifies points of control in the pathway that were previously unidentified. Most importantly, this study implicates this pathway in the establishment of host associations and virulence for the causal agent of rice blast disease, Magnaporthe oryzae, the most important disease of rice worldwide.
| Rice blast, caused by the fungal pathogen Magnaporthe oryzae, is a recurrent and devastating problem worldwide [1]. Severe disease outbreaks can destroy upwards of 90% of rice yields for an entire field, region, or country resulting in a dramatic impact on human welfare and regional economies. The process starts when an asexual spore lands on a rice leaf. Given suitable moisture and temperature, the spore germinates with a short germination tube at the tip of which a specialized infection structure, an appressorium, emerges. The formation of an appressorium is essential for successful disease as it facilitates breaching the plant cuticle and cell wall, allowing access to the underlying tissues. Following penetration, the entry peg forms an un-branched hyphal strand that subsequently matures into branched bulbous infection hyphae. The fungus fills the infected cell in what is considered a biotrophic state before invading adjacent cells and switching to a necrotrophic state where it ramifies through the host tissues killing cells. In the final stage, the fungus produces new asexual spores that are spread to neighboring plants [2], [3]. While knowledge of the core signal pathways regulating each phase of this process continues to resolve, the key determinants controlling environmental perception and cellular response are as yet not fully understood [4]. Specifically, we have little knowledge of the upstream receptors used by the fungus to detect stimuli, nor do we know how downstream factors specifically interact to affect expression of genes deployed during infection related development, establishment of host associations, and invasive growth.
Calcium signaling has been implicated in regulating growth and development in M. oryzae including the infection process [5]–[9]. The components of Ca2+ signaling have been studied in many organisms and are relatively well understood. Ca2+ signaling starts when G-protein coupled receptors are activated by an external stimulus. Phospholipase C (PLC) is activated to hydrolyze phosphatidyl inositol-1,4-bisphosphate (PIP2) into inositol 1,4,5-triphosphate (IP3) and diacylglycerol. IP3 activates Ca2+ release from intracellular stores into the cytosol. Ca2+ ions bind to and activate calmodulin, which in turn, activates the Ca2+/calmodulin-dependent serine/threonine protein phosphatase calcineurin. Calcineurin is a heterodimer consisting of catalytic (CNA) and regulatory (CNB) subunits. In fungi, calcineurin mediated Ca2+ signaling has been shown to be required for growth, development, response to stress, and pathogenesis [10]. It was necessary for survival during environmental stresses such as ions (Mn2+, Li+, Na+), high pH, high temperature, ER stress, and prolonged incubation with mating pheromone α-factor in Saccharomyces cerevisiae [11], [12]. It is essential for growth and virulence of Candida albicans and Cryptococcus neoformans [13]–[16], and controls the dimorphic transition from mycelia to yeast in Paracoccidioides basiliensis [17]. Effects of gene deletion or chemical inhibition in filamentous fungi typically have pleiotropic effects. For example, a cnaA deletion mutant in Aspergillus fumigatus was viable but severely affected in hyphal morphology, sporulation, conidial architecture, pathogenicity, and invasive growth [18], [19]. Reduction of calcineurin activity by the immunosuppressant drug cyclosporine A, resulted in reduction of mycelial growth and alteration in hyphal morphology as shown in Neurospora crassa [20], [21], A. nidulans [22], A. oryzae [23], and M. oryzae [24]. RNA silencing in M. oryzae showed similar effects, specifically a reduction in mycelial growth, sporulation, and appressorium formation in MCNA knock down mutants [8].
Calcineurin functions mainly through the activation of the transcription factor CRZ1 (Calcineurin Responsive Zinc Finger 1). Upon activation by increased intracellular Ca2+ and calmodulin, calcineurin dephosphorylates CRZ1 leading to its nuclear localization. As a major mediator of calcineurin signaling, crz1 deletion mutants in a variety of fungi showed similar phenotypes as calcineurin mutants [5], [25]–[28]. However, differences have been noted. CRZ1 in C. albicans was not involved in tolerance to antifungal agents (fluconazole, terbinafine) and only slightly affected in virulence, which is in contrast to the calcineurin mutants [28]. On the other hand, CRZ1 is strongly associated with virulence both in human and plant pathogenic fungi [5], [25], [26], [28], [29]. The B. cinerea CRZ1 ortholog BcCRZ1 was required for growth, conidial and sclerotial development, and full virulence while being dispensable for conidia-derived infection of bean plants [25]. In M. oryzae, the Δmocrz1 deletion mutant showed decreased conidiation and was not able to cause disease when spray inoculated. Mutant conidia were not distinguishable from that of wild type and formed appressoria at a similar level as wild type. Importantly, a significant portion of appressoria failed to penetrate rice sheath tissue. The mutant could colonize and cause disease when the conidia were infiltrated directly into the host tissue, thus bypassing the penetration process and suggesting that MoCRZ1 plays a role in appressorium mediated penetration and establishing a biotrophic association with its host [5].
Comprehensive genome-wide expression analysis in S. cerevisiae identified 163 genes regulated in a calcineurin/CRZ1 dependent manner by the stimulation of Ca2+ or Na+. These genes were associated with a diverse range of cellular processes including signaling pathways, ion/small molecule transport, cell wall synthesis/maintenance, and vesicular trafficking [30]. In C. albicans, microarray analysis revealed 60 genes to be transcriptionally activated by exogenous Ca2+ treatment through calcineurin/CRZ1 regulation using cnaΔ/Δ and crz1Δ/Δ mutants. Analysis of putative functions revealed that about 60% of these genes were involved in cell wall organization, cellular organization, cellular transport and homeostasis, cell metabolism, and protein fate [28]. Many of the genes regulated through calcineurin signaling in these two species belonged to similar functional groups, although only 9 genes were found to be commonly regulated [28]. To date, no genome-wide study has been conducted to identify regulated genes by direct binding of CRZ1 to promoter regions.
Here we report the use of chromatin immunoprecipitation coupled with non-coding region tiling arrays (ChIP-chip) analysis and whole-genome expression studies to identify target genes directly regulated by MoCRZ1. To our knowledge, this is the first report of ChIP-chip technology being applied to filamentous fungi. From this analysis, we can model the Ca2+/calcineurin signaling and control pathways that in-part influence infection related development and establishment of a compatible host association for this devastating plant pathogen. Further, this work reveals divergence within the fungal kingdom of the suites of genes and processes directly regulated by this ubiquitous signaling pathway.
A MoCRZ1::eGFP construct was co-transformed into fungal protoplasts along with a hygromycin resistance conferring vector. Transformants were single spore isolated and screened under the epi-fluorescent microscope. MoCRZ1-eGFP fluorescence was faint and evenly dispersed through the cytosol in mycelia with nuclear localization detected in hyphal tips (data not shown). Nuclear translocation of CRZ1 in response to Ca2+ is a well conserved phenomenon and was shown previously to occur in M. oryzae [5]. Following mycelia treatment with CaCl2, eGFP fluorescence was localized to the nucleus (Figure 1A), as expected. Addition of the calcineurin inhibitor FK506 completely blocked nuclear accumulation of the fluorescent protein. The MoCRZ1::eGFP over-expression line showed normal growth, appressoria development, and virulence to susceptible rice cultivar Nipponbare (data not shown), and was selected for subsequent ChIP-chip analysis.
Experimental design for ChIP-chip analysis is depicted in Figure 1B. CaCl2 treated mycelia were used to enrich MoCRZ1 occupied genomic fragments, while mycelia treated with CaCl2 and FK506 were used as the negative control. Expression of PMC1 (P-type ATPase) was analyzed for each sample by RT-PCR as shown in Figure 1C to confirm the effect of each treatment, i.e., up-regulated in the calcium treated mycelium and blocked by FK506. PMC1 is a previously described target of MoCRZ1 [5] and was used throughout this study as positive control marker. Enrichment of MoCRZ1 bound DNA fragments in ChIPed fractions when compared to input DNA (non-ChIPed) was confirmed by real-time PCR (Figure 1D). As expected, the fold change of PMC1 was 4.54±1.13 in ChIPed DNA from Ca2+ treated mycelia over input DNA from Ca2+ + FK506 treated mycelia, while that of β-tubulin was 1.78±0.56 (Figure 1D). Input and IPed DNA was amplified and subsequently re-amplified to amass sufficient DNA (∼8.5 ug) for labeling and hybridization to the array. Enrichment of MoCRZ1 bound DNA in the ChIPed fraction was validated by PCR amplification of PMC1 at each step (data not shown). ChIPed DNA labeled with Cy5 was co-hybridized with Cy3 labeled input DNA to the NimbleGen M. oryzae intergenic region specific tiling array (see Materials and Methods for array description).
Two complementary approaches were applied to analyze ChIP-chip hybridization data to identify putative MoCRZ1 binding sites and the genes it regulates. Initially, 42 peaks were identified as having a false discovery rate less than 0.2 and being common in both biological replications of the Ca2+ treatment but not in the Ca2++FK506 treatment. The 42 peaks were within 1 kb upstream of 37 predicted genes. Following this initial analysis, we used NimbleGen's SignalMap software to manually interrogate the ratio signal tracks across the genome to identify short sequence stretches showing a normal distribution profile of signal intensities upstream of annotated ORFs (Figure 1B bottom). Sequence tracks showing this profile were accepted only if they appeared in both biological replications of the Ca2+ treatment with no or lower signal intensities in the Ca2++FK506 treatment. If the binding signals were located between two divergently transcribed ORFs, both ORFs were regarded as possible candidates. This manual analysis resulted in the identification of 346 genes evenly distributed through the genome with no apparent bias (Figure 2, Table S1). Importantly, the 37 genes resulting from the first automated analysis were captured in the set of 346. Manual analysis produced more putative targets than the automated because SignalMap reports the probe ID with the highest signal intensity. In most cases, a single binding site did not share the identical probe as having the highest signal between biological replicates, thereby disqualifying them from the automated analysis. Data was deposited in the Gene Expression Omnibus (GEO) at NCBI under the accession number of GSE18180 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?token=dlarvwwmsoquoxs&acc=GSE18080).
Expression microarray analysis was conducted to corroborate genes predicted by ChIP-chip to be regulated by MoCRZ1. The experimental design is described in Figure 3A. Wild type strain KJ201 was treated with CaCl2 without or with FK506 to identify calcium and calcineurin dependent genes, respectively. MoCRZ1 deletion mutant (Δmocrz1) was also treated with CaCl2 to identify MoCRZ1 regulated genes. Four biological replicates for each of the 4 treatments were selected for hybridization to the Agilent M. oryzae whole genome microarray chip version 2. Signal intensities from the single channel hybridization were normalized to the average expression level of all probes among the 16 data sets. Pair wise comparison between treatments was conducted as depicted in Figure 3A, in which (a), compared Ca2+ treated/no treatment in wild type strain KJ201 (CA/CK); (b), Ca2+ treated/Ca2++FK506 treated in KJ201 (CA/CAFK); (c), Ca2+ treatments in KJ201/Ca2+ treatments in Δmocrz1 (CA/CRZ). Genes were regarded as differentially expressed if their average signal intensity among 4 replicates was above 20 in a minimum of one condition and the expression ratio is greater than 2 fold with P<0.05 (Student's t-test). Changes in gene expression of the 346 genes identified from ChIP-chip were analyzed. Of the 346, we found 309 with expression in each condition, with 121 and 19 genes up- or down- regulated, respectively, in the Ca2+ treated KJ201 condition in at least one comparison (Table S1, Figure 3B). It was noteworthy that the expression level of some MoCRZ1 target genes was lower in the Ca2+ activated wild type condition than in calcineurin and/or MoCRZ1 defective conditions suggesting that MoCRZ1 can act as repressor. These 140 (121+19) genes represent those directly bound to and regulated by MoCRZ1, and form the set used for analyses described below. The full dataset was deposited in NCBI GEO with the accession number of GSE18185 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?token=ttmdlgumsmsoury&acc=GSE18185). SuperSeries GSE18193 combining the ChIP-chip and microarray data were also generated (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?token=tnutnsemyikamlm&acc=GSE18193).
The 15 most up-regulated genes in calcium treated wild type strain KJ201compared to that of the Δmocrz1 mutant were selected for real-time RT-PCR to validate expression data. The results in Figure 4 show that each gene is transcriptionally more activated in all three comparison, i.e., Ca2+ treated vs. untreated control (a), Ca2+ treated vs. Ca2+ + FK506 treatment (b), and Ca2+ treated wild type vs. Δmocrz1 (c). Although the magnitude of fold changes was much higher than that from microarray in most cases, real-time RT-PCR data supported the microarray results.
Functional categorization was conducted in two ways. At first, hierarchical classification according to the expression pattern resulted in two groups. This analysis was followed by GO annotation using an InterPro to GO module incorporated in the Comparative Functional Genomics Platform (Figure 3C and Table S2, S3) [31]. Group I contains 64 genes of which expression was tightly regulated in a Ca2+/calcineurin/MoCRZ1 dependent fasion, i.e., up-regulated in all three comparisons. Group II comprises 76 genes whose expression was differentially regulated in three comparisons. Twenty four genes of group I and 36 of group II were assigned with GO terms, 14 and 22 to biological process, 20 and 33 to molecular function, 10 and 19 to cellular component, respectively (Table S2). Second, genes were annotated through literature with their specific functions assigned according to the functional classes of Cyert [32] (Table 1). Sixty-two of 140 genes identified by both ChIP-chip and microarray analyses could be assigned to one of 7 functional groups (Table 1). Consistent with the role of MoCRZ1 in providing tolerance to ionic and cell wall stress, genes involved in small molecule transport or ion homeostasis and cell wall synthesis/maintenance were highly represented. Among them was PMC1, which provides support for the validity of these results. Furthermore, the AAA family of ATPase as a whole was highly represented as direct targets of MoCRZ1, as well as members of major facilitator superfamily of multidrug-resistance proteins. Considering cell wall synthesis/maintenance genes, a number of GPI-anchored cell surface glycoproteins were captured in addition to the previously known downstream genes like chitin synthase activator (Chs3) and chitin syntase 1. Small secretory proteins, including effectors and cell wall degrading proteins, are regarded as key molecules acting at the interface between the plant and microbe. Efficient secretion of these proteins is assumed to be essential during the interaction between host and pathogen. Among the targets identified were genes comprising the vesicle mediated secretory pathway, including rhomboid family membrane protein (MGG_07535), Sso1/2 type SNARE protein (MGG_04090) known to be localized at secretory vesicles from Golgi to plasmamembrane, homocysteine S-methyltransferase (MGG_04215), golgi apyrase (MGG_07077), and a protein required for assembly of ER-to-Golgi SNARE complex (MGG_01489). Proper protein folding in the ER mediated by co-chaperone LHS1 [33], and efficient Golgi performance involving exocytosis entailing functions of the integral membrane P-type ATPase encoded by MgAPT2 [34], have been reported to be necessary for protein secretion and biotrophic phase infection in this fungus.
A major group of genes found in this study to be regulated by MoCRZ1 are those involved in cellular signaling and transcription. Among them were genes encoding serine/threonine protein kinases (MGG_04660, MGG_07287, MGG_06928), a phosphatase (MGG_00552), and a Rho guanyl nucleotide exchange factor (MGG_11178). In addition, genes comprising calcium signaling machinery were also common. Genes encoding annexin (MGG_06360), lysophospholipase 3 (MGG_07287), PX domain-containing protein (MGG_11649), calcineurin binding protein (CBP1; MGG_03218) and the calcineurin temperature suppressor CTS1 (MGG_01150) were identified. Of note, the expression of CBP1 and CTS1 was highly increased in all three comparisons. Binding in the promoter of these two genes and regulation of their transcription strongly suggests a previously uncharacterized level of negative feedback regulating calcineurin activity. MoCRZ1 functions to activate 12 genes from diverse families of transcription factors. Significantly, MoCRZ1 itself was identified, suggesting an autoregulatory role not previously reported. The expression of MoCRZ1 was induced by exogenous calcium treatment, but not altered by the inhibition of calcineurin activity with FK506. Regarding the fact that calcineurin dephosphorylates MoCRZ1 upon activation by calcium and that FK506 blocks nuclear localization of MoCRZ1, inactivation of calcineurin regulates function only at post-translational level. This data suggests an additional level of regulation at transcription.
Δmocrz1 mutants are defective in post appressoria formation penetration and establishment of biotrophic host association. Appressoria from this mutant background have defects in penetration, however those that successfully penetrated fail to incite disease. We examined MoCRZ1 target genes for previously defined roles in fungal virulence. Of the 140 target genes queried to pathogen-host interactions database (PHI-base) version 3.1 [35], [36], 16 had matches to more than one entry from plant or human pathogens using a stringent cut-off value e<−20 (Table 2). Three proteins, MoCRZ1 itself [5], MGG_03530 encoding chitin syntase activator Chs3 [37] and PMC1 [8] were previously characterized to regulate virulence in M. oryzae. Similarly, members of ATPases family, PDE1 [38] and MgAPT2 [34], are known to be involved in M. oryzae pathogenicity. Two genes encoding serine/threonine protein kinases (MGG_04660 and MGG_06928) had 45 and 27 hits, respectively. Genes involved in drug resistance (MGG_05723 and MGG_10869), MGG_07230 alpha-1,3-mannosyltransferase CMT1, MGG_07287 lysophospholipase 3, MGG_05727 ankyrin repeat protein, MGG_03288 bZIP transcription factor, and MGG_09361 homolog of CgDN3 were similarly listed as being involved in fungal virulence.
To identify the MoCRZ1 binding motif, the exact binding sequences of MoCRZ1 (∼50–1247 bp) revealed from ChIP-chip studies were retrieved from the promoters of genes in common between ChIP-chip and microarray expression studies and subjected to motif signature analyses (Figure 5A). Initially, 106 sequences from each of the 83 genes differentially regulated in the WT/Δmocrz1 comparison (Figure 3B) were analyzed with MEME [39] and MDScan [40] (Figure 5A). There were more sequences than genes as 21 genes had 2 peaks in their promoter region and 2 genes had 3. Candidate motifs from both algorithms were manually interrogated and enumerated to identify the top 3 candidates, which were subsequently screened against randomly retrieved 106 intergenic sequences with an average length of 509 bp (Figure 5A). The most enriched motif of CAC[AT]GCC was identified in 33 sequences in front of 24 genes, a 16X enrichment in MoCRZ1 bound sequences. The most common motif of TTGNTTG was found in 68 promoter sequences in front of 42 genes with 4X enrichment. Motif TAC[AC]GTA occurred in 22 promoter sequences of 18 genes with 4X enrichment. Fifty-six genes had at least one motif, while all three of these motifs occurred in front of 5 genes including PMC1, CTS1, MGG_01494 encoding a cell wall protein, and two genes encoding conserved hypothetical proteins MGG_03539 and MGG_06359 (Table S4). These motifs were searched against yeast motif database using TOMTOM [41]. The top match for CAC[AT]GCC was MET28 (p-value = 0.0013), while the second match was CRZ1 with significant p-value (0.0022), showing Crz1p of S. cerevisiae has this motif in its promoters although it was not previously identified as a calcineurin dependent response element (CDRE) (Figure 5B). Pbx1b (p-value = 0.00039) and Zec (p-value = 0.00045) were best two matches for TTGNTTG, while no significant match was returned for TAC[AC]GTA. Binding of MoCRZ1 to the promoter region was confirmed by Electrophoretic Mobility Shift Assay (EMSA). A 209 bp PCR fragment having 1 TTGNTTG and 2 CAC[AT]GCC motifs from the MoCRZ1 promoter region was bound to purified MoCRZ1 protein (Figure 5C, left panel). A 325 bp fragment of CBP1 (MGG_03218) having 1 TTGNTTG and 1 CAC[AT]GCC motifs was also shown to bind to purified MoCRZ1 (Figure 5C, right panel).
The most common signal transduction pathways in nature, MAPK, cAMP, and calcium mediated, have been shown to be involved in all aspects of growth, development, and pathogenicity of M. oryzae and several other fungal pathogens of plants. Recently, a transcription factor, MoCRZ1, relaying calcium signals from calcineurin has been characterized [5], [42]. As a major mediator of calcineurin signaling, crz1 deletion mutants in a variety of fungi showed similar phenotypes as calcineurin mutants such as sensitivity to Ca2+ and other ionic stresses as well as cell wall stresses. In addition, Δmocrz1 showed defects in development and pathogenicity, including reduced conidiation and defective appressorium mediated penetration [5], [42]. MoCRZ1 is required for the calcineurin-dependent transcriptional induction of downstream genes such as PMC1, encoding P-type ATPase, CHS2 and CHS4, encoding chitin synthase. In this study, we gained a genome-wide perspective of the direct downstream targets of MoCRZ1 signaling using a combined approach of ChIP-chip and expression microarray analysis. CRZ1 in filamentous fungi have common as well as unique roles compared to those of their yeast ortholog. As such, we find that the suite of genes regulated by MoCRZ1 contains a high percentage specific to M. oryzae.
Our combined approach identified 140 direct targets of MoCRZ1 whose expression was concurrently regulated in a calcineurin/CRZ1 dependent manner. Sixty-two of these genes could be grouped in the same categories that were used to functionally assign yeast genes [32]. These groups include cell wall synthesis, ion or small molecule transport, vesicle transport, lipid or sterol synthesis, degradative enzymes, and signaling and transcription. This is expected as the Δmocrz1 mutant also exhibited sensitivity to calcium ions and chemicals perturbing cell wall integrity as did the yeast crz1 mutant [5]. However, the large diversity in the suite of individual target genes is compelling. In S. cerevisiae, genome wide expression profiling identified 153 calcium/calcineurin/Crz1p dependent genes [30]. Comparison between these two sets showed that only 15 out of 140 MoCRZ1 targets had 12 yeast orthologs whose expression was regulated by calcium/calcineurin/Crz1p (Table 3). When we compared our gene list to the 120 A. fumigatus genes whose expression was changed by exposure to calcium for 10 min [26], only 21 matched to 14 A. fumigatus genes, with only 6 having reciprocal best blast hits (Table 3). When the same analysis was applied to the 141 A. fumigatus genes whose expression was modulated by AfcrzA, as recently reported by Soriani et al. [43], 28 MoCRZ1 targets having 15 A. fumigatus orthologs were found with only 3 matching the 14 genes identified previously (data not shown). The observed diversity may reflect divergently evolved molecular features modulated by CRZ1 in each species to cope with its unique environment. This diversity was also reflected in the crz1 mutant phenotypes across the species. For example, crz1 mutants in different species showed a spectrum of ion sensitivity; ΔcrzA of A. fumigatus was more sensitive to Mn2+, but Δmocrz1 of M. oryzae was not. BcCRZ1 of B. cinerea was dispensable for conidia mediated infection, but MoCRZ1 was necessary. These data suggest that although the core calcium signaling machinery including calmodulin and calcineurin is highly conserved across the species, their mechanism of action has diverged. Similar suggestions have been proposed by Karababa et al. [28]. Kraus and Heitman [44] also made note of species-specific action mechanisms through which calcium signaling involving calmodulin and calcineurin was manifested in three different species, S. cerevisiae, C. albicans, and C. neoformans. Additional evidence supporting this hypothesis is found in the differences in CDRE (calcineurin-dependent response elements) sequences [27], [30], [45], [46]. In S. cerevisiae, nucleotide stretches of 5′-GNGGC(G/T)CA-3′ was reported as the Crz1p-binding site by in vitro site selection. A similar sequence, 5′-GAGGCTG-3′, was also identified as a common motif in the upstream 500 bp regions of 40 genes with ≥4.0 fold Crz1-dependent expression profile [30]. Two similar sequences, 5′-GTGGCTC-3′ and 5′-GAGGCTC-3′, were reported as CDREs from the genus Aspergillus in the A. nidulans chsB and A. giganteus afp promoters [47]. A slightly divergent motif, 5′-G(C/T)GGT-3′, was identified as a common regulatory sequence from the 60 upstream regions of calcineurin/Crz1p-dependent genes of C. albicans [28]. In contrast, the motif sequences obtained in this study shows further divergence from that of S. cerevisiae or C. albicans. Although, core hepta nucleotide, 5′-GGCTC-3′, was found in the probe sequence of 10 genes including PMC1 (MGG_02487) and MGG_03530 (chitin synthase activator), the occurrence of the full S. cerevisiae or C. albicans motif sequences is not enriched/overrepresented among the 106 sequences we analyzed (data not shown).
In addition to PMC1, several other genes implicated in the calcium signaling pathway regulating calcineurin activity were identified. Among them were calcineurin binding protein CBP1 (MGG_03218) and the calcineurin temperature suppressor CTS1 (MGG_01150), suggesting feedback regulation of calcineurin activity mediated by MoCRZ1. In addition, MoCRZ1 bound its own promoter to activate expression. CBP1 shows homology to CbpA in A. fumigatus and Cbp1 of C. neoformans, which in turn, are orthologous to RCN1 of S. cerevisiae, an inhibitor of calcineurin called calcipressin. Over-expression of RCN1 in a pmc1 mutant background conferred Ca2+ tolerance by activation of vacuolar Ca2+/H+ exchanger Vcx1p, expression of which was negatively regulated by calcineurin. Expression of RCN1 (YKL159c) was regulated by calcineurin and Crz1p, suggesting negative feedback regulation [30], [48]. However, both stimulatory and inhibitory regulation of calcineurin by RCN1 was reported [49]. RCN1 expression at endogenous level and phosphorylation by GSK-3 kinase, positively regulated calcineurin activity [49]. Degradation of phosphorylated RCN1 is required for precise calcineurin activity in response to changes in Ca2+ concentration [50]. Negative feedback regulation of calcineurin by CbpA was also reported in A. fumigatus, where it down regulates cnaA expression as well as that of downstream genes vcxA and chsA encoding vacuolar Ca2+/H+ exchanger and chitin synthase A, respectively [51]. Expression of CbpA (Afu2g13060) was known to be up-regulated in response to Ca2+, which was CrzA dependent [26]. This feedback loop seems to extend at least to the level of calcineurin. Roles and action mechanisms of Cbp1 in the calcium/calcineurin signaling pathway also seems to be diverged in a species specific manner. In C. neoformans, Cbp1 does not stimulate or inhibit calcineurin expression, and does not seem to participate in a feedback loop. Taken together, these data lead us to propose that a negative or positive feedback loop, which includes MGG_03218, regulates the calcium/calcineurin signal transduction pathway in M. oryzae.
Phospholipid-binding protein Cts1 (calcineurin temperature suppressor) was identified and characterized in C. neoformans as able to restore growth defect at 37°C in calcineurin-deficient strains and to confer resistance to the calcineurin inhibitor FK506 [52]. Δcts1 mutants were synthetically lethal in combination with a calcineurin mutation. However, no direct interaction between Cts1 with either the catalytic or regulatory subunit of calcineurin was reported. With these data, they suggested that Cts1 acted in either parallel pathways or a branched pathway to compensate, at least in part, for the loss of calcineurin function [52]. MGG_01150, an ortholog of Cts1, was found to be a direct target of MoCRZ1. Its calcineurin dependent expression pattern is opposite to that of Cts1. Unlike the elevated expression of Cts1in calcineurin deficient strains, MGG_01150 expression was activated by Ca2+ treatment, which was blocked by calcineurin inhibitor FK506 and abolished in the Δmocrz1 mutant. Therefore, it is evident that MGG_01150 is a component of the calcineurin signaling pathway in M. oryzae unlike its counterpart in C. neoformans.
Our data revealed that MoCRZ1 binds to its own promoter to activate expression in a Ca2+ /calcineurin dependent manner. Therefore, MoCRZ1 regulation appears to occur at the posttranslational and transcriptional levels via activation by calcineurin and positive autoregulation respectively. Calcineurin/CRZ1 dependent expression of CRZ1 was also reported in C. albicans suggesting a common mechanism of regulation across the fungal species [28]. However, expression dynamics of the catalytic (MCNA: MGG_07456) and regulatory (MCNB: MGG_06933) subunit were not altered in the Δmocrz1 mutant compared to wild type in response to Ca2+ treatment (data not shown). This suggests that the proposed feedback regulation does not include direct regulation of calcineurin expression by CRZ1.
Involvement of CRZ1 in fungal virulence has been recently demonstrated in both human and plant pathogenic fungi [5], [10], [25], [26], [28], [29]. Signals related to these virulence traits seemed to be transmitted to a diverse range of downstream genes, as 18 genes out of 140 direct targets including MoCRZ1 have been found to be related to fungal virulence in both human and plant pathogens. Gene repertoire ranges from cell wall synthetic enzymes, proteins conferring resistance to antifungal agents encoded by major facilitator type transporter, calcium homeostasis to transcription factors. Three genes (MoCRZ1, MGG_03530 encoding chitin synthase activator 3, and MGG_02487) have been functionally characterized in M. oryzae [5], [8], [37]. Association of MGG_03530 encoding chitin synthase activator 3 (Chs3) with virulence was found in a T-DNA insertion strain with reduced growth rate on nutrient rich media, reduced conidiation rate with aberrant conidia morphology, and reduced appressorium formation and virulence [37]. Filamentous fungal genomes contain up to 10 chitin synthase genes of 7 classes [53]. Different CHS were regarded as to have functional redundancy in a variety of developmental processes because single mutation of a class I or II gene did not result in a marked phenotype. Therefore, specific roles for individual genes have not yet been clearly assigned and the association with fungal virulence has been controversial. However, several lines of evidence implicate class V myosin-like CHS in virulence in the maize anthracnose pathogen Colletotrichum graminearum [54] and in Fusarium oxysporum, the tomato wilt fungus [55]. One Class III chitinases, BcChs3a of Botrytis cinerea had important roles in virulence, especially on leaf tissue colonization, grape vines and Arabidopsis thaliana [56]. The M. oryzae genome contains 7 predicted chitin synthases, of which the expression of 5 were induced and 1 repressed in response to exogenous calcium treatment (data not shown). Only one (MGG_01802 encoding class II chitinase) was directly regulated by MoCRZ1. Therefore, calcium seems to regulate expression of most chitin synthases in diverse pathways.
Two genes encoding small molecule transporting P-type ATPases (MGG_02487 Ca transporting ATPase and MGG_12922 phospholipid-trasnporting ATPase) were found to have homologs implicated in fungal pathogenicity. Knock-down of MGG_02487 encoding PMC1 by RNAi technology resulted in no conidiation, growth retardation, and reduced melanization [8]. The association of PMC1 and fungal virulence was not investigated in other fungi. Other evidence on the involvement of Ca2+ transporting ATPase in fungal virulence arises from the study of PMR1 of C. albicans [57]. PMR1 is a Golgi membrane located Ca2+ transporting P-type ATPase, and is known to work cooperatively with PMC1 in the maintenance of cytosolic Ca2+ homeostasis. Capmr1Δ mutant of C. albicans had a weakened cell wall, probably due to the glycosylation defect and showed severely attenuated virulence in a murine model of systemic infection [57]. Two genes encoding Drs2 family of P-type ATPases, PDE1 and MgAPT2, were functionally characterized to act in appressorium formation and invasive growth [34], [38]. MgAPT2 was necessary for the normal development of Golgi apparatus that is required for secretion of a subset of extracellular enzymes via exocytosis [34], [58].
This study is the first of its kind where ChIP-chip technology has been applied to filamentous fungi. The correlation of comprehensive whole genome expression data with results from ChIP-chip have allowed for significant refinement of the predicted targets of MoCRZ1. This refinement alone allowed for the identification of a predicted signature binding motif for this transcription factor. This study reveals conserved elements of the calcium/calcineurin signaling pathway, as well as elucidates species specific differences that we propose function to regulate the system and allow for responses tailored to biology of the organism. Calcium signaling is a ubiquitous and complicated aspect of cell physiology. This study represents a major advance in our understanding of this pathway in M. oryzae and provides the launching point for the functional characterization of the genes and interactions it implicates. Figure 6 depicts our proposed model resulting from this work and includes our new findings of predictive roles for MoCRZ1 in autoregulation, feedback inhibition, and secretion.
Strains of M. oryzae were maintained on oatmeal agar (50 g of oatmeal per liter) and grown at 22 °C under constant fluorescent light to promote conidiation. Mycelial blocks from actively growing margins of colony were inoculated into complete media (CM) liquid media at 25 °C by shaking for 3 days. After thorough washing with sterile distilled water, the mycelia were treated with 200 mM CaCl2 with or without 10 µg/ml FK506 for the indicated time. Mycelia were harvested and immediately frozen with liquid nitrogen and stored at −80°C before use. FK506 (Sigma, St. Louis, MO) stock solution in 5 mg/ml was prepared with DMSO, and stored at −20°C until used.
Protoplasts generation and transformation were performed following established protocols [59]. Protoplasts were generated from young mycelia grown in complete media with 10 mg/ml Lysing Enzyme (Sigma, St. Louis, MO) in 20% sucrose. Protoplasts were harvested by filtration through 4 layers of miracloth (Calbiochem, Darmstadt, Germany), washed twice with STC (20% sucrose, 50 mM Tris-HCl, 50 mM CaCl2, pH 8.0) followed by centrifugation at 5,000 rpm for 15 min at 4°C, and resuspended to 5×107 protoplasts/ml. GFP tagging construct in TOPO cloning vector (PMoCRZ1::MoCRZ1::GFP) was co-transformed with pCX63 containing hygromycin resistance cassette by the mediation of 40% polyethyleneglycol. After incubation for 7–10 days at 25°C, hygromycin resistant colonies were transferred to V8 juice agar media. The fluorescing transformants observed under the microscope with epifluorescent optics (Nikon eclipse 80i, Melville, NY) were purified through single spore isolation. Nuclear translocalization of MoCRZ1 was observed after treatment with 200 mM CaCl2 with or without 10 µg/ml FK506 for 1 hour at room temperature.
Total RNA was isolated from frozen mycelial powder using an Easy-Spin RNA extraction kit (iNtRON Biotechnology, Seoul, Korea). Five micrograms of total RNA was reverse-transcribed into first-strand cDNA by oligo dT priming using the SuperScript first-strand cDNA synthesis kit according to the manufacturer's instructions (Invitrogen Life Technologies, Carlsbad, CA). Resulting cDNA was diluted to 1∶20 with sterile water. Real-time RT-PCR was performed according to the established protocol [59] using iQ SYBR Green Supermix (Bio-rad, Hercules, CA) on an iCycler iQ5 Real-Time PCR Detection System (Bio-rad). Fold changes were calculated by 2−ΔΔCt, where ΔΔCt = (Ctgene of interest-Ctcontrol gene)test condition-(Ctgene of interest-Ctcontrol gene)control condition. Test and control conditions are as same as in Figure 3A, where (a) compares expression level between Ca2+ treated vs. no treatment in wild type strain KJ201, (b) Ca2+ vs. Ca2++FK506 in KJ201, (c) Ca2+ treatments in KJ201 vs. in Δmocrz1. Primer sequences were listed in Table S5.
Young mycelia grown in liquid media were treated with 200 mM CaCl2 with or without 10 µg/ml FK506 for 1 hour with shaking. The harvested mycelia were divided with one part being immediately frozen in liquid nitrogen for future RNA isolation and the other treated with 1% formaldehyde in buffer A (0.4 M sucrose, 10 mM Tris-HCl, pH 8.0, 1 mM EDTA, 1 mM phenylmethylsulfonyl fluoride, and 1% formaldehyde) for cross-linking for 20 min. Mycelia were harvested with excess amount of distilled water after stopping cross-link with 0.1 M glycine for 10 min, frozen in liquid nitrogen, ground into a fine powder in a chilled mortar and pestle, and stored at −80°C until used. Chromatin immunoprecipitation was conducted according to published procedures with modification [60], [61]. Nuclear DNA was then isolated from cross-linked mycelia with Plant Nuclear Isolation Kit (Sigma, St. Louis, MO) and sheared into fragments by sonication to 200- to 1,000-bp with an average size of 500 bp. Sonication was conducted on ice with an amplitude of 10% using 30×30 s pulses (30 s between bursts) using Biorupter (Cosmo Bio, Tokyo, Japan). After pre-clearing nuclear lysates with Salmon sperm/protein A agarose (Upstate, Temecula, CA) for 4 hours at 4°C, immunoprecipitations were performed with either 1 µg of rabbit control IgG (ab46540-1, Abcam, Cambridge, MA) or 0.5 µl of antiGFP antibody (ab290, Abcam) for overnight at 4°C. A small aliquot of DNA (30%) was saved for input DNA (input). Immunoprecipitated DNA was captured with proteinA agarose beads (Upstate, Temecula, CA) for 4 hours, and then washed twice with LNDET buffer (0.25 M LiCl, 1% NP40, 1% deoxycholate, 1 mM EDTA) and twice with TE buffer. The DNAs were reverse-cross linked at 65 °C overnight in elution buffer (1% SDS and 0.1 M NaHCO3) containing 1 mg/ml proteinase K, and purified using PCR purification kit (Qiagen). Real-time PCR was performed with 1 µl each of pulled-down DNA and input DNA as template following the procedures described above. Fold changes for control gene (β-tubulin) and putative target gene (PMC1) were calculated by 2−ΔΔCt, where ΔΔCt = (Ctinput DNA-CtChIPed DNA) Ca2+ treated sample - (Ctinput DNA-CtChIPed DNA) Ca2+/FK506 treated sample. Primer sequences for the promoter region of PMC1 and β-tubulin were listed in Table S5. For ChIP-chip experiments, 10 µl ChIPed DNA and 10 ng input DNA were amplified using GenomePlex Whole Genome Amplification Kit (Sigma). Amplified DNA was then labeled with Cy3 or Cy5 fluorescent dyes for input or immunoprecipitated DNA, respectively, and hybridized to NimbleGen Magnaporthe grisea promoter tiling arrays according to the manufacturer's instruction (NimbleGen Systems of Iceland). Probes for tiling array were designed to have about 70 nucleotides per 100 bp of promoter and intergenic region based on annotation of M. grisea genome version 5. After getting peak intensity, peak data files (.gff) were generated from the scaled log2-ratio data using NimbleScan. It detects peaks by searching for 4 or more probes whose signals are above the cutoff values using a 500 bp sliding window. The ratio data was then randomized 20 times to evaluate the probability of “false positives”. Each peak was then assigned a false discovery rate (FDR) score based on the randomization. Peaks with FDR score ≤0.2 were regarded as positive.
Mycelia of wild type KJ201 and Δmocrz1 strain were treated with 200 mM CaCl2 with or without 10 µg/ml FK506, with water as control. Initially, samples were harvested at 0, 15, 30, and 60 min. after treatment. PMC1 expression level was checked by RT-PCR with the highest expression at the 30 min. time point. Four biological replicates of wild type and mutant mycelia were harvested after 30 min. treatment with chemicals, frozen immediately with liquid nitrogen. Total RNA was isolated described as above. After validation of sample quality by RT-PCR, total RNA was sent to Cogenics (Morrisville, NC) for hybridization to the Agilent M. grisea whole genome microarray chip version 2.0 using the single channel hybridization design. Quality of RNA was determined with Agilent Bioanalyzer. Five hundred nanograms of total RNA was converted into labeled cRNA with nucleotides coupled to fluorescent dye Cy3 using the Quick Amp Kit following the manufacturer's instructions (Agilent Technologies, Palo Alto, CA). After analyzing the quality with Agilent Bioanalyzer, Cy3-labeled cRNA (1.65 µg) was hybridized to M. grisea 2.0 4×44 k microarrays. The hybridized array was washed and scanned, and the data were extracted from the scanned image using Feature Extraction version 10.2 (Agilent Technologies). An error-weighted average signal intensity of two probes within a chip was used for normalization with Lowess normalization module implanted in JMP Genomics software. An average expression of all probes among 16 data sets was used as the baseline. Pairwise comparison between treatments was conducted to get the expression profiles of each probe. Genes were regarded as differentially expressed if their average signal intensity among 4 replicates was above 20 in a minimum of one condition and expression ratio is greater than 2 fold with P<0.05 (Student's t-test).
The two commonly used motif discovery programs, MEME [39] and MDScan [40], were used to identify the MoCRZ1 binding motif. Input data consisted of the exact binding sequences retrieved from the promoters of 83 genes with differential expression in the WT/Δmocrz1 comparison (Figure 3A). Candidate motifs from both algorithms were manually interrogated and enumerated to identify the 3 top candidates, and queried against the yeast motif database using TOMTOM [41]. Enrichment was calculated over the 106 background sequence set which was randomly retrieved from intergenic region of the whole genome. Consensus motif sequences were calculated using WebLogo server at http://weblogo.berkeley.edu [62].
Protein expression vector was constructed by ligation of MoCRZ1 cDNA encompassing full ORF into pGEX-6P-1 (Invitrogen, Carlsbad, CA) having GST tag at the N terminus. The resulting construct was transformed into the E. coli strain BL21 (DE3) pLysS (Novagen) after verifying the cDNA sequences. Induced protein was purified with GST agarose beads (Sigma) based on the procedures of Frangioni et al.[63].
Probe DNA was prepared by PCR with Biotin labeled primer at the 5′ end, followed by gel purification. Cold probe was amplified with the same primer sequence without Biotin labeling. Primer sequences were listed in Table S5. Binding of putative motif sequences to MoCRZ1 protein was performed using LightShift Chemiluminescent EMSA kit following the manufacturer's manual (PIERCE, Rockford, IL). Reaction mixtures containing 10 ng of purified MoCRZ1 and biotin labeled probe were incubated for 20 min. at room temperature. The reactions were electrophoresed on 5% polyacrylamide gel in 0.5×TBE, and transferred to a positively charged nylon membrane (Hybond N+, GE Healthcare). Signals were detected using Chemiluminescent Nucleic Acid Detection Module (PIERCE) according to the manufacturer's instruction.
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10.1371/journal.ppat.1002930 | Structural Basis of Differential Neutralization of DENV-1 Genotypes by an Antibody that Recognizes a Cryptic Epitope | We previously developed a panel of neutralizing monoclonal antibodies against Dengue virus (DENV)-1, of which few exhibited inhibitory activity against all DENV-1 genotypes. This finding is consistent with reports observing variable neutralization of different DENV strains and genotypes using serum from individuals that experienced natural infection or immunization. Herein, we describe the crystal structures of DENV1-E111 bound to a novel CC′ loop epitope on domain III (DIII) of the E protein from two different DENV-1 genotypes. Docking of our structure onto the available cryo-electron microscopy models of DENV virions revealed that the DENV1-E111 epitope was inaccessible, suggesting that this antibody recognizes an uncharacterized virus conformation. While the affinity of binding between DENV1-E111 and DIII varied by genotype, we observed limited correlation with inhibitory activity. Instead, our results support the conclusion that potent neutralization depends on genotype-dependent exposure of the CC′ loop epitope. These findings establish new structural complexity of the DENV virion, which may be relevant for the choice of DENV strain for induction or analysis of neutralizing antibodies in the context of vaccine development.
| Within each Dengue virus (DENV) serotype, viruses are subdivided into genotypes based upon the protein sequence variation. Infection with a given serotype is believed to induce neutralizing antibodies that provide long-term immunity against secondary infection by a strain of the same serotype. However, recent studies suggest that some classes of neutralizing antibodies fail to inhibit infection equivalently for all genotypes within a DENV serotype. DENV1-E111 is an example of an antibody that differentially neutralizes infection of DENV-1 strains. We used structural and molecular approaches to determine that DENV1-E111 binds to an epitope in domain III of the envelope protein. Although the epitope sequence varied between DENV-1 genotypes, inhibitory activity of the antibody remained unequal when we exchanged the amino acids within the epitope among genotypes. Docking of our structures onto DENV virion models revealed that the DENV1-E111 epitope was inaccessible, suggesting that the antibody recognizes an uncharacterized virus conformation. Our studies suggest that DENV virion structures differ in a genotype-dependent manner, which can impact the inhibitory activity of antibodies that recognize cryptic epitopes.
| Dengue viruses (DENV) are mosquito-borne viruses of the Flavivirus genus, which include other significant human pathogens such as West Nile (WNV), Japanese encephalitis, and yellow fever viruses. Infection with DENV can cause symptoms in humans ranging from a mild febrile illness (Dengue Fever, DF) to a more severe hemorrhagic fever (DHF) and life-threatening dengue shock syndrome (DSS). Currently, it is estimated that DENV infects ∼50 to 100 million people per year resulting in ∼250,000 to 500,000 cases of DHF/DSS [1]. The four serotypes of DENV (DENV-1, DENV-2, DENV-3, and DENV-4) comprise a genetically related yet antigenically distinct serocomplex, varying from one another by 25 to 40% at the amino acid level. Each DENV serotype is further divided into genotypes, which can vary up to 3% [2], [3]. Currently, there are no specific antiviral therapies or vaccines approved for use in humans, and treatment of severe disease remains supportive in a tertiary care setting.
The humoral response contributes to protection and also, paradoxically to the pathogenesis of severe DENV disease. Infection with a given serotype is believed to induce durable levels of neutralizing antibodies that provide life-long immunity against subsequent challenge by a strain of the same serotype [4]. However, secondary infection with a heterologous serotype increases the relative risk of developing DHF and DSS [5]. A favored hypothesis is that during secondary infection poorly neutralizing cross-reactive antibodies from the primary infection enhance infection of the heterologous virus in cells bearing Fc-γ receptors [6]. Recent studies in non-human primates and mice have confirmed that passive transfer of anti-DENV monoclonal or polyclonal antibodies can augment replication of a heterologous DENV in challenge models, and in some cases cause a lethal vascular leakage syndrome that resembles DSS [7]–[9].
Humoral protection against DENV correlates with the induction of a neutralizing antibody response against the envelope (E) protein on the surface of the virion ([10] and reviewed in [11]). The ectodomain of the DENV E protein is composed of three domains [12]: DI is a central nine-stranded β-barrel domain, DII consists of two finger-like protrusions from DI and contains the hydrophobic peptide required for virus fusion, and DIII is an immunoglobulin-like domain on the other side of DI that has been proposed to interact with as yet uncharacterized host receptor(s). Neutralizing monoclonal antibodies (MAbs) against the different DENV serotypes map to all three domains [13]–[19], although many potently inhibitory mouse MAbs localize to DIII [13]. To date, three epitopes have been established on DIII [7], [20], [21]: MAbs binding the lateral ridge or A-strand epitope are relatively inhibitory, whereas MAbs recognizing the AB loop neutralize infection less efficiently or not at all [22], presumably due to poor epitope accessibility on the virion.
Cryo-electron microscopy (cryo-EM) studies have revealed that the E proteins of mature flavivirus virions form anti-parallel dimers that lie flat against the surface of the virion and are arranged with T = 3 quasi-icosahedral symmetry [23], [24]. In this configuration, E proteins exist in three distinct chemical environments defined by their proximity to the 2-, 3-, or 5-fold axis of symmetry [23], [24]. While 180 copies of the E protein are present on all flavivirus virions, the different environments imposed by the quasi-icosahedral symmetry make some epitopes unequally accessible. Epitope exposure also may be affected by neighboring E proteins in adjacent symmetry units, or by the presence or absence of prM in the case of immature, partially mature, and mature virions [25]–[30]. The arrangement of the E proteins on the surface of the virion can be modulated over time and across a range of temperatures due to the intrinsic conformational heterogeneity of virions [31], [32] Consequently, the accessibility of epitopes can vary across structurally distinct epitopes, ultimately affecting the number of sites available for antibody binding and neutralization.
Recently, we generated a panel of 79 MAbs against DENV-1 to define how antibodies neutralized different DENV-1 genotypes [17]. Within this panel, 15 MAbs were potently neutralizing, and most mapped to previously identified epitopes in DIII, although few retained strong inhibitory activity against heterologous DENV-1 genotypes. Prior studies have described disparate neutralization of DENV strains corresponding to different genotypes within a serotype with serum from natural infection [33]–[35] or after immunization with live-attenuated tetravalent vaccine candidates [34], [36]–[38]. One such DIII-specific neutralizing MAb from our panel, DENV1-E111 (henceforth termed E111) potently neutralized infection of a genotype 2 DENV-1 (strain 16007, EC50 of ∼4 ng/ml), but inhibited infection of a genotype 4 virus poorly (strain Western Pacific-74 (West Pac-74), EC50 of ∼15,200 ng/ml). Sequence analysis of the variation between residues 296–400 of DIII for 16007 or West Pac-74 revealed only two differences (amino acids 339 and 345), with amino acid 345 as the only residue that varied in all five DENV-1 genotypes.
Here, we determined the crystal structures of an E111 single-chain variable fragment (scFv) in complex with DIII of 16007 and the E111 Fab in complex with DIII from West Pac-74. E111 bound to a previously uncharacterized epitope centered on the CC′ loop of DIII, which should not be exposed on the virion according to existing flavivirus atomic models; our structural data defining the CC′ loop epitope of E111 was supported by extensive mutagenesis and binding analyses. While E111 showed a higher affinity and longer half-life of binding to DIII of 16007 (genotype 2) compared to DIII from several other DENV-1 genotypes, this did not explain the disparity in neutralization potency for viruses from all five genotypes. Mutation at position 345 of West Pac-74 DIII to the corresponding residue in 16007 resulted in increased E111 binding, but only a small improvement in neutralization potency, suggesting that differences in amino acids within the epitope among genotypes could not account for the phenotype. However, neutralization of DENV-1 West Pac-74 with E111 was enhanced by incubating virus-antibody complexes at higher temperature or for longer times, whereas this treatment failed to equivalently impact inhibition of strain 16007 by E111. Our experiments suggest that the conformational ensemble of DENV virion structures differs in a genotype-dependent manner, which impacts the neutralizing activity of antibodies that recognize nominally cryptic epitopes.
We initially formed complexes of E111 Fab with soluble, bacterially expressed DIII (residues 293–399) cloned from 16007 and West Pac-74 DENV-1 strains (Figure S1, and data not shown). Several conditions yielded diffracting crystals of the 16007 DIII-E111 Fab complex but failed to diffract better than ∼6.0 Å resolution. As an alternative strategy, we cloned the heavy (VH) and light chain (VL) variable domain sequences from the E111 hybridoma to create an scFv. Two constructs of the E111 scFv were generated, with either the VL or VH sequence at the N-terminus, separated with a (GGGGS)3 linker, and a C-terminal hexahistadine tag for affinity purification. These inserts were cloned into the pAK400 vector that contains a pelB leader sequence for targeting the polyprotein transcript to the oxidative environment of the bacterial periplasm [39]. Sequential purification by nickel affinity and size exclusion chromatography revealed two species of scFv: a non-disulfide domain-swapped dimer and a monomer. For our structural analysis, we used the monomeric species with VL at the N-terminus (Figure S1A).
We determined the structure of E111 scFv in complex with DIII of DENV-1 16007 to 2.5 Å resolution (model statistics are in Table 1). There were no major structural perturbations to the immunoglobulin-like β-sandwich topology of DIII found in other flavivirus E proteins, with a root mean squared displacement of 0.7 Å compared to unbound DIII. The E111 scFv adopted the predicted variable domain assembly (Figure 1A). The binding interface had an average degree of shape complementarity (Sc = 0.68, with Sc = 1.0 a perfect score) for antibody-antigen interactions and 2,095 Å2 of combined surface area. The light and heavy chains engaged DIII equivalently, with a combined buried surface area of 1,017 Å2 (460 Å2 of DIII and 557 Å2 of the light chain) versus 1,078 Å2 (550 Å2 of DIII and 528 Å2 of the heavy chain) (Figure 1C). The interaction between E111 and DIII of strain 16007 was dominated by hydrophobic interactions, in addition to six direct hydrogen bonds and fourteen water-mediated networks at the interface of the complex (Table S1).
We obtained crystals of DIII of West Pac-74 in complex with E111 Fab that diffracted to 3.8 Å resolution (Figure S1B). There were two complexes in the asymmetric unit, and non-crystallographic symmetry restraints were applied in the refinement (model statistics are in Table 1). The two Fabs have essentially identical structures with the notable exception of the elbow angles between the variable and constant domains ((149.0° versus 133.5° as calculated by the RBOW server [40]). As with the co-crystal structure with the E111 scFv and DIII of 16007, there were limited conformational changes in the West Pac-74 DIII upon E111 Fab ligation (R.m.s.d = 0.8 Å). The E111 Fab-DIII interface also had a similar average degree of shape complementarity (Sc = 0.65) and total buried surface area (2,076.5 Å2). Overall, the E111 scFv and Fab structures (DIII and Fv domains) varied little from one another in terms of structure (R.m.s.d = 0.3 Å) or orientation of engagement of DIII (Figure S1C).
E111 engaged discontinuous segments of DIII of 16007 and West Pac-74 including the N-terminal linker (residues 300–301), C-strand, CC′ loop, C′-strand (residues 334–351), EF loop (372), and FG loop (residues 382–384) (Figure 1D); together, these form a single convex surface patch of 25 residues. A total of 20 residues of E111 contacted DIII: 8 from the light chain and 12 from the heavy chain. The heavy and light chains both formed contacts with three of the same amino acid residues (S338, G344, and A345). The E111 binding site was centered on the CC′ loop (7 of 25 residues), a previously uncharacterized epitope for flavivirus neutralizing MAbs (Figure 1E). Analysis of the CC′ loop sequences from other DENV serotypes revealed significant variation (Figure 1F), which likely explains the type-specificity (i.e., does not bind or neutralize other DENV serotypes) of E111 [17].
We previously observed reduced binding of E111 to DENV-1 West Pac-74 strain (genotype 4) in a virus capture ELISA, which correlated with a ∼4,100-fold decrease in neutralization efficiency [17]. E111 contacted every residue in the CC′ loop of 16007 DIII, as well as residues on the adjacent C- and C′-strands. Sequence variation between 16007 (genotype 2) and West Pac-74 (genotype 4) occurs at two DIII positions, 339 and 345, both of which are directly contacted by E111.
As variation within the CC′ loop and surrounding regions might affect the differential binding and neutralization of E111 for different DENV-1 genotypes, we generated a library of soluble DENV-1 DIII proteins based upon natural sequence variation of all five DENV-1 genotypes and tested their binding kinetics at 25°C to E111 by surface plasmon resonance (SPR) (Figure 2A, 2F, and Table 2). Whereas E111 had a KD of 18.0±0.08 nM and a half-life of 194 seconds for DIII from 16007, its interaction with West Pac-74 DIII was weaker with a KD of 415±24 nM and half-life of 6.5 seconds (Figure 2B and 2C). Mutagenesis of a T→S at position 339 had a similar affinity as wild type 16007 DIII (KD of 16.6±0.17 nM; t1/2 = 222.4 seconds (Figure 2D)). The crystal structures show that the additional methyl group present in the 16007 Thr residue does not contact E111, whereas the Ser/Thr hydroxyl groups both make equivalent hydrogen bonds with TyrL30B in CDR1 of the E111 light chain (see Figure 1E). In contrast, an A→V change at position 345 of 16007 DIII (to the residue in West Pac-74) decreased the affinity of binding such that kinetics became comparable to West Pac-74 DIII with a KD of 1143±60 nM and half-life of 5.3 seconds (Figure 2E). The side chain of Ala 345 of 16007 makes limited contact with E111, and the additional two methyl groups in Val 345 of West Pac-74 appear to be tolerated sterically at the E111 interface with only minor structural perturbations. However, position 345 and the adjacent CC′ loop residues participate in an extensive network of hydrogen bonds with E111 (Figure S1D), and we speculate that Val 345 leads to a dramatically faster off-rate by subtle destabilization of this interface. Due to the low resolution of the E111 Fab-West Pac-74 DIII structure, ordered water molecules could not be modeled, making precise comparison of the interfaces difficult.
While the on-rates for E111-DIII interactions were relatively constant, the off-rate governed the differences in affinity for the DIII variants (Figure 2F). DIII from strain 3146 SL varies in six positions from 16007, including a valine at position 345. Kinetic analysis with 3146 SL DIII revealed a decreased half-life (5.5 seconds) as compared to 16007. Substitution of individual amino acids corresponding to variation in strain 3146L (D341N (CC′ loop) or A369T (E-strand)) had little negative effect on the half-life of strain 16007 (t1/2 of 173 seconds and 182 seconds, respectively). One amino acid difference (V380I (F-strand)) in 3146 SL caused a small increase in the half-life (t1/2 of 296 seconds) when inserted into DIII of 16007, despite its side chain location ∼10 Å from DIII. An A→I change at position 345 was the only difference in DIII sequence between strains 16007 (genotype 2) and TVP-5175 (genotype 3); this single amino acid substitution reduced the half-life (t1/2 of 2.2 seconds) and affinity of binding (KD of 1981±441 nM). Precise kinetic measurements with DIII from TVP-2130 (genotype 1) were limited by non-specific interactions, and thus not analyzed (data not shown). However, a DIII variant of 16007 that included the unique variation of strain TVP-2130 in the CC′ loop (L351V) showed a decreased half-life with E111 (t1/2 of 32.9 seconds) likely due to the disruption of optimal hydrophobic contact with Tyr30B of the variable light chain CDR1 loop. As a control, insertion of a triple mutation of K310E/T329E/K361T into the 16007 DIII lateral ridge and A-strand epitopes did not alter significantly E111 binding affinity or half-life (t1/2 of 155 seconds). SPR binding studies with the E111 scFv showed a similarly reduced half-life for West Pac-74 DIII compared to 16007 DIII (Table 2). A similar kinetic pattern of E111-DIII interactions also was observed at 37°C (data not shown). Overall, our genetic and biophysical studies support the crystallographic analysis and establish the CC′ loop as important for recognition of DIII by E111.
We determined the inhibitory activity of E111 against strains representing the three other genotypes of DENV-1: TVP-2130 (genotype 1), TVP-5175 (genotype 3), and 3146 SL (genotype 5). Only strains corresponding to genotypes 2 and 5 (16007 and 3146 SL) were neutralized potently by E111 with EC50 values of 3.8±2.0 ng/ml and 22±10 ng/ml, respectively (Figure 3A and Table 3). In comparison, E111 neutralized strains of genotype 1, 3, and 4 poorly with EC50 values ranging from 9,700±3,200 ng/ml to greater than 25,000 ng/ml. Although rather extreme differences in E111-mediated neutralization were observed with different genotypes, this pattern failed to correlate with the KD or half-life of binding with recombinant DIII by SPR (see Table 2). These results suggest that DIII epitope sequence-independent factors (e.g., CC′ epitope accessibility on the virion or secondary binding sites in other domains) likely contribute to the differential genotype neutralization by E111.
Our SPR data demonstrated that substitution of a single residue (A→V) at position 345 of soluble 16007 DIII reduced the E111 binding half-life to that observed with DIII of West Pac-74. To test the effect of a reciprocal V→A change at position 345 in the West Pac-74 strain, we used a reverse genetic system: DENV-1 West Pac-74 reporter virus particles (RVP) [32], [41] incorporating a single V345A mutation were analyzed for sensitivity to MAb neutralization. Whereas DENV1-E103 MAb (which maps to residues T303, G328, T329, D330, and P332 on the lateral ridge of DIII [17]) neutralized both wild-type and V345A DENV-1 West Pac-74 RVP equivalently (Figure 3B), E111 showed only moderately enhanced neutralization of the V345A-containing RVP (3.6-fold, P<0.05, Figure 3C). Although the V345A change improved neutralization of West Pac-74 by E111, it failed to restore the sensitivity seen with DENV-16007 RVP or the fully infectious virus. Thus, either additional amino acid residues accounted for the genotypic difference in neutralization or the epitope was not displayed equivalently on the two viruses.
Because of the differential neutralization of West Pac-74 and 16007 by E111, we evaluated its epitope in the context of full-length E protein structures. We docked our scFv–DIII complex onto the available structure of the pre-fusion DENV E protein dimer (PDB ID 1OAN [12]), and compared this to other characterized DIII-specific anti-flavivirus neutralizing MAbs (Figure 4A). E111 engaged the face of DIII opposite to the one seen previously with 1A1D-2 and 4E11 (A-strand) [31], [42] or WNV-E16 (lateral ridge) [25]. The E111 Fab was rotated in a downward orientation compared to the WNV E16 Fab (Figure 4A) or the A-strand DENV Fabs docked onto the same structure (data not shown). Based on this docking it appears that E111 does not bind the outer exposed surface of the DENV-1 E protein but rather a determinant that is localized to the interior of the virus.
Antibody neutralization of flaviviruses can occur by blocking attachment, internalization, and/or endosomal fusion. Prior to viral fusion, the E proteins on the surface of the virus dissociate from their dimeric hairpin arrangement to form trimeric spikes upon acidification in the late endosome. This rearrangement is essential to allow the newly exposed fusion loop to insert into the endosomal membrane. While the exact structural transitions of the E proteins from dimer to trimer remain unknown, DIII is displaced ∼70° from the pre-fusion structure and settles adjacent to DI in the post-fusion state [43]–[45]. We examined structurally how E111 could disrupt a post-attachment step by docking our Fab-DIII complex onto the structure of the DENV-1 post-fusion trimer [45]. While the Fab does not clash with the adjacent E protein in the DENV-2 pre-fusion dimer structure (Figure 4A), the light chain of E111 sterically would inhibit formation of the post-fusion trimer (Figure 4B) by clashing with the neighboring DI of an adjacent E protein. Thus, from a structural perspective, E111 likely hinders the necessary conformational change from E protein homodimer to homotrimer, and limits viral fusion and infection.
To begin to understand the mechanism of E111-mediated neutralization, we performed pre- and post-attachment neutralization assays [10], [18], [25], [46]. E111 MAb was incubated with DENV-1 16007 before or after virus binding to BHK21-15 cells, and infection was measured by the plaque reduction assay. E111 efficiently neutralized DENV-1 when premixed with the virus before cell attachment or when added after the virus had attached to the cell surface (Figure 4C). This result suggests that E111 has the capacity to neutralize infection after virus attachment has occurred, and is consistent with previously observed patterns of inhibition seen for potently neutralizing DIII-specific antibodies [18], [25], [46], [47].
We next docked our E111-DIII structures onto the cryo-EM-derived model of the mature DENV virion [23]. With three envelope glycoproteins in the asymmetric unit, there are three potential E111-binding environments. However, in the mature DENV model, the E111 epitope was not accessible on the surface in any of the three symmetry environments (Figure 5A). Instead, the E111 epitope was buried in E protein contacts on the virion surface (Figure S2A–E).
Because some anti-flavivirus MAbs (e.g., DII fusion loop-specific) show differential neutralization of mature and partially mature virions, we hypothesized that intrinsic differences in particle maturation among different DENV-1 genotypes might impact neutralization by E111. However, the distinct neutralization profiles by E111 of DENV-1 16007 and West Pac-74 were not explained by differential epitope accessibility due to variation in the maturation state of the viruses (Figure S3). As in the mature virion model, the E111 epitope also appeared inaccessible on the cryo-EM model of the immature DENV virion [48], due to the trimeric arrangement of prM-E, which positions the epitope farther into the virus interior (Figure 5B and Figure S2 F–H).
The cryo-EM model of DENV-2 in complex with the 1A1D-2 Fab describes one conformational ensemble that is a consequence of “breathing” of a virus particle [31]. The 1A1D-2 epitope is partially inaccessible in the unbound conformation of the mature virion, and an increase in temperature allows for dissociation of E protein homodimers and greater exposure of the A-strand of DIII, a major component of the 1A1D-2 epitope. Although there are major rearrangements of the E proteins in this structure, E111 binding still would be prohibited by steric clashes of adjacent E protein monomers (Figure 5C and Figure S2 I–K) at the 3- and 5-fold axes of symmetry. While access of the 2-fold axis is not hindered by contacts with neighboring E proteins, its orientation would inhibit an immunoglobulin from binding this site. Based on these models, it appears unlikely that E111 binds to DENV-1 in the conformations that have been described by cryo-EM to date.
Changes in time and temperature of binding can expose otherwise cryptic epitopes and enhance neutralizing activity of some MAbs [31], [32], [49]. Given our structural, biophysical, genetic, and virological data, we hypothesized that the CC′ epitope on West Pac-74 (genotype 4) was less well exposed compared to 16007 (genotype 2). Alternatively, a difference in the range of the ensemble structures sampled by the two viruses could contribute to the differential neutralization by E111. We compared the time- and temperature-dependence of neutralization of E111 with 16007 and observed little change in EC50 values after incubation of 16007 in the presence of antibody at 37°C or 40°C from 1 to 7 or 4.5 hours, respectively (Figure 6A and E); this suggests that the CC′ loop epitope generally is accessible among the ensemble of conformations sampled by 16007 under steady-state conditions. Similarly, a modest change in the pattern of neutralization was observed with E111 and West Pac-74 RVP after incubation at 37°C up through 7 hours (Figure 6B). However, we observed a marked increase in neutralization when E111 and West Pac-74 RVP were incubated at 40°C for 4.5 hours, with a 20-fold (P<0.001) reduction in the EC50 value (Figure 6F). By comparison, 16007 exhibited only a 3.5-fold fold increase in potency over the same interval. A shift in EC50 was observed with both 16007 and West Pac-74 after 22 hours at 37°C suggesting that over time, a greater number of E111 epitopes become exposed for binding (Figure 6A and B).
Because our SPR binding and structural data (Figures 1 and 2) did not correlate with neutralization experiments in which amino acids of 16007 and West Pac-74 were exchanged (Figure 3), we speculated that the interaction between E111 and amino acid 345 on DIII might be modulated by epitope accessibility in a genotype-dependent manner. We evaluated the effects on time and temperature on E111 neutralization of the reciprocal pair of DENV-1 RVP, V345A West Pac-74 and A345V 16007. We observed enhanced E111 neutralization of V345A West Pac-74 as a function of increased time and temperature (Figure 6C and G); by 22 hours at 37°C or 7 hours at 40°C, the EC50 value of V345A West Pac-74 RVP neutralization approached that of the wild type 16007 RVP (Figure S4). Neutralization of the A345V 16007 RVP by E111 also increased with time and temperature (Figure 6D and H), although there was no difference in EC50 value compared with wild type 16007 RVP. Overall, these experiments suggest that under steady-state conditions, the ensemble of structures with respect to exposure of the CC′ loop epitope are different between strains 16007 and West Pac-74. Virion conformations sampled by individual DENV-1 genotypes likely vary with temperature and differ from those described in existing cryo-electron microscopy models.
Antibody neutralization of flaviviruses requires multiple antibodies to bind a single virion until a neutralization threshold is reached. The ability of a MAb to bind a given viral epitope depends on its concentration, the affinity of its interaction with the infectious virus particle, and the accessibility of the epitope on the virion [11]. While some epitopes are readily accessible on the surface of mature DENV, others are partially or completely inaccessible [27], [29], [31], [50]. However, antibodies that recognize partially or completely occluded sites on the mature virion can still neutralize flavivirus infection because of particle heterogeneity with respect to maturation [29], [50] and/or by sampling of alternate ensemble structures or “breathing”, which allows for intermittent display of cryptic epitopes [31], [32]. Here, our structural studies show that E111 binds to a novel CC′ loop epitope on DIII that does not appear to be affected by particle maturation. Although the CC′ epitope is predicted to be inaccessible on both the mature and immature virion, E111 still potently neutralizes some but not all DENV-1 genotypes. While the amino acid sequence of DIII varies among genotypes in and around the CC′ loop, which affects E111 binding by SPR, there was a limited relationship between the kinetics of binding in vitro and the potency of genotypic neutralization in cell culture. Thus, some aspect of E111 recognition and neutralization appears independent of the epitope sequence. While E111-mediated neutralization of strain 16007 was less affected by changes in time or temperature of incubation, neutralization of West Pac-74 was enhanced substantially after incubation with E111 at higher temperatures and for longer times. These experiments suggest that at steady state, DENV-1 16007 has a broader ensemble of conformations compared to West Pac-74, allowing for enhanced exposure of particular DIII-specific epitopes for MAb neutralization. This phenomenon could explain in part why so many (13 of 15) of our DIII-specific MAbs strongly neutralized infection of strain 16007 but not West Pac-74 despite the relatively few amino acid changes in DIII [17]. Several of our other anti-DENV-1 MAbs map to the lateral ridge epitope on DIII, which should be fully exposed on the virion [51], and, in principle, not require temperature or time-dependent changes in structure for enhanced epitope accessibility. Nonetheless, in on-going studies, DIII lateral ridge epitope-specific MAbs (e.g., DENV1-E102, DENV1-E103, DENV1-E105, and DENV1-E106) all neutralized infection by DENV-1 West Pac-74 more efficiently after an increase of temperature and duration of incubation (K. Dowd and T. Pierson, unpublished results). Thus, for DENV-1, structural perturbations to the virion may influence neutralization by MAbs recognizing ostensibly more and less exposed epitopes in a strain-dependent manner.
Within DIII of different DENV-1 genotypes, the greatest sequence variation occurs within and surrounding the CC′ loop (4 of 9 sites). In comparison, the CC′ loop residues of other DENV serotypes are highly conserved: for DENV-2 and DENV-3 genotypes, only 2 of 8 and 1 of 11 sites, respectively, show amino acid variation within or proximal to the CC′ loop. Neutralizing antibodies that localize to the CC′ loop are not restricted to DENV-1. We recently mapped four inhibitory DENV-2 MAbs to residues within the CC′ loop by yeast surface display [18]. Several of our DENV-2-specific CC′ loop MAbs protected against DENV-2 challenge both as pre-exposure prophylaxis and post-exposure therapy in mice. Due to the lack of a reproducible mouse model for DENV-1 16007 infection, we have not assessed directly the therapeutic efficacy of E111 under conditions where it is highly neutralizing. Nonetheless, E111 was effective as prophylaxis against DENV-1 West Pac-74 in an immunocompromised AG129 mouse model of infection [17], despite its relatively poor EC50 value in cell culture.
Flavivirus virions can undergo structural re-arrangements with an increase of temperature, which can facilitate binding of antibodies to epitopes with limited accessibility [31], [32]. Indeed, DENV-1 RVP showed markedly enhanced neutralization by E111 that was dependent on both time and temperature of incubation with antibody. While these pre-incubation conditions alone improved neutralization of West Pac-74 RVP by E111, insertion of the V345A substitution (from 16007 into West Pac-74) was required to shift the neutralization curve to achieve an EC50 value of wild type 16007. This observation is consistent with a role for amino acid 345 in E111 engagement and correlates with differences in the binding of V345A and wild type DIII of West Pac-74 observed by SPR. Thus, while a lack of E111 epitope accessibility explains why West Pac-74 was not efficiently neutralized under steady-state conditions, prolonged time and higher temperature of incubation promoted sampling of a broader ensemble of structures that revealed the differential effect of residue 345 on neutralization of West Pac-74. Interestingly, the reciprocal mutation, A345V, when substituted into 16007 had essentially no impact on neutralization by E111, regardless of the time and temperature of incubation. While wild type DIII of 16007 binds E111 with a 37-fold longer half-life than the A345V variant, the on-rates were equivalent. Thus, E111 binding and neutralization may be preferentially determined by the on-rate kinetics of antibody attachment through stabilization of a potentially transient/infrequent conformation present in the 16007 ensemble of structures. Indeed, a mutant DIII of 16007 (K343I), which showed a substantially enhanced half-life of binding interaction (∼45 minutes) with E111, did not affect neutralization potency (S. K. Austin, M. Diamond, and D. Fremont, unpublished results).
Currently, there are no cryo-electron microscopy models of DENV-1, whereas several models of DENV-2 have been described [23], [31], [52]. These models were used as surrogates of DENV-1 in an attempt to understand how E111 engaged its epitope in the context of a virion. Due to the packing of individual E protein monomers in the particle, there are limitations of accessibility of antibodies to portions of the E protein depending upon its particular symmetry environment. Examination of the available cryo-electron microscopy models of DENV failed to explain how E111 binds to the CC′ loop on the virion, as it is completely inaccessible in all models, in all symmetry environments. DENV-2 particles are believed to sample ensemble of conformations [32], as shown in the captured intermediate of the cryo-electron microscopy reconstruction of DENV-2 with the 1A1D-2 Fab [31]. Despite a sizable increase in the relative E protein surface area exposed in the 1A1D-2 captured intermediate, from a structural perspective there was still insufficient accessibility to allow engagement by E111. Our crystallographic, kinetic, and functional data all support a role for the CC′ loop in E111 recognition yet the existing atomic models cannot explain how it engages the virion. We speculate that a particular structural ensemble allows exposure of the CC′ loop and binding of E111 for certain DENV-1 genotypes. Indeed, we know little about the alternate conformational states sampled by flaviviruses, as only two cryo-electron microscopy models of transitional flavivirus states exist: a low pH model of WNV E16 Fab and WNV, and the 1A1D-2 Fab binding to DENV-2 at physiological pH [31], [53]. Further investigation using antibody captured virus conformations are needed to explore the breadth of structures sampled by flaviviruses.
Our structural and functional characterization of E111 has implications for vaccine development and assessment. While natural infection with DENV is believed to confer durable protective immunity against homologous DENV serotypes, several papers have reported disparate neutralization titers of homotypic strains and genotypes after natural infection or immunization. The neutralization potency of patient sera during the course of an DENV-3 epidemic varied substantially for DENV-3 strains corresponding to distinct genotypes [33]. A study of sera from individuals experiencing DENV-1 infections also showed variable neutralizing activity against different DENV-1 strains [35]. Moreover, pooled sera from monkeys immunized with a tetravalent chimeric live attenuated DENV vaccine revealed a range (e.g., ∼12-fold for DENV-1 strains) of variability in EC50 neutralization titers against individual strains of a given DENV serotype [38]. It remains possible that the differences are even larger, as full neutralization profiles or EC90 values were not reported in this latter study.
Studies examining how genotypic variation affects neutralization with MAbs [16]–[18], [54], [55] suggest that natural sequence variation among genotypes of a DENV serotype impacts the potency of antibody neutralization. Analogously, many neutralizing antibodies against HIV, influenza, and hepatitis C viruses fail to inhibit related stains and/or serotypes [56]. While the cryptic nature of the CC′ loop may be a special case [17], we propose that disparate neutralization of DENV-1 strains by monoclonal or polyclonal antibodies could be due to or at least be affected by differences in the ensemble of conformations sampled by the virion. Selection of DENV strains that sample a greater diversity of conformations as vaccine candidates could broaden the repertoire of neutralizing antibodies against DENV. Such strains could better expose and present the spectrum of epitopes available, and thereby induce a more diverse neutralizing antibody repertoire. Alternatively, the use of DENV strains or formulations with a limited structural ensemble could focus the neutralizing antibody response on specific epitopes whose accessibility is independent of time and temperature, and thus, more effective at neutralizing a diverse range of strains, regardless of particle conformation. Although a monovalent formalin-fixed DENV-2 vaccine induced strongly neutralizing antibodies against the parent strain in mice and monkeys, it was never evaluated for activity against a range of strains corresponding to different genotypes [57]. Clearly, further empirical studies are necessary to assess directly how virion ensembles affect immunogenicity as well as pathogenesis. Finally, the conformational diversity of DENV strains used for diagnostic evaluation of polyclonal serum could affect the interpretation of its neutralizing potential; for example, the choice of a DENV strain that cycles through limited structural conformations at 37°C for neutralization assays could underestimate the quality of the inhibitory activity of the antibody response in human serum.
In summary, we have defined a novel structural epitope on the CC′ loop of DIII of DENV, which is not accessible in the existing cryo-electron microscopy reconstruction models of DENV particles. Our experiments also suggest that the ensemble of conformations of the DENV virion structure varies in a genotype-dependent manner, which impacts the neutralizing activity of antibodies and has direct implications for the development and analysis of candidate DENV tetravalent vaccines.
An untagged form of DENV-1 DIII (strain 16007, residues 293 to 399) was cloned into the pET21a vector (Novagen) and expressed by autoinduction [58] in BL21 bacterial cells (Agilent). Isolated inclusion bodies were solubilized and oxidatively re-folded, as previously described [59]. Variants of the DENV-1 16007 strain (residues 293–399) were generated by site-directed mutagenesis (QuikChange, Agilent) using unique primer sets (Table S2). E111 scFv was engineered with a (GGGGS)3 linker between the VL and VH and domains and a C-terminal hexahistadine tag, cloned into the pAK400 vector, and expressed in the periplasm of bacteria. The bacteria were lysed and the E111 scFv was purified by nickel affinity and size exclusion chromatography. The scFv was complexed with excess DIII and purified by size exclusion chromatography. The E111 scFv-DIII complexes were crystallized at 10 mg/ml by sitting-drop vapor diffusion at 20°C using 20% polyethylene glycol (PEG) 3350, 0.2 M potassium sulfate, and 5% glycerol. Crystals were cryo-protected in a solution containing 35% glycerol and cooled in liquid nitrogen.
After protein A affinity purification, the E111 IgG was cleaved with immobilized papain (Pierce Biotechnology), and Fabs were recovered, as the Fc and uncleaved IgG were removed by passage over a second protein A affinity column. West Pac-74 DIII and E111 Fab were mixed and isolated by size exclusion chromatography on a S75 Superdex column. The E111 Fab-DIII complexes were crystallized at 15.8 mg/ml by sitting-drop vapor diffusion at 20°C using 0.1 M MES pH 5.3, 20% PEG 6000 (final pH 6.0) with 1% glycerol. The crystals were cryo-protected in the mother solution supplemented with 20% ethylene glycol and cooled in liquid nitrogen.
Data were collected at APS beamline 19–ID (Argonne National Laboratories) at 293° K and at a wavelength of 1.007 Å using a CCD detector. Data were processed, scaled, and merged with HKL-2000 [60]. Crystallographic phasing for the E111 scFv-DIII complex was obtained by molecular replacement (PHENIX [61]) using the predicted scFv model given by the PIGS server [62] and the atomic structure of DENV-1 16007 DIII (PBD accession number 3IRC [17]). The crystals belong to the space group P43212 with the unit cell dimensions of a = b = 135.224 and c = 52.221, with one E111 scFv-DIII complex per asymmetric unit. An atomic model was iteratively built in COOT [63] and refined in PHENIX, and contained 328 amino acids (residues 298–396 from DIII, 1–114 of the E111 VH, and 1–107 of the E111 VL, (Chothia numbering), 147 water molecules, four chloride ions and one sulfate and glycol molecule each. The final 2.5 Å resolution model was refined to an Rwork = 19.6% and Rfree = 23.9% for all F>0, with excellent geometry and Ramachandran angles (97.4% favored and 0.3% outliers).
Data for the E111 Fab-West Pac-74 DIII complex were initially processed with centered orthorhombic symmetry with subsequent identification of pseudo-merohedral twinning. The crystals actually belong to space group P21 and suffer ∼30% twinning with the operator h,-k,-h-l. The data was successfully phased by molecular replacement using the E111 scFv-DIII complex and the constant domains from PDB ID 4AEH with two molecules per asymmetric unit. The atomic model was iteratively build in COOT and refined in REFMAC [64] and PHENIX using jelly body and reference model restrains, respectively. The structure contained 1060 amino acids (residues 299–395 from DIII, 1–212 from the light chain, and 1–212 from the heavy chain (Chothia numbering). The final 3.8 Å resolution model was refined to an Rwork = 23.7% and Rfree = 27.8% for all F>0, with excellent geometry and Ramachandran angles (97.0% favored and 0.4% outliers). The atomic coordinates and structure factors have been deposited in the Protein Data Bank (www.rcsb.org) under accession numbers 4FFY and 4FFZ for the scFv and Fab complexes, respectively.
Kinetic information on the interaction between E111 and DIII variants was obtained using a Biacore T100 instrument. Approximately 500 response units (RU) of E111 or control MAb/scFv (WNV E16) was immobilized using amine coupling to a Series S CM5 chip. Once stabilized, a two-fold dilution series of the DENV DIII variants were injected over the chip at a flow rate of 65 ml/min for 180 seconds and allowed to dissociate for 1,000 seconds. DIII had dissociated over this time period and additional regeneration was not necessary. Data was processed using the Biacore Evaluation Software (Version 1.1.1) by double referencing and a 1∶1 Langmuir fit of the curves. All curves were reference subtracted from a flow cell containing the negative control WNV E16 MAb/scFv. Maximum response units were plotted versus concentration and this curve was fitted to determine the KD. Results were generated from at least three independent experiments, with a minimum of six binding curves per experiment.
PRNT were performed with the five DENV-1 genotype strains with E111 on Vero cells as described previously [17]. In some experiments, pre- or post-attachment studies were performed as a variation [18], [46]. Briefly, serially diluted MAbs were mixed 1∶1 with 102 PFU of 16007 DENV-1 virus in DMEM containing 10% FBS and incubated for one hour at 4°C. The virus-MAb mixture was then added to the cells at 4°C, and after washing, incubated at 37°C for one additional hour. Alternatively, cells and media were chilled to 4°C before 102 PFU of virus was added and incubated for one hour. Unbound virus was washed away with chilled media before the addition of E111 MAb. After one hour at 4°C, cells were washed with warm media and overlaid with 2% low-melt agarose (SeaPlaque) in modified Eagle medium and 4% FBS and incubated at 37°C for 6 days. PRNT50 values were determined using non-linear regression analysis (Graph Pad Prism4).
DENV-1 RVP were generated as described previously [32], [41]. Plasmids expressing the wild type or mutant capsid (C)-prM-E genes of DENV-1 (strain 16007 or West Pac-74) were co-transfected into HEK293T cells with a plasmid encoding a sub-genomic WNV replicon expressing GFP. E protein variants were engineered by site-directed mutagenesis (QuikChange, Agilent) and confirmed by sequencing. Standard neutralization assays with RVP were performed by incubating serial dilutions of antibody with DENV-1 RVP for 1 hour at 37°C, followed by addition of Raji-DC-SIGNR cells. Infection was carried out at 37°C and monitored by flow cytometry 48 hours later for GFP expression. To assess the role of temperature on MAb activity, neutralization assays were performed as above, and designated as “reference” neutralization profiles. Additional RVP-antibody complexes, following the initial 1 hour incubation at 37°C, were further incubated at 37°C or 40°C for incremental lengths of time, followed by infection of Raji-DC-SIGNR cells. Relative infectivity was determined after comparison to infectivity of DENV-1 RVP incubated at the same temperature in parallel in the absence of antibody.
RVP were produced from HEK293T cells to represent various stages of maturation (standard (containing a heterogeneous mixture of partially mature and mature) or mature (produced in the presence of an over-expression of furin)) according to published protocols [29]. Standard neutralization assays with RVP were performed by incubating serial dilutions of antibody with DENV-1 RVP for 1 hour at 37°C, followed by addition of Raji-DC-SIGNR cells. Infection was carried out at 37°C and monitored by flow cytometry 48 hours later for GFP expression. To assess the role of temperature on MAb activity, neutralization assays were performed as above, and designated as “reference” neutralization profiles.
(a) E protein dimer. Docking of the E111-DIII structure and the WNV E16 Fab-DIII (PDB 1ZTX) onto the pre-fusion dimer structure of DENV2 (PDB 1OAN) was based upon superimposition of DIII. (b) E protein trimer. The same procedure was used for docking of the E111 scFv onto the post-fusion DENV-1 trimer structure (PDB 3G7T). (c) Virions. The coordinates for the full mature (PDB 1KR4), immature (PDB 3C6D), and 1A1D-2-bound (PDB 2R6P) DENV-2 virus assemblies were downloaded from VIPERdb [65] (http://viperdb.scripps.edu/). The surface of the virus was clipped to reveal the interior of the virion models. All structural representations were colored and rendered using PyMOL (The PyMOL Molecular Graphics System, Version 1.4–1.5.1 Schrödinger, LLC., http://www.pymol.org).
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10.1371/journal.ppat.1005714 | Hepatitis C Virus Core Protein Promotes miR-122 Destabilization by Inhibiting GLD-2 | The liver-specific microRNA miR-122, which has essential roles in liver development and metabolism, is a key proviral factor for hepatitis C virus (HCV). Despite its crucial role in the liver and HCV life cycle, little is known about the molecular mechanism of miR-122 expression regulation by HCV infection. Here, we show that the HCV core protein downregulates the abundance of miR-122 by promoting its destabilization via the inhibition of GLD-2, a non-canonical cytoplasmic poly(A) polymerase. The decrease in miR-122 expression resulted in the dysregulation of the known functions of miR-122, including its proviral activity for HCV. By high-throughput sequencing of small RNAs from human liver biopsies, we found that the 22-nucleotide (nt) prototype miR-122 is modified at its 3′ end by 3′-terminal non-templated and templated nucleotide additions. Remarkably, the proportion of miR-122 isomers bearing a single nucleotide tail of any ribonucleotide decreased in liver specimens from patients with HCV. We found that these single-nucleotide-tailed miR-122 isomers display increased miRNA activity and stability over the 22-nt prototype miR-122 and that the 3′-terminal extension is catalyzed by the unique terminal nucleotidyl transferase activity of GLD-2, which is capable of adding any single ribonucleotide without preference of adenylate to the miR-122 3′ end. The HCV core protein specifically inhibited GLD-2, and its interaction with GLD-2 in the cytoplasm was found to be responsible for miR-122 downregulation. Collectively, our results provide new insights into the regulatory role of the HCV core protein in controlling viral RNA abundance and miR-122 functions through miR-122 stability modulation.
| Viruses may benefit from altering the host cell’s normal miRNA milieu, by either lowering or increasing antiviral or proviral miRNA levels. Our results reveal a mechanism by which virus infection can regulate miRNA abundance by modulating the activity of cellular enzymes responsible for miRNA 3′-terminal modifications. Hepatitis C virus (HCV) uses microRNA-122 (miR-122) as a proviral host factor to increase viral genome abundance, but HCV infection results in diminished miR-122 levels by unknown mechanisms. We demonstrated that the HCV core protein plays a role in reprogramming the cellular profile of miR-122 isomers by inhibiting GLD-2, a non-canonical cytoplasmic poly(A) polymerase, to promote miR-122 destabilization. Our results raise several important questions. First, how is only a specific subset of miRNAs in the liver affected by GLD-2 inhibition by the HCV core protein? Second, how does HCV regulate pre-miR-122 processing in its loop region? Third, does GLD-2 inhibition by the HCV core protein affect the translation of a specific group of host mRNAs? With many unanswered questions, our results propose a novel feedback regulatory mechanism for controlling HCV RNA abundance by the viral capsid protein, which is capable of modulating miR-122 activity and stability through the inhibition of GLD-2.
| Hepatitis C virus (HCV), a positive-sense single stranded RNA virus, causes chronic hepatitis and liver cirrhosis, often leading to the development of hepatocellular carcinoma. The HCV genome is composed of a long open reading frame (ORF) that is flanked by untranslated regions (UTRs) at both the 5′ and 3′ ends. The ORF encodes a polyprotein of approximately 3010 amino acids that is processed by cellular and viral proteases into 10 polypeptides, including structural (core protein and envelope proteins E1 and E2) and non-structural (NS) proteins [1].
MicroRNA-122 (miR-122) is the most abundant miRNA in the liver [2, 3]. miR-122 binds to two closely spaced target sites in the 5′-UTR of the HCV genome to promote viral RNA stability and accumulation by diverse mechanisms [4–8]. Sequestration of miR-122 with miravirsen, an antisense oligonucleotide targeting miR-122, resulted in a prolonged and dose-dependent decrease in HCV RNA titers in a clinical study [9]. In addition to its proviral function for HCV, miR-122 regulates hepatic function and cholesterol and fatty-acid metabolisms [10, 11]. Interference of miR-122 function using an antisense oligonucleotide decreased cholesterol levels in plasma [11]. In addition, it was found that miR-122 downregulates its target genes that may be involved in tumorigenesis and metastasis, thus acting as a tumor suppressor [12–15].
Functional relationships between viruses and cellular miRNAs are likely to play important roles in viral pathogenesis and in modulating virus replication by either promoting or limiting replication [16, 17], as also studied with HCV using various miRNAs that directly bind to the viral genome [18]. In addition, the interface between viruses and miRNAs can alter predefined cellular regulatory networks of miRNAs by re-programming miRNA types and abundance. miRNA expression is controlled at various stages of transcription and processing, and its level and function can be further regulated by mature sequence modifications. miRNA 3′ modification is known to regulate its stability or turnover [19], and recent high-throughput sequencing studies revealed extensive 3′-terminal modifications on miRNAs in animal cells [20]. Despite the ample evidence of post-transcriptional modification on mature miRNAs, the impact of miRNA 3′-end modifications is largely unknown.
Previous studies illustrated that miR-122 levels are decreased in HCV-infected cells [21]. In addition, a decreased miR-122 level was detected in sera from patients with HCV [21, 22]. However, little is known regarding how the cellular abundance of miR-122 is regulated by HCV. We report in this study that the HCV core protein inhibits GLD-2’s terminal mononucleotidyl transferase activity, resulting in the downregulation of miR-122 isomers bearing non-templated 3′-end single-nucleotide additions and thereby the dysregulation of miR-122 function. Our results demonstrate a novel role of the HCV core protein in regulating viral replication and translation through GLD-2-mediated miR-122 stability control.
We analyzed miR-122 expression levels in human liver biopsies from healthy subjects and patients with HCV (S1 Table show the characteristics of the liver biopsies used in this study) and observed decreased miR-122 levels in liver specimens from patients (66 ± 3.7%, 72 ± 0.7%, and 74 ± 1.9% decreases in liver biopsies HCV-1, HCV-2, and HCV-3, respectively from patients infected with HCV) compared with those in the normal liver N-1 (Fig 1A). In the hepatocellular carcinoma cell line Huh7, which is widely used in HCV studies because of its capability to support HCV replication, the miR-122 level was found to be approximately 100-fold lower than that in the normal liver N-1 (1.25% of miR-122 levels in N-1). In this cell line, HCV infection elicited a 70 ± 1.5% decrease in miR-122 levels at 2 days post infection (Fig 1B) when ~30–40% of cells were infected as determined by immunostaining of core protein (S1A Fig). The miR-122 level decreased gradually during the course of HCV infection as early as day 1 post-infection (S1B Fig).
To identify the viral proteins involved in this regulation of miR-122 expression, we monitored miR-122 levels in Huh7-derived cell lines that individually express the HCV core protein, NS5B, and NS proteins (NS3 to NS5B from an HCV genotype 1b subgenomic replicon, which constitutively replicates in the R-1 cell line; Fig 1C, top). We observed a significant decrease in the expression of the mature form of miR-122 when the HCV core protein was expressed (30 ± 11.5% decrease; Fig 1C and 1D), whereas its levels were not influenced by NS proteins. Notably, the expression of miR-122 precursors (primary and precursor forms of miR-122) was not altered by the core protein (Fig 1E), demonstrating that miR-122 transcription and biogenesis were not affected by core protein expression.
In Huh7 cells transiently expressing the HCV core protein, decreased miR-122 levels were also observed, as assessed by real-time quantitative RT-PCR (53 ± 1.6% of an empty vector-transfected control) and northern blot analysis (35% decrease compared with miR-122 levels in the 6-μg empty plasmid-transfected control) (Fig 1F), whereas miR-221 levels were not altered upon core protein expression (Fig 1G). Transient expression of the core protein did not reduce cell viability (Fig 1H). Its expression level in Huh7 cells was in the range of 0.2–0.46 ng/μg total protein. This level was comparable to that (0.3 ng/μg total protein) in Huh7 cells infected with HCV (MOI = 0.25) and that (0.17 ng//μg total protein) in the liver of HCV-infected SCID mouse with chimeric liver repopulated with human hepatocytes (S1C and S1D Fig show the percentage of core protein-positive cells in the chimeric liver and serum HCV RNA titer in the infected mouse, respectively). The downregulating effect of the HCV core protein was also observed both in R-1 cells (Fig 1I) and in mouse primary hepatocytes (Fig 1J), which express the same mature form miR-122 as in human hepatocytes.
We performed a series of experiments to evaluate the impact of miR-122 downregulation. First, using a psiCHEK-2_CULT1(WT) dual-luciferase reporter vector that contains the CULT1 3′-UTR, a known target of miR-122 [23], we demonstrated that the reporter activity, which was suppressed by miR-122 duplex transfection, could be rescued by the HCV core protein both in Huh7 and R-1 cells (Fig 2A). As expected, this rescuing effect and target gene-suppressing activity disappeared when the miR-122-binding site in the dual reporter was mutated.
miR-122 is involved in the cholesterol synthesis pathway, and the plasma cholesterol level decreases upon the inhibition of miR-122 using an antisense oligonucleotide [10]. Cellular cholesterol metabolism plays direct or indirect roles in the HCV life cycle [24]. We found a decrease in the cellular total cholesterol content in the core protein-expressing stable cell line Huh7/core (Fig 2B), elaborating a recent observation of hypocholesterolemia induced by HCV infection [25] and suggesting potential regulatory role of HCV core protein in cellular cholesterol biosynthesis [10].
HCV RNA abundance is regulated by miR-122 binding to the HCV 5′-UTR [4, 5, 26]. miR-122 transfection into R-1 cells increased HCV subgenomic RNA titer (Fig 2C), confirming the previous findings. As shown in Fig 2C and 2D, the expression of the core protein in R-1 cells and its expression in Huh7 prior to HCV (JFH-1) infection resulted in significant decrease in HCV RNA levels (50.8 ± 2.8% and 61 ± 5.4% of control, respectively). As expected, we observed that miR-122 added exogenously could rescue the inhibitory effects of core protein in R-1 cells.
Furthermore, using a dual luciferase reporter system that expresses the Renilla luciferase reporter (Rluc) in a cap-dependent manner and firefly luciferase (Fluc) reporter by HCV internal ribosome entry site (IRES)-mediated initiation, we were able to demonstrate the gradual inhibition of HCV IRES-mediated translation with an increase in the HCV core protein expression (maximum 51 ± 5.9% decrease compared with 6-μg empty vector-transfected control; Fig 2E).
Recent studies illustrated that post-transcriptional 3′-terminal modification of the mature or precursor forms of miRNAs can regulate their turn-over by promoting either stabilization or destabilization [20, 27, 28]. We intended to test the possibility that HCV infection affects this miRNA modification process to alter the profile of miR-122 isomers. Therefore, we performed high-throughput sequencing of small RNA libraries from human liver biopsy specimens from healthy individuals (N-1 and N-2) and patients with HCV (HCV-1 to HCV-3) (S2 Table shows details on the small RNA types and their read counts). Among diverse miRNA [578 (for HCV-2) to 775 (for HCV-3) species identified by aligning the reads to 1,080 prototype miRNAs in the miRNA database] expressed in human liver tissues, miR-122 was the most abundantly expressed miRNA. We detected various miR-122 species in the range of 16 to 25 nucleotide (nt) in length (S3 Table). Because miR-122 isomers of 21, 22, and 23 nt in length are three prominent species, which were detectable by northern blot analysis of total RNA from Huh7 cells and primary hepatocytes (S2A and S2C Fig), and other individual isomers comprised <2% of the total read counts of all forms of miR-122 species (with a read frequency of >50 reads per million) in biopsies, we focused on these three isomers for further sequence analysis.
Interestingly, the proportion of individual miR-122 isomers was substantially altered in patient liver biopsies. As shown in Fig 3A, there was a >2-fold increase in the proportion of the 21-nt miR-122 isomer compared with that in the normal liver N-1. In contrast, the proportion of the 23-nt isomer dramatically decreased, whereas the ratio for the 22-nt miR-22 prototype species only slightly decreased. The 23-nt isomer, including five different species, had either a templated nucleotide [i.e., the 3′-end U residue derived from precursor miR122 (pre-miR-122); Fig 3B, top shows the partial primary miR-122 (pri-miR-122) secondary structure and sequence] or a non-templated nucleotide (i.e., isomers bearing a dinucleotide of 3′-GA, 3′-GG, and 3′-GC in the 23-nt isomer in which the penultimate G is derived from a template or a 3′-AU dinucleotide; underlined sequences represent non-templated additions) at their 3′ ends. In addition to the 3′-end mono-adenylated isomer, we could also detect miR-122 isomers carrying a 3′-mono-G or -C tail in the liver biopsies, but these isomers represented <0.2% of the total read counts of all miR-122 species (S3 Table). In fact, isomer profile analysis for the top 50 most abundantly expressed miRNAs in the liver biopsies revealed that all miRNAs bear a single 3′ terminal non-templated or templated nucleotide (any of four ribonucleotide residues) with adenylate and uridylate being two major terminal nucleotides (S3 Fig).
Notably, in patient liver specimens, all of the miR-122 isomers with a non-templated nucleotide added to the 3′-end of 22-nt prototype miR-122 exhibited a similar decrease in their proportions (Fig 3B). In particular, the proportion of two major 23-nt isomers, namely the A-tailed and U-tailed isomers, was decreased by approximately 2-fold in HCV-infected liver samples (Fig 3C) compared with that in normal liver tissue specimens (N-1 and N-2). Similar results were also observed both in Huh7 cells transiently expressing core protein and in HCV-infected Huh7 cells (Fig 3D), in which only 3′-end mono-adenylated or uridylated species were detected probably due to ~100-fold lower miR-122 levels in Huh7 cells (Fig 1A and S4 Table).
We found that HCV-mediated inhibition of 3′-end monoadenylation and uridylation is limited to a specific set of miRNAs in the liver (S4 Fig). Analysis of small RNA sequencing datasets for each species of the 24-nt miR-122 isomers (four different species of 24-nt isomer harboring an AA, AU, UU, and UA dinucleotide; underlined sequences represent non-templated nucleotides) also revealed that their relative ratios decreased by >2-fold in HCV-infected liver tissues, although the total read counts of the 24-nt long miR-122 isomers was relatively low (approximately 1% of total reads; S3 Table). The results together demonstrate that regardless of the types of 23- and 24-nt miR-122 isomers bearing a single nucleotide or dinucleotide, their relative proportions were decreased in liver tissues from patients with HCV. In particular, the non-templated addition of adenylate residues, which is the major modification event of miR-122, was substantially inhibited by HCV.
Having found that HCV infection interferes with miR-122 3′-end tailing in liver tissues, we investigated whether the HCV core protein inhibits this modification. miRNA tailing can be catalyzed by various nucleotidyl transferases including non-canonical poly(A) polymerases and terminal uridylyl transferases. We silenced each of a series of known terminal nucleotidyl transferases using specific small interfering RNAs (siRNAs) and found that miR-122 abundance is specifically regulated by GLD-2, a non-canonical cytoplasmic poly(A) polymerase [29] [also known as terminal uridylyl transferase 2 (TUTase-2)] (Fig 4A). Silencing of GLD-2 expression with increasing doses of siRNA (Fig 4B), which was not associated with a substantial cell viability decrease (Fig 4C), decreased the cellular abundance of three major miR-122 isomers (21-, 22-, and 23-nt isomers) (Fig 4D), resulting in an overall 49% decrease in their miRNA levels and consequent decreases in HCV subgenomic RNA and viral protein (NS3 and NS5B) levels (Fig 4B and 4D). As observed in GLD-2 knock-downed cells, the 23-nt miR-122 isomers as well as the two other major isomers (21-nt and 22-nt species) concurrently decreased both in Huh7 cells transiently expressing the core protein and in HCV-infected cells (S2A and S2B Fig), suggesting that the miR-122 level reduction does not occur in a step-by-step manner in the order of decreasing length of isomers. Furthermore, when the core protein was expressed in GLD-2 knock-downed cells, miR-122 levels decreased further (Fig 4E, see the cells treated with 1 or 5 nM siGLD-2). However, when GLD-2 expression was substantially silenced with higher concentrations of siRNA, this inhibitory effect of core protein was diminished, suggesting that miR-122 level regulation by core protein is a GLD-2-dependent event.
Because miR-122 abundance was specifically regulated by GLD-2 silencing and the miR-122 isomers’ 3′ end was found to carry four different single ribonucleotides, we assessed the possibility that GLD-2 adds ribonucleotides other than adenylate to the 3′ end of miR-122 and sought to test whether GLD-2 catalytic activity is inhibited by the HCV core protein. We addressed these questions using highly purified recombinant GLD-2 and core proteins (Fig 5A). Initially, we tested the adenylation activity of the purified recombinant GLD-2 with an N-terminal histidine tag and found that wild-type GLD-2, but not its inactive form GLD-2(D215A), monoadenylates miR-122-5p (Fig 5B). This result was unexpected because GLD-2 was originally identified as a non-canonical cytoplasmic poly(A) polymerase that acts on certain mRNAs [29, 30] but was consistent with previous studies illustrating GLD-2-mediated miR-122 3′ adenylation [19, 31]. Importantly, we found that GLD-2 was capable of adding any of the four ribonucleotides to the miR-122 3′ end with almost similar efficiency (Fig 5C). Similarly, GLD-2 added any of four ribonucleotides to the 3′ end of eight other miRNAs (S5A Fig) randomly selected from the top 50 most abundantly expressed miRNAs in human liver, with different efficiencies (S5B Fig). Surprisingly, miR-122-5-p was most efficiently modified by GLD-2. Further analysis of GLD-2 template specificity using ribonucleotide homopolymers (20-nt) revealed that (A)20 is the best template of GLD-2 for 3′-end adenylation, uridylation, guanylation, and cytidylation (S5C Fig). These findings suggest that GLD-2 has a certain degree of selectivity on small RNAs for 3′ end modification. Using the miR-122-5p that was most efficiently adenylated by GLD-2, we revealed that the HCV core protein inhibits GLD-2’s 3′-end monoadenylation activity, whereas severe acute respiratory syndrome coronavirus (SARS-CoV) capsid protein had little effect on GLD-2 activity (Fig 5D).
Because GLD-2 silencing lowered miR-122 levels, we assessed whether the HCV core protein expression downregulates GLD-2, but we did not observe a reduction in GLD-2 expression (S6A Fig). Translin, a DNA-binding protein, was reported to bind to miR-122 and increase its stability [32]. Translin mRNA expression level was not altered by the core protein (S6B Fig). These results together suggest that the inhibition of GLD-2-catalyzed 3′ non-templated nucleotide addition by the core protein is responsible for the downregulation of miR-122 following HCV infection.
To verify the interaction between the core protein and GLD-2, we performed coimmunoprecipitation experiments using cell lysates from the Huh7 cells in which Flag-tagged HCV core protein or NS5B protein was ectopically expressed. Fig 6A demonstrates the ability of the core protein to interact with endogenous GLD-2. HCV core protein did not interact with Ago2, whereas its association with Hsp60 [33] could be confirmed. This result suggests that miR-122 level regulation was not mediated through Ago2-core protein interactions. Coimmunofluorescence staining showed that the HCV core protein, which predominantly resided on the perinuclear region, partially colocalized with GLD-2, which was previously identified to be located in both the nucleus and cytoplasm [28], when they are present in the cytoplasm (Fig 6B).
In an additional experiment using various GFP-tagged core proteins (Fig 6C, top panel), we found that the full-length core protein GFP-C(1–191) and its truncation mutants GFP-C(76–191) and GFP-C(99–191), which localized in the cytoplasm due to the presence of the C-terminal region spanning amino acids 174–191, downregulate miR-122 expression. In contrast, other truncation mutants [GFP-C(1–75), GFP-C(1–121), and GFP-C(1–173)], which translocated to the nucleus, failed to decrease miR-122 levels (Fig 6C and 6D), indicating that GLD-2 inhibition-mediated miR-122 expression regulation by the core protein is a cytoplasmic process. These results also indicate that the RNA-binding activity of the core protein is not involved in this regulation because GFP-C(76–191) and GFP-C(99–191), lacking the known RNA-binding domain (amino acids 1–75) [34], did also reduce miR-122 levels. GFP-C(151–191), in which 52 amino acids was further deleted from GFP-C(99–191) and thus lacks the most of the domain 2 of HCV core protein known to be required for its lipid droplet association [35], failed to decrease miR-122 level (Fig 6E). This mutant showed a weaker affinity to GLD-2 as revealed by co-immunoprecipitation experiments (Fig 6F). More importantly, this deletion mutant was barely colocalized with GLD-2 in the cytoplasm, whereas GFP-C(1–191) and core protein expressed in Huh7 cells by transfection of HCV RNA colocalzied with GLD-2 in the perinuclear regions and lipid droplet-like structures (Fig 6G). HCV RNA (JFH-1 in vitro transcripts)-transfected Huh7 cells showed enhanced colocalization of the core protein with GLD-2 compared with the cells expressing the GFP-fused core protein alone, suggesting that core protein-GLD-2 interaction in cells might be affected by viral protein expression and/or viral genome replication.
We investigated whether miR-122 3′-end modification with four different ribonucleotides has any impact on miRNA activity and stability. In HeLa cells, which express an undetectable amount of miR-122 (S7 Fig), all of the 23-nt miR-122 isomers (pre-annealed imperfect duplex miRNAs modified with a single nucleotide addition added to their 3′ ends) significantly increased the reporter-suppressing activity compared with the findings using the 21-nt isomer in a reporter assay with the psiCHEK-2_CULT1(WT) plasmid (Fig 7A). Similarly, these miR-122 isomers were more effective in enhancing HCV IRES-mediated translation compared with the shorter forms of miR-122, the 21- and 22-nt isomers (Fig 7B). We found that the single nucleotide-extended miR-122 isomers displayed improved stability compared with the 22-nt isomer when their cellular stability was assessed by northern blot analysis for the residual amounts of miR-122-5p following the incubation of individual duplex miRNAs in HeLa cell extracts (Fig 7C). These results demonstrate that miR-122 isomers modified by GLD-2-mediated 3′ non-templated addition display increased miRNA activity and stability, implying that GLD-2 inhibition by the HCV core protein promotes miR-122 destabilization to dysregulate its functions in the liver and in the HCV life cycle.
In this study, we examined the mechanism by which the cellular abundance of miR-122 is downregulated by HCV. Diverse regulatory mechanisms, which include the transcription regulation and post-transcriptional turn-over control of mature miRNA, might explain the differential expression of miRNAs in HCV-infected cells. We demonstrated that the HCV core protein inhibits GLD-2 and thereby promotes miR-122 destabilization, which leads to the downregulation of HCV RNA abundance.
Inhibition of GLD-2 by the HCV core protein affects multiple miRNAs in addition to miR-122 by decreasing the proportion of mono-adenylated isomers. Deep sequencing analysis of small RNAs from liver biopsies revealed that among the top 50 most abundantly expressed miRNAs in liver tissues, only six miRNAs exhibited a decrease in their 3′-end monoadenylated isomer levels upon HCV infection. The cellular abundance of these miRNAs was diverse (S3A Fig), suggesting that the selection of the miRNA template does not appear to be determined by the abundance of miRNAs alone. Instead, there might be a certain template specificity for GLD-2. However, the six miRNAs did not have any conserved sequences (S4A Fig).
GLD-2 has no known RNA-binding motif [29, 30]. Thus, it is recruited by the phosphorylated CPEB/CPSF complex to the 3′-UTR of target mRNAs for cytoplasmic poly (A) addition [36]. Similarly, in a cellular context, helper protein might also govern GLD-2 specificity toward miR-122. Notably, the miRNA species, for which the adenylated isomer levels were decreased by HCV infection, were not perfectly matched to the GLD-2 depletion-sensitive (in terms of stability) miRNAs in human fibroblasts [19]. Thus, the type of miRNAs post-transcriptionally modified by GLD-2 might be determined by cell-type specific cofactors used by GLD-2 to recognize a specific set of miRNAs. Nevertheless, GLD-2 appears to a higher affinity for miR-122-5p preferentially for mono-ribonucleotide addition to its 3′-end compared with either other liver resident miRNAs we tested (S5B Fig). Surprisingly, GLD-2 displayed a much higher terminal transferase activity on 20-mer homopolymer (A) than other homopolymeric RNA templates (S5C Fig). These results suggested that miRNA sequence and/or structure might also important determinants for GLD-2 template selectivity.
Given that miR-122 is a specific template of GLD-2, one important question is whether the non-templated addition of all four different nucleotides to miR-122 is catalyzed by GLD-2 alone. An early study claimed that C. elegans GLD-2 from reticulocyte lysates programmed with a plasmid encoding GLD-2 exhibited a strict ATP dependency on a C35A10 substrate [29]. GLD-2 monoadenlyation activity on miR-122 was also previously demonstrated with an immunoprecipitated mouse GLD-2 [19, 31] and a recombinant GDL-2 fused to the C-terminal end of GST [19]. Interestingly, the GST-fused GLD-2 used in the latter study displayed a weak preference for ATP over UTP on miR-122. Our terminal nucleotidyl transferase assays conducted using His-tagged recombinant GLD-2 revealed that any of the four ribonucleotides can be added to the 3′-end of miR-122 without preference for ATP (Fig 5C), raising the possibility that the non-templated additions of all four different nucleotides to the miR-122 3′ end might be catalyzed by GLD-2. Supporting this hypothesis, we detected the 23-nt miR-122 isomers modified by the non-templated addition of a single A, G, or C residue in five different liver biopsies analyzed. The ability of GLD-2 to use UTP and ATP as a substrate was also observed in the 24-nt isomers in which the U-tailed 23-nt isomer (it can be derived by the non-templated addition or aberrant processing of pre-miR-122) was further extended by the addition of a single uridylate or adenylate residue. Owing to the high abundance of miR-122 in the liver, the analysis of our small RNA sequencing datasets enabled us to identify previously uncovered 24-nt miR-122 species that carry a dinucleotide (AA and AU; all represent non-templated nucleotides that always start with A) or a 25-nt species bearing a triple nucleotide sequence (UUU; underlined sequences represent non-templated nucleotides). In these miR-122 species, the non-templated addition of uridylate residues was repeatedly observed. These results further suggest that GLD-2 may have an intrinsic capability of using diverse nucleotides as substrates. If consistent with the in vitro findings for GLD-2, the inhibition of GLD-2 by the HCV core protein would lower the proportion of miR-122 isomers bearing any single non-templated nucleotides at their 3′ ends. Indeed, the read frequency of all of the abovementioned miR-122 species was substantially decreased in liver biopsies from patients with HCV (S3 Table). A study illustrated that the Zcchc11-dependent uridylation of miR-26a and 26b attenuates miRNA-target repression [37]. However, uridylation of miR-122 enhanced its stability and miRNA activity, highlighting the importance of the fine regulation of miRNA 3′-end modification for executing the optimal functions of individual miRNAs. Finally, it should be stated that there was a substantial decrease in the proportion of the single U-tailed abundantly present 23-nt miR-122 isomer (aberrantly processed or terminally modified one), suggesting that pre-miR-122 processing in its single-stranded loop region might also be influenced by HCV infection (30%–33% of 22-nt canonical miR-122 in normal liver versus 14%–18% in patient liver biopsies). This interesting issue is currently under further investigation.
In addition to its role as a structural protein, the HCV core protein is also known to be involved in the development of hepatocellular carcinoma [38]. The tumor suppressor activity of miR-122 is well characterized in cells and miR-122 knockout mice [12–15]. Thus, our results imply that the HCV core protein may contribute to liver cancer development by decreasing miR-122 levels. Decreased miR-122 expression resulting from core protein expression during the course of chronic HCV infection may in part explain the oncogenic phenotype previously observed in cell culture and transgenic mice expressing the core protein [38]. Furthermore, the core protein-expressing transgenic mice and miR-122 knockout mice both are characterized by hepatic inflammation and fibrosis, followed by the development of hepatocellular carcinoma with age, further supporting the role of the core protein in liver diseases through its ability to promote miR-122 destabilization by inhibiting GLD-2. Studies have suggested that let-7i-5p and miR-29a-3p act as tumor suppressors because of their ability to repress oncogenes [39, 40]. It remains to be investigated whether the functions of these anti-cancer miRNAs are regulated by alteration in their 3′-end modification as observed in liver biopsies from patients with HCV (S4 Fig). If this is confirmed, GLD-2 inhibition by the core protein can be one of the mechanisms accelerating tumor development by chronic HCV infection through a previously uncovered pathway. This hypothesis is supported by the finding that GLD-2 is downregulated in several cancers, as revealed by a gene expression meta-analysis [41], implying that the loss of GLD-2 could be involved in carcinogenesis.
Generally, cytoplasmic polyadenylation by GLD-2 with the help of GLD-3, as illustrated in a recent structural analysis of these proteins [30], is required for the stability and translational activity of mRNAs [30, 42]. So, what might be the consequence of GLD-2 inhibition by the core protein? We can speculate that cytoplasmic poly (A) addition to some host mRNAs 3′-UTR can be inhibited by the HCV core protein. Inhibition of host translation by virus infection is mediated by the inactivation of translation initiation factor 2 through its phosphorylation by PKR, a type I interferon inducible gene [43]. Because many viruses, including HCV, suppress or abrogate the type I interferon signaling pathway and PKR activation by diverse mechanisms [44], whereas subgroups of host mRNAs are yet subjected to down-regulation, it is tempting to speculate that GLD-2 inhibition by the HCV core protein affects host mRNA translation. It remains to be characterized what host mRNAs are subjected to this regulation to understand the impact of the chronic inhibition of GLD-2 in liver pathophysiology.
In summary, our results provide a novel mechanism by which the HCV core protein controls viral replication levels through the downregulation of miR-122. The beneficial effect of miR-122 in HCV replication and translation can be repressed by the accumulation of the core protein, which determines the degree to which the host supports viral replication and thus plays a role in establishing persistent infection. Suppressing miR-122 levels in addition to sequestering of miR-122 by the HCV genome as reported recently [45] might be viral strategies to control viral titers. These miR-122 suppressing effect would also contribute in promoting liver diseases and cancer development.
The pcDNA3.1-Flag-core plasmid expressing the Flag epitope-tagged full-length HCV core protein was described previously [46]. The pcDNA3.1-Flag-NS5B expressing the Flag epitope-tagged full-length HCV NS5B was previously constructed [47]. The pEGFP-C(1–191), pEGFP-C(1–75), pEGFP-C(1–121), pEGFP-C(1–173), pEGFP-C(76–191), pEGFP-C(99–191) [48], and pEGFP-C(151–191), in which the numbers in parentheses indicate the amino acid positions in the core protein, were used for the expression of the enhanced green fluorescence protein (EGFP)-fused HCV core protein and its deletion derivatives. psiCHECK-2_CULT1(WT) and psiCHECK-2_CULT1(MT) were kindly provided by Liang-Hu Qu (Sun Yat-Sen University, China). These dual luciferase reporters contain the 3′-UTR fragment of cut-like homeobox 1 (i.e., CULT1), a transcriptional repressor of genes specifying terminal differentiation in hepatocytes, carrying either the miR-122 target sequence or its mismatched target sequence (Fig 2A) [23]. The pTrcHisB-GLD-2 plasmid was constructed by inserting GLD-2 cDNA amplified by RT-PCR using forward (5′-CCGGCTAGCATGTTCCAAACTCAATTTTGGG-3′) and reverse (5′-CGAAGCTTTTATCTTTTCAGGACAGCAGCTC-3′) primers into the pTrcHisB vector (Invitrogen, Carlsbad, CA, USA) via the NheI and HindIII sites. The pTrcHisB-GLD-2(D215A) was generated by site-directed mutagenesis to express an inactive GLD-2(D215A) carrying an Ala substitution for Asp215 at the catalytic active site of GLD-2 [19]. The bicistronic vector pDual-IRES, which expresses a cap-dependent Rluc reporter and an HCV IRES-dependent Fluc reporter was described previously [49]. The JFH1 plasmid [50] was used to produce infectious HCV particles. Antibodies were obtained as follows: rabbit polyclonal anti-GFP antibody from Santa Cruz Biotechnology (Santa Cruz, CA, USA), rabbit polyclonal anti-GLD-2 antibody, rabbit polyclonal anti-NS5A antibody, mouse monoclonal anti-NS5B antibody (clone 10D6), and mouse monoclonal anti-core protein antibody (clone C7-50) from Abcam (Cambridge, MA, USA), rabbit polyclonal anti-Ago2 antibody and mouse monoclonal anti-Hsp60 antibody (clone EPR4211) from Cell Signaling Technology (Danvers, MA, USA), and mouse monoclonal anti-α-tubulin antibody (clone DM1A) from Calbiochem (La Jolla, CA, USA). SARS-CoV capsid protein and the HCV core protein, which were expressed in Escherichia coli as N-terminal His-tagged full-length forms, were prepared as previously described [46, 51]. miRNAs were obtained from ST Pharm (Seoul, Korea). The sequences of miRNAs used in this study are shown in S5A Fig.
Huh7 human hepatocellular carcinoma cells (ATCC, Manassas, VA, USA) were grown in Dulbecco’s modified Eagle’s medium supplemented with 10% fetal bovine serum, 2 mM l-glutamine, 100 U/ml penicillin, 100 μg/ml streptomycin, and 0.1 mM nonessential amino acid under standard culture conditions (5% CO2, 37°C). The Huh7 stable cell lines Huh7/core and Huh7/NS5B, which express Flag-tagged core and NS5B proteins, respectively, were obtained previously by transfecting pcDNA3.1-Flag-core [46] and pcDNA3.1-Flag-NS5B [47]. The Huh7-derived cell line R-1 harboring a genotype 1b HCV subgenomic replicon was previously described [52]. Primary hepatocytes from BALB/c mice were isolated using a two-step perfusion method and grown on collagen-coated culture plates as previously described [53]. Huh7 cells were infected with HCV particles, which were recovered from Huh7 cells transfected with full-length HCV (genotype 2a, JFH1 clone) RNA at a multiplicity of infection (MOI) of ~0.25, as previously described [54].
Total RNA was extracted from human liver biopsies (N-1, N-2, HCV-1, HCV-2, and HCV-3) using Trizol reagent (Invitrogen). Details on the human liver biopsy specimens used in this study are given in the Supplemental Information S3 Table. The small RNA fraction was enriched from total RNA extracted from human liver biopsies (N-1, N-2, HCV-1, HCV-2, and HCV-3) using a mirVana miRNA isolation kit (Ambion, Austin, TX, USA). Similarly, small RNA fraction was prepared from Huh7 cells transiently expressing HCV core protein or infected with HCV. The cDNA library for small RNA was prepared for Illumina sequencing using a Truseq Small RNA Sample Preparation kit (Illumina, San Diego, CA, USA), according to the manufacturer’s protocol. The amplified PCR products were analyzed by electrophoresis on an agarose gel, and the DNA band of an appropriate size was then excised from the gel. The cDNA amplicons were analyzed on a Bioanalyzer High Sensitivity DNA chip (Agilent, Santa Clara, CA, USA). The deep sequencing libraries were sequenced on an Illumina Genome Analyzer GA II (Illumina) using the 54-bp single read protocol. Sequencing data were analyzed using the bowtie-1.0.1 program.
Solexa sequence reads were subjected to adapter removal using the FASTX-toolkit (http://hannonlab.cshl.edu/fastx_toolkit/). After removal of redundant reads, the reads (16–30 nt) were mapped to the human reference genome hg19 in the UCSC Genome Browser Database [55] using the Bowtie program (version 1.0.1) with the following options: -v 2 -m 10 –best—strata. The reads that could be aligned to the human genome were moved to the “mapped” dataset, and suppressed reads were discarded to prevent multiple mapping. After mapping, only unique reads of “mapped” dataset were annotated using the miRBase database (release 19) (http://www.mirbase.org) for miRNA [56]; the Rfam database (release 11) (http://rfam.xfam.org) for rRNA, various noncoding small RNAs, and repeat sequences [57]; and the genomic tRNA database (http://gtrnadb.ucsc.edu/Hsapi19/) [58]. Individual mapped sequence reads were compiled into a set of unique sequences with the read counts for each sequence reflecting the relative abundance. The unique sequence read counts were normalized to the total read counts of the “mapped” dataset in millions to give reads per million.
The HCV genome copy number was estimated by real-time qRT-PCR using an HCV 5′-UTR-specific TaqMan probe as previously described [59]. Mature miRNA (miR-122 and miR-221) quantification was performed using TaqMan miRNA assays (Applied Biosystems, Foster, CA, USA), according to the manufacturer’s instruction. Glyceraldehyde-3-phosphate dehydrogenase (GAPDH), miR-122 precursor, and GLD-2 levels were estimated by qRT-PCR using a SYBR Green assay kit (Takara, Kyoto, Japan). The primers used for qRT-PCR were as follows: GAPDH (forward primer, 5′-GAAGGTGAAGGTCGGAGTC-3′; reverse primer, 5′-GAAGATGGTGATGGGATTTC-3′), miR-122 precursors (pri-miR-122 and pre-miR-122: forward primer, 5′-GCCTAGCAGTAGCTATTTAGTGTG-3′; reverse primer, 5′-CCTTAGCAGAGCTGTGGAGT-3′), and GLD-2 (forward primer, 5′-GTCTAGAGCTGTGTCATTACAGCA-3′; reverse primer, 5′- TCGCTTAATCTCTTCCTTCCTCG-3′). U6 snRNA levels were determined as described previously [60]. Gene expression levels, normalized to GAPDH unless otherwise specified, were determined using the ΔΔCt method as previously described [61]. Data are shown as the mean ± SD of three experiments, each involving triplicate PCR assays.
Total RNA (20 μg) isolated from Huh7 cells using Trizol reagent was resolved by electrophoresis on a 15% denaturing polyacrylamide gel, transferred onto a positively charged nylon membrane (Roche Diagnostics, Mannheim, Germany), and fixed to the membrane by UV crosslinking. The membrane was pre-hybridized for 30 min and then hybridized with a 5′-radiolabeled antisense probe overnight. The probe was generated using [γ-32P] ATP and T4 polynucleotide kinase (Takara). After washing, the blot was analyzed with a PhosphorImager. Probe sequences were as follows: 5′-ACAAACACCATTGTCACACTCCA-3′ (miR-122-5p), 5′-GAAACCCAGCAGACAATGTAGC-3′ (miR-221), and 5′-CCTGCTTAGCTTCCGAGATCA-3′ (5S rRNA).
Cell lysates were prepared in a lysis buffer (1% Triton X-100, 100 mM Tris-HCl, pH 8.0, 150 mM NaCl, 10 mM NaF, 1 mM Na3VO4, and 17.5 mM β-glycerophosphate) supplemented with a protease inhibitor cocktail (Roche Diagnostics). Flag-tagged HCV core protein and NS5B were immunoprecipitated using anti-Flag-M2 affinity resin (Sigma-Aldrich, San Jose, CA, USA) from cell lysates, following incubation for 1 h on a rotator at 4°C in the presence of 2 μg/ml RNaseA (Sigma-Aldrich). Immunoprecipitates or lysates (30 μg) were separated by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and were blotted onto nitrocellulose membranes. For detection of HCV HCVcore protein in chimeric mice, liver tissues were homogenized, mixed with an equal volume of a standard RIPA lysis buffer (50 mM Tris-HCl, pH 7.5, 150 mM NaCl, 1% NP40, 0.1% SDS, 0.5% sodium deoxycholate) supplemented with a protease inhibitor cocktail, and cleared by centrifugation. The clear lysates (100 μg) were analyzed by immuoblotting. Membranes were analyzed using various primary antibodies and appropriate horseradish peroxidase-conjugated secondary antibodies. Immunoblots were developed using the ECL detection kit (GE Healthcare Life Sciences, Piscataway, NJ, USA).
Huh7 cells seeded at 3 × 105 cells/well in a 6-well plate were grown overnight and transfected with a dual luciferase reporter plasmid (1 μg) using Fugene HD (Promega, Madison, WI, USA), according to the manufacturer’s protocol. Luciferase assays were performed 48 h after transfection using the Dual-Glo luciferase assay system (Promega).
Total cholesterol was extracted from cells as described previously [62]. Cholesterol content was determined using the Amplex Red cholesterol assay kit (Molecular Probes, Eugene, OR, USA).
siRNAs targeting GLD-2 and other nucleotidyl transferases were designed according to the GLD-2 mRNA sequence (GenBank NM 001114394) and previous studies [63], respectively, and were obtained from ST Pharm (Seoul, Korea). Their antisense sequences (all siRNAs have a UU-3′ overhang) were as follows: 5′-CGAGCACAUUCACUAACAA-3′ (TUTase-1; also known as mtPAP, PAPD1, and Hs4), 5′-CUGAACAAUUGCCUUAAGU-3′ (GLD-2; also known as TUTase-2 and PAPD4), 5′-GGACGACACUUCAAUUAUU-3′ (TUTase-3; also known as PAPD5 and TRF4-2), 5′-UGAUAGUGCUUCAGGAAUU-3′ (TUTase-4; also known as ZCCHC11, Hs3, and PAPD3), 5′-CUACGGUACCAAUAAUAAA-3′ (TUTase-5; also known as PAPD7 and TRF4-1), 5′-GCAGCCAAUUACUGCCGAA-3′ (TUTase-6; also known as U6 TUTase, PAPD2, Hs5, and TUT1), and 5′-GAAAAGAGGCACAAGAAAA-3′ (TUTase-7; also known as ZCCHC6 and PAPD6). siRNAs were transfected into cells using Lipofectamine RNAiMAX (Invitrogen), according to the manufacturer’s instructions
E. coli BL21 transformed with pTrcHisB-GLD-2 were cultured in LB medium containing 100 μg/ml ampicillin. Cells were cultured at 37°C to 0.8 OD at 600 nm, and protein expression was induced at 25°C by the addition of 1 mM isopropyl-β-d-thiogalactopyranoside for 12 h. Cell pellets from a 2-l culture were washed once with phosphate-buffered saline (PBS) and resuspended in 40 ml of binding buffer (50 mM Tris-HCl, pH 8.0, 300 mM NaCl, 10 mM imidazole, 10 mM β-mercaptoethanol, 10% glycerol, 1% Nonidet P-40). Cells were sonicated on ice and centrifuged at 35,000 × g for 15 min. After centrifugation, the supernatant was bound to Ni-nitrilotriacetic acid agarose resin (Qiagen, Hilden, Germany) pre-equilibrated with the binding buffer. Bound proteins were eluted with the binding buffer containing imidazole (50–500 mM). Fractions containing GLD-2 were then dialyzed against buffer A [50 mM Tris-HCl, pH 8.0, 1 mM dithiothreitol (DTT), 50 mM NaCl, 5 mM MgCl2, and 10% glycerol], and aliquots were stored at −80°C.
Terminal nucleotidyl transferase reactions were set up in a total volume of 25 μl containing 50 mM Tris-HCl (pH 7.5), 50 mM NaCl, 5 mM MgCl2, 1 mM DTT, 20 U of RNase inhibitor (Promega), 10 μCi of [α-32P] rNTP (3000 Ci/mmol, Amersham Pharmacia Biotech), 250 ng (33.7 pmol) of miRNA or 30 pmol ribonucleotide homopolymer (20-nt), and 3.75 pmol of purified GLD-2. After 30 min of incubation at 37°C, reactions were stopped by adding 60 μl of an acid phenol emulsion [phenol:chloroform:10% SDS:0.5 M EDTA (1:1:0.2:0.4)] and 20 μg of glycogen. RNA products were precipitated with 2.5 volumes of cold 5 M ammonium acetate-isopropanol (1:5) and washed with 80% cold ethanol. After heat denaturation, the RNA samples were subjected to electrophoresis on an 8 M urea–15% polyacrylamide gel and were visualized by autoradiography. Densitometric quantification of radioactivity was performed using a Fuji BAS-2500 PhosphorImager.
Huh7/core or HCV-infected Huh7 cells were cultured in four chamber slides to 50% confluency. After 48 h, cells were fixed with 4% paraformaldehyde in methanol for 15 min at room temperature, washed 3 times with cold PBS, and then permeabilized with PBS containing 0.2% Triton X-100 for 30 min at room temperature. After washing three times with PBS, the cells were treated with a blocking solution (3% horse serum in PBS) for 30 min at room temperature. Cells were further incubated with an anti-GLD2 antibody overnight at 4°C and then washed three times with PBS. Cells were further incubated with an Alex Flour 647-conjugated anti-rabbit IgG antibody (Invitrogen) and a FITC-conjugated mouse monoclonal anti-core antibody (clone IE5, Abcam) for 2 h at room temperature and then washed three times with PBS. Nuclei were visualized by staining with 1 μM 4′, 6′-diamidino-2-phenylindole (DAPI) in PBS. Confocal images were collected on an LSM 510 META confocal laser-scanning microscope (Carl Zeiss, Oberkochen, Germany). Colocalization of GLD-2 with HCV core protein was determined by using the colocalization plugin module in the NIH ImageJ/Fiji software (v. 1.50).
Immunostaining for HCV core protein in mouse liver tissues was performed using an anti-core antibody (C7-50) as described previously [64].
Duplex miRNAs (10 pmol) were incubated with HeLa cell lysate (40 μg) in a 110-μl reaction buffer (50 mM Tris-HCl, pH 8.0, 150 mM NaCl, 0.1% NP-40, and 10% glycerol) supplemented with complete EDTA-free 1× protease inhibitor cocktail (Roche Diagnostics) for 0, 30, 60, and 120 min. RNA was extracted from a 25-μl aliquot of the reaction mixture using Trizol LS reagent (Invitrogen) for northern blot analysis.
Viability of R-1 cells transiently expressing the Flag-tagged core protein was measured using MTS [3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide] reagent as previously described [64].
Liver biopsy samples from two healthy volunteers and three patients with HCV were obtained from the National Biobank of Korea, and written informed consent was obtained from all subjects (Pusan National University Hospital, Busan Korea, IRB Approval No: 2011–3). The study protocol was approved by the institutional review board of Korea Advanced Institute of Science and Technology (IRB-14-040). All animal experiments were performed by certified personnel in an approved animal facility of the Yonsei Univeristy, in accordance with the Korean Food and Drug Administration guidelines. BALB/c male mice (6 weeks old; LaonBio Inc, Korea) were used to isolate primary hepatocytes. This study was approved by the Institutional Animal Care and Use Committee (IACUC) of the Yonsei University (Permit No: IACUC-A-201408-274-01). Liver tissues from HCV (genotype 1b patient serum)-infected SCID chimeric mice (uPA+/+SCID+/+) in which mouse liver was repopulated with human hepatocytes were obtained from Phoenix Bio (Hiroshima, Japan). The experimental protocols for the chimeric mice were approved by the ethics board of the Hiroshima Prefectural Institute of Industrial Science and Technology, Hiroshima, Japan.
Statistical analyses were performed using GraphPad Prism 6.01 (GraphPad Prism Software Inc., La Jolla, CA, USA). Results are presented as the mean ± SD from at least three independent experiments, unless otherwise stated. The p-value was calculated using a one-tailed unpaired Student’s t-test. P values < 0.05 were considered statistically significant.
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10.1371/journal.pcbi.1002743 | Incorporating 16S Gene Copy Number Information Improves Estimates of Microbial Diversity and Abundance | The abundance of different SSU rRNA (“16S”) gene sequences in environmental samples is widely used in studies of microbial ecology as a measure of microbial community structure and diversity. However, the genomic copy number of the 16S gene varies greatly – from one in many species to up to 15 in some bacteria and to hundreds in some microbial eukaryotes. As a result of this variation the relative abundance of 16S genes in environmental samples can be attributed both to variation in the relative abundance of different organisms, and to variation in genomic 16S copy number among those organisms. Despite this fact, many studies assume that the abundance of 16S gene sequences is a surrogate measure of the relative abundance of the organisms containing those sequences. Here we present a method that uses data on sequences and genomic copy number of 16S genes along with phylogenetic placement and ancestral state estimation to estimate organismal abundances from environmental DNA sequence data. We use theory and simulations to demonstrate that 16S genomic copy number can be accurately estimated from the short reads typically obtained from high-throughput environmental sequencing of the 16S gene, and that organismal abundances in microbial communities are more strongly correlated with estimated abundances obtained from our method than with gene abundances. We re-analyze several published empirical data sets and demonstrate that the use of gene abundance versus estimated organismal abundance can lead to different inferences about community diversity and structure and the identity of the dominant taxa in microbial communities. Our approach will allow microbial ecologists to make more accurate inferences about microbial diversity and abundance based on 16S sequence data.
| Microbial ecologists cannot observe their study organisms directly, so they use molecular sequencing to measure the abundance of different microbes living in the wild. The most commonly used method for measuring the abundance of different microbes is to collect a DNA sample from an environment and sequence a particular gene, the 16S SSU rRNA gene (“16S”) from those samples. The abundance of 16S sequences from different microbes is then used as a surrogate measure of the abundance of the microbial taxa in the community. One problem with the use of the 16S gene as a measure of microbial abundance is that many microbes have multiple copies of the gene in their genome. Thus, variation in 16S gene abundances can be caused by both genomic copy number variation and variation in the abundance of organisms. In this study we present a computational method that allows estimation of the abundance and genomic 16S copy number of microbes based on environmental sequencing of the 16S gene. We use simulations and analysis of microbial community data sets to demonstrate that estimating the abundance of organisms from 16S data improves our ability to accurately measure the diversity and abundance of microbial communities.
| The SSU rRNA gene (also known as the 16S rRNA gene, referred to as “16S” hereafter) is widely used in studies of microbial ecology as a “barcode gene” [1] to quantify microbial community structure and diversity [2], [3]. The widespread adoption of 16S as a microbial barcode gene has been driven by several desirable properties of the gene, including the fact that it is universal across bacteria and archaea, it can be easily amplified from a wide diversity of taxa at one time by the polymerase chain reaction (PCR), it is phylogenetically informative, and it can be used to identify and phylotype sequences based on extensive databases of 16S sequences with associated taxonomic and phylogenetic information [2], [4]. In 2011 there were 3,574 publications in the Web of Science database matching a search for the terms “16S and (communit* or diversit* or abundance*)”.
There are numerous advantages to using 16S as a microbial community barcode gene, but also numerous disadvantages including amplification and sequencing bias and error [5], [6], difficulty with the accurate taxonomic identification and binning of short sequences [7]–[10], and a lack of benchmark studies to guide decisions about quality control, filtering, and analysis of 16S sequence data sets derived from novel sequencing technologies. Another disadvantage of the 16S gene that is particularly relevant to inferring microbial abundance from 16S gene sequence abundances is that genomic 16S copy number varies a great deal across the tree of life [11]–[13]. For example, among bacterial taxa with fully sequenced genomes, 16S copy number varies from a single copy in Erythrobacter litoralis to fifteen copies in Photobacterium profundum [14], [15]. As a result of this variation in copy number, the variation in the relative abundance of 16S gene sequences in an environmental sample can be attributed both to variation in the relative abundance of different organisms, and to variation in genomic 16S copy number among those organisms ([12], [16]–[18]; Figure 1). The use of a single-copy protein coding gene such as rpoB as a microbial barcode would avoid this problem [11], [19], [20], but these genes are not as widely used as the 16S gene, and there are biases inherent in the use of every barcode gene and sequencing technology. Though metagenomic data will help in allowing the use of genes that have less variation in copy number [20], PCR amplification of 16S genes is still the method of choice in many environmental surveys. The vast majority of such studies either explicitly or (usually) implicitly assume that the relative abundance of 16S gene sequences is an accurate measure of relative abundance of the organisms containing those sequences in analyses of community diversity and composition. The degree to which this assumption is warranted, and the effect of treating 16S gene abundance as a surrogate measure of organismal abundance on estimates of microbial community structure, is unknown.
In this study we present a method for phylogenetic estimation of 16S copy number and organismal abundance that allows us to improve estimation of microbial abundance and community structure by accounting for copy number variation among taxa. Our specific objectives are threefold. First, we demonstrate that 16S gene abundance is a function of both organismal abundance and 16S gene copy number, and show how this relationship can influence the ability to estimate community structure and diversity from sequence data. Second, we develop a method that allows estimation of organismal 16S gene copy number and abundance as a function of 16S gene abundances in environmental samples, and assess the performance of this method with simulated data sets. Finally, we apply our method to several empirical data sets to illustrate the practical effects of treating 16S gene abundance as a measure of organismal abundance on measures of microbial community diversity, structure, and composition.
Our interest lies in relating the observed abundances of 16S genes in biological samples to the abundance of cells, or organisms, from which these genes arise. For any taxon i within a biological community, the relationship between the abundance of 16S genes from that taxon, Gi, and the abundance of organisms from that taxon, Ni, is determined by the genomic 16S copy number of that taxon, Ci, where Gi = NiCi. Defining the relative 16S gene abundance of taxon i, , and the relative organismal abundance of taxon i, , it follows that:(1)Here, the summation is across all taxa i within the biological community, and is thus a constant. Because = 1, equation 1 shows that in communities where all taxa have 16S copy number equal to one, all sampled taxa will have identical 16S gene and organismal relative abundance. As the 16S copy number of one or more taxa increases, disparity between the 16S gene abundance and organismal abundance of individual taxa grows.
We can also readily explore community-level patterns of microbial abundance. We characterize the taxa-gene distribution, P(G), as the fraction of taxa in a biological sample with 16S gene abundance G. Similarly, we characterize the taxa-abundance distribution, P(N), as the fraction of taxa with N organisms. These two distributions are related by:(2)Here, the summation is over all possible combinations of N and C with product equal to G, and P(N,C) is the joint probability of a taxon having an abundance N and copy number C. In the case where organismal abundance and copy number are independent of one another, this simplifies to:(3)where P(C) is the distribution of copy number across taxa within the biological community.
To understand the potential differences between gene abundance distributions and the organismal abundance distributions from which they are derived, we used two approaches. First we qualitatively compared the shapes of the distributions of P(N) and P(G). To model the taxa-abundance distribution, P(N), we simulated biological communities assuming a zero-truncated Poisson lognormal distribution [21]. We chose the lognormal distribution for illustrative purposes because it is one of the most widely discussed taxa-abundance distributions in biology [22], [23]. To model the distribution of genomic 16S copy number across taxa, P(C), we simulated biological communities with a zero-truncated Poisson distribution. We chose the Poisson distribution because it approximated the empirical copy number distribution in our reference data set (Supporting Figure S1). For each simulated community we calculated the resulting taxa-gene distribution, P(G), from equation 3.
Second, we examined how sampling from the simulated biological communities with corresponding distributions P(N) and P(G) resulted in different biodiversity estimates. Our motivation for this was to understand the differences expected when sampling genes versus individuals from communities. To do this we sampled a fixed number of genes, or individuals, from the simulated communities. We focused on a key attribute of the sample distributions: the numbers of taxa that are unobserved, or hidden behind the ‘veil line’ of the sampled taxa-abundance and taxa-gene distributions [22]. For each sample we used standard parametric tools to estimate the number of unobserved taxa for P(N) and P(G) (reviewed in [24]). We tested whether estimating the total taxa richness based on P(G) versus P(N) could lead to different inferences about diversity using an ANOVA to compare predicted taxa richness using these two different distributions.
Environmental sequencing studies that utilize the 16S gene as a barcode provide a measure of 16S gene relative abundance gi. Given the relationship between 16S gene relative abundance gi, copy number Ci, and organismal relative abundance ni outlined above (Equation 1), we can estimate ni given information on gi and Ci. But the genomic copy number Ci of the 16S gene (referred to as “copy number” hereafter) is usually not observed directly from environmental sequence data because the full genomes of the organisms containing the gene are not sequenced. Metagenomic studies could theoretically address this issue [19], , but metagenomic sequencing generally provides insufficient sampling depth to provide full genome coverage for all of the organisms in diverse communities. To overcome this challenge, we use methods from comparative biology and leverage phylogenetic signal in copy number to estimate copy number and organismal abundance for organisms for which we observe only 16S gene abundances.
The general approach we use to estimate copy number and organismal abundance from environmental 16S sequences is to place those sequences onto a reference phylogeny of organisms for which genomic 16S copy number is known (Figure 2). Using ancestral state reconstruction via phylogenetically independent contrasts [26], [27], we can then obtain an estimate of genomic 16S gene copy number, , for any taxon i. By combining the estimated copy number , and the observed relative gene abundance of taxon i, gi, we can obtain an estimate of the relative abundance of taxon i following Equation 1:(4)
To illustrate the impact of variation in copy number on empirical estimates of microbial community structure and diversity, we reanalyzed data from two previously published studies: a survey of microbial communities along an oceanic depth gradient using Sanger sequencing [41], and a survey of the skin, gut, and mouth microbiome of a human female using pyrosequencing (subject F1-3 from [42]).
For each data set, we estimated the relative abundance for each OTU using our copy number estimation pipeline. We then asked whether accounting for copy number variation influenced several commonly used measures of community structure and diversity for each data set. We estimated the fit of gi and abundance distributions from these data sets to a lognormal model of relative abundance distributions. We classified each sequence in the empirical data sets to the taxonomic order level using the RDP taxonomic classifier [43] and evaluated changes in the relative abundance of bacterial orders based on gi versus . We measured overall community dissimilarity among samples from each study using the weighted UniFrac phylogenetic distance metric [44], based on the both the gi and values, and then performed a hierarchical clustering with complete linkage to evaluate the overall similarity of samples in each study.
Plots of simulated P(N) and P(G) abundance distributions (Figure 3) indicated that the shape of these distributions are different. For the simulation parameters we considered, treating Gi as a measure of organismal abundance lead to an underestimation of the abundance of rare taxa and overestimation of the abundance of the most abundant taxa compared to the distribution of Ni (Figures 3 and 4). Estimates of total species pool richness fit using a parametric method [23] were significantly lower for Gi than for Ni (ANOVA; all P<0.01; Figure 4).
Copy number variation can have substantial effects on inferences about numerous aspects of community diversity and structure including relative abundance distributions, the estimated abundance of different taxa, and the overall similarity of ecological communities. In both empirical data sets, rank abundance distribution plots of and gi revealed that failure to account for copy number variation resulted in gi underestimating the relative abundance of the most abundant taxa and overestimating the relative abundance of the rarest taxa relative to (Figure 7). The fit of empirical rank abundance distributions of and gi to a log-normal distribution model was much better for than for gi (human microbiome: AIC(gi) = −200903, AIC() = −215791; ocean: AIC(gi) = −4573.7, AIC() = −4808.1).
In addition to changes in the overall shape of rank-abundance distributions, the relative abundance of several microbial taxa also changed substantially after accounting for copy number variation among taxa. In the human microbiome data set, these changes did not greatly modify the overall abundance structure of the community (Figure 8). However, in the ocean data set the relative abundance of several taxa differed greatly when based on gene versus organismal abundance estimates (Figure 8). For example, the relative abundance of sequences assigned to Cyanobacteria Family II nearly doubled and this taxonomic group went from being the ninth most abundant based on gene abundance (gi = 0.04) to the second most abundant based on estimated organismal abundance ( = 0.09).
The use of organismal versus gene abundances did not have a major effect on the clustering of ocean communities based on their phylogenetic similarity, with samples tending to cluster together with other samples from similar depths regardless of whether gi or was used to calculate weighted UniFrac similarity of samples (results not shown). However, for the human microbiome data set, using gi versus as the abundance measure changed the overall similarity of communities from different habitats as measured by hierarchical clustering of communities based on the weighted UniFrac phylogenetic distance metric (Figure 9). Based on gene abundances, microbial communities from the inner ear/earwax clustered with communities from the sole of the foot (Figure 9A), but based on estimated organismal abundance the inner ear/earwax community formed a distinct cluster with communities from the nostril, and these two communities from relatively moist skin microhabitats were compositionally distinct from all other microbial communities on drier skin sites and the gut and mouth (Figure 9B).
We have demonstrated how data on the sequence and abundance of 16S genes in environmental samples can be used to accurately estimate 16S gene copy number and improve estimates of organismal abundance in microbial communities. Using simulated and empirical data sets, we have shown that treating gene abundance as if it were equivalent to organismal abundance can lead to misleading inferences about microbial community structure and diversity. Our simulations indicate that genomic 16S copy number can be estimated accurately for environmental sequences through the use of phylogenetic reference data, and that failure to account for copy number variation among taxa in environmental samples can lead to the observed relative abundance of 16S sequences (gi) being weakly correlated with the true abundance of organisms in the community (ni).
Our findings have wide-ranging implications for studies treating 16S gene sequence abundances as a measure of organismal abundances in communities. In some simulations, less than 30% of the variance in true organismal abundance was explained by observed gene abundance (Figure 6). The weak correlations between observed 16S gene abundance and true organismal abundance suggest that estimation of organismal abundance from gene abundance and copy number should be a routine part of any 16S sequencing study, since it will reduce one of the numerous potential biases inherent to inferring microbial community structure from environmental sequencing data. Analyses of several empirical data sets indicated that copy number variation can affect numerous aspects of community structure that are commonly measured by studies using the 16S gene, including relative abundance distributions, estimates of the abundance of different taxa, and overall measures of community diversity and similarity.
The effects of copy number variation on community structure will not be consistent across studies, as they will depend on the relative copy number of taxa in a particular community, and on the distribution of, and relationship between, Ni and Ci in that community. Our simulations of gene and organismal abundance distributions, P(G) and P(N), indicate that these distributions can have different properties. Under the simulation parameters we explored, there was a tendency for P(G) to have lower abundances for the rarest species and higher abundances for the most abundant species in comparison with P(N). Estimates of species richness based on gene abundances were also consistently lower than estimates based on organismal abundances. These differences are likely due both to the fact that P(G) is a function of P(C) and P(N) (cf. Equations 2 and 3) leading to a difference in the shape of gene and organismal abundance distributions, and due to sampling depth being effectively lower for gene abundance distributions than for organismal abundance distributions for a given number of genes/individuals sampled, since multiple copies of the genes of each organism make up the pool of genes in the community. We simulated P(N) and P(C) as statistically independent distributions, but it is also possible to imagine situations where P(N) and P(C) are correlated (e.g. where abundant taxa have consistently higher or lower 16S copy number), which could further obscure relationships between gene abundance and organismal abundance.
In the abundance distributions for the empirical data sets we examined, we observed that gene abundances were generally higher for the rarest taxa and lower for the most abundant taxa compared to estimated organismal abundances, a pattern opposite that seen in our simulations. This discrepancy highlights the fact that relationships between gene and organismal abundances will vary depending on numerous factors including the distribution of organismal abundances and copy numbers as well as the relationship between organismal abundance and copy number in natural communities, and further highlights the need to estimate copy number and organismal abundance for empirical data sets.
There was not always a large effect of using gene versus organismal abundance to measure community structure in the empirical data sets we examined, but we did see major impacts on our inferences about community structure in some data sets, including changes in estimates of the identity of the common and rare taxa within communities and the similarity of communities among different habitats. If there is not a consistent difference in copy number between abundant and rare taxa, there could be little effect of adjusting relative abundance to account for copy number, but the only way to assess differences in gene versus organismal abundances for a particular community will be to estimate copy number and organismal abundance for the taxa in that community.
There is great interest in understanding the structure and dynamics of the “rare biosphere”, the rare microbial taxa whose detection in ecological communities was only possible with the advent of high-throughput sequencing technology and deep sequencing of environmental samples [45]. In our simulations and analyses of ecological communities, we found that estimates of the relative abundance of rare taxa were consistently affected by variation in copy number, likely due to the fact that the effects of copy number on detection probability and abundance estimation will be strongest for the rarest taxa in a community [46]. It will be useful to disentangle the effects of copy number variation versus ecological rarity per se on our perception of the ecology of the rare biosphere.
The phylogenetic method for copy number estimation we present in this study could be applied to predict any microbial trait for which reference sequence and trait data are available, and will help to further develop a trait-based approach to microbial ecology [47]. Numerous hypotheses about the environmental distribution of microbial traits including genomic 16S copy number have been proposed [48], [49] and it will be possible to test these hypotheses using estimation of the traits of microbial communities. This approach will complement metagenomic approaches to understanding the distribution of microbial traits and functions, since it could be applicable to phenotypic traits of microbes that cannot be directly measured from metagenomic data such as genomic copy number or ecological attributes of taxa such as growth rate or pathogenicity.
Since uncertainty in copy number estimates depends on the branch length separating environmental sequences from reference sequences, there will be greater uncertainty in estimates of copy number for sequences from poorly known and unculturable bacterial clades lacking close relatives in reference genomic data sets. However, for the empirical data sets we analyzed, the largest standard error of copy number predictions was less than one copy per sequence, even for the environmental sequences distantly related to all taxa in the reference data set. Our ability to estimate copy number accurately will be improved as the genomes of greater numbers of uncultured and rare microorganisms continue to be sequenced. The method we present in this study can be used with any set of reference sequences, and as greater numbers of genomes from uncultured and phylogenetically diverse microbes are sequenced [28], we expect that our ability to estimate copy number and abundance will become even more accurate.
Understanding patterns of organismal abundance across space, time and environments lies at the core of microbial biodiversity and biogeography research. The ability to estimate copy number and abundance for microorganisms based on environmental sequences opens the door to the application of numerous ecological methods developed for estimating taxa richness, taxa range distributions, and community similarity while taking variation in detection probability into account. Future studies utilizing the copy number and abundance estimation approach we have developed will improve our understanding of the structure and dynamics of microbial communities.
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10.1371/journal.pgen.1000647 | Tnni3k Modifies Disease Progression in Murine Models of Cardiomyopathy | The Calsequestrin (Csq) transgenic mouse model of cardiomyopathy exhibits wide variation in phenotypic progression dependent on genetic background. Seven heart failure modifier (Hrtfm) loci modify disease progression and outcome. Here we report Tnni3k (cardiac Troponin I-interacting kinase) as the gene underlying Hrtfm2. Strains with the more susceptible phenotype exhibit high transcript levels while less susceptible strains show dramatically reduced transcript levels. This decrease is caused by an intronic SNP in low-transcript strains that activates a cryptic splice site leading to a frameshifted transcript, followed by nonsense-mediated decay of message and an absence of detectable protein. A transgenic animal overexpressing human TNNI3K alone exhibits no cardiac phenotype. However, TNNI3K/Csq double transgenics display severely impaired systolic function and reduced survival, indicating that TNNI3K expression modifies disease progression. TNNI3K expression also accelerates disease progression in a pressure-overload model of heart failure. These combined data demonstrate that Tnni3k plays a critical role in the modulation of different forms of heart disease, and this protein may provide a novel target for therapeutic intervention.
| Heart failure is the common final outcome of many forms of acute and chronic heart disease. The prognosis of heart disease is highly variable between patients, and these differences in the phenotypic expression (symptoms, course, and final outcome) are in part due to genetic factors that have proven difficult to directly identify in the human population. To overcome this limitation, we employed a disease-sensitized mouse model of dilated cardiomyopathy to identify genes that modify the progression and outcome of the phenotype. Here we report the identification of a novel heart disease modifier gene, Tnni3k, that accelerates disease progression in two distinct mouse models of cardiomyopathy. This gene appears to play a critical role in modulating heart disease phenotypes and may provide a novel target for therapeutic intervention.
| Heart failure is the common final outcome for many forms of acute and chronic heart disease. The prognosis of heart failure is highly variable between patients, and the differences in phenotypic expression (symptoms, disease progression and course, and final outcome) create difficulties in the construction of predictive models [1]. Previous research has suggested that genetic factors can considerably modify the progression and outcome of heart failure [2]. However, these factors are difficult to identify directly in the human population because of wide genetic variability, uncontrollable environmental factors, and the intervention of medical therapy.
We have employed a disease-sensitized mouse model to map genetic factors that modify the progression and outcome of heart disease. In the Calsequestrin transgenic mouse, cardiac-specific overexpression of the calcium binding protein Calsequestrin (CSQ) leads to dilated cardiomyopathy [3]. This murine model recapitulates many of the hallmarks of human dilated cardiomyopathy including cardiac enlargement, depressed contractile function, abnormal beta-adrenergic receptor signaling and premature death [4]. Although all mice that overexpress CSQ develop dilated cardiomyopathy, disease progression and outcome varies greatly depending on the genetic background (inbred mouse strain) harboring the Csq transgene (Csqtg) [5],[6]. These differences are due to modifier genes whose multiple alleles differentially modulate the phenotype.
We have exploited these strain-specific phenotypic differences to map seven different loci (Heart failure modifier or Hrtfm) that modify the progression of cardiac dysfunction and the outcome of heart failure [5]–[7]. Hrtfm2, mapping to mouse chromosome 3, was identified in a cross between inbred strain DBA/2J (DBA), which harbors the original Csqtg, and C57BL/6 (B6). In this cross, the B6 allele at Hrtfm2 imparted a dominant, disease-accelerating effect on both cardiac dysfunction (as measured by echocardiography) and reduced survival [5]. Subsequently, Hrtfm2 was re-identified in a second cross between DBA/Csqtg animals and inbred strain AKR/J (AKR) [7]. In this cross, the AKR allele of Hrtfm2 also imparted a dominant, disease-accelerating effect on cardiac dysfunction and survival. The phenotypic effects of Hrtfm2 were robust, accounting for 30% of the genetic variability for survival and 22% for cardiac dysfunction. Capitalizing on the ancestral nature of the Hrtfm2 allele (ie, mapping within a murine haplotype block that has been retained throughout evolution and now found in multiple inbred strains), we employed haplotype-sharing analysis to effectively narrow the candidate interval from 16.5 Mb to 2 Mb, a region containing only 7 known genes [7]. We had previously suggested the Tnni3k gene as an attractive candidate based on its location within the shared haplotype interval and its biological significance as a cardiac-specific kinase that reportedly interacts with cardiac Troponin I (cTnI) [8]. Here we report the molecular characterization of allelic variation at the murine Tnni3k gene, and present in vivo functional evidence showing that Tnni3k underlies the heart failure modifier locus, Hrtfm2.
As part of an effort to identify candidate genes for the Hrtfm loci, we performed microarray expression analysis of normal heart tissue from the inbred strains used in our mapping crosses to identify genes showing innate differences in transcript levels. Of the genes mapping within the Hrtfm2 linkage peak, only one gene exhibited significantly different transcript levels between the less susceptible strain DBA and the more susceptible strains B6 and AKR. Transcript levels of Tnni3k were elevated 12-fold in B6 and AKR compared with DBA, whereas levels of other transcripts mapping within the interval were not significantly different (Figure 1A). These expression differences were validated by more-sensitive qRT-PCR analysis, where Tnni3k message levels were found to be 25-fold higher in B6 and AKR strains than those in DBA (Figure 1B). In parallel, we genetically isolated the Hrtfm2 locus by creating a congenic line that carried AKR alleles across Hrtfm2 (an approximately 20 Mb region between rs13477425 and rs13477504) and DBA alleles throughout the rest of the genome. Quantitative RT-PCR showed that Tnni3k transcript levels in hearts from DBA.AKR-Hrtfm2 congenic mice were comparable to levels observed in B6 and AKR (AKR being the source of the Hrtfm2 locus), and not that seen in DBA (the genomic background), suggesting that the Tnni3k expression differences were driven by cis-acting sequence elements within the Hrtfm2 locus, rather than trans-acting factors mapping elsewhere in the genome.
We analyzed heart tissue prepared from six inbred mouse strains to determine if these differences in levels of Tnni3k transcript would be observed at the protein level. We chose three additional strains that shared either the DBA or B6 haplotype at Tnni3k (Table S1). As predicted by the transcript levels, robust levels of TNNI3K protein were detected in B6, AKR, 129×1/SvJ (129) and the DBA.AKR-Hrtfm2 congenic, which share the B6 haplotype. Surprisingly, no apparent protein was detected for DBA, C3H/HeJ and BALB/cByJ (BALB/c) strains, which share the DBA haplotype (Figure 1C). Therefore, within the limits of detection of the antiserum, TNNI3K protein was apparently absent from hearts of strains sharing the DBA haplotype at the Tnni3k locus. The latter strains effectively represent Tnni3k null or extreme hypomorphic genotypes with no apparent effect on development or survival, and with no obvious pathological consequence.
The Tnni3k coding region of the mapping strains differed by a single, relatively conservative, non-synonymous coding SNP (rs30712233, T659I). By sequencing Tnni3k cDNA from the strains, we noted another, more consequential, strain-specific sequence difference. All strains sharing the B6 haplotype showed a single major transcript identical to the published cDNA. By contrast, all strains sharing the DBA haplotype exhibited a mixture of two transcripts consisting of the published transcript and a second transcript containing a 4-nucleotide insertion between exons 19 and 20 (Figure 2A). This insertion was not present in the genomic DNA, but instead represented the addition of the first 4 nucleotides from intron 19 into the Tnni3k transcript. The insertion created a frameshift resulting in a premature termination codon immediately downstream (Figure 2B). We determined that the frameshifted transcript accounted for approximately 70% of the message in DBA heart mRNA, but was not present in B6 or AKR (Figure 2C). This transcript was not found in any of the EST databases for mouse or any other species, suggesting that it represented an aberrant message created by defective splicing, possibly caused by the use of a second ‘gt’ splice donor site 4 nucleotides downstream of the normal donor site.
The genomic region surrounding exons 19 and 20 harbors over 50 SNPs. Although in principle any of these could have caused the aberrant splicing, we focused on the SNP nearest to the splice donor junction. B6 and related strains (AKR, 129, MRL) possess an ‘a’ nucleotide at rs49812611, whereas DBA and related strains (A/J, C3H/HeJ (C3H), BALB/c) possess a ‘g’. This SNP lies at the +9 position for the normal splice site, but importantly, this SNP lies at the +5 position with reference to the aberrant splice site. Thus, DBA and related strains harbor the consensus ‘g’ nucleotide at the +5 position for the aberrant site. During mRNA processing, the ‘g’ at the +5 splice donor position pairs with a ‘c’ in the U1 or U6 snRNA, resulting in a preference for ‘g’ at this position. Weight matrix scores for splice donor strength [9],[10] for each possible splice donor site confirmed that the second (aberrant) splice site was the strongest splice site in the region only when the ‘g’ nucleotide is present at rs49812611 (Figure 2D).
We tested the hypothesis that rs49812611 is the cause of aberrant splicing using an in vitro splicing system. Genomic DNA spanning exons 18 through 20 from both B6 and DBA were sub-cloned and transfected into 293T cells (Figure 3A). These in vitro constructs recapitulated the splicing pattern observed in vivo, confirming that the splicing defect was caused by cis-acting sequences residing within the cloned 4 kb genomic fragment (Figure 3B). Site-directed mutagenesis was used to investigate the role of rs49812611 in aberrant splicing. A single change at this SNP completely reversed the splicing pattern. DBA genomic DNA altered to carry the ‘a’ allele at rs49812611 generated no aberrant splice product, whereas the B6 DNA carrying the ‘g’ allele exhibited the aberrant product (Figure 3B). These results showed that rs49812611 was responsible for the presence or absence of the aberrantly spliced message, although the extent of aberrant splicing may be modulated by other flanking sequence variation.
Since Tnni3k was originally identified as a positional candidate for Hrtfm2 due to differences in transcript levels between the mapping strains, we hypothesized that nonsense-mediated decay (NMD) was responsible for the drastically reduced levels of the frameshifted message seen in DBA. We investigated this in the mouse cardiomyocyte cell line, HL-1 [11], which shares the DBA haplotype at Tnni3k. We first confirmed that HL-1 cells expressed both aberrant and normal Tnni3k at levels comparable to wild-type DBA hearts, with the majority of the message being the aberrant variant that includes the 4-nucleotide insertion. HL-1 cardiomyocyte cells were then treated with either cycloheximide and emetine, two drugs commonly used to block NMD [12]. Treatment with either drug increased the level of aberrantly spliced transcript relative to the normally spliced message (Figure 4A). As predicted, these treatments increased levels of total Tnni3k mRNA 16-fold (Figure 4B), supporting a major role for NMD in the observed differences in transcript levels between strains.
Although these experiments determined the molecular mechanism underlying the observed differences in Tnni3k transcript levels, they did not address the in vivo role of Tnni3k in the progression of cardiomyopathy. We next investigated whether Tnni3k was the gene underlying the Hrtfm2 locus. We created transgenic mouse lines that expressed human TNNI3K protein in the heart. TNNI3K protein is highly conserved between human and mouse (91% identity), and transgenic expression of the human transcript enabled discrimination between the endogenous murine transcript and that derived from the transgene. Three independent founder lines were created, and qRT-PCR indicated that the human transgene was expressed at levels ranging from 5 to 20-fold above the endogenous B6 mouse transcript, depending on the founder. F1 generation mice from all three lines survived over a year, and cardiac function in 12 and 21-week transgenic animals were indistinguishable from wild-type animals. Consequently, TNNI3K expression alone did not result in overt cardiomyopathy or decreased survival due to heart failure. This was not unexpected, since in the absence of the Csq transgenic disease-sensitizer, there were no measurable differences in heart function between B6 and DBA animals, even though B6 express robust levels of TNNI3K whereas DBA shows no detectable protein.
By repeated backcrosses to DBA, the TNNI3K transgenes were introgressed into the DBA background that shows no detectable endogenous murine TNNI3K protein to test the hypothesis that in the presence of the Csq transgenic sensitizer, increased expression of TNNI3K would accelerate disease progression. The backcrossed transgenic lines continued to express robust levels of the human TNNI3K protein (Figure S1). Two lines were chosen for all subsequent experimental crosses, and phenotypic data from N5 (or N6) animals from both lines were combined, as there was no discernable difference in the data derived from either transgenic line.
In addition to an apparently normal lifespan, TNNI3K transgenic animals did not show any signs of cardiac pathology by echocardiography. By contrast, expression of TNNI3K in the context of the Csq transgenic sensitizer resulted in profoundly premature death (Figure 5). Of the four possible genotypes from a cross between Csq (sensitizer) and TNNI3K (modifier) transgenic lines, only the double transgenic mice showed a decrease in survival (p<0.00001). The observed survival differences were profound. All other genotypes survived on average at least 150 days, but all animals expressing both Csq and TNNI3K died within 21 days. This extreme premature death phenotype resembled that which we had previously observed when attempting to introgress the Csq transgene into the B6 background, which exhibits robust levels of endogenous mouse TNNI3K protein [5]. Starting with the sensitizer in the DBA background [4], we were unable to move the Csq transgene beyond the second generation, as N2 animals died within 40 days, precluding further backcrosses with B6 mice [5].
We next determined whether natural levels of the murine TNNI3K protein would also exhibit disease-accelerating effects. This was investigated by crossing the congenic mice harboring the AKR allele at Hrtfm2 with mice containing the Csq transgene, both held for many generations in the DBA background. The progeny from this cross would harbor a only single AKR allele of Tnni3k, the appropriate genotype for Hrtfm2 which exhibited dominant effects in the original mapping crosses [5],[7]. When the congenic mice DBA.AKR-Hrtfm2, were crossed with the Csq sensitized mice, the Csqtg offspring with even only a single AKR Hrtfm2 allele showed a decrease in survival (on average 107 days) compared to those with two DBA Hrtfm2 alleles (more than 150 days, Figure 5). The Csq-sensitized Hrtfm2 congenic line also survived longer than Csq-sensitized F1(DBA/B6) animals, which survived on average to only 50 days [5]. In the original mapping cross, Hrtfm2 contributed approximately 30% of the genetic variance towards the survival trait [5]. Thus, as expected, the isolated B6/AKR Hrtfm2 allele contributed a robust but only partial effect on reduced survival when compared to the F1 animals, as other modifier loci had been crossed out of the congenic line.
To determine whether the premature death was related to cardiac dysfunction, we performed echocardiography on animals with all four possible genotypes resulting from cross between the Csq and TNNI3K transgenic lines. Echocardiography was performed at 14 days, the earliest possible age for reproducible echocardiographic data. Due to the extremely accelerated disease course and profound reduction in survival, only six of fourteen double transgenic mice survived to their scheduled 14-day echocardiogram. Fractional shortening in these TNNI3Ktg/Csqtg mice was significantly decreased compared to the other three genotypes of animals (P<0.0232), demonstrating severely abnormal heart function of the double transgenic animal (Figure 6A and 6B, Table S2). Even by 14 days, hearts from the TNNI3Ktg/Csqtg mice were larger than those of the other genotypes, and by histological staining showed obvious chamber dilation (Figure 6C). Thus, the double transgenic animals developed dilated cardiomyopathy by 14 days (or earlier) and all mice of this genotype died before this or shortly thereafter due to heart failure. Many of the double transgenic animals displayed bradycardia (a severe slowing of the heart rate), clearly evident in the echocardiograms. This phenotype, while a feature of the natural disease progression in the Csq transgenic model, is normally observed only in adult animals just prior to heart failure [5]. Thus, this hallmark of the natural progression of the Csq transgenic model is also greatly accelerated with overexpression of TNNI3K.
We also investigated the echocardiographic parameters of the DBA.AKR-Hrtfm2/Csqtg (see Figure 5). Echocardiography performed on these mice at 4 and 8 weeks of age showed decreased fractional shortening in DBA.AKR-Hrtfm2/Csqtg mice compared to the DBA/Csqtg littermates, indicating more severe level of cardiomyopathy (Figure 7, Table S3). Furthermore, from age 4 to 8 weeks, percent fractional shortening decreased more rapidly in DBA.AKR-Hrtfm2/Csqtg mice (36%, p = 0.0004) than the littermates (18%, p = 0.186), suggesting that the presence of even a single AKR-Hrtfm2 locus (essentially half the normal AKR/B6 level of TNNI3K expression) can accelerate the progression of the Csq-induced cardiomyopathy.
We next investigated whether TNNI3K expression would exhibit a disease accelerating effect in a model of cardiomyopathy that was unrelated to Calsequestrin over-expression. Transverse aortic constriction (TAC) induces left ventricular hypertrophy in response to pressure overload [13]. We performed TAC on TNNI3K transgenic animals and wild-type littermate controls. Cardiac function was analyzed by echocardiography at 4 and 8 weeks following TAC surgery. At 4 and 8 weeks post-surgery, the transgene-positive mice showed greater diastolic and systolic dysfunction (increased left-ventricular end diastolic diameter (LVEDD) and left-ventricular end systolic diameter (LVESD)), and significantly reduced fractional shortening compare to the control mice (Figure 8A–8C, Table S4). This confirmed that TNNI3K expression has a detrimental effect on heart function outside the context of the transgenic Csq sensitizer.
We had previously mapped 7 loci (Hrtfm1-7) that modify heart disease progression using the Calsequestrin (Csq) transgenic mouse model [5]–[7]. Here we report Tnni3k (cardiac Troponin I-interacting kinase) as the gene underlying Hrtfm2. We have shown that the murine Tnni3k locus harbors an ancestral SNP in intron 19 that activates a cryptic splice site, generating an aberrant transcript that undergoes NMD, leading to drastically reduced message levels and an apparent absence of TNNI3K protein. In DBA and other inbred mouse strains sharing the same haplotype at Tnni3k, drastically reduced levels of TNNI3K protein have no obvious effect on normal development or physiology, suggesting that any trace amounts of protein that remain are sufficient for its normal function, or that the lack of protein is compensated by functional redundancy of another gene. In vivo transgenic and congenic mouse lines confirm that TNNI3K levels are a significant determinant of the rate of disease progression and outcome, since expression of this protein accelerates disease progression in two independent and unrelated models of cardiomyopathy. However, we did not observe a simple, linear relationship between the level of TNNI3K transgenic overexpression and the strength of the modifying effect. In these models, the levels of overexpression in the transgenic lines may have crossed the threshold required for maximal phenotypic effects.
The modifying effects of TNNI3K expression were not dependent on the allele at the nonsynonymous coding SNP (rs30712233, T659I). DBA and most inbred mouse strains encode Threonine at this position. Most other species also encode Threonine at the homologous position. By contrast, B6 and AKR inbred strains encode the Isoleucine variant. The human TNNI3K transgene employed in the validation experiments coincidentally encodes the highly conserved Threonine variant. The modifying effects of the human transgene carrying the conserved variant (695T) directly parallel, and are even stronger, than those observed using the congenic line containing the B6/AKR variant (695I). Thus, robust phenotype modifying effects were observed independent of the murine Tnni3k coding variant. Nonetheless, these experiments did not address whether the 695I polymorphism also alters TNNI3K protein function.
TNNI3K was identified as a cardiac-specific protein kinase that interacted with cardiac Troponin I (cTnI) in the yeast-two hybrid interaction assay [8], however, cTnI has not been established as a phosphorylation target. TNNI3K protein contains seven ankyrin repeats in the N-terminus followed by a dual-specificity protein kinase domain and a short C-terminal serine-rich domain. The overall domain structure of TNNI3K resembles that of Integrin-linked Kinase (ILK). ILK mediates communication from the cellular matrix to intracellular signaling molecules such as PKB and GSK3β, and plays important roles in cardiac growth, contractility and repair [14],[15]. Sequence and structural homology might imply similar functions for TNNI3K. A yeast two-hybrid interaction screen with a C-terminal fragment of TNNI3K identified several additional sarcomeric proteins as putative binding partners such as cardiac α-actin and myosin binding protein C [8]. These studies suggest that TNNI3K might modulate sarcomere function through interactions with key components of the sarcomeric complex. However, to date, none of these proteins has been validated as a phosphorylation target of TNNI3K in cardiomyocytes, and the in vivo function of TNNI3K remains unknown.
Recently, expression of TNNI3K was shown to be protective in a different cardiomyopathic disease context [16],[17]. In a murine model of cardiac ischemia, intramyocardial transplantation of Tnni3k-overexpressing P19CL6 cells promoted cardiomyogenesis and improved cardiac function. We note that P19CL6 cells were originally derived from the C3H lineage of mice [18], which share with DBA, the “null” haplotype for Tnni3k (see Figure 1). Thus, the resulting phenotype may have been due to the restoration of Tnni3k expression in otherwise null cells, rather than to overt overexpression of the gene. A locus for susceptibility to coxsackievirus B3-induced myocarditis maps to a locus on distal mouse chromosome 3 (Vms1, viral myocarditis susceptibility locus) that virtually overlaps Hrtfm2 and which includes Tnni3k in its confidence interval [17]. In this disease model, inbred strain C57BL/10 provides the protective allele at the locus, and strain A/J provides the susceptibility allele. Assuming that Tnni3k also underlies the Vms1 locus, viral-induced myocarditis might represent another disease context where expression of TNNI3K is protective. These combined data suggest that expression of TNNI3K may be detrimental in certain pathological conditions such as pressure overload or aberrant sarcomeric calcium regulation, but protective in other disease contexts. In either scenario, TNNI3K appears to play a critical role in modulating disease progression and outcome in heart disease.
Since protein kinases are critical cell cycle regulators, kinase inhibitors have become a major avenue for the development of novel cancer therapeutics. TNNI3K may be an ideal candidate for the development of small molecule kinase inhibitors for categories of heart disease where TNNI3K expression is detrimental. In these cases, selective inhibition of TNNI3K would be particularly useful as it might slow disease progression, which may prove beneficial in treating individuals with rapidly progressing disease. In other scenarios of disease, augmentation of TNNI3K activity or protein levels may instead prove beneficial. Further investigation of TNNI3K function in these and other cardiomyopathic mouse models will lead to increased understanding of its role in both normal and pathological contexts, and may provide a novel target for therapy for heart disease.
All mice were handled according to approved protocol and animal welfare regulations of the Institutional Review Board at Duke University Medical Center. All inbred mouse strains used in the course of this study were obtained from Jackson Laboratory (Bar Harbor, ME). Transgenic mice overexpressing Csq [3] were maintained on a DBA/2J genetic background.
Through repeated backcrossing to DBA/2J, a congenic mouse was created which retains AKR genomic DNA at the Hrtfm2 locus in the DBA genetic background. At generation N2, breeders were selected which were heterozygous at Hrtfm2 and homozygous DBA at the other mapped modifier loci [7]. Genome-wide SNP genotyping was carried out using the Mouse MD linkage panel with 1449 SNPs (Illumina, San Diego, CA). By generation N6, the animals were homozygous for DBA alleles throughout the genome and only showed heterozygosity for an approximately 20 Mb interval on chromosome 3, the region containing Hrtfm2. Once we had reached the generation N10 backcross, the DBA.AKR-Hrtfm2 mouse was maintained by intercross.
Whole hearts removed from age- and sex-matched wild type animals from each of the three primary strains (B6, DBA, AKR) were used to examine RNA transcript levels. Total RNA was isolated using the RNeasy Kit (Qiagen, Valencia, CA). Microarray analysis was done on an Affymetrix Mouse probe set (Mouse 430 2.0 Array, Affymetrix, Santa Clara, CA). Analysis was done using GeneSpring GX 7.3 Expression Analysis (Agilent Technologies, Santa Clara, CA). For the TaqMan expression analysis, total RNA was extracted from whole mouse hearts using TRIzol reagent (Invitrogen, Carlsbad, CA). cDNA was synthesized from 1 µg total RNA using the High Capacity cDNA Archive Kit (Applied Biosystems, Foster City, CA) and used as the template for qRT-PCR. Tnni3k cDNA was amplified using the predesigned gene expression assay (TaqMan, ABI, assay ID: Mm01318633_m1). Beta-actin (Actb) was used as the endogenous control (TaqMan, ABI, catalogue number 4352341E). All amplifications were carried out in triplicate on an ABI Prism 7000 Real Time PCR system and analyzed with ABI software. All statistical analyses were done using an unpaired, two-tailed T-test.
Whole heart protein lysates were prepared using flash-frozen heart tissue resuspended in lysis buffer with protease inhibitors. Lysates were analyzed by SDS-PAGE and Western blot performed with standard methods. A polyclonal peptide antiserum was developed to the C-terminal 14 amino acids of mouse TNNI3K protein (LHSRRNSGSFEDGN). Antiserum from 2 rabbits was purified on a Protein A column (GenScript, Piscataway, NJ). TNNI3K antibody was used at a 1∶1000 dilution in TBST with 5% dry milk. Secondary anti-rabbit antibody conjugated to HRP followed by incubation with Pierce SuperSignal West Pico Chemiluminescant Substrate (Thermo Fisher Scientific, Rockford, IL) and exposure to X-OMAT film (Kodak) to visualize protein bands. Western blot analysis was used to confirm specificity of the antibody. As predicted, the mTNNI3K antibody detects a 90 kDa protein from lysates prepared from 293T cells transiently transfected with a full length Tnni3k expression vector and in protein lysates from wild-type mouse hearts.
cDNAs were subjected to qRT-PCR using primers designed to detect either a 116 bp or a 120 bp cDNA PCR product. The forward primer was targeted 25 bp upstream of the predicted 4 base insertion and was fluorescently labeled: 5′-6FAM-AGATTTCTGCAGTCCCTGGAT-3′ while the unlabeled reverse primer was targeted 48 bp downstream of the predicted 4 base insertion with the sequence: 5′-AAGACATCAGCCTTGATGGTG-3′. Accumulation of both fragments was quantified using the GeneMapper analysis program on the ABI Prism 3730 DNA Sequencer (Applied Biosystems). Ratios of properly spliced and mis-spliced products were calculated based on relative amplification of both cDNA products.
To create the Tnni3k genomic splicing constructs, DBA genomic DNA and B6 BAC clone RP23-180023 were used as templates to generate genomic 4 kb fragments that included part of intron 17, exon 18, intron 18, exon 19, intron 19, exon 20 and part of intron 20. The sequence of the forward PCR primer was 5′-ACTTACTTATGTGCTTCTCTTAGTTATGTGC-3′; the reverse primer was 5′-GGATTTAAACATAGGTGTGTACCTAATTGT-3′. PCR products were sub-cloned into pSPL3 (Invitrogen). Clones were verified by direct sequencing. Human embryonic kidney HEK293T (293T) cells (ATCC, Manassas, VA) were maintained in Dulbecco's Modified Eagle's Medium (DMEM, Gibco) containing 10% fetal bovine serum at 37°C in 5% CO2. Cells were grown on 35 mm2 plates and transfected with 1 µg plasmid DNA using FuGene reagent (Roche, Indianapolis, IN) according to the manufacturer's protocol. RNA was extracted with TRIzol (Invitrogen) 24 hr post-transfection and RT-PCR was carried out using standard methods.
HEK293T cells were grown to approximately 80% confluence in 6-well plates, then transfected using with 1 µg of DBA- or B6-pSPL3 plasmid mixed with FuGene reagent. All transfections were performed in triplicate. Total RNA was extracted with TRIzol 20 hr post-transfection. RT-PCR was carried out using standard methods. Ratios of properly spliced and aberrantly spliced products for the Tnni3k construct were determined by the fluorescent RT-PCR assay described above.
A single base was changed at rs49812611 (IVS19+9), in the DBA-pSPL3 construct (G→A) and the B6-pSPL3 construct (A→G) using the QuikChange Site-Directed Mutagenesis Kit (Stratagene, LaJolla, CA) with PfuTurbo proofreading DNA polymerase. All clones were sequenced to verify proper incorporation of the SNP.
HL-1 cardiomyocytes [11] were cultured in Claycomb Medium (SAFC Laboratories, Lenexa, KS) supplemented with Fetal Bovine Serum at 10%, 2 mM L-Glutamine, 100 mg/ml Penicillin/Streptomycin, and 100 mM fungizone. Cells were cultured at 37°C with 5% CO2. Although the HL-1 cardiomyocytes were derived from a heart isolated from a mixed B6-DBA mouse [11] direct sequencing of genomic DNA from the cell line showed that it is homozygous for DBA alleles at the Tnni3k locus. HL-1 cells were treated with 5.7×10−2 mM cycloheximide or 3.3×10−2 mM emetine. Each treatment was performed in triplicate and RNA was isolated from cells 24 hours post treatment. RT-PCR was performed on RNA isolated from cells treated with NMD blocking drugs and untreated controls. Ratios of properly spliced and aberrantly spliced products were measured using the fluorescent RT-PCR splicing assay as described above. Total transcript levels were determined using the Tnni3k TaqMan assay described above.
A full-length 2.5 kb human TNNI3K cDNA was amplified from normal human heart RNA following RT-PCR and cloned into a vector downstream of the murine α-myosin heavy chain (αMHC) promoter. An artificial minx intron was inserted upstream of the TNNI3K start codon. The construct was linearized and an 8 kb fragment containing the αMHC promoter, cDNA and SV40 polyadenylation sequence was purified and used for microinjection. B6SJLF1/J blastocysts were injected with the linearized transgene and subsequently implanted into surrogate mice. The resulting founder animals were genotyped for presence of the TNNI3K transgene using a 5′ primer in the αMHC promoter and a 3′ primer in the TNNI3K transgene. Three transgenic lines were chosen for backcrossing to the DBA strain. Western blot analysis of heart lysates with a polyclonal antibody (Bethyl Laboratories, Montgomery, TX) raised against a human C-terminal TNNI3K peptide (FHSCRNSSSFEDSS) confirmed similar levels of expression of the TNNI3K transgene in each line (Figure S1). This was repeated for several generations of backcrossing to DBA. Southern blot analysis of DNA from founder animals and subsequent generations (N2–N3) indicated that two founder lines carried 10–20 copies of the transgene while the third line appeared to have >100 copies. qRT-PCR with SYBRgreen (Invitrogen) was performed on heart cDNA from several transgenic mice to determine the relative expression difference between endogenous mouse Tnni3k and transgenic human TNNI3K expression.
Transthoracic two-dimensional M-mode echocardiography was performed between 12 and 18 weeks of age in conscious mice using either a Vevo 770 echocardiograph (Visual Sonics, Toronto, Canada) or an HDI 5000 echocardiograph with a 15-MHz frequency probe (Phillips Electronics, Bothell, WA). Measurements of cardiac function include heart rate, posterior and septal wall thickness, left-ventricular end diastolic diameter (LVEDD) and left-ventricular end systolic diameter (LVESD). Fractional shortening (FS) was calculated with the formula: FS = (LVEDD−LVESD)/LVEDD, as previously described [4].
Hearts were fixed in 10% neutral buffered formalin, dehydrated in 75%, 90% and 100% ethanol, and embedded in paraffin; sections 5 mm in thickness were cut and then stained with Masson's trichrome stain.
Mice were anesthetized with a mixture of ketamine (100 mg/kg) and xylazine (2.5 mg/kg), and transverse aortic constriction (TAC) was performed as previously described [13]. TAC was performed on 14 TNNI3K transgene-positive animals and 14 transgene-negative (wild-type) littermates at 10 weeks of age. One of the transgene-negative controls and three transgene-positive animals died following surgery, which is a normal complication of this procedure. The remaining 24 mice were then analyzed by echocardiography (as described above), at 4 and 8 weeks following the surgery.
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10.1371/journal.pntd.0005337 | Intensive trapping of blood-fed Anopheles darlingi in Amazonian Peru reveals unexpectedly high proportions of avian blood-meals | Anopheles darlingi, the main malaria vector in the Neotropics, has been considered to be highly anthropophilic. However, many behavioral aspects of this species remain unknown, such as the range of blood-meal sources. Barrier screens were used to collect resting Anopheles darlingi mosquitoes from 2013 to 2015 in three riverine localities (Lupuna, Cahuide and Santa Emilia) in Amazonian Peru. Overall, the Human Blood Index (HBI) ranged from 0.58–0.87, with no significant variation among years or sites. Blood-meal analysis revealed that humans are the most common blood source, followed by avian hosts (Galliformes-chickens and turkeys), and human/Galliforme mixed-meals. The Forage Ratio and Selection Index both show a strong preference for Galliformes over humans in blood-fed mosquitoes. Our data show that 30% of An. darlingi fed on more than one host, including combinations of dogs, pigs, goats and rats. There appears to be a pattern of host choice in An. darlingi, with varying proportions of mosquitoes feeding only on humans, only on Galliformes and some taking mixed-meals of blood (human plus Galliforme), which was detected in the three sites in different years, indicating that there could be a structure to these populations based on blood-feeding preferences. Mosquito age, estimated in two localities, Lupuna and Cahuide, ranged widely between sites and years. This variation may reflect the range of local environmental factors that influence longevity or possibly potential changes in the ability of the mosquito to transmit the parasite. Of 6,204 resting An. darlingi tested for Plasmodium infection, 0.42% were infected with P. vivax. This study provides evidence for the first time of the usefulness of barrier screens for the collection of blood-fed resting mosquitoes to calculate the Human Blood Index (HBI) and other blood-meal sources in a neotropical malaria endemic setting.
| Anopheles darlingi is the major malaria vector in the Amazon. This species has been commonly described as highly anthropophilic throughout its geographic range, although little is known about its feeding preferences. Scant information is available regarding the origin of An. darlingi blood-meals. In the context of malaria elimination programs, the Human Blood Index (HBI) may provide crucial information regarding mosquito-human contact related to transmission dynamics. Additionally, collection of resting An. darlingi is challenging, mainly because the resting behavior of this species has not been well characterized. Our study, conducted from 2013–2015 in three localities in Loreto Department in the Peruvian Amazon, showed for the first time the efficacy of the barrier screen methodology for collecting recently blood-fed An. darlingi in a neotropical setting for the purpose of identifying the source of their blood-meals. Our data show that An. darlingi feeds on humans, Galliformes, dogs, pigs and goats, and that 30% of the mosquitoes fed on more than one type of host. Despite this opportunistic feeding behavior, however, An. darlingi is primarily anthropophilic. We hypothesize that mosquito population structure is associated with feeding preferences, which may affect the pattern of malaria transmission in the area.
| The Human Blood Index (HBI), formerly known as the anthropophilic index or human blood ratio, is the proportion of recently-fed mosquitoes, usually vector species that have taken a human blood-meal [1]. This index is a very important component of the formulae used to determine vectorial capacity and varies depending on mosquito species, collection area and season or time of collection [2]. From an epidemiological standpoint, it is crucial to be able to accurately identify mosquito blood-meals for studies of transmission dynamics of viral and parasitic pathogens [3]. For example, in Equatorial Guinea, the calculation of this index before and after indoor interventions to reduce malaria did not detect any mosquito behavioral differences, and researchers concluded that control strategies in this region were ineffective [4]. In Central Kenya, anthropophily decreased in An. gambiae after the introduction of long lasting insecticide nets (LLINs) and zooprophylaxis [5]. However, in southern Zambia, after two years of LLIN intervention, the main vector, Anopheles arabiensis, remained highly anthropophilic [6]. In Tanzania the HBI showed a change in the main blood-source in An. arabiensis but not in An. funestus after the use of spatial repellent coils [7].
Another index to quantify host selection patterns is the incidence of multiple blood-meals from the same host species (cryptic) or from two or more different host species (patent) [8]. Evidence that malarial mosquitoes take partial blood-meals from multiple hosts may be interpreted as interrupted blood-feedings that could increase the probability of both acquiring and transmitting Plasmodium [9]. On the other hand, Burkot and colleagues [10] contend that fewer gametocytes would be ingested per meal, resulting in lower mosquito infection rates.
Anopheles darlingi, the primary regional malaria vector in the Amazon Basin, is anthropophilic in the Iquitos region [11], although both human biting rate (HBR) and entomological inoculation rate (EIR) vary widely [12] depending on the setting [13–15]. The An. darlingi feeding site in this region is exophagic and/or endophagic, depending on local circumstances (e.g., vegetation cover, type of house) and host availability [11, 12, 14,15].
In 2015, Loreto Department reported 95% of the total malaria cases in Peru (59,349 of 62,220 total) with Plasmodium vivax as the most prevalent human parasite followed by P. falciparum, with 46,924 and 12,425 cases, respectively [16]. Parker and collaborators [13] demonstrated that high HBR, EIR, and infectivity of An. darlingi are a signature of remote riverine malaria hot spots and hyperendemicity in certain areas of the Peruvian Amazon, upending previous notions that transmission is hypoendemic throughout the peri-Iquitos region [11,12]. Recent studies also detected very high seasonal HBR and moderate EIR in the peri-Iquitos region [14, 15]. Most malaria cases occur during the rainy season, from December to June [17] and a correlation was detected between An. darlingi abundance and peak river levels, but there was no significant correlation between river level and malaria case numbers [12, 14, 15]. In this last study, mosquitoes positive for Plasmodium were collected in peridomestic areas within approximately 10 m of the main house entrance, (a caveat being that very few An. darlingi were found indoors despite extensive searching), suggesting that most malaria is transmitted exophagically, where humans have little protection against mosquito bites.
Despite being the dominant malaria vector in Amazonia, few studies have documented the blood-meal sources for An. darlingi. In Amapá state, Amazonian Brazil, an ELISA analysis found that 13.1% of blood-meals were human; most resting An. darlingi had fed on cattle, pigs and dogs [18]. Notwithstanding the relatively low level of HBI, these communities are endemic for malaria, and An. darlingi is considered to be the most effective local vector [19]. In Peru, no studies have been published on the identity of An. darlingi blood-meals, but potential non-human hosts in rural residences near Iquitos include common peridomestic animals, dogs and chickens, and several potential wild mammalian hosts [12].
Although resting mosquitoes are optimal for calculating HBI, adequate sample sizes can be difficult to obtain in some habitats [18–20]. Little information exists on host preference and resting behavior of An. darlingi. The location of resting sites of An. darlingi could be useful for focal vector control if such mosquitoes are clustered non-randomly in the landscape. The development of barrier screens as a method for collecting anophelines outdoors has been tested successfully in the South East Pacific [20] and recently in southern Zambia [21].
This study was designed to address the following questions regarding An. darlingi feeding behavior in the Peruvian Amazon: i) are barrier screens a useful tool to collect resting blood-fed An. darlingi in the area; ii) what is the degree of anthropophily (HBI) in An. darlingi in contrast to more opportunistic behavior; iii) what is the influence of available host biomass and iv) is there evidence of seasonal age-structure in An. darlingi.
This study was approved by the Human Subjects Protection Program of the University of California San Diego, La Jolla, California and by the Ethical Boards of Universidad Peruana Cayetano Heredia and Asociación Benéfica PRISMA, Lima, Peru.
The strategy of the barrier screen method of collecting mosquitoes outdoors is to intercept and capture mosquitoes transiting between blood feeding and resting sites [20]. Two possible scenarios can be identified: 1) intercepting mosquitoes entering a village seeking a blood-meal after emergence or oviposition; and 2) intercepting blood-fed mosquitoes leaving the village and seeking resting sites for egg development (swamp, creek, stream, forest). In this Peruvian study, barrier screens were placed to intercept mosquitoes flying between house-forest and house-river depending on the specific characteristics of the locality. Mosquito collections were performed in three villages in Loreto Department: Lupuna (LUP) and Cahuide (CAH) in the peri-Iquitos area, and Santa Emilia (SEM), in a remote area ~150 km from Iquitos (Fig 1). Detailed descriptions of these villages are in [15, 22]. In 2013, from March to May, a pilot study was conducted using a single screen in LUP and CAH placed at different points within each village (between the creek/river and village houses). Specimens were collected for 4 nights (6PM- 6AM) each month.
Each barrier screen was constructed from a lightweight window screen mesh approximately 15 m long and 2 m high (S1 Fig). Screens were then attached to poles with thin wire. Permission from the inhabitants/owners was obtained prior to any activity, including setting up the barrier screens and performing mosquito collections. Resting mosquitoes from the barrier screens were sampled by manually searching the surface of the screens with a mouth aspirator every hour for 15 minutes on each side, and the location (next to house, forest or river) and height (˃ or ˂ 1m above ground) of mosquitoes was recorded. Mosquitoes were captured and stored by hour of collection and screen side separately. In 2014 (monthly) and 2015 (January-June), the design was slightly modified to include four barrier screens in LUP and CAH to better represent the An. darlingi population in each locality. When multiple screens were used per village, data from each screen was maintained separately. In SEM, a remote village along the Nahuapa River, collections were performed with two barrier screens for two nights in May-June 2014 and May-September 2015. Additionally, in 2015, daytime mosquito collections (6AM-6PM) with barrier screens were performed two days monthly from January-June in LUP and CAH, and from May-July in SEM. Screen orientation, wind speed and direction were recorded for every collection with a Windmate 300 Wind/Weather Meter. A census questionnaire of domestic hosts present in the study villages was performed in October 2014 in LUP and CAH and May 2015 in SEM (S1 Table, Fig 2). Because the first study was performed a year prior and the animal composition could have changed, the questionnaire included a retrospective question to assess the presence of potential past hosts.
All specimens collected were morphologically identified using entomological keys [23–25] and abdominal status recorded (unfed, blood-fed or gravid). Mosquitoes were stored and labeled individually with silica gel and placed at 4°C until subsequent analysis.
To estimate the female age composition of the population, in March-April 2014 and February-June 2015 in LUP and CAH a proportion of females were dissected to determine the parity rates per hour, trap and side of trap [26]. Parity is also used as an indicator of mosquito survival under natural conditions. Mosquito longevity (life expectancy) was estimated using Davidson’s methodology (1954) Age=1loglP, where l is the natural logarithm of the constant P (daily survival rate). (P) was calculated P = PRgc, where PR is the ratio of parous mosquitoes and the total number of females dissected, and gc is the duration of the gonotrophic cycle in days [27]. A limitation of this calculation is the assumption of accurate estimates of the length of the gonotrophic cycle. We have assumed that two or more blood-meals are required for the first oviposition and that the temporal feeding pattern is not regular, and therefore, we followed the method of calculations proposed by Garret-Jones and Grab [28]. Various studies have estimated the gonotrophic cycle of An. darlingi to be 2–3 days [29, 30, respectively]. Recently, it was calculated to be 2.19 days in the rainy season and 2.43 in the dry season [31]. Calculations in our study were performed using the 2.19 day estimate based on the timing of our An. darlingi collections (the rainy season).
Individual An. darlingi were bisected between the head/thorax and abdomen and DNA was extracted manually using the DNeasy Blood & Tissue kit (Qiagen). A PCR-RFLP protocol was performed to detect the most common host in the area [32] for all mosquito abdomens in 2013–2015, except for a subsample (60%) of mosquitoes collected in LUP 2014 (due to a extended sample size). In addition, samples were tested for Galliformes (Gallus gallus and turkeys; see census and proportion of chickens; Fig 2, S1 Table) following [33], rat and didelphis [34], and monkey [35]. A subsample of the unidentified blood samples was sequenced for the mitochondrial COI gene [36] and then compared with sequences in GenBank using BLASTn (http://www.ncbi.nmln.nih.gov) or BOLD SYSTEMS v2.5 (http://www.barcodinglife.org). The best match with identity of 95% or above was recorded.
Detection of Plasmodium infection was conducted using real-time PCR of the small subunit of the 18S rRNA, with a triplex TaqMan assay (Life Technologies), as described in [37]. First, DNA was extracted from each specimen of An. darlingi, then the RT-PCR was conducted on pools of DNA of head/thoraces of five mosquitoes, and finally the pools were analyzed for detection of P. vivax and P. falciparum. Specimens from positive pools were tested individually to calculate infection rate (IR).
HBI was calculated as the proportion of mosquitoes fed on a specific host divided by the number of mosquitoes analyzed (mixed blood-meals were added to totals of each host). To adjust the HBI, mosquitoes with unidentified blood-meals were excluded. This index was calculated monthly in each locality and Chi-square (χ2) analyses were performed to compare statistical differences temporally and among sites. Host data recorded in the census was used for the calculation of the forage ratio (wi) [38, 39] and selection index (Bi) [40], to quantify the preference of mosquitoes for available blood resources. The forage ratio for species i was calculated as wi=oipi, where oi is the proportion of host species i in the blood-meals, and pi is the proportion of available host in the environment. Forage ratios >1.0 indicate preference and < 1.0 avoidance and selection of another host; ~1.0 means neither preference nor avoidance. The selection index Bi was calculated with the formula Bi=wi∑i=1nwi, where wi is the forage ratio for species i and n is the number of blood sources available.
Wind speed was measured at 6:00pm, 12:00am, and 6:00am each collection night in LUP, CAH, and SEM in 2015. For each collection night, mosquito density was aggregated into four 3-hour collection periods (6-9pm, 9pm-12am, 12-3am, and 3-6am). The wind speed at 6:00pm was assigned to the 6-9pm collection time, the wind speed at 12:00am was assigned to the 9pm-12am and 12-3am collection times, and the wind speed at 6:00am was assigned to the 3-6am collection time. The mosquito density was plotted against wind speed for each collection period at each location (n = 48 collection periods each for LUP and CAH, and 40 collection periods for SEM) using the ggplot2 package in RStudio v0.98.1091 [41].
A null-model analysis was used to test whether An. darlingi feeding habits were random or structured among the three villages, as in [36] and [42]. All specimens with identified blood-meals from 2013–2015 for LUP, 2013–2015 for CAH, and 2014–2015 for SEM were included, and specimens with mixed blood-meals were counted once for each host identified in the blood-meal. We calculated a C-score comparing the blood- meal sources of mosquitoes from the three villages using Ecosim 7.0 and we used the R bipartite package [43] to generate a host-vector quantitative interaction network for the three localities, as in [36].
In 2013, all specimens caught on the screens were collected and identified to determine the potential use of screens for collecting not only Anophelinae but also other Culicidae, potential vectors of parasites and arboviruses. A total of 322 mosquitoes in LUP and 514 in CAH were collected in 6 nights (72 h collection) (Table 1); 94.4% (304/18) of mosquitoes collected in LUP and 89.7% (461/53) of all mosquito species in CAH were females. Anopheles darlingi comprised 78.9% and 61.5% of these collections in LUP and CAH, respectively, and Culex quinquefasciatus was the second most common species identified in both localities (Table 1). Only one additional species of anopheline, Anopheles forattini, was identified (in LUP).
With respect to screen position, in LUP 63.4% of the An. darlingi were collected on the side facing the houses (In) and 36.6% on the side facing the creek (Out), although this difference was not significant (Kolmogorov-Smirnov test; p = 0.4). On both sides of the screen, most of the specimens were collected <1m from the ground (Below; Table 2) (range 76.5–90.2%). In CAH, 61.8% of the mosquitoes were collected on the house side and 38.2% on the creek side, and 93.1% and 84.5% (In and Out, respectively) were caught <1 m from the ground. No differences were found between LUP and CAH for side of the barrier screen. Only 1.62% in LUP and 6.57% in CAH of the An. darlingi females were determined by visual inspection to be blood-fed, with no differences between screen sides (Table 3).
In 2014, using multiple barrier screens per locality, a total of 4,593 An. darlingi females were collected in LUP, 175 in CAH and 216 in SEM (Table 2). One specimen of Anopheles dunhami in LUP and eighteen Anopheles benarrochi B in SEM were also identified as in [14]. In LUP, no significant differences were detected between the sides of four screens tested independently. However, when data were grouped over months there was a significant difference between mosquitoes collected on the side of the houses (In) and creek/vegetation side (Out) (Wilcoxon test; p = 0.0313). In CAH, the four barrier screens were not homogeneous, with significant differences in number of mosquitoes collected from each side (K-S; In: p = 0.0082 and Out: p = 0.0356), and when In/Out were compared by month (K-S; p = 0.0022). There were also significant differences between collections in LUP and CAH (K-S, p = 0.0336). In SEM, captures in May (two screens) and in June (four screens), were not significantly different between screens.
In 2015, in LUP, 1,019 female mosquitoes were collected, 233 in CAH and 277 in SEM. Most specimens were captured resting < 1m from the ground with little variation among years and sites (Table 2).
Differences in mosquito density by time of collection and side of barrier screen were tested (Fig 3) with time of collection split into four three-hour periods (6-9pm, 9pm-12am, 12-3am, and 3-6am). In both LUP and CAH in 2015, there was a significant difference in the distribution of mosquito collection location (side of screen) by time period (Kruskal-Wallis p < 0.0001 for both sites), with higher proportions of mosquitoes found on the In (facing house) side of the screen from 9pm-12am and 12-3am than from 6-9pm and 3-6am. In LUP and CAH in 2013 and 2014, and in SEM in 2015, there was no significant difference in mosquito density by time of collection (Kruskal-Wallis p>0.05).
Plots of mosquito density against wind speed for each locality in 2015 are shown in Fig 4. Overall, there was a negative but non-significant correlation between mosquito density and wind speed (Pearson’s r = -0.09, p = 0.3). The correlation between mosquito density and wind speed was also negative in LUP (Pearson’s r = -0.25, p = 0.1) and SEM (Pearson’s r = -0.27, p = 0.09), but was positive in CAH (Pearson’s r = 0.14, p = 0.34) (Fig 4).
To investigate the diurnal behavior of An. darlingi, barrier screen collections were performed in LUP and CAH from January to June, and in SEM from May to June from 6AM to 6PM twice January-June 2015. In LUP a total of 59 An. darlingi were collected during this period and female activity was reported from 6AM to 9AM and from 2PM to 5PM. In CAH, the number of collected specimens was 23, with an activity similar to LUP. In SEM, 33 mosquitoes were collected, with an extension of the flying activity until 8AM, and beginning again in the evening at 4PM. In LUP, 20.3%, in CAH, 34.8% and in SEM 54.5% of diurnal An. darlingi specimens were collected on the house side (In).
A total of 583 An. darlingi females from LUP were dissected in 2014 (12% of the total) and 19 in CAH (11%); in 2015, n = 633 in LUP (62%) and n = 153 (65%) in CAH were dissected. The monthly mean parity rate in LUP in 2015 was ~ 55% (range 45.6–66.7) and in CAH it was ~ 51% (range 27.8–64.5) (Table 4). No significant differences were found between months or between localities, although in February, the rate was slightly higher compared to June. Mosquito age in LUP in March—April 2014 was 7.47 and 14.21 days, respectively, whereas in 2015 it ranged from 14.21–23.90 days. In CAH, mosquitoes collected in March 2014 were estimated to survive 14.98 days, and between 3.73–20.24 days in 2015 (Table 4).
Blood-meal source was determined for 4,417 An. darlingi females (S2 Table). A total of 3,214 mosquitoes from LUP, 729 from CAH and 474 from SEM were analyzed. Single-host blood-meals were the highest percentage among the blood-meals detected (69.98%) and human was the most common blood source (42.5%), followed by Galliformes (25.1%) and dog (1.42%; Fig 5). Only 4% of the samples could not be identified to blood-meal source. Multiple blood-meals were found in 1,272 mosquitoes and accounted for 30% of the blood- meals, with 1,262 double feeds in the three localities, and triple feeds (n = 10) only identified in LUP.
In total, seventy-three samples with non-identified blood-meal source by PCR-RFLP, were sequenced for 16S ribosomal DNA [36] and mammalian cytochrome-b [32]. Only ten were identified as of human origin with the 16S protocol, whereas 23 were consistent with human for cytochrome-b.
The distribution of blood-meal source in An. darlingi presented little temporal or spatial variation. Evaluation of the proportion of feeds on single different hosts showed that in LUP, no significant differences between years were detected by one-way ANOVA analysis; paired Wilcoxon-tests were not significant when comparing years 2013–2014 with 2015 or 2013 and 2014. In CAH, no significant differences between the years 2013–2014, 2014–2015 or among the 3 years were found. In SEM, a non-parametric Mann-Whitney test was not significant comparing 2014 and 2015. For locality comparison, data from the same years and different localities were compared. In 2013, there were no significant differences between LUP and CAH, and in 2014 and 2015 a one-way ANOVA test did not show differences between sites.
HBI was calculated monthly (S3 Table) and annually (Table 5) per locality. In 2013, no significant differences were detected in LUP or CAH. Mean HBI per year was non-significant among localities (LUP, CAH, SEM) and years 2014–2015.
The Forage Ratio and Host Selection Index were calculated, accounting for single and multiple blood-meals (Table 6). Humans were the preferred source, closely followed by Galliformes, in all three settings for both years. When the Forage Ratio was analyzed, the weight per host was used instead of the numerical presence at the site [36] (S4 Table), Galliformes were by far the preferred host, with humans as the second most favoured. For example in LUP, the Galliforme forage ratio ranged from 10.35 to 17.96 and the human forage ratio from 0.58–0.72. The null model test indicated that the mosquito feeding patterns were aggregated among the localities, indicating that diet overlapped more than expected between the localities, although this finding was only marginally significant (C-score: 0.33, p = 0.08). The quantitative interaction network of blood-meal source by locality (Fig 6) supported patterns of organization based on the above-mentioned trophic preferences (humans and Galliformes) from the three mosquito populations (LUP, CAH, SEM).
A total of 5,387, 362 and 455 mosquitoes in LUP, CAH and SEM, respectively, collected on barrier screens, were tested for Plasmodium. The Infection rate (IR) of mosquitoes varied among sites and seasons, ranging from 0.20–3.85 in LUP, 0.51–14.3 in CAH and 0–2.04 in SEM (Table 7). A logistic regression model analysis determined that IR was significantly higher in CAH (p = 0.02) and SEM (p = 0.003) vs. LUP. No specimens from the diurnal collections in the three localities (n = 116) were positive for P. vivax, independent of the collection season.
Ours is the first study to conclusively demonstrate that An. darlingi readily feeds on Galliformes. Overall, the feeding preference of An. darlingi in the Peruvian Amazon is more variable than previous studies have assumed. In addition, a consistent pattern of blood-meal source was observed at each site every year of collection: mosquitoes feeding only on humans, only on chickens, or on both hosts. This consistency could suggest the co-occurrence of different subpopulations within a metapopulation, with local adaptation as the main driving force.
A single metapopulation was initially detected in An. darlingi in the Iquitos area with AFLPs [44] and microsatellite markers [45]. However, using 2x the number of microsatellites, a population replacement event was detected between 2006 and 2012 and two subpopulations were detected, one significantly more prevalent in highway compared with riverine habitat [20]. This recent genetic structure could explain some of the heterogeneity in feeding preferences of An. darlingi among localities [45, 46]. Additional studies, focused on intrinsic host preference, vector density and social practices of the human population might elucidate the basis for the described behavior and whether some An. darlingi populations are under selective pressure for host preference or whether this pattern is strongly correlated with host availability.
Similar HBI across the dry and rainy seasons and between populations infers that mosquitoes maintain their host preference behavior independent of local ecological conditions. In an earlier investigation of HBI of An. darlingi in riverine villages in Amapá State, Brazil [18], researchers reported high among-village variance (HBI 0.131–0.435) and ~10% of mixed blood-meals overall, mainly from cattle and pigs. In contrast, in our study, there was virtually no variance in HBI among localities, HBIs were higher (0.58–0.79) and ~30% of blood-meals were mixed, with Galliformes as the primary alternate host. Because HBI is an integral parameter of the vectorial capacity formula (the daily rate of malaria transmission from a single infected human, assuming every bite from an infected mosquito leads to transmission) [2], our data suggest that An. darlingi is a more effective vector in the peri-Iquitos area compared with Amapá state, Brazil. Curiously, in Tanzania, An. arabiensis avoids, and may be repelled by, the volatiles of chickens [47]. Subgenera Nyssorhynchus (An. darlingi) and Cellia (An. arabiensis) were estimated to have diverged ~94 million years ago [48]; therefore their olfactory responses are expected to have evolved differentially.
The present study provides evidence of the successful use of barrier screens to collect blood-fed An. darlingi mosquitoes in Amazonian Peru. Initially, in 2013, we conducted preliminary barrier screen collections with Procopack aspirators in LUP and CAH from 5 to 8 AM for 6 days/collection in March-May in at least 10 houses each time, but only one An. darlingi specimen was caught. Interestingly, in Iquitos the Procopack effectively collected indoor resting Culicidae including Aedes aegypti and Culex pipiens complex [49]. One explanation for our failure to find An. darlingi using the Procopack despite extensive searching could be due to its singular resting and biting behavior in this region.
Anopheles darlingi resting behavior varies across its range [50]: in Venezuela, Guyana [51] and in Brazil, in Matto Grosso and in southern Amazonas [52, 53] it rests during the day inside houses (endophily). In contrast, in Suriname, using exit traps, a peak departure from the dwelling was observed at sunrise [54] and in Brazil An. darlingi was resting indoors only at night [55]. In Amapá state, Brazil, resting mosquitoes were collected after sunrise (6AM-7AM) under houses and in peridomestic vegetation [18]. In French Guiana, no resting An. darlingi were collected indoors after pyrethroid spray, from pit-shelters or in the shade in the peridomestic area [56]. In our study, overall differences detected between screen sides may reflect the relative nearness of screens to houses, resulting in the interception of a higher proportion of blood fed An. darlingi leaving the peridomestic area, compared with questing females, entering the village from numerous resting and/or breeding sites. In CAH, we hypothesize that additional differences among screens and between months could result from a much smaller population of An. darlingi intercepted in this village. Our results constitute a major accomplishment: the use of barrier screens in this setting to overcome the difficulty of performing host-independent sampling for determining blood-meal sources.
The success of individual mosquito blood-meal identification in this study (range of 92.2–99.3%), was remarkably high when compared to visually blood-fed mosquitoes (0.92%-14.44%). When analysis is restricted only to the latter, information from partial blood-meals or partially digested blood is missed, leading to underestimation of the proportion of host sources (up to 18.7%); hence, a miscalculation of HBI [57]. One limitation of our study was the lack of identification of potential wild animal hosts; use of novel targeted high-throughput sequencing [58] would rectify this.
In LUP, the age of the mosquito population at each time point is enough to sustain the sporogonic cycle of P. vivax (range 7.24–9.13 days; calculated by the Moshkovsky method in [31]), whereas in CAH the population is, in general, younger, but with non-dangerously aged mosquitoes only in May and June. The proportion of young females might be explained by differential dispersal and aggregation of different age classes of An. darlingi populations, as previously reported for An. farauti in Papua New Guinea [59]. Use of 2.19 days of the gonotrophic cycle [31] could have produced a miscalculation in the age parameter. For instance, gravid females may experience delays while searching for suitable oviposition sites or there could be variation in extrinsic environmental conditions within this population of An. darlingi [60]. Because of the natural development of the parasite within the mosquito, a longer life-span is related to a higher potential to transmit malaria [61]. Parity is also associated with seasonality, i.e., mosquitoes generally survive longer during the rainy season [62,63], but see [64].
Overall, our study provides unreported information of the blood-meal preferences of An. darlingi in the peri-Iquitos area, which will be the base-line to compare potential changes in the behavior of these mosquito populations. HBI, together with other malaria metrics such as HBR or EIR, should be taken into consideration for surveillance and epidemiological studies of malaria transmission.
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10.1371/journal.ppat.1002037 | A Large and Intact Viral Particle Penetrates the Endoplasmic Reticulum Membrane to Reach the Cytosol | Non-enveloped viruses penetrate host membranes to infect cells. A cell-based assay was used to probe the endoplasmic reticulum (ER)-to-cytosol membrane transport of the non-enveloped SV40. We found that, upon ER arrival, SV40 is released into the lumen and undergoes sequential disulfide bond disruptions to reach the cytosol. However, despite these ER-dependent conformational changes, SV40 crosses the ER membrane as a large and intact particle consisting of the VP1 coat, the internal components VP2, VP3, and the genome. This large particle subsequently disassembles in the cytosol. Mutant virus and inhibitor studies demonstrate VP3 and likely the viral genome, as well as cellular proteasome, control ER-to-cytosol transport. Our results identify the sequence of events, as well as virus and host components, that regulate ER membrane penetration. They also suggest that the ER membrane supports passage of a large particle, potentially through either a sizeable protein-conducting channel or the lipid bilayer.
| Biological membranes represent a major barrier during viral infection. While the mechanism by which an enveloped virus breaches the limiting membrane of a host cell is well-characterized, this membrane penetration process is poorly understood for non-enveloped viruses. Indeed, most available insights on membrane transport of non-enveloped viruses are built upon in vitro studies. Here we established a cell-based assay to elucidate the molecular mechanism by which the non-enveloped SV40 penetrates the endoplasmic reticulum (ER) membrane to access the cytosol, a critical step in infection. Strikingly, we uncovered SV40 breaches the ER membrane as a large and intact viral particle, despite the conformational changes it experiences in the ER lumen. This result suggests that the ER membrane can accommodate translocation of a large protein complex, possibly through either a sizeable protein channel or the ER membrane bilayer. In addition to this finding, we also pinpoint viral and host components that control the ER-to-cytosol membrane transport event. Together, our data illuminate the cellular mechanism by which a non-enveloped virus penetrates the limiting membrane of a target cell during infection.
| The mechanism by which non-enveloped viruses such as simian virus 40 (SV40) and the murine polyomavirus (mPy) penetrate the host cell's membrane to cause infection is enigmatic. However, a general model describing how they breach this membrane based largely on in vitro studies is emerging [1], [2]. In this model, the virus undergoes conformational changes by interacting with host factors, culminating in the formation of a hydrophobic viral particle or release of a lytic peptide. They then engage the limiting membrane to disrupt its integrity, enabling the virus to cross the membrane. As it is unknown whether this scenario reflects the pathway in cells, establishing a cell-based assay that monitors non-enveloped virus membrane penetration affords the opportunity to study this event's physiological mechanism. Important questions include: what reaction sequence initiates membrane penetration? What is the nature of the viral conformational change and identity of the membrane penetrating species? What viral and host components control the penetration process, and how is membrane transport achieved?
Here we address SV40's membrane transport process. Structurally, SV40 is composed of 72 pentamers of the VP1 coat assembled into an icosahedral viral capsid [3], [4]. Each VP1 pentamer engages the internal proteins VP2 and VP3 through hydrophobic interactions [5]. VP1 also binds to the ∼5 kb viral DNA genome buried within the virus through electrostatic interactions. Three additional forces support the overall viral architecture. First, disulfide bonds present throughout the virus stabilize it [4]. Second, the VP1 C-terminus invades a neighboring VP1 pentamer to provide inter-pentamer support [3]. And third, calciums bound to the virus clamp together different pentamers to increase capsid stabilization [4].
To infect cells, SV40 VP1 binds to the glycolipid ganglioside GM1 on the host cell surface [6], inducing membrane tubulation that initiates internalization [7]. The virus-receptor complex is then transported to the pH neutral caveosomes [8] or the low pH endolysosomes [9]. Regardless of the pathway, the virus subsequently sorts to the endoplasmic reticulum (ER). Upon arrival of the virus-receptor complex to the ER [10], SV40 is proposed to disassemble to cross the ER membrane and reach the cytosol [11]. From the cytosol, a subviral core particle transports into the nucleus where transcription and replication of the viral DNA ensue, leading to lytic infection or cell transformation.
Reactions controlling SV40's ER-to-cytosol transport, a decisive infection event, are not fully understood. How do ER-initiated events propel the virus to the cytosol? What is the identity of the membrane penetrating species? What viral, ER, and cytosolic components regulate this process? While a report suggests that the ER associated degradation (ERAD) machinery mediates SV40 infection [12], how this machinery geared normally to handle endogenous proteins much smaller than SV40 (∼50 nm in diameter) promotes membrane transport of the larger viral particle is unclear.
Here we established a cell-based assay to elucidate SV40's ER-to-cytosol membrane penetration. Our data demonstrate that, upon ER arrival, SV40 is released into the ER lumen and undergoes sequential disulfide bond modification as it moves to the cytosol. Despite these reactions, a large and intact SV40 intermediate penetrates the ER membrane to reach the cytosol where it disassembles. We also pinpoint viral and host components that regulate the penetration process. This assay thus provides the opportunity to illuminate SV40's membrane penetration mechanism in a cellular setting.
We first tested whether brefeldin A (BFA), a drug that can impede COPI-dependent retrograde transport from the cell surface to the ER, blocks arrival of SV40 to the ER and infection as reported previously [11], [13]. A convenient method to measure SV40 ER arrival is to monitor conformational changes imparted on the virus in the ER. For instance, when SV40 arrives in the ER, ER-resident protein disulfide isomerase (PDI) family members disrupt its disulfide bonds [12]. When a whole cell extract (WCE) derived from infected cells was analyzed by non-reducing SDS-PAGE, VP1 monomer was detected [12]. Accordingly, simian CV-1 cells were incubated with SV40 (m.o.i. 30) for 12 hrs at 37°C. The cells were solubilized with SDS to generate a WCE, and the samples analyzed by non-reducing SDS-PAGE followed by immunoblotting with VP1-specific antibodies. We detected formation of both VP1 monomer and a species whose size corresponds to a VP1 dimer (Figure 1A, lane 1). An additional VP1 species at the top of the gel was also detected, which is likely derived from the intact virus. The VP1 monomer and dimer levels decreased when cells were treated with BFA at infection (0 h.p.i.) (Figure 1A, compare lane 2 to 1). A similar VP1 monomer level was observed when the samples were subjected to reducing SDS-PAGE (Figure 1A, compare lanes 3 and 4).
BFA was added to cells 4 hrs post infection (4 h.p.i.) to avoid perturbing viral entry. After 8 additional hrs, cells were harvested and analyzed as above. Under this condition, we found that the VP1 monomer and dimer levels also decreased when compared to control cells (Figure S1A, top panel, compare lane 2 to 1), indicating that BFA likely acted at an intracellular step required for ER sorting. Analyses using confocal microscopy further demonstrated that when cells were treated with BFA 4 h.p.i., co-localization between SV40 (green) and ER (red) decreased (Figure S1B, compare right and left panels). Collectively, these results indicate that ER transport is required to generate VP1 monomer and dimer.
To assess BFA's effect on viral infection, control and BFA-treated cells were incubated with SV40, and immunofluorescence microscopy was used to score expression of the virally encoded T antigen (TAg) in the nucleus as before [14]. We found that BFA decreased SV40 infection potently (Figure 1B). This result demonstrates that ER transport is critical for SV40 infection, consistent with previous observations [11], [13]. Thus BFA blocks SV40 trafficking to the ER and infection.
To establish an ER-to-cytosol transport assay for SV40, outlined in Figure 1C, we modified our semi-permeabilized cell-based assay developed previously to probe translocation of cholera toxin (CT) from the ER to the cytosol [15]. In this modified assay, SV40-infected CV-1 cells were treated with a low digitonin concentration (0.1%) to gently permeabilize the plasma membrane while leaving intracellular membranes, including the ER membrane, intact (Figure 1C, step 1). The permeabilized cells were centrifuged at medium-speed (16,000 g) to generate two fractions: a supernatant fraction (S1) that should contain cytosolic proteins, virus that reached the cytosol from the ER, and any endosomal vesicles harboring virus that did not sediment at the medium-speed spin, and a pellet fraction (P1) that should contain the plasma membrane, intracellular organelles including the ER and nucleus, and SV40 that either did not undergo ER-to-cytosol transport or did but is further imported into the nucleus. P1 contents were extracted by Triton X-100 and SDS. When S1 and P1 were subjected to reducing SDS-PAGE followed by immunoblotting, we found the cytosolic marker Hsp90 is predominantly in the S1 (Figure 1D, compare second and fifth panels from top), while the ER lumenal protein PDI was present only in the P1 (Figure 1D, compare 6th and 3rd panels from top). Similar to Hsp90, the cytosolic protein actin also appeared in S1 but not P1 using this fractionation method (Figure S1C, top and bottom panels, compare lane 1 to 2). Hence, this one-step fractionation procedure efficiently separates cytosolic from ER contents, similar to our previous report [15].
When cells were incubated with wild-type (WT) SV40 at 4°C, a condition that blocks endocytosis, and the cells subjected to the fractionation procedure, no VP1 was detected in the S1 (Figure 1D, lane 1, compare first and fourth panels from top). In contrast, when the cells were incubated with SV40 at 37°C for 8 hrs (8 h.p.i.) to allow entry, a portion of VP1 was found in the S1 (Figure 1D, lane 2, compare first and fourth panels from top). When cells treated with BFA at infection (0 h.p.i.) were incubated with SV40 at 37°C for 8 hrs, the VP1 level present in the S1 decreased (Figure 1D, top panel, compare lanes 3 to 2). Similar results were observed when cells were incubated with SV40 at 37°C for 10 hrs and 12 hrs: for both time points, appearance of SV40 in the S1 was blocked significantly by BFA (Figure 1D, top panel, compare lanes 5 to 4 and lanes 7 to 6). Moreover, when BFA was added to cells 4 h.p.i. and the cells harvested after 8 additional hours, the S1 VP1 level also decreased significantly (Figure 1E, top panel, compare lane 2 to 1). Thus, by blocking ER arrival (Figure 1A, S1A, and S1B), BFA also attenuates the subsequent ER-to-cytosol transport of SV40.
We showed previously that BFA also blocked ER-to-cytosol transport of CT [15], [16]. To intoxicate cells, CT via its B subunit (CTB) binds to GM1 on the cell surface, becomes rapidly endocytosed into invaginating vesicles, transported to the early and recycling endosomes, then followed by retrograde sorting through the Golgi and to the ER [17]. In the ER, the catalytic CTA1 undergoes ER-to-cytosol translocation to reach the cytosol where the toxin induces cytotoxicity. We had demonstrated that BFA blocked ER-to-cytosol transport of CTA1 in both HeLa [15] and 293T [16] cells. Here, when CV-1 cells treated with BFA at intoxication were subjected to the semi-permeabilized assay (Figure 1C), the S1 CTA1 level (analyzed 90 min post-intoxication) was significantly decreased when compared to control cells (Figure S1D, top panel, compare lane 1 to 2). This finding is consistent with our previous findings [15], [16] and further substantiates BFA's ability to generally perturb ER-to-cytosol transport processes by disrupting ER arrival.
As SV40 also relies on a nocodazole-sensitive step to reach the ER critical for infection [8], we showed that when cells were treated with nocodazole at infection, the S1 VP1 level 12 h.p.i. was blocked completely when compared to control cells (Figure 1F, top panel, compare lane 1 to 2). Hence nocodazole effectively perturbed SV40's ER-to-cytosol transport, presumably by blocking viral transport to the ER.
A more detailed time-course experiment using the semi-permeabilized system demonstrated that significant VP1 level started to appear in the S1 approximately 6 h.p.i., although a low VP1 level appeared in the S1 at 4 h.p.i. (Figure S1E, top panel). Because a previous study demonstrated that SV40 arrives to the ER approximately 6 h.p.i. [18], the low VP1 level in the S1 at 4 h.p.i. is unlikely virus that underwent ER-to-cytosol transport. Instead, it may represent virus that either leaked from a membrane compartment due to digitonin treatment or in transport vesicles en route to the ER which did not pellet after medium-speed centrifugation.
To test the former possibility, we asked whether digitonin causes leakage of CTB from membrane vesicles. CTB is used because it is much smaller than SV40, binds to ganglioside GM1 (akin to VP1), and is also targeted to the ER similar to SV40. Accordingly, cells were intoxicated with CT for either 5 min (where CTB is found in vesicles/endosomes) or 90 min (where CTB is found in a mixture of endosomes, Golgi, and ER). Following digitonin treatment, cells were subjected to 16,000 g medium-speed centrifugation to generate S1 (Figure S1F, see diagram and top and bottom panels, lane 1). S1 was treated with or without 2% SDS and subjected to high-speed centrifugation (100,000 g) to generate a supernatant (sn) and pellet fractions. Under this condition, vesicles harboring CTB should pellet, while CTB that leaked due to membrane rupture by digitonin should appear in the sn. We found that, at both time points, CTB appeared only in the pellet but not the sn (Figure S1F, top and bottom panels, compare lane 5 to 3). If SDS was added to S1 to artificially solubilize vesicles prior to high-speed centrifugation, CTB appeared in the sn but not pellet instead (Figure S1F, top and bottom panels, compare lane 6 to 4). We conclude that digitonin treatment did not cause CTB leakage from vesicles. Thus, because CTB is much smaller than SV40, it is unlikely that digitonin disrupted any membrane vesicles to cause leakage of SV40.
To test the idea that VP1 in the S1 at 4 h.p.i. represents SV40 in transport vesicles that did not sediment after medium-speed centrifugation, we first used limited proteolysis because this method distinguishes between membrane-encased virus versus naked virus. Because of the low VP1 level in the S1 at 4 h.p.i., a higher amount of this sample was used to visualize VP1. We found that VP1 in the S1 at 4 h.p.i. is resistant to trypsin digestion, in contrast to virus at 12 h.p.i. (Figure S1G, compare top and bottom panels, lanes 1 to 2 and 3). These findings indicate that SV40 in the S1 at 4 h.p.i. is likely contained in membrane vesicles, while those at 12 h.p.i. are not.
To further support this view, we subjected SV40 in the S1 at both 4 and 12 h.p.i., as well as purified WT SV40, to OptiPrep gradient flotation. The majority of VP1 at 4 h.p.i. floated to lighter density fractions when compared to purified SV40 (Figure S1H, compare top and bottom panels). In contrast, VP1 at 12 h.p.i. displayed very little flotation when compared to purified SV40 (Figure S1H, compare middle and bottom panels). These results demonstrate that the low SV40 level in the S1 at 4 h.p.i. is membrane-bound, presumably reflecting transport vesicles carrying SV40 that have not arrived to the ER. By contrast, virus at 12 h.p.i. is naked and not in vesicles, consistent with the property of a viral particle that has penetrated the ER membrane. We conclude that VP1 in the S1 at the 12 h.p.i. time point, as well as at the earlier 8 and 10 h.p.i. time points (see below), represents the virus pool that reached the cytosol from the ER.
An increase in cytosol-localized SV40 should allow more viral particles to enter the nucleus to cause infection. We found that increasing the m.o.i. increased both the S1 VP1 level at 12 h.p.i. (Figure S1I, top panel, lanes 1–6) and infection (Figure S1I, bottom graph). This correlation is consistent with the view that virus in S1 at 12 h.p.i. represents cytosol-localized virus poised to enter the nucleus to promote infection.
To further verify that the semi-permeabilized assay reflects SV40's ER-to-cytosol transport, we reasoned that down-regulation of ER-resident factors implicated in SV40 infection should block ER-to-cytosol transport as well. As ERp57 down-regulation decreased virus infection [12], we showed that ERp57 knock-down also decreased the S1 VP1 level at 12 h.p.i. (Figure 1G, top panel, compare lane 1 to 2). Similarly, we found that down-regulation of a novel ER-resident DNA J protein required for efficient SV40 infection also decreased the amount of S1 VP1 (manuscript in preparation). Finally, as treating cells with dithiothreitol (DTT) was shown to attenuate infection [12], we found that DTT treatment decreased both the S1 SV40 level (at 12 h.p.i.) and infection (Figure S1J, top panel, compare lane 2 to 1, and right graph). These findings further validate the semi-permeabilized system as an ER-to-cytosol transport assay.
In CV-1 cells, the earliest expression of new VP1 occurred at 20 h.p.i. (Figure 1H, middle panel, arrow), consistent with an earlier report in the same cell line [19]. This finding demonstrates that VP1 in the S1 derived from cells incubated with SV40 for 8, 10, and 12 hrs (Figure 1D, top panel, lanes 2, 4, and 6) is input but not de novo synthesized virus. We note that TAg expressed at 14 h.p.i. (Figure 1H, top panel, arrow head), suggesting that only a small proportion of virus in the P1 at the 8, 10, and 12 h.p.i. time points represents nuclear-localized virus. When control and BFA-treated cells were incubated with a biotinylated SV40 for 12 hrs, and the cells subjected to the ER-to-cytosol transport assay, biotinylated VP1 (as detected by streptavidin binding) was detected in the S1 derived from control and to a lesser extent BFA-treated cells (Figure S1K, top panel, compare lane 1 to 2). This finding further proves that the input virus reaches the cytosol.
Do other viral components undergo ER-to-cytosol transport? In addition to immunoblotting, the S1 from control and BFA-treated cells infected with SV40 for 12 hrs were subjected to PCR analyses using primers designed to amplify an SV40 genome fragment. We found presence of the viral genome in S1 derived from control but not BFA-treated cells (Figure 1I, top panel, compare lane 1 to 2). Similarly, using a VP2/VP3-specific antibody, we detected VP2 and VP3 in S1 derived from control but not BFA-treated cells (Figure 1J, top panel, compare lane 1 to 2). The higher VP3 intensity when compare to VP2 is not due to preferential antibody binding to VP3 as VP2 contains all of VP3 except VP2 has an additional N-terminal extension. Instead, this observation is likely because the input SV40 particle contains more VP3 than VP2 (below), similar to a previous report [20]. These results demonstrate that VP2, VP3, and the viral genome are co-transported with VP1 from the ER to the cytosol.
We next analyzed ER events that prime SV40 for membrane penetration by taking further advantage of the semi-permeablized system. We hypothesize that, upon ER arrival, SV40 remains bound to GM1 on the lumenal surface of the ER membrane, as the related mPy associates with its ganglioside receptor GD1a when this virus reaches the ER [10]. We postulate that SV40 is next released into the lumen by detaching from GM1. Here it undergoes conformational changes that enable the virus to re-engage the ER membrane, ultimately penetrating this bilayer to reach the cytosol. At steady state, there should be a virus pool attached to GM1 on the ER membrane, in the ER lumen, trapped on the ER membrane in the act of penetration, and in the cytosol. Analyzing specific SV40 conformations in each pool should reveal the sequence of events and the mechanism guiding membrane penetration.
P1 in our assay ought to contain SV40 attached to GM1 on the ER membrane (as well as on the plasma membrane and other organelles), in the ER lumen, and trapped on the ER membrane in transit to the cytosol. In contrast, S1 should contain virus that reached the cytosol (or in transport vesicles at the earlier time point). Because GM1 is enriched in membrane microdomains referred to as lipid rafts [18], SV40 attached to GM1 should localize to lipid rafts. Contents in this microdomain are often found to be resistant to Triton X-100 extraction [21]. Thus, SV40 that reaches the ER but remains bound to GM1 is resistant to Triton X-100 extraction, while those virus released into the ER lumen or trapped on the ER membrane en route to the cytosol are extracted by this detergent. Contents resistant to Triton X-100 extraction can be extracted by SDS.
Accordingly, P1 derived from cells incubated with SV40 for varying times were solubilized with Triton X-100 (Figure 1C, step 2). After centrifugation, the resulting supernatant contains the Triton X-100 extractable material (S2), while the new pellet contains Triton X-100 insoluble material that was extracted by SDS (P2). The S2 and P2 samples were subjected to immunoblot analysis. We found that while VP1 is present in P2 throughout the entire course of the experiment (Figure 2A, bottom panel, lanes 1–7), VP1 only appeared in the S2 starting at 6 h.p.i. (Figure 2A, top panel, compare lanes 4–7 to lanes 1–3). Under these conditions, PDI and most of the ER membrane protein calnexin are found in S2 but not P2 (Figure 2A, lane 9 and 10, compare top and bottom panels), as expected for an ER lumenal and membrane protein not enriched in lipid rafts.
VP1's appearance in S2 derived from cells incubated with virus for 12 hrs is blocked completely when cells are pretreated with BFA (Figure 2A, top panel, compare lane 8 to 7). S2 VP1 also decreased significantly if BFA is added 4 h.p.i. (Figure 2A, top panel, compare lane 9 to 7), again demonstrating that BFA blocked an intracellular step important for SV40 sorting to the ER. As a control, we found that CTB, which is also found in lipid raft-enriched membranes, remains exclusively in the P2 and not S2 (Figure 2B, top panel, compare lane 2 to 1), indicating that Triton X-100 did not non-specifically disrupt lipid raft membrane domains to release SV40. These results demonstrate that ER transport is required to generate Triton X-100-extractable virus, consistent with the hypothesis that SV40 detaches from GM1 upon ER arrival. Thus, while SV40 in P2 represents virus concentrated in membrane rafts due to its interaction with GM1, SV40 in S2 represents virus that reached the ER and is released into the ER lumen, either preparing for membrane penetration or trapped on the ER membrane in transit to the cytosol. SV40's appearance in the ER starting at 6 h.p.i. in this assay is in agreement with previous studies [12], [18], and is consistent with the notion that SV40 arrives in the cytosol after 6 h.p.i. (Figure S1E, top panel).
P2, S2, and S1 contain SV40 at different stages of membrane penetration. To examine the nature of SV40's disulfide bonds in these fractions, samples from the three fractions generated from cells infected with SV40 for 12 hrs were subjected to non-reducing SDS-PAGE followed by immunoblotting with VP1-specific antibodies. VP1 monomer, dimer, and virus at top of the gel were detected in P2 (Figure 2C, top panel, lane 1). In S2, a faint species corresponding to a VP1 higher oligomer, dimer, and more monomer (when compared to its P2 level) were observed (Figure 2C, top panel, lane 2). By contrast, only VP1 monomer was detected in S1 (Figure 2C, top panel, lane 3). When all three fractions were subjected to reducing SDS-PAGE, VP1 monomer was the only species observed (Figure 2C, bottom panel, lanes 1-3).
Thus, when the virus initially arrives in the ER attached to the membrane, disulfide bond disruption is initiated, generating VP1 monomer and dimer (Figure 2C, lane 1). When the virus is released into the ER lumen or becomes subsequently trapped on the ER membrane en route to the cytosol, intact virus is converted to the VP1 higher oligomer, and the dimer is further reduced to the monomer (Figure 2B, compare lane 2 to 1). Finally, upon cytosol arrival, complete disruption of the disulfide bonds ensues, generating VP1 monomer (Figure 2B, compare lane 3 to 2). These results demonstrate a sequential rearrangement of SV40's disulfide bonds as it moves from the ER to the cytosol. We note that as monomer and dimer were not detected in any of the fractions using non-SDS biochemical methods (below), they likely still consist of VP1 pentamers that remain in contact with the core viral particle via non-covalent interactions.
As complete disruption of disulfide bonds that generates VP1 monomer (in a non-reducing SDS condition) is a hallmark of cytosol-localized SV40, we performed a time-course experiment using a non-reducing SDS-PAGE and showed that VP1 monomer appeared in S1 at approximately 8 h.p.i. (Figure 2D, lanes 5–7). These findings further support the assertion that SV40 begins to arrive to the cytosol sometime after 6 h.p.i., also consistent with our measurement of SV40 ER arrival at approximately 6 h.p.i.
The disulfide bond arrangement of ER- and cytosol-localized SV40 is distinct (Figure 2B, top panel, compare lane 2 to 3). However, whether this difference affects the global viral conformation is unknown. We therefore evaluated the virus structures in S1 and S2 using four independent biochemical approaches.
We first used conformation-specific antibodies for this purpose. Two monoclonal VP1 antibodies (CC10 and BC11) were shown to neutralize SV40 infection, but did not recognize denatured virus during immunoblotting [22]. We found that these antibodies precipitated the VP1 pentamer (not shown). Hence, the CC10 and BC11 antibodies recognize structural features of the intact pentamer, but not unfolded virus whose epitopes critical for antibody recognition are disordered. We reasoned that, at a sub-saturating antibody concentration where there is insufficient antibody to bind to all available VP1, a given CC10 or BC11 antibody should precipitate more VP1 if the virus is assembled and intact than disassembled and uncoated. In contrast, at a saturating antibody concentration, a similar VP1 level would be precipitated by the antibodies regardless of the viral structural state. Thus, using antibodies at a sub-saturation condition could potentially reveal the global structural state of SV40.
Accordingly, at 12 h.p.i., cells were subjected to the semi-permeabilized assay, and virus in S1 and S2 immunoprecipitated with a mixture of increasing amounts of the VP1 monoclonal antibodies. VP1 in S1 precipitated less efficiently than VP1 in S2 when a low (i.e. 0.04 µg) level of antibodies was used (Figure 3A, top panel, compare lane 1 to 4). However, the difference in the precipitation efficiency gradually disappeared when higher levels of antibodies (i.e. 0.2 and 1 µg) were used (Figure 3A, top panel, compare lanes 2 and 3 to lanes 5 and 6). A control antibody did not precipitate VP1 from S2 (Figure 3A, top panel, lane 8). Thus, in our experimental conditions, 0.04 µg represents a sub-saturating antibody concentration in which differences between the structural organization of SV40 in S1 and S2 can be revealed. Specifically, that 0.04 µg of the SV40 antibodies precipitated less VP1 from S1 than S2 suggests that virus in S1 underwent disassembly.
VP2/VP3 in S2 co-precipitated with VP1 specifically (Figure 3B, top panel, compare lane 2 to 4), with an efficiency similar to that observed when purified WT SV40 was used as the starting material (Figure 3B, top panel, compare lane 2 to 6). In addition, the SV40 genome also co-precipitated with VP1 from S2 specifically (Figure 3C, compare lane 2 to 4). In contrast, VP2 and VP3 in S1 co-precipitated weakly with VP1 when compared to the efficiency observed using purified WT SV40 (Figure 3D, top panel, compare lane 1 to 3), even when 5-fold more S1 than S2 was used for immunoprecipitation. The SV40 genome co-precipitated with VP1 in S1 specifically (Figure 3E, compare lane 2 to 4). Our results suggest that the ER-localized SV40 is more assembled and intact than the cytosol-localized virus, and retains strong binding to the internal viral components. The cytosol-localized virus likely experienced disassembly, and displays less interaction with its internal proteins.
As a second method to probe SV40's conformations in the ER and cytosol, S1 and S2 prepared from cells infected with SV40 for 12 hrs were subjected to gel filtration analyses. Our data showed that essentially all the viral particles in S2 are found in fractions similar to purified WT SV40 (estimated to be >660 kDa in our system due to resolution of the column) (Figure 4A, compare second and third panels from top). For simplicity, these viral particles are referred to as “large” particles (Figure 4A). In contrast, a virus pool in S1 was found in fractions that corresponded to “small” particles approximating 150 kDa, while another portion was located in fractions corresponding to the large particle (Figure 4A, top panel). The 150 kDa species likely represents the VP1 pentamer. These results demonstrate that all the SV40 particles in the ER are large, while virus in the cytosol exists as large and small particles.
We next used continuous (20–40%) sucrose gradient sedimentation as a third approach to examine SV40's structure in the ER and cytosol (Figure 4B). Again, whereas all the virus in S2 sedimented to bottom heavier fractions similar to purified WT SV40 corresponding to the large particle (Figure 4B, compare second and third panels from top), a portion of virus in S1 was found in the top lighter fractions corresponding to the small particle and another portion in the heavier fractions corresponding to the large particle (Figure 4B, top panel). The virus remained in these lighter fractions even when S1 was pretreated with Triton X-100 prior to sedimentation (not shown), indicating that SV40 in these fractions is not due to flotation caused by membrane encapsulation. PCR analysis further demonstrated that the large but not small viral particles in S1 contain the viral genome (Figure 4C, compare bottom and top panels). This result is consistent with our co-immunoprecipitation analysis demonstrating that the cytosol-localized SV40 binds to the genome (Figure 3E).
To estimate the proportion of SV40 in S1 and S2 that are small and large, these samples (along with purified WT SV40) were layered over a sucrose cushion (20%) and centrifuged (Figure 4D). The large particle is expected to penetrate the sucrose cushion and sediment, while the small particles should remain near the top of the cushion. When the sedimented material (labeled large) and material near the top of the cushion (labeled small) were subjected to immunoblotting, approximately 50% of virus in S1 were found in the small fraction and 50% in the large fraction (Figure 4D, compare lane 1 to 2). In contrast, essentially all of the virus in S2 and a sample containing purified WT SV40 was large (Figure 4D, compare lane 4 to 3 and 6 to 5). This size distribution is consistent with the gel filtration (Figure 4A) and continuous sucrose sedimentation (Figures 4B and 4C) findings.
Results using four distinct biochemical strategies (i.e. immunoprecipitation, gel filtration, continuous sucrose gradient sedimentation, and sucrose cushion sedimentation) demonstrate unambiguously that SV40 in the ER is a large particle, while the virus in the cytosol exists as small and large particles. The simplest explanation of these findings is that ER-localized SV40 penetrates the ER membrane as a large and intact particle, reaching the cytosol where it disassembles into small particles. The remaining core particle after cytosol-mediated disassembly, which remains relatively large and cannot be distinguished from the large ER-localized particle using either gel filtration or sucrose gradient analysis, contains the genome and is likely the predecessor to the form that enters the nucleus.
Alternatively, it is possible that the ER-localized large particle disassembles into small particles in the ER, become discharged into the cytosol where they re-assemble into a large particle. To test whether the cytosol supports large particle assembly in our system, we analyzed SV40 virion formation by transfecting cells with the viral genome. Using this method, VP1 monomer should be made in the cytosol, followed by its oligomerization into pentamers in this compartment. The pentamers are expected to import into the nucleus for full assembly into the large SV40 particle. We found that when cells were transfected with the SV40 genome for 48 hrs, subjected to the semi-permeabilized assay, and the S1 and P1 analyzed by sucrose gradient sedimentation, only small particles were found in the S1 (Figure 4E, top panel, fractions 1–4). These small particles represent the cytosol-localized pentamers. By contrast, VP1 appeared in virtually all fractions in the P1 (Figure 4E, bottom panel). (The pellet was subjected to repeated freeze-thaw to extract virus from the nucleus). VP1 in the top fractions corresponds to nuclear-localized pentamers imported from the cytosol while those in the heavier fractions correspond to viral particles in the nucleus undergoing assembly. Thus, when cells were transfected with the SV40 genome, VP1 pentamers are generated in the cytosol and imported into the nucleus to form large particles, consistent with the established SV40 assembly process [20]. Importantly, these results demonstrate that the cytosol does not support large particle formation from small particles.
We next sought to visualize the large SV40 particle in the S1 cytosol. Buffer, WT SV40, S1 derived from mock-infected cells (i.e. mock-infected S1), and S1 derived from SV40-infected cells for 12 hrs (i.e. SV40-infected S1) were immunoprecipitated with VP1-specific antibodies, and the immunoprecipitate captured by magnetic beads. Samples were subjected to SDS-PAGE and silver stained. A distinct band corresponding to VP1 was detected in samples derived only from WT SV40 and SV40-infected S1 (Figure 5A, lanes 2 and 4). In addition, a band corresponding to VP3 was also found in the WT SV40 and SV40-infected S1 immunoprecipitate (Figure 5A, lanes 2 and 4), consistent with the co-immunoprecipitation result presented in Figure 3D. When the immunoprecipitate derived from WT SV40 was subjected to negative stain EM, a mostly homogenous population of spherical particles approximately 50 nm could be seen (Figure 5B, a and b).
Interestingly, while spherical particles approximating 50 nm could also be observed in the SV40-infected S1 immunoprecipitate (Figure 5C, a and b (white arrow)), others appeared to be slightly distorted, appearing as elongated spheres with what seems to be pores in the middle (Figure 5C, b (white arrow head)). Even more distorted SV40 particles around 50 nm could also be found in the S1 immunoprecipitate. In these cases, some of their overall structures were poorly defined (Figure 5D, a and d), while others appeared again to be elongated spheres with a doughnut-shaped pore in the middle (Figure 5D, c) or contained a clover leaf-shaped hole (Figure 5D, b). Thus S1 SV40 particles are heterogeneous in structure, and likely represent the large particle pool identified in our biochemical assays.
In addition to elucidating the ER membrane penetration mechanism, we characterized the viral components regulating this process. As VP2, VP3, and viral genome co-transport with VP1 to the cytosol (Figure 1), we asked whether these internal components control ER-to-cytosol transport. To address whether the minor coat proteins play a role, we generated SV40 mutant viruses lacking VP2 (SV40 (-VP2)), VP3 (SV40 (-VP3)), or both (SV40 (-VP2/-VP3)) (Figure 6A, top and bottom panel, compare lanes 2–4 to 1). VP3's band intensity is higher than VP2 in WT SV40 (Figure 6A, bottom panel, lane 1), indicating more VP3 than VP2 per viral particle, as reported previously [20].
We first determined whether the mutant viruses reach the ER with equal efficiency as WT SV40 by assessing their ability to undergo both ER-dependent disulfide disruption and release from GM1-enriched lipid raft membranes. Cells were incubated with WT or mutant SV40 for 6 hrs, and the S2 prepared. When S2 was subjected to non-reducing SDS-PAGE, SV40 (-VP3) displayed a similar VP1 banding pattern as WT SV40 (Figure 6B, compare lane 3 to 1). In contrast, very low signal was detected in S2 derived from cells infected with SV40 (-VP2) or SV40 (-VP2/-VP3) (Figure 6B, compare lanes 4 and 2 to 1). As expected, when the S2 was subjected to reducing SDS-PAGE, a similar VP1 level was seen between WT and SV40 (-VP3), and essentially no signal was detected from samples derived from SV40 (-VP2) or SV40 (-VP2/-VP3) (Figure 6C, compare lanes 1 and 3 to 2 and 4). The VP1 level was similar in all samples in the P2 (Figure 6C, lanes 5–8), indicating that the total cell-associated virus is the same between WT and mutant viruses. These results demonstrate that SV40 (-VP3), but not SV40 (-VP2) or SV40 (-VP2/-VP3), reaches the ER with similar efficiency as WT SV40 at 6 h.p.i.; SV40 (-VP2) and SV40 (-VP2/-VP3) likely entered the cells but failed to sort to the ER.
We next asked whether the mutant viruses undergo ER-to-cytosol transport by assessing the S1 VP1 level at both 8 and 12 h.p.i. using the semi-permeabilized system described in Figure 1. We found that the S1 VP1 level for all mutant viruses decreased significantly at both time points when compared to WT SV40 (Figure 6D, top and fourth panels, compare lanes 2–4 to 1). The mutant viruses also promoted infection poorly when compared to WT SV40 (Figure 6E). As SV40 (-VP3) reaches the ER from the cell surface with similar efficiency as WT SV40 at 6 h.p.i., we conclude that VP3 plays a critical role in ER-to-cytosol transport. Because SV40 (-VP2) and SV40 (-VP2/-VP3) did not reach the ER, they are expected to not undergo subsequent ER-to-cytosol transport. Thus, our results cannot distinguish a role of VP2 in the ER-to-cytosol penetration process. Of interest, VP2 and VP3 were shown previously to be necessary for nuclear entry [23].
To address the viral genome's role in facilitating ER exit of SV40, we enriched for SV40 that lacked the genome (SV40 (-genome)) on a CsCl gradient. As expected, infection caused by SV40 (-genome) was attenuated severely when compared to WT SV40 (Figure 6F, approximately 9% of WT). When cells incubated with this mutant virus for 12 hrs were subjected to the semi-permeabilized assay, the S1 VP1 level decreased when compared to the VP1 level derived from cells infected with WT SV40 (Figure 6G, top panel, compare lane 2 to 1). SV40 (-genome) and WT SV40 underwent similar ER-dependent disulfide rearrangement (Figure 6H, compare lane 2 to 1) and release from lipid raft membrane domains (Figure 6H, compare lane 4 to 3). We conclude that in addition to VP3, the SV40 genome appears to also mediate its ER-to-cytosol transport.
What might be the driving force that discharges SV40 into the cytosol from the ER membrane? The proteasome has been shown to extract some misfolded proteins from the ER membrane into the cytosol [24], [25]. As proteasome inhibition decreased SV40 infection [12], we tested the proteasome's role in cytosol release of SV40 by using MG132, a proteasome inhibitor. When DMSO or MG132 was added simultaneously with SV40 to cells for 12 hrs, VP1 in S1 decreased in cells treated with MG132 when compared to DMSO (Figure 7A, top panel, compare lane 6 to 1; quantified in Figure 7B). The VP1 level in S1 was restored to a similar level as the DMSO-treated cells when MG132 was added increasingly later after incubation of cells with SV40 (Figure 7A, top panel, compare lane 6 to lanes 2–5; quantified in Figure 7B). The time range when proteasome inhibition no longer affects virus arrival to the cytosol (i.e. approximately 9–11 h.p.i.) occurs slightly after arrival of SV40 to the cytosol (i.e. approximately 8 h.p.i.). Addition of epoxomicin, a more specific proteasome inhibitor, to cells also decreased the S1 VP1 level at 12 h.p.i. (Figure 7C, top panel, compare lane 2 to 1), consistent with the MG132 effects. These findings indicate that the proteasome plays an important function in promoting virus release into the cytosol.
MG132 decreased SV40 infection when this drug was added simultaneously with SV40 to cells (Figure 7D, 0 h.p.i., compare square to circle), similar to a previous finding [12]. The infection level was restored partially if MG132 was added 9 or 11 h.p.i. (Figure 7D, circles), consistent with restoration of the S1 VP1 level when this drug was added at the same time points post-infection (Figure 7A and 7B). The correlation between the time-dependent effects of MG132 on viral infection and release into the cytosol underscores the proteasome's role in controlling SV40's ER-to-cytosol transport.
As inhibiting the proteasome prevents SV40 release into the cytosol, we hypothesized that such perturbation should concomitantly cause an increase in ER-localized virus. To assess the ER-localized SV40 level, we measured formation of viral disulfide bonded intermediates in the ER. Cells were incubated with SV40 for 12 hrs and subjected to the semi-permeabilized assay. The resulting P1 was used to generate S2 and P2. These fractions were subjected to non-reducing SDS-PAGE followed by immunoblotting with VP1-specific antibodies. We detected formation of VP1 monomer, dimer, and a low level of the higher oligomer in the S2 (Figure 7E, left panel, lane 2). BFA added at infection blocked the generation of these products (Figure 7E, left panel, compare lane 2 to 1), consistent with results observed in a WCE sample (Figure 1A). When cells were incubated simultaneously with MG132 and SV40, the VP1 monomer, dimer, and higher oligomer levels in the S2 increased when compared to control cells (Figure 7E, left panel, compare lane 3 to 2). Similarly, proteasome inhibition also increased VP1 monomer in P2 when analyzed by a non-reducing gel (Figure 7E, right panel, compare lane 6 to 5). Thus blocking the proteasome activity caused a build-up of virus in the ER lumen and those that remained attached to GM1 on the ER membrane. These findings further demonstrate a role of the proteasome in controlling exit of SV40 to the cytosol.
How non-enveloped viruses penetrate biological membranes is understood poorly [2]. Here we established a cell-based assay to examine ER-to-cytosol membrane transport of the non-enveloped SV40. Our findings drew four major conclusions, depicted in Figure 8. First, upon ER arrival, SV40 attached to GM1 on the ER membrane is released into the ER lumen, and undergoes sequential disulfide bond disruption to reach the cytosol (steps 1 and 2). Disulfide bond disruption triggers conformational changes that prime the virus for membrane penetration (step 3). This step may involve VP2 and VP3 exposure. Second, despite ER remodeling events, a large and intact viral particle penetrates the ER membrane to reach the cytosol, potentially through either the lipid bilayer (step 4a) or a sizeable protein-channel (step 4b). Third, viral VP3 and potentially the genome, as well as the host proteasome, regulate SV40 release into the cytosol (step 5). And fourth, SV40 disassembles in the cytosol (step 6). We will discuss these points separately.
Using a semi-permeabilized system, we found that SV40 is released into the ER lumen upon ER arrival, presumably by detaching from GM1. What might be the driving force for this reaction? ER factors may induce physical changes to VP1 that decreases its affinity for GM1. Alternatively, when GM1 reaches the ER, it may partition into the ER bilayer, thereby reducing SV40's affinity for the membrane.
In the ER, disruption of SV40's disulfide bonds by the PDI family members ERp57 and PDI imparts conformational changes on the viral particle, priming it for membrane penetration [12]. Using non-reducing SDS-PAGE, our analyses dissected this reaction into several steps. First, when SV40 attached to GM1 reaches the ER, its disulfide bonds are disrupted, generating VP1 monomer and dimer. Next, when the virus is released into the ER lumen, monomer and dimer, as well as a higher oligomer (which could be an intermediate for the dimer and monomer) continues to form. Because only a large viral particle was detected in the ER using non-SDS methods, disulfide bond disruption is not sufficient to generate VP1 monomer and dimer; the ER-localized SV40 likely represents VP1 pentamers that remain attached to the core viral particle via non-covalent interactions.
Finally, when the virus is discharged into the cytosol, all the intermediate species undergo a thorough disruption of the disulfide bonds to produce only VP1 monomer. During these steps, SV40's interchain Cys9-Cys9 and Cys104-Cys104 disulfide bonds [4], [12] are likely disrupted. As a species resembling VP1 pentamer (but not monomer) is detected in the cytosol using non-SDS methods, the monomer must be held together non-covalently. The sequential manner by which SV40's disulfide bonds are disrupted as it moves from the ER into the cytosol reveals the coordinated manner by which the host dismantles the virus.
Using four independent biochemical approaches, our results unambiguously established that the conformations of the ER- and cytosol-localized viral particles are different. Specifically, we demonstrate that ER-localized SV40 is large and intact, and contains VP2, VP3, and the genome. No small viral particles were detected in the ER. In contrast, both large and small viral particles are present in the cytosol. These particles display weak VP1-VP2/VP3 binding. Furthermore, our EM analyses detected large 50 nm viral particles in the cytosol, although they appear to be heterogeneous in structure. The simplest interpretation of these data is that a large and intact viral particle in the ER penetrates the ER membrane to reach the cytosol where it disassembles.
Another potential explanation is that the ER-localized large particle disassembles to small particles that then discharge rapidly to the cytosol where they re-assemble into a large particle. However, this complex scenario is unlikely because it would require an unprecedented efficiency in removing all the small particles from the ER to the cytosol to preclude their detection in the ER. Moreover, it is also inconsistent with the established SV40 assembly process in which the nucleus but not the cytosol supports large virion assembly [20]. In our system, we further demonstrate that the cytosol does not provide an environment conducive for large particle assembly.
While a precise measurement of the large membrane penetrating species is not available, sucrose gradient analyses indicate that its size is similar to the native 50 nm SV40 virion. This proposed size raises the question of whether the virus crosses a protein-conducting channel or the ER lipid bilayer. A previous study implicated a role of Derlin-1, a component of an ER membrane complex used during ERAD [26], in SV40 infection [12]. Should Derlin-1 function as a channel, massive Derlin-1 oligomerization is required to accommodate viral transport. That biological membranes can support transport of large complexes is not without precedent, as a 9 nm gold particle decorated with the peroxisome-targeting signal can be transported into the peroxisome interior [27].
An alternative to the protein channel-based mechanism is a lipid-based strategy. Our in vitro findings on mPy provide insight into how this process may occur. The PDI family member ERp29 untangles the VP1 C-terminal arm of mPy in a reaction that requires reduction of the virus disulfide bonds and removal of the virus-bound calciums [28]. VP2 and possibly VP3 are then exposed, generating a hydrophobic viral particle that binds, integrates, and perforates the ER membrane [28], [29]. These reactions initiate mPy's penetration across the ER lipid bilayer. Interestingly, a different version of the lipid-based model was hypothesized [30]. In this model, a pore in the ER membrane created when a lipid droplet leaves the membrane enables SV40/mPy to gain access to the cytosol. No experiments have validated this idea thus far.
While SV40 VP2 and VP3 have been implicated in nuclear entry [23], our findings demonstrate that at least VP3 plays a role in SV40's ER-to-cytosol transport; our results cannot distinguish any function of VP2 in this process. In vitro, SV40 VP2 and VP3 can integrate into the ER membrane [31]. Integration of these proteins into the ER membrane may create a pore through which the viral genome is injected [31]. Alternatively, VP2 and VP3 may act as lytic factors [32], perforating the ER membrane to allow passage of a subviral particle. As the ER and nuclear membranes are continuous, a subviral particle could bypass the cytosol and reach the nucleus directly after penetrating the ER membrane. However, the findings that cytosol arrival is required for SV40 infection [33], that interaction between VP3's nuclear localization signal and importins is necessary for nuclear entry [34], and that ER machineries dedicated to ERAD are crucial for infection [12], point to the ER-to-cytosol transport pathway as the dominant infectious route. As SV40's genome stabilizes its overall viral architecture [12], its absence likely destabilizes the virus structure. This could in turn lead to incorrect conformational changes that perturb ER-to-cytosol transport.
The host proteasome also plays a pivotal function in controlling SV40's ER-to-cytosol transport. Since the proteasome extracts some misfolded ER proteins to the cytosol [24], [25], it may also discharge SV40 into the cytosol. Establishing a cell-free reconstituted system will reveal if the proteasome plays a direct role in viral release.
Our data also suggest that VP2 controls SV40 sorting to the ER from the cell surface. In addition, VP3 may also be involved in this process, should an SV40 mutant virus lacking VP3 reach the ER inefficiently after 6 h.p.i.. Further experiments are required to clarify how VP2 regulates ER sorting, and whether VP3 plays any role.
Our analyses demonstrated that SV40 disassembles in the cytosol. The starting substrate for this reaction is a large particle that reaches the cytosol from the ER. Indeed, large particles approximating 50 nm were detected in the cytosol by EM. Their heterogeneous nature may reflect the various disassembly intermediates. Of particular interest is the viral intermediate containing a doughnut-shaped pore in the middle of its structure. This species might represent a viral particle in which a 5-coordinated VP1 pentamer is released to generate a pore. Release of the 5-coordinated VP1 pentamer from intact SV40 in vitro was previously hypothesized to be involved in ER-to-cytosol transport [12].
Our biochemical results also show that the large cytosol-localized virus disassembles to generate small particles approximating the size of a pentamer and lacks the genome. This disassembly reaction may be aided by the low calcium concentration in the cytosol which would promote loss of calcium ions from the cytosol-localized virus, thereby further destabilizing VP1 capsomer interaction. The remaining core particle (relatively large particle in Figure 8), which harbors the genome, is likely targeted to the nucleus to cause infection. As the cytosol-localized viral intermediates observed by EM are large, they are unlikely the species that enter the nucleus. Because previous studies showed that Hsp70 uncoats mPy in vitro [35] and binds to SV40 in cells [36], this cytosolic chaperone may convert the large SV40 particle to the small particle in our assay. Whether cytosolic disassembly is coupled to nuclear entry is unknown, and unraveling it will provide insight into another critical step in SV40's infection pathway.
Polyclonal antibodies against Hsp90 and PDI were purchased from Santa Cruz Biotechnology, monoclonal antibodies against PDI from Abcam, large T antigen from Santa Cruz Biotechnology, MG132 and epoxomicin from EMD chemicals, BFA from Epicenter, proteinase K and monoclonal antibodies against HA from Roche, and TCEP from Thermo Scientific. All other reagents were from Sigma. The pUCSV40 encoding SV40 genome and polyclonal antibodies against VP1 were generous gifts from Dr. H. Handa (Tokyo Institute of Technology), polyclonal antibodies against VP3 from Dr. H. Kasamatsu (University of California, Los Angeles) and monoclonal antibodies against VP1 from Dr. W. Scott (University of Miami).
CV-1 cells were incubated with SV40 (m.o.i. = 3–50) at 4°C, washed, and incubated at 37°C. At indicated time points, cells were trypsinized (scraped off for the mutant viruses), permeabilized with HN buffer (50 mM Hepes, pH 7.5, 150 mM NaCl, and protease inhibitors) containing 0.1% digitonin on ice for 10 min, and centrifuged at 16,100 g for 10 min. The resulting supernatant is referred as S1. The pellet was resuspended in SDS sample buffer and is referred as P1. Where indicated, P1 was incubated in HN buffer containing 1% Triton X-100 on ice for 10 min and centrifuged at 16,100 g for 10 min. This second supernatant is referred as S2. The Triton X-100-insoluble pellet was resuspended in SDS sample buffer and is referred to as P2. For non-reducing SDS-PAGE, NEM (10 mM) was added to all buffers.
SV40 monoclonal antibodies (CC10 and BC11) or an HA monoclonal antibody were added to S1 and S2 and incubated on ice for 3 hrs. Protein G-Dynabeads (Invitrogen) were used to capture the antibody-virus complex. The beads were isolated using a magnet stand (Dynal), washed with a high salt buffer (50 mM Hepes, pH 7.5, 500 mM NaCl, 1% Triton X-100), and the bound proteins eluted with an acidic buffer (50 mM glycine, pH 2.8).
CV-1 cells were incubated with the indicated viruses at 4°C for 2 hrs. The cells were washed and incubated at 37°C. 24 h.p.i., cells were fixed with 1% paraformaldehyde, treated with 0.2% Triton X-100, and incubated in 3% milk. The cells were stained with a mouse monoclonal SV40 large T antigen antibody, followed by Alexa Fluor-488-conjugated secondary antibody (Invitrogen). In each experiment, approximately 1,000 cells were counted to assess the extent of large T antigen expression.
S1 and S2 were loaded onto a Bio-Sil 600 gel filtration column (Bio-Rad) and separated with HN buffer. Forty fractions (0.5 ml each) were collected and 0.1 ml of fractions 9-30 was separated by SDS-PAGE, followed by immunoblotting with VP1 monoclonal antibodies.
S1 and S2 were loaded onto a 0.5 ml preformed 20–40% sucrose gradient and centrifuged at 49,500 rpm for 50 min at 4°C in an SW 55Ti rotor. After centrifugation, 10 fractions were collected from the top.
S1, S2, and WT SV40 were layered over a 20% sucrose solution, centrifuged, and the sedimented material and material near the top of the cushion were subjected to immunoblotting.
Cells were transfected (Lipofectamine 2000, Invitrogen) with the SV40 genome for 48 hrs, harvested, and subjected to the ER-to-cytosol assay to generate S1 and P1. P1 was freeze-thawed to extract virus from the nucleus. Both fractions were analyzed by sucrose gradient sedimentation.
WT and SV40 mutants were purified using the OptiPrep gradient system, except SV40 (-genome) was purified by CsCl gradient. Briefly, SV40-infected or viral genome-transfected CV-1 cells were lysed in a buffer containing 50 mM Hepes (pH 7.5), 150 mM NaCl, and 0.5% Brij58 on ice for 30 min and centrifuged at 16,100 g for 10 min. The supernatant was loaded onto a discontinuous 20 and 40% OptiPrep gradient and centrifuged at 49,500 rpm for 2 hrs at 4°C in an SW 55Ti rotor. A viral particle fraction between 20% and 40% OptiPrep was collected with a needle. For separation of virion and empty particle, supernatant was loaded onto a 1.516, 1.443, 1.37, 1.296, 1.222, and 1.148 g/ml discontinuous CsCl gradient (1 ml each) and centrifuged at 35,000 rpm for 3 hrs at 4°C in an SW 41Ti rotor. Fractions corresponding to virion and empty particle were collected. Each fraction was transferred into a 5×41-mm open-top tube (Beckman) and centrifuged at 49,500 rpm for 12 hrs at 4°C in an SW 55Ti rotor. A fraction corresponding to virion or empty particle was collected.
Purified SV40 was labeled with EZ-Link Sulfo-NHS-LC-Biotin (Thermo) according to the manufacturer's protocol.
33 nM ERp57-specific (5′-UGAAGGUGGCCGUGAAUUATT-3′) (Invitrogen) or control (Ambion) siRNAs were transfected into CV-1 cells using the Lipofectamine 2000 system according to the manufacturer's protocol. At 36 hrs post-infection, cells were infected with SV40 at m.o.i. = 5 and subjected to the ER-to-cytosol membrane penetration assay.
Samples from S1, immunoprecipitation, or sucrose gradient fractions were incubated in 10 mM Tris-HCl (pH 8.5) containing 0.2 mg/ml proteinase K. After proteinase K was heat-inactivated, the samples were subjected to a PCR reaction using a set of primers (GCAGTAGCAATCAA CCCACA [forward] and CTGACTTTGGAGGCTTCTGG [reverse]).
CV-1 cells plated on 18 mm glass plates were washed with DMEM, chilled at 4°C, and incubated with SV40 (m.o.i. = 1) at 4°C for 1 hr. Cells were washed extensively to remove unbound viruses, incubated in DMEM at 37°C for 10 hrs, fixed with 1% paraformaldehyde, incubated with a mouse monoclonal SV40 VP1 and rabbit polyclonal PDI antibody, followed by an Alexa Fluor 594 and Rhodamine conjugated secondary antibodies. Images were taken with an Olympus FV-500 confocal microscopy equipped with 100x objective. The ER images derived from the PDI signal were subjected to the FFT Bandpass Filter embedded in Image J (NIH) as described previously [36].
4-fold more S1 at 4 h.p.i. was used to ensure that the VP1 level is similar between S1 at 4 and 12 h.p.i. The samples were incubated with 30 or 100 µg/ml trypsin for 1 hr on ice and the reaction was stopped by the addition of 1 mM TLCK for 10 min on ice. The samples were separated by SDS-PAGE followed by immunoblotting with SV40 VP1 monoclonal antibodies.
S1 or purified SV40 was mixed with the same amount of 60% OptiPrep solution. 100 µl of the mixed sample was placed at the bottom of a Beckman centrifuge tube (7×20 mm), and 100 µl of 20% OptiPrep was loaded onto the sample. The tube was centrifuged in a Beckman TLA100 rotor for 1 hr at 100,000 rpm. Fractions were collected from the top (20 µl each), separated by SDS-PAGE, and immunoblotted with SV40 VP1 monoclonal antibodies.
CV-1 cells were intoxicated with 30 nM CT and subjected to semi-permeabilization with 0.1% digitonin as described in the ER-to-cytosol membrane penetration assay. S1 and P1 fractions were analyzed by SDS-PAGE followed by immunoblotting with CTA, PDI, and Hsp90 antibodies.
Cells were washed with DMEM, chilled at 4°C in 10 ml of DMEM for 20 min, and incubated with 30 nM CT at 4°C for 2 hrs. Cells were then washed with cold PBS to remove unbound CT and incubated in 10 ml of DMEM at 37°C to allow entry. At the indicated time points, cells were washed with cold PBS, scraped off the plate in 1 ml of PBS containing 10 mM NEM, and collected in a microcentrifuge tube. S1, prepared as described above, was incubated with or without 2% SDS at 25°C for 10 min. The samples were subjected to high-speed centrifugation in a Beckman TLA100 rotor for 30 min at 100,000 g. The resulting supernatant and pellet fractions after high-speed spin, and the original S1, were analyzed by SDS-PAGE followed by immunoblotting with polyclonal CTB antibodies.
S1, prepared from cells (7.5×106 cells) infected with SV40 for 12 hrs, was incubated with 1% Triton X-100 to solubilize any membrane material, centrifuged in a Beckman TLA100 rotor for 30 min at 100,000 g to concentrate the virus, the resulting pellet resuspended in 100 µl of HN buffer, and subjected to immunoprecipitation as described above. The virus-antibody-bead complex was captured by a magnet stand (Dynal) and washed with HN buffer containing 1% Triton X-100. The magnetic beads were resupended in 20 µl of HN buffer. For negative staining, 5 µl of each sample containing magnetic beads were absorbed onto a grow-discharged copper grid (Electron Microscopy Sciences) and stained with 1% uranyl acetate. The samples were observed using a Philips CM-100 at 80 kV.
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10.1371/journal.pgen.1003921 | Neuron-Specific Feeding RNAi in C. elegans and Its Use in a Screen for Essential Genes Required for GABA Neuron Function | Forward genetic screens are important tools for exploring the genetic requirements for neuronal function. However, conventional forward screens often have difficulty identifying genes whose relevant functions are masked by pleiotropy. In particular, if loss of gene function results in sterility, lethality, or other severe pleiotropy, neuronal-specific functions cannot be readily analyzed. Here we describe a method in C. elegans for generating cell-specific knockdown in neurons using feeding RNAi and its application in a screen for the role of essential genes in GABAergic neurons. We combine manipulations that increase the sensitivity of select neurons to RNAi with manipulations that block RNAi in other cells. We produce animal strains in which feeding RNAi results in restricted gene knockdown in either GABA-, acetylcholine-, dopamine-, or glutamate-releasing neurons. In these strains, we observe neuron cell-type specific behavioral changes when we knock down genes required for these neurons to function, including genes encoding the basal neurotransmission machinery. These reagents enable high-throughput, cell-specific knockdown in the nervous system, facilitating rapid dissection of the site of gene action and screening for neuronal functions of essential genes. Using the GABA-specific RNAi strain, we screened 1,320 RNAi clones targeting essential genes on chromosomes I, II, and III for their effect on GABA neuron function. We identified 48 genes whose GABA cell-specific knockdown resulted in reduced GABA motor output. This screen extends our understanding of the genetic requirements for continued neuronal function in a mature organism.
| Living organisms often reuse the same genes multiple times for different purposes. If one function of a gene is essential, death or arrest of the mutant masks other functions. Understanding the functions of essential genes is particularly critical in the nervous system, which must maintain plasticity and fend off disease long after development is complete. However, current strategies for generating conditional knockouts rely on making a new transgenic animal for each gene and thus are not useful for forward genetic screens or for other experiments involving a large number of genes. We have developed a technique in C. elegans for generating gene knockdown in selected neuron sub-types in response to feeding RNAi. Using this technique, we performed a screen aimed at identifying essential genes that are required for the function of mature GABAergic neurons. By knocking these genes down in only GABAergic neurons, we can circumvent the muddying effects of pleiotropy and find essential genes that function cell intrinsically to promote GABA neuron function. The genes we identified using this method provide a more complete understanding of the complex genetic requirements of post-developmental neurons.
| In C. elegans, there are two basic ways to generate mosaic gene expression: knocking gene function down in specific cells of an otherwise normal animal; or rescuing wild type gene function in a mutant animal. Examples of the first method include triggering local RNAi by targeted expression of hairpin or double-stranded RNA [1], [2]; examples of the second include the use of unstable DNA elements or the targeted expression of wildtype coding sequences [3]. However, all of these methods require the construction of a new transgenic animal for each gene of interest. The requirement for one strain per gene limits the usefulness of these techniques for questions involving many genes, because it is impractical to construct so many transgenic animals.
Gene knockdown in C. elegans can be induced by feeding RNAi [4], and the development of whole-genome feeding RNAi libraries means that RNAi can be used for large-scale genetic analysis, up to and including whole-genome screens [5]–[7]. Moreover, many of the cellular mechanisms that mediate feeding RNAi have been described. In particular, interfering RNA species enter cells using the dsRNA channel SID-1 [8]–[10], while within each cell the Argonaute protein RDE-1 is required to achieve gene knockdown [11]. This molecular understanding has been used to develop a new method for mosaic gene expression that is generated by feeding RNAi, and thus is compatible with the study of many genes. For example, a muscle-specific rde-1 mosaic enables muscle-specific knockdown in response to feeding RNAi [12]. Similarly, manipulating sid-1 expression can alter the response of touch neurons to feeding RNAi [13].
Neurons, however, present particular problems for feeding RNAi. Most C. elegans neurons are resistant to feeding RNAi [14]–[16]. Genetic backgrounds have been developed that enhance the sensitivity of neurons to feeding RNAi, such as the lin-15B; eri-1 mutant [17] and neuronal expression of sid-1 [13]. However, these same genetic backgrounds can also result in increased transgene silencing in the nervous system [17]–[19]. Such transgene silencing could limit expression of the transgenes driving mosaic rescue of RNAi, even while these mutations enhance RNAi sensitivity. Thus, an approach that allows feeding RNAi to generate tissue-specific gene knockdown in neurons that can be generalized to a variety of neuronal subtypes is not currently available. Here, we describe a strategy in C. elegans that allows feeding RNAi to generate cell-specific knockdown in a wide variety of neuronal subtypes. We use this method to examine the genetic requirements of mature GABA motor neurons.
We chose to restrict RNAi sensitivity to selected neurons using rde-1 mosaic animals [12]. At the same time, we also sought to increase RNAi sensitivity in the selected neurons, since many neurons are resistant to RNAi. We used two complementary techniques to increase neuronal sensitivity. First, we used a genetic background (lin-15B; eri-1) that enhances the sensitivity of all neurons to RNAi [17]. Second, we overexpressed the double-stranded RNA transporter sid-1 only in the selected neurons [13]. Thus, our strain carries three background mutations (lin-15B; eri-1; rde-1) and expresses two transgenes in select neurons (rde-1(+) and sid-1(+)) (Fig. 1A). We initially found that our approach was subject to significant transgene silencing effects, likely caused by the combination of the lin-15B; eri-1 background and rde-1 overexpression (see Fig. 2A) [17], [18]. To avoid transgene silencing, we combined the rde-1(+) and sid-1(+) rescue fragments into a single transcriptional unit, separated by an SL2-specific trans-splice site [20]. This artificial operon was placed in a MosSCI-compatible [21] MultiSite Gateway vector for easy manipulation, single-copy integration, and expression under cell-specific promoters.
We first targeted GABA-releasing neurons. We used Gateway recombination to place the rde-1(+); sid-1(+) operon behind the Punc-47 promoter, which drives expression exclusively in the GABA motor neurons (Fig. 1B) [22]. This construct was inserted into the genome as a single copy using the MosSCI technique [21], and this transgene was crossed into the lin-15B; eri-1; rde-1 mutant background to generate a strain of animals in which the interfering response of exogenous dsRNA is limited to GABA neurons.
To determine the effectiveness of our approach, we expressed nuclear-localized GFP in all somatic cells of our GABA-specific RNAi strain and also marked the GABA neurons with mCherry. We fed these animals RNAi against GFP and found that, compared to control RNAi, GFP was efficiently knocked down in GABA neurons but was still present in other cells – including muscles, intestine, skin, and non-GABA neurons (Fig. 2A, B). This suggests that the RNAi response is limited to the tissue in which our artificial operon is expressed and does not spread to other adjacent cells or tissues.
We also tested the viability of our strain when challenged with RNAi against an essential gene. ama-1 encodes the large subunit of C. elegans RNA polymerase II [23], and RNAi against ama-1 results in a larval arrest phenotype with penetrance approaching 100%. By contrast, the GABA-specific strain, and the others described below, were completely resistant to ama-1 RNAi-induced arrest, demonstrating that gene function can be studied in the GABA neurons of an otherwise normal animal even when these genes have essential functions in other tissues.
Next, we sought to determine the effectiveness of our approach against endogenous, single-gene targets. We took advantage of the robust and specific behavioral ‘shrinker’ phenotype generated by lack of either pre- or post-synaptic components of GABA neurotransmission. The GABA-specific shrinker phenotype is readily distinguished from the paralyzed phenotype caused by loss of basal neurotransmission components. We compared the effect of feeding RNAi between a standard neuron-sensitized strain (lin-15B; eri-1) and our GABA-specific strain. We performed RNAi against two GABA-specific genes: unc-25, which encodes glutamic acid decarboxylase [24] and is required in GABA neurons for GABA neurotransmission; and unc-49, which encodes the GABAA receptor [25] and is required in muscles for GABA neurotransmission. We also targeted two components of basal neurotransmission, both of which are required in all neurons for synaptic vesicle release: unc-13, which encodes UNC-13 [26], and snb-1, which encodes synaptobrevin [27]. We found that, as expected, knockdown of GABA genes in the neuron-sensitized strain resulted in a GABA-specific shrinker phenotype, while knockdown of basal neurotransmission genes resulted in an uncoordinated phenotype. By contrast, in our GABA-specific strain, knockdown of the neuronal GABA gene unc-25 resulted in a shrinker phenotype, while knockdown of the muscle GABA gene unc-49 had no effect. Further, in our GABA-specific strain, knockdown of basal neurotransmission genes also resulted in a shrinker phenotype (Fig. 2C). To quantify behavioral changes due to GABA-specific knockdown, we utilized an aldicarb-sensitivity assay to indirectly measure GABA output. Aldicarb is an acetylcholinesterase inhibitor that causes acute paralysis due to accumulation of acetylcholine at neuromuscular junctions (NMJs). Loss of inhibitory GABA input leads to hypersensitivity to aldicarb, causing more rapid paralysis [28]. As expected, GABA-specific knockdown of unc-25 as well as the basal neurotransmission genes unc-13 and snb-1 led to hypersensitivity to aldicarb, while RNAi against unc-49 had no effect (Fig. 2D).
In addition to synaptic genes, we sought to target a broadly-expressed gene involved in maintenance of the nervous system. unc-70 encodes β-spectrin, a component of the plasma membrane skeleton that is expressed in all cells [29]. Animals lacking unc-70 generate spontaneous breaks in their neurons as a result of mechanical stress [30]. Neuron-sensitized (lin-15B; eri-1) animals fed dsRNA against unc-70 are slow to grow, dumpy, and paralyzed. Knockdown of unc-70 in the GABA-specific strain, however, caused an aldicarb hypersensitivity phenotype (Fig. 2D), with no other obvious phenotypes. When the GABA neurons of these worms were examined, we observed defects consistent with lack of unc-70, including branched processes, broken axons – some of which terminated in regenerative growth cones – as well as substantial degeneration of the dorsal nerve cord, especially where disconnection of distal fragments was apparent (Fig. 2E). Together, these data demonstrate that our approach allows knockdown of endogenous genes within selected neurons, while preventing knockdown of those genes in other cells. Moreover, this method allows for dissection of the site of gene action, easily separating pre- and post-synaptic functions, as well as neuron sub-type-specific effects of gene knockdown.
To determine whether our system for controlling feeding RNAi was adaptable to other sets of neurons, we used other promoters to drive expression of our artificial rde-1(+); sid-1(+) operon, made single-copy MosSCI integrations of each construct, and placed each resulting MosSCI transgene in the lin-15B; eri-1; rde-1 mutant background. Next, we characterized the resulting strains by challenging them with ama-1 RNAi. Three of these new strains – those using the Pdat-1, Punc-17, and Peat-4 promoters – satisfied the test of ama-1 RNAi resistance in this context, and further experiments with these three strains are discussed below. However, with two other promoters – Prab-3 and Pmig-13 – we found that the resulting strain was not resistant to ama-1 RNAi. Thus, these promoters are not suitable for the analysis of essential genes using our system. By contrast, the Pdat-1, Punc-17, and Peat-4 promoters appear to be tightly controlled, suggesting that the three strains using these promoters can be used for neuron-specific RNAi.
Pdat-1 drives expression in the dopaminergic neurons [31], which comprise eight cells in adult hermaphrodites [32]. One function of dopamine release from these cells is to control a behavioral response to food called “basal slowing,” in which animals slow their rate of locomotion when they encounter a bacterial lawn [33]. We used a basal slowing assay to evaluate the ability of our Pdat-1 strain to restrict RNAi to the dopaminergic neurons. The cat-2 gene encodes tyrosine hydroxylase and is required for dopamine synthesis [34]. Mutant animals that lack cat-2, or animals in which all eight dopamine neurons have been ablated, do not exhibit basal slowing [33]. Basal slowing response is also absent in mutants that lack dop-3, which encodes a D2 dopamine receptor and is not expressed in the dopaminergic neurons [35]. Thus, basal slowing requires factors both intrinsic and extrinsic to the dopamine neurons. We found that both a standard sensitized RNAi strain (lin15B; eri-1) and the Pdat-1 strain exhibited a robust basal slowing response on control RNAi (Fig. 3A, B). Further, the basal slowing response was completely blocked in both strains on RNAi against cat-2, which is required in the dopamine neurons themselves. By contrast, RNAi against dop-3 blocked basal slowing in the control strain but did not affect basal slowing in the Pdat-1 strain. We also tested basal slowing following feeding RNAi directed against unc-13 or snb-1. In these experiments, the standard lin-15B; eri-1 strain could not be tested because knockdown of these genes resulted in an uncoordinated behavioral phenotype (Fig. 2C). By contrast, knockdown of unc-13 and snb-1 in the Pdat-1 strain eliminated the basal slowing response (Fig. 3B). Further, although the basal slowing response was eliminated by knockdown of unc-13 or snb-1, these knockdowns did not affect the rate of locomotion (p = 0.0816 and p = 0.5566 respectively, compared to control off food). These data demonstrate that feeding RNAi in the Pdat-1 strain is effective in dopamine neurons but is blocked in other cells.
Next, we targeted glutamatergic neurons by driving expression of our artificial operon under the Peat-4 promoter. In C. elegans, one behavior mediated by glutamatergic neurotransmission is reversal in response to nose touch [36]. Glutamatergic control of this behavior requires the gene eat-4, which encodes VGLUT, the glutamate synaptic vesicle transporter [37] that functions intrinsically in glutamatergic neurons. Glutamate control of the nose touch response also requires the gene glr-1, which encodes an AMPA-type ionotropic glutamate receptor [38], [39] and is required in the post-synaptic cells that respond to glutamate. We fed neuron-sensitized (lin15B; eri-1) animals dsRNA against eat-4 and glr-1. Under both conditions, these animals were deficient in their ability to respond to nose touch when compared to controls (Fig. 3C). Thus, feeding RNAi can target both pre- and post-synaptic components of glutamatergic neurotransmission to generate a glutamate-specific behavioral defect. We then tested the glutamatergic neuron-specific RNAi strain. On control RNAi, these animals exhibited a normal nose touch response, similar to the standard sensitized strain (Fig. 3C and D, gray bars; p = 0.3421). Feeding RNAi against eat-4 resulted in a loss of nose touch response compared to empty vector fed controls, similar to the loss in the standard strain (Fig. 3D). Thus, RNAi of an endogenous gene in glutamatergic neurons is effective in the glutamate-specific strain. By contrast, RNAi against glr-1 did not affect the ability of the glutamate-specific strain to respond to nose touch. This result suggests that unlike the standard sensitized strain, the glutamate-specific strain is insensitive to RNAi outside the glutamate neurons. In support of this, we found that feeding RNAi against the basal neurotransmission genes unc-13 and snb-1 resulted in a glutamate-specific behavioral defect in nose touch, rather than a general defect in movement. In addition, glutamate-specific RNAi worms fed dsRNA against unc-70 were also impaired in their response to nose touch when compared to controls (Fig. 3D), suggesting that unc-70 is important for maintaining the integrity of glutamate-releasing neurons.
We also targeted cholinergic neurons using the Punc-17 promoter, which drives expression in acetylcholine-releasing neurons, including the excitatory motor neurons that innervate the body wall muscles and are required for proper locomotion [40], [41]. We first measured the thrashing rate in liquid of neuron-sensitized (lin-15B; eri-1) worms and found that they exhibited a significant decrease when fed bacteria producing dsRNA against snb-1 or unc-13 compared to empty vector control (Fig. 2E). We then knocked these genes down in the acetylcholine-specific RNAi strain and observed a similar decrease in the rate of thrashing (Fig. 2F). We also fed the acetylcholine-specific RNAi worms dsRNA against unc-70. These worms were impaired in their thrashing rate but displayed no other phenotypes indicative of systemic RNAi of unc-70, suggesting that RNAi in this strain is restricted to the acetylcholine neurons.
Neurons are complex cells, and synaptic transmission and maintenance of normal neuronal function requires the concerted action of a large number of genes. Although many such genes have been identified by forward genetic screens, we hypothesized that these screens may have missed important requirements in neurons for essential genes. Essential genes – those required for the growth of an organism to a fertile adult – are difficult to recover in screens for neuronal function because of death, arrest, or sterility of the mutant. Yet such genes might have critical roles in neurons. Accordingly, we sought to expand our ubderstanding of the genetic requirements for proper GABA neuron function by screening our GABA-specific RNAi strain against a large number of essential genes.
We began by curating a list of all essential genes by reported RNAi phenotype of lethal, arrested, or sterile using WormMart (wormbase.org), and we arrayed corresponding Ahringer RNAi [6] clones into a custom essential gene RNAi library. Our primary screen consisted of 1,782 essential RNAi clones from chromosomes I, II, and III. Using the GABA-specific RNAi strain described above, we screened animals fed these clones for hypersensitivity to aldicarb-induced paralysis. Due to the strict experimental control needed for proper neuronal RNAi and phentoyping, such as log-phase culture and age-matched progeny, we were successfully able to screen 1,320 clones (outlined in Table S1) targeting essential genes for their effect on GABA output. From the primary screen, we identified 79 clones (∼6%) that produced aldicarb hypersensitivity of at least two standard deviations above the mean (Figure 4A).
We also examined the morphology of the GABA nervous system in each experiment for branching, degeneration, or cell death. We found that in contrast to the functional defects, we observed no morphological defects in any of the 1,320 RNAi experiments. Thus, the functional deficits we observe in response to RNAi are the result of altered neurotransmission rather than cell death or degeneration. However, our screen was conducted on young adult animals, so it is possible that longer-term knockdown would result in morphological phenotypes for some genes.
To validate the primary screen hits, we retested each of the clones in at least three independent trials. We selected those that retested above the cutoff from the primary screen, which was well above that needed for statistical significance (p<0.0001 for all selected clones compared to control). These clones were then sequenced to confirm the targeted gene. We discarded any clone that could not be mapped to a single gene target, including clones that mapped to more than one gene, intergenic region, or intron. We identified 48 genes (Figure 4C, Table 1) whose knockdown in GABA neurons led to aldicarb hypersensitivity, and thus decreased GABA motor output. Eighty-three percent (40) of these genes have predicted human orthologs [42], suggesting that we have identified a largely conserved set of genes that are important for post-developmental GABA neuron function.
The technique and strains presented here enable cell-specific knockdown in designated neurons simply by performing feeding RNAi. Our results in GABA, dopamine, glutamate, and acetylcholine-releasing neurons suggest that the technique can be used to limit feeding RNAi to any neuron or group of neurons. However, since RDE-1 acts at a rate-limiting step early in the exogenous RNAi pathway, small amounts of misexpression can trigger an amplified interference response – hence, tightly controlled promoters are required to drive expression of the transgene to ensure specificity.
One major use of this technique will be to rapidly determine the site of action of particular genes. For example, our results demonstrate that the dopamine receptor dop-3 does not function in the dopamine neurons, and the glutamate receptor glr-1 does not function in the glutamate neurons.
Another major use will be to easily determine the function in specific neurons of genes that are ubiquitously expressed. For example, our data demonstrate that GABA, acetylcholine, dopamine, and glutamate neurons all rely on unc-13 and snb-1 for neurotransmission. Also, we show that unc-70 is required in GABA, acetylcholine, and glutamate neurons for proper function.
Finally, our technique enables new kinds of forward genetic screens, as we have demonstrated with our GABA-specific RNAi screen of essential genes. Essential genes make up a substantial portion of the genes in the genome, but are virtually inaccessible to traditional genetic screens. These genes, however, are some of the most conserved – while only ∼38% of all C. elegans genes have human orthologs [42], ∼76% of the genes we selected for their essential RNAi phenotype have predicted orthologs. By using a strain that limits RNAi to non-essential neurons (such as our GABA-specific strain), it is now possible to screen for neuronal functions of genes that normally have lethal, sterile, or other pleiotropic phenotypes.
We have identified 48 genes whose cell-specific knockdown lead to deficits in GABA neurotransmission. As expected, we found components of essential cellular processes such as energy metabolism, transcription, and translation. Additionally, components of several important and conserved signaling and gene regulation pathways were identified, such as miRNA (drsh-1), Wnt signaling (mig-5), and Hpo signaling (wts-1). These pathways have been studied extensively for their role in development, but our data suggest that these pathways may be important post-developmentally for maintenance of GABA neuron function. The genes identified in this study provide a more complete understanding of the complex genetic requirements of post-developmental neurons. Additional studies will be required to determine the mechanism through which these genes act to promote GABA neuron function, whether through specific modulation of neuronal functions such as neurotransmitter release, or general cellular health and metabolism.
The four strains presented here enable the rapid knockdown of many single gene targets in a given neuron sub-type. The efficiency of RNAi in each strain varies, possibly due to differences in expression levels of our bi-cistronic transgene when driven by various promoters. In the case of the GABAergic neuron-specific strain, we are able to recapitulate the null phenotype of unc-25(e156) with unc-25 RNAi (Figure S1A, p = 0.0994). The efficiency of the dopaminergic-specific RNAi strain is comparable to that of the standard sensitized RNAi strain – when fed RNAi against cat-2 (Figure 3A, B), basal slowing response is abolished to a similar level in both strains. The efficiency of RNAi in the glutamatergic neuron-specific RNAi strain is also comparable to the standard sensitized strain when nose touch behaviors are compared after feeding with eat-4 RNAi (Figure 3C, D, p = 0.5205). Finally, RNAi in the cholinergic neuron-specific RNAi strain is slightly less efficient than the standard sensitized strain – there is a small, but significant difference in the thrashing rates when these strains are fed RNAi against unc-13 (Figure 3E, F, p = 0.0430) or snb-1 (p = 0.0478). Additionally, RNAi of unc-13 in the cholinergic-specific RNAi strain is unable to fully recapitulate the slowed thrashing rate of unc-13(e51) mutants (Figure S1B, p = 0.0003), due to a combination of decreased penetrance and effect size. All the strains presented, however, show dramatic behavioral defects when fed RNAi against genes that are required for those neurons to function.
An interesting feature of our system is that we do not observe effects for knockdown of gene function during development. For example, although GABA neurotransmission is required at all developmental stages for normal movement, our GABA-specific strain does not exhibit behavioral defects until the L4 and adult stage. Similarly, no developmental defects were observed during our GABA-specific screen of essential genes. A likely reason for this is that in our system, RNAi is not initiated until rde-1 is expressed, and expression from the promoters we use does not begin soon enough to affect behavior at earlier stages. Although this delay means that the developmental functions of genes cannot be studied with our system, it also allows bypassing developmental effects for genes that function both during development and afterward. For example, if a neuronal gene functions in axon guidance and also functions in neurotransmission, our system will allow specific analysis of this later role.
To our knowledge, our approach is the first in any metazoan that enables cell-specific knockdown in any chosen neuron sub-type (with an available promoter), in a way that is high throughput enough to be compatible with questions involving large numbers of genes – including whole-genome screens. As additional specific sensitized strains are developed in addition to the four presented here, it will be possible to combine analysis of neural circuits with genetics, knocking down specific genes in specific parts of circuits and determining the effect on output. In general, we expect this technique will be useful for two major classes of applications: first, to rapidly determine the site of gene function by knocking down a gene of interest in specific neurons; and second, to perform forward genetic screens in a mosaic context, defeating the muddying effects of pleiotropy and biasing the hits toward genes that function intrinsically in the neurons of interest.
All entry clones were generated using Phusion DNA polymerase (Finnzymes) and Gateway BP Clonase II (Life Technologies). rde-1 and sid-1 were amplified from genomic and cDNA, respectively, from start to stop codons and cloned into pDONR221 (Life Technologies). The bi-cistronic rde-1:SL2:sid-1 entry clone was generated using In-Fusion PCR cloning kit (Clonetech) in two steps: first, the 245 bp SL2-specific trans-splice site from the gpd-2/gpd-3 intergenic region [20] was inserted upstream of the start codon of the sid-1 entry vector, then the rde-1 gene was inserted upstream of the SL2 site. Pdat-1 and Peat-4 promoter entry clones were made by PCR amplification of 717 bp and 2582 bp, respectively, upstream of the corresponding gene start site and cloned into pDONR-P4-P1R. Punc-47 and Punc-17 promoter entry constructs [43] were a gift from Gunther Hollopeter, University of Utah, Salt Lake City, UT. Expression clones were generated using Gateway LR clonase II Plus (Life Technologies) and inserted into pCFJ150 [21], a Gateway three-fragment compatible destination vector for MosSCI containing a C. briggsae unc-119 rescue fragment and genomic regions flanking the ttTi5605 Mos1 insertion, to generate: pCF1021 (Punc-47::rde-1:SL2:sid-1::let-858UTR), pCF1028 (Punc-17::rde-1:SL2:sid-1::let-858UTR), pCF1035 (Pdat-1::rde-1:SL2:sid-1::let-858UTR), pCF1044 (Peat-4::rde-1:SL2:sid-1::let-858UTR). The unc-70 RNAi construct was made by inserting 1697 bp of unc-70 coding sequence between the SpeI and KpnI sites of L4440 (clone pPD129.36, Fire Kit, Addgene). Primers and templates are outlined in Table S2.
All mutant C. elegans strains were provided by Caenorhabditis Genetics Center and maintained at 20°C as previously described [44]. Transgenic C. elegans lines carrying the transgenes as single copy insertions were created as described [21], [45] using insertion site ttTi5605, then verified by PCR and Sanger sequencing. These insertions were then crossed into lin-15B(n744); eri-1(mg366); rde-1(ne219) mutant animals and genotyped by PCR, Sanger sequencing, and resistance to ama-1 RNAi. XE1583 was created by microinjection of XE1375 with 15 ng µl−1 of pTG96 [46] as described [47]. The following strains were used in this study: N2, KP3948 (lin-15B(n744) X; eri-1(mg366) IV), XE1375 (lin-15B(n744) X; eri-1(mg366) IV; rde-1(ne219) V; wpSi1[Punc-47::rde-1:SL2:sid-1, Cbunc-119(+)] II; wpIs36[Punc-47::mCherry] I), XE1474 (lin-15B(n744) X; eri-1(mg366) IV; rde-1(ne219) V; wpSi6[Pdat-1::rde-1:SL2:sid-1, Cbunc-119(+)] II), XE1581 (lin-15B(n744) X; eri-1(mg366) IV; rde-1(ne219) V; wpSi10[Punc-17::rde-1:SL2:sid-1, Cbunc-119(+)] II), XE1582 (lin-15B(n744) X; eri-1(mg366) IV; rde-1(ne219) V; wpSi11[Peat-4::rde-1:SL2:sid-1, Cbunc-119(+)] II), XE1583 (lin-15B(n744) X; eri-1(mg366) IV; rde-1(ne219) V; wpSi1[Punc-47::rde-1:SL2:sid-1, Cbunc-119(+)] II; wpIs36[Punc-47::mCherry] I; wpEx180[Psur-5::sur-5:gfp:NLS]), CB156 (unc-25(e156) III), MT7929 (unc-13(e51) I).
RNAi was induced by feeding as described [15], with modifications. We found the following conditions were optimal for RNAi in these strains. Standard NGM agar [44] was supplemented with 25 µg ml−1 carbenicillin and 1 mM isopropyl β-D-1-thiogalactopyranoside (IPTG), poured into 6 cm dishes or 12-well plates, and allowed to dry for 7 days at room temperature (RT). E. coli HT115 carrying the appropriate RNAi clones were grown in LB containing 100 µg ml−1 carbenicillin and 50 µg ml−1 tetracycline at 37°C overnight. This saturated culture was then seeded 1∶40–1∶200 into LB containing 100 µg ml−1 carbenicillin and grown at 37°C until it reached an OD600 of 0.6–0.8, then several drops were seeded onto plates, making sure the culture dried within 1–2 hrs, and induced at RT for 48 hrs. L1 worms were then transferred to the plates (3 per 12-well plate well or 6 per 6 cm plate) and allowed to grow at 20°C for approximately 7 days until young adult (L4+1 day) F1 progeny were visible. The following RNAi clones were used: L4440 empty vector control (clone pPD129.36, Fire Kit, Addgene), GFP (clone pPD128.110, Fire Kit, Addgene), snb-1 (clone T10H9.4, Ahringer Library [6], unc-13 (clone ZK524.2, Ahringer Library), cat-2 (clone B0432.5, Ahringer Library), dop-3 (clone T14E8.3, Ahringer Library), eat-4 (clone ZK512.6, Ahringer Library), glr-1 (clone C06E1.4, Ahringer Library), unc-25 (clone Y37D8A.23, Vidal ORFeome Library [7], unc-49 (clone T21C12.1, Vidal ORFeome Library). See Supplemental Table S1 for the list of clones used in the screen.
Young adult hermaphrodites were mounted in a slurry of 0.1 µm diameter polystyrene beads (Polysciences) on a 5% agarose pad and imaged using a UltraVIEW VoX (Perkin Elmer) spinning disc confocal microscope using a 60× CFI Plan Apo VC, NA 1.4, oil objective. Images were pseudo colored in Fiji [48].
Aldicarb hypersensitivity was measured as described [28]. Briefly, NGM agar was poured into 12-well plates and allowed to dry for 14 days at RT. Plates were then weighed, top-spread with 30 mM aldicarb (Ultra Scientific) to a final concentration of 750 µM, and allowed to dry for 6 hrs. Plates were then seeded with 5 µl of OP50 culture and allowed to grow overnight at RT. 25 young adult worms were then transferred to each well, and after 100 min, the number of paralyzed (defined as the cessation of all spontaneous movement) worms was counted. Each experiment was performed in triplicate.
Basal slowing was measured as described [33]. Locomotion for 20 worms was measured for each RNAi and each treatment.
Thrashing was measured by picking a single young adult worm into a drop of M9 buffer [44] on a glass slide at RT. After equilibrating for 30 sec, the number of body bends (complete movement of the anterior of the worm from one extreme to the other and back) was counted for 30 sec. Rates were measured for 10 worms for each RNAi treatment.
Response to nose touch was measured as described [36]. 10 animals were tested for each condition, 5 trials/animal. The percentage of reversals per total trials was calculated.
RNAi was performed as described above, with cultures grown in 96-deep-well plates. Two positive (unc-25) and two negative (L4440) controls were included for each 96 clones screened. Only cultures grown to log-phase were seeded onto plates as measured by OD600 using a Perkin Elmer Victor 2 plate reader. Aldicarb hypersensitivity was performed as above. Morphology of GABA neurons was examined using a Leica M165FC epi-fluorescent dissecting microscope under 500× magnification.
Two-tailed, unpaired Student's t-tests were used to compare GFP RNAi and aldicarb hypersensitivity data in Figures 2 and 4, as well as basal slowing and thrashing in Figure 3. Two-tailed Fisher's exact test was used to compare nose touch behaviors in Figure 3.
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10.1371/journal.pgen.1003017 | Long Telomeres Produced by Telomerase-Resistant Recombination Are Established from a Single Source and Are Subject to Extreme Sequence Scrambling | Considerable evidence now supports the idea that the moderate telomere lengthening produced by recombinational telomere elongation (RTE) in a Kluyveromyces lactis telomerase deletion mutant occurs through a roll-and-spread mechanism. However, it is unclear whether this mechanism can account for other forms of RTE that produce much longer telomeres such as are seen in human alternative lengthening of telomere (ALT) cells or in the telomerase-resistant type IIR “runaway” RTE such as occurs in the K. lactis stn1-M1 mutant. In this study we have used mutationally tagged telomeres to examine the mechanism of RTE in an stn1-M1 mutant both with and without telomerase. Our results suggest that the establishment stage of the mutant state in newly generated stn1-M1 ter1-Δ mutants surprisingly involves a first stage of sudden telomere shortening. Our data also show that, as predicted by the roll-and-spread mechanism, all lengthened telomeres in a newly established mutant cell commonly emerge from a single telomere source. However, in sharp contrast to the RTE of telomerase deletion survivors, we show that the RTE of stn1-M1 ter1-Δ cells produces telomeres whose sequences undergo continuous intense scrambling via recombination. While telomerase was not necessary for the long telomeres in stn1-M1 cells, its presence during their establishment was seen to interfere with the amplification of repeats via recombination, a result consistent with telomerase retaining its ability to add repeats during active RTE. Finally, we observed that the presence of active mismatch repair or telomerase had important influences on telomeric amplification and/or instability.
| Indefinite growth of tumor cells requires a mechanism to maintain telomeres. While most cancers use telomerase for this, some maintain long and heterogeneous telomeres using a recombination-dependent mechanism termed alternative lengthening of telomeres (ALT). What causes ALT and how their long and heterogeneous telomeres form and are maintained are not well understood. In this study, we use mutationally tagged telomeric repeats to probe the mechanisms by which highly elongated telomeres are generated by recombination in an ALT–like yeast mutant. Our data show that most or all lengthened telomeres in a newly established mutant cell are commonly generated by amplifying sequence from a single telomere source. This is consistent with the roll-and-spread model, which proposes that a single circle of telomeric DNA can be the ultimate source of all newly amplified telomeres. Other evidence showed that the telomeres of the mutant are exceptionally dynamic. Rapid terminal deletions preceded telomere elongation at the establishment of the mutant state. Also, patterns of telomeric repeats present in long telomeres became rapidly scrambled. These findings may have implications for the establishment and maintenance of long telomeres in human ALT cells.
| Recombination can maintain telomeres in many situations where telomerase is absent. Natural examples of this include the chromosomal telomeres in the mosquito Anopheles [1]–[2] and the mitochondrial telomeres in certain ciliates and yeasts [3]–[5]. Of particular importance are the 5–10% of human cancer cells where telomerase activity is undetectable and telomeres are maintained by a mechanism termed Alternative Lengthening of Telomeres (ALT) (for a review, see [6]). ALT cells are characterized by long and heterogeneous telomeres [7]–[10] and the presence of ALT-associated PML bodies (APB) that contain telomeric DNA as well as telomeric and recombinational proteins [8], [11]–[14].
Several lines of evidence suggest that recombination is involved in maintaining telomeres when the long and heterogeneous telomeres already exist in ALT cells. Plasmid tags introduced into a telomere can be duplicated to other telomeres or at the same telomere in ALT cells but not in telomerase positive cells [15]–[16]. Extrachromosomal telomeric circles (t-circles), likely products of intratelomeric recombination, are abundant in ALT cells [17]–[19]. Telomeric sister chromatid exchanges (t-SCE) occur at highly elevated rates in ALT cells [20]–[22]. However, the details of how recombination can establish these long and heterogeneous telomeres from normal-length telomeres in ALT cells are still unknown.
Recombinational telomere elongation (RTE) has been described in yeast mutants lacking telomerase in the species Sacchromyces cerevisiae [23], Kluyveromyces lactis [24], Candida albicans [25] and Schizosacchromyces pombe [26]. The recombination in these cases is thought to be caused by the shortening telomeres eventually losing part or all of their protective capping function. These mutants commonly display gradual growth senescence when telomeres are gradually shortening that is followed by the formation of better growing post-senescence survivors with longer telomeres [23]–[24], [27]. Two types of RTE were initially described in telomerase deletion mutants of S. cerevisiae. Both depend upon RAD52, suggesting that they require homologous recombination (HR). Type I RTE is characterized by amplification of subtelomeric Y′ elements and short tracts of telomeric repeats, and is dependent upon the canonical mitotic HR pathway involving RAD51, RAD54, RAD55, and RAD57. Type II RTE is characterized by lengthened tracts of telomeric repeats and is dependent upon a different pathway involving RAD50, RAD59, and SGS1 [28]–[33]. Only type II RTE normally occurs in K. lactis telomerase deletion mutants (ter1-Δ) [24]. Studies, particularly in K. lactis, have suggested that type II RTE occurs through a roll-and-spread mechanism, where a t-circle is used as a template to lengthen one short telomere which in turn can be used as a template to lengthen other telomeres via break-induced replication (BIR) events [34]–[37]. Consistent with this model, post-senescence survivors derived from cells with two kinds of telomeric repeats often contain repeating patterns in most or all lengthened telomeres [36]. Additionally, when a DNA circle containing telomeric repeats is transformed into a K. lactis telomerase deletion mutant, its sequence becomes efficiently amplified onto telomeric ends as long tandem arrays [36]. T-circles are also abundant in yeast mutants with telomere dysfunction [34], [38]–[39]. Furthermore, sequence from a single telomere is used as the source of all lengthened telomeres in K. lactis post-senescence survivors [37]. Type II RTE in S. cerevisiae has also been suggested to involve rolling circle copying of t-circles [39].
RTE can also be triggered by perturbation of telomeric capping proteins. For example, in S. cerevisiae, a cdc13-1 yku70 mutant can generate type II survivors without gradual growth senescence [40]. In K. lactis, telomerase deletion mutants containing telomeric repeats with defects in Rap1 binding develop much longer telomeres than equivalent mutants with only wild type repeats [37], [41]. Of particular interest is the stn1-M1 mutant of K. lactis [42]. Stn1 is a part of the Cdc13/Stn1/Ten1 (CST) complex that binds to the 3′ single-stranded telomeric overhang and protects the telomere termini from degradation and engagement in recombination (for a review see [43]). Stn1 also regulates telomerase recruitment and telomeric C-strand synthesis, the latter via its interaction with Polα/primase [44]–[45]. In many ways, the stn1-M1 mutant displays more similarity to ALT than do ter1-Δ mutants. It shares with ALT a steady state of very long and highly heterogeneous telomeres that are produced by recombination as well as the immediate presence of chronic but slight growth defects instead of the gradual growth senescence and survivor formation seen in ter1-Δ mutants [8], [42], [46]. Both ALT cells and stn1-M1 cells show high levels of telomere instability including elevated telomere recombination, rapid telomere deletions, and abundant extrachromosomal telomeric DNA including t-circles [10], [21], [38], [42], [47]–[48]. Finally, the phenotypes of stn1-M1 and of most ALT cells, in sharp contrast to that of ter1-Δ mutants, are largely not affected by whether telomerase is present or not, a property that we refer to as telomerase-resistant [8], [42]. While telomeric recombination in ter1-Δ cells appears repressed once telomeres are even moderately elongated, the telomere recombination in stn1-M1 cells is thought to occur at telomeres of all sizes. To distinguish the fundamental differences between telomere capping defects in the two mutants, the RTE in stn1-M1 mutant was termed type IIR for its “runaway” lengthening characteristics [42]. Given its similarities with ALT cells, the stn1-M1 mutant may therefore be a useful model system to obtain more clues about mechanisms that establish long telomeres in ALT cells.
In this work, we utilize mutationally tagged telomeric repeats to study the mechanism of type IIR RTE in stn1-M1 mutant during the establishment stage where long telomeres are generated from much shorter telomeres. Our results are consistent with predictions of the roll-and-spread model in demonstrating that sequence from one telomere is commonly spread to most or all telomeres of newly formed stn1-M1 mutants. Our results also suggest that rapid telomere truncations routinely precede the generation of long telomeres and that the presence of active mismatch repair or telomerase can impact the outcomes observed.
To study the type IIR RTE that forms highly elongated telomeres in the stn1-M1 mutant of K. lactis, we generated stn1-M1 mutants from two kinds of precursors with mutationally tagged telomeric repeats. Previously, similar approaches were informative in studying the type II RTE that forms the more modestly elongated telomeres in K. lactis telomerase deletion (ter1-Δ) mutants [36]–[37]. The experimental setup for generating stn1-M1 cells from the first kind of precursor is diagramed in Figure 1A. An stn1-M1 ter1-Δ mutant was first transformed with a plasmid (pSTN1-TER1(ApaL)) containing both the STN1 and the TER1-20C(ApaL) genes. The TER1-20C(ApaL) gene forms a telomerase that adds mutated ApaL repeats onto all telomeric termini. ApaL repeats are phenotypically silent but contain a single base change that both forms an ApaLI site and eliminates the native RsaI site [49] (Figure 1A). A transformant was then serially passaged for ten streaks to allow telomeres to shorten to near normal length and to incorporate ApaL repeats at their termini. These passaged cells are referred to as the ApaL precursor cells.
All telomeres in the ApaL precursor cells had acquired ApaL repeats at termini as indicated by their EcoRI-digested telomeric fragments being shorter after ApaLI digestion (Figure 1B and data not shown). In contrast, telomeric fragments in a wild type control were digested away with RsaI but resistant to ApaLI (Figure 1B–1C). To estimate the number of ApaL repeats at telomeric termini of ApaL precursors, we digested their DNA by BsrBI+ApaLI and BsrBI+RsaI (Figure 1C and data not shown). BsrBI cleaves 3 bp away from 10 of 12 telomeres in K. lactis (Figure 1A), and ApaLI and RsaI specifically cleave ApaL and wild type (WT) repeats, respectively. The size ranges of the telomeric signal in BsrBI+ApaLI and BsrBI+RsaI digestions reflect the size ranges of internal WT repeats and terminal ApaL repeats, respectively. From this, we estimated that the terminal 100–300 bp of the 350–600 bp total telomere length was composed of ApaL repeats. This was verified by cloning and sequencing two telomeres from an ApaL precursor. One cloned telomere contained 11 basal WT repeats and three terminal ApaL repeats and the other contained ∼9 basal WT repeats and five terminal ApaL repeats (Figure S1A). To our surprise, clone 2 also contained a 13 bp repeat located between the WT and ApaL repeats (Figure S1A). The sequence of this 13 bp repeat suggested it arose when the terminal bases of a telomere annealed to the middle instead of to the 3′ end of the telomerase RNA template (Figure S1B). This half-sized repeat was not likely formed by the ApaL telomerase because it contained the native RsaI site of WT repeats rather than an ApaLI site.
We then selected for newly generated stn1-M1 ter1-Δ mutants by plating the ApaL precursors on medium containing 5-flouro-orotic acid (5-FOA) to select for loss of the pSTN1-TER1(ApaL) plasmid. As the loss of plasmid sequences simultaneously deletes both telomerase and the wild type STN1 gene, the generation of long telomeres in the newly formed stn1-M1 ter1-Δ cells should depend solely on recombination. Although the ter1-Δ mutation is, by itself capable of causing RTE, it is not likely to interfere with the type IIR RTE brought on by the stn1-M1 mutation. This is because the telomeric recombination induced by the ter1-Δ mutation is confined to occurring when telomeres become <∼150 bp in length [37] while telomeric recombination in stn1-M1 occurs even at very long telomeres. Consistent with this, the growth and telomere phenotypes of the stn1-M1 mutant are epistatic to those of the ter1-Δ mutant.
Because the pSTN1-TER1(ApaL) plasmid became integrated into a chromosome at the stn1-M1 locus in the ApaL precursor during passaging (data not shown), the rate of recovering stn1-M1 ter1-Δ mutants (generated by homologous recombination looping out the plasmid from the chromosome) was relatively low. A group of ten newly generated stn1-M1 ter1-Δ mutants was initially analyzed. All ten mutant clones showed the long and heterogeneous pattern of telomeres that is the characteristic of the stn1-M1 phenotype when EcoRI digests were observed in a Southern blot (Figure 1D). Telomeric signals in nine of these ten mutants (clones A2–A10, Figure 1D) were not obviously cleaved by ApaLI. Hybridization of the same filter to a subtelomeric probe (Figure 1E) showed that most signal from most clones was unchanged by ApaLI digestion. These results indicate that telomeres in these nine mutants were composed almost entirely of WT repeats. Consistent with this interpretation, telomeric signals from these nine mutants were virtually cleaved away by RsaI, which specifically cleaves WT repeats (Figure 1D). These results are very surprising, because the stn1-M1 phenotype forms rapidly without a period of growth senescence and should therefore not undergo any gradual sequence loss of terminal ApaL repeats before the formation of long telomeres by RTE [42]. Each of the A2–A10 clones did show a small percentage of the heterogeneously smeared subtelomeric signal shifted down to one or more bands, generally of ∼1 kb, from ApaLI digestion. This indicates that one or a small number of the telomeres in each clones retained at least one ApaL repeat near their base.
Strikingly, the telomeric signal of one stn1-M1 ter1-Δ mutant (clone A1) was cleaved into very small fragments by both ApaLI and RsaI (Figure 1D). When the same digests were run on a high-percentage agarose gel, the small fragments were observed to be composed largely of a ∼125 bp band in the ApaLI digests and a ∼50 bp band in the RsaI digests (Figure 1F). The former was predicted to contain four WT repeats and two half ApaL repeats and the latter was predicted to contain one ApaL repeat and two half WT repeats. These data suggest that telomeres in this stn1-M1 ter1-Δ mutant may contain repeating structures that consist of four WT repeats and one ApaL repeat as the repeating unit. To test this, we cloned and sequenced 38 telomeric fragments from this clone, which were produced by partial ApaLI digestion. Although mostly very small ApaLI fragments were recovered (Figure S2), the results showed that 19 of 51 (37%) blocks of WT repeats were ∼100–125 bp, of which 15 (79%) consisted of three WT repeats and one half WT repeat of the same sequence that was recovered from the ApaL precursor. Although these results rule out the presence of perfectly repeating patterns when DNA was isolated from the A1 mutant, the widespread presence of a particular pattern of repeats could suggest that telomeres with more perfect repeating patterns originally existed but was disrupted by numerous later recombination events.
We next analyzed 83 additional stn1-M1 ter1-Δ mutants generated from ApaL precursors. 73 of these clones had telomeric signals that were essentially uncleaved by ApaLI but were nearly fully cleaved by RsaI indicating that the lengthened telomeres were composed of virtually all WT repeats (data not shown). However, ten clones had telomeric signals that were cleaved partly or entirely into short fragments by both ApaLI and RsaI digestions (Figure 1G, upper panel). The same digests of these ten mutants were then run on a high-percentage agarose gel to resolve short DNA fragments (Figure 1G, lower panel). Several of these mutants, including B19, B20, C12, C23 and C44 showed favored fragment sizes in both ApaLI and RsaI digests (indicated by white arrows) which could be indicative of degraded repeating patterns. In each of these mutants, the most common fragment size of ApaL repeats was smaller than that of WT repeats. The other mutants examined, B8, B12, B16, C4 and C60, sometimes exhibited favored short fragments in RsaI digests but not obviously any in ApaLI digests. The average size of the telomeric signal in ApaLI digests of these clones tended to migrate at greater average size than that seen in the other clones. In some clones, most notably C4, the ApaLI digestion produced ladders of bands that included sizes consistent with the presence of the “half” telomeric repeats as was present in the A1 clone.
Two newly generated stn1-M1 ter1-Δ mutants that exhibited the best evidence of repeating patterns (clones A1 and C12) were serially passaged for 5–10 streaks on YPD plates and periodically examined for their telomeric DNA structure by Southern blots of ApaLI and RsaI digests run on high-percentage agarose gels (Figure 1H). The initial banding pattern of these mutants became more complicated after passaging. Specifically, the favored fragments of the two mutants in ApaLI digests, initially ∼110–150 bp, became more variable at later streaks and tended to produce new fragments of larger sizes. This observation supports the idea that telomeres in stn1-M1 mutants are extremely dynamic and are prone to high rates of recombination that can rapidly alter their structure.
Base mismatches in DNA can reduce the rate of homologous recombination [50]. Therefore, the base mismatch between WT and ApaL repeats is likely to interfere with the recombination between telomeres that contain them. To test this, we disrupted the MSH2 gene (required for mismatch repair) in an ApaL precursor, and generated 17 stn1-M1 ter1-Δ msh2-Δ mutants by losing pSTN1-TER1(ApaL) as described above. Telomeric signals in 5 of these mutants showed little or no cleavage by ApaLI (data not shown). However, telomeric signals in the other 12 mutants were cleaved into broad ladders of bands with ∼25 bp steps by both ApaLI and RsaI (Figure 2A and data not shown). Unlike what was seen in mutants derived from a MSH2 background, these mutants showed no obvious favored fragments that might have indicated the presence of a degraded pattern. The appreciably higher percentage of stn1-M1 mutants made in the msh2-Δ background that had amplified ApaL repeats might also be related to mismatch repair. Alternatively, we cannot rule out the possibility that the increased incorporation of ApaL repeats occurring in the precursor during the additional cell divisions (equivalent to 2–3 streaks) needed to disrupt the MSH2 gene altered the result.
We speculated that stn1-M1 ter1-Δ mutants made in a msh2-Δ background had sufficiently high levels of telomeric recombination to rapidly break down any repeating structure that might have been formed initially. To test this idea, we attempted to knock out the MSH2 gene in the A1 clone of stn1-M1 ter1-Δ mutant that had highly favored small telomeric fragments in both ApaLI and RsaI digests (Figure 1D). Among 72 clones transformed with the knockout cassette, only one had the MSH2 gene successfully disrupted. As homologous gene disruption rate, even with the fragment used in this experiment, is generally 10–40% in STN1 cells, it is conceivable that very high levels of telomeric recombination might interfere with homologous recombination elsewhere in the genome of stn1-M1 cells. The one stn1-M1 msh2-Δ clone we did recover was serially passaged on YPD plates for three streaks and its telomeres were examined after each streak by Southern blot (Figure 2B). The same procedure was carried out with a control transformant that still had the intact MSH2 gene. Upon disruption of MSH2, the simple banding patterns of the A1 clone, particularly the larger fragment in ApaLI digests, become distinctly heterogeneous as soon as the samples could be analyzed (Figure 2B). This heterogeneous pattern is similar to those from stn1-M1 ter1-Δ mutants generated directly in a msh2-Δ background shown in Figure 2A. In contrast, the control transformant having the intact MSH2 exhibited a banding pattern that remained distinctly more stable. This result demonstrates that the elevated telomeric recombination in a msh2-Δ background is sufficient enough to rapidly break down a repeating structure that might have been formed initially in telomeres of a newly generated stn1-M1 ter1-Δ mutant. A diagram summarizing our experiments with stn1-M1 ter1-Δ mutants generated from ApaL precursors is shown in Figure 2C.
RTE in ter1-Δ mutants of K. lactis regularly involves the spreading of sequence from a single telomere to all other telomeres to generate modest elongated telomeres [37]. To test whether the stn1-M1 mutant can also use this mechanism to generate highly elongated telomeres, we constructed telomeric DNA fragments consisting of the subtelomeric sequence, a HIS3 marker and Bcl telomeric repeats (Figure 3A). The subtelomeric sequence is shared by 11 of 12 K. lactis telomeres allowing the transformed fragments to replace a native telomere through homologous recombination. The Bcl repeats each contain a phenotypically silent base change that generates a BclI site [51]–[53]. DNA fragments containing either ∼11 or ∼40 Bcl repeats were transformed into stn1-M1 ter1-Δ cells containing pSTN1-TER1 integrated at the stn1-M1 locus (Figure 3A). While the shorter telomeric fragment generated a new telomere of normal length (Figure 3B), the long telomeric fragment generated a telomere substantially longer than those of wild type cells (Figure 3F). The resulting transformants are hereafter referred to as “normal length Bcl precursors” and “long Bcl precursors”, respectively.
Next, we generated 55 clones of stn1-M1 ter1-Δ mutants from four normal length Bcl precursors by plating the precursors on 5-FOA-containing medium that selected for the loss of the pSTN1-TER1 plasmid. Telomeric signals in 49 of these clones appear essentially identical in both EcoRI and EcoRI+BclI digests (Clones 1, 3, 4 and 5 in Figure 3B and data not shown) which indicated that telomeres in these clones were composed mostly or entirely of WT repeats with very few or no Bcl repeats. Other data were also consistent with this view. The weak signal observed in these clones with a probe specific to Bcl telomeric repeats was not sensitive to BclI digestion, suggesting that it was due to background hybridization to the heavily amplified wild type repeats (Figure 3C). Additionally, subtelomeric signals in these clones were largely resistant to BclI digestion, except for ∼1.5 kb fragments generated in some clones (Clones 2, 4 and 5 in Figure 3D–3E) that also hybridized to HIS3 probe, consistent with them being derived from the original integrated telomere. We classified these 49 clones as having no amplification or spreading of Bcl repeats (Figure 4, column 1). Our results suggest that Bcl repeats were actively avoided as a source of sequence to be amplified during establishing the long telomeres in the stn1-M1 ter1-Δ mutant.
The remaining 6 of 55 clones, including clone 2 in Figure 3B, showed amplification of Bcl repeats estimated to be 3–5 times that present in the single telomere of the precursor strain (Figure 4 and data not shown). At least two of these clones showed extra copies of the HIS3 gene (data not shown), suggesting that the modest amplification of Bcl repeats occurred primarily by subtelomeric break-induced replication (BIR) events that produced extra copies of the tagged telomere but without the Bcl repeats otherwise becoming amplified.
Of the 55 total clones, 41 (including 38 of 49 clones with no amplification and spreading of Bcl repeats) showed no HIS3 signal (Figure 3E and data not shown). This indicates that these mutants lost the HIS3-tagged telomere and probably all the Bcl repeats originally attached to that telomere. Such frequent loss of the subtelomeric HIS3 was not entirely surprising as that stn1-M1 cells showed very high rates of loss of a URA3 gene inserted at the same subtelomeric location [42]. These deletions are likely BIR events that replace one telomere with sequence from another telomere. The same mechanism is likely responsible for occasions in some mutants where the HIS3-tagged telomere became duplicated.
Analysis of 76 newly generated clones of stn1-M1 ter1-Δ mutants from long Bcl precursors showed somewhat different results (Figure 4, column 2). While 53 clones (70%) showed no amplification or spreading of Bcl repeats, the other 23 clones (30% of the total) did show some degree of amplification and spreading of Bcl repeats. Most notably, telomeric signals in three of these clones, including clones 6 and 13 in Figure 3F, can be completely or nearly completely cleaved away by BclI digestion, suggesting that telomeres in these clones were composed mostly or entirely of Bcl repeats (Figure 4). Consistent with this interpretation, the Bcl repeat-specific signals of these three clones were intense in EcoRI digests but were eliminated by BclI digestion (Figure 3G). Furthermore, the long smeared subtelomeric signal of these clones in EcoRI digests was largely or entirely cleaved by BclI into fragments that were generally <2 kb (Figure 3H). Interestingly, these three clones showed no amplification of the subtelomeric HIS3 gene (Figure 3I and data not shown), suggesting that the Bcl repeats were amplified by inter-telomeric recombination rather than by subtelomeric duplication.
As indicated in Figure 4, 8 of the 76 clones derived from the long Bcl precursor (including clone 1 in Figure 3F–3I) were classified as having intermediate amplification and spreading of Bcl repeats based on: 1) total telomeric signal that was partially cleaved by BclI (Figure 3F); 2) Bcl-specific telomeric signal that was at least 5 times that of the precursor (Figure 3G and data not shown); and 3) subtelomeric signal that was substantially cleaved into one or more short fragments by BclI digestion (Figure 3H). These results suggest that both Bcl and WT repeats were amplified and copied onto multiple telomeres in these mutants. This group of clones had no obvious telomeric fragments of 50–500 bp in EcoRI+BclI digests (Figure 3F and data not shown), suggesting that the amplified Bcl and WT repeats were not generally interspersed closely together. Indeed, the signal from WT repeats that remained after BclI digestion was generally very long, suggesting that WT repeats in these clones were often in long continuous arrays (Figure 3F and data not shown). Twelve other clones (including clone 4 in Figure 3F–3I) were classified as having slight amplification and spreading of Bcl repeats based on lesser degrees of both amplification of Bcl repeats and subtelomeric signal that was cleaved by BclI (Figure 4, column 2).
One clone of the stn1-M1 ter1-Δ mutant, clone 15 in Figure 3F–3I, showed a unique outcome. It displayed a ∼9–10 kb band that hybridized intensely with both telomeric and subtelomeric probes but was resistant to BclI digestion (Figure 3F and 3H). These results are consistent with the possibility that this clone contained tandem arrays composed of both WT telomeric repeats and subtelomeric sequences, but not Bcl repeats. HIS3 was detected in the clone but apparently was not within the amplified fragment (Figure 3I). Conceivably, the putative tandem arrays originated from a native telomere rather than the Bcl telomere. This clone is reminiscent of type I post-senescent survivors in Sacchromyces cerevisiae, which are characterized by amplified subtelomeric Y′ elements and short tracts of telomeric repeats [31], [54]. Although type I-like survivors with alternating telomere and non-telomeric sequences can occur in K. lactis cells that are transformed with a circle containing telomeric repeats and the URA3 gene [35], [48], to our knowledge, this is the first report of RTE amplifying subtelomeric sequences in K. lactis.
23 of 76 stn1-M1 ter1-Δ mutants (30%) derived from long Bcl precursors had lost HIS3 signal (Figure 3I and data not shown). All 23 of these clones showed no amplification and spreading of Bcl repeats. This loss rate of HIS3 was significantly less (p<0.001, in Fisher exact test) than that seen in mutants derived from normal Bcl precursors where 41 of 55 clones (75%) showed no HIS3 signal. This result suggests that a long telomere stabilizes the adjacent subtelomeric sequences from being lost during the establishment of an stn1-M1 ter1-Δ mutant.
To test whether the low frequency of spreading Bcl repeats to other telomeres in stn1-M1 ter1-Δ mutants was affected by mismatch repair, we constructed normal length Bcl precursor and long Bcl precursor strains containing pSTN1-TER1 and a msh2-Δ mutation (Figure 5A). After screening for loss of pSTN1-TER1, we identified 55 stn1-M1 ter1-Δ msh2-Δ mutants from three normal length Bcl precursors and 38 mutants from two long Bcl precursors. The results showed that the msh2-Δ background permitted a substantially higher frequency of amplification and spreading of Bcl repeats than a MSH2 background did. Eight of 55 stn1-M1 ter1-Δ msh2-Δ mutants (15%) derived from normal length Bcl precursors (including clones 1–2 in Figure 5B–5E) showed complete or near complete spreading of Bcl repeats (Figure 4, column 3). Two mutants (4%), including clone 6 in Figure 5B–5E, showed intermediate amplification and spreading of Bcl repeats and the remaining mutants (45 of 55; 81%), including clones 5 and 11–13 in Figure 5B–5E, showed no amplification (Figure 4, column 3).
Remarkably, 30 of 38 mutants (78%) derived from long Bcl precursors (including clones 5–7, 9, and 10 in Figure 5F–5I) showed complete or near complete spreading of Bcl repeats (Figure 4, column 4). At least 25 of these clones appeared to contain a small percentage of WT repeats interspersed among the Bcl repeats, as indicated by telomeric signal of <500 bp present in EcoRI+BclI digestion (e.g., clones 5–7, 9, and 10 in Figure 5F). However, as these telomeric signals were not intense, it is likely that the WT repeats were not interspersed throughout the telomeres. Only two clones (6%), including clone 8 and 11 in Figure 5F–5I, showed intermediate amplification and spreading of Bcl repeats (Figure 4, column 4). As the disruption of mismatch repair presumably permits Bcl repeats to recombine with WT repeats in an unperturbed fashion, we conclude that the long telomeres formed during the establishment of an stn1-M1 ter1-Δ mutant are regularly derived primarily from a single telomere source.
45 of 55 (82%) stn1-M1 ter1-Δ msh2-Δ mutants derived from normal Bcl precursors (Figure 5E and data not shown) and 21 of 38 (55%) mutants derived from long Bcl precursors (Figure 5I and data not shown) had lost the HIS3 genes. This difference was significant (p = 0.0005 in Fisher exact test). Strikingly, many clones that had complete or nearly complete spreading of Bcl repeats (4 of 8 mutants from the normal length Bcl precursor and 14 of 30 from the long Bcl precursor) had lost the HIS3 genes (clone 5 and 7 in Figure 5I and data not shown). On the other hand, 4 of 18 clones that had complete or near complete spreading of Bcl repeats contained estimated 5–10 copies of HIS3 genes (data not shown). These results showed that the spreading of Bcl repeats to all other telomeres could occur either with or without concurrent spreading of the subtelomeric HIS3 to other telomeres.
To test the effect of telomerase on amplification and spreading of Bcl repeats, we constructed normal length Bcl precursors and long Bcl precursors in both stn1-M1 TER1 MSH2 and stn1-M1 TER1 msh2-Δ backgrounds that were complemented by plasmid pSTN1. These precursors were similar to those used above except that the subtelomeric marker gene was URA3 and the complementing plasmid carried STN1 and HIS3 (Figure 6A). We generated stn1-M1 mutants from all four precursor types by screening for loss of pSTN1 after streaking onto YPD plates and identifying clones with the rough colony phenotype characteristic of stn1-M1 mutants. Results from this showed that telomerase significantly inhibited the amplification and spread of Bcl repeats (Figure 6; Figure S3; and Figure 4, columns 5–6). None of 37 stn1-M1 TER1 MSH2 clones derived from normal-length precursors showed amplification and spreading of Bcl repeats (Figure S3A–S3D and Figure 4, column 5), and only 1 of 38 stn1-M1 TER1 MSH2 clones from the long precursors showed detectable amplification and spreading (clone 9 in Figure S3E–S3H and Figure 4, column 6). The one mutant that did show amplification (an intermediate level) was estimated to contain three copies of URA3 (data not shown). This may suggest that amplification and spreading of Bcl repeats in this clone occurred primarily through subtelomeric BIR events that produced extra copies of the tagged telomere rather than by telomere-telomere recombination.
Similar outcomes were seen with stn1-M1 TER1 msh2-Δ mutants. There, the frequency of amplification and spreading of Bcl repeats was markedly lower than that seen in stn1-M1 ter1-Δ msh2-Δ mutants (Figure 4, columns 7–8). None of 28 stn1-M1 TER1 msh2-Δ mutants obtained from the long precursors showed complete amplification and spreading of Bcl repeats, and only 7 of 28 mutants showed intermediate or slight amplification. Clone 9 in Figure 6B–6E is the sole example of intermediate amplification. Among stn1-M1 TER1 msh2-Δ mutants derived from a normal length Bcl precursor, only 3 of 25 mutants, including clones 6 and 7 in Figure S3I–S3L, showed slight Bcl amplification (Figure 4, column 7).
The subtelomeric URA3 marker was comparatively stable in the stn1-M1 TER1 mutants. Only 7 of 67 stn1-M1 TER1 msh2-Δ mutants and no stn1-M1 TER1 MSH2 mutants (0 of 39) had lost URA3 (Figure S3D, S3H, S3L; Figure 6E; and data not shown). These results suggest that an active telomerase in stn1-M1 mutants helps to stabilize the adjacent subtelomeric sequences against loss or amplification.
Accumulated previous evidence suggests that a roll-and-spread mechanism is involved in generating the moderately elongated telomeres formed in K. lactis ter1-Δ post-senescence survivors (reviewed in [27]). Our studies here suggest that this mechanism, involving amplification of sequence from a single t-circle is also involved in establishing the more ALT-like highly elongated telomeres in the K. lactis stn1-M1 mutant. Some support for the importance of t-circles in stn1-M1 cells already existed. Previous data had shown that t-circles are abundant in stn1-M1 cells [38]. Additionally, a recent study showed that introducing t-circles into stn1-M1 cells leads to tandem arrays of the circle's sequence becoming incorporated at multiple telomeres [48].
The use of mutationally tagged repeats was critical to earlier studies on how RTE occurs in a ter1-Δ mutant [35]–[37]. However, the type II RTE in this mutant is episodic, occurring when telomeres drop below a critical length and essentially shutting off once telomeres are moderately lengthened [24]. The transient stability of the lengthened telomeres allowed Southern blots that examined the structure of telomeric DNA from populations of cells to be very informative. We anticipated that the telomerase-resistant type IIR RTE of the stn1-M1 mutant, with its apparently continuous high rate of telomeric recombination, would be more problematic to study by this method as any long telomere initially generated would be expected to be unstable and would become altered by further recombination events in many if not most cells of any culture large enough to be studied. The first tagging approach used in this study involved generating stn1-M1 MSH2 mutants from precursors with ApaL repeats at all telomeric termini. Among the mutant clones that had both amplified WT and ApaL repeats, roughly half had telomeric blocks of both types of repeats that had single favored sizes. Although other interpretations cannot be ruled out, this result is consistent with a roll-and-spread mechanism that derived all amplified telomeres from the sequence of a single small t-circle if the uniformly repeating patterns predicted from rolling circle copying of a t-circle containing both WT and ApaL repeats had been extensively disrupted by later recombination events.
Other results clearly demonstrated that ongoing recombination in stn1-M1 mutants can disrupt existing signs of repeating patterns. This was most strikingly seen when a stn1-M1 ter1-Δ MSH2 mutant with a very non-random WT/ApaL repeat pattern had its MSH2 gene disrupted. As soon as this msh2-Δ derivative could be examined, its telomeric repeats had acquired a much more randomized pattern similar to those seen in stn1-M1 ter1-Δ mutants established directly in a msh2-Δ background. As discussed further below, loss of mismatch repair is expected to elevate recombination rates between WT and ApaL repeats to levels that would occur if no mismatches were present. Thus, even the complete absence of preferred sizes of blocks of WT and ApaL repeats seen in stn1-M1 ter1-Δ msh2-Δ clones is not inconsistent with a single t-circle having been the original source of the amplified telomeres.
A result of particular importance in our study was that Bcl repeats present at a single telomere in a precursor cell were sometimes the source of virtually all of the amplified telomeric DNA sequences in newly generated stn1-M1 mutants. This effect was most prominent in a strain lacking both telomerase and MSH2. This mutant combination is probably the most relevant of those examined that used Bcl repeats to study telomeric recombination. This is because the absence of telomerase assures that all telomeric lengthening is due to recombination while the absence of mismatch repair presumably permits recombination between wild type and Bcl telomeres to occur at the same frequency as recombination between two wild type telomeres in otherwise equivalent circumstances. In the ter1-Δ msh2-Δ strain, the percentage of stn1-M1 mutants that displayed complete or near complete amplification and spreading of Bcl repeats was 14% in mutants derived from the normal length Bcl precursors and 78% in mutants derived from the long Bcl precursors (Figure 4). These results are very similar to those seen in an earlier study with K. lactis ter1-Δ mutants, where total spreading of Bcl repeats to all telomeres from a single normal length Bcl telomere and a single long Bcl telomere was measured at 10% and 94%, respectively [37]. As was proposed with ter1-Δ mutants, we suggest that a roll-and-spread mechanism, where rolling circle amplification of a single t-circle followed by spreading of that sequence to all telomeres by BIR events, can account for these observations. When all twelve telomeres are the same length and each has the same chance to be amplified and spread, the predicted frequency of clones where Bcl repeats have been amplified and spread would be roughly one twelfth. The much greater frequency of spreading of Bcl repeats that is observed from mutants derived from the long Bcl precursors indicates that a longer telomere is much better able than a shorter telomere at promoting the spread of its sequence to other telomeres. The roll-and-spread model predicts that this could occur because the long Bcl telomere is used directly as a template for lengthening other telomeres and/or is better at forming t-circles. Although t-circles are common in established stn1-M1 cells, the fact that all amplified telomeric sequences can be derived from a single telomere may suggest that t-circle formation is limiting during the initial establishment of the mutant state. Additionally, the copying of sequence from a lengthened telomere onto other telomeric ends might be facilitated by the physical clustering of uncapped telomeres. In favor of this, it has been reported that multiple double-strand DNA breaks (DSBs) are often recruited to a single Rad52-containing focus for DNA repair in S. cerevisiae [55].
The amplification and spreading of Bcl repeats in newly generated stn1-M1 mutants was strongly inhibited by the mismatch repair system. This is not surprising as mismatch repair can inhibit recombination involving sequences with imperfect homology [50]. For example, in S. cerevisiae, 1% and 6% sequence divergence can reduce mitotic recombination ∼20 and ∼140 fold, respectively [56]. Therefore, the 4% sequence divergence between Bcl repeats and WT repeats in our study may significantly reduce the recombination between the telomeres containing them. Mismatch repair has been suggested to inhibit recombination by rejecting or processing the heteroduplex formed by strand invasion occurring with homeologous sequences [50]. Loss of MSH2 was previously shown to facilitate the recombination that generates post-senescence survivors in telomerase deletion mutants of S. cerevisiae and K. lactis [57]. This was attributed to increased recombination between the homeologous telomeric repeats in S. cerevisiae and between the homeologous subtelomeric sequences in K. lactis. Deficiency in mismatch repair can also facilitate ALT-like telomerase-independent telomere elongation in human colon cancer cell lines and in gastric carcinomas [20], [58]. Conceivably, this effect stems from the degenerate telomeric repeats that are common in the basal regions of human telomeres [59]–[60].
In stn1-M1 mutants formed in a ter1-Δ MSH2 background, there was not only a substantial reduction in the frequency of clones exhibiting total spreading of Bcl repeats but also, when spreading was observed, it was far more likely to be partial, accompanied by substantial amplification of WT repeats as well. We suggest that in these clones, formation of a t-circle from the Bcl telomere will occur efficiently (as no mismatches would be present during intratelomeric recombination) but copying sequence from a t-circle composed purely of Bcl repeats onto any of the 11 wild type telomeres would be impeded. When copying of a Bcl repeat t-circle did occur, the impeded ability of the Bcl repeats to recombine with the resident wild type telomeres could allow time for wild type t-circles to form and have their sequences amplified.
Interestingly, mismatch repair was apparently not an appreciable barrier to recombination between Bcl and WT repeats in STN1 during the formation of post-senescence survivors in ter1-Δ mutants [37]. One possibility to account for this is that the extremely short telomeres in senescing cells of these mutants recombine in a different way than the somewhat longer telomeres in newly forming stn1-M1 mutants. This is supported by the fact that the type II RTE in S. cerevisiae depends on a pathway involving Rad50 when occurring at very short telomeres in a senescing telomerase deletion mutant but depends on the more standard Rad51-dependent pathway when occurring at longer telomeres uncapped by defects in telomere capping proteins [30], [40]. Also, Rad51-dependent recombination is inhibited by mismatch repair 13-fold more than Rad50-dependent recombination [61]. The formation of post-senescence survivors in K. lactis STN1 ter1-Δ mutants has recently been shown to require both the Rad50- and the Rad51- pathways (Basenko and McEachern, unpublished data).
Telomerase is active in stn1-M1 cells, but its presence does not grossly affect the phenotype of the mutant [42]. This indicates that the recombination by itself is capable of generating and maintaining the very long and unstable telomeres of stn1-M1 cells and that the recombinational processes of type IIR RTE in stn1-M1 cells are not suppressed by the presence of telomerase. Our results here demonstrate that the presence of telomerase at the establishment of the stn1-M1 state can largely inhibit the amplification and spreading of Bcl repeats. While we cannot rule out the possibility that telomerase fundamentally alters the mechanism by which telomeres are maintained in stn1-M1 cells, we believe this is unlikely. Rather, we suggest that sequence addition by telomerase masks the effects of recombination. In particular, we suggest that the stn1-M1 mutation, like certain telomeric repeat mutations shown to produce type IIR RTE, is disrupted not only in telomeres' ability to block recombination but also in their ability to negatively regulate sequence addition by telomerase [37], [41]. Consistent with this possibility is our observation that telomerase inhibits the spreading of Bcl repeats not only from a normal length telomere, but also from long Bcl telomere in stn1-M1 cells. The latter is resistant to telomerase addition in wild type K. lactis cells [37]. With multiple telomerase-synthesized WT telomeric repeats added onto the ends of both long and normal length Bcl telomeres, t-circles formed from terminal deletions of these telomeres would likely often be composed only of WT repeats. This would of course interfere with the ability of the Bcl repeats to amplify and spread to other telomeres. The relative contribution of recombination and telomerase to new telomeric repeat synthesis in stn1-M1 TER1 cells is not fully known. In experiments we performed where TER1-20C(ApaL) was present during the establishment of newly generated stn1-M1 mutants, we found that ApaL repeats were present but only as a minority of the telomeric repeats in each of multiple clones examined (data not shown). This argues that recombination is the predominant mechanism for telomeric repeat amplification in stn1-M1 cells. It is reasonable to believe, however, that the contribution of telomerase might be greatest at the earliest stages of formation of the stn1-M1 state, before recombination has abundant long telomeric sequences available for it to copy.
One unexpected result from our work was that precursor cells with ApaL repeats present at the termini of all telomeric ends produced stn1-M1 mutants where the ApaL repeats were generally completely absent. This occurred in both msh2-Δ and MSH2 backgrounds and therefore does not appear to require mismatch repair. As the ApaL telomeric base change does not appear to alter telomere function [49], this effect is also not likely to be due to selection against amplification of ApaL repeats. The simplest explanation for this outcome would be that a significant fraction of telomeric termini, at least a third of the ∼350–600 bp telomeres by our estimates, is routinely deleted prior to the initiation of recombination events that elongate telomeres when the stn1-M1 mutant state is established. Telomere shortening also precedes RTE in yeast telomerase deletion mutants. In that case, however, the shortening occurs very gradually over many tens of cell divisions from replicative sequence loss [54], [62]–[63]. Such gradual telomere shortening cannot explain the terminal sequences loss we see in stn1-M1 cells. Most newly generated spores of the stn1-M1 mutant die within a few cell divisions [42]. This indicates that they experience a severe growth problem immediately after their generation and suggests that the terminal telomeric loss occurs very rapidly. Indeed, the rapid telomeric deletion might help account for the poor viability of stn1-M1 spores.
A number of mechanisms have been proposed for generating telomeric deletions (for a review see [64]). One well studied mechanism is telomere rapid deletion (TRD) which is thought to be an intratelomeric recombination event that occurs after a telomeric 3′ overhang strand invades into the double-stranded region of the same telomere (for a review see [65]). TRD can lead to telomere deletion in wild type yeast cells that contain artificially elongated telomeres [66]–[68]. Processes similar to TRD, requiring the recombination protein XRCC3, can lead to shortening of both normal and dysfunctional mammalian telomeres [18], [69]. TRD has been proposed to be a mechanism that can generate t-circles [34], [68].
An obvious question that arises from our data is why telomeric truncations would predominate at the earliest stage of the formation of a mutant that ultimately generates and maintains highly elongated telomeres. At least two possibilities exist. One is that the physiological conditions at the earliest stage of stn1-M1 mutants, when telomere deletions occur, are different from those at later stages, when telomere elongation predominates. Conceivably, later stages might be influenced by the presence of chronic DNA damage and react differently to dysfunctional telomeres compared to cells at the earliest stage. This idea is supported by the finding that S. cerevisiae telomerase deletion mutants showed expression changes in hundreds of genes during the senescence caused by shorting telomeres [70]. Another possibility is that telomeric deletions are always more frequent than recombination events that lengthen telomeres in stn1-M1 cells. In this scenario, net lengthening might only predominate over shortening once long telomeres or t-circles are present and abundant enough to serve as efficient templates for elongation events that can generate long extensions. Some observations support this possibility as well. In wild type K. lactis cells, deletions from TRD are approximately an order of magnitude more frequent than telomere elongation by recombination [67]. Also, telomeric deletions are very frequent in stn1-M1 cells and in other mutants undergoing type IIR RTE [41], [48].
Taken together, our results suggest that the establishment of long and heterogeneous telomeres in stn1-M1 via type IIR RTE may involve the following events as summarized in Figure 7. First, upon initiating the stn1-M1 state, the uncapped telomeres rapidly undergo net deletion to generally lose at least one third of the repeats from telomeric termini. Next, an occasional telomeric recombination event results in the production of a small t-circle that is used as a template to lengthen one or more telomeres. These t-circles are likely derived from the more basal repeats of a telomere as indicated by the absence of amplification of ApaL repeats in most clones in our experiments. Finally, the initial elongated telomere(s) serve as the templates for lengthening most or all remaining telomeres, generally before other t-circles can form and compete for being copied. Another study of ours [48] that examined the stability of tandem arrays at telomeres in stn1-M1 cells, suggests that once the long telomere state is established, the maintenance of it likely includes secondary formation and copying of t-circles. However, the spreading of sequence from a single source to all telomeres in these secondary amplifications was rare or absent, presumably because cells already contained many potential templates (both t-circles and linear telomeric tracts) that could be copied to generate long telomeric arrays.
The extent to which our results with stn1-M1 cells bear on human ALT cells remains to be determined. T-circles are known to be abundant in ALT cells [17], however, there are conflicting data regarding their importance. While Ku mutations inhibit formation of t-circles and block proliferation of ALT cells [71], mutations in Xrcc3 and Nbs1 have been reported to eliminate t-circles without blocking ALT cell growth [72]. Our study suggests that it will be important to examine not only the maintenance of long telomeres in established ALT cells but also the role of t-circles during the initial establishment of the ALT cell state. Our data also suggest that a single telomeric template DNA might commonly be the source of elongation of multiple, even all, other telomeres. Interestingly, multiple telomeres in ALT cells have been shown to cluster into single foci which co-localize with PML bodies in a manner that could potentially facilitate recombination between telomeres [73]. However, there are other reasons to believe that a single telomere is unlikely to be the source of all telomere elongation during the establishment of ALT. These include that there far more telomeres in a mammalian cell and that the establishment of the ALT state might not be nearly as abrupt as the establishment of the stn1-M1 state. Our finding that telomerase can have some effects on telomeric recombination in stn1-M1 cells may have parallels in ALT. Indeed, expression of telomerase was shown to reduce the number of telomeres clustered in single foci in the ALT cells and could therefore decrease the frequency of recombination between telomeres [73]. Finally we would note that ALT does not arise in human cells from a simple lack of telomerase activity. This suggests that ALT likely requires mutations that allow telomeric recombination to occur at elevated frequencies. Mutations in yeast such as the stn1-M1 mutation may suggest that alterations in human telomere binding proteins might be a possible cause or contributor to the ALT phenotype. Much further work will be needed to fully understand the mechanisms involved in the establishment and maintenance of ALT.
All K. lactis strains used are derivatives of wild type (WT) 7B520 [74]. K. lactis stn1-M1, stn1-M1 ter1-Δ strains were described previously [42]. The precursors of K. lactis stn1-M1 msh2-Δ and stn1-M1 ter1-Δ msh2-Δ were constructed as follows. The MSH2 gene was first amplified as a 4.4 kb fragment from genomic DNA of 7B520 cells by PCR (forward primer: 5′AGGGATCCGGGAGGCTCCAATAACAACA3′; reverse primer: 5′ACCTCGAGTTGCGAGTGATTCGTTCAAG3′) and cloned into the BamHI and XhoI sites of pBluescript IIKS(−) resulting in pMSH2. Then, pMSH2 was digested by BglII and EcoRI to delete an 891 bp fragment of the ORF of MSH2, and a 1.4 kb PCR-amplified (forward primer: 5′AGGCAGATCTGGATGGCGGCGTTAGTATCG3′; reverse primer: 5′AGGAATTCCCAGCGACATGGAGGCCCAG3′) fragment of KANMX gene from the genomic DNA of SAY557 [75] was inserted into the BglII+EcoRI− digested pMSH2 to produce the pMSH2::KANMX. The 3.2 kb disruption cassette containing MSH2::KANMX was then amplified from pMSH2::KANMX by PCR (forward primer: 5′ATATTGCAGAGGAGCGAGGA3′; reverse primer: 5′CTTGTACGGACGGGTCATCT3′) and was transformed into either stn1-M1 cells complemented with pSTN1 or stn1-M1 ter1-Δ cells complemented with pSTN1-TER1 or pSTN1-TER1(ApaL). The knockout of the MSH2 gene was confirmed by Southern blotting and hybridization to a MSH2 probe.
The plasmid pSTN1 was constructed in following two steps. First, a 3.4 kb fragment containing the 1.3 kb ORF of the STN1 gene and 1.6 kb upstream and 0.5 kb downstream sequences was obtained by PCR (forward primer: 5′ACGAGCTCTGGCAACCCACTTGTGACTA3′; reverse primer: 5′ACCTCGAGTGCTCAGCCAATTTCTGTTG3′) using the genomic DNA of the 7B520 strain as the template. Second, the PCR fragment, which contains flanking SacI and XhoI sites, was inserted into the polylinker SacI and XhoI sites of pKL313(HIS3) [51] to generate the pSTN1. Plasmids pSTN1-TER1 and pSTN1-TER1(ApaL) were constructed as follows. First, the STN1 gene was cloned as a 3.4 kb fragment from genomic DNA of 7B520 cells by PCR (forward primer: 5′ACGAGCTCTGGCAACCCACTTGTGACTA3′; reverse primer: 5′ACGGTACCTGCTCAGCCAATTTCTGTTG3′) into the SacI and KpnI sites of pCXJ18 [76] to result in plasmid pCXJ18-STN1. The 2.6 kb TER1 or TER1(ApaL) gene fragments flanked by XbaI and KpnI sites were obtained from pJR31 [37], and pJR31 derivative that contained a mutation in the template of TER1 changing T20 to C to create an ApaLI restriction site by oligonucleotide mediated site-directed mutagenesis as described elsewhere [77]. Subsequently, the 2.6 kb TER1 or TER1(ApaL) fragment was cloned into the pCXJ18-STN1 to generate pSTN1-TER1 or pSTN1-TER1(ApaL).
The URA3-tagged single Bcl telomeres containing ∼11 and ∼40 Bcl-telomeric repeats were described before [37]. The HIS3-tagged single Bcl telomeres were based on the URA3-tagged single Bcl telomeres, which were cleaved by PstI and NruI to excise the URA3 gene and replace it with a 1.2 kb HIS3 fragment amplified from pKL313 [51] by PCR (forward primer: 5′ACAGTGCTGCAGCGGCATCAGAGCAGATTGTA3′; reverse primer: 5′ACTGAGTCGCGATCTGTGCGGTATTTCACACC3′). Either URA3-tagged or HIS3-tagged single Bcl telomeres were transformed into stn1-M1 cells with a MSH2 or a msh2-Δ genetic background complemented by pSTN1, or stn1-M1 ter1-Δ cells with a MSH2 or a msh2-Δ genetic background complemented by pSTN1-TER1 respectively. Ura+ or His+ colonies were then examined by Southern blotting to confirm that URA3 or HIS3-tagged single Bcl telomeres had replaced a single native telomere by subtelomeric recombination.
K. lactis transformation was done by electroporation as described for S. cerevisiae [78]. Passaging of complemented cells was carried out by serial streaking of single colonies on rich medium (YPD plates) at 30°C. Strains were streaked every 3 days down to single cells that grew into colonies. Each streak was estimated to be 20–25 cell divisions.
The yeast genomic DNA sample from clone A1 in Figure 2A was partially digested with ApaLI. This was terminated by an equal volume of 12.5 mM EDTA added to the digestion reaction. The ApaLI partially digested DNA was ligated with the ApaLI-digested pACYC177 plasmid, and transformed into DH5α cells. The clones with telomeric fragments were confirmed by a Southern blot hybridized to telomeric probe. Positive clones were then sequenced.
Yeast genomic DNA samples digested with restriction enzymes were run on 0.8% or 3% agarose gels and then transferred onto Hybond N+ membrane in 0.4 M NaOH. All hybridization were carried out in Na2HPO4 and SDS as described [79]. The γ-32P-labeled telomeric probe is Klac 1–25 [63] The temperature of hybridization and washing for this probe was between 45–50°C. The γ-32P-labeled Bcl telomeric probe was KTelBcl (GATCAGGTATGTGG) [51] The temperature of hybridization and washing was 40°C and 34–36°C respectively. The subtelomeric probe was generated from pKL11-B (Insert of ∼1 kb telomeric EcoRI-SmaI fragment into pBluescript SK−), which was digested with XbaI and ligated back together to excise all the telomeric sequence and was then digested by EcoRI and XbaI to generate a ∼600 bp subtelomeric fragment for probe. The URA3 probe was described before [80]. The RAD50 gene probe was the purified PCR product from K. lactis genomic DNA (Forward primer: 5′GATAGGTCTACCGCGACCAA3′; Reverse primer: 5′GCGTAAGAGGACGCATTCAT3′). Subtelomeric, URA3, and RAD50 probes were prepared using a random priming kit (NEB). Temperature of hybridization and washing for these probes was 65°C. The membranes were autoradiographed and visualized using a Molecular Dynamics Storm PhosphorImager.
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10.1371/journal.pntd.0006240 | Trypanosoma cruzi vaccine candidate antigens Tc24 and TSA-1 recall memory immune response associated with HLA-A and -B supertypes in Chagasic chronic patients from Mexico | Trypanosoma cruzi antigens TSA-1 and Tc24 have shown promise as vaccine candidates in animal studies. We evaluated here the recall immune response these antigens induce in Chagasic patients, as a first step to test their immunogenicity in humans. We evaluated the in vitro cellular immune response after stimulation with recombinant TSA-1 (rTSA-1) or recombinant Tc24 (rTc24) in mononuclear cells of asymptomatic Chagasic chronic patients (n = 20) compared to healthy volunteers (n = 19) from Yucatan, Mexico. Proliferation assays, intracellular cytokine staining, cytometric bead arrays, and memory T cell immunophenotyping were performed by flow cytometry. Peripheral blood mononuclear cells (PBMC) from Chagasic patients showed significant proliferation after stimulation with rTc24 and presented a phenotype of T effector memory cells (CD45RA-CCR7-). These cells also produced IFN-γ and, to a lesser extent IL10, after stimulation with rTSA-1 and rTc24 proteins. Overall, both antigens recalled a broad immune response in some Chagasic patients, confirming that their immune system had been primed against these antigens during natural infection. Analysis of HLA-A and HLA-B allele diversity by PCR-sequencing indicated that HLA-A03 and HLA-B07 were the most frequent supertypes in this Mexican population. Also, there was a significant difference in the frequency of HLA-A01 and HLA-A02 supertypes between Chagasic patients and controls, while the other alleles were evenly distributed. Some aspects of the immune response, such as antigen-induced IFN-γ production by CD4+ and CD8+ T cells and CD8+ proliferation, showed significant association with specific HLA-A supertypes, depending on the antigen considered. In conclusion, our results confirm the ability of both TSA-1 and Tc24 recombinant proteins to recall an immune response induced by the native antigens during natural infection in at least some patients. Our data support the further development of these antigens as therapeutic vaccine against Chagas disease.
| Chagas disease, caused by the protozoan parasite Trypanosoma cruzi, is a neglected tropical disease, affecting mostly marginated populations and there are no effective drugs or vaccines for prevention and/or treatment. Chronic Chagasic cardiomyopathy is the mortal form of the disease; it affects 30% of infected patients and develops after 20–30 years of infection. We propose the development of a therapeutic vaccine to prevent disease progression. Our vaccine is based on the combination of two parasite proteins: Tc24 and TSA-1. In this study we assessed the response of mononuclear cells from Mexican Chagasic patients to the stimulation by both antigens. We found that both proteins are able to recall a secondary immune response, induced during natural infection in some patients, which is characterized by memory cells that can proliferate, differentiate and produce cytokines. We also showed that the immune response is associated with some of the histocompatibility allele supertypes specific of our study population. Our results support the further development and testing of both vaccine candidate antigens in future clinical trials.
| Chagas disease is a chronic disease caused by the protozoan parasite Trypanosoma cruzi, transmitted by hematophagous triatomine bugs through the direct contact of their infected feces with the skin, following a blood meal. Other transmission routes include blood transfusion, organ transplantation, congenital and oral transmission (through ingestion of food or water contaminated with infected feces) [1].
At least 5.7 million people are infected and 70.2 million are at risk of infection worldwide [2], with the majority in Latin America, but new cases of autochthonous transmission have been reported in the southern USA [3]. Moreover, due to migration patterns of infected populations, the disease has a growing presence in non-endemic regions, such as Europe or Asia [4,5]. In Mexico, the estimated prevalence is at least 0.65%, with 1.1 million people infected with T. cruzi. However, new cases of Chagas disease are underreported and the prevalence is likely much higher [6,7].
Chagas disease has two clinical phases. The acute phase presents as either asymptomatic infection or with nonspecific signs and symptoms such as fever, but parasitemia is elevated. Some acute cases (2–6%) can lead to death due to myocarditis and meningoencephalitis, mostly in children. The chronic phase is initially asymptomatic, with no clinical or physical signs of disease. However, about 20–40% of patients develop clinical symptoms after 20–30 years of infection. Chronic Chagasic cardiomyopathy is the most frequent and severe clinical manifestation. Some signs of Chagasic cardiomyopathy include abnormalities of the conduction system characterized by right bundle branch block or progressive dilated cardiomyopathy, both leading to heart failure and death. Involvement of the digestive system, such as organomegaly and gastrointestinal motor disorders, may also develop in some patients [8,9].
Control of T. cruzi infection requires the activation of both CD4+ Th1 and CD8+ T cells. Once activated, IFN-γ-producing CD4+Th1 cells induce activation and differentiation of CD8+ T cells into cytotoxic T lymphocytes, which may clear cells infected by the parasite [10–12]. However, these effector T cells die of apoptosis within a few days after activation and only a fraction of primed CD4+ and CD8+ T lymphocytes persist as antigen-specific memory T cells, which can protect against secondary challenges [13]. Albareda et al. 2009 showed that chronic infection promotes the exhaustion of CD4+ and CD8+ long-term memory cells, characterized by a loss of proliferation capacity and effector functions such as cytokine production. In this context, patients in early stages of chronic infection have higher frequencies of CD4+ and CD8+IFN-γ-producing cells compared to subjects with advanced disease. This suggests that induction of long-term memory cells with effector capabilities also occurs in some patients [14,15]
Benznidazole and Nifurtimox, the only two trypanocidal drugs available for Chagasic patients, can have severe side effects and limited efficacy in adults and advanced chronic patients [16,17]. A recent study indicates that Benznidazole treatment has little effect on cardiac outcomes or preventing death in patients with moderate to advanced cardiomyopathy [17]. Thus, a therapeutic vaccine aimed at preventing or at least delaying the development of chronic Chagas disease, would be an attractive and cost effective alternative or complement to current drug treatment [4,18,19].
The antigens TSA-1 (Trypomastigote surface antigen-1) and Tc24 (flagellar calcium binding protein of 24 kDa) have been proposed as candidates for an immunotherapeutic vaccine [18]. The prophylactic and immuno-therapeutic immunization with DNA vaccines encoding these antigens can decrease cardiac tissue damage and amastigote nests density in murine and canine models of T. cruzi infection [20–22]. A recombinant Tc24 protein formulated with monophosphoryl-lypid-A [23] or CpG and nanoparticles [24] can also reduce parasitemia, cardiac parasite burden and inflammatory cell infiltrate density in immunized mice compared to controls. These antigens thus appear promising for a human vaccine. Currently, a cysteine-mutagenized form of Tc24 is undergoing scale-up and production for possible clinical testing [25]. However, the extent of their recognition and processing by the human immune system and potential HLA restriction is still unclear. Most studies with Chagasic patients have focused on carriers of the A2 supertype, especially the HLA-A*0201 allele, due to its high frequency (about 45%) in Latin American populations [26,27] or with patients with unknown HLA.
In this study, we evaluated the recall immune response induced during natural infection against both rTSA-1 and rTc24 vaccine candidates using peripheral blood mononuclear cells (PBMC) from Chagasic patients and controls, as a first step towards future clinical trials of this vaccine candidate in humans. We assessed HLA diversity of our study population to understand its role in the immunogenicity of our vaccine candidates.
We included 20 Chagasic patients and 19 seronegative healthy controls, matched for age and gender (Table 1). Eleven of the Chagasic patients reside in the city of Merida, and the other eight patients in the rural communities of Sudzal and Teya, Yucatan. None of the Chagasic patients had received treatment before enrollment in the study. Inclusion criteria for both groups were established as follows: adults above 18 years old, born in Mexico, with parents and grandparents born in Mexico, resident in the Yucatan endemic area for at least 15 years, without history of autoimmune, immunosuppressive or infectious diseases.
Diagnostic of T. cruzi infection was confirmed in Chagasic patients when at least two anti-T. cruzi antibody tests were positives, including two rapid tests (Chagas Stat-pak (CHEMBIO) and Trypanosoma detect (INBIOS)), and two ELISAs ((Chagatest Recombinant V.3.0 (Wiener Lab.) and an in-house ELISA based on whole parasite lysate). All seronegative controls were negative to all serological tests. An electrocardiographic recording was performed to assess clinical cardiac alterations in both groups. All participants provided written informed consent and the study was approved by the Institutional Bioethics Committee of the Regional Research Center “Dr. Hideyo Noguchi” of the Autonomous University of Yucatan (Reference #CIRB-012-0017).
The recombinant protein Tc24 (24.7 kDa) was expressed and purified as previously described [23]. Briefly, the Tc24 coding sequence subcloned into the yeast expression vector pPICZα was used for expression in Pichia pastoris X-33 and the recombinant protein was purified by affinity chromatography. For TSA-1, the coding sequence was subcloned into E. coli expression vector pET41a and purification was achieved by Ni-affinity chromatography under denaturing condition and then refolded by size exclusion chromatography. Endotoxin levels were measured with the Charles River Endosafe-PTS. The integrity and size of the recombinant proteins were analyzed by SDS-PAGE electrophoresis following purification and just before use, after storage at -80° C.
Epimastigote lysate was obtained from the H1 T. cruzi strain grown in 10% fetal bovine serum-LIT medium at 27°C. Parasites were washed twice with 1X phosphate-buffered saline (PBS), disrupted by 3 cycles of freezing (-80°C) and thawing (37°C), then sonicated in ice a total of 3 times using 45 pulses during 15 s, resting 30 s between cycles. The lysate obtained was centrifuged at 12,000 rpm during 20 min at 4°C and the soluble phase was collected. The soluble parasite lysate was assayed for protein concentration using Bradford reagent, aliquoted and stored at -80°C until used.
For plasma collection, 2 mL of whole blood anticoagulated with EDTA were centrifuged at 4500 rpm during 10 min. Plasma obtained was separated, aliquoted and stored at -80°C until use. Plasmatic IgG antibodies against TSA-1 or Tc24 were measured using ELISA. Ninety six-well immunoassay plates were sensitized with carbonate buffer containing 0.2 μg/mL rTSA-1 or 1.25 μg/mL rTc24 overnight at 4°C. Plates where washed two times with wash buffer (0.05% tween 20-PBST, pH 7.4). Next, plates were blocked with 1% BSA-PBST for two hours. After washing three times with PBST, plasmas at 1:100 dilutions were added to the respective wells and incubated for 2 hours at room temperature. Plates where then washed 3 times with PBST and anti-human IgG peroxidase conjugate was added at 1:6000 dilution for incubation during 1 h at room temperature. After 3 washes with PBST, 100 μL of a substrate solution containing OPD/ citrate buffer-0.1% hydrogen peroxide was added to each well. The reaction was developed in the dark at room temperature for 30 min and stopped by adding 3N HCl. Optical densities (O.D) were read at 490 nm using a spectrophotometer. The mean O.D of each group were graphed and analyzed using Graph-PRISM software.
Twenty mL of blood was collected from each participant by venipuncture. Approximately 16 mL of blood were collected in heparinized tubes (Vacutainer, BD) for PBMC purification and 4 mL in EDTA tubes for plasma and DNA isolation. Blood was diluted 1:1 with Dulbecco’s phosphate-buffered saline (DPBS) pH 7.4 (KCl 2.67 mM, KH2PO4 1.47 mM, NaCl 137.93 mM, Na2HPO4 8.06 mM) and mononuclear cell layer was separated using Ficoll histopaque-1077® (Sigma, USA) density gradient. Fresh PBS/heparinized blood was added to a Ficoll-histopaque solution at 2:1 proportion and centrifuged at 400 x g for 40 min. PBMCs were collected and washed twice with PBS pH 7.4. Cells were suspended in complete RPMI-1640 medium (RPMIc) (Gibco, USA) containing antibiotics, non-essential amino acids and 10% Fetal Bovine Serum (FBS).
Analyses of in vitro proliferative response were performed using a proliferation dye (VPD-450, BD, USA). PBMCs were adjusted to 1x106/mL in PBS and incubated for 10 min with 1 μM VPD-450 at 37°C. Cells were then washed and re-suspended in RPMIc adjusted to 2 x 105 cells/well in 96-well plates. Cells were stimulated with 5 μg/mL Concanavalina A (Con A) (Sigma, USA), 20 μg/mL TSA-1 or Tc24 recombinant proteins or RPMI (unstimulated culture) at 37°C in 5% CO2. Stimulations with 20 μg/mL Bacillus of Calmette-Guérin (BCG) or 20 μg/mL T. cruzi-soluble antigen (TcSA) were used as additional controls. After 120 hours, cells were harvested, washed twice with PBS and stained for T cell phenotyping with anti-CD3-Alexa488, anti-CD4-PE and anti-CD8-PercP-Cy5.5 (all from BD, San Jose CA, USA) conjugated antibodies for 20 minutes at 4°C. Cells were fixed with 4% paraformaldehyde in PBS and 50,000 events were acquired in a FacsVerse Cytometer (BD, San Jose CA, USA), results were analyzed in FlowJo 8.7 software. A general procedure for the identification of T cell subpopulations is shown in S1 Fig. Stimulation indices were obtained by dividing the percentage of divided cells in response to antigens (experimental conditions) between the percentages of divided cells in response to RPMI (unstimulated cells). A stimulation index cut-off value of 2 was used to classify responders and non-responders [28]. For some subjects, cell counts were insufficient for running all the in vitro assays, and subjects with inconsistent results for methodological controls (ConA, BCG or RPMI) were removed from further analysis.
PBMCs were adjusted to 1 x106 cells/mL in 24-well plate and stimulated with the antigens mentioned above for 22–24 hours at 37°C in 5% CO2. Four to six hours before the end of this incubation, 200 μL aliquots of the supernatant were collected for cytokine measurement, and 200 μL of fresh RPMIc medium with Brefeldin A (10 μg/mL) were added to block cytokine secretion. At the end of the incubation, cells were collected, washed twice with PBS and stained with anti-CD3-Alexa488, anti-CD4-PE and anti-CD8-PercP-Cy5.5 conjugated antibodies (all from BD, San Jose CA, USA). After fixation and permeabilization with Cytofix-Cytoperm (BD, San Jose CA, USA), cells were further stained with anti-INFγ-PE-Cy7 and anti-IL-10-APC (BD, San Jose CA, USA) conjugated antibodies for 20 minutes at 4°C, then fixed with 4% paraformaldehyde. Isotype-matched antibodies and basal fluorescence were assayed as controls. 100,000 events were acquired in a FACSVerse cytometer and then analyzed in FlowJo vX.0.7 (S1 Fig). The ratio of antigen-specific T cells producing cytokines was obtained by dividing the percentage of cells expressing cytokines in response to antigens (experimental conditions) by the percentage of cells expressing cytokines in response to RPMI (unstimulated cells).
Cytokine concentrations (INF-γ, TNF, IL-10, IL-5, IL-4 and IL-2) were determined in the supernatants of stimulated PBMCs after 18–20 hours of incubation, using a cytometric bead array (CBA) kit (human Th1/Th2 kit, BD Pharmingen). Samples were processed following the manufacturer’s instructions. Briefly, 50 μL of supernatant aliquots were incubated during 3 hours with the capture beads and PE-detection reagent, then washed with wash buffer and centrifuged at 200 x g, for 5 min. Samples were acquired in a FACSVerse analyzer and cytokine concentrations were determined using BD CBA software. Samples were included when values were above the limit of detection of the cytokine: 2.4 pg/mL for IL5, 2.6 pg/mL for IL2 and IL4, 2.8 mg/mL for TNF and IL10, and 7.1 pg/mL for IFN-γ.
Following antigen stimulation for 120 hours in 24-well plates as described above, PBMCs were collected, washed twice and stained with anti-CD4-V421, anti-CD45RA-FITC and anti-CCR7-PECy7 conjugated antibodies (all from BD, San Jose CA, USA) for 20 minutes at 4°C. Cells were then collected, washed twice and fixed with 4% paraformaldehyde. 100,000 events were acquired in a FACSVerse cytometer and then analyzed in FlowJo vX.0.7 to identify naïve T cells as well as effector and central memory T cells (S1 Fig). The ratio was obtained by dividing the percentage of cells expressing memory markers in response to antigens (experimental conditions) by the percentages of cells expressing memory markers in response to RPMI (unstimulated cells).
A high resolution typing was performed using SeCore HLA sequence based typing kits (Thermo Fisher Scientific) for HLA-A and HLA-B, according to the manufacturer’s instructions. Briefly, genomic DNA was extracted from 200 μL of EDTA-anticoagulated blood (For both, Chagasic patients and healthy donors), using DNAeasy blood and tissue kit (Qiagen, Hilden Germany). HLA-A and HLA-B gene fragments were amplified using primers provided by the manufacturer and purified using ExoSAP-IT Walthman, MA, USA. Sequencing was performed using Applied Biosystems BigDye Terminator purification kit and analyzed in an ABI 3130 DNA sequencer (Applied Biosystems, Foster City, CA, USA). HLA alleles were identified using UTYPE software version 6.0 (Invitrogen, Carlsbad, USA). Four digits alleles were used for identification of their respective supertype, according to the classification proposed by Sidney et al. 2008 [29]. Two-digit allele families were used to obtained allele frequencies for each population.
The results are presented as individual point graphs, or as mean ± SEM, as indicated. Normality of the data was evaluated using the Komolgorov-Smirnov test. Parametric t tests or U Mann-Whitney test were used to compare the means of Chagasic patients and healthy donors for proliferation assays, immunophenotyping of memory T cells, ICS and ELISA assays. Contingency tables for responders and non-responders per group were build for proliferation assays, immunophenotyping and ICS, and comparison of proportion data between groups were analyzed by Chi square or Fisher. Graphs were built and statistics obtained by using GraphPad-Prism 7 software. Differences were considered significant for P values < 0.05.
HLA supertype frequencies were compared by Fisher test and odds ratios were calculated with their 95% confidence interval. A network of the various components of the immune response following antigen stimulation was constructed in Cytoscape 3.5.0 [30] to visualize the overall immune response. Circular nodes represent the major immune parameters tested and the edges connecting the parameters show significant differences between Chagasic patients and controls.
A total of 20 patients were confirmed as positive for T. cruzi antibodies using serological commercial tests and our in-house ELISA against total soluble T. cruzi antigens. They lived in the city of Merida or in rural Yucatan and had been diagnosed with T. cruzi infection for the first time in 2004–2006. They were between 31 and 76 years old. Age-matched seronegative healthy donors (n = 19) were from the same region and ascendency.
EKG recording indicated that most patients and controls had normal EKG (no arrhythmias or other conduction disorders) (Table 1), and minor alterations such as incomplete right bundle branch block (IRBBB) were found similarly in patients and controls (F = 7.38, d.f = 4, P = 0.09). These electrocardiographic alterations were not specific for Chagas disease and Chagasic patients could thus be classified in the asymptomatic chronic phase of Chagas disease.
We evaluated the specific humoral immune response against Tc24 and TSA-1 antigens triggered by natural infection. We observed significantly higher anti-Tc24 (Fig 1A, P<0.0001, t test) and anti-TSA-1 (Fig 1B, P = 0.001, t test) IgG antibody levels in plasma from Chagasic patients compared to the seronegative controls. This confirmed that antibodies induced during natural infection recognized both recombinant antigens Tc24 and TSA-1 and suggested the presence of B cell-responses specific for these proteins in Chagasic patients.
To evaluate the presence of Tc24 and TSA-1 antigen-specific memory T cells induced upon natural infection in Chagasic patients, we first measured the proliferative recall response. Proliferation assays were carried out by stimulating PBMCs from Chagasic patients and healthy donors with rTSA-1 or rTc24 antigens. BCG vaccine, T. cruzi SA, Concanavalin A (ConA) and RPMI were used as controls. ConA-stimulated cells from all subjects presented a very high stimulation index (range of 50–120, S2A Fig). Similarly, methodological controls using cell stimulation with BCG and TcSA showed antigen-specific T cell proliferation in BCG-vaccinated and Chagasic patients, respectively (S2B and S2C Fig).
Proliferation assays with our recombinant proteins showed that after stimulation with rTc24, CD3+ and CD4+ T cells from Chagasic patients tended to have a higher stimulation index compared to the seronegative group. In addition, we found a significantly higher proportion of Chagasic patients with CD4+ T cells that proliferated after stimulation with rTc24 compared with controls (Fig 2A; P = 0.04, Fisher test). Similarly, more Chagasic patients had CD3+ T lymphocytes that proliferated (stimulation index ≥2) after stimulation with rTSA-1 compared to the control group (Fig 2B; P = 0.04, Fisher test). On the other hand, we found no differences in the CD8+ T cell population recalled by Tc24 or TSA-1. Together, these results indicated presence of Tc24 and TSA-1-specific memory cells induced during natural infection in Mexican Chagasic patients.
We then characterized the phenotype of these memory cells using CD45RA and CCR7 as memory markers, as previously described [31,32]. Cell stimulation with ConA induced a strong response in both controls and Chagasic patients (S3A Fig), and stimulation with TcSA induced a trend to a higher ratio of TCM memory cells in seropositive patients when compared to the seronegative group (S3B Fig). On the other hand, when using rTc24 the results showed a significantly higher ratio (P = 0.03, t test) of Tc24-specific CD4+ TEM cells, as well as a significantly lower ratio of Tc24-specific TCM (P = 0.003, U Mann-Whitney test) and TNAIVE cells (P = 0.03, U-Mann-Whitney test) in Chagasic patients compared to seronegative individuals (Fig 3). We also found a significant lower ratio (P <0.05) on naïve CD4+ T cell population (TNAIVE) of seropositive patients compared to seronegative group, after stimulation with TSA1 (S4 Fig).
We then analyzed the T cell cytokine production in response to antigen stimulation using two approaches: an intracellular cytokine staining assay to phenotype INF-γ and IL10 producing T cells, and a CBA to measure Th1 and Th2 secreted cytokines (INF-γ, TNF, IL10, IL5, IL4 and IL2). Again, stimulation with ConA resulted in a strong response in both groups of subjects (S5A Fig) and stimulation with TcSA induced some antigen-specific cytokine production in Chagasic patients (S5B Fig). Intracellular cytokine staining also showed that PBMC from Chagasic patients had a significantly higher ratio of CD4+ T cells producing INF-γ after stimulation with both TSA1 and Tc24 (Fig 4A), when compared to the control group (P = 0.03 and P = 0.04, respectively, U Mann-Withney). The ratio of IL-10-producing CD4+ T cells tended to be higher in Chagasic patients, but this did not reach statistical significance (Fig 4B). Also, cytokines produced from CD8+ T cells after stimulation with TSA1 or Tc24 were comparable between both groups (Fig 4C and 4D).
The profile of cytokine secretion of PBMC, measured by CBA, in response to rTSA-1 (Fig 5A) or rTc24 (Fig 5B) confirmed a high INF-γ secretion by cells from Chagasic patients compared to the control group. Pro-inflammatory TNF was the cytokine secreted with the highest levels in the supernatant of PBMC after stimulation, however no significant differences were observed between groups, neither for IL10 secretion levels. On the other hand, IL2, IL4 and IL6 were not detected in response to rTc24 or TSA-1 in both groups.
To visualize the different immune parameters induced by TSA-1 and Tc24, we constructed a network with nodes representing the various immune components measured above. Edges link the parameters showing differences between Chagasic patients and seronegative controls, which are also indicated by the size of the nodes. As shown in Fig 6, Both Tc24 and TSA-1 antigens activated several T cell populations, and the cytokine profile was predominantly biased towards IFN-γ and a Th1 pro-inflammatory profile. Indeed, Tc24 induced a shift from naïve and central memory cells to effector memory T cells, together with the activation of IFN-γ production, particularly by CD4+ cells. However, IL2 and TNF production tended to decrease. TSA-1 stimulation induced both IFN-γ and IL-2 production but led to decreases in IL10, naive and central memory T cell populations, suggesting an immune response biased to Th1.
To understand the role of HLA allele diversity in the immunogenicity of our vaccine candidates we first identified the HLA repertoire of our study population. The HLA-A and B genotypes of Chagasic patients and healthy donors were determined using high resolution typing by sequencing. For HLA-A, eight allele families were identified in Chagasic patients compared to 12 allele families in the control group, corresponding to at least 4 A-supertypes. For HLA-B, there were 9 alleles families in the Chagasic patients compared to 12 in the control group including 3 supertypes in Chagasic patients and 4 different supertypes in the group control (S1 Table).
The most frequent HLA-A allele supertype in our population was A03 (33/71, 46.5%), followed by A24 (17/71, 23.9%), A02 (11/71, 15.5%) and A01 (10/71, 14.1%). For HLA-B allele supertypes, the most frequent allele was B07 (25/52, 35.2%, followed by B27 (14/52, 19.7%), B44 (11/52, 15.5%), and B62 (2/52, 2.8%). The predominance of A03 supertype observed in our population is markedly different from other indigenous and mestizo populations in Mexico, in which A02 alleles predominate, highlighting the unique characteristics and genetic origin of the Yucatan population (S2 Table).
While most HLA-A and B supertypes were distributed evenly between controls and Chagasic patients, there were significant differences for HLA-A01 and A02 (Table 2). Indeed, A01 was significantly less frequent, while A02 was significantly more frequent in patients compared to controls. HLA-B44 also tended to be less frequent in patients, but this did not reach statistical significance (Table 2). These results suggested potential association of these HLA alleles with T. cruzi infection in this population.
To evaluate the relationship between the HLA allele supertypes of the study participants and the variables of the immune response against TSA-1 and Tc24, we tested for potential differences in immune response based on the HLA supertype. Interestingly, we did not detect significant HLA restriction for most of the immune parameters that were evaluated, with the notable exceptions of IFN-γ production by CD4+ and CD8+ T cells, as well as CD8+ T cell proliferation. Indeed, for TSA-1 but not for Tc24, CD8+ proliferation was restricted to HLA-A03 (Fig 7A); for Tc24 but not for TSA-1, the CD8+IFN-γ response was restricted to HLA-A01, A02 or A03 compared to A24 (Fig 7B), while for TSA-1 and Tc24, HLA-A01 was associated with a low CD4+IFN-γ response, compared to HLA-A02, A03 or A24 alleles (Fig 7C). In spite of these HLA restrictions, and based on their allele frequency of our study population, as well as in other Mexican populations (S2 Table), we can expect to have at least a partial response to either of the candidate antigens in a majority of individuals (28–70%).
Based on the promising results obtained with TSA-1 and Tc24 antigens as vaccine candidates in pre-clinical models [23,24,33,34], we investigated here the ability of the TSA-1 and Tc24 recombinant proteins to recall a specific immune response in PBMC of Chagasic patients, as a first step towards clinical trials. Overall, our measurements of specific antibodies, T cell proliferation, memory cell phenotyping and cytokine production confirm the recall response induced by both Tc24 and TSA-1 T. cruzi recombinant antigens in at least some naturally infected patients and therefore, support their future development as therapeutic vaccines.
The higher proliferation response induced by rTSA1 and rTc24 in CD3+ and CD4+ T cells of seropositive patients suggested the presence of antigen-specific memory cells induced by natural T. cruzi infection in these Mexican patients. Proliferation response to recombinant proteins has been previously reported [35,36] suggesting a protective role to T. cruzi infection. Moreover, using T. cruzi soluble antigen, we also found T. cruzi-specific proliferative response in the T cell compartment (CD3+, CD4+ and CD8+) of seropositive subjects, whose response was higher when was compared to seronegative individuals, as has been previously reported (S3 Fig). Such proliferative response is also remarkable in the context of immune exhaustion in chronic Chagas disease. Indeed, it has been demonstrated that CD4+ and CD8+ T cells of chronic Chagasic patients have lower proliferation ability compared to patients in the indeterminate phase [15,37–39]. This is in agreement with the classification of our cohort of patients in the indeterminate stage based on the very mild cardiac alterations that they presented.
Nonetheless, not all Chagasic patients responded to the antigens, which may be due their HLA supertypes (See below) and/or to potencial limitations of our experimental approach, highlighting the complexity of studying immune responses in patients compared with mouse models.
Next, we characterized the phenotype of memory cells that proliferated in response to stimulation with our candidate proteins. Peripheral blood T cells can be divided into naïve and memory cells. This division is based on their functions and cell surface markers. Together, expression of the CD45RA+ isoform and the chemokine receptor CCR7 permit further discrimination of central memory T cells (CD45RA-CCR7+) and effector memory T cells (CD45RA-CCR7-) [31,32]. Fiuza et al. (2009) have described the memory profile of peripheral CD4+ and CD8+ T lymphocytes as well as its cytokine secretion, before and after in vitro antigenic stimulation (using total soluble antigen of T. cruzi) between the different clinical forms of Chagas disease. They found that Chagasic patients (in indeterminate and cardiac stages) have lower percentages of naïve CD4+ and CD8+ T cells as well as higher percentages of memory CD4+ T cells in infected individuals. They also observed that individuals in the indeterminate phase presented more TCM CD4+ T cells, and suggested that it may induce a regulatory mechanism to protect the host against the exacerbated inflammatory response elicited by the infection [40]. However, these results were obtained by recalling the whole memory cell compartment with total soluble antigens. In our study, we also observed a higher frequency of TCM memory cells after ASTc stimulation in seropositive patients when compared to seronegative group (S3 Fig), as previously reported in Brazilian chronic Chagasic patients [40]. The central memory T cell (TCM) subpopulation has a higher sensitivity to antigen stimulation, is less dependent on co-stimulation and can work as CPA to dendritic and B cells. The process of differentiation of memory T cells subsets is still not clearly understood, but it is generally accepted that naïve cells antigenic activation can originate TCM and these later differentiate into TEM cells [31,40].
After stimulating PBMCs with our protein antigens, we observed a higher ratio of Tc24-specific CD4+ TEM cells, as well as a lower ratio of Tc24-specific TCM cells in Chagasic patients compared to seronegative controls. This finding can represent a switch in memory T cell compartment in response to stimulation with TSA-1 and Tc24 recombinant proteins, with activation of TCM memory T cells generating TEM cells. Functionally, TEM have a higher capacity for cytokine production and less proliferative response than TCM and TNAIVE [31]. On the other hand, the naïve T cell population was expected to have the same frequency between experimental groups. The lower ratio of naïve T cells found in Chagasic patients could be due to a lower basal frequency of CD4+ naïve T cells, as has been previously reported in chronic Chagasic patients. This lower frequency of naïve cells could also be explained by the to continuous re-stimulation of this cell compartment during the chronic infection.
The profile of memory cells against recombinant proteins has been poorly studied in humans; most of the findings were made using total soluble T. cruzi antigens or focused on CD8+ populations. Albareda et al 2006, showed that the percentage of CD8+CD45RA-CCR7- (TEM) T cells in individuals with indeterminate Chagas disease was significantly higher than in the uninfected group after co-culture with T. cruzi infected autologous dendritic cells [14]. In contrast, the percentage of central memory cells (CD8+CD45RA-CCR7+) was similar to the controls, suggesting that subjects in the early stages of disease have memory T cells capable of rapid effector functions [14]. We observed a similar phenomenon in CD4+ cells after stimulation with rTc24, which could mediate protection against the development of cardiac pathology. Recently, Egui et al (2015) reported a memory T phenotype specific of the TcCA-2 protein, characterized by CD8+ TNAIVE cells in patients in asymptomatic stage and mainly CD8+ effector memory cells (TEM and TEMRA) in chronic cardiac patients [41]. However, predominance of TNAIVE cells in CD8+ cells specific of the TcCA-2 protein suggests a low antigenicity, at least during the early stages of the disease. We did not evaluate the CD8+ cells memory compartment here, but future studies could focus on CD8+ T cells specific of TSA1 and Tc24 recombinant proteins.
Characterization of the cytokine profile in response to stimulation with TSA-1 or Tc24 further confirmed their activation of the immune response. It is known that IFN-γ and IL10 cytokines have antagonist functions due to the pro-inflammatory role of IFN-γ, while IL10 can mediate regulatory or anti-inflammatory functions. CD4+ T cells of Chagasic patients trended to express high amounts of both IFN-γ and IL10 in response to TcSA stimulation, relative to seronegative subjects (S5 Fig). Additionally, we found that TcSA tended to induce somewhat higher IFN-γ-specific CD4+ T cells compared to IL10-producing CD4+ cells in Chagasic patients. This finding was consistent with a previous study, which suggested that chronic Chagasic patients have more CD4+ memory cells producing both IFN-γ and IL10 cytokines [40]. In our study, after stimulation with TSA-1 or Tc24, CD4+ T cells of seropositive subjects had a higher IFN-γ production. Moreover, the number of patients showing IFN-γ in response to Tc24 stimulation was significantly higher in Chagasic patients compared with seronegative controls. This suggests that Tc24 protein tended to recall a Th1 immune response in most of the patients. However, no differences were found in either IFN-γ or IL10 production in CD8+ T cells. It is known that in vitro activation of CD8+ cells requires specialized antigen-presenting cells, which may not have been present in sufficient proportion in our culture conditions.
In addition, measurement of secreted cytokines following stimulation with rTSA-1 or rTc24 showed enhanced levels of IFN-γ in Chagasic patients, compared to seronegative controls. This finding confirmed the ability of our vaccine candidate proteins to induce cytokine production. TNF, another pro-inflammatory Th1 cytokine, had the highest concentration detected by CBA (1500–5000 pg/mL) in comparison with IFN-γ (3–8 pg/mL) and IL10 (200–300 mg/mL), however no differences were found when comparing TNF levels between groups (patients and controls). Overall, the profile of secreted cytokines in response to TSA-1 and Tc24 showed no clear polarization for TSA-1 and a bias towards Th1 for Tc24.
The role of HLA diversity was also tested as one of the factors that can influence immunogenicity of antigens. Interestingly, HLA diversity of our study population was markedly different from that of other indigenous or mestizo populations in Mexico, highlighting the uniqueness of the Yucatan population (S2 Table). The most frequent alleles observed corresponded to A03 and B07 supertypes, while most studies conducted on human Chagas disease have focused on patients with the A02 allele family, which is of high frequency in Latin American populations. Thus, cytotoxic T lymphocytes specific for peptides derived from TSA-1, ASP-1 and ASP-2 have been detected in Chagasic patients with HLA-A02 from Guatemala [42].
Additional CD8+ epitopes restricted to A02 alleles and derived from different antigens including cruzipain, FL-160, KMP-11, HSP-70, PFR1-4, and other trans-sialidases proteins have been identified in HLA-A*0201 Chagasic patients [26,27,41,43–46]. Our findings suggest the necessity to include a greater diversity of HLA supertypes in Chagas disease studies. For example, Alvarez and collaborators (2008) screened transialidase proteins from the T. cruzi genome against six commons HLA-I supertypes and identified that peptides predicted to bind the A02 supertype were most frequently recognized in Chagasic patients (regardless of their HLA), followed by peptides binding to the A03 and A24 supertypes [44]. Thus, vaccine candidates should be tested in patients with a variety of HLA profiles, as shown by Lasso et al. (2016), who reported an epitope of KMP-11 protein that can be presented in the context of more than one HLA-I supertype (A02, A24 and A01) in Colombian Chagasic patients [27].
While HLA allele frequencies were mostly similar between Chagasic patients and seronegative controls, we did detect some significant differences, suggesting that HLA-A 01 and A02 supertypes could be associated with protection and susceptibility to T. cruzi infection, respectively. Most studies have focused on the role of HLA class II alleles in T. cruzi infection and/or disease progression, with HLA-DQ1 and DQ7 [47], DRB1 [48–52] and DR4 [53] being found associated with susceptibility or resistance to infection/disease progression in different populations. However, Class I HLA, such as HLA-C*03, was associated with susceptibility to the development of Chagasic cardiomyopathy in Venezuelan patients [54], HLA-B*39 was found to be more frequent in Mexican Chagasic patients compared to healthy controls and A*68 and B*35 (allele families) were associated with disease progression [53].
As expected, we detected some HLA associations with the immune response recalled by TSA1 and Tc24 antigens, particularly for CD8+ T cell proliferation and IFN-γ production. The association of HLA-class I alleles with CD4+ responses was not expected, but may be due to the main role of this CD4+ population (Th1) when acting as collaborators to activate CD8+ cells. We do not discard a possible mechanism of cross-presentation with CD8+ cells recognizing peptides of TSA-1 and Tc24 trough HLA class II molecules. Overall, our findings suggest that peptides from TSA-1 and Tc24 proteins could have different HLA supertype restriction, highlighting the advantage of a vaccine composed of both proteins, which should thus be able to be effective over a wide range of individuals and HLA haplotypes. More studies using peptides derived from TSA-1 and Tc24 are needed to assay peptide restriction to HLA molecules.
In conclusion, we showed that TSA-1 and Tc24 antigens prime the immune system during natural T. cruzi infection, and induce a long lasting humoral and cellular immune response that can be recalled in vitro after at least 10 years of chronic infection. These findings support the immunogenicity of both TSA-1 and Tc24 as potential vaccine candidates in humans. The use of these antigens as a therapeutic vaccine, alone or in combination with drug therapy may help control the development of chronic cardiac disease caused by T. cruzi. These results represent an important step towards the initiation of pre-clinical trials of such vaccine in non-human primates and future clinical trials.
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10.1371/journal.pcbi.1003809 | Strategies for Controlling Non-Transmissible Infection Outbreaks Using a Large Human Movement Data Set | Prediction and control of the spread of infectious disease in human populations benefits greatly from our growing capacity to quantify human movement behavior. Here we develop a mathematical model for non-transmissible infections contracted from a localized environmental source, informed by a detailed description of movement patterns of the population of Great Britain. The model is applied to outbreaks of Legionnaires' disease, a potentially life-threatening form of pneumonia caused by the bacteria Legionella pneumophilia. We use case-report data from three recent outbreaks that have occurred in Great Britain where the source has already been identified by public health agencies. We first demonstrate that the amount of individual-level heterogeneity incorporated in the movement data greatly influences our ability to predict the source location. The most accurate predictions were obtained using reported travel histories to describe movements of infected individuals, but using detailed simulation models to estimate movement patterns offers an effective fast alternative. Secondly, once the source is identified, we show that our model can be used to accurately determine the population likely to have been exposed to the pathogen, and hence predict the residential locations of infected individuals. The results give rise to an effective control strategy that can be implemented rapidly in response to an outbreak.
| Public health strategies for infectious disease control can benefit greatly from our growing capacity to predict human movement behaviour. This is facilitated by modern methods of electronic data generation and storage that allow us to track detailed human movement patterns. Here we develop a mathematical model of the dynamics of non-transmissible infections that is informed by a new data set describing detailed movements of the population of Great Britain. We apply the model to three outbreaks of Legionnaires' disease. We demonstrate how the method can assist during the crucial early stages of an outbreak by providing predictions of the infection source location and individuals with a high exposure risk.
| The development of epidemiological models to inform public health strategies for infectious disease control has been greatly aided by incorporating an understanding of human movement behaviour, drawing on the increasing availability of data describing human movement patterns [1]–[5]. Recent studies emphasise the need to include information on a range of movement activities in addition to home-workplace commuting, such as irregular and stochastic movements, in order to accurately predict important properties of epidemics such as the rate of spatial spread of transmissible infections [6] and the location of sources of non-transmissible infections [4]. Models informed by detailed data describing a range of movement activities, such as mobile phone data and simulated traffic flow data, can then be used to develop targeted intervention strategies, for example vaccinating high risk individuals [2] and increasing surveillance on high risk travel routes [5].
In this study we utilize a source of human movement data that has not previously been applied to inform infectious disease control: a high-resolution database developed within the retail sector that describes travel behaviour for work, shopping and educational activities by the population of Great Britain. This database, which we term the Great Britain Human Movement (GBHM) database, has been developed by a commercial retail planning consultancy in order to forecast the sales potential of retail development sites. Location-specific estimates of consumer demand are generated using fine-scale predictions of population movements to parameterise a spatial interaction model [7], [8] (see Section S9 of Text S2). The model is informed by socio-demographic and travel data from a range of sources including publicly available data from the United Kingdom census [9] as well as commercial information on customer travel and demography collected from store loyalty cards and electronic point-of-sale records [7], [10] (see Sections S1 and S9 of Text S1 and Text S2).
Infectious diseases that are never or very rarely transmitted between humans, including Legionella pneumophilia (Legionnaires' disease), H5N1 (avian flu), and inhalational anthrax, are typically contracted from a localized infection source. When an outbreak is detected the primary public health concerns are to locate (and treat) the source of infection and rapidly identify the individuals who are likely to have been exposed; these combined actions aim to prevent further infections and enable early treatment of affected individuals [4]. Both of these objectives require a detailed understanding of the population's movements. A location's potential for being the infection source is influenced by the total number of infected and uninfected individuals that visited the location. Additionally, an understanding of population movements can help to identify high risk groups and hence target surveillance for undetected infections [4].
Here we develop a mathematical model for the dynamics of non-transmissible infections that is informed by the commercial GBHM database. We explore the model's ability to inform the response to outbreaks of Legionnaires' disease, a potentially life-threatening form of pneumonia that is contracted when a susceptible human inhales aerosolized water containing the bacteria Legionella pneumophilia [11]. There have been many community-acquired Legionella outbreaks associated with environmental sources including cooling towers [12]–[16], whirlpool spas [17], [18] and supermarket mist machines [19]. Locating such infection sources is often difficult and time-consuming for public health workers [20], [21].
We use the model to predict the location of the infection source and the individuals in the population with a high risk of exposure for three outbreaks of Legionnaires' disease that have occurred in Great Britain. Our analysis asks whether predictive capacity is improved by increasing the detail of the data describing population movement patterns. We find that the most accurate prediction of the source location is obtained when individual-level information about the travel histories of infected individuals is used to inform the model. However, given the debilitating effect of Legionnaires' disease, obtaining movement histories from infected individuals is time-consuming and sometimes impossible; we therefore need to consider alternative sources of movement data [17], [22]. Using the GBHM database to estimate movement probabilities still predicts the source location to within a narrow local area. Moreover, the home locations of high-risk individuals are predicted with high accuracy using this movement database. When simple dispersal kernels are used instead of these more detailed movement estimates, the predictive accuracy and confidence declines significantly. This suggests that relating human movement patterns to the particular urban geography of the study region is important to shaping our predictions.
Predictions of the spatial movement patterns of all individuals in England, Scotland and Wales are provided by a large data set (the GBHM database). These movement predictions are informed by commercial and public data describing a range of individual movement activities including commuting between home and workplace, shopping trips and visits to schools and higher education institutions. A detailed description of the data sources and prediction methodology is given in sections S1 and S9 of Text S1 and Text S2 while the main features of the data are described here.
The total area of England, Scotland and Wales is subdivided into hexagonal spatial units of 500 m in diameter, resulting in approximately 21 million spatial units. The database contains estimates of the number of individuals residing in each hexagon (based on census data) stratified by socio-demographic variables including age, gender and employment status (full-time employed, part-time employed, unemployed, economically inactive or full-time education) (Figure 1). For an individual with a given residence hexagon and socio-demographic type, the database provides estimates of the probabilities of visiting all locations in the landscape; these visits are subdivided by activity type: work, education at schools or universities, shopping for food and non-food consumables and other unknown activities. The probabilities also depend on the time and day of the week in which the activity is undertaken. The week is divided into 28 components, four components for each day, which are defined as night (8pm–6am), peak morning (6am – 10am), day (10am–4pm) and peak evening (4pm–8pm) (see section S1 of Text S1).
The data show that spatial movement patterns vary considerably with the reason for travel. Shopping destinations receive high rates of visitation because shopping activity is distributed across a relatively small number of destinations, with individuals preferring to shop close to home. In contrast, work activity is distributed across a larger number of destinations and so visitation to these locations is generally less intense (see section S1 of Text S1).
Data describing three outbreaks of Legionnaires' disease in different areas of England (Stoke-on-Trent, Hereford and Barrow-in-Furness) were used to assess the performance of our methodology and the impact of movement data quality. For each of the outbreaks the source of infection and its location have been identified by traditional field epidemiology and confirmed by laboratory studies.
The Stoke-on-Trent outbreak consists of 23 laboratory-confirmed cases of Legionnaires' disease identified between May and August 2012 [17]; age, gender, place of residence, occupation and date of symptom onset were recorded for all cases. In addition, the Health Protection Agency in the West Midlands conducted repeated interviews of each case (or sometimes their relatives) to obtain detailed travel histories over a period of 2 weeks prior to the date of symptom onset. This time period encompasses the bulk of the estimated maximum incubation period of Legionella pneumophilia (<10 days in ∼90% of cases) [18], [20] (see section S1.3 of Text S1).
The outbreak in the city of Hereford has 28 cases that were identified between October to November 2003 [13]. The data provide demographic information on only 19 of the cases including age, gender, place of residence and occupation. Travel history data for the cases is not available.
The Barrow-in-Furness outbreak was the largest with 179 confirmed cases reported in August 2002 [16]. For this outbreak the data provide the residential locations of 96 of the cases but does not include any information about the demography or travel history of the cases.
We define a region R surrounding the outbreak area within which each hexagonal unit could possibly contain the source of infection. We assume that the infection rate within the source hexagon, , is a constant value, . Further, individuals who reside in hexagon S are assumed to experience a different infection risk ( where is a constant) when they are at home compared to individuals visiting the location. For every individual i in the population the probability of becoming infected by a source located within hexagon S is (1)
where is the probability that individual i is present in location S on day part d and not at home and is the probability that individual i is at home in location S on day part d. D is the period over which the individuals are exposed to a risk of infection and is the duration of a day part, which is approximated to one-quarter of a day. The values of and can either be estimated from recorded travel-history patterns (if such information is available), estimated from the GBHM data-base, or approximated using household location data and a simple movement kernel. (A derivation of (1) and the associated probabilities is given in sections S1 and S2 of Text S1).
We now use the infection probabilities (1) to determine the likelihood that individuals in set I become infected while the rest (set U) remain uninfected over a time period Tc:(2)
To predict the location of the source of Legionella pneumophilia infection we first determine the maximum likelihood values of the infection rates and for each of the possible source hexagons . This is achieved using an open-source Bayesian Markov Chain Monte Carlo (MCMC) Gibbs sampling algorithm [23] to estimate the marginal posterior distribution for each fixed value of S, using the above likelihood expression (eqn 2). Uniform priors are used for the parameters and to ensure non-negative estimates. With this choice of prior distributions the mode of the marginal posterior approximates the maximum likelihood value [24] (see section S2 of the Text S1).
We then rank the set of possible source hexagons in order of decreasing values of the Deviance Information Criterion (DIC) [24], [25]. We select a hexagon, S, as the preferred source location if the DIC value obtained from the estimate of is lower by at least 3 compared to the DIC values for all other source locations [25]. We chose to predict the source location based on the DIC in order to account for differences in the effective number of parameters of the fitted models.
To assess how more detailed information on population movement patterns improves prediction of the infection source location, we considered three different levels of movement data richness, referred to as Levels 1, 2 and 3.
Level 1 is the most detailed and uses travel histories of cases to estimate their movement probabilities. However comprehensive travel histories are time- consuming to obtain especially as most individuals infected with Legionella are in a state of poor health. Repeated interviews of the cases and their relatives are often required [17], [22]. Therefore, we considered a subsample of the full set of travel histories to approximate the amount and quality of data that is likely to be available during an outbreak. Specifically, we considered only the first half of the total set of cases to develop symptoms, and of these we randomly selected only 50% of the destinations recorded for each case. Movement probabilities for uninfected individuals are constructed as in Level 2, below. This first level of data richness is only available for the Stoke-on-Trent outbreak.
Level 2 assumes that travel histories are not available, which represents the situation that public health agencies face in the early stages of an outbreak. The GBHM database was used to estimate the probabilities that all individuals, both infected and uninfected, visited location S. Due to the greater incidence of Legionnaires' disease in older individuals [11], [12], [26], only people of over 34 years of age are considered in the uninfected population.
Level 3 uses the least amount of individual-level movement information; the movement probabilities of infected individuals were estimated by a single power-law function of distance from home (see section S3 of Text S1). Such power-law kernels have been shown to accurately represent the distribution of workplace commuting journeys [3], but will not reflect attractiveness of particular locations or individual-level heterogeneities. The parameters of the power-law distribution were estimated using the GBHM database (see sections S2 & S3 of Text S1).
Once a location has been identified as a potential source of infection our methodology can be used to predict the risk of infection to all individuals in the population. Identifying the home locations of high-risk exposed individuals can help design intervention strategies that target these individuals [22] and provides an additional verification of our methodology. Using the GBHM database and the known source of infection two quantities of interest are calculated: the probability of each hexagon containing a case and the expected number of cases living within a given distance from the source.
We calculated the predicted probability of obtaining one or more infected individuals residing within a given hexagon, H:(3)
where Hi is the set of individuals residing in hexagon H and is probability of infection for individual i given by the mode of the posterior distribution for the model corresponding to the true source hexagon (see section S2 of Text S1).
Naturally, the probabilities depend strongly on the distance of the residential hexagon from the source due to the strong distance-dependent nature of travel (see sections S1 and S3 of Text S1). We therefore compare values of for the observed case home hexagons to the values for other hexagons a similar distance from the source. If the GBHM database accurately represents the subset of individuals most likely to visit a destination (beyond simple distance-dependence) then hexagons that contain home locations of the true/reported cases will have values that are above average for their given distance.
To further assess the model's prediction of the spatial prevalence we calculated the cumulative number of cases expected to live within a distance r from the source hexagon, , as(4)
where Hr is the set of all hexagons within a distance r from the source. The observed number of residential hexagons of cases within a given distance can then be compared to the expected value .
We assess the ability of our methodology to predict the infection source location, considering the three outbreaks and three different levels of richness of the data describing human movement patterns. For each outbreak and data richness level, we compare values of the Deviance Information Criterion (DIC) for each hexagon that could possibly contain the source of infection. We will focus on the Stoke-on-Trent outbreak, for which we have more detailed information, but consider the Hereford and Barrow-in-Furness outbreaks with the same framework. Examples of the results of the MCMC algorithm for all of the analyses presented in this study are provided in section S4 and Table S5.1 in Text S1.
Using the recorded travel history information (richness Level 1), our method correctly identifies the hexagon containing the true source (Figure 2A). This hexagon is clearly preferred with a DIC value that is 27.6 and 99.9 lower than the second and third preferred source locations respectively (Figure 3A; red squares). Hence our method confidently and accurately predicts the infection source location when it is informed by travel history data, even though we only use the first 12 cases and randomly exclude half of all reported destinations in their travel histories.
Using the Great Britain Human Movement (GBHM) database (richness Level 2), the preferred hexagon is only 1 km away from the true source (Figure 2B). The differences between the DIC value of the preferred hexagon and the second and third ranked models are less than those obtained for Level 1 although there is still a clear preference ( = 6.7 and 13.7 respectively; Table S5.1). Hexagons predicted likely to contain sources of infection occur in clusters that are often positioned on major shopping locations. The true source is the centre of such a cluster; however its DIC is relatively high as it is a less popular shopping destination with other more popular ones nearby (Figures 2B,D). The GBHM database predicts that 16 out of the 23 cases have a non-zero probability of visiting the source hexagon, in contrast the most preferred hexagons have a non-zero probability for all of the cases.
The results obtained for data richness Levels 1 and 2 demonstrate that travel histories from infected individuals are key to pinpointing the exact infection source location for the Stoke-on-Trent outbreak. This is because, even if the GBHM database perfectly captures the set of all likely movements (and assigns them a probability), the travel histories of cases eliminates particular locations while providing definite knowledge of movement to others - they provide a realization from the probability distribution. It is this categorical information that allows the source of infection to be accurately located using the travel-history data. Moreover, even partial travel-history data can be sufficient to determine the source location.
The GBHM database demonstrates that both the spatial location and the attractiveness of a destination are important factors in predicting the source location. Figure 3A (blue circles) shows that there is a subset of locations with substantially lower DIC values than the majority; these locations are destinations with strong attractiveness (Figure 2D). Within this subset of attractive destinations the models show an overall trend of preferring locations closer to the true infection source (Figure 3A). The map of the attractiveness of the destinations represented in the GBHM database (Figure 2D) shows that the top preferred source hexagon (indicated by the pink arrow) is not the most attractive destination in the area, but it is both close to the true source and more attractive than the other destinations that are closer to the true source.
Taken together, Figures 2 and 3A show that using the GBHM database enabled prediction of the infection source location to within a local area and also identified a ranked set of plausible candidate destinations based on their attractiveness to the infected individuals and the wider population. It therefore provides a useful alternative in the early stages of an outbreak when comprehensive travel history information is not available (see also the Discussion).
Assuming an isotropic, homogeneous, power-law movement kernel for all individuals (richness Level 3) leads to strong preference for home locations due to the localization of the movement kernel. The preferred source hexagon (Figure 2C) contains the home locations of two cases and is approximately 2 km away from the true infection source. This hexagon is clearly preferred compared to the second and third ranked models ( = 5.8 and 12.7 respectively; Table S5.1) which are also home locations of cases (see also the distribution of the residential locations in Figure S6.1 of Text S1). Notably, the DIC values using the movement kernel are far more uniform across all possible source locations compared to those obtained using richer movement data (Figure 3A), and our method does not provide a clear discrimination of the likely locations of the infection source apart from the cases' home locations.
For the outbreaks in Hereford and Barrow-in-Furness travel history data were not available, therefore only data richness Levels 2 and 3 were considered. For both of these outbreaks the urban area under consideration is relatively small compared to that of Stoke-on-Trent (see Figure S6.1 of Text S1) which allows more accurate prediction of the source location.
For the Hereford outbreak and data richness Level 2 the three most preferred hexagons are indistinguishable with DIC values of 328.0, 329.3 and 329.9 respectively (Table S5.1). The true source of infection corresponds to second lowest DIC value (see the map in Figure S7.1A of Text S1). For data richness Level 3 the two most preferred models are indistinguishable ( = 2.9), and true source of infection is associated with the lowest DIC value (see Figure S7.2A of Text S1). These results are attributable to the close clustering of case home locations, with 3 out of 19 of the cases residing in the hexagon that contained the infection source (see Figure S6.1 of Text S1). This means that infection in the home is more likely and hence is substantially greater than zero (see Figure S4.1D of Text S1). Although both levels of data richness allow the source location to be accurately predicted, the GBHM database again provides greater discrimination between the candidate locations, with a clear trend preferring locations closer to the true source (Figure 3B).
For the Barrow-in-Furness outbreak and data richness Level 2, the preferred source location corresponds to a hexagon that is adjacent to the true infection source (see the map in Figures S7.1B). This hexagon is clearly preferred with a DIC that is lower by 47.4 and 57.4 compared to the second and third most preferred models (Table S5.1 of Text S1). For data richness Level 3 the preferred source location is about 1 km away from the hexagon containing the true source, and this hexagon is clearly preferred, with a DIC that is lower by 16.3 and 17.5 compared to those for the second and third most preferred hexagons respectively (see Figure S7.2B and Table S5.1 of Text S1). The source of this outbreak is part of a shopping destination in the centre of town; such locations are predicted by the GBHM database to be highly attractive with several cases travelling considerable distances from home to the source (see Figure S6.1 of Text S1). This explains both the low estimate of (even though 3 out of 96 of the cases resided in the source hexagon, see Figure S4.1C,E of Text S1) and the greater accuracy gained using data richness Level 2 (Figure 3C). Again there is less discrimination between the models for data richness Level 3 (Figure 3C).
We now use the model informed by the GBHM database to predict the cumulative number of home locations containing cases within a given distance from the infection source (eqn 4). These predictions agree well with the observed data for all three outbreaks, providing good estimates of both the local gradient and eventual asymptote (Figure 4A–C). The distance range plotted differs for each outbreak, reflecting the variation in the scale of human movement patterns associated with the different urban geographies of each outbreak location. For example the distribution for the Barrow-in-Furness outbreak (Figure 4C) features infections at longer distances from the source because in this relatively low-density rural environment some individuals necessarily travel further distances to reach urban centres (see Figure S6.1 of Text S1). These long-range effects are well captured by the model and this data richness level (Figure 4C). In contrast, for the Stoke-on-Trent outbreak (Figure 4A) the model tends to over-estimate the proportion of the cases that lived close to the infection source. This can be explained by the highly contained dispersal of the Legionella pneumophilia associated with this outbreak (due to the source being housed indoors) [17], so that only a small proportion of individuals who visited the source hexagon were actually exposed to the infection source.
For a given distance from the source, the model-predicted probability that a hexagon contains a case, , provides a more detailed assessment of our ability to capture spatial structure. Values of are relatively high for those hexagons that are observed case home locations (Figure 4D–F), indicating that the model provides a fine scale identification of areas where infected individuals are likely to reside, as well as the radial pattern about the source (see also Figure S8.1 of Text S1).
For the three outbreaks (Stoke-on-Trent, Hereford and Barrow-in-Furness) the probability is greater than expected in 16 out of 20 (80%, p<0.02), 9 out of 13 (69%, p≈0.29) and 48 out of 56 (86%, p<10−6) case home locations respectively. The accuracy of these predictions relies on the location-specific nature of individual movement patterns that are informed by the urban geography of the outbreak region. This indicates that a detailed knowledge of the human movement landscape in the region surrounding an infection source can be of substantial benefit to infectious disease control by assisting in active case finding [22] and predicting the size and spatial extent of an outbreak.
We have assessed the ability of models informed by detailed human movement data to predict important spatial features of non-transmissible infections, focusing on Legionella pneumophilia outbreaks. Our analysis compares the predictive capacity afforded by detailed travel histories from infected individuals with that given by estimated movement patterns produced using simulation models that consider varying levels of movement complexity. Public-health management can benefit greatly from predictive tools that can be implemented prior to obtaining travel history data, which typically requires repeated interviewing of infected individuals, or often their relatives, depending on the individual's state of health [17], [22]. Epidemic curves for Legionnaires' disease outbreaks show that the majority of infections can occur in less than a week in some instances [12], [27], so rapid response informed by prompt analysis can be crucial.
The computational methods developed here provide a clear strategy to help reduce the incidence of infection in the early stages of a non-transmissible infection outbreak. Our database describing population movement patterns for Great Britain can be used to predict the local area containing the infection source and also identify plausible candidate source locations. In addition, the method can be used to predict the number and spatial distribution of future cases, giving public health organisations an advanced warning of the spatial extent of an outbreak, as well as a narrower target for more costly, detailed data collection. Our method can be applied iteratively to support an outbreak investigation by combining predicted travel patterns of the wider population with travel history information for infected individuals as this data becomes available. The resulting geographically detailed prediction of the infection landscape can be used as a visual tool to focus case and source finding activities on likely areas of high infection risk. This can help to make efficient use of the limited resources available to outbreak control teams.
Using the GBHM database (under the Level 2 analysis), our method provides a spatially precise prediction of the infection risk landscape because it is based on a fine scale identification of attractive and unattractive points in the landscape e.g. shopping centres versus empty fields. The method essentially ranks these locations by the likelihood that they contain the infection source based on predicted travel patterns of the infected individuals and the wider population. We have found that the top preferred source location is typically a relatively attractive site. Generally this predicted location does not exactly match the true source location, but it is within about 1 km from the true source for the three outbreaks analysed.
In contrast, when our method uses isotropic movement kernels centred on the case home locations to predict the movements of infected individuals (the Level 3 analysis), we essentially obtain a ranking of these home location neighbourhoods by their likelihood of being an infection source. The home locations of cases, and their surrounds, are obvious and sensible places for public health workers to look for infection sources. We believe that the GBHM database adds valuable information to predicting the infection risk landscape by identifying candidate source locations that are likely common sources of attraction to all of the cases, and are therefore also likely sources of infection. By predicting short range as well as long range journeys to particular attractive centres, the database is able to accurately predict infection risk over a large spatial area (Figure 4).
Our analysis has adopted a simple representation of the process of pathogen dispersal and dissemination, assuming that the infection is localized within the source hexagon and that the infection rate is constant across the entire period. The modelling study by Egan et al. [20] concludes that the assumption of a constant infection rate is appropriate for several L. pneumophilia outbreaks, although the true form of the variation clearly depends on the infection source and may also depend on weather conditions [21], [28]. Our method could be extended to consider more extensive pathogen dispersal at the cost of added computational expense; this may be usefully applied to analyse Legionella dispersal from cooling towers, which can potentially disperse the pathogen up to 7 km [14]. Detailed models of pathogen dispersal have been developed for L. pneumophilia [29], [30] and other non-transmissible pathogens [4], [31], but frequently rely on complex dispersion modelling influenced by local meteorological conditions.
Our model also simplifies the variation in susceptibility to L. pneumophilia infection that exists within the human population, assuming that susceptibility is constant and confined to older age classes. Susceptibility is known to increase with age and to be higher in males, particularly smokers [11], [12], [26]. However, there is currently a lack of knowledge regarding how infection risk depends on the inhaled dose of the pathogen [20], [22]. This limits our ability to quantify the variation in susceptibility within the population.
In conclusion we have demonstrated that the detailed understanding of human movement patterns and their interaction with the urban landscape, that have been developed largely for commercial reasons, can be successfully applied to prediction of non-transmissible infections. Representing detailed human movement behaviour in epidemiological models to incorporate spatial, social and demographic heterogeneity is demanding both in terms of necessary data and computational resources [2]. However, the first of these is likely to be addressed by the foreseeable growth in Big Data [32] and large databases that document human movement and behavioural patterns, such as data that is collated and managed by the retail [7], [8], [10] and communications sectors [5], [33]. Our ability to control and contain the spread of infectious disease will therefore continue to benefit from our growing capacity to predict human movement behavior and assess its impact on infection dynamics.
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10.1371/journal.pcbi.1002886 | Interpretation of Genomic Variants Using a Unified Biological Network Approach | The decreasing cost of sequencing is leading to a growing repertoire of personal genomes. However, we are lagging behind in understanding the functional consequences of the millions of variants obtained from sequencing. Global system-wide effects of variants in coding genes are particularly poorly understood. It is known that while variants in some genes can lead to diseases, complete disruption of other genes, called ‘loss-of-function tolerant’, is possible with no obvious effect. Here, we build a systems-based classifier to quantitatively estimate the global perturbation caused by deleterious mutations in each gene. We first survey the degree to which gene centrality in various individual networks and a unified ‘Multinet’ correlates with the tolerance to loss-of-function mutations and evolutionary conservation. We find that functionally significant and highly conserved genes tend to be more central in physical protein-protein and regulatory networks. However, this is not the case for metabolic pathways, where the highly central genes have more duplicated copies and are more tolerant to loss-of-function mutations. Integration of three-dimensional protein structures reveals that the correlation with centrality in the protein-protein interaction network is also seen in terms of the number of interaction interfaces used. Finally, combining all the network and evolutionary properties allows us to build a classifier distinguishing functionally essential and loss-of-function tolerant genes with higher accuracy (AUC = 0.91) than any individual property. Application of the classifier to the whole genome shows its strong potential for interpretation of variants involved in Mendelian diseases and in complex disorders probed by genome-wide association studies.
| The number of personal genomes sequenced has grown rapidly over the last few years and is likely to grow further. In order to use the DNA sequence variants amongst individuals for personalized medicine, we need to understand the functional impact of these variants. Deleterious variants in genes can have a wide spectrum of global effects, ranging from fatal for essential genes to no obvious damaging effect for loss-of-function tolerant genes. The global effect of a gene mutation is largely governed by the diverse biological networks in which the gene participates. Since genes participate in many networks, no singular network captures the global picture of gene interactions. Here we integrate the diverse modes of gene interactions (regulatory, genetic, phosphorylation, signaling, metabolic and physical protein-protein interactions) to create a unified biological network. We then exploit the unique properties of loss-of-function tolerant and essential genes in this unified network to build a computational model that can predict global perturbation caused by deleterious mutations in all genes. Our model can distinguish between these two gene sets with high accuracy and we further show that it can be used for interpretation of variants involved in Mendelian diseases and in complex disorders probed by genome-wide association studies.
| Advances in next-generation sequencing technologies have considerably reduced the cost of genome sequencing. As a result, there has been an avalanche of personal genomic data with numerous individual genomes sequenced in the last few years [1]–[4]. Variants in protein-coding genes are of special interest due to their stronger likelihood of functional effects. A comprehensive understanding of the functional impact of variants in coding genes requires their integration with various levels of annotations, such as primary sequence of the gene, three-dimensional structures of its protein products and biological networks where genes interact with each other. Functional annotation of single nucleotide variants (SNVs) at genomic sequence level results in their classification as nonsynonymous (which includes missense and nonsense), splice site disrupting or synonymous. Similarly, small insertions and deletions (indels) in coding genes can be classified as frame-shift or in-frame. Nonsense and splice site disrupting SNVs as well as frame-shift indels are mostly assumed to lead to loss-of-function (LoF) of genes [5]. On the other hand, missense SNVs and in-frame indels may or may not be damaging [6].
It is well understood that genes and their protein products rarely act in isolation but rather work closely with other genes and/or their products to form various networks and pathways which accomplish specific goals, for example, signal transduction, metabolism etc. Thus, a comprehensive understanding of the functional impact of variants necessitates the inclusion of these interactions between genes. Network-based approaches are thus often used to study human disease [7]. One feature that has emerged from past studies of disease genes and networks is that protein products of genes associated with similar disorders have a higher likelihood of physical interaction with each other [8]. It has also been noted in many studies that functionally essential genes are more likely to encode for hub (i.e. highly connected) proteins in the physical protein-protein interaction (PPI) network in both yeast [9] and humans [8]. Moreover, hub proteins are likely to be under stronger negative selection constraints in humans and positive selection tends to occur on network periphery [10]. Similar studies on signaling pathways have revealed that as one goes from extracellular space to the nucleus in the cell, negative selection constraints on genes encoding corresponding proteins tend to increase [11]. Selection studies have also been performed on metabolic pathways where enzyme connectivity signifies the number of other metabolic enzymes that produce the enzyme's reactants or consume its products. For example, in a yeast network of 584 metabolites and comprising about 16% of all yeast genes, Vitkup et al found that highly connected enzymes evolve slower than less connected enzymes [12]. Montanucci et al also reported that genes encoding highly connected enzymes in N-glycosylation metabolic pathway exhibit stronger purifying selection constraints and tend to evolve slowly in primates [13].
In order to obtain a higher resolution understanding of the relationship between selection constraints and networks, some studies have also integrated three-dimensional protein structures with PPI network to obtain structural interaction network (SIN). Kim et al showed that in yeast, hubs in PPI with more than two interaction interfaces are more likely to be essential than those with two or less interfaces [14]. Using structurally resolved human PPI network, Wang et al showed that disease-causing missense SNVs and in-frame insertions and deletions tend to be enriched at the interaction interfaces of proteins associated with corresponding disorders [15]. They also showed that the disease specificity of different mutations of the same gene can be explained by their location on the interaction interfaces. Another important feature that has emerged from studies of genomic variants on protein structure (without consideration of network interactions) is that benign missense polymorphisms tend to occur at solvent exposed sites on protein structure, while disease-causing missense SNVs tend to be more buried [16].
Previous studies examining the relationship of functional significance and selection properties of genes with network topology have mostly focused on networks with a singular mode of interactions between genes or their protein products, for example physical protein-protein interactions. However, a gene and its protein products can be involved in various biological networks and its role and consequently its centrality can vary across these networks. For example, SIX5 is a transcription factor gene that targets 360 genes in the human regulatory network but interacts with only one protein in the physical PPI network [17], [18]. This gene is of high functional significance since its disruption causes Branchio-oto-renal syndrome, a developmental disorder characterized by the association of branchial arch defects, hearing loss and renal anomalies [19]. In this study we examine the relationship of functional essentiality and selection with various biological networks – protein-protein interaction (PPI), phosphorylation, signaling, metabolic, genetic and regulatory. This enables us to understand the functional importance and selection constraints on genes in a global systemic approach. Moreover, although it has been shown that low evolutionary conservation of LoF- tolerant genes and their large distance from recessive disease genes in PPI network can be used to predict disease causation of variants [5], their unique properties in diverse biological networks have not been exploited before. Here, we use the distinguishing network and evolutionary properties of functionally essential and LoF-tolerant genes to build a predictive model for global damage caused by novel variants. Using this model, we are able to compute functional indispensability scores for all protein-coding genes.
The biological networks studied in this work include – PPI, phosphorylation, metabolic, signaling, genetic and regulatory (Materials and Methods). Some of these networks represent direct physical interactions between proteins, for example, PPI. On the other hand, genetic and regulatory networks contain indirect interactions between gene pairs. Additionally, some networks such as phosphorylation, metabolic, signaling and regulatory are directional with an upstream and downstream gene, whereas PPI and genetic interactions are undirected. While a gene can have a vital role in one pathway or network, it might not be as crucial in another network. Therefore, we pool together data from all the above-mentioned biological networks to construct a unified global network, which we term Multinet (Materials and Methods). The Multinet enables the analyses of the genes via their roles in the individual networks and the combined network.
We note that some interactions between two different networks can be shared. For example, an interaction in which gene A phosphorylates gene B can occur in both phosphorylation and PPI networks. However, we find that out of ∼110,000 interactions in our data set, only 881 interactions occur in more than one network. Thus the vast majority of interactions in our data are unique or non-redundant. This observation reiterates the fact that interactions of genes vary across different networks and it is crucial to include all the networks while analyzing the relationship between functional importance and selection constraints with global network centrality. The distribution of 881 interactions which occur in more than one network is shown in Supporting Figure S1. The numbers of genes and unique interactions in each network are shown in Supporting Table S1.
In this section we investigate the relationship between functional significance of genes and their properties in various biological networks. All human protein-coding genes are divided into four categories based on their known disease susceptibilities and functional impact. A ‘gene significance score’ ranging from 3 to 0 is assigned to each gene: 3 for essential genes, 2 for all genes with disease-causing mutations in HGMD, 0 for LoF-tolerant genes and 1 for all the remaining genes that do not fit into any of the above categories (Materials and Methods). We then correlate these significance scores with the degree centralities of the genes in all networks. Degree centrality of a gene in any network is defined as the number of its interacting partners in that network. In order to estimate the total number of interacting partners of a gene, we use its connectivity (number of interactions) in the Multinet (Materials and Methods).
We find that gene significance scores show positive correlation with degree centralities in most networks, though it is statistically significant only in PPI and signaling network and Multinet (Figures 1 and 2A; Supporting Table S2). Thus, in general, essential genes tend to be more connected in biological systems consistent with previous findings [8]. Surprisingly, we find a small but significant negative correlation between gene significance score and metabolic degree (Spearman correlation coefficient or SCC = −0.07, pvalue = 0.028). We also find that, unlike most other degree centralities, the metabolic degree centrality of genes shows a significant positive correlation with the number of paralogs (duplicated copies) (SCC = 0.15; pvalue = 8.26e-07) (Supporting Table S3; Materials and Methods). Thus, it is possible that in case of a LoF mutation in a participating enzyme, the metabolic pathway can be re-routed to an alternate path, possibly involving a duplicated gene of the disabled enzyme. Our observation in the human metabolic network is in agreement with a previous study by Vitkup et al, in which they found that highly connected enzymes are no more likely to be essential than less connected enzymes in yeast metabolic network [12]. In this study we find that not only are essential genes unlikely to be highly connected in human metabolic network, LoF-tolerant genes (whenever present in metabolic network) are indeed more connected than essential genes (Supporting Table S7). This result demonstrates a major contrast between the structure of the metabolic network and other networks. In most biological networks, highly connected genes tend to have fewer duplicated copies; hence LoF mutations in them can have serious phenotypic consequences. Since this distinct trend of high degeneracy at hub proteins is observed only in the metabolic network, we further posit that this might be an evolutionary mechanism to increase tolerance towards damaging mutations. The uniqueness of such a ‘protective’ effect somewhat suggests an implicit level of greater functional importance of metabolic pathways as compared to other networks of gene interactions.
Interestingly, we find that gene significance scores are positively correlated with the number of networks the gene is involved in (Figures 1 and 2B). This indicates that genes involved in many networks can act as information bottlenecks between different systems and thus tend to be more essential.
We next examine the relationship between selection constraints on genes and their network properties. We estimate evolutionary constraints over long time-scale by dN/dS (ratio of missense to synonymous substitution rates) computed from human-chimp ortholog alignments (Materials and Methods). dN/dS<1 indicates purifying selection while values close to 1 indicate neutral selection and dN/dS>1 indicates positive selection. We find that dN/dS values of genes are negatively correlated with their degree centralities in all networks, though they reach significance in PPI, phosphorylation, regulatory and Multinet networks (Supporting Table S4). This shows that highly connected genes tend to be under stronger purifying selection constraints over long evolutionary time-scale, in agreement with previous studies [10].
Furthermore, we analyze patterns of genetic variation in modern-day humans in relation to biological networks. We compute average heterozygosity of each gene to estimate its genetic variability using missense SNPs (single nucleotide polymorphisms) and their corresponding allele frequencies in three sets of populations from 1000 Genomes Pilot data (Materials and Methods) [4]. We find that there is a significant negative correlation between Multinet degree and heterozygosity of missense SNPs for all three populations, indicating more genetic variation at the periphery of networks (the correlation is also significant for some populations in PPI, phosphorylation and regulatory networks) (Supporting Table S5). Interestingly, we do not find a significant correlation of heterozygosity of synonymous SNPs with Multinet degree (Supporting Table S6; Materials and Methods). Putting together, these results suggest that reduced genetic variability of highly connected genes with respect to missense SNPs is indeed due to selection constraints.
When network edges between two genes correspond to physical interactions between their protein products, molecular level details of the interaction can be obtained by integrating three-dimensional protein structures with the underlying network data. Therefore, in order to understand the reasons for selection constraints in PPI network at higher resolution, we integrated three-dimensional protein structures with network interaction data to create structural interaction network (SIN) (Figure 3A; Materials and Methods) [14], [15], [20]. SIN is a subset of the larger PPI network and consists of 2,102 genes and 11,433 interactions.
SIN construction allows us to estimate the number of interfaces used by a protein to interact with other proteins (Figure 3A; Materials and Methods). We find that there is a significant positive correlation between gene significance scores and the number of interfaces used by their protein products in SIN (Figure 1). Thus, protein products of essential genes tend to use more interaction interfaces than those of LoF-tolerant genes. We also find that the number of interfaces used by the protein to interact with other proteins in SIN is positively correlated with their degree centrality in PPI network (SCC = 0.18, pvalue = 1.06e-09). This shows that hub proteins tend to have more interaction interfaces. Thus, it is likely that higher number of interfaces possessed by protein products of essential genes could partly be a result of their higher degree centrality in PPI network.
We next examine the impact of missense SNVs on protein structure in relation to SIN. We find that, in general, residues with disease-causing missense SNVs tend to be more buried inside protein structure than polymorphic residues (Figure 3B). Our observation is consistent with previous findings which have reported that missense mutations buried inside protein structure tend to be more deleterious than those on surface [16]. However, these previous studies treated all proteins equally and did not differentiate between hub and non-hub proteins in PPI network. When we treat hub (degree centrality> = 50) and non-hub proteins separately, we find that accessible surface area for residues with missense disease mutations is higher for hub proteins (Wilcoxon rank sum pvalue = 0.014; Supporting Figure S2). We also observe a significant positive correlation between the degree centrality of protein and the accessible surface area of their residues undergoing disease mutations (SCC = 0.028, pvalue = 3.12e-03). These results show that hub proteins tend to have a higher fraction of missense disease mutations on their exposed surface. This result is very reasonable in light of our observation that hub proteins tend to have more interaction interfaces (see preceding paragraph), thereby having a higher fraction of their exposed surface under selection constraints.
In order to further examine the close correlation of network and evolutionary properties with gene essentiality we use a logistic regression model to differentiate essential genes from LoF-tolerant genes (Materials and Methods). Network features used to train the logistic regression model include degree centralities in Multinet and all networks separately (PPI, phosphorylation, signaling, metabolic, genetic and regulatory), number of networks the gene is involved in and number of interfaces used in SIN. Selection properties used in the model include human-chimp dN/dS ratios and average heterozygosities of both synonymous and missense SNPs in modern human populations. The average values of these features for LoF-tolerant and essential genes along with corresponding Wilcoxon rank sum pvalues are provided in Supporting Table S7 (see also Figure 1). Using these features we obtain an excellent classification accuracy for 140 LoF-tolerant and 115 essential genes with AUC = 0.914 (Figure 4A; Materials and Methods).
Network properties that contribute significantly to the model include degree centralities in regulatory, genetic and metabolic networks as well as number of networks the gene is involved in (Materials and Methods). On further examination of network participation of LoF-tolerant and essential genes, we find that most LoF-tolerant genes are not involved in any network and some of them are involved in a very small number of networks (Figure 4B). On the other hand, most essential genes are involved in many networks (Figure 4C). Genes involved in a variety of networks serve as information bottlenecks between different systems and hence are more likely to be essential. We note that absence in some networks could partially be due to missing network data in our study and/or a bias in existing databases. Essential genes are more likely to have been the focus of previous research studies, for example PPI studies, and hence more likely to be present in our PPI network. They also tend to have more regulatory interactions and thus are more likely to be present in our regulatory network (which consists of 118 transcription factors and their target genes: the most comprehensive human regulatory network available to our knowledge) [17]. However, the strength of our model lies in its use of many different network properties to minimize the biases resulting from the use of a single network property or data resource. Furthermore, to test the robustness of our model, we computed the AUC for separation of LoF-tolerant and essential genes multiple times – each time randomly removing 10% of the edges from a network and rebuilding the Multinet. After repeating this for all the networks, we find minimal change in the AUC (ranging from 0.914 to 0.912), which shows that our model is quite robust to changing some edges in individual networks.
We next perform an independent validation of our model by applying it on all genes that are neither LoF-tolerant nor essential. Interestingly, we find that predicted functional indispensability scores are in the following order: genes with known disease-causing mutations have significantly higher scores than genes identified in genome-wide association (GWA) studies (Wilcoxon rank sum pvalue = 7.62e-05), which are in turn significantly higher than all the remaining neutral genes (Wilcoxon rank sum pvalue<2.2e-16) (Figure 4D). Genes identified in GWA studies are associated with phenotypic consequences, while they are not necessarily the causal genes. Hence it is reassuring that genes with known disease-causing mutations in HGMD receive significantly higher scores than those identified in GWA studies. This validation exercise demonstrates that our model can help researchers pick candidate disease genes in clinical sequencing studies. We have provided the predicted scores for all the genes in Supporting Table S8. We note that the predicted functional indispensability scores are continuous scores unlike the discrete gene significance scores used to compute correlations in an earlier section.
Genes and their protein products work in collaboration with other genes to form biological systems that perform specific tasks. For a systemic understanding of the role a gene plays, there is a need to integrate different modes of gene interactions. In this work we pool together interaction data from various biological systems (PPI, phosphorylation, signaling, metabolic, genetic and regulatory) to create a unified Multinet, enabling the computation of degree centrality of the genes in their individual networks and in the context of the entire Multinet (Supporting Table S8). Subsequent analysis of functional significance and evolutionary properties of genes allows us to relate genomic sequence variants in individual genes to their functional effects in individual and global networks. We find that highly connected genes in the Multinet and genes that participate in many biological systems tend to be more functionally significant, have fewer paralogs and resist mutations in healthy humans. While we also observe similar trends in most of the constituent networks of the Multinet, the metabolic network seems to be an exception. Highly connected genes in the metabolic network tend to have more paralogs and are more tolerant to LoF mutations.
Next, we combine three-dimensional protein structural information with PPI network to create structural interaction network (SIN) and understand selection on protein structure at molecular level detail. We find that functionally essential genes (which are more likely to encode for hub proteins) tend to use more interfaces to interact with other proteins. We also observe that hub proteins in PPI network contain a higher fraction of disease-causing mutations on their solvent exposed surface, as compared to non-hub proteins. Thus, although generally missense SNVs on exposed protein surface are more likely to be benign, our results show that those on the surface of hub proteins are more likely to be deleterious [21].
Finally, we integrate network and selection properties of genes to build a logistic regression model which can separate LoF-tolerant and essential genes with high accuracy (AUC = 0.91). Application of the model on all genes shows that it predicts higher functional indispensability scores for genes with known disease-causing mutations than genes identified in GWA studies, which themselves have higher scores than remaining neutral genes. The predicted functional indispensability scores for all genes are made publicly available and can be used to predict candidate disease genes in future clinical studies. These scores are indicators of global damage caused by deleterious mutations in coding genes – including nonsense and missense SNVs and in-frame and frame-shift indels. As mentioned above, nonsense SNVs and frame-shift indels are mostly assumed to disable gene function. However, missense SNVs and in-frame indels are more complex since they may or may not have a deleterious impact. Various methods exist to predict the functional effects of missense SNVs, for example, SIFT and PolyPhen [21], [22]. While these methods examine the tolerance of individual sites in genes to missense mutations, they do not take into account the functional significance of the entire gene. For example, a moderately deleterious missense SNV in a highly significant gene can be equally or more damaging than a strongly deleterious missense SNV in a less significant gene. Our method to compute functional indispensability scores for entire genes can be combined with scores predicted by SIFT and PolyPhen to obtain a more comprehensive view of the functional effects of genomic variation.
We note that even though our model is very robust to the removal of some edges in individual networks, the incomplete and biased nature of existing biological networks data may constitute a caveat in our study. However, to our knowledge, this is the first comprehensive genome-wide study linking genetic variants at population scale as well as disease variants with a vast body of available network resources. Models developed and applied in this study can be further expanded as more interaction data is obtained and further population genetics projects are undertaken, particularly with the future phases of the 1000 Genomes project.
Human protein-protein interaction and genetic interaction networks were extracted from BIOGRID (release 3.1.83) [18] containing 43,722 and 263 interactions, respectively. Regulatory network (relationship between transcription factors and target genes) was from ENCODE data [17]. Metabolic enzyme network contained directed linkages from upstream enzymes to downstream enzymes, based on compound reactions in KEGG [23]. Phosphorylation network in human contains 28,637 directed kinase-substrate interactions between 2,392 genes [24]. The signaling network in this study is constructed based on 1,011 interactions and 527 proteins (downloaded July 2011) from human signaling pathways obtained from the SignaLink database (http://www.signalink.org/) [25]. SignaLink offers an easily-downloadable and well-curated set of interactions from eight major signaling pathways found in humans that are not tissue-specific, namely EGF/MAPK, Ins/IGF, TGF-β, Wnt, Hedgehog, JAK/STAT, Notch and NHR (Nuclear Hormone Receptors). Manual data curation was performed in SignaLink by extensive literature survey of primary experimental evidence of these interactions, resulting in expansion of verified interaction data for the corresponding signaling pathways in protein interaction databases such as the KEGG [26], Reactome [27] and NetPath [28], while maintaining substantial overlaps with these databases. A detailed description of the curation process and comparisons between these databases and SignaLink can be found in [25]. Throughout the article, connectivity of the gene in PPI, phosphorylation, signaling and metabolic networks refers to connectivity of the protein product of the gene.
Interactions from all the above networks were combined to create Multinet. If a gene pair interacts in multiple networks or shows both upstream and downstream connection in a directional network, the interaction is counted once in Multinet.
The list of 140 LoF-tolerant genes was obtained from MacArthur et al [5]. This list contains genes that show homozygous LoF mutations in at least one individual in 1000 Genomes pilot data [4]. The list of 115 essential genes was obtained from Liao et al [29]. These genes exhibit clinical features of death before puberty or infertility when LoF mutations occur. The list of 2,451 disease genes was obtained from HGMD (Human Gene Mutation Database) [30]. All the genes with any disease-causing mutation (DM tag in HGMD) were used. If any gene occurred in more than one category, its category was decided in a hierarchical fashion as follows: essential, followed by disease followed by LoF-tolerant. The remaining 19,267 genes were assigned the category of neutral. The list of genes identified in GWA studies was obtained from the NHGRI GWAS catalogue (https://www.genome.gov/26525384#download).
Number of paralogs for each gene and dN/dS values for human-chimp orthologs were obtained from Ensembl using BioMart [31].
SNPs in modern-day humans and their allele frequencies were obtained from the low-coverage pilot phase of the 1000 Genomes Project [4]. This phase consisted of 60 individuals of CEU (Utah residents with Northern and Western European Ancestry), 59 individuals of YRI (Yoruba in Ibadan, Nigeria) and 60 individuals of CHB+JPT (Han Chinese in Beijing, China and Japanese in Tokyo, Japan) populations. Heterozygosity value is calculated as 2pq, where p and q correspond to the frequencies of the two alleles. Average heterozygosity for a gene is defined as the average heterozygosity of the SNPs in that gene, where heterozygosities of missense and synonymous SNPs are computed separately.
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10.1371/journal.pntd.0000518 | Natural Infection of the Ground Squirrel (Spermophilus spp.) with Echinococcus granulosus in China | Echinococcus granulosus is usually transmitted between canid definitive hosts and ungulate intermediate hosts.
Lesions found in the livers of ground squirrels, Spermophilus dauricus/alashanicus, trapped in Ningxia Hui Autonomous Region, an area in China co-endemic for both E. granulosus and E. multilocularis, were subjected to molecular genotyping for Echinococcus spp. DNA. One of the lesions was shown to be caused by E. granulosus and subsequently by histology to contain viable protoscoleces.
This is the first report of a natural infection of the ground squirrel with E. granulosus. This does not provide definitive proof of a cycle involving ground squirrels and dogs or foxes, but it is clear that there is active E. granulosus transmission occurring in this area, despite a recent past decline in the dog population in southern Ningxia.
| Echinococcus granulosus and E. multilocularis are important zoonotic pathogens that cause serious disease in humans. E. granulosus can be transmitted through sylvatic cycles, involving wild carnivores and ungulates; or via domestic cycles, usually involving dogs and farm livestock. E. multilocularis is primarily maintained in a sylvatic life-cycle between foxes and rodents. As part of extensive investigations that we undertook to update available epidemiological data and to monitor the transmission patterns of both E. granulosus and E. mulilocularis in Ningxia Hui Autonomous Region (NHAR) in northwest China, we captured small mammals on the southern slopes of Yueliang Mountain, Xiji, an area co-endemic for human alveolar echinococcosis and cystic echinococcosis. Of 500 trapped small mammals (mainly ground squirrels; Spermophilus dauricus/alashanicus), macroscopic cyst-like lesions (size range 1–10 mm) were found on the liver surface of approximately 10% animals. One of the lesions was shown by DNA analysis to be caused by E. granulosus and by histology to contain viable protoscoleces. This is the first report of a natural infection of the ground squirrel with E. granulosus. We have no definitive proof of a cycle involving ground squirrels and dogs/foxes but it is evident that there is active E. granulosus transmission occurring in this area.
| The canid adapted intestinal tapeworms, Echinococcus granulosus and E. multilocularis are important zoonotic pathogens that cause serious disease in humans [1]; both are endemic to Ningxia Hui Autonomous Region (NHAR) in northwest China [2],[3]. E. granulosus can be transmitted through either sylvatic cycles, involving wild carnivores and ungulates; or via domestic cycles, usually involving dogs and farm livestock. A common source of infection for dogs is hydatid infected offal from sheep, which often harbour the common G1 genotype (sheep-dog strain) responsible globally for most cases of human cystic echinococcosis (CE) [1],[4]. E. multilocularis is primarily maintained in a sylvatic life-cycle between foxes and rodents, with human infections considered as a relatively rare accidental event caused by spill-over from the wildlife cycle in European countries [5]. Synanthropic transmission cycles are believed to be responsible for the high prevalence of human alveolar echinococcosis (AE) in Alaska and on the eastern Tibetan Plateau, whereby domestic dogs predating on rodents in and around villages are considered to be the primary source of infection causing human AE [6],[7].
A report of E. granulosus in plateau pika (Ochotona curzoniae) in Qinghai Province [8] appears retrospectively almost certainly to be due to E. shiquicus, a new Echinococcus species described in 2005 that infects Tibetan foxes (Vulpes ferrilata) on the Tibetan Plateau [9]. Work in the 1980s in NHAR indicated that the transmission modes for co-hyperendemic AE and CE involved domestic dogs/livestock (mainly sheep) for CE and foxes/rodents for AE [10].
Extensive investigations that we undertook in 2001–2007 to update available epidemiological data and to monitor the transmission patterns of both E. granulosus and E. mulilocularis in NHAR, indicated that owned dogs were a risk factor for human AE (involving a dog/rodent cycle) as well as CE (involving a dog/domestic livestock cycle) [3],[11]. An increase in susceptible rodent populations due to deforestation and over use of farmland for agriculture have been emphasised as important zoonotic risk factors for human AE in NHAR and in other Chinese settings [11],[12]. As part of these ongoing studies, we captured small mammals on the southern slopes of Yueliang Mountain, Xiji County (Figure 1) (E, 105°64′–105°89′; N, 36°03′–36°18; altitude ranging from 2000–2200 m) in July, 2007. This is an area known to be co-endemic for both human AE and CE [3], and where high seroprevalence for echinococcosis among village-children has been recorded [13]. Of 500 trapped small mammals (mainly ground squirrels; Spermophilus dauricus/alashanicus referred to also as S. dauricus, Myospalax fontanieri and Mus musculus), macroscopic cyst-like lesions (size range 1–10 mm) were found on the liver surface of approximately 10% animals. Lesions were subjected to molecular genotyping and histopathological examination. None were attributable to E. multilocularis but one lesion was identified unambiguously as E. granulosus, subsequently shown by histology to contain viable protoscoleces. This is the first report of a natural infection of the ground squirrel with E. granulosus.
This study was reviewed and approved by the Ethics Committee of Ningxia Medical University. All small mammals were humanely euthanized soon after being trapped. Animals were identified, dissected and the obtained livers fixed in absolute ethanol for DNA analysis and histology.
Prior to histopathology, involving sectioning, haematoxylin/eosin staining and microscopic examination by standard procedures, liver lesions were transported to the Cestode Zoonoses Laboratory (University of Salford, U.K.) for molecular genotyping. Genomic DNA was extracted from these lesions using the DNeasy tissue kit (Qiagen, Hilden, Germany) according to the manufacturer's instructions and used as a template for the amplification of a fragment within the mitochondrial 12S rRNA gene [14],[15]. Amplified cestode-specific DNA was gel purified using the PureLink™ quick gel extraction kit (Invitrogen, Paisely, U.K.) and commercially sequenced (Cogenics, Takeley, U.K.). The identity of one of these samples was confirmed in another laboratory (Department of Parasitology, Asahikawa Medical College, Asahikawa) by partial sequencing of the mitochondrial cox1 gene as described [16].
Comparison of the generated sequence data with those held on the NCBI database (www.ncbi.nlm.nih.gov) through the use of BLAST program revealed one sample had 100% homology (254 bp) with the mitochondrial 12S rRNA gene of E. granulosus genotype G1 (common sheep-dog strain) (accession nos. DQ408422, AF297617, AB031350, AB024515). Compared with previously published gene sequences (AF297617, E. granulosus G1 (common sheep-dog strain); AB 018440, E. multilocularis), cox1 sequence (789 bp) for the sample was nearly identical to that of the published E. granulosus G1 sequence with the exception that three transitional changes were present at positions 243 (G/A), 530 (C/T) and 594 (T/C) for the isolate. The sequence shared only 80% identity with the published E. multilocularis cox1 gene sequence.
Subsequent histological examination of this ground squirrel liver lesion revealed the presence of a thick laminated layer, thin germinal layer and presence of brood capsules containing viable E. granulosus protoscoleces (Figure 2).
There are numerous previous reports of small mammal species infected with E. multilocularis in China and Europe [12],[17]. It is well accepted that microtine rodent species are the main reservoir hosts of E. multilocularis, though other rodent groups and even lagomorphs (hares and pikas) may also be naturally infected [6],[18]. However as far as we know, apart from experimental infection of rodents using either protoscolex or oncosphere injection [19],[20], or oral administration of viable eggs [21], this is the first report of a rodent species naturally infected with the metacestode stage of E. granulosus. Other non rodent small mammals harbouring lesions of E. granulosus (identified morphologically) have been described in hares in Argentina [22] and rabbits in Australia [23],[24]. The current observation has shown, for the first time, the rodent, Spermophilus dauricus, is susceptible and can be infected naturally with E. granulosus that can become viable, producing fertile cysts.
Land cover in the southern mountainous areas of NHAR has undergone important changes since the second half of the 20th century. The area was largely deforested in the 1970s–80s, and in the rolling hills around the southern Liupan Shan, total tree clearance was completed in the mid 90s. Now, the landscape consists entirely of fields for production of wheat, potatoes, beans, alfalfa, etc. During the late 1970s valleys and lower slopes were generally used for agricultural crop production while the upper slopes and hill tops were reserved for grazing. At the time there were no livestock restrictions and grazing pressure was intense. In the late 90s, massive reforestation campaigns carried out to prevent soil erosion led to extensive re-planting of trees and restrictions in sheep numbers allowable per family. The landscape changes had a subsequent major effect on small mammal communities [25] and may have played a role modifying cestode transmission patterns involving small mammals. For instance the opening of the landscape during the deforestation process may have increased the area of habitats favourable to Spermophilus dauricus [25]. On the other hand, the dog population has been recovering after the banning of indiscriminate rodenticide use in NHAR from 2002 [12]. The high susceptibility of various host species present together with high parasite prevalence may have increased the infection of definitive and intermediate hosts for both E. granulosus and E. multilocularis. It is possible that free roaming dogs not only could get infected with E. granulosus after feeding on discarded sheep offal containing larval E. granulosus but also perhaps through predation on Spermophilus dauricus. This large rodent species is one of the commonest in the area and largely occurs in fields, fallows and in the early stages of re-forestation. The red fox (Vulpes vulpes) which mostly feeds on small mammals is also a potential candidate for E. granulosus transmission in this area of NHAR since this canid species has been shown to be susceptible by experimental infection [11], although it usually harbours smaller worm burdens than dogs, and it has been found naturally infected in Australia and Europe [26]–[31].
Although we have no definitive proof of a cycle involving ground squirrels and dogs/foxes, it is clear there is active E. granulosus transmission occurring in this area, despite the recent past decline in the dog population in southern Ningxia [3],[12]. Possible misidentification of morphological specimens of Echinococcus obtained from small mammals may have occurred in the past [10]. Therefore, in further epizootiological surveillance of echinococcosis, it would be useful to apply DNA typing of metacestodes from small mammals and copro-DNA techniques [32] for unambiguous identification of fox or dog infections in order to provide accurate baseline data on transmission and to inform a model [33],[34] for future integrated control options.
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10.1371/journal.pntd.0007171 | Inadequate knowledge about snakebite envenoming symptoms and application of harmful first aid methods in the community in high snakebite incidence areas of Myanmar | Every year millions of people in developing countries suffer from snakebite, causing a large number of deaths and long term complications. Prevention and appropriate first aid could reduce the incidence and improve the health outcomes for those who suffer bites. However, many communities where snakebite is a major issue suffer from a lack of information about prevention and first aid measures that a family or community member could take to prevent severe envenoming, complications and poor outcomes. Myanmar suffers from a high burden of snakebites with a large number of deaths. As part of a health services and community development program, a community survey was conducted to identify communities’ knowledge about snakebite and their sequelae, and knowledge and practice about first aid and health services use.
4,276 rural residents of Kyaukse and Madaya townships in the Mandalay region were recruited by cluster sampling, involving random selection of 144 villages and random sampling of 30 households from each village. One adult member of each household was interviewed using a structured questionnaire.
The incidence of snakebite was 116/100,000 people. Respondents reported 15 different types of snakes in the area, with Russell’s Viper, Cobra and Green snakes as the most common. 88% of the people informed that working in the fields and forests was when most of the bites occur. A majority knew about snakebite prevention methods such as wearing long boots. However, only a few people knew about the specific symptoms caused by snakebites. Only 39% knew about the correct methods of first aid. More than 60% mentioned tourniquet as a first aid method, though this may cause significant complications such as ischaemia of the limb. 88% said that they would take a snakebite victim to a government hospital, and 58% mentioned availability of antivenom as the reason for doing this. At the same time, the majority mentioned that traditional methods existed for first aid and treatment and 25% mentioned at least one harmful traditional method as an effective measure that they might use.
The community is aware of snakebites as a major public health issue and know how to prevent them. However, the high incidence of snakebites point to lack of application of preventive methods. The community recognise the need for treatment with antivenom. However, inadequate knowledge about appropriate first aid methods, and a reliance on using tourniquets require a targeted education program. Existing knowledge in communities, albeit insufficient, provides a good starting point for mass media educational campaigns.
| Snakebite is a major public health problem, particularly in developing countries in the tropics, and every year millions of people suffer from snakebite causing a large number of deaths and long term complications. Communities’ knowledge about snakebite prevention practices and appropriate basic first aid could reduce the number of snakebites and improve the health outcomes for those who suffer bites. However, many communities where snakebite is a major issue may lack information about prevention and first aid measures that a family or community member could take to prevent severe complications and poor outcomes. Myanmar suffers from a high burden of snakebites with a large number of deaths. We conducted a community survey in two townships to identify communities’ knowledge about snakebite prevention, first aid and health services use. The survey informed that a large majority of people were aware that working in the fields and forests was when most of the bites occur. Similarly, a majority has the knowledge about snakebite prevention methods such as wearing long boots. However, the majority did not know about the correct methods of first aid, with many people mentioning tourniquet as a first aid method. While the community is aware of how to prevent snakebites, the fact that number of snakebites is high points to lack of application of those preventive methods. The inadequate knowledge about appropriate first aid methods with a reliance on using tourniquets informs about the need for public health education programs.
| Many people in developing countries suffer from snakebites, causing many deaths and long-term complications. Venomous snakebites cause local and systemic problems such as shock, bleeding, kidney injury, paralysis, infections, long term pituitary dysfunction and local necrosis. Global annual snakebite incidence is estimated as about 4.5 to 5.4 million patients, with more than 100,000 deaths [1]. However, the number of deaths may be substantially higher [2], with 45,000 deaths annually in India alone [3], based on community surveys, which detect higher rates than government hospital statistics.
Prevention and appropriate first aid are important public health measures to reduce incidence and severity of snakebite envenoming. Communities’ knowledge and implementation of snakebite prevention, appropriate first aid, and choice of medical care can help reduce the burden. At present, communities have limited understanding of correct first aid methods. Many choose potentially harmful methods [4]. Inadequate emphasis on public health education, with a resulting failure to promote appropriate preventive and first aid, adds to the challenges of addressing this issue.
Traditional methods for treating snakebite are commonly used [5]. Most of them are useless and some; such as making incisions, sucking the venom, and application of tight tourniquets; are deleterious [6]. In Sri Lanka, despite high levels of formal education and good awareness about snakebite, more than 43% of bite victims sought care from traditional healers [7]. Even those who had relatively easy access to allopathic treatment still sought traditional healing [7]. One reason for the persistent use of techniques that may be useless or downright deleterious, is that some, such as electroshock, cryotherapy, herbs, oils, and suction devices applied to the wound, are still claimed to be effective [8].
One commonly used first aid measures that is to tie a tourniquet around the bitten limb, in the belief that it slows the spread of venom from bite site through veins and lymphatics. In the past, public health educational campaigns promoted the use of tourniquets [8]. The pressure pad technique, originally described by Anker et al [9], utilises a pad tightly applied to the bite site. This technique delayed the leakage of venom into the systemic circulation in Russell’s viper bite victims in Myanmar [10], without the potential for vascular occlusion and limb ischaemia associated with application of a tourniquet. However, tourniquets remain the most commonly used first aid method in developing countries. In Bangladesh, more than 95% of snake bitten patients had applied them [11], in Nepal, 70% of people described the use of tourniquets as appropriate first aid for snakebite [12] and even in Sri Lanka, where communities’ knowledge about snakes and snakebite is relatively sound, 35% of snakebite victims applied tourniquets [13].
Snakebite is a major public health issue in Myanmar with more than 15,000 bites and more than 1,000 deaths annually [14]. We conducted a community survey in the Mandalay region of Myanmar to identify population-based incidence and communities’ knowledge of snakebite, prevention, first aid and use of health services. The survey was conducted as part of a health system development project of the Myanmar Ministry of Health and Sports and Ministry of Industry together with the University of Adelaide and other Australian and international institutions funded through an Australian Government foreign aid grant. Information about community knowledge and practices is essential for an effective health education program and an effective health system response.
The survey was conducted in the rural townships of Kyaukse and Madaya in the Mandalay region, which is among the highest snakebite incidence regions in Myanmar. It was conducted in rural areas as most snakebites occur in rural farming communities. Relatively few people live in the urban town centres of these townships (Kyaukse 16%, Madaya 9%). Most live within the smaller agricultural villages that surround them. Population of both Madaya and Kyaukse is about 258,000 each. A majority of the 15–64 years of age people, 51.4% in Kyauske and 54% in Madaya work in agriculture, forestry and fishing [15]. Main crops include rice, wheat, legumes and vegetables. The sampling unit was the household, in which an adult member was interviewed in each of those selected. The sample size was 4,500 households with about 20,000 household members.
Cluster sampling was used, as recommended in the WHO Vaccination Coverage Cluster Surveys [16], with three stages of sampling i.e. (i) stratification by township, (ii) random selection of 75 villages from each of the two townships, (iii) random sampling of 30 households from each village selected from lists of households provided by the local government health departments. Census data on the population of each village were used for stage ii so that random sampling of villages was done with a probability proportional to their sizes.
An interviewer-administered questionnaire was used to collect data. The structured questionnaire targeted the respondents’ knowledge of local snake fauna, the symptoms of snakebite, preventive and first aid methods, traditional healing and of health service access. Some of the questions were open-ended so as to capture the potential variety of practices and knowledge of the community members (Table 1).
The statistical analysis was conducted using SPSS (IBM SPSS Statistics Version 24) after the survey data were weighted for respondent sampling probability. All numeric findings are adjusted for the three stage sampling methodology. Chi-squared test was used to calculate p values to assess difference between the knowledge among men and women, between respondents who lived in high snakebite incidence villages and those who lived in low incidence villages, and the respondents with and without family history of snakebite during the last ten years.
The research was conducted with ethics approval from the Human Research Ethics Committee at the University of Adelaide and Ethics Committee at the Department of Medical Research at the Ministry of Health in Myanmar.
The survey was conducted in 74 villages in Kyaukse township and 70 villages in Madaya townships, with 4,276 respondents. The participation rate was 94%. Half (49,9%) of respondents were women. The majority, 41.9%, of the respondents were 41–60 years of age, 35.5% were 18–40 years and 20.5% were above 60 years of age.
Mandalay region is a high snakebite incidence area. The respondents reported that among these 4,276 households 24 people were bitten in the last one year. Incidence, types of snakes and activities associated with the snakebite have been reported in a previous publication [14].
Very few respondents had an adequate understanding of the symptoms caused by Russell’s Viper bite, the snake responsible for most envenomings in this region. Similarly, only a few respondents had accurate knowledge about the symptoms caused by cobra bite. Bleeding, which is among the main symptoms of Russell’s Viper envenoming, was mentioned by only 2.9% of respondents, while neurological symptoms, which rarely if ever occur with Russell’s Viper bite, were mentioned by about 30% of respondents. The majority of the respondents appeared not to have adequate knowledge about the specific symptoms of snakebite. However, more than half mentioned, in generic terms, that snakebite causes serious health consequences (Tables 2 & 3). There was no difference between survey respondents who reported that one of their family members suffered from snakebite during the last ten years, and those with no direct exposure to the consequences of snakebite.
There was no statistically significant difference between men and women’s knowledge of symptoms. Similarly, there was no statistically significant difference in the knowledge of those who were less than 40 years of age and those above 40 years. The respondents from 40 villages reported three or more snakebites for each of those 40 villages during the last ten years. There was no statistically significant difference in knowledge of respondents from those villages which reported 3 or more snakebites and those villages which had none.
Many respondents had knowledge about some of the preventive methods, such as wearing long boots (79.4%), using a torch when in dark (58.9%), using a stick to check before working [for example, collecting harvested] crops (28.9%), and clearing bushes around homes (11.5%). A few of the respondents mentioned wearing gloves, using mosquito bed nets, and not sleeping on the floor. A few others mentioned saying prayers or offering goods to spirits to help prevent snakebites. There was no statistically significant difference in prevention methods knowledge of respondents from those villages which reported 3 or more snakebites and those villages which had none.
When asked which first aid method they would provide to a snakebite victim, many respondents (62%) mentioned application of a tourniquet (Table 4). Best practice first aid methods include pad and bandage over the bite site, splinting the limb, carrying the patient and not letting them walk if the bite is on lower limb. Most (72%) respondents did not mention any of these best practice methods.
When asked if there were any effective traditional methods for treating snakebite, 37.4% answered that there were some effective methods to treat snakebite. One in four mentioned one or more harmful traditional method such as cutting the bite site. Kyaukse township has a larger district hospital, and relatively fewer people from this region mentioned potentially harmful traditional methods compared to those from Madaya township. In Madaya 42.9% (CI 38.0–47.8) people mentioned traditional methods compared to 31.1% (CI 27.4–35.1) in Kyaukse. Harmful methods were mentioned by 29.0% and 20.0% people in Madaya and Kyaukse respectively. Harmful traditional methods included sucking out the blood from the bite site, cutting or tattooing around the bite, burning the bite area, drinking something to cause vomiting, and rubbing/massaging traditional medicine into the bite area. Non harmful traditional methods included using a black stone (considering that the stone is applied without incising the wound), drinking holy water, saying prayers, drinking coconut water, or applying a dead chick to the bite site.
When asked where they would first take the snakebite victim, a large majority (88%) of the respondents mentioned the teaching hospital in nearby Mandalay city, or a township hospital or smaller station hospital near their village. Another 4% mentioned government health centres. Only 1.6% said that they would take the snakebite victim first to a traditional healer. The main reasons for accessing government health facilities included availability of antivenom (58.3%), and the better quality of care (19.7%). Only 10.8% of the respondents answered that their reason for using the health centre/hospital was the short distance and travel time to the health centre/hospital in their areas. While 55.2% mentioned one or more best practice treatment methods, many mentioned applying tourniquet (44%) and one or more other harmful methods such as cutting or incising the bite site (13%).
The high incidence of snakebites despite people’s having adequate knowledge about prevention suggests that preventive methods are not being practised. Despite a high number of bites at the population level, snakebite is a rare event for an individual. Therefore, people may not perceive themselves at risk of being bitten. Preventive action requires that people not only perceive a threat of high severity but also perceive themselves at risk of suffering from that health problem [17]. Health education campaigns could use the foundation of good preventive knowledge to help people perceive the risk and turn knowledge into practice. Other reasons such as difficulty wearing boots in a hot tropical climate or wearing them while working in rice fields have been noted, but light weight, low cost boots proved to be acceptable to farmers in a pilot study [18].
Communities’ knowledge about snakebite symptoms is very limited. This reflects the fact that, despite snakebite being a major public health issue, health education has been limited. Even those with a snakebite in the family have limited understanding of symptoms, including renal problems, despite the fact that the majority of bites in this area are by Russell’s Viper whose bites carry a high risk of causing acute kidney injury. This lack of awareness about renal and other specific effects may also reflect the general level of health literacy in the community. While community’s lack of knowledge about symptoms may not be so problematic as far as treatment at health facilities is concerned where healthcare provider would be able to differentiate, in most of the cases, between cobra and Russell’s viper bite, it may cause delays in accessing care; for example, as many believed that Russell’s Viper bite causes neurological symptoms, they may not seek care if these symptoms system do not appear. Community-based health education and mass media-based awareness campaigns need to focus on delivering messages about symptoms and their implications for the need for seeking appropriate care.
Many mentioned tourniquets as a first aid measure. Observations at the hospitals in the region confirm that many victims arrive at the hospitals with a tourniquet applied. Tourniquets and ligatures have long been used by indigenous peoples to decrease the spread of venom [19]. Avau considers that the reasons for the lack of use of the pressure pad technique is that it has only recently been introduced to health education and that some literature still doubts its effectiveness causing uncertainty among health educators and communities [8]. There is a need for mass media-based awareness campaigns to alert people to the damaging effects of a tightly applied tourniquet.
Although few respondents answered that they would take a victim to a traditional healer, many reported knowing about traditional healing methods that were potentially harmful. Relatively fewer people from the township that had a larger hospital with a reputation for effective management of snakebite reported use of traditional healing methods. This probably reflects the impact of community trust in the capacity of formal health care services in that district. Access to services at a well-respected larger hospital with effective care and with abundant supplies of antivenom, potentially affects people’s use of traditional services. However, a previous qualitative participatory research project conducted by us in three of the villages where our current survey was conducted, suggested that traditional healers were trusted and used despite improved access to modern biomedical treatment and availability of antivenom [5]. Traditions are important to people as they are rooted in faith and cultural belief systems, local rituals, and practices promoted by loved ones and elders. However, it is important that the communities are aware that these practices are potentially harmful and that visiting a traditional healer may cause delays in reaching a health facility where antivenom is available if needed. Therefore, it is important that a sensitive but assertive approach is taken whereby allopathic modern medicine is not seen to be in competition with traditional methods, but the community is informed of the need for careful assessment of the risks associated with some of these practices. Empowering people with simple scientific knowledge about the cause of various symptoms will help more people to utilise health facilities early and avoid using harmful traditional methods.
This survey was conducted in townships close to a large city, with health care and antivenom available at many large government hospitals. This would not affect findings about knowledge about prevention and first aid, but might have affected responses to questions such as ‘where would you take a snakebite victim’. The interviews were conducted by primary care staff who might have caused response bias for the questions about use of traditional healers.
Despite the above-mentioned limitation, the survey, based on interviews with a large number of community women and men, highlighted the gaps in communities’ knowledge about prevention, first aid and treatment. The fact that despite the high prevalence of snakes and snakebites in the research area, the community members had inadequate knowledge, indicates that existing health promotion and health education initiatives have had little impact on understanding and practices. There is an immediate need for health education campaigns about snakebite prevention and first aid methods that target primary care workers in the public sector and communities through those primary care workers. While effectiveness of mass media educational campaigns for communities’ knowledge about snakebite prevention and first aid is not assessed yet, considering the need to reach out to large populations in high snakebite incidence regions of Myanmar FM radio and television campaigns might also be considered, and their impact evaluated, for improved knowledge about prevention and first aid. Health education programs must also involve local community women and men and community based organisations in planning and implementing such programs, as it helps avoid cultural bias; a challenge that besets many modern health program [20]. In this research setting, participatory assessment and planning involving locals informed that such participation leads to an in depth understanding about the local beliefs and practices [5].
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10.1371/journal.pntd.0002424 | Ex Vivo Innate Immune Cytokine Signature of Enhanced Risk of Relapsing Brucellosis | Brucellosis, a zoonotic infection caused by one of the Gram-negative intracellular bacteria of the Brucella genus, is an ongoing public health problem in Perú. While most patients who receive standard antibiotic treatment recover, 5–40% suffer a brucellosis relapse. In this study, we examined the ex vivo immune cytokine profiles of recovered patients with a history of acute and relapsing brucellosis.
Blood was taken from healthy control donors, patients with a history of acute brucellosis, or patients with a history of relapsing brucellosis. Peripheral blood mononuclear cells were isolated and remained in culture without stimulation or were stimulated with a panel of toll-like receptor agonists or heat-killed Brucella melitensis (HKBM) isolates. Innate immune cytokine gene expression and protein secretion were measured by quantitative real-time polymerase chain reaction and a multiplex bead-based immunoassay, respectively.
Acute and relapse patients demonstrated consistently elevated cytokine gene expression and secretion levels compared to controls. Notably, these include: basal and stimulus-induced expression of GM-CSF, TNF-α, and IFN-γ in response to LPS and HKBM; basal secretion of IL-6, IL-8, and TNF-α; and HKBM or Rev1-induced secretion of IL-1β, IL-2, GM-CSF, IFN-Υ, and TNF-α. Although acute and relapse patients were largely indistinguishable by their cytokine gene expression profiles, we identified a robust cytokine secretion signature that accurately discriminates acute from relapse patients. This signature consists of basal IL-6 secretion, IL-1β, IL-2, and TNF-α secretion in response to LPS and HKBM, and IFN-γ secretion in response to HKBM.
This work demonstrates that informative cytokine variations in brucellosis patients can be detected using an ex vivo assay system and used to identify patients with differing infection histories. Targeted diagnosis of this signature may allow for better follow-up care of brucellosis patients through improved identification of patients at risk for relapse.
| Brucellosis is a disease caused by transmission of bacteria of the Brucella genus from infected animals to humans. The main route of infection occurs through consumption of contaminated dairy products or contact with infected animals. While most patients treated with antibiotics will be cured of the infection, between 5–40% of patients experience a relapse of brucellosis. The mechanisms underlying these recurring infections remain poorly understood. In this study, we examined blood cells from control donors, patients who previously had acute infections, and patients who previously had relapsing infections. We identified an inflammatory cytokine signature from measurements of unstimulated and stimulated cells that showed statistically significant differences between relapsing and non-relapsing brucellosis patients. Future applications of this assay system may allow for better follow-up care of brucellosis through the diagnosis of this cytokine signature and predictive or improved identification of patients at risk for relapse.
| Brucellosis in humans is a zoonotic infection caused by Gram-negative facultative intracellular bacteria of the Brucella genus. Four species are typically responsible for human infections, B. abortus, B. melitensis, B. suis, and B. canis, and are transmitted from animal reservoirs including infected cows, goats or sheep, pigs, and dogs, respectively. Infection occurs by ingestion of contaminated unpasteurized milk or cheese or through contact with blood or materials from infected animals [1]. B. melitensis is recognized as not only the most virulent species, needing only a few organisms (10–100) to establish infection, but also the predominant species responsible for the brucellosis burden in Perú [2], [3]. Brucella spp. are of particular interest because they are easily aerosolized, which is underscored by the designation of brucellosis as the most common laboratory-acquired infection [4] and Brucella spp. as a category B agent on the Centers for Disease Control bioterrorism hazard list.
Approximately 5–40% of patients treated for brucellosis suffer a relapse, with the wide variation in risk historically being attributed to the duration and combination of antibiotic treatment [5]. However, few investigations have focused on the variation of the innate immune reaction to Brucella spp. and its impact on the rate of relapse. While studies have examined the association of genetic polymorphisms in cytokines and other immunity-related genes with brucellosis susceptibility [6], [7], less emphasis has been placed on the overall functional cytokine reaction of patients who demonstrate brucellosis susceptibility or relapse.
Brucella spp. are able to survive and replicate within macrophages, and effective control of brucellosis requires a potent Th1 response to activate cellular mediated immunity which is driven by the production of IFN-γ, IL-2, and TNF-α [8]–[12]. A Th2 response, driven by IL-4 and IL-10, is detrimental to combating brucellosis as it promotes humoral immunity and suppresses macrophage activation [13], [14].
In this study, we examined the ex vivo cytokine profiles of patients with a history of brucellosis in the absence of stimuli and after toll-like receptor (TLR) and heat-killed Brucella melitensis (HKBM) stimulation. This approach is unique because we assessed human cytokine expression and secretion in fully recovered patient blood cells to determine if there is a brucellosis cytokine signature present at baseline, that may underlie a person's response to B. melitensis infection. While previous studies employ animal models, cell lines, or look at post-treatment serum cytokine levels [15], we assessed the ex vivo immune reaction of primary cells from human patients. We found that several cytokines showed altered expression and secretion in both unstimulated and stimulated conditions. Patients with a history of acute or relapsing brucellosis can be accurately identified by a robust inflammatory cytokine signature, months and even years after successful treatment. This signature consists of increased secretion of TNF-α and IL-2 in response to HKBM and LPS, IL-1β in response to Rev1 and LPS, IFN-γ in response to HKBM, and basal IL-6. This work demonstrates that cytokine variations in brucellosis patients can be detected using an ex vivo assay system and can be used to distinguish between relapse and acute patients. Targeted diagnosis of this signature may allow for improved treatment of brucellosis by identifying patients at risk for relapse.
The study was approved by the Human Research Protection Program of the University of California, San Diego, and the Comité de Ética of Universidad Peruana Cayetano Heredia (UPCH), Lima, Perú. All patients provided written informed consent prior to enrollment in the study.
Sixteen patients with a previously confirmed history of acute brucellosis (6 males and 10 females; 44.8±12.5 years, “acute”) and 6 patients previously diagnosed with relapsing brucellosis (2 male and 5 females; 39±15.2 years, “relapse”) were enrolled in the study. Brucellosis was confirmed by serology, positive culture, or both methods (Supporting Table S1). At the time of sample collection all patients were 18 years of age or older, had completed treatment and were asymptomatic for brucellosis for 6 months or more, had a normal physical examination, and showed no signs or symptoms of other illness. 11 healthy volunteers with no history of brucellosis were also enrolled as negative controls (5 males and 6 females; 30.8±7.3 years, “control”).
Volunteers provided 120 mL of venous blood or underwent leukapheresis. Peripheral blood mononuclear cells (PBMCs) were isolated using Ficoll Paque (GE Healthcare) as previously described [16].
Isolated PBMCs were cultured in RPMI-1640 (Sigma) with 10% fetal bovine serum at a density of 2.5×106 cells per well of a 24-well plate at 37°C with 5% CO2. After isolation, cells were allowed to rest for 4 hours and were then stimulated with either PBS (resting, basal), a TLR4 agonist, lipopolysaccharide B5:055 from Escherichia coli (LPS, 1 µg/ml, Sigma), a TLR2/1 agonist, the synthetic triacylated lipoprotein Pam3CSK4 (1 µg/ml), a TLR3 agonist, low molecular weight polyinosine-polycytidylic acid (Poly(I∶C), 10 µg/ml), a TLR7/8 agonist, the imidazoquinoline compound R848 (3 µg/ml), a TLR9 agonist, the synthetic CpG ODN 1668 (CpG, 5 mM), heat-killed Brucella melitensis vaccine strain Rev1 (Rev1, 65 CFU/ml) or a heat-killed, virulent B. melitensis patient isolate (HKBM, 65 CFU/ml). All manipulations of live Brucella melitensis vaccine strain Rev1 and the B. melitensis patient isolate were carried out under BSL3 conditions at UPCH, Lima, Peru. After 18 h of stimulation, the supernatant was removed and preserved at −80°C and the cells were washed with PBS and frozen for subsequent RNA isolation.
After the culture supernatant was removed, PBMCs were washed in PBS, centrifuged, and the cell pellets were frozen at −80°C. Cells were thawed, lysed, homogenized, and total RNA was extracted using the QIAshredder and RNeasy kits per the manufacturer's instructions (Qiagen). RNA was eluted in 30 µl of RNase-free water, and 1 µg was reverse-transcribed into cDNA using the iScript cDNA synthesis kit according to the manufacturer's instruction (Bio-Rad).
Quantitative real-time PCR (qPCR) was performed to measure the mRNA expression level of the housekeeping gene GAPDH, and several inflammatory cytokines (GM-CSF, IFN-γ, IL-1β, IL-10 and TNF-α). Using a CFX384 Real-Time Detection System (Bio-Rad), each reaction was performed in triplicate in a final reaction volume of 5 µl, including 2.5 µl SsoAdvanced SYBR Green Supermix (Bio-Rad), 1.0 µl cDNA template, and 1.0 µl (100 nM final concentration) of each primer. Primers were designed for each gene using Primer3 (Supporting Table S2). After amplification, threshold cycle (CT) values were generated using the Bio-Rad CFX Manager Software 1.6. The fold change of gene expression was calculated as previously described [17].
A multiplex bead-based immunoassay was used to quantify cytokine levels secreted into the culture supernatant after stimulation. Using the Human Cytokine 10-Plex Panel for the Luminex platform, the following cytokines were measured according to the manufacturer's instruction: GM-CSF, IFN-γ, IL-1β, IL-2, IL-4, IL-5, IL-6, IL-8, IL-10 and TNF-α (Invitrogen). Briefly, either recombinant protein standards or 50 µl of each culture supernatant sample were first incubated, in duplicate, with antibody-conjugated fluorophore beads, and then with protein-specific biotinylated antibodies. Finally, following the addition of Streptavidin-RPE, samples were analyzed using the Bio-Plex 200 system (Bio-Rad). Data analysis was performed using the manufacturer provided software and the included recombinant proteins were used to generate standard curves to determine the sensitivity of the assay.
Significance values were calculated using the R software environment for statistical computing. For each pairwise comparison, Welch's t-test was used to estimate the probability that the two samples have equal mean. Probabilities less than 0.05 suggest significant differences between the two samples and are indicated by an asterisk.
Prior to classification, all response variables were log10 transformed, centered, and scaled to unit variance. Unless otherwise stated, variables for which more than four patients were missing, or for which two or more patients belonging to the same category were missing, were discarded. Missing values in the remaining 70 response variables were imputed from their conditional means [18]. Specifically, for each missing value, a linear regression model was identified by forward model selection using Akaike's information criterion (AIC). Regressors were chosen from the 32 response variables for which no data was missing, including patient category. Forward selection was terminated when there was no further reduction in the AIC, or when the complexity of the model reached 12 regressors. Imputation by conditional means was chosen because of the relatively high correlation observed between variables [19], [20].
Linear discriminant analysis (LDA) was performed in R using the ‘lda’ function. Accuracy of the resulting linear discriminant function, or classifier, was then assessed using the ‘predict’ function in conjunction with leave-one-out cross-validation. To identify the optimal classifier for a given cross-section of the data, LDA was performed using all pairwise combinations of variables contained in the cross-section. Top-performing pairs, defined as those pairs of variables that trained a classifier with the highest accuracy, were then used to seed model selection. During model selection, a variable was chosen at each step whose inclusion in the classifier resulted in the greatest increase in accuracy, up to a backtracking factor of 0.03 (1 patient). Since a multiplicity of models could satisfy this selection criteria, each selection was performed 20 times. The model ultimately identified by forward selection was taken to be that which yielded the highest classification accuracy while using the fewest number of variables.
To quantify the induction of cytokine gene expression in response to inflammatory stimuli, we first measured the resting, or basal, expression in unstimulated PBMCs. We found that basal expression of IL-1β and GM-CSF was significantly higher in relapse patients than in controls, while TNF-α was significantly higher in both acute and relapse patients compared to control (Figure 1).
Next, PBMCs were stimulated overnight with LPS, heat-killed B. melitensis (HKBM) or R848. In response to LPS, relapse patients exhibited higher expression of GM-CSF and IL-10 and significantly higher TNF-α and IFN-γ than either controls or acute patients (Figure 2A). This trend was also observed in response to HKBM, except relapse and acute patients exhibited similarly and significantly elevated levels of GM-CSF, TNF-α, and IL-10 (Figure 2B). Thus while cytokine gene expression in response to LPS appears to discriminate well between relapse and either acute or controls, the response to HKBM appears to discriminate between control subjects and either acute or relapse patients.
In summary, relapse patients uniquely demonstrated elevated basal IL-1β and GM-CSF expression compared to control donors. In comparison to both acute and control donors, relapse patients exhibit increased IFN-γ expression after HKBM stimulation and increased TNF-α expression after LPS.
To test whether the differences observed in cytokine gene expression were also manifest in the synthesis and secretion of cytokine proteins, we used a multiplex bead-based immunoassay to quantify ex vivo cytokine secretion in the culture supernatant of unstimulated and stimulated PBMCs. We measured the concentrations of GM-CSF, IFN-γ, IL-1β, IL-2, IL-4, IL-5, IL-6, IL-8, IL-10, and TNF-α. In unstimulated cells we found that the basal secretion of IL-6, IL-8, and TNF-α was elevated in both acute and relapse patients compared to control subjects. IL-8 was higher in relapse patients than in acute patients, while basal IL-2 was increased in relapse patients compared to controls (Figure 3). All differences were significant (p<0.05).
Next we stimulated PMBCs with LPS, heat-killed B. melitensis (HKBM) or heat-killed B. melitensis vaccine strain Rev1 (Rev1). As observed in our gene expression data, after stimulating with HKBM, Rev1, or LPS, secretion of GM-CSF, IFN-γ, and TNF-α was significantly elevated in relapse patients compared to control subjects (Figure 4). Additionally, IL-1β and IL-2 secretion was significantly elevated in acute and relapse patients compared to control donors after both HKBM and Rev1, but not LPS, stimulation. Several of the cytokine concentrations measured in response to other stimuli fell out of the observable range of the assay (Supporting Figure S1).
To test whether the differences observed in cytokine gene expression and protein secretion were sufficient to accurately discriminate between patients that did and did not experience a relapse in brucellosis, we trained a linear discriminant classifier using different cross-sections of the data and assessed its accuracy by leave-one-out cross validation. Linear discriminant analysis (LDA) is a supervised learning method that maximizes separation in the data – defined here as the ratio of variances between patient categories to the variance within – using a linear recombination of response variables, in this case our observed gene expression or cytokine secretion measurements. Using LDA in conjunction with a model selection strategy allowed us to ask whether a subset of the response variables that we assayed could accurately classify patients as control, acute, or relapse.
First, cross-sections of the cytokine gene expression and protein secretion data were chosen such that all response variables were of the same cytokine or generated using the same stimulus. We refer to these as “cytokine” and “stimulus” cross-sections, respectively. A classifier trained on a cytokine cross-section is said to be trained “across stimuli”, and vice versa. Response variables for which more than four patients were missing, or for which two or more patients belonging to the same category were missing, were discarded. Missing values in the remaining 70 response variables were imputed from their conditional means [18]. Linear discriminant functions were then identified for each cross-section using a forward model selection strategy with backtracking (see Methods).
On average, we found that higher classification accuracy was achieved by training across stimuli than across cytokines. Training across the four gene expression or eight protein secretion stimuli yielded accuracies of 0.679±0.073 and 0.642±0.119, respectively, compared to 0.598±0.045 and 0.606±0.116 across cytokines (Figures S2, S3, S4). This result is likely due to the higher cross-correlation observed between cytokines in response to a single stimulus, compared to the cross-correlation observed in a single cytokine in response to multiple stimuli.
Second, we observed that the cytokine secretion assay was superior at discriminating between acute and relapse patients compared to gene expression. With expression, only the IFN-γ cross-section correctly classified more than one relapse patient (Figure S2D). Conversely, four cytokine secretion cross-sections (IL-1, IL-6, IL-10, and TNF-α) and two stimulus cross-sections (Pam3CSK4 and R848) correctly classified half or more relapse patients (Figures S3, S4). This result is likely due to better separation in the response variables between acute and relapse patients in the cytokine secretion data compared to gene expression (Figure S5).
Indeed, clustering the patients hierarchically by Euclidean distance in their gene expression or cytokine secretion profiles, we found that the gene expression profile for every relapse patient most closely matches that of an acute patient (Figure 5A). Similarly, control subject 70005 and acute patient 10288 cross-cluster with acute and control subjects, respectively. Consequently, these seven patients are misclassified in over half of the 20 qPCR models identified by forward selection. In contrast, five of the six relapse patients cluster together according to their cytokine secretion profile, resulting in significantly better classification performance (Figure 5B). Among the other patients, control subject 70005 and acute patient 10288 were again the most often misclassified, suggesting that these two may be outliers in their respective patient categories.
Examining the optimal gene expression model identified by forward selection, we found that it classified 28 of 33 patients correctly. Distinguishing between acute and relapse patients was the primary source of misclassification, with 83% relapse sensitivity (one false negative), but 71% precision (two false positives) (Figure 6A). In contrast, four cytokine secretion models correctly classified 32 of 33 patients. These models also classified five of six relapse patients correctly, but with perfect precision and fewer variables than gene expression (Figure 6B). Interestingly, these models all share the following eight response variables: TNF-α and IL-2 in response to HKBM and LPS, IL-1β in response to Rev1 and LPS, IFN-γ in response to HKBM, and basal IL-6. Pairing these variables with, for example, IL-1β and GM-CSF in response to HKBM, or TNF-α and GM-CSF in response to Rev1, achieves 97% patient classification accuracy. We therefore propose that these variables constitute an innate immune cytokine signature for accurate identification of patients at risk for brucellosis relapse.
Here we present evidence that patients with a history of acute-and-cleared or relapsing brucellosis can be distinguished with a robust inflammatory cytokine signature even months or years after successful treatment. Currently, under standard treatment, many patients experience relapsing brucellosis, the cause of which remains poorly understood. In this study we stimulated PBMCs from patients with a past history of acute or relapsing brucellosis and measured ex vivo innate inflammatory cytokine expression and secretion to determine if at a clinically normal baseline there was a cytokine signature that might be associated with relapsing infection.
Brucella spp. are intracellular pathogens whose effective control and elimination requires a potent cell-mediated Th1 immune response [9], [21], [22]. We found that relapse brucellosis patients demonstrated higher basal IL-1β and GM-CSF gene expression compared to control donors, increased IFN-γ expression after heat-killed B. melitensis (HKBM) stimulation and higher TNF-α expression after LPS stimulation compared to both acute brucellosis patients and control donors. Surprisingly, this indicates relapse patients are capable of inducing the expression of cytokines needed to mount a Th1 response. However increased IL-10 gene expression after stimulation with HKBM in both acute and relapse brucellosis patients, but not after LPS stimulation, may suggest a possible Brucella spp. specific elevated Th2 response. Th2 cytokines like IL-10 have been shown to downregulate immunity to Brucella spp. [23], [24].
Additionally, relapse patients produced more TNF-α protein compared to control donors and secrete more GM-CSF compared to both groups. Indeed, previous studies indicate GM-CSF secretion can stimulate IL-1β and TNF-α secretion by monocytes after in vitro B. abortus challenge [25]. Taken together, the ex vivo innate immune cytokine expression and secretion of acute or relapse patients indicates a functional and Th1-dominated response. IL-2, TNF-α, and IFN-γ secretion have previously been shown to be increased during brucellosis [26], [27], and recent studies also suggest that adequate levels are required for control of the infection as genetic polymorphisms in these genes may increase susceptibility to, or duration of, disease [6], [28]. In accordance with our findings, others have shown elevated IFN-γ after ex vivo B. melitensis antigen stimulation in patients less than one year after diagnosis [29]. Here we confirm that this remains true even several years after the resolution of infection.
Though gene expression of the Th2 cytokine IL-10 was elevated in some brucellosis patients, IL-10 protein secretion was not significantly altered in these patients under any stimulation condition; IL-4, another important Th2 cytokine, was not highly secreted in any condition (Supporting Figure S1). However, one key limitation of the study was the multiplex approach used to determine cytokine protein levels: several of the cytokines measured in the assay fell above or below the standard range defined in the manufacturer's protocol and some concentration values were extrapolated or not detected. Due to the limited quantity of patient sample and culture supernatant, individual optimization for each cytokine and standard in the 10-cytokine kit was not possible. To address this issue in future studies, multiplex kits with improved standard ranges could be used or individual conventional ELISA assays might be useful for key cytokines which still fall outside the detection of the multiplex assay.
In summary, this study demonstrates that innate immune cytokine variations can be detected between patients with a history of acute or relapsing brucellosis and control donors using an ex vivo assay system. Standard clinical methods for monitoring brucellosis treatment outcomes remain unreliable: antibody titers used for serological diagnosis of brucellosis and circulating B. melitensis DNA load used for diagnosis by PCR, have been shown to persist for years after successful treatment [30]–[34]. In contrast, we show that an ex vivo cytokine signature can accurately distinguish between relapse and acute patients, and may provide a novel approach to monitor clinical outcomes. Further work would be required to validate this ex vivo assay as a method for predicting or confirming actively relapsing infections.
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10.1371/journal.pbio.0060008 | The Viral Oncoprotein LMP1 Exploits TRADD for Signaling by Masking Its Apoptotic Activity | The tumor necrosis factor (TNF)-receptor 1–associated death domain protein (TRADD) mediates induction of apoptosis as well as activation of NF-κB by cellular TNF-receptor 1 (TNFR1). TRADD is also recruited by the latent membrane protein 1 (LMP1) oncoprotein of Epstein-Barr virus, but its role in LMP1 signaling has remained enigmatic. In human B lymphocytes, we have generated, to our knowledge, the first genetic knockout of TRADD to investigate TRADD's role in LMP1 signal transduction. Our data from TRADD-deficient cells demonstrate that TRADD is a critical signaling mediator of LMP1 that is required for LMP1 to recruit and activate I-κB kinase β (IKKβ). However, in contrast to TNFR1, LMP1-induced TRADD signaling does not induce apoptosis. Searching for the molecular basis for this observation, we characterized the 16 C-terminal amino acids of LMP1 as an autonomous and unique virus-derived TRADD-binding domain. Replacing the death domain of TNFR1 by LMP1′s TRADD-binding domain converts TNFR1 into a nonapoptotic receptor that activates NF-κB through a TRAF6-dependent pathway, like LMP1 but unlike wild-type TNFR1. Thus, the unique interaction of LMP1 with TRADD encodes the transforming phenotype of viral TRADD signaling and masks TRADD's pro-apoptotic function.
| For viral infection to succeed, viral proteins must interact with the cellular signaling machinery of its target cell. An oncoprotein encoded by the Epstein-Barr virus (EBV) called latent membrane protein 1 (LMP1) is a primary contributor to the transformation of human B cells by the virus and the development of EBV-associated B cell malignancies by recruiting signaling molecules provided by the host. One such molecule, the cellular adapter protein TRADD, is among the few direct interaction partners of LMP1. But because TRADD promotes cell death (apoptosis) in the cellular tumor necrosis factor-receptor 1 (TNFR1) signaling pathway, it seems counterintuitive that TRADD could play a role in LMP1 biology, since LMP1 promotes cell survival and proliferation. We provide genetic evidence that TRADD is critical for LMP1 to assemble its transforming signaling network. LMP1 requires TRADD to recruit and activate I-κB kinase β and, thus, to induce canonical NF-κB signaling. Simultaneously, LMP1 masks TRADD's pro-apoptotic activity. We show that LMP1 carries a unique and autonomous viral TRADD-binding domain, which dictates an unusual structure of the LMP1-TRADD complex and the nonapoptotic phenotype of TRADD signaling, irrespective of the receptor context in which this domain is located. Thus, DNA tumor viruses alter the functional properties of cellular signaling molecules to exploit them for their own purpose of cell transformation.
| Latent membrane protein 1 (LMP1) is the primary oncogene of Epstein-Barr virus (EBV), which is a human DNA tumor virus of the gamma-herpes virus family that preferentially infects and transforms human B lymphocytes [1]. LMP1 is critical for B lymphocyte transformation by EBV and has oncogenic potential when expressed in the B cell compartment of transgenic mice [1]. Despite the fact that LMP1 promotes cell survival and proliferation, it recruits the pro-apoptotic tumor necrosis factor (TNF)-receptor 1–associated death domain protein (TRADD) [2]. TRADD has been described as the central adapter protein of TNF-receptor 1 (TNFR1), and TRADD mediates TNFR1 induction of apoptosis as well as the activation of NF-κB and c-Jun N-terminal kinase (JNK) [3]. In contrast, the role of TRADD in LMP1 signaling has been unresolved.
LMP1 is a transmembrane protein composed of 386 amino acids (aa) that mimics a constitutively active receptor [4]. Six transmembrane helices mediate the spontaneous formation of LMP1 oligomers in the membrane, which is an essential and sufficient prerequisite to induce signal transduction at the C-terminal cytoplasmic signaling domain (aa 186–386) of the molecule. Two functionally independent regions within the LMP1 signaling domain are involved in B lymphocyte growth transformation and the induction of signal transduction: the C-terminal activating region 1 (CTAR1, aa 194–231) and CTAR2 (aa 351–386) [5]. CTAR1 harbors a P(204)xQxT/S consensus motif, which binds TNFR-associated factors (TRAFs) and induces the noncanonical NF-κB pathway through I-κB kinase α (IKKα) [6–9]. CTAR2 triggers the canonical NF-κB pathway via IKKβ followed by phosphorylation and degradation of I-κB [8,10]. Although IKKβ has an indirect role in CTAR1 function, most likely by up-regulating the expression of critical factors for noncanonical NF-κB signaling, its kinase activity is primarily activated by CTAR2 [8,9,11]. IKKα rather acts as a negative regulator of CTAR2-induced NF-κB activity, whereas IKKγ is required for some but not all canonical CTAR2 signaling [8–10]. The JNK pathway is generally triggered at CTAR2, although CTAR1 can also contribute to JNK activation in some cell lines [12–17]. The motif P(379)VQLSY(384) of CTAR2 is essential for activation of NF-κB and JNK [15,18]. Studies in TRAF-deficient mouse cells suggested critical roles of TRAF3 and TRAF6 in CTAR2 signaling to NF-κB and JNK [10,11,19,20], but it is still unclear how CTAR2 assembles its signaling complex. In contrast to CTAR1, TRAF molecules have no affinity for CTAR2. There is no experimental evidence for a direct or indirect physical interaction of CTAR2 with TRAF3 [5], but TRAF6 translocates to sites of active LMP1, and its indirect interaction with CTAR2 might involve the transcriptional corepressor BS69 as a mediator [19,21].
During the search of CTAR2-binding proteins, TRADD has been pulled out from a yeast–two-hybrid screen using CTAR2 as a bait [2]. Early experiments have indicated a potential role for TRADD in LMP1 signaling. Overexpression of TRADD potentiated NF-κB activation by LMP1 in HEK293 epithelial cells, and co-expression of TRADD harboring a mutated death domain interfered with LMP1 signaling to NF-κB in the same cell line [2,15]. A role for TRADD in CTAR2-triggered JNK activation has also been postulated [14]. However, it cannot be excluded that TRADD overexpression caused unspecific effects unrelated to LMP1 signaling in these studies. More recent experiments showed no significant effect of RNAi-mediated TRADD down-regulation on CTAR2-induced NF-κB and JNK activity in HEK293 cells [11,19]. Therefore, it is currently believed that TRADD is dispensable for LMP1 signaling, and no molecular role of TRADD in organizing the CTAR2 signaling complex has been demonstrated. In analogy to TNFR1 signaling, it has been suggested that TRADD recruits TRAF2 to CTAR2 [14]. However, no experimental evidence for a physical interaction of TRAF2 with the signaling complex at CTAR2 is available. Recent data from TRAF2-deficient cell lines and RNAi experiments showed that TRAF2 is fully dispensable for activation of NF-κB and JNK by LMP1-CTAR2 [10,19,20]. Until today, no definite evidence for a role of TRADD in LMP1 signaling has been provided due to the lack of TRADD-deficient animals or cell lines.
Upon activation with the pro-inflammatory cytokine TNFα, TNFR1 recruits TRADD via direct interaction between the death domains of both molecules [22]. TRADD is believed to serve as a platform for the binding of TRAF2 and RIP1 to set up the TNFR1 complex at the plasma membrane, inducing JNK and NF-κB [23]. Subsequently, TRADD binds the Fas-associated death domain protein (FADD) to activate caspase 8–dependent apoptosis. In contrast to TNFR1, LMP1 is devoid of a death domain, and TRADD's death domain is dispensable for TRADD interaction with LMP1, suggesting that the molecular architectures of the TNFR1-TRADD and LMP1-TRADD complexes are different [15]. However, it is unknown whether this unique type of TRADD recruitment is determined by LMP1 and whether it has relevance for the biological function of LMP1. Although amino acid residues Y384 and Y385 of CTAR2 are known to be essential for TRADD recruitment [2], the functional TRADD-binding site of LMP1 has not been well defined.
Here, we report the first genetic knockout, to our knowledge, of the TRADD gene, which we have generated in human B lymphocytes to characterize TRADD's role in LMP1 signal transduction. Using TRADD-deficient cells, we found that TRADD has an essential role in LMP1 activation of canonical NF-κB signaling by mediating the recruitment and activation of IKKβ. In contrast, JNK induction by LMP1 is independent of TRADD, demonstrating that the NF-κB and JNK pathways bifurcate upstream of TRADD at the level of CTAR2. We narrowed down the functional TRADD-binding domain of LMP1 to the 16 C-terminal amino acids of LMP1. The deletion of this domain resulted in a loss of IKKβ binding to LMP1. We were able to transplant the nonapoptotic phenotype of LMP1-induced TRADD signaling to TNFR1 by replacing its death domain by the TRADD-binding sequence of LMP1. Thus, the viral oncoprotein LMP1 exploits TRADD for nonapoptotic signaling, which is intrinsically encoded by the viral TRADD-binding domain but not by the receptor context in which it operates.
Studies on the role of TRADD in LMP1 signaling were hampered by the lack of TRADD-deficient mice or cell lines. To solve this problem, we chose to delete the TRADD gene from human B lymphocytes, which are the primary target cells of EBV and, thus, the natural compartment of LMP1 expression. The EBV-negative human B cell lines DG75, BJAB, and BL2 were selected as candidates for the TRADD knockout due to their diploidy of the TRADD locus (unpublished data). We cloned the human TRADD gene from a genomic blood cell DNA library in order to construct the two gene targeting vectors pTRADDko.1 (first TRADD allele) and pTRADDko.2 (second TRADD allele) for the consecutive deletion of both TRADD alleles from human somatic cells (Figure 1A and Materials and Methods). Both constructs differed in their 3′ arms homologous to the TRADD gene to prevent recombination with the already disrupted TRADD allele while targeting the second allele. In DG75 cells, each step of the gene targeting procedure was verified by Southern blotting, resulting in DG75 TRADD–/– clones with no encoding TRADD sequences left in the genome (Figure 1B). In contrast to DG75 cells, BJAB and BL2 cells did not support homologous recombination of the targeting vectors with the TRADD locus at a detectable frequency (Table S1). The targeting efficiency in DG75 cells was approximately one out of 50 GFP-positive, hygromycin B- and ganciclovir-resistant clones for both TRADD alleles (Table S1). As expected, no TRADD protein was expressed in the DG75 TRADD–/– clones (Figure 1C). DG75 TRADD–/– cells were fully viable and showed similar growth properties to wild-type cells at normal cell densities.
TRADD overexpression and RNAi studies have suggested that TRADD mediates TNFR1 activation of the NF-κB pathway [22,24]. To demonstrate that DG75 TRADD–/– cells are suitable tools to study the role of TRADD in signal transduction, we stimulated TRADD+/+ and TRADD–/– cells with soluble human TNFα and monitored the activity of the NF-κB pathway (Figure 2A). DG75 wild-type cells responded to TNFα stimulation with a rapid phosphorylation and subsequent degradation of I-κB. In contrast, the knockout of TRADD completely abolished TNFα-induced I-κB phosphorylation. This result delivered genetic evidence for an essential role of TRADD in TNFR1 signaling to NF-κB and showed that canonical NF-κB signaling is intact in DG75 cells.
TRADD interacts with the CTAR2 domain of LMP1, which signals through IKKβ to activate the I-κB–dependent NF-κB pathway [8,10,11]. To investigate a potential role of TRADD in LMP1-induced NF-κB signaling, we performed IKKβ kinase assays in DG75 TRADD+/+ and TRADD–/– cells, because IKKβ activity is the most proximal and specific readout of CTAR2 signaling on the NF-κB axis (Figure 2B). Due to the high endogenous NF-κB activity levels in DG75 cells, NF-κB reporter gene assays did not result in reliable induction levels after LMP1 expression (unpublished data). HA-LMP1Δ371–386, which lacks the 16 C-terminal amino acids of LMP1, is defective in CTAR2 signaling and served as null control. Expression of wild-type HA-LMP1 resulted in a 2.8-fold activation of IKKβ in TRADD+/+ cells, monitored as I-κBα substrate phosphorylation and IKKβ autophosphorylation (Figure 2B). Thus, CTAR2 triggered canonical NF-κB signaling in wild-type cells. Strikingly, CTAR2 lost its potential to induce IKKβ in TRADD–/– cells, which clearly showed that CTAR2 requires TRADD to activate the IKKβ pathway (Figure 2B). To demonstrate specificity of this effect for LMP1, we analyzed CD40 signaling in wild-type and TRADD-deficient cells. CD40 does not recruit TRADD and was therefore anticipated to signal also in the absence of TRADD. Confirming this presumption, a constitutively active chimera composed of the LMP1 transmembrane domain and the CD40 signaling domain, LMP1-CD40, induced IKKβ activity in TRADD+/+ and TRADD–/– cells to similar levels.
To exclude that defects unrelated to TRADD had caused the defective LMP1 signaling in DG75 TRADD–/– cells, we tested whether ectopic expression of TRADD restored LMP1 activation of IKKβ in TRADD-deficient cells (Figure 2C). To express TRADD at physiological levels, TRADD–/– cells were co-transfected with the pACYC184-1012.4 vector, which carries the complete human TRADD gene under the control of its endogenous promoter (see Materials and Methods). Exogenous TRADD expression alone did not induce IKKβ, further confirming that TRADD protein levels lay within a normal range which did not result in TRADD autoactivation. Notably, reintroduction of TRADD enabled LMP1 to activate IKKβ in TRADD–/– cells, verifying TRADD's essential role in CTAR2 signaling on the NF-κB axis (Figure 2C). In summary, our results provided strong genetic evidence for a critical role of TRADD in LMP1 signal transduction. In fact, LMP1 requires TRADD to induce the IKKβ/NF-κB pathway.
To further dissect the signaling pathways originating at CTAR2, we tested whether the LMP1-induced JNK pathway required TRADD for its activation. Both HA-LMP1 and LMP1-CD40 readily activated JNK in HA-JNK1 kinase assays in DG75 wild-type cells (Figure 2D). CTAR2 but not CTAR1 triggers JNK activation in DG75 cells, because mutation of Y384 to G within CTAR2 fully abrogated JNK activation as compared with the LMP1Δ194–386 control, which lacks the complete signaling domain. By analyzing JNK signaling in TRADD–/– cells, we observed that CD40 activation of JNK1 was independent of TRADD, as expected. Surprisingly, also LMP1 induced JNK1 in TRADD–/– cells to levels that were similar to those of wild-type cells (Figure 2D). Thus, activation of the JNK pathway by LMP1 does not involve TRADD, demonstrating that the CTAR2-triggered NF-κB and JNK pathways bifurcate upstream of TRADD at the level of CTAR2.
In TNFR1 signaling, TRADD mediates TRAF2 and probably also RIP1 binding to the activated signaling complex [3]. Subsequently, the IKK complex is recruited to the receptor through interactions of IKKα/β with TRAF2, and IKKγ with RIP1 [25–27]. Activation of IKKs is achieved through the TAB2/TAK1 complex, which binds to ubiquitinated RIP1 [28]. We show here that TRADD is necessary for IKKβ activation by the CTAR2 domain of LMP1. However, TRAF2/5, RIP1, TAB2, and IKKγ—the key players of TRADD signaling to NF-κB in the TNFR1 context—are all dispensable for LMP1-induced NF-κB activity [10,29]. This raised a question about the molecular function of TRADD in the LMP1 pathway to IKKβ.
To examine whether LMP1 recruits IKKβ and to evaluate whether TRADD has a role in this process, we performed co-immunoprecipitation assays with HA-tagged LMP1 in DG75 wild-type and TRADD-deficient cells (Figure 3A). IKKβ co-precipitated with HA-LMP1 from TRADD+/+ cells. Thus, IKKβ physically interacts with the LMP1 signaling complex. In addition, we found that TRADD was present in the complex together with HA-LMP1 and IKKβ. Deletion of the 16 C-terminal amino acids of LMP1 resulted in a loss of TRADD and IKKβ interaction with LMP1, demonstrating that aa 371–386 of LMP1 are essential for the recruitment of the NF-κB–inducing signaling complex to CTAR2. Remarkably, IKKβ was absent in HA-LMP1 immunoprecipitations from DG75 TRADD–/– cells (Figure 3A). These data support an essential role of TRADD in mediating the binding of IKKβ to the LMP1 signaling complex at CTAR2. In contrast to TRADD and IKKβ, we could not detect a physical interaction of TRAF3 or TRAF6 with CTAR2 in our immunoprecipitations from DG75 cells (unpublished data), making it unlikely that both TRAFs have crucial roles in the binding of IKKβ to CTAR2.
Next, we tested whether IKKβ also interacts with LMP1 in EBV-transformed lymphoblastoid cell lines (LCLs) (Figure 3B). For this purpose, primary human B cells were transformed with a recombinant maxi-EBV, which carried HA-LMP1 instead of the wild-type LMP1 gene. The resulting lymphoblastoid cell line LCL 3 endogenously expressed HA-LMP1 at levels comparable to untagged LMP1 of the control LCL 721 (Figure 3B). Endogenous IKKβ co-precipitating with HA-LMP1 was detected in anti-HA immunoprecipitations from HA-LMP1 cells (LCL 3, right panel) but not from LMP1 cells which served as a control for the specificity of anti-HA immunoprecipitation (LCL 721, left panel). These data verified the physical interaction of endogenous IKKβ with the LMP1 signaling complex also in LCLs.
Here we provide evidence for an active role of TRADD in LMP1 signal transduction. Nevertheless, LMP1 has anti-apoptotic properties, and LMP1 signaling does not trigger cell death, even if the NF-κB pathway is blocked (see: Figure 5) [29]. In contrast, TNFR1 induces caspase-dependent apoptosis through TRADD (see: Figure 5) [24,30]. The TRADD interaction sites of LMP1 and TNFR1 are not related, because LMP1 has no death domain to bind TRADD. Based on these observations, we asked whether the unique TRADD-binding site of LMP1 might determine the non-apoptotic phenotype of virus-induced TRADD signaling. Alternatively, the receptor context of LMP1′s TRADD-binding domain might dictate the outcome of TRADD signaling. To answer this question, we transferred LMP1′s TRADD-binding domain into the context of the TNFR1 signaling domain and evaluated the apoptotic potential of the resulting chimera.
Amino acids 371–386 of LMP1 were essential for TRADD-binding (Figure 3A). We speculated that this motif might also be sufficient for TRADD interaction and, thus, contains the functional LMP1 TRADD-binding site (LTB). Therefore, we replaced the TNFR1 death domain of the HA-tagged LMP1-TNFR1 chimera by LMP1 aa 371–386, resulting in the construct HA-LMP1-TNFR1-LTB (Figure 4A). Due to spontaneous oligomerization of the LMP1 transmembrane part of the molecule, HA-LMP1-TNFR1 and its derivatives induce constitutive signaling specified by the type of fused signaling domain [4,31]. Thereby, all chimeras based upon HA-LMP1-TNFR1 are independent of a ligand and their signaling readouts can be directly compared with HA-LMP1. HA-LMP1, HA-LMP1-TNFR1, and all derivatives were expressed to similar levels in HEK293 cells (Figure 4B).
First, it was important to show that aa 371–386 of LMP1 were sufficient to mediate TRADD interaction. Both, LMP1 and TNFR1 partially localize to, and signal from, membrane lipid rafts [23,32,33]. Therefore, we tested if HA-LMP1-TNFR1-LTB recruited TRADD into lipid rafts in HEK293 cells. A substantial fraction of HA-LMP1, HA-LMP1-TNFR1, and its derivatives localized to the lipid raft fraction (Figure 4C). Because of a reduced solubility of HA-LMP1-TNFR1 in Triton X-100 (unpublished data), this assay delivered qualitative rather than quantitative data regarding the amounts of expressed HA-LMP1-TNFR1 and its derivatives. HA-LMP1 recruited TRADD to rafts. Also HA-LMP1-TNFR1 relocated a major fraction of TRADD into this signaling-active compartment of the membrane. The deletion of the TNFR1 death domain completely abolished the ability of HA-LMP1-TNFR1 to interact with TRADD, as was shown by the absent shift of TRADD into the raft fraction in the presence of HA-LMP1-TNFR1ΔDD. From this observation, we also concluded that aa 206–331 of the TNFR1 signaling domain were not sufficient to mediate TRADD binding to TNFR1. When we replaced the death domain of TNFR1 by aa 371–386 of LMP1, the resulting HA-LMP1-TNFR1-LTB chimera efficiently recruited TRADD into lipid rafts (Figure 4C). This result showed that the 16 C-terminal amino acids of LMP1 are not only essential but also sufficient for TRADD interaction and, thus, comprise the functional TRADD-binding domain of LMP1.
To further substantiate our findings, we performed immunoprecipitation assays in HEK293 cells. Wild-type TRADD co-precipitated with HA-LMP1, whereas HA-LMP1-TNFR1ΔDD failed to pull down TRADD (Figure 4D). Confirming aa 371–386 as the self-sufficient TRADD-binding site of LMP1, TRADD bound to HA-LMP1-TNFR1-LTB and HA-LMP1 equally well (Figure 4D).
Mutation of aa 296–299 to alanines in TRADD's death domain strongly impaired TRADD binding to TNFR1 as well as TRADD activation of NF-κB, but had no negative effect on TRADD's interaction with LMP1 in glutathione S-transferase (GST) pulldown assays [15,34]. To evaluate whether this unique type of interaction between LMP1 and TRADD is encoded by the TRADD-binding site of LMP1, we tested if TRADD (296–299A) interacted with HA-LMP1-TNFR-LTB in immunoprecipitations (Figure 4E). HA-LMP1 efficiently bound TRADD (296–299A). After its transfer into the context of the TNFR1 signaling domain, the TRADD-binding site of LMP1 was still able to recruit TRADD (296–299A), albeit to a somewhat lesser extend than wild-type LMP1 (Figure 4E). Taken together, these experiments showed that aa 371–386 of LMP1 compose an autonomous viral TRADD-binding domain, which determines the unique type of interaction with TRADD, irrespective of the neighboring receptor context.
Next, we tested if the TRADD-binding site of LMP1 not only recruited TRADD when replacing the death domain of TNFR1, but also induced signal transduction (Figure 5A). Expression of wild-type HA-LMP1 caused a 17.0-fold induction of NF-κB activity in HEK293 cells. The contribution of CTAR2 was a 6.4-fold NF-κB activation, as determined by transfection of HA-LMP1(AAA), which carries an inactivating P(204)xQxT to AxAxA mutation in its CTAR1 domain. The HA-LMP1-TNFR1-LTB chimera also activated NF-κB to levels that were comparable to HA-LMP1(AAA), demonstrating that aa 371–386 of LMP1 retained their full competence to induce signal transduction after the transfer to the TNFR1 signaling domain. In addition, also JNK1 was activated by the HA-LMP1-TNFR1-LTB chimera, further confirming the functionality of LTB in the HA-LMP1-TNFR1-LTB chimera (Figure S1). The TRADD-binding site of LMP1 induced similar NF-κB levels as the TNFR1 death domain, demonstrating that both TRADD-binding domains are equally efficient with respect to NF-κB activation (Figure 5A).
In contrast to TNFR1, LMP1 signaling to NF-κB is dependent on TRAF6 [10,11,21]. To show that LMP1 aa 371–386 induced LMP1-type signaling in the context of the TNFR1 signaling domain, we evaluated if HA-LMP1-TNFR1-LTB activation of NF-κB is dependent on TRAF6. In contrast to HA-LMP1-TNFR1, the HA-LMP1-TNFR1-LTB chimera indeed required the reconstitution of TRAF6 expression in TRAF6–/– mouse embryonic fibroblasts (MEFs) to induce NF-κB (Figure 5B). Taken together, these results indicate that the TRADD-binding site of LMP1 was fully functional in the context of the TNFR1 signaling domain and encoded an LMP1-type signaling and interaction with TRADD.
Finally, we investigated whether the TRADD-binding site of LMP1 had the potential to induce cell death in the context of the TNFR1 signaling domain. Cell death assays in human BJAB B lymphocytes showed that expression of wild-type HA-LMP1 did not interfere with cell survival in the absence of NF-κB signaling, which was inhibited by co-transfection of dominant-negative I-κBα(S32/36A) (Figure 5C). In contrast, TNFR1 signaling induced by HA-LMP1-TNFR1 strongly reduced the survival rate of BJAB cells to 45.3 % of the controls. Cell death induced by HA-LMP1-TNFR1 was caspase-dependent, because the pan-caspase inhibitor zVAD-fmk fully rescued cell survival after HA-LMP1-TNFR1 expression (Figure 5C). Moreover, HA-LMP1-TNFR1 required TRADD to exert its killing activity in B lymphocytes (Figure 5D). Because TNFR1 has been demonstrated to induce apoptosis specifically through a TRADD- and caspase-dependent pathway [24], HA-LMP1-TNFR1–induced cell death is most likely due to apoptosis. Most notably and in contrast to HA-LMP1-TNFR1, the HA-LMP1-TNFR1-LTB chimera did not induce cell death (Figure 5C). Thus, although being fully functional with regard to the binding of TRADD and the induction of signal transduction, the TRADD-binding site of LMP1 had no apoptotic potential in the context of the TNFR1 signaling domain. We concluded from these results that the unique TRADD-binding site of LMP1, but not the context of the receptor signaling domain, encodes the nonapoptotic type of viral TRADD signaling.
To further corroborate our findings, we transferred LMP1 aa 371–386 into the context of wild-type TNFR1, replacing TNFR1′s death domain (Figure 6A). The resulting chimera, TNFR1-LTB, was devoid of any other LMP1 sequences except of LMP1′s TRADD-binding site. TNFR1ΔDD lacking its death domain served as a control. After electroporation, all constructs were expressed to similar levels in TNFR1/TNFR2-double deficient MEFs (Figure 6B). We used TNFR-deficient cells for our experiments to avoid cross-talk or the formation of heterocomplexes between the exogenously expressed TNFR1-LTB chimera and endogenous wild-type TNF-receptors. Activation of the TNFR1 constructs was achieved by spontaneous patching upon high exogenous expression in TNFR-deficient cells. Both, TNFR1 and TNFR1-LTB induced NF-κB to comparable levels in TNFR-deficient MEFs in the presence or absence of caspase inhibition (Figure 6C). Thus, the TRADD-binding domain of LMP1 was functional also in the context of wild-type TNFR1. However, whereas TNFR1 expression resulted in a massive induction of caspase-dependent apoptosis in TNFR-deficient MEFs, TNFR1-LTB did not affect viability, even if TNFR1-LTB was transfected at 5-fold higher DNA concentrations than wild-type TNFR1 (Figure 6D). These results confirmed our conclusions that aa 371–386 of LMP1 make up a self-sufficient, viral TRADD-binding domain which encodes a unique and nonapoptotic type of TRADD signaling.
Our results provide definite evidence that the cellular pro-apoptotic TRADD protein is a critical signaling mediator of the EBV oncoprotein LMP1, and they show that DNA tumor viruses have developed means to modulate the molecular and functional properties of cellular signaling molecules. We have demonstrated that EBV masks TRADD's pro-apoptotic activity and that this unique viral function is intrinsically encoded by the TRADD-binding domain of the LMP1 molecule. Moreover, this property of the 16 C-terminal amino acids of LMP1 is transferable to other receptors such as TNFR1. EBV exploits TRADD for NF-κB signaling by LMP1-CTAR2, which contributes important growth factor-like signals for efficient proliferation of EBV-transformed B lymphocytes [35,36]. Quantitative analysis has revealed that a recombinant maxi-EBV harboring a LMP1(Y384G) mutant has a ∼90% reduced potential of inducing long-term B cell proliferation compared to wild-type virus [37]. LMP1 also transforms rat fibroblasts in culture [38]. Whereas CTAR1 but not CTAR2 was the essential domain for in vitro transformation of Rat1 cells in one report [39], other studies supported a critical role of CTAR2 in oncogenic transformation of rat fibroblasts in vitro as well as in vivo [40,41]. Notably, tumor formation by LMP1-transformed Rat1 cells in nude mice essentially requires NF-κB activity and a functional CTAR2 domain [41]. According to our data, it is therefore highly likely that TRADD is critically involved in mediating these functions of CTAR2, extending the biological spectrum of TRADD activity to cell proliferation and transformation.
So far, studies on the role of TRADD in LMP1 signaling were solely based on the overexpression of TRADD and TRADD mutants, or RNAi-mediated knockdown of TRADD [2,11,14,15,19]. Such experiments yielded conflicting results with respect to the potential role of TRADD in LMP1 signal transduction (see Introduction). The knockdown of TRADD in HEK293 epithelial cells even suggested that TRADD is fully dispensable for LMP1 signaling [11,19], although residual TRADD protein might have rescued pathway activities in RNAi experiments. Alternatively, this result might reflect different cell type–specific functions of TRADD. To be able to investigate TRADD's molecular role in LMP1 signaling in a clean genetic system, we generated a TRADD knockout by deleting both TRADD alleles from human DG75 B lymphocytes. This is a biologically relevant cell system, because human B lymphocytes are the target cells of EBV. DG75 cells also express TNFR1 and readily responded to TNFα stimulation with activation of the NF-κB pathway. The knockout of TRADD abrogated TNFα-mediated NF-κB activation in DG75 cells, demonstrating that TNFR1 activation of the canonical NF-κB pathway critically depends on TRADD and that TRADD signaling is intact in DG75 cells.
The molecular mechanisms by which CTAR2 of LMP1 activates NF-κB are poorly understood. It has been unclear which direct interaction partners of CTAR2 mediate NF-κB activation. TRADD was believed to be dispensable for LMP1 signaling to NF-κB [11]. TRAF3 and TRAF6 are required for NF-κB activation by CTAR2, but TRAF molecules do not interact with CTAR2 directly [5]. In addition, knockdown of BS69, a potential mediator of TRAF6 binding to CTAR2, did not affect LMP1 signaling to NF-κB [42]. Here we demonstrate that the CTAR2 domain of LMP1 recruits IKKβ for signaling and that IKKβ recruitment and, thus, activation by LMP1 depends on TRADD. In TNFR1 signaling, activation of IKKβ can be functionally separated from its recruitment to the signaling complex. IKKβ directly binds to TRAF2, which gets recruited to TNFR1 via TRADD [25,26,30]. The activation of IKKβ requires the interaction of IKKγ, the regulatory subunit of the IKK complex, with ubiquitinated RIP1 [28]. In contrast, TRAF2, RIP1, and IKKγ are dispensable for LMP1 signaling [10,29].
How does NF-κB signaling at CTAR2 work? We propose a mechanism of IKKβ activation at CTAR2 that involves two pathways: a TRADD-dependent IKKβ recruitment pathway and a parallel activation pathway that is critically mediated by TRAF6 (Figure 7). Such a mechanism resolves the paradox that TRADD is solely required for CTAR2-triggered NF-κB signaling, whereas TRAF6 is essential for both NF-κB and JNK activation. Because TRAF2 is dispensable for NF-κB induction by LMP1 [10], a yet-undefined factor must be postulated that bridges IKKβ binding to TRADD in LMP1 signaling. We exclude TRAF3 or TRAF6 as candidates, because TRADD has affinity for only TRAF1 and TRAF2 [43], and we did not detect TRAF3 or TRAF6 in immunoprecipitations together with TRADD and IKKβ (unpublished data). Potential candidates might include p62/PKCζ [44]. Despite their role in canonical NF-κB signaling, a physical and/or functional interaction of TRAF3 and TRAF6 with CTAR2 must be independent of TRADD, because both TRAFs are critical for JNK activation by CTAR2, which is still intact in TRADD–/– cells. The transcriptional corepressor BS69 was suggested as the potential mediator of TRAF6 interaction with CTAR2 [42], although a recent report showed that endogenous BS69 is exclusively located within the nucleus interacting with chromatin [45]. However, cytoplasmic BS69 protein levels beyond the detection limit of the applied BS69 antibody possibly suffice to mediate LMP1 signaling. It is also conceivable that additional and yet-unknown factors might be involved in mediating the interaction of TRAFs with CTAR2.
Upon overexpression, TRADD is a highly apoptotic protein [22]. In TNFR1 signaling, TRADD mediates the activation of caspase-dependent apoptosis through FADD [22,24,30]. In contrast, LMP1 is a transforming protein with anti-apoptotic properties that does not induce programmed cell death, even if NF-κB signaling is blocked (Figure 5) [5,29]. We showed here that TRADD is essential for NF-κB induction by CTAR2, a domain that delivers signals critical for long-term proliferation of EBV-transformed B lymphocytes [35,37]. Thus, TRADD has a unique biological role in LMP1-induced cell proliferation and, therefore, contributes to B cell transformation by EBV. How does LMP1 achieve to exploit TRADD without inducing apoptosis? Our results demonstrated that aa 371–386 of LMP1 encompass an autonomous and unique viral TRADD-binding domain that encodes the nonapoptotic properties of TRADD signaling. This domain has no sequence homology to any known TRADD-binding site of cellular receptors or signaling molecules. Accordingly, the interaction between LMP1 and TRADD does not require a functional death domain in either of the two molecules. Our data demonstrated that this unique molecular structure of the LMP1–TRADD complex is intrinsically determined by the TRADD-binding site of LMP1 and can be transferred together with the nonapoptotic phenotype of TRADD signaling to TNFR1. Therefore, the unique interaction of TRADD with the TRADD-binding site of LMP1 must prevent apoptosis induction by TRADD. Accordingly, we could not detect an interaction of TRADD with the apoptosis mediator FADD in the presence of LMP1 (unpublished data). Sequences within the TRADD death domain, which are required for apoptosis induction, might be masked by components of the LMP1–TRADD complex. Based on our domain-swapping experiments, we can further exclude the possibility that signals originating elsewhere at the LMP1 signaling domain, for instance at CTAR1, are required to suppress apoptosis induction by TRADD, because the 16 C-terminal amino acids of LMP1 did not induce apoptosis in the context of wild-type TNFR1.
We also showed that LMP1 and its anticipated functional analogue, CD40, substantially differ in their receptor-proximal mechanisms activating signal transduction. Very much in contrast to LMP1, CD40 does not require TRADD for signaling. Thus, although both molecules share similarities regarding their role in supporting B-cell proliferation [46], they work through different receptor-proximal signaling molecules. This is in line with previous observations that LMP1 assembles a more efficient signaling complex than CD40 and that LMP1, but not CD40, signaling is dependent on TRAF3 [20,33,47].
In summary, our experiments defined an essential and unique role for TRADD in signaling of the viral oncoprotein LMP1. The viral TRADD-binding site of LMP1 determines the nonapoptotic and TRAF6-dependent type of TRADD signaling which leads to the activation of NF-κB. Thus, the human DNA tumor virus EBV has developed strategies to alter the functional and molecular properties of cellular signaling molecules to exploit them for its own purpose of cell transformation.
pHEBO empty vector, pCMV-HA-LMP1 based on pHEBO, pSV-LMP1-CD40, pcDNA3-p35, pRK5 empty vector, pRK-myc-TRADD, pRK-myc-TRADD(296–299A), pCMV-I-κBα(S32/36A), pcDNA3-Flag-IKKβ, and the NF-κB reporter plasmid 3xκBL have been described previously [15,21,48,49]. pEGFP-C1 is commercially available (BD Clontech). The following expression vectors were cloned on the basis of pCMV-HA-LMP1 by PCR approaches: pCMV-HA-LMP1(AAA), pCMV-HA-LMP1(Y384G), pCMV-HA-LMP1Δ194–386, pCMV-HA-LMP1Δ371–386 and pCMV-HA-LMP1(AAA/Δ371–386). The LMP1-TNFR1 fusion was generated by overlap-extension PCR and cloned into the background of pCMV-HA-LMP1 to obtain pCMV-HA-LMP1-TNFR1. The vectors pCMV-HA-LMP1-TNFR1ΔDD and pCMV-HA-LMP1-TNFR1-LTB were generated by PCR approaches based upon pCMV-HA-LMP1-TNFR1. To generate pRK5-TNFR1, the sequence of human TNFR1 was amplified by PCR from a TNFR1 cDNA (kind gift from H. Wajant) and cloned into pRK5. pRK5-TNFR1ΔDD and pRK5-TNFR1-LTB were cloned by PCR approaches. To generate pRK5-HA-JNK1, human JNK1α1 cDNA (kind gift from M. Karin) was N-terminally fused with a hemagglutinin (HA)-tag by PCR and cloned into pRK5. All constructs were verified by sequencing.
The human TRADD gene was cloned from the blood cell PAC library RPCI,3–5 (P. de Jong, Roswell Park Cancer Institute). A 13,702-bp EcoRI fragment of the clone RPCIP704M111012 encompassing the complete TRADD gene was partially sequenced and subcloned into pACYC184, resulting in the vector pACYC184-1012.4. Based on this TRADD clone, we constructed the gene targeting vectors pTRADDko.1 and pTRADDko.2. The 5′ homologous arms of both vectors were identical, whereas the constructs differed in their 3′ arms homologous to the TRADD gene to prevent recombination of pTRADDko.2 with the already disrupted TRADD allele. 107 DG75 cells were electroporated with 20 μg of linearized pTRADDko.1. The cells were selected in RPMI medium containing 10% fetal calf serum in the presence of 50% cell-free pre-conditioned medium, 400 μg ml−1 hygromycin B and 40 μM ganciclovir (Cymeven, Hoffmann-La-Roche) for simultaneous positive and negative selection. Successful gene targeting was verified by Southern blot analysis of EcoRI-digested cellular DNA using the external probe 1 and the internal probe 2. Subsequently, the hygromycin-resistance (Hyg-R) and GFP expression cassettes were removed from the disrupted TRADD gene locus by Cre-mediated recombination. Due to the use of the modified loxP66 and loxP71 sites [50], no functional loxP sequence resided in the genome after Cre reaction. To target the remaining wild-type TRADD allele, resulting TRADD+/– clones were subjected to a second gene targeting round using the pTRADDko.2 vector.
107 DG75 cells were electroporated in a BioRad Gene Pulser at 240 V and 975 μF with 5 μg of the indicated LMP1 constructs and 1 μg Flag-IKKβ. Total transfected DNA was adjusted to 18 μg with inert DNA. Five transfection samples were pooled per immunoprecipitation. Twenty-four h post transfection, the cells were lysed in IP-lysis buffer (50 mM HEPES, pH 7.5, 150 mM NaCl, 0.1% NP40, 5 mM EDTA, Roche protease inhibitor cocktail). HA-tagged proteins were immunoprecipitated by the anti-HA (12CA5) antibody (Roche) which had been covalently coupled to protein A sepharose beads (Amersham) using dimethyl pimelimidate (Pierce). After immunoprecipitation, beads were washed three times with IP-lysis buffer and precipitated proteins were analyzed on immunoblots. HEK293 cells were transfected in five 10-cm dishes per immunoprecipitation with 3.5 μg of LMP1 constructs, 0.5 μg of TRADD constructs, and 1.5 μg of pcDNA3-p35 using the polyfect reagent (Qiagen). LCL 3 lymphoblastoid cells were generated by in vitro transformation of primary human B lymphocytes with a recombinant maxi-EBV in which the wild-type LMP1 gene had been replaced by HA-LMP1 as described [37]. HA-LMP1 was immunoprecipitated from LCL 3 lysates using anti-HA (12CA5)-coupled protein A sepharose beads (see above). The LCL 721 [51] expressing untagged LMP1 was used as a control. For standard immunoblotting, cells were lysed in IP-lysis buffer containing 0.1% NP40 as a detergent (see above). The following primary antibodies were used: Actin (I-19), CD40 (C-20), I-κBα (C-21), IKKβ (H-470), JNK1 (C-17), TNFR1 (H-5), TRADD (H-278), αTubulin (B-5-1-2) (all from Santa Cruz Biotech.), HA (12CA5) (Roche), Flag (M2) (Sigma), TRADD (mouse, BD Transduction Lab.), and phospho-I-κBα(Ser32) (New England Biolabs).
For lipid raft recruitment assays, HEK293 cells were transfected with the indicated constructs in 10-cm dishes. Twenty-four h post transfection, the cells were lysed in TXNE buffer (25 mM Tris-HCl, pH 7.4, 150 mM NaCl, 5 mM EDTA, 0.5% Triton X-100, Roche protease inhibitor cocktail) at 4 °C and subsequently fractionated in an OptiPrep (Progen) density gradient as described [52]. Membrane lipid rafts were found enriched in the second fraction from top out of eight 500-μl fractions. The raft-specific sphingolipid GM1 served as a raft marker and was detected on dot blots using HRP-coupled cholera toxin subunit B (Sigma).
5 × 106 DG75 cells were electroporated with 5 μg of the indicated constructs together with 3 μg of pRK5-HA-JNK1 for JNK assays or with 2 μg of the LMP1 constructs and 5 μg of pcDNA3-Flag-IKKβ for IKKβ assays. GFP and p35 were co-transfected. To rescue TRADD expression, DG75 TRADD–/– cells were co-transfected with 1 μg of pACYC184-1012.4 (see above). Total amounts of transfected DNA were adjusted to 15 μg with inert DNA. As described [21], the cells were lysed 24 h post transfection, HA-JNK1 or Flag-IKKβ was immunoprecipitated by the anti-HA (3F10) or anti-Flag (M2) antibodies, respectively, and immunocomplex kinase assays were performed using purified GST-c-Jun or GST-I-κBα as substrates.
3 × 106 B lymphocytes were electroporated in a Bio-Rad gene pulser with 10 μg of the indicated LMP1 constructs together with 1 μg of pEGFP-C1 and 3 μg of pCMV-I-κBα(S32/36A) to block the NF-κB pathway. Twenty-four h post transfection, the cells were stained with propidium iodide (PI), and GFP+/PI– cells were quantified by flow cytometry in a FACSCalibur cytometer (Becton Dickinson) as described [31]. The number of successfully transfected viable cells (GFP+/PI–) in the empty vector reference was set to 100% survival. The reduction of green living cells in a test sample versus the reference was a direct measure for the cell death rate induced by the co-transfected gene [31].
107 TNFR1/TNFR2 double-deficient MEFs [53] were electroporated at 240 V and 975 μF in a BioRad Gene Pulser with the given amounts of the indicated TNFR1 constructs together with pEGFP-C1. Total transfected DNA was adjusted to 16 μg with inert DNA. Six h post transfection, the cells were stained with Cy5-labeled annexin V (Biocat) and analyzed for GFP expression and annexin V staining by flow cytometry. AnV-Cy5+/GFP+ cells were indicative for successfully transfected cells undergoing apoptosis.
TRAF6-deficient MEFs [54] and HEK293 cells were transfected in six-well plates with the indicated constructs together with pcDNA3-p35, the NF-κB reporter 3xκBL, and CMVβGal (BD Clontech) using Polyfect (Qiagen). Twenty-four h post transfection, the cells were lysed in luciferase lysis buffer (100 mM KPi, pH 7.8, 1 mM DTT, 1% Triton X-100). Firefly luciferase and β-galactosidase were measured as described [21]. 107 TNFR1/TNFR2 double-deficient MEFs were electroporated with the indicated constructs together with the NF-κB reporter 3xκBL and pCMV-RL (Promega). Twenty-four h post transfection, firefly and renilla luciferase activities were measured and analyzed using the Dual-Luciferase reporter assay system (Promega). Luciferase activities were standardized for β-galactosidase or renilla luciferase activities, respectively.
The GenBank (http://www.ncbi.nlm.nih.gov/Genbank/index.html) accession number for human TRADD gene is AY995114.
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10.1371/journal.pcbi.1004513 | Combining Search, Social Media, and Traditional Data Sources to Improve Influenza Surveillance | We present a machine learning-based methodology capable of providing real-time (“nowcast”) and forecast estimates of influenza activity in the US by leveraging data from multiple data sources including: Google searches, Twitter microblogs, nearly real-time hospital visit records, and data from a participatory surveillance system. Our main contribution consists of combining multiple influenza-like illnesses (ILI) activity estimates, generated independently with each data source, into a single prediction of ILI utilizing machine learning ensemble approaches. Our methodology exploits the information in each data source and produces accurate weekly ILI predictions for up to four weeks ahead of the release of CDC’s ILI reports. We evaluate the predictive ability of our ensemble approach during the 2013–2014 (retrospective) and 2014–2015 (live) flu seasons for each of the four weekly time horizons. Our ensemble approach demonstrates several advantages: (1) our ensemble method’s predictions outperform every prediction using each data source independently, (2) our methodology can produce predictions one week ahead of GFT’s real-time estimates with comparable accuracy, and (3) our two and three week forecast estimates have comparable accuracy to real-time predictions using an autoregressive model. Moreover, our results show that considerable insight is gained from incorporating disparate data streams, in the form of social media and crowd sourced data, into influenza predictions in all time horizons.
| The aggregated activity patterns of Internet users have enabled the detection and tracking of multiple population-wide events such as disease outbreaks, financial markets performance, and preferences in online movie selections. As a consequence, a collection of mathematical models aiming at monitoring and predicting these events in real-time have been proposed in the past decade. As we discover new methods and data sources suitable to track these events, it is not clear whether more information will lead to improved predictions. In the context of digital disease detection at the population level, we show that it is advantageous to combine the information from multiple flu activity predictors in the US instead of simply choosing the best performing flu predictor. Our findings suggest that the information from multiple data sources such as Google searches, Twitter microblogs, nearly real-time hospital visit records, and data from a participatory surveillance system, complement one another and produce the most accurate and robust set of flu predictions when combined optimally.
| Predicting the dynamics of seasonal and non-seasonal influenza outbreaks remains a great challenge [1]. They cause up to 500,000 deaths a year worldwide and an estimated 3,000 to 50,000 deaths a year in the United States of America (US) [2]. Frequently, their severity cannot be assessed in a timely manner, and thus, systems capable of providing estimates of influenza incidence are critical to allow health officials to properly prepare for and respond to influenza-like illness (ILI) outbreaks. The US Centers for Disease Control and Prevention (CDC) continuously monitor the level of ILI circulation in the US population by gathering information from physicians’ reports that record the percentage of patients seen in clinics who exhibit influenza-like illnesses (ILI) symptoms. While CDC ILI data provides public health officials with an important proxy of influenza activity in the population, its availability has a known lag-time of at least 7 to 14 days. This means that by the time the data is available, the information is already 1 or 2 weeks old.
Many attempts have been made to estimate the ILI activity in the US ahead of the release of CDC reports, some using a combination of statistical and mechanistic SIR models [3,4,5] and others using non-traditional Internet-based information systems such as: Google [6,7], Yahoo [8], and Baidu [9] Internet searches, Twitter posts [10,11,12], Wikipedia article views [13,14], Flu Near You [15,16], and clinicians’ databases (such as UpToDate) queries [17]. We will focus on non-traditional Internet-based approaches here. Google Flu Trends (GFT) [6], a widely accepted digital disease detection system that uses the Google search volume of specific terms to predict ILI in the US and other countries, continuously provides real-time estimates of ILI. Even though GFT was initially hailed as a success, its inaccuracies in multiple time periods of high ILI have led to doubts about the utility of these data [18]. While Google and external researchers have worked to update and reevaluate the methodology behind GFT [19, 20, 21, 22, 23, 24], alternative and independent methods to estimate ILI in real-time are still needed.
We propose a methodology based on machine learning algorithms capable of providing real-time (“nowcast”) and forecast estimates of ILI by leveraging data from multiple sources including: Google searches, nearly real-time hospital visit records provided by athenahealth, Twitter posts, and data from Flu Near You, a participatory surveillance system. While models using these data sources to predict ILI may capture different flu incidence signals in the population, we show that they complement one another when we combine them to predict CDC’s ILI. Our main contribution consists of optimally combining multiple ILI estimates, generated independently with each data source, into a single prediction of ILI utilizing machine learning ensemble approaches. Our methodology exploits the information in each data source and produces accurate weekly ILI predictions for up to four weeks ahead of the release of CDC’s ILI reports, effectively producing forecasts three weeks into the future. We evaluate the predictive ability of our ensemble approach during the 2013–2014 and 2014–2015 flu seasons for each of the four weekly time horizons.
We collected CDC-reported ILI, considered the ground truth for this study, from the ILINet website (http://gis.cdc.gov/grasp/fluview/fluportaldashboard.html). We used five independent data sets to develop our ILI weak predictors: (a) near real-time hospital visit records from athenahealth, a medical practices management company; (b) Google Trends, a Google service that provides approximate search volumes for specific queries (www.google.com/trends), (c) influenza-related Twitter microblogging posts, (d) FluNearYou, a participatory surveillance system to self-report ILI; and (e) Google Flu Trends. All datasets were accessed and downloaded on March 16, 2015.
We chose three different machine learning algorithms: Stacked linear regression, Support Vector Machine regression, and AdaBoost with Decision Trees Regression, in order to optimally combine the five ILI estimates, produced independently with the five available data sources. We chose this set of machine learning algorithms since each one of them is known to have distinct strengths in combining information [30]. While the linearity assumption may be restrictive, we chose Stacked Linear Regression for simplicity. We chose Support Vector Machines (SVM) with radial basis function kernels because they map the input space to an infinite dimensional, nonlinear feature space, thus allowing more freedom on the functional relationship between the target and independent variables. Both Stacked Linear Regression and Support Vector Machines are global methods that apply the same rules to all of the data. We chose AdaBoost with Decision Trees because it has the power to learn local rules.
In the following paragraphs we describe the main features of each methodology.
Stacked linear regression is a machine learning methodology commonly used in finance to combine weak predictors of stock prices [30, 31]. The goal of this methodology is not to identify which (so called) “weak predictor”, vk(t), is the best one to predict the quantity y(t) (in our case flu activity), but to linearly combine the information contained in all the “weak predictors” to obtain a more accurate and robust single predictor of a quantity y(t). A multivariate approach is used to determine the best linear combination of weak predictors capable of producing the best prediction of the quantity y(t) over a training period. Since the weak predictors are, by construction, highly correlated (indeed, each individual predictor was designed to minimize the square error between the predictions and flu activity), a way to discard redundant information is needed. Regularized approaches that penalize the size of the multiplying coefficients, αk, in the multivariate regression, such as Ridge or LASSO regularizations (L2 and L1, respectively), are good candidates to handle this. We chose LASSO regularization for our ensemble approach since we are interested in identifying models with the smallest number of independent variables (vk(t)). Additionally, a non-negative constraint for each multiplicative coefficient αk is imposed. This linear combination is then used to predict the value of y(t) for values of t outside of the training period.
Support Vector Machine (SVM) models [32] are similar to multivariate linear regression models with the important difference that non-linear functions can be chosen as the best relationship between the variables. This is achieved by introducing transformations (called kernels) that map the independent variables to higher dimensional feature spaces. The independent variables can even be mapped to an infinite dimensional feature space with the use of a radial basis function (RBF) kernel. SVM models are fitted by minimizing an epsilon-insensitive cost function where errors (between the predictions and the observed values) of magnitude less than epsilon are ignored in the cost function. This approach typically leads to better generalization of the chosen model on out-of-sample data. The SVM kernel type, margin width, and regularization hyper parameters were chosen via cross-validation on the training data.
Decision Tree models are created by recursively splitting the input space, creating local models in each region of the input space. Decision trees, however, have been shown to be unstable as small changes in the data can lead to drastically different tree structures. Boosting methods, such as Adaptive Boosting (AdaBoost), are often employed to fix this problem. Adaptive Boosting (AdaBoost) regression [33] fits a sequence of weak learners (in this case decision trees) on sequentially reweighted versions of the training data. At each iteration, the weights are individually modified so that the training examples incorrectly predicted by the previous decision tree are given more importance when training the next decision tree. The final prediction is obtained by taking the weighted median of the predictions outputted by the ensemble of weak learners (AdaBoost.R2 algorithm: [33]).
In all of the aforementioned regression approaches the goal was to use all available information, in a given point in time, to produce accurate predictions of CDC’s %ILI one, two, three, and four weeks ahead of the release of CDC reports, effectively predicting ILI three weeks into the future. At a given point in time, historical values up to two weeks prior to current date were available for all data sources (CDC, FNY, ATH, GT, GFT, and TWT). In addition real-time ILI estimates were available, with one-week lag, for ATH, GT, GFT, TWT. With this information, we produced predictions for every week starting on July 06, 2013 and up to February 21, 2015. For our first prediction, on the week of July 06, 2013, the first training set included 31 weeks worth of historical data from all data sources. For subsequent weeks, we dynamically increased the training set to include all available information at the given date, from all data sources.
As a reference, we produced ILI predictions using only historical CDC reported ILI. We achieved this via an autoregressive model with three weekly lagged components as independent variables (equation 1 in Paul et al 2014 [11]). We trained this model for the time period 11/06/11–2/08/15, and produced out-of-sample predictions for the four weekly time horizons during the time period of our study. We used the same procedure as the ARX model for Twitter, training on the 2011–2012 and 2012–2013 flu seasons, and producing predictions on the 2013–2014 and 2014–2015 flu seasons. These predictions were used to assess the added value provided by our digital disease detection systems’ information.
We report 5 evaluation metrics to compare the performance of the five independent predictors and the multiple ensemble methods: Pearson correlation, root mean squared error (RMSE), maximum absolute percent error (MAPE), Root Mean Square Percent error (RMSPE), and hit rate.
The definitions of all evaluation metrics are given below. Our notation is as follows: yi denotes the observed value of the CDC’s ILI at time ti, xi denotes the predicted value by any model at time ti,
y¯
denotes the mean or average of the values {yi} and similarly
x¯
denotes the mean or average of the values {xi}.
Pearson Correlation, a measure of the linear dependence between two variables during a time period [t1, tn], is defined as:
r=∑i=1n(yi−y¯)(xi−x¯)∑i=1n(yi−y¯)2∑i=1n(xi−x¯)2
Root Mean Squared Error (RMSE), a measure of the difference between predicted and true values is defined as:
RMSE=1n∑i=1n(yi−xi)2
Root Mean Squared Percent Error (RMSPE), a measure of the percent difference between predicted and true values is defined as:
RMSPE=1n∑i=1n(yi−xiyi)2×100
Maximum Absolute Percent Error (MAPE), a measure of the magnitude of the maximum percent difference between predicted and true values, is defined as
MAPE=(maxi|yi−xi|yi)×100
Hit Rate, a measure of how well the algorithm predicts the direction of change in the signal (independently of the magnitude of the change), is defined as:
HitRate=∑i=2n(sign(yi−yi−1)==sign(xi−xi−1))n−1×100
where the symbol = = denotes an if statement that returns the value 1, if the signs of predicted and observed changes are the same, and 0 otherwise.
These metrics were calculated for the time period: July 06, 2013 to February 21, 2015.
Table 1 presents the performance of the 5 real-time (nowcast) weak predictors as measured by each individual evaluation metric. This table is labeled “last week” since at a given point in time the revised version of all these estimates is only available on the Sunday of the reported week (or Monday of the subsequent week) and thus the information effectively predicts the %ILI of last week. For context, we included the metrics of three additional real-time predictions: (1) the baseline autoregressive predictions described in the previous section; (2) the CDC’s Virology data, and (3) the best real-time ensemble method predictions, produced with a support vector machine (with RBF kernel).
As Table 1 shows, the real-time ensemble predictions outperform any individual weak predictor in all but one metric (the hit rate). A 0.989 Pearson correlation and an average error of about 0.176%ILI (RMSE) make the ensemble approach a very accurate predictor. The ensemble predictions are very robust as indicated by the size of the MAPE, which measures how much the ensemble method is off-target with respect to the revised CDC ILI estimates. The worst performance was 23.6%, which is comparable to the LASSO’s 20.2% MAPE. See Table 2. This error is smaller than two thirds of the smallest MAPE of any of the individual weak predictors. In terms of hit rate, which reflects the ability of the method to predict the upward or downward tendency of the CDC’s ILI (in addition to the Pearson correlation and independently of producing an accurate point estimate, as captured by RMSE), athenaheath data (ATH) offers the best results.
Furthermore, Table 1 quantitatively shows the added value of using real-time digital disease detection information over a simple historical autoregressive approach. This can be seen by the improvement of the Pearson correlation from 0.930 to 0.989, the near three-fold reduction on the RMSE, and the maximum absolute error cut in half.
The top panel of Fig 1 graphically shows the revised CDC’s ILI along with the predictions of: the 5 data sources, the baseline, and the best ensemble approach (SVM RBF), as a function of time. The errors for each predictor are displayed in the bottom panel of Fig 1. The real-time estimates produced with our ensemble method are capable of predicting the timing and magnitude of the two peaks of the 2014–2015 season exactly, whereas they predict the peak of the 2013–2014 season with a one-week lag. Overall predictions track very accurately the CDC’s revised %ILI. This can also be seen in the top left panel of Fig 2.
Since none of the five weak predictors produce predictions into the future (forecasts), we do not have the equivalent of Table 1 for the three forecast time horizons (labeled “this week”, “next week”, and “in two weeks”). Table 2 presents the performance of 4 different machine learning ensemble approaches and the baseline autoregressive predictions for the four time horizons. Figs 2, 3 and 4 show these results graphically. Ensemble predictions produced with the AdaBoost method show the best accuracy (lowest RMSE) and robustness (lowest MAPE), for the three forecast time horizons. Correlation is also highest with AdaBoost in all three horizons. While the hit rate seems to be highest for different methods in different time horizons, Adaboost has an overall best performance as observed in Figs 3 and 4. We highlight the fact that our ensemble predictions one week into the future, labeled “this week”, have comparable accuracy to real-time GFT predictions, as measured by RMSE.
As shown in Table 2, our ensemble approach produces better results than the baseline AR3 autoregressive model in all similarity metrics and all time horizons. This fact shows quantitatively the value of using social media and crowd-sourced data in improving influenza predictions in future %ILI predictions. Specifically, the average error (RMSE) of our ensemble predictions nearly halves the errors of the autoregressive predictions in all time horizons. Pearson correlations of our ensemble approach predictions improve their autoregressive counterparts, from 0.845 to 0.960, in the one week forecast; from 0.759 to 0.927, in the two-week forecast, and from 0.683 to 0.904, in the three week forecast. Note also that our forecast estimates in all time horizons (up to four weeks ahead of the release of CDC’s reports) show at least comparable accuracy to “real-time” estimates obtained with a purely autoregressive model.
The ability of the ensemble approach forecasts to capture the timing and magnitude of the peaks in the flu seasons decays as the time horizon increases, as observed in Fig 2. Indeed, one-week forecasts predict the 2013–2014 peak with a one-week lag and with a percent error of about 10%, and they predict the two 2014–2015 peaks with a one-week lag and with percentage errors less than 2%. The two-week forecasts capture the 2013–2014 peak with a one-week lag and show percentage errors of about 10%, and they predict the two 2014–2015 peaks with a two-week lag and percentage errors up to 20%. Finally, the three-week forecasts capture the 2013–2014 peak with a two week lag and show percentage errors of about 20%, and they predict the two 2014–2015 peaks with a two-three week lag and with percentage errors up to 25–30%.
Our results show that our real-time ensemble predictions outperform every real-time flu predictor constructed independently with each data source. This fact suggests that combining information from multiple independent flu predictors is advantageous over simply choosing the best performing predictor. This is the case not only for real-time predictions but also for the one, two and three week forecasts presented.
Specifically, we show that our methodology can produce predictions one week ahead of GFT’s real-time estimates with comparable accuracy. We also show that our ensemble forecasts (up to three weeks into the future) always improve predictions produced with a baseline autoregressive model, thus proving quantitatively the added value of incorporating search and social media data in our flu prediction models.
It is interesting to highlight that the correlation and RMSE of the ensemble approach real-time predictions (Corr: 0.989 and RMSE: 0.176) are similar to the differences between revised and unrevised CDC reports (Corr: 0.993 and RMSE: 0.162). This means that our real-time ensemble model is as accurate a predictor of the revised CDC’s ILI estimates as the unrevised CDC data is. Thus, it is possible that we may be reaching the limit of what is possible, in terms of producing an accurate predictor of revised CDC’s ILI.
Our ensemble estimates correlate better with CDC’s ILI than CDC’s Virology data (which measures lab-confirmed cases of influenza) does with CDC’s ILI. This suggests that our (search and social media) data sources, when combined appropriately, track closely people showing symptoms and not necessarily those that are confirmed with influenza. It is important to mention that CDC’s Virology data (http://www.cdc.gov/vaccines/pubs/surv-manual/chpt06-influenza.html) is not necessarily considered to be a good predictor of ILI and tends to be even more lagged than CDC’s ILI due to the slowness of laboratory testing [34,35,36].
Doubts have emerged regarding the value of digital disease detection methods as a consequence of the multiple discrepancies between GFT’s predictions and the observed CDC’s ILI estimates [18,19, 20, 21, 22, 24, 28, 29]. We highlight the fact that even when one of the independent predictors produces unreliable estimates, our ensemble estimates are robust and accurate. This is observed specifically during the 2014–2015 flu season when ATH and GFT overestimated the flu season peak magnitude by more than 30% and approximately 15%, respectively, and the real-time ensemble approach estimates were right on target.
An additional attribute of our approach is that even if the ground truth (now CDC reports) were chosen to come from a different (and potentially more appropriate) source, our methodology would seamlessly adapt to predicting any target signal.
While the results presented here are for influenza-like illnesses at the national level within the US, our approach shows promise to be easily extended to accurately track not only influenza in other countries where multiple data sources may be available [37,38] but also other infectious diseases. Indeed, infectious diseases such as Dengue [39, 40, 41] or Malaria [42], for which multiple surveillance methods are in place would benefit from combining information in a similar way to the one proposed here. Moreover, disease surveillance data at finer spatial resolutions tend to be scarcer and often unreliable [43], and thus, approaches like ours may help produce more accurate and robust disease incidence estimates, at higher spatial resolutions, by drawing data from multiple sources.
Using weekly information from reports published by the CDC as our gold standard for national flu activity may not necessarily be ideal. Indeed, two data sources considered in this study, athenahealth and Flu Near You, aim at tracking the percentage of the general population with ILI symptoms independently. While athenahealth can be thought of as a subsample of the CDC-reported %ILI (since it calculates the %ILI in a similar fashion to the CDC, except with the information from those patients seeking medical attention in facilities managed by athenahealth), Flu Near You aims at providing an estimate of flu activity from a potentially distinct population (people willing to report their health status in weekly surveys via a mobile phone app). Interestingly, while the sectors of the population sampled by the CDC and FNY maybe distinct (they may overlap when people report their symptoms using the FNY app and they seek medical attention), Fig 1 and a recent study [16] show that their ILI estimates track one another quite well (Pearson correlation of .948) suggesting that both FNY and CDC datasets may be good proxies of ILI activity in the population. Finally, the best ensemble methodology may change for future flu seasons, and thus, continuous monitoring of the multiple methodologies’ performances should be conducted as new predictions are produced.
We presented a methodology that optimally combines the information from multiple real-time flu predictors to produce more accurate and robust real-time flu predictions than any other existing system. Moreover, our ensemble approach is capable of using real-time and historical information to accurately forecast flu estimates one, two, and three weeks into the future.
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10.1371/journal.pcbi.1000510 | Predicting the Evolution of Sex on Complex Fitness Landscapes | Most population genetic theories on the evolution of sex or recombination are based on fairly restrictive assumptions about the nature of the underlying fitness landscapes. Here we use computer simulations to study the evolution of sex on fitness landscapes with different degrees of complexity and epistasis. We evaluate predictors of the evolution of sex, which are derived from the conditions established in the population genetic literature for the evolution of sex on simpler fitness landscapes. These predictors are based on quantities such as the variance of Hamming distance, mean fitness, additive genetic variance, and epistasis. We show that for complex fitness landscapes all the predictors generally perform poorly. Interestingly, while the simplest predictor, ΔVarHD, also suffers from a lack of accuracy, it turns out to be the most robust across different types of fitness landscapes. ΔVarHD is based on the change in Hamming distance variance induced by recombination and thus does not require individual fitness measurements. The presence of loci that are not under selection can, however, severely diminish predictor accuracy. Our study thus highlights the difficulty of establishing reliable criteria for the evolution of sex on complex fitness landscapes and illustrates the challenge for both theoretical and experimental research on the origin and maintenance of sexual reproduction.
| One of the biggest open questions in evolutionary biology is why sexual reproduction is so common despite its manifold costs. Many hypotheses have been proposed that can potentially explain the emergence and maintenance of sexual reproduction in nature, and currently the biggest challenge in the field is assessing their plausibility. Theoretical work has identified the conditions under which sexual reproduction is expected. However, these conditions were typically derived, making strongly simplifying assumptions about the relationship between organisms' genotype and fitness, known as the fitness landscape. Building onto previous theoretical work, we here propose different population properties that can be used to predict when sex will be beneficial. We then use simulations across a range of simple and complex fitness landscapes to test if such predictors generate accurate predictions of evolutionary outcomes. We find that one of the simplest predictors, related to variation of genetic distance between sequences, is also the most accurate one across our simulations. However, stochastic effects occurring in small populations compromise the accuracy of all predictors. Our study both illustrates the limitations of various predictors and suggests directions in which to search for new, experimentally attainable predictors.
| Sexual reproduction is widespread among multi-cellular organisms [1]. However, the ubiquity of sex in the natural world is in stark contrast to its perceived costs, such as the recombination load [2] or the two-fold cost of producing males [3],[4]. Given these disadvantages it is puzzling that sexual reproduction has evolved and is maintained so commonly in nature. The “paradox of sex” has been one of the central questions in evolutionary biology and a large number of theories have been proposed to explain the evolution and maintenance of sexual reproduction [5]. Currently, the most prominent theories include (i) the Hill-Robertson effect [6]–[8], (ii) Muller's ratchet [9], (iii) the Red Queen hypothesis [10],[11], and (iv) the Mutational Deterministic hypothesis [12],[13]. While originally described in various different ways, the underlying benefit of sex can always be related to the role of recombination in breaking up detrimental statistical associations between alleles at different loci in the genome. What fundamentally differentiates the theories is the proposed cause of these statistical associations, assigned to either the interactions between drift and selection (Fisher-Muller effect, Muller's ratchet, and Hill-Robertson effect) or gene interactions and epistatic effects (Red Queen hypothesis and Mutational Deterministic hypothesis).
The present list of hypotheses is certainly not exhaustive, with new ones continuously being proposed, complementing or replacing the existing ones [14]. However, it is not new hypotheses that are most needed, but the real-world evidence that allows us to distinguish between them. The major question that still remains is whether the assumptions and requirements of different theories are fulfilled in the natural world. Accordingly, there has been considerable effort to experimentally test these assumptions, mainly for the epitasis-based theories (reviewed in [15]–[17]). However, an even more basic and crucial problem underlies all work on evolution of sex: how does one choose, measure, and interpret appropriate population properties that relate to different theories [17]–[19]. The difficulty stems from the often large divide between the theoretical and experimental research: theories are frequently formulated as mathematical models and rely on simplistic fitness landscapes or small genome size (e.g. two locus, two allele models) [13], [20]–[25]. As a result, it may be unclear how a property established based on these simplified assumptions relates to actual properties of natural populations.
In this study we attempt to bridge the gap between the theoretical and experimental work and to identify which population measures are predictive of the evolution of sexual reproduction by simulating the evolution of both sexual and asexual populations on fitness landscapes with different degrees of complexity and epistasis. The measures we use are the change of mean fitness, of additive genetic variance, or of variance in Hamming distance as well as four epistasis-based measures, physiological, population, mean pairwise, and weighted mean pairwise epistasis. While this certainly is not an exhaustive list, we took care to include major quantities previously considered in theoretical and experimental literature (e.g. [26]–[28]). With some exceptions [29]–[32], earlier work generally focused on the smooth, single peaked landscapes, while here we also use random landscapes and NK landscapes (random landscapes with tunable ruggedness). Some studies of more complex rugged landscapes tested whether they would select for sex but have not found a simple and unique answer, even in models with only two-dimensional epistasis [33],[34]. A recent paper, which uniquely combines experimental and theoretical approaches and simulates evolution of sex on empirical landscapes, also finds that landscape properties greatly affect the outcome of evolution, sometimes selecting for but more often against sex [35]. However, what specifically distinguishes our study is the goal of not only determining when sex evolves but also of quantifying our ability to detect and predict such outcome in scenarios where we know how the evolution proceeds.
Whether the more complex landscapes we are using here are indeed also more biologically realistic is open to debate as currently little is known about the shape and the properties of real fitness landscapes (for an exception see for example [35],[36]). Our goal is to move the research focus away from the simple landscapes mostly investigated so far to landscapes with various higher degrees of complexity and epistasis, and to probe our general understanding of the evolution of sexual reproduction on more complex fitness landscapes.
Notably, we find that some of the measures routinely used in the evolution of sex literature perform poorly at predicting whether sex evolves on complex landscapes. Moreover, we find that genetic neutrality lowers the predictive power of those measures that are typically robust across different landscapes types, but not of those measures that perform well only on simple landscapes. The difficulty of predicting sex even under the ideal conditions of computer simulations, where in principle any detail of a population can be measured with perfect accuracy, may be somewhat sobering for experimentalists working on the evolution of sex. We hope, however, that this study will evoke interest among theoreticians to tackle the challenge and develop more reliable predictors of sex that experimentalists can use to study the evolution of sex in natural populations.
We investigated the evolution of sex in simulations on three types of fitness landscapes with varying complexity (smooth, random and NK landscapes) and used seven population genetic quantities (ΔVarHD, ΔVaradd, ΔMeanfit, Ephys, Epop, EMP, and EWP, Table 1) as predictors of change in frequency of the recombination allele (see Methods for more details). We calculated predictor accuracy (the sum of true positives and true negatives divided by the total number of tests) and used it to assess their quality on 110 smooth landscapes with varying selection coefficients and epistasis, 100 random landscapes, and 100 NK landscapes each for K = 0,…,5. All landscapes are based on 6 biallelic loci and they were generated such that an equal number of landscapes of each type select for versus against sex in deterministic simulations with infinite population size. Hence, random prediction by coin flipping is expected to have an accuracy of 0.5.
Figure 1 shows the accuracy of the predictors for the different landscape types. Increasing levels of blue indicate greater accuracy of prediction. For the simulations with infinite population size (deterministic simulations) we ran a single competition between sexual and asexual populations to assess whether sex was selected for. For simulations with finite population size (stochastic simulations), we ran 100 simulations of the competition phase and assessed whether the predictor accurately predicts the evolution of sex in the majority of these simulations. Focusing on the top left panel we find that for deterministic simulations most predictors are only highly accurate in predicting evolutionary outcomes for the smooth landscapes. The exception is the poor performance of ΔMeanfit, which is not surprising, as theory has shown that for populations in mutation-selection balance ΔMeanfit is typically negative [2]. According to our use of ΔMeanfit as a predictor, it always predicts no selection for sex when negative and thus is correct in 50% of cases, due to the way the landscapes were constructed. For the NK0 landscapes, all predictors perform poorly, because such NK landscapes have no epistasis by definition (see Methods). For infinite population size, theory has established that in absence of epistasis there is no selection for or against sex. Indeed, in our simulations the increase or decrease in the frequency of sexual individuals is generally so small (of order 10−15 and smaller) that any change in frequency can be attributed to issues of numerical precision. Generally, the accuracy of most predictors is much weaker for complex landscapes (NK and random landscapes) than for the simpler, smooth landscapes. The predictors that have highest accuracy across different landscape types are ΔVarHD and Epop.
To test whether combinations of the predictors could increase the accuracy of prediction of the evolution of sex we plot for each landscape the value of the predictors ΔVarHD, ΔVaradd and ΔMeanfit against each other and color code whether the number of sexual individuals increased (red) or decreased (blue) during deterministic competition phase (see Figure 2). If the blue and red points are best separated by a vertical or a horizontal line, then we conclude that little can be gained by combining two predictors. If, however, the points can be separated by a different linear (or more complex) function of the two predictors, then combining these predictors would indeed lead to an improved prediction. Figure 2 shows the corresponding plots for the smooth, the random, and the NK2 landscapes. For the smooth landscapes the criterion ΔVarHD>0 or ΔVaradd>0 are both equally good in separating cases where sex evolved from those where it did not. As already shown in Figure 1, ΔVarHD is generally a more reliable predictor of the evolution of sex than ΔVaradd in the more complex random or NK landscapes. Epistasis-based theories suggest that the selection for sex is related to a detrimental short-term effect (reduction in mean fitness) and a possibly beneficial long-term effect (increase in additive genetic variance) [28]. The plots of ΔVaradd against ΔMeanfit, however, do not indicate that combining them would allow a more reliable prediction of the evolution of sex. Generally, the plots show that blue and red points either tend to overlap (in the more complex landscapes) or can be well separated using horizontal or vertical lines (in the smooth landscapes) such that combining predictors will not allow to substantially increase the accuracy of prediction. This is also the case for all other landscapes and all other pairwise combinations of predictors (data not shown). It is possible that some of the effect described in [28] and expected here are too small to be detected with the level of replication in our study. However, as the level of replication used in this computational study goes way beyond what can be realistically achieved in experimental settings we expect that these effects would also not be detected in experimental studies.
We also used a linear and quadratic discriminant analysis to construct functions to predict the outcome of competitions between the two modes of reproduction. For these purposes, half of the data set was used for training and the other half for testing of the discriminant functions, and the procedure was repeated separately for each of the three population sizes (1,000, 10,000, and 100,000) and the deterministic case. In no case did these methods improve the accuracy of predictions (data not shown). While there certainly are other, potentially more sophisticated techniques that could be used here, our analysis indicates that there may not be much additional information in our metrics that could be extracted and used to increase the accuracy of the predictions.
All predictors performed much worse for simulations with finite population size (Figure 1), most likely because the selection coefficient for sex is weak [19],[20]. To further examine the effect of finite population size on the evolution of sex on different landscape types we analyzed 100 independent simulations of the competition phase starting from the genotype frequencies obtained from the burn-in phase on each landscape. Figure 3 shows the fraction of cases in which the frequency of sexual individuals increased for three population sizes (1,000, 10,000, and 100,000), plotted separately for those landscapes in which frequency of the recombination modifier increased or decreased in deterministic simulations. For almost all landscapes the fraction of cases in which sex evolves is close to 50%, indicating that selection for sexual reproduction is indeed extremely weak, and can thus easily be overwhelmed by stochastic effects (in contrast to simulations with infinite populations where selection coefficients of any size will always produce a consistent observable effect). As a consequence, even for relatively large population sizes the outcome of the competition between sexual and asexual populations is largely determined by drift. Such weak selection may in part due to the small number of loci used for these simulations and stochastic simulations with larger genomes have indeed been shown to result in stronger selection for or against sex [37],[38]. However, accurate deterministic simulations are computationally not feasible for large genome sizes, because of the need to account for the frequency of all possible genotypes in deterministic simulations (see Supporting Information (Text S1) for more details).
According to the Hill-Robertson effect (HRE) [8],[21] selection for recombination or sex may be stronger in populations of limited size, because in such populations the interplay between drift and selection can generate negative linkage disequilibria, which in turn select for increased sexual reproduction. The strength of HRE vanishes for very small populations and for populations of infinite size [21]. In an intermediate range of population sizes, the HRE increases with increasing number of loci (as does the range of population sizes in which the effect can be observed) [38] and for large genome size it can be strong enough to override the effect of weak epistasis [37]. In our simulations, however, HRE is weak, as is evidenced by the fact that, in the NK0 landscape, which by definition have no epistasis, the fraction of runs in which sex evolves is only very marginally above 50% (Figure 3).
Our results indicate that for finite population size the predictors generally perform poorly. Of course this does not imply that they could not be better than a simple coin toss. However, the results suggest that these predictors will likely be of limited use, as any experiment will have difficulties to reach even the replicate number that we have used to generate Figure 1.
We also examined additional fitness landscapes, characterized by increased neutrality (for full details and figures see Text S1). We found that the allelic diversity at neutral loci both decreases the accuracy and generates a systematic bias in the previously best performing predictors, Epop and ΔVarHD. In contrast, other predictors investigated here, ΔVaradd, ΔMeanfit, Ephys, EMP, and EWP are not affected by including neutral loci, but still have poor accuracy of prediction on more complex fitness landscapes.
Our computer simulations highlight the difficulties in predicting whether the frequency of sexual individuals will increase in populations evolving on complex fitness landscapes. The predictors of the evolution of sex used here are derived from previous studies on simpler landscapes and are based on standard population genetic measures such as variance of Hamming distance, mean fitness, additive genetic variance, physiological or population epistasis, mean pairwise epistasis and weighted mean pairwise epistasis. Not surprisingly, all predictors are highly accurate on the simplest landscape type, the smooth landscapes, in which log fitness is a monotonic, weakly curved function of the Hamming distance to the fittest sequence. Interestingly, the simplest measure, ΔVarHD, which is based on the change of Hamming distance after versus before recombination, turns out to be among the most robust predictors of the evolution of sex across the range of fitness landscapes tested here (Figure 1). Notably, ΔVarHD requires no fitness data, but only genetic information, and should thus be easier to obtain experimentally, at least when compared to the measures that require both information on the mutations present and on their fitness effects. Intuitively, ΔVarHD measures whether recombination has the effect of spreading out a population or condensing it over the space of all possible genotypes. Spreading out the population over genotype space (i.e. increasing genetic variation) may increase phenotypic variation, which in turn leads to more efficient selection on fitness affecting loci and eventually to selection for sexual reproduction. Another measure, population epistasis, Epop, turns out to be an equally robust predictor of the evolution of sex. Epop may be more convenient than Ephys in experimental studies because there is no need to generate a large number of mutants for the analysis. However, neither of these two predictors manages to attain a high degree of accuracy on complex landscapes under stochastic conditions. Using combinations of predictors also does not appear to increase the overall ability to predict evolutionary outcome in our simulations (Figure 2). Our results are in general agreement with previous work on the evolution of sex on rugged and complex landscapes [32]–[35]. For example, we had to generate many landscapes before finding 50 of each type on which sex evolves, with sometimes less than 1 in 10 landscapes promoting sex under the deterministic scenario (data not shown.)
Finding such poor performance of all the predictors is a somewhat sobering result. A possible criticism of our approach is that we have focused in our simulations on small genomes in mutation selection balance. In such a situation the selection for sex is particularly weak and hence likely to be overwhelmed by stochastic effects. An alternative scenario for which effects could be stronger is that of populations in which a substantial fraction of beneficial mutations have not yet gone to fixation [21]. Preliminary simulations of this alternative scenario suggest that, under a narrow range of parameters (low mutation rate, single mal-adapted founding genotype, large population size), sex indeed evolves more frequently than in simulations starting from mutation selection balance (data not shown). Generally, however, the quality of the predictors does not substantially increase. More work is needed to characterize and fully examine predictors in adapting populations, highlighting these scenarios as interesting future directions, but outside of the scope of the present study.
Our goal here was not to address all of the different theories on evolution of sex and our simulations are certainly not well suited for investigating the Red Queen hypothesis ([10] and for a recent review see [39]) which is based on fluctuating selection or the Fisher-Muller hypothesis [6],[7],[21] which is based on the effect of beneficial mutations. To do so properly would require an entirely new setup, including for example, changing fitness landscapes and/or presence of parasitic individuals in case of the Red Queen hypothesis and the continuous presence of novel beneficial mutations in case of the Fisher-Muller hypothesis. Instead our study focuses on those hypotheses for the evolution of sex/recombination such as the Mutational Deterministic hypothesis [12],[13] or the Hill-Robertson effect [8],[37] that work at mutation-selection balance.
The central message of our study is that the prediction of the evolution of sex is difficult for complex fitness landscapes, even in the idealized world of computer simulations where in principle one can measure any detail of a given population and fitness landscape. Here we put the emphasis on predictors that are experimentally measurable and are based on conditions for the evolution of sex established in the population genetic literature using simple fitness landscapes. We have however included EMP and EWP, two predictors which would be more difficult to measure experimentally, but are based on the most fundamental and general theoretical treatment of the evolution of sex [28]. Of course, while our choice of predictors, landscapes and selection regimes is comprehensive, we are aware that it can never be exhaustive or complete – there will always be other options to try out and test. Future work will have to focus on identifying more reliable predictors of the evolution of sex that can be used in conjunction with experimental data. Additionally, a better characterization of properties of natural fitness landscapes is badly needed to improve our understanding of the forces selecting for the evolution of sex. As it stands, ΔVarHD, our best candidate for a predictor of the evolution of sex, has nevertheless important shortcomings. In particular, it never reaches high levels of accuracy on many of the landscapes. Still, ΔVarHD at least suggests a potential direction for future research: a focus on predictors that would take advantage of the rapidly increasing number of fully or partially sequenced genomes and allow us to determine the advantage of sex in large numbers of taxa, bringing us closer to fully understanding the evolution of sex.
All simulations of the evolution of a haploid population on a given fitness landscape are divided into a “burn-in” and a “competition” phase. In the burn-in phase an asexually reproducing population is allowed to equilibrate on the landscape starting from random initial genotype frequencies. In the competition phase we determine whether the frequency of an allele coding for increased recombination increases in the population.
The burn-in phase consists of repeated cycles of mutation and selection. Genotype frequencies after selection are given by the product of their frequency and relative fitness before selection. In all simulations mutations occur independently at each locus with a mutation rate μ = 0.01 per replication cycle. This high mutation rate was chosen in order to obtain sufficient levels of genetic diversity. However, we also tested mutation rates up to 10 times lower and found no qualitative differences in the results (data not shown).
In the competition phase the population undergoes recombination in addition to mutation and selection in each reproduction cycle. To this end a recombination modifier locus is added to one end of the genome, with two alleles m and M, each present in exactly half of the population. Recombination between two genotypes depends on the modifier allele in both genotypes, with the corresponding recombination rates denoted by rmm, rmM, and rMM. For the simulations discussed in the main text we used rmm = rmM = 0 and rMM = 0.1. For this parameter choice individuals carrying distinct modifier alleles cannot exchange genetic material and thus any effect of increased recombination remains linked to the M allele. Sexual and asexual individuals compete directly with each other, and we refer to this scenario as the evolution of sex. In contrast, if rmm<rmM<rMM, then genetic material can be exchanged between all individuals. We refer to this scenario as the evolution of recombination. For the sake of simplicity, we primarily consider the evolution of sex in the main text, but analogous simulations of the evolution of recombination scenario led to qualitatively indistinguishable results (Text S1). Moreover, for the evolution of sex scenario we also tested values of rMM ranging from 0.01 to 0.3 (data not shown), which produced qualitatively indistinguishable results. All recombination values refer to a probability of recombination happening between neighboring loci with one recombination event per genome. The position of the crossover point is chosen randomly. No mutations occur between m and M alleles at the modifier locus.
Recombination, mutation and selection as described above are deterministic and are calculated assuming infinite population size. To examine stochastic effects, we also considered populations with 1,000, 10,000, and 100,000 individuals. Those simulations included a step in which the frequencies of genotypes are sampled from a multinomial distribution according to their frequencies as calculated based on infinite population size.
The burn-in phase always consists of 2500 generations of mutation and selection. We confirmed that 2500 generations were typically sufficient for the system to go into mutation-selection balance from random initial genotype frequencies (data not shown). The competition phase consists of 250 generations of recombination, mutation and selection. For infinite population size we ran a single competition phase for each burn-in phase. For finite-size populations, the outcome was estimated as the average of 100 simulations of the competition phase.
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10.1371/journal.pgen.1005667 | Epigenetic Control of Salmonella enterica O-Antigen Chain Length: A Tradeoff between Virulence and Bacteriophage Resistance | The Salmonella enterica opvAB operon is a horizontally-acquired locus that undergoes phase variation under Dam methylation control. The OpvA and OpvB proteins form intertwining ribbons in the inner membrane. Synthesis of OpvA and OpvB alters lipopolysaccharide O-antigen chain length and confers resistance to bacteriophages 9NA (Siphoviridae), Det7 (Myoviridae), and P22 (Podoviridae). These phages use the O-antigen as receptor. Because opvAB undergoes phase variation, S. enterica cultures contain subpopulations of opvABOFF and opvABON cells. In the presence of a bacteriophage that uses the O-antigen as receptor, the opvABOFF subpopulation is killed and the opvABON subpopulation is selected. Acquisition of phage resistance by phase variation of O-antigen chain length requires a payoff: opvAB expression reduces Salmonella virulence. However, phase variation permits resuscitation of the opvABOFF subpopulation as soon as phage challenge ceases. Phenotypic heterogeneity generated by opvAB phase variation thus preadapts Salmonella to survive phage challenge with a fitness cost that is transient only.
| A tradeoff can increase the adaptive capacity of an organism at the expense of lowering the fitness conferred by specific traits. This study describes a tradeoff that confers bacteriophage resistance in Salmonella enterica at the expense of reducing its pathogenic capacity. Phase variation of the opvAB operon creates two subpopulations of bacterial cells, each with a distinct lipopolysaccharide structure. One subpopulation is large and virulent but sensitive to phages that use the lipopolysaccharide O-antigen as receptor, while the other is small and avirulent but phage resistant. In the presence of a phage that targets the O-antigen, only the avirulent subpopulation survives. However, phase variation permits resuscitation of the virulent opvABOFF subpopulation as soon as phage challenge ceases. This transient tradeoff may illustrate the adaptive value of epigenetic mechanisms that generate bacterial subpopulations in a reversible manner.
| The study of differentiation in bacterial species that undergo developmental programs has played a historic role in biology [1,2,3]. In addition, phenotypic differences between colonies [4] and within colonies [5,6] were described many years ago in bacterial species that do not undergo development. Despite their technical limitations, these early studies contributed to bring about the idea that phenotypic heterogeneity might be a common phenomenon in the bacterial world [7]. This view has been confirmed by single cell analysis technologies [8,9,10,11,12]. Furthermore, theoretical analysis has provided evidence that phenotypic heterogeneity can have adaptive value, especially in hostile or changing environments [13,14,15]. In certain cases, the adaptive value of subpopulation formation is illustrated by experimental evidence [16,17,18].
Formation of bacterial lineages is governed by diverse mechanisms, including programmed genetic rearrangement [19] and contraction or expansion of DNA repeats at genome regions [20,21]. In other cases, however, lineage formation is controlled by epigenetic mechanisms: certain cell-to-cell differences serve as physiological signals, and signal propagation by a feedback loop generates an inheritable phenotype [12,22]. Cell-to-cell differences can be a consequence of environmental inputs or result from the noise intrinsical to many cellular processes [10,12,15]. In turn, the feedback loops that propagate the initial state beyond can be relatively simple or involve complex mechanisms like the formation of inheritable DNA adenine methylation patterns in the genome [12,23,24]. Some feedback loops are stable enough to cause bistability, the bifurcation of a bacterial population into two distinct phenotypic states [22].
If a feedback loop is metastable, reversion of the epigenetic state will occur after a certain number of cell divisions. Reversible bistability is usually known as phase variation, and typically involves reversible switching of gene expression from OFF to ON or from low to high expression [25,26,27]. Examples of phase variation have been described mostly in bacterial pathogens, and subpopulation formation is frequently viewed as a strategy that may facilitate evasion of the immune system during infection of animals [25,26]. This view is supported by the observation that phase-variable loci often encode envelope components or proteins involved in modification of the bacterial envelope [25,26].
Some phase-variable envelope modifications controlled by DNA adenine methylation play roles in bacteriophage resistance. For instance, phase variation in the gtrABC1 cluster protects S. enterica against the T5-like phage SPC35, probably by an indirect mechanism [28]. In Haemophilus influenzae, DNA adenine methylation controls phase-variable resistance to bacteriophage HP1c1 but the underlying mechanism remains hypothetical [29]. Phase variation can also contribute to phage resistance without alteration of the bacterial surface. For instance, certain genes encoding restriction-modification systems show phase variation [30,31].
In this study, we describe a phase variation system that confers resistance to bacteriophages that use the lipopolysaccharide (LPS) O-antigen as receptor. The genome of Salmonella enterica contains a horizontally-acquired locus, known as opvAB or STM2209-STM2208 [32]. The opvA and opvB genes form a bicistronic operon [32] and encode inner membrane proteins [32]. OpvA is a small peptide of 34 amino acids, and OpvB is a larger protein of 221 amino acids with homology to the Wzz superfamily of regulators of LPS O-antigen chain length [32]. We show that expression of the S. enterica opvAB operon confers resistance to bacteriophages P22 (Podoviridae), 9NA (Siphoviridae), and Det7 (Myoviridae) by modification of the phage receptor, the LPS O-antigen. Because expression of opvAB is phase-variable, bacteriophage resistance occurs in the subpopulation of opvABON cells only. This subpopulation, which is extremely small, preadapts Salmonella to survive phage challenge albeit at the cost of reduced virulence. However, because the opvABON state is reversible, the virulence payoff is temporary, and a virulent bacterial population resuscitates as soon as phage challenge ceases.
OpvA and OpvB were previously shown to be inner membrane proteins involved in LPS synthesis [32]. Because the LPS is known to have a helical distribution in the cell envelope [33], the OpvA and OpvB subcellular localization was investigated. For this purpose, a chromosomal opvB::mCherry fusion was constructed downstream of the opvB gene (so that the strain remains OpvAB+). In a wild type background, expression of opvB::mCherry was low in most cells (Fig 1A). However, rare cells with high levels of expression of opvB::mCherry were detected (Fig 1A), an observation consistent with the occurrence of phase variation skewed towards the OFF state [32]. Expression of opvB::mCherry was also monitored in an opvAB-constitutive (opvABON) strain engineered by elimination of GATC sites upstream of the opvAB promoter [32]. In an opvABON background, all cells displayed high levels of fluorescence, similar to those of the rare fluorescent cells visualized in a wild type background (Fig 1B). In fluorescent cells, OpvB was seen forming helical intertwining ribbons in the inner membrane (Fig 1A and 1B).
The subcellular distribution of OpvA was examined using a plasmid-borne opvA::mCherry fusion. This experimental choice was based on the consideration that construction of an mCherry fusion in the upstream gene opvA would likely prevent opvB expression because of a polarity effect. In the strain carrying plasmid-borne opvA::mCherry, intense fluorescence was observed in all cells (Fig 1C), presumably because opvA::mCherry overexpression from the multicopy plasmid abolished phase variation. This construction was useful, however, to permit clear-cut observation of helical intertwining ribbons formed by OpvA (Fig 1D). The evidence that OpvA and OpvB may have a similar or identical distribution in the bacterial envelope is consistent with the physical interaction previously described between OpvA and OpvB [32].
Constitutive expression of opvAB leads to the production of a particular form of O-antigen in the S. enterica LPS, with a modal length of 3–8 repeat units [32]. A diagram of LPS structure is presented in S1 Fig, together with an electrophoretic separation of O-antigen chains and a diagram of the differences in LPS structure between opvABOFF and opvABON pubpopulations. To investigate the role of individual OpvA and OpvB proteins in control of O-antigen chain length, non-polar mutations in opvA and opvB were constructed in the wild type and in an opvABON background. In the wild type, lack of either OpvA or OpvB did not alter the electrophoretic profile of LPS (Fig 2), an observation consistent with two known facts: the subpopulation of cells that express opvAB in wild type Salmonella is very small [32], and an OpvAB−mutant displays an LPS profile identical to that of the wild type [32]. In contrast, OpvA−opvBON and opvAON OpvB−mutants showed differences with the parental opvABON strain and also with the wild type:
These observations suggested that the function of OpvA might be to prevent the formation of normal O-antigen so that OpvB could then impose its preferred modal length. To test this hypothesis, LPS structure was analyzed in an OpvA−opvBON background in the absence of either WzzST or WzzfepE. The results support the view that OpvB needs OpvA to prevent O-antigen formation by customary modal length regulators. In the absence of WzzST, OpvB alone was able to produce an O-antigen similar to that found in the opvABON strain (Fig 2). In contrast, lack of WzzfepE did not seem to facilitate OpvB function, suggesting that OpvB may mainly compete with WzzST. This preference may be related to the fact that both WzzST and OpvB convey relatively short preferred modal lengths: 3–8 for OpvB [32] and 16–35 for WzzST [36,37,38] compared with >100 for WzzfepE [34].
The LPS O-antigen is a typical receptor for bacteriophages [39] and modification of the O-antigen can confer bacteriophage resistance [40]. On these grounds, we tested whether opvAB expression increased Salmonella resistance to the virulent phages 9NA [41,42] and Det7 [43,44]. We also tested the historic phage P22, using a virulent mutant to avoid lysogeny [45]. Three strains (wild type, opvABON and ΔopvAB) were challenged with 9NA, Det7, and P22, which belong to different bacteriophage families and use the O-antigen as receptor. The experiments shown in Fig 3 were carried out by inoculating an exponential culture of S. enterica with an aliquot of a phage suspension at a multiplicity of infection (MOI) >10, and monitoring bacterial growth afterwards. The results can be summarized as follows:
A tentative interpretation of these observations was that the wild type strain contained a subpopulation of opvABON cells that survived phage challenge. Because opvAB phase variation is skewed towards the OFF state [32], the small size of the opvABON subpopulation and the regular formation of phage-sensitive opvABOFF cells caused growth retardation (albeit to different degrees depending on the phage). In contrast, the opvABON strain grew normally, an observation consistent with the occurrence of phage resistance in the entire bacterial population. This interpretation was supported by analysis of the LPS profiles of wild type and opvABON strains grown in the presence of P22, 9NA, and Det7 until stationary phase (OD600 ~4) (Fig 3). After phage challenge, the wild type contained an LPS different from the LPS found in LB (Fig 3D), and similar or identical to the LPS found in the opvABON strain (Fig 2; see also [32]). In contrast, the LPS from the opvABON strain did not change upon phage challenge (Fig 3D).
Confirmation that challenge of the wild type with P22, 9NA, and Det7 selected opvABON S. enterica cells was obtained by flow cytometry analysis (Fig 4). Expression of opvAB was monitored using a green fluorescent protein (gfp) fusion constructed downstream opvB (so that the strain remains OpvAB+). In the absence of phage, most S. enterica cells expressed opvAB at low levels; however, a small subpopulation that expressed opvAB at high levels was also detected. Phage challenge yielded mostly S. enterica cells with high levels of opvAB expression. These observations provide additional evidence that phages P22, 9NA, and Det7 kill the opvABOFF subpopulation, and that opvABON cells overtake the culture.
If the above model was correct, we reasoned, cessation of phage challenge should permit resuscitation of a phage-sensitive subpopulation as a consequence of opvAB phase variation. This prediction was tested by isolating single colonies from cultures in LB + phage. After removal of phage by streaking on green plates, individual isolates were cultured in LB and re-challenged with P22, 9NA, and Det7 (≥ 20 isolates for each phage). All were phage-sensitive and their LPS profile was identical to that obtained before phage challenge. Representative examples are shown in Fig 5. Unlike the wild type, individual isolates of the ΔopvAB strain remained phage-resistant after single colony isolation and were considered mutants (see below).
Challenge of a ΔopvAB strain with phages P22, 9NA, and Det7 prevented growth for 5–6 h, and growth resumed afterwards (Figs 3 and 5). To investigate the cause(s) of phage resistance in the absence of OpvAB, individual colonies were isolated from stationary cultures of a ΔopvAB strain in LB + P22, LB + 9NA, and LB + Det7. Phage was removed by streaking on green plates. Independent isolates (each from a different culture) were then tested for phage resistance. Sixty seven out of 72 independent isolates turned out to be phage-resistant, thus confirming that they were mutants. Analysis of LPS in independent phage-resistant mutants revealed that a large fraction of such mutants displayed visible LPS anomalies (Fig 6). The few mutant isolates (5/67) that did not show LPS alterations may have LPS alterations that cannot be detected in gels or carry mutations that confer phage resistance by mechanisms unrelated to the LPS. Whatever the case, these experiments support the conclusion that resistance of S. enterica to phages P22, 9NA, and Det7 in the absence of OpvAB is mutational.
To determine whether isolates resistant to one phage were also resistant to other phages that target the O-antigen, cross-resistance was tested by growth in LB upon phage inoculation. Sixty seven mutants (24 P22-resistant, 24 9NA-resistant, and 19 Det7-resistant) were tested (S1 Table). The main conclusions from these experiments were as follows:
Because the LPS plays roles in the interaction between S. enterica and the animal host [34,46,47], we tested whether OpvAB-mediated alteration of O-antigen chain length affected Salmonella virulence. For this purpose, competitive indexes (CI's) [48] were calculated in the following experiments: (i) oral and intraperitoneal inoculation of BALB/c mice; (ii) infection of mouse macrophages in vitro; and (iii) exposure to guinea pig serum, an assay that provides reductionist assessment of the capacity of the pathogen to survive the bactericidal activity of complement [47]. As controls, CI's were also calculated in LB (Table 1). In all virulence assays, the CI of the opvABON strain was found to be lower than those of the wild type and the ΔopvAB strain. Because the wild type, the opvABON strain and the ΔopvAB strain show similar or identical growth rates in LB, the conclusion from these experiments was that expression of opvAB reduces Salmonella virulence (Table 1).
A tradeoff is established whenever the adaptive capacity of an organism is increased at the expense of lowering the fitness conferred by specific phenotypic traits [49]. Tradeoffs have been mainly studied in sexually reproducing organisms but they occur also in microbes [50,51,52,53]. In pathogens, for instance, acquisition of mutational resistance to antimicrobial compounds often affects fitness [54,55], and may require loss of virulence as a payoff [56]. Bacteriophage resistance has been also shown to impair virulence in a variety of bacterial pathogens [57].
In this study, we describe a tradeoff that confers bacteriophage resistance at the expense of reducing virulence in the human pathogen Salmonella enterica. This tradeoff is however unusual because phage resistance is not mutational but epigenetic, and because the phage-resistant, avirulent phenotype is reversible.
The opvAB operon is present in most Salmonella serovars (S2 Table). Its products are inner membrane proteins that form intertwining ribbons (Fig 1) reminiscent of those formed by the LPS in the outer membrane [33]. Synthesis of OpvA and OpvB causes a decrease of long O-antigen chains and an increase of short O-antigen chains in the LPS (Fig 2; see also [32]). Genetic evidence presented in Fig 2 suggests that OpvA may prevent the formation of normal O-antigen, allowing OpvB to compete with the WzzST modal length regulator. A similar phenomenon occurs in Pseudomonas aeruginosa, where the Iap transmembrane peptide encoded by bacteriophage D3 disrupts endogenous O-antigen biosynthesis allowing a phage-encoded O-antigen polymerase to produce a different O-antigen [58]. OpvB confers a predominant modal length of 3–8 units, while the wild type LPS shows modal lenghts of 16–35 units and of >100 units (Fig 2; see also [32]). As a consequence of the dramatic change in LPS structure caused by opvAB expression, S. enterica becomes resistant to bacteriophages 9NA, Det7, and P22 (Figs 3 and 4), an observation consistent with the fact that the O-antigen is the bacterial surface receptor used by these bacteriophages [39,59].
Expression of opvAB undergoes phase variation under the control of DNA adenine methylation and the transcriptional regulator OxyR [32]. Because opvAB phase variation is skewed towards the OFF state [32], S. enterica populations contain a major subpopulation of opvABOFF (phage-sensitive) cells and a minor subpopulation of opvABON (phage-resistant) cells. In the presence of a bacteriophage that targets the O-antigen, the opvABOFF subpopulation disappears and the opvABON subpopulation is selected (Figs 3 and 4). Hence, the existence of a small subpopulation of phage-resistant cells preadapts S. enterica to survive phage challenge. In OpvAB−S. enterica, acquisition of phage resistance is mutational only, and a frequent mechanism is alteration of LPS structure (Fig 6). Because the LPS plays major roles in bacterial physiology including resistance to environmental injuries and host-pathogen interaction [60], opvAB phase variation may have selective value by providing S. enterica with a non-mutational, reversible mechanism of phage resistance. This mechanism offers the additional advantage of protecting Salmonella from multiple phages, perhaps from all phages that bind the O-antigen (note that the phages used in this study belong to three different families: Podoviridae, Siphoviridae, and Myoviridae).
Acquisition of phage resistance in opvABON cells requires a payoff: reduced virulence in both the mouse model and in vitro virulence assays (Table 1). In a phage-free environment, this payoff may be irrelevant because the avirulent subpopulation is minor as a consequence of skewed switching of opvAB toward the OFF state: 4 x 10−2 for ON→OFF switching vs 6 x 10−5 for OFF→ON switching [32]. In other words, only 1/1,000 S. enterica cells can be expected to be avirulent in a phage-free environment. The virulence payoff is therefore enforced in the presence of phage only, and its adaptive value may be obvious as it permits survival. On the other hand, the fitness cost of OpvAB-mediated phage resistance can be expected to be temporary because phase variation permits resuscitation of the virulent opvABOFF subpopulation as soon as phage challenge ceases (Fig 5). Resuscitation may actually be rapid as a consequence of skewed switching towards the opvABOFF state.
Phase variation systems that contribute to bacteriophage resistance have been described previously. For instance, certain restriction-modification systems show phase-variable expression [31]. However, protection by restriction-modification systems can be expected to be incomplete as only a fraction of infecting phage genomes are modified [61]. Phase variation can also confer phage resistance by preventing infection, and an interesting example is the gtrABC1 cluster which protects S. enterica against the T5-like phage SPC35 [28]. Although the receptor of SPC35 is the BtuB vitamin transporter, GtrABC-mediated glycosylation of the LPS O-antigen may reduce SPC35 adsorption by an indirect mechanism [28]. In Haemophilus influenzae, phase-variable resistance to bacteriophage HP1c1 may involve changes in LPS [29]. Because these studies did not investigate the impact of phase variation on bacterial fitness, it remains unknown whether the tradeoff associated with opvAB phase variation is unusual or commonplace. However, if one considers that envelope structures play multiple roles in bacterial physiology aside from serving as phage receptors, it is tempting to predict that phase-variable bacteriophage resistance may frequently involve fitness costs. Whatever the payoff, however, phase-variable resistance may have a crucial advantage over mutation by creating phenotypic heterogeneity in a reversible manner.
Strains of Salmonella enterica used in this study (Table 2) belong to serovar Typhimurium, and derive from the mouse-virulent strain ATCC 14028. For simplicity, S. enterica serovar Typhimurium is routinely abbreviated as S. enterica. For the construction of strain SV7643, a fragment containing the promoterless mCherry gene and the kanamycin resistance cassette was PCR-amplified from pDOC-R, an mCherry-containing derivative of plasmid pDOC [62] using primers HindIII-opvB-mCherry-5 and NdeI-opvB-mCherry-3. The construct was integrated into the chromosome of S. enterica using the Lambda Red recombination system [63]. For the construction of strains SV5675, SV6786, SV6791, and SV8020, targeted gene disruption was achieved using plasmid pKD13 [63] and oligonucleotides listed in S3 Table: wzzB5-PS4 + wzzB3-PS1 for wzzST disruption, fepE5-PS4 + fepE3-PS1 for wzzfepE disruption, STM2209-PS4tris + STM2209-PS1 for opvA disruption, and STM2208-PS4 + STM2208-PS1 for opvB disruption. The kanamycin resistance cassettes were then excised by recombination with plasmid pCP20 [63]. For the construction of strain SV6727, a fragment containing the promoterless green fluorescent protein (gfp) gene and the chloramphenicol resistance cassette was PCR-amplified from pZEP07 [64] using primers STM2208stop-GFP-5 and STM2208stop-GFP-3. The fragment was integrated into the chromosome of S. enterica using the Lambda Red recombination system [63]. An opvB::gfp transcriptional fusion was formed downstream of the opvB stop codon, and the strain remained OpvAB+. For the construction of strains SV7645, SV8117, and SV8118, plasmid pKD46 was introduced in SV6401, and the PCR products used for construction of strains SV7643, SV8020 and SV5675 were integrated into the chromosome of SV6401 using the Lambda Red recombination system [63].
Bertani's lysogeny broth (LB) was used as standard liquid medium. Solid LB contained agar at 1.5% final concentration. Green plates [65] contained methyl blue (Sigma-Aldrich, St. Louis, MO) instead of aniline blue. Antibiotics were used at the concentrations described previously [66].
Bacteriophages 9NA [41,42] and Det7 [43] were kindly provided by Sherwood Casjens, University of Utah, Salt Lake City. Bacteriophage P22 H5 is a virulent derivative of bacteriophage P22 that carries a mutation in the c2 gene [45], and was kindly provided by John R. Roth, University of California, Davis. For simplicity, P22 H5 is abbreviated as P22 throughout the text.
A DNA fragment containing opvA and the native opvAB promoter was PCR-amplified using primers KpnI-opvA-plasmidoGFP-5 and KpnI-opvA-plasmidoGFP-3 (S3 Table). The amplification product was cloned into pDOC-R [62]. The resulting plasmid produces an OpvA-mCherry fusion protein.
Bacterial cells from 1.5 ml of an exponential culture in LB at 37°C (OD600 ~0.15) were collected by centrifugation, washed in phosphate saline buffer (PBS), and resuspended in 1 ml of the same buffer. Cells were fixed in 4% formaldehyde solution and incubated at room temperature for 30 minutes. Finally, cells were washed, resuspended in PBS buffer, and stored at 4°C. Images were obtained by using an Olympus IX-70 Delta Vision fluorescence microscope equipped with a 100X UPLS Apo objective. Pictures were taken using a CoolSNAP HQ/ICX285 camera and analyzed using ImageJ software (Wayne Rasband, Research Services Branch, National Institute of Mental Health, MD, USA). Z-stacks (optical sections separated by 0.2 μm) of mCherry fluorescence were taken with the same microscope. Maximal intensity projections are shown.
To investigate LPS profiles, bacterial cultures were grown in LB overnight. Bacterial cells were harvested and washed with 0.9% NaCl. The O.D.600 of the washed bacterial suspension was measured to calculate cell concentration. A bacterial mass containing about 3 x 108 cells was pelleted by centrifugation. Treatments applied to the bacterial pellet, electrophoresis of crude bacterial extracts, and silver staining procedures were performed as described by Buendia-Claveria et al. [67].
Bacterial cultures were grown at 37°C in LB or LB + phage (P22, 9NA, or Det7) until exponential (OD600 ~0.3) or stationary phase (OD600 ~4). Cells were then diluted in PBS. Data acquisition and analysis were performed using a Cytomics FC500-MPL cytometer (Beckman Coulter, Brea, CA). Data were collected for 100,000 events per sample, and analyzed with CXP and FlowJo 8.7 software.
Overnight cultures were diluted 1:100 in 3 ml LB and grown in aeration by shaking at 37°C until they reached an optical density OD600 ~0.3. One hundred μl of a bacteriophage lysate (P22 H5, 9NA, or Det7) were added (M.O.I. ≥10), and OD600 was subsequently measured at 1 h intervals.
Eight-week-old female BALB/c mice (Charles River Laboratories, Santa Perpetua de Mogoda, Spain) were inoculated with pairwise combinations of the wild type, an opvABON strain, and a ΔopvAB strain at a 1:1 ratio. Bacterial cultures were previously grown overnight at 37°C in LB without shaking. Oral inoculation was performed by feeding the mice with 25 μl of PBS containing 0.1% lactose and 108 bacterial colony-forming units (CFU). Intraperitoneal inoculation was performed with 104 CFU in 200 μl of PBS. Bacteria were recovered from the spleen and the liver of infected mice at 2 days post-infection (intraperitoneal challenge) or 5 days post-infection (oral challenge). A competitive index (CI) was calculated as described elsewhere [48]. To permit strain discrimination, ATCC 14208 was tagged with trg::MudJ (Kmr), an allele that is neutral for virulence [68]. When necessary, cross-streaking on green plates with P22 H5 was used to discriminate phage-resistant isolates [65]. Infection of cultured J774 mouse macrophages, inoculation of guinea pig serum (Sigma-Aldrich), and calculation of competitive indexes in vitro followed previously described protocols [68]. The Student's t test was used to determine whether the CI's were significant.
Animal research adhered to the principles mandatory in the European Union, as established in the Legislative Act 86/609 CEE (November 24, 1986) and followed the specific protocols established by the Royal Decree 1201/2005 of the Government of Spain (October 10, 2005). The protocols employed in the study were reviewed by the Comité Ético de Experimentación of the Consejo Superior de Investigaciones Científicas (CSIC), and were approved by the Consejería de Medio Ambiente, Comunidad de Madrid, Spain, on December 12, 2014 (permit number PROEX 257/14).
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10.1371/journal.pgen.1004533 | The TRIM-NHL Protein LIN-41 Controls the Onset of Developmental Plasticity in Caenorhabditis elegans | The mechanisms controlling cell fate determination and reprogramming are fundamental for development. A profound reprogramming, allowing the production of pluripotent cells in early embryos, takes place during the oocyte-to-embryo transition. To understand how the oocyte reprogramming potential is controlled, we sought Caenorhabditis elegans mutants in which embryonic transcription is initiated precociously in germ cells. This screen identified LIN-41, a TRIM-NHL protein and a component of the somatic heterochronic pathway, as a temporal regulator of pluripotency in the germline. We found that LIN-41 is expressed in the cytoplasm of developing oocytes, which, in lin-41 mutants, acquire pluripotent characteristics of embryonic cells and form teratomas. To understand LIN-41 function in the germline, we conducted structure-function studies. In contrast to other TRIM-NHL proteins, we found that LIN-41 is unlikely to function as an E3 ubiquitin ligase. Similar to other TRIM-NHL proteins, the somatic function of LIN-41 is thought to involve mRNA regulation. Surprisingly, we found that mutations predicted to disrupt the association of LIN-41 with mRNA, which otherwise compromise LIN-41 function in the heterochronic pathway in the soma, have only minor effects in the germline. Similarly, LIN-41-mediated repression of a key somatic mRNA target is dispensable for the germline function. Thus, LIN-41 appears to function in the germline and the soma via different molecular mechanisms. These studies provide the first insight into the mechanism inhibiting the onset of embryonic differentiation in developing oocytes, which is required to ensure a successful transition between generations.
| Reprogramming into a naïve, pluripotent state during the oocyte-to-embryo transition is directed by the oocyte cytoplasm. To understand how this reprogramming is controlled, we searched for C. elegans mutants in which the activation of embryonic genome, a landmark event demarcating the switch from a germline- to embryo-specific transcription, is initiated precociously in germ cells. This screen identified a novel function for LIN-41, a member of the TRIM-NHL protein family, in preventing a premature onset of embryonic-like differentiation and teratoma formation in developing oocytes, thus ensuring a successful passage between generations. This is the first example of such a regulator in cells that are poised for embryonic development. Interestingly, the majority of molecular “roadblocks” to reprograming that have been identified so far are epigenetic regulators. However, we propose that, at least in germ cells, LIN-41-like regulators may fulfill an analogous role in the cytoplasm, which has possible implications for the generation of human pluripotent stem cells.
| There is a special relationship between germ cells and pluripotency, i.e,. the ability to adopt alternative cell fates. First, germ cells transmit the pluripotent potential to recreate all types of cells in a new individual. Second, germ cells give rise to pluripotent cell lines such as embryonic germ or carcinoma cells and oocyte cytoplasm has the capacity to reprogram somatic nuclei [1], [2]. Finally, in disease, germ cells can abnormally differentiate into diverse somatic cell types, forming teratomas. However, during normal development, the ability to differentiate into all three embryonic germ layers is restricted to the cells of the early embryo. Combined, these observations suggest that the reprogramming potential of germ cells is kept at bay by repressive mechanisms. Depletion of several chromatin modifiers, either alone or combined with an ectopic overexpression of somatic cell fate-specifying transcription factors, can induce reprogramming of C. elegans germ cells into somatic cells [3]–[5]. The loss of these factors appears to primarily impact proliferating (pre-meiotic) germ cells and affects chromatin-based regulation. In contrast, our previous work in the same animal demonstrated that a conserved RNA-binding protein, GLD-1/Quaking, prevents teratomatous differentiation of post-mitotic germ cells [6], [7]. Importantly, in gld-1 mutants, the germline-to-soma transition is accompanied by a precocious onset of embryonic (or zygotic) genome activation (EGA), suggesting a causal connection between EGA and pluripotency. In other animals, the connection between EGA and pluripotency has been also postulated based on the temporal correlation between EGA and the acquisition of a pluripotent chromatin landscape [8], [9].
These observations prompted us to examine whether new regulators of pluripotency can be identified based on a precocious onset of EGA in the germline. Here, we report the discovery of one such novel regulator of pluripotency, LIN-41/TRIM71. LIN-41 belongs to the TRIM-NHL protein family [10]. These proteins contain a TRIpartite Motif (TRIM) consisting of a RING finger domain (commonly endowing a protein with E3 ubiquitin ligase activity, for example [11]–[13]), two B-Box motifs and a coiled-coil domain. Additionally, they also carry six so-called NHL repeats (named after NCL-1, HT2A and LIN-41) and may contain a filamin domain, which have been implicated in both protein-protein and protein-RNA interactions [13]–[17]. Consistently, different molecular functions have been attributed to LIN-41-like proteins, but many questions remain open; for example, it is not clear whether all the domains function together and/or are used in a tissue context-dependent manner [11], [14], [18]–[20]. The TRIM-NHL family includes well-known regulators of self-renewal and differentiation. For example, in Drosophila melanogaster, Brat inhibits neuroblast self-renewal, cell growth and ribosome synthesis in the larval brain [21]–[24] and Mei-P26 restricts growth and proliferation in the ovarian stem cell lineage [25]. Defects in TRIM-NHL proteins have also been associated with human pathologies, for example TRIM32 has been implicated in the Bardet–Biedl Syndrome and the Limb-Girdle Muscular Dystrophy [12], [26], [27]. Recently, human LIN-41 has been shown to promote reprogramming of differentiated cells into induced pluripotent stem cells (iPSCs) [28]. Here, we demonstrate a role for LIN-41 in controlling pluripotency during development of an animal. In C. elegans, LIN-41 is a well-known component of the somatic heterochronic pathway, which temporally controls the transition from larval to adult cell fates [29], [30]. The lin-41 germline phenotype described here indicates that, by preventing the onset of embryonic events in developing oocytes, LIN-41 also ensures a successful transition between generations. However, based on our analyses on both existing and newly created LIN-41 mutations, LIN-41 appears to function in the germline and the soma via two distinct molecular mechanisms. Our study identifies the first cytoplasmic “molecular roadblock” to reprogramming in developing oocytes and we propose it to be required to delay the onset of embryonic differentiation until after fertilization.
To understand how the onset of pluripotency is controlled during C. elegans development, we executed a genetic screen to identify factors that prevent EGA in the adult germline. To monitor EGA, we created a strain expressing GFP from an early embryonic promoter, vet-4 (very early transcript 4) [31], [32]. Thus, to identify novel regulators of developmental plasticity, we searched for mutants expressing the EGA-GFP in the adult germline (Figure 1A). In addition to a new allele of gld-1, this screen yielded two mutants that, in contrast to the embryo-specific EGA-GFP expression in wild-type animals, expressed EGA-GFP within the gonads (Figure 1B). Several lines of evidence suggested that the phenotype of the two mutant strains was caused by alterations in the same gene, lin-41 (Figures 1C and S1A–D). In these mutants (alleles rrr3 and rrr4, Figure 1C), the EGA-GFP expression was restricted to the proximal region of the oogenic germline (Figure 1B). Consistent with this, RNAi-mediated depletion of lin-41 resulted in a similar expression of EGA-GFP in the gonad (Figure S1C) and a transgenic construct expressing LIN-41 fully rescued the germline defects of lin-41(rrr3) animals (Figure S1D). To further examine the role of LIN-41 in controlling EGA, we verified that the endogenous vet-4 is also abnormally transcribed in lin-41(rrr3) gonads. Indeed, by in situ hybridization, we could detect vet-4 to be expressed in the proximal gonads of lin-41(rrr3), but not wild-type animals (Figure 1D). Next, to examine the extent of embryonic-like transcription in lin-41(rrr3) gonads, we monitored the levels of vet-4 and other additional early embryonic transcripts by reverse transcription and quantitative PCR (RT-qPCR) (Text S1). We found that these transcripts were expressed in mutant, but not wild-type gonads (Figure 1E), further demonstrating that, in lin-41 mutants, embryonic transcription is prematurely activated in the germline. Importantly, we detected no obvious changes in levels or expression pattern of GLD-1 in lin-41(rrr3) gonads (Figure S2), suggesting that the gonadal phenotype of lin-41 mutants is not caused by defective expression of GLD-1.
In wild-type animals, Pol II-dependent transcription is repressed in oocytes, which is seemingly at odds with the embryonic-like transcription in the proximal gonads of lin-41 animals. To investigate this potential discrepancy, we examined the transcription-initiating phosphorylation of serine 5 (Ser5P) within the C-terminal domain (CTD) of Pol II [33]. In contrast to wild-type gonads, Ser5P was detected in the majority of the cells in the proximal gonads of lin-41(rrr3) animals (Figure 2A), indicating ongoing Pol II-dependent transcription. Apart from EGA, the onset of embryonic development is marked by the degradation of germline mRNAs and proteins [34]. To examine this aspect of the germline-to-soma transition in lin-41 animals, we followed the expression levels of RME-2, a yolk receptor present in oocytes [35], and PGL-1, a constitutive component of germ cell-specific RNA/protein granules [36]. In contrast to wild-type animals, which express RME-2 in developing oocytes and PGL-1 throughout the germline, we found that both proteins were absent from the proximal lin-41(rrr3) gonads (Figure 2B–C), indicating that cells in this gonadal region lose germline identity. To test this further, we monitored expression of several transcripts that are normally expressed in somatic lineages. By RT-qPCR (Text S1), we found that several of these transcripts (for example the myogenic hlh-1/MyoD) were abnormally expressed in lin-41(rrr3) gonads (Figure 2D). Additionally, we examined the expression of several hox genes, which control the positional identities of cells during animal body formation [37]. While the hox transcripts were not expressed in wild-type gonads, they were strongly expressed in lin-41(rrr3) gonads (Figure 2D). Finally, we analyzed the expression of the muscle lineage markers UNC-120 and muscle myosin, the intestine lineage marker ELT-2 and a GFP reporter driven from a pan-neuronal unc-119 promoter (nGFP). We observed that lin-41(rrr3) gonads contained numerous cells expressing muscle and neuronal markers (Figures 2E and S3; 44/45 examined gonads contained cells expressing UNC-120, 10/18 cells expressing muscle myosin, and 57/57 cells expressing the nGFP). Only few gonads contained ELT-2-expressing cells (3/35 gonads and only in few cells), which might reflect a competitive advantage of some differentiation programs in the lin-41 teratoma. During embryogenesis, most body-wall muscles of an adult animal are specified by the transcription factor PAL-1/CDX [38]. The PAL-1-dependent transcription is relatively well understood and involves the activation of its direct targets, such as HLH-1/MyoD and UNC-120/SRF [39]. In wild-type oocytes, expression of PAL-1 is insufficient for the induction of its target genes (Figure S4A–B) [40]. Nevertheless, we observed that the numbers of UNC-120-expressing cells in lin-41(rrr3) gonads were significantly reduced upon pal-1 RNAi (Figure S4B). Thus, the differentiation into muscles in lin-41 gonads appears, at least partly, to mimic the pathway driving muscle formation in embryos. Together, these findings indicate that lin-41 germ cells in the proximal gonad undergo a dramatic reprogramming, which results in the acquisition of an embryonic-like state and teratomatous differentiation.
To better understand the germline-to-soma transition in lin-41 animals, we examined cells in lin-41(rrr3) gonads in a time-course experiment (Figure 3A). Until immediately after the end of spermatogenesis, the morphology and numbers of germ cells in lin-41 and wild-type gonads appeared similar. However, concomitantly with the onset of oogenesis, differences between the lin-41 and wild-type germlines began to emerge. The proximal region of wild-type gonads contained fully-grown oocytes harboring chromosomes arrested at the diakinesis stage of meiosis I. In stark contrast, the proximal region of lin-41 gonads contained oocyte-like cells that were about to divide, as evidenced by the presence of highly condensed chromosomes (marked by the phosphorylation of histone H3 on serine 10, Ser10P [41], and microtubule spindles (Figure 3A–B). Consistent with entering a mitotic cell cycle, cells in the proximal lin-41(rrr3) gonads did not express HIM-3 (Figure 3C), a synaptonemal complex component [42]. Wild-type oocytes eliminate centrosomes, presumably to ensure the correct ploidy in embryos [31], [43], [44]. In contrast, by monitoring a constitutive centrosome component, SPD-2 [45], we found that centrosomes were present in the proximal lin-41(rrr3) gonads (Figure S5). These centrosomes could duplicate (Figures 3D and S5) and were able to nucleate microtubule spindles (Figure 3D). Finally, in addition to the cell cycle markers, we monitored expression of an EGA reporter (EGA-mCherry) and the muscle-lineage marker UNC-120 and observed that their expression followed the onset of mitosis (Figure 3A). Taken together, the absence of LIN-41 leads to the elimination of germline proteins, induction of EGA, a change from the meiotic to the mitotic cell cycle and somatic-like differentiation. Thus, rather than completing oogenesis, cells in the proximal lin-41 gonads execute events that, in wild-type development, only occur during embryogenesis.
In addition to the germline defects, lin-41(rrr3) animals displayed somatic abnormalities: decreased size (dumpy phenotype), appeared sick and occasionally bursted through the vulva. These phenotypes have been extensively described by Slack and colleagues and are caused, at least in part, by a precocious translation of the transcription factor LIN-29 [29]. To determine whether the gonadal phenotype reflects LIN-41 function in the germline, or it is indirectly caused by the loss of LIN-41 in the soma, we created a transgene driving lin-41 expression from a heat-shock promoter (hsp-16.41). Due to a general insensitivity of germ cells to the heat-shock promoter-driven expression [46], this transgene was not expressed in the germline but, when crossed into the lin-41(rrr3) mutant background and cultivated at an elevated temperature (24–25°C, which is apparently enough to drive sufficient expression of lin-41 in the soma), it rescued the somatic lin-41 defects (the transgenic animals no longer appeared sick or short; Figure 4A). Despite the somatic rescue, these animals still developed teratomas (Figure 4B, 50/50 examined animals), suggesting that, in controlling the germline-to-soma transition, LIN-41 functions autonomously in the germline. To examine this further, we immunostained gonads using antibodies raised against LIN-41 and found that, indeed, LIN-41 was present in the cytoplasm of germ cells starting from the late pachytene stage and culminating in the fully-grown oocytes (Figure 4C). Interestingly, LIN-41 was often absent from the most-proximal oocytes (Figures S1D and S6), suggesting a possible connection between oocyte maturation and/or ovulation and LIN-41 levels. LIN-41 expression was limited to the oogenic germline (i.e., it was absent in sperm, e.g., S1D), suggesting that the germline-to-soma transition in lin-41 gonads is caused by the loss of LIN-41 function in the developing oocytes.
In the soma, LIN-41 is thought to associate with and repress the mRNA encoding a transcription factor, LIN-29, and LIN-29 depletion suppresses the somatic defects of lin-41 mutants [29]. In contrast to these observations in the soma, lin-29 mRNA appears to be either poorly or not at all expressed in the germline (our unpublished results and [47]). Consistently, we found that RNAi-mediated depletion of LIN-29 did not suppress the germline defects of lin-41(rrr3) mutants (though, it suppressed the somatic defects, as expected [29]). We obtained similar results in lin-41; lin-29 double mutants (Figure S7). Thus, LIN-41 may function in the germline and soma via distinct targets and/or mechanisms.
The domain structure of LIN-41 reflects the diversity of functions that have been associated with TRIM-NHL proteins (Figure 1C). Several of these proteins function as E3 ubiquitin ligases, which require a functional RING domain [10], [30]. However, a sequence alignment of the C. elegans LIN-41 RING domain with those of other Caenorhabditis species indicates that a highly conserved proline, critical for canonical E3-E2 interactions [48], is not found in the nematode LIN-41 RING domains (Figure 5A). Moreover, mutating five cysteine residues that are critical for the RING domain zinc finger structure (C114S, C117S, C130S, C151S, C154S; Figure 5A) resulted in a protein that rescued both somatic and germline defects of lin-41(rrr3) animals (Figure 5B). Although we cannot rule out the possibility that LIN-41 associates with additional factors to regulate ubiquitination, these results suggest that the nematode LIN-41 does not function as a direct E3 ubiquitin ligase.
The coiled-coiled, filamin and NHL domains of the human homolog of LIN-41, TRIM71, constitute the minimal region responsible for binding mRNA and inhibiting translation [19], which is the function attributed to the C. elegans LIN-41 in the soma [29]. One previously isolated lin-41 allele, ma104, is a transposon insertion into the sequence coding for the filamin domain [29]. Intriguingly, lin-41(ma104) mutants display the somatic defects, but the animals are apparently fertile [29]. We confirmed this and, although the brood size in lin-41(ma104) animals is decreased [29], we found no obvious differences in the levels or localization of the germline LIN-41ma104 (Figure 6A) and no evidence for a precocious EGA in the gonads (0/35 worms were expressing the EGA reporter in their gonads). To understand the effect of the ma104 mutation on the LIN-41 protein, we examined the cDNA product of the lin-41(ma104) allele. We found that the ma104 mutation resulted in an insertion of 16 amino acids into the filamin domain (Figure 6B). To gain a mechanistic insight into the ma104 mutation, the filamin domain (residues 691–821) was subcloned, overexpressed in bacteria, and the protein purified to homogeneity. The protein was crystallized and the structure determined at high resolution (1.68 Å; for data collection and refinement statistics, see Table S1). The final crystallographic model encompasses residues 691–729 and 758–820, whereas a long insert (730–757) that is only found in Caenorhabditis LIN-41 protein sequences could not be built due to high flexibility. We found that the LIN-41 filamin structure exhibits a classical immunoglobulin (IG)-like domain fold consisting of seven β-strands arranged in two antiparallel β-sheets (Figure 6C) [49]. A structural search with the LIN-41 filamin domain against the Protein Data Bank (PDB), using DALI, identified the filamin domains most structurally similar to that of LIN-41, which yielded the filamin domains from the Dictyostelium discoideum gelation factor, the human TRIM45 and Filamin-A (PDB IDs 1QFH, 1WLH, 2DS4 and 3RGH, respectively) with root-mean-square deviation values for Cα positions between 1.6 and 2.0 Å [50]. Both crystal packing analysis using PISA [51] and SEC MALS experiments of the protein in solution reveal the oligomeric state of this protein domain as monomeric. Importantly, the 16-residue insertion present in the ma104 allele maps to the mid-section of the second β-strand and is very likely perturbing the filamin IG-like fold (Figure 6B–C). Specifically, the 16-residue insert will prevent completion of one of the β-sheets resulting in solvent access to the hydrophobic protein core of the IG-like β-sandwich and thereby severely destabilizing the fold. This makes it very unlikely that the filamin domain of the ma104 allele is properly folded to exert its biological function.
In the fly Brat and the mammalian TRIM71/LIN-41, the NHL domain is essential for mRNA regulation [14], [19], [52]. One of the lin-41 alleles reported here, rrr4, introduces a premature stop codon within the first NHL repeat (Figure 1C), potentially triggering mRNA degradation via nonsense-mediated mRNA decay (NMD). Indeed, inhibiting NMD (by depleting an NMD component, SMG-2 [53]), restored the wild-type expression pattern and levels of LIN-41rrr4 (Figure 7A). LIN-41rrr4 is expected to lack the NHL domain and we found that the gonads expressing this LIN-41 variant displayed lin-41-like germline and somatic defects (Figure 7A). Thus, the NHL domain appears to be essential for LIN-41 functions in both germ and somatic cells.
The NHL domain structure of Brat forms a six-bladed β-propeller [52]. Several point mutations in Brat and TRIM71 that disrupt mRNA regulation affect residues on the electropositive side of the NHL domain [19], [23], [52], [54] (Figures S8, S9), highlighting the importance of this surface for mRNA regulation. Point mutations in the NHL domain have been also reported in LIN-41 (Figures 7B and S9A–B) [29]. Importantly, although these mutations display defects in the soma, the animals are fertile, suggesting that the mutant proteins fulfill the gonadal functions [29]. To better interpret these mutations, we initially attempted, unsuccessfully, to express the LIN-41 filamin-NHL or a NHL-only domain constructs for protein structure determination. Thus, we created a homology model of the LIN-41 NHL domain based on the crystal structure of the Brat NHL domain. Interestingly, we found that most of the existing point mutations in the LIN-41 NHL domain also affect amino acids residing on the electropositive surface of the NHL domain (Figure 7B). These observations suggest that i) the electropositive surface of the NHL domain plays a conserved function in mRNA regulation and ii) the germline and somatic functions of the NHL domain involve different mechanisms. To explore this further, we introduced an additional mutation (Y941A) on the electropositive surface of the NHL domain (Figures 7B and S9). Potentially, this mutation is more informative than the other existing NHL mutations because mutation of the corresponding residue (Y702A) in TRIM71 is known to abolish mRNA regulation [19]. In contrast to the deletion of the whole NHL domain from otherwise rescuing (FLAG- and GFP-tagged) LIN-41 protein, which, as expected, caused defects in both the soma and the germline, we found that the LIN-41Y941A variant largely suppressed the germline defects and sterility of lin-41 animals (Figure 7C), including the precocious expression of the endogenous vet-4 transcript (Figure 7D), though it continued to display the somatic defects. Thus, if the same domains/residues determine mRNA regulation in LIN-41 as in TRIM71, the germline function of LIN-41 might be independent from mRNA binding.
In contrast to the much-publicized regulation of pluripotency via DNA and chromatin modifications, the potential for cytoplasmic regulation has largely been neglected. Our findings suggest that proteins like LIN-41 and GLD-1 can function in the cytoplasm as molecular “roadblocks” to reprogramming, analogous to the nuclear factors. Similarly, components of P-granules (germline-expressed RNPs) have been recently reported to facilitate maintenance of germline identity in proliferating germ cells, i.e., at the stage prior to GLD-1 expression [55]. Whether teratomatous differentiation in the absence of P-granules reflects precocious activation of embryonic transcription, or has a different etiology, remains to be determined. However, in contrast to P-granules, that impact multiple aspects of RNA metabolism, GLD-1 and LIN-41 are expected to have more specific functions. Intriguingly, the germline-to-soma transition in the absence of LIN-41 or GLD-1 involves similar events: loss of germline proteins, retention of centrosomes, execution of mitosis and activation of the embryonic genome. Two GLD-1 mRNA targets, important for the germline-to-soma transition, encode the CDK-2 partner protein CYE-1/cyclin E and the transcription factor PAL-1/Cdx [6], [31]. However, cyclin E and PAL-1 are co-expressed with LIN-41 in the developing wild-type oocytes [56], suggesting that their expression is not regulated by LIN-41. Thus, GLD-1 and LIN-41 may regulate pluripotency via different targets and/or mechanisms. While GLD-1 directly binds and regulates the expression of its mRNA targets, the molecular function of LIN-41 remains elusive. Our analysis suggests that the germline function of LIN-41 may be independent from mRNA binding, though it does not exclude its role in posttranscriptional regulation, for example as a component of a regulatory RNP. In addition to binding RNA, several TRIM-NHL proteins have been shown to modulate functions of other proteins, for example, by their sequestration (e.g., TRIM3 appears to regulate p21 [13], [17]) or by linking structural proteins (e.g., Wech bridges Talin and ILK for proper embryonic muscle attachment [16]). These interactions depend, at least in part, on the NHL domains of both proteins. Thus, LIN-41 could regulate the germline-to-soma transition by associating, via its NHL domain, with another protein.
LIN-41-mediated regulation of cell fate transition between generations is somewhat reminiscent of LIN-41 function in the hetrochronic pathway in the soma. However, specific mutations within the NHL and filamin domains of LIN-41 result mainly in somatic but not germline defects (this study and [29]). In addition, LIN-41-dependent repression of LIN-29 appears to be restricted to the soma. Thus, although LIN-41 regulates developmental transitions in both germ- and somatic cells, it may do so through different molecular mechanisms and/or targets.
In the soma, down-regulation of LIN-41, which is mediated by the let-7 miRNA, allows terminal differentiation [57], [58]. In the germline, LIN-41 levels decrease in the most-proximal oocytes, so that LIN-41 is absent from the early embryos. An interesting possibility is that the absence of LIN-41 is a trigger for the onset of embryonic differentiation. In order to test this hypothesis, we attempted to over-express LIN-41 from the heat shock promoter in very early embryos. However, LIN-41 was efficiently expressed only after gastrulation (our unpublished observation), i.e., several cell divisions after EGA, making the experiment inconclusive. The down-regulation of LIN-41 occurs while Pol II-dependent transcription is globally repressed in the oocytes, suggesting that the regulation occurs at the mRNA or the protein level. To test for possible regulation at the mRNA level, we expressed a rescuing LIN-41 under the control of a truncated 3′UTR missing most of the sequence, including the let-7 binding sites [58]. While the expression of this LIN-41 protein started earlier (more distally) in the germline, suggesting posttranscriptional regulation of lin-41 mRNA in this part of the gonad, LIN-41 was still down-regulated in the oocytes and embryos (our unpublished observation), hinting at a possible regulation at the protein level. If so, testing the functional significance of LIN-41 degradation will require the dissection of regulatory motifs in the protein (for example phosphorylation).
TRIM-NHL proteins are known to control the proliferation versus differentiation decision in germ- and neuronal stem cell lineages [23], [25], [59] and, intriguingly, C. elegans LIN-41 has been recently reported to control the regenerative ability of neurons [60]. While these examples highlight the importance of TRIM-NHL proteins for maintaining homeostasis in self-renewing tissues, these proteins have not previously been implicated in controlling pluripotency during development. In our study, we describe LIN-41 as a critical component of the timing mechanism controlling the oocyte reprogramming capacity. To our knowledge, this is the first example of such a regulator in cells that are ready for embryonic development, providing the initial glimpse into a pathway controlling one of the most fundamental developmental transitions. While the in vivo roles of the mammalian LIN-41/TRIM71 are poorly understood, the murine TRIM71 is expressed and functions in developing embryos [61], [62]. TRIM71 is also preferentially expressed in embryonic stem (ES) cells [18], which are derived from pluripotent embryonic cells. In ES cells, TRIM71 represses the expression of Cdkn1, an inhibitor of the cell cycle progression, thereby promoting proliferation [18]. While this role appears opposite to LIN-41 function in the C. elegans germline, TRIM71 presumably associates with many mRNAs, making additional roles likely. Intriguingly, the human LIN-41 has been recently shown to facilitate reprogramming of fibroblasts into iPSCs [28]. In this context, LIN-41, combined with several “pluripotency” transcription factors, can circumvent the requirement for c-Myc in reprogramming [28]. c-Myc facilitates reprogramming in several ways, including by inhibiting differentiation [63], and LIN-41 appears to play a similar role by repressing mRNAs encoding pro-differentiation factors [28]. Although the targets and, perhaps, the mechanisms may differ, it is striking that the C. elegans LIN-41 appears to fulfill an analogous function in the germline. Thus, dissecting LIN-41 targets and the mechanism are exciting objectives for the future research.
N2 animals were maintained as previously described [64] and were grown at 20°C unless stated otherwise. For alleles and transgenic lines, see Supplemental Material. For RNAi, L1 larvae (L4 for fog-2), grown at 25°C, were fed with bacteria expressing dsRNAs (targeting lin-41, pal-1, fog-2 or smg-2 from the Open Biosystem library or lin-29 from the Ahringer library) and screened one day after the L4-to-adult molt in the same (lin-41, pal-1 and lin-29) or in the second generation (fog-2 and smg-2). A bacterial strain carrying an “empty” vector was used as a negative control (mock RNAi).
EMS mutagenesis [64] was performed on a strain (# 1284, see Text S1) carrying the EGA-GFP (integrated at two chromosomal locations to increase GFP fluorescence). F2 animals derived from ≈10.000 F1s were screened. Candidate mutations were identified as previously described [65]. Each mutant was back-crossed four times against the parental strain before genome sequencing. Genomic DNAs (gDNAs) were isolated using Gentra Puregene Tissue Kit 4 g (Qiagen). DNA libraries were created from 50 ng of gDNA (Nextera DNA kit from Illumina). The sequencing data were generated using Hi Seq 2000 (Illumina).
Sequence reads were aligned to the May 2008 C. elegans assembly (obtained from http://hgdownload.soe.ucsc.edu/goldenPath/ce6/chromosomes/) using “bwa” [66]; version 0.6.1-r104) with default parameters, but only retaining single-hit alignments (“bwa samse -n 1” and selecting alignments with “X0:i:1”). The resulting alignments were converted to BAM format, sorted and indexed using “samtools” [67]; version 0.1.18). In order to quantify contamination by Escherichia coli, reads were similarly aligned to a collection of E. coli genomes (NCBI accession numbers NC_008253, NC_008563, NC_010468, NC_004431, NC_009801, NC_009800, NC_002655, NC_002695, NC_010498, NC_007946, NC_010473, NC_000913 and AC_000091), which typically resulted in less than 1% aligned reads. Sequence variants were identified using GATK [68]; version 1.5.31) indel realignment and base quality score recalibration, followed by SNP and INDEL discovery and genotyping for each individual strain using standard hard filtering parameters, resulting in a total of six to eight thousand sequence variations in each strain compared to the reference genome. Finally, the number of high quality (score > = 500) single nucleotide substitutions of EMS-type (G/C→A/T transitions [69], not found in other any other mutant strain or in the parent strain (typically less than 1% of the total number of variants per strain) were counted in sequential windows of 1 Mb to identify regions of increased variant density.
RNA was isolated from gonads dissected from one day-old (after the L4-to-adult molt) animals. cDNA was synthesized with oligo(dT) primers using the ImProm II Reverse transcription system from Promega according to manufacturer's instructions. cDNA was used for qPCR with the Absolute QPCR SYBR green ROX mix (AbGene) on an ABI PRISM 7700 system (Applied Biosystems). qPCR reactions were performed as previously described [31]. At least one primer in each pair is specific for an exon-exon junction. Human carrier RNA was added to each sample before RNA extraction, allowing normalization to hGAPDH. Standard curves for quantification were generated from a serial dilution of input cDNA for each primer pair. The amount of target present in each replicate was derived from a standard curve; an average was calculated for the triplicates. To compare total mRNA levels, the qPCR results were normalized to human GAPDH and to the wild-type values for each primer pair and fold enrichments were calculated. For primers used, see Text S1.
Immunostaining experiments were performed as previously described [70] with the following antibodies: PGL-1 [36] (dilution 1∶1000); SPD-2 [45] (“969LA”, 1∶800); GFP (Roche, 1∶700); phospo-Histone H3 Ser10 (“Ser10P”, Millipore, 1∶200); muscle myosin [71] (“5–6”, 1∶2.500); and UNC-120 (courtesy of Michael Krause, 1∶500). Immunostainings against RME-2 [35] (“INT”, dilution 1∶100), GLD-1 [72] (dilution 1∶5) and LIN-41 (courtesy of Helge Grosshans, “4796”, 1∶2.000) were performed as previously described [73] and against the Ser5P of Pol II CTD [74] (“3E8”, 1∶5) according to Seydoux and Dunn [33]. Immunostainings against HIM-3 [42] (courtesy of Monique Zetka, dilution 1∶500) were performed as previously described [75]. Secondary antibodies used in this study: goat anti-mouse IgG alexa-488 (Molecular Probes, 1∶600,), goat anti-rabbit IgG alexa-568 (Invitrogen, 1∶750) and goat anti-rat IgG alexa-568 (Molecular Probes, 1∶500). In situ hybridizations against the vet-4 mRNA were performed as previously described [31]. Unless indicated otherwise, the gonads were dissected from 1 day-old adults. Zeiss AxioImager Z1 microscope equipped with an Axiocam MRm REV 2 CCD camera was used for capturing pictures. Images were then exported into Adobe Photoshop CS4 and processed in an identical manner. A spinning disk multipoint confocal microscope equipped with an EM-CCD Cascade II camera (Photometrics) was used for capturing images for Figure 3D. Pictures were, then, deconvolved with the Huygens software and then processed in Imaris XP 7.1.1.
The affinity-purified (ELISA) rabbit anti-LIN-41 antibody (“4796”) was provided by Helge Grosshans (Magdalene Rausch & Helge Grosshans, unpublished data) and created against the VKNLKLSVLISQAESLQSKQIDLQQAIQTATKLMDSSDCDEMVLRQVFEKLASCQMGNEGTEPNNNILNVLMLACQVNEDDRLKFTAPQDGILLNKARQF sequence (residues 587–686). The rabbit was raised by SDIX in Newark, DE, USA.
The LIN-41 point mutant transgene constructs “RING” and “Y941A” were created from the wild-type LIN-41 transgenic template by site-directed mutagenesis (Stratagene QuikChange method), whereas the deletion construct “ΔNHL” was created via two-step PCR. In any case, Phusion High-Fidelity DNA Polymerase (Fermentas) was used. For primers used see Text S1.
The filamin domain (residues 691–821) of C. elegans LIN-41 (isoform B of Q9U489) was cloned into pOPINF [76] using In-Fusion (Clontech Laboratories Inc). The resulting expression construct was transformed into BL21 DE3 cells and the protein expressed via auto-induction at 20°C for 20 hours. Cells were harvested, then resuspended in lysis buffer (50 mM Tris, pH 7.5, 500 mM NaCl, 20 mM imidazole, 0.2% Tween-20) and frozen at −80°C. The cell suspension was thawed and freshly supplemented with Complete EDTA-free protease inhibitors (Roche Diagnostics) and 3 U/ml Benzonase (Sigma) before passing through an Avestin EmulsiFlex-C3 cell disruptor. The clarified lysate was incubated with NiNTA affinity resin (Qiagen) in batch mode and the bound protein eluted in 50 mM Tris, pH 7.5, 500 mM NaCl, 125 mM imidazole. The protein was fractionated on a Superdex 75 HiLoad 16/60 (GE Healthcare) gel filtration column in GF buffer (20 mM Tris, pH 7.5, 200 mM NaCl, 2 mM TCEP and 0.02% NaN3). The single peak fraction was pooled and digested overnight at 4°C with 3C protease to remove the N-terminal histidine tag. The released protein tag and 3C protease were removed by a second nickel-affinity step and the untagged filamin domain was further purified over a Superdex 75 column in GF buffer and concentrated to 7.5 mg/ml.
All crystallization experiments were performed at 20°C using the sitting-drop vapour diffusion method via a Phoenix robot (Art Robbins) dispensing 100 nl drops. Removal of the N-terminal histidine tag from the filamin domain was needed to obtain crystals. The untagged filamin domain readily crystallized in many conditions. Crystals grown in 1.1 M sodium malonate, 0.1 M HEPES, pH 7.0, 0.5% v/v Jeffamine ED-2001, were harvested and cryoprotected in mother liquor containing 25% ethylene glycol. These crystals diffracted to 1.68 Å resolution at the SLS PX-III beamline and belonged to space group C2221 with one molecule per asymmetric unit. Diffraction data were integrated and scaled using XDS [77] and the structure was solved by the molecular replacement method using PHASER [78]. Phases from this solution were calculated and used for automatic model building with BUCCANEER [79]. The LIN-41 filamin structure was further improved by the crystallographic simulated annealing routine followed by individual B-factor refinement in PHENIX [80] and several rounds of manual rebuilding in COOT [81] and refinement in BUSTER [82]. The final structure was validated using COOT. Structural images for figures were prepared with PyMOL (http://pymol.sourceforge.net/). Atomic coordinates and structure factors for the LIN-41 filamin domain have been deposited in the PDB with entry code 4UMG.
Amino acid sequences of the C. elegans LIN-41 (Uniprot Q9U489, 830–1147) and Homo sapiens TRIM71 (Uniprot Q2Q1W2, 591–868) NHL domains were submitted to the HHPRED server for homology detection and structure prediction [83]. The structure of the D. melanogaster Brat NHL domain (PDB 1Q7F) was the top hit in both searches resulting in very high scores for the LIN-41 NHL domain (Score = 241.22, E-value = 1e-33, 28% sequence identity) and the TRIM71 NHL domain (score = 22.54, E-value = 4.5e-35, 31% identity). The top alignments were edited for minimal local corrections and sent to the HHPRED MODELLER pipeline for modeling. Loops lacking template information for both the C. elegans and the human NHL domain models were removed in the final models. Structural figures were prepared using PyMOL (www.pymol.org).
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10.1371/journal.ppat.1004325 | Bacillus Calmette-Guerin Infection in NADPH Oxidase Deficiency: Defective Mycobacterial Sequestration and Granuloma Formation | Patients with chronic granulomatous disease (CGD) lack generation of reactive oxygen species (ROS) through the phagocyte NADPH oxidase NOX2. CGD is an immune deficiency that leads to frequent infections with certain pathogens; this is well documented for S. aureus and A. fumigatus, but less clear for mycobacteria. We therefore performed an extensive literature search which yielded 297 cases of CGD patients with mycobacterial infections; M. bovis BCG was most commonly described (74%). The relationship between NOX2 deficiency and BCG infection however has never been studied in a mouse model. We therefore investigated BCG infection in three different mouse models of CGD: Ncf1 mutants in two different genetic backgrounds and Cybb knock-out mice. In addition, we investigated a macrophage-specific rescue (transgenic expression of Ncf1 under the control of the CD68 promoter). Wild-type mice did not develop severe disease upon BCG injection. In contrast, all three types of CGD mice were highly susceptible to BCG, as witnessed by a severe weight loss, development of hemorrhagic pneumonia, and a high mortality (∼50%). Rescue of NOX2 activity in macrophages restored BCG resistance, similar as seen in wild-type mice. Granulomas from mycobacteria-infected wild-type mice generated ROS, while granulomas from CGD mice did not. Bacterial load in CGD mice was only moderately increased, suggesting that it was not crucial for the observed phenotype. CGD mice responded with massively enhanced cytokine release (TNF-α, IFN-γ, IL-17 and IL-12) early after BCG infection, which might account for severity of the disease. Finally, in wild-type mice, macrophages formed clusters and restricted mycobacteria to granulomas, while macrophages and mycobacteria were diffusely distributed in lung tissue from CGD mice. Our results demonstrate that lack of the NADPH oxidase leads to a markedly increased severity of BCG infection through mechanisms including increased cytokine production and impaired granuloma formation.
| The vaccine Mycobacterium bovis BCG is administrated to prevent early age tuberculosis in endemic areas. BCG is a live vaccine with a low incidence of complications. However, local or disseminated BCG infection may occur, in particular in immunodeficient individuals. Chronic granulomatous disease (CGD), a deficiency in the superoxide-producing phagocyte NADPH oxidase, is a primary immune deficiency and one of the most frequent congenital defects of phagocyte in humans. Here we analyze the role of the phagocyte NADPH oxidase NOX2 in the defense against BCG. An extensive literature review suggested that BCG infection is by far the most common mycobacterial disease in CGD patients (220 published cases). We therefore studied BCG infection in several CGD mouse models showing that these were highly susceptible to BCG infection with a mortality rate of ∼50%. As compared to the wild type, CGD mice showed a markedly increased release of cytokines, an altered granuloma structure, and were unable to restrain mycobacteria within granulomas. Rescue of the phagocyte NADPH oxidase in macrophages was sufficient to protect mice from BCG infection and to sequester the mycobacteria within granulomas. Thus, superoxide generation by macrophages plays an important role for the defense against BCG infection and prevents overshooting release of proinflammatory cytokines.
| M. bovis BCG (Bacillus Calmette Guérin) is an attenuated strain of M. bovis, used as a vaccine against tuberculosis. BCG vaccination has a proven efficacy only early in life (<1 year of age), in particular against tuberculous meningitis and miliary tuberculosis. Thus, the WHO recommends vaccination of newborns in endemic areas [1]. However, BCG is a live vaccine, which may persist and become a pathogen. In some individuals, in particular those with immune defects, BCG vaccination may lead to severe local or to disseminated infection [2], [3]. BCG is also used as local treatment for bladder cancer [4], where in some cases it may lead to symptomatic infection, from cystitis to life threatening dissemination [5]. However, there is emerging evidence for increased risk of BCG infection in patients lacking the phagocyte NADPH oxidase (chronic granulomatous disease, CGD) [6]–[8]. Indeed studies looking at underlying risk factors in patients presenting BCG infection suggest that approximately 20% of such patients suffer from CGD [9] and in many instances, BCG infection is the first manifestation of CGD [10].
The phagocyte NADPH oxidase NOX2 is a superoxide producing enzyme, involved in the host defense against numerous bacteria and fungi. Genetic loss of function of NOX2 is a primary immunodeficiency referred as chronic granulomatous disease (CGD). CGD may be caused by mutations in the gp91phox/NOX2 protein which is coded by the Cybb gene or one of its subunits, in particular p47phox, which is coded by the Ncf1 gene [11]. CGD patients suffer from severe and recurrent bacterial and fungal infections as well as from hyperinflammatory and autoimmune diseases in particular discoid lupus [12]. Until about 10 years ago, it was thought that the phagocyte NADPH oxidase was not relevant for the defense against mycobacteria [13]. Whether mice carrying CGD mutations show an increased susceptibility to infection with Mycobacterium tuberculosis remains controversial [14], while their susceptibility to BCG infection has so far not been studied.
Host defense mechanisms against mycobacteria are typically initiated by phagocytosis through macrophages, inducing inflammation and subsequently cell-mediated immunity involving Th1-type immune responses. These coordinated mechanisms result in granuloma formation. Granulomas are highly organized structures generated by interactions between myeloid and lymphoid cells that characterize the adaptive immune response to mycobacteria. In general granulomas sequester mycobacteria and thereby limit their dissemination. Granulomas are formed through cellular recruitment and are associated with production of cytokines and chemokines [15]. Among these cytokines, TNF and IFN-γ are the main players contributing to activation of macrophage host defense mechanisms [16]. Neutrophils are able to kill mycobacteria in vitro, but the in vivo relevance of neutrophils in the mycobacterial host defense remains a matter of debate [17].
Here we have first analyzed the relevance of BCG infection in CGD patients and then investigated the role of NADPH oxidase-generated ROS in experimental BCG infection. Mice lacking a functional phagocyte NADPH oxidase showed a markedly enhanced severity to BCG infection. Rescue of phagocyte NADPH oxidase function in macrophages was sufficient to reverse the phenotype to the mild disease observed in wild-type mice. We identified increased cytokine generation and poorly organized granuloma formation as mechanisms involved in the exacerbated severity of BCG infection in NADPH oxidase-deficient mice.
To understand the relevance of mycobacterial infections for CGD patients, we performed an extensive review of the existing studies and case reports on this topic. A previous literature-based study from 1971 to 2006 reported 72 cases [6]; here we report a total of 297 cases of mycobacterial infections in CGD patients [6], [8], [9], [18]–[23]. M. bovis BCG infection was by most frequently reported (74%; i.e. 220 cases); 20% of cases were caused by Mycobacterium tuberculosis infection, and the rest by different nontuberculous mycobacteria (Fig. 1A). Most of the BCG cases were local or regional infections (BCG-itis). Relatively little information was found on the treatment and the outcome of these local infections, however one article mentions the necessity for surgical excision [6]. However, systemic BCG infections (BCG-osis) in CGD patients were not uncommon (31 cases; Fig. 1B). In 6 of the 31 cases, the outcome has been documented: 3 of the patients died and 3 of the patients survived [6], [24]–[28].
We therefore studied BCG infection in CGD mouse models. To exclude epistatic effects, we investigated different types of CGD mice (Ncf1 mutants, Cybb-knock-out mice), as well as different genetic backgrounds (C57Bl/10.Q, C57Bl/6). Taken together, the following mouse lines were used:
Mice were injected intravenously with BCG (107 CFU). Wild-type mice resisted the infection during the 4 week observation. In contrast, Ncf1 mutant mice showed early mortality: 50% of mice died after 10 days and only 33% of the mice survived after 4 weeks (Figure 2A). The high mortality of Ncf1 mutant mice was associated with a rapid weight loss, which was absent in wild-type controls (Figure 2B). To investigate whether the genetic background or the type of CGD mutation was responsible for the high susceptibility, we investigate other types of CGD mice. BCG-infected Ncf1 mutant mice in a C57Bl/6 background showed also a higher mortality as compared with their wild-type controls (Figure 2C). Similarly to Ncf1 mutant mice in C57Bl/10.Q background, Ncf1 mutant mice in a C57Bl/6 background showed a rapid weight loss (Figure 2D). However the median survival time was around 20 days in the C57Bl/6 background, as opposed to ∼8 days in the C57Bl/10.Q background which suggest a contribution of the mouse genetic background to BCG susceptibility. Given that the survival trends in the different genetic backgrounds appeared to be similar, suggesting a minor role for epistasis and a major role of the NOX2 subunit mutation, BCG infection was studied in Ncf1 rescue mice that we have previously characterized [31], [32]. Ncf1 rescue as wild-type mice resisted the infection during the 4 weeks observation and no mortality or no important weight loss was observed (Figure 2A and B). Finally, BCG infection in Cybb-deficient mice also led to a high mortality and weight loss as compared to wild type controls (Figure 2E and F). These observations strongly suggest that in absence of ROS production by NADPH oxidase (see below), mice are more susceptible to mycobacterial infection. Furthermore, the normal survival of Ncf1 rescue mice implies that ROS production in mononuclear phagocytes is crucial.
To further understand the causes of the early mortality of Ncf1 mutant mice, mice were sacrificed at day 3 post-infection for lung histopathological examination. In the absence of BCG infection, no histological differences were observed between wild-type, Ncf1 mutant and rescue mice (Figure S1). However, upon BCG infection, Ncf1 mutant mice presented severe inflammatory lesions in the lungs with extended hemorrhagic lesions, intravascular thrombosis, decrease of the alveolar spaces (Figure 3A.b) and hypertrophy of pleural cells (Figure 3B.b). Ncf1 mutant mice also showed accumulation of inflammatory cells composed essentially by neutrophils concentrated as microabscesses (Figure 3A.e). In contrast, wild-type and Ncf1 rescue did not show massive hemorrhagic lesions and only moderate inflammation with mixed inflammatory cells observed in lungs (Figures 3A.a, c, d and f).
The surviving Ncf1 mutant mice (5 out of 15) were also analyzed at 4 weeks post-infection. Histopathological examination of Ncf1 mutant lungs revealed extensive inflammatory lesions reducing notably the alveolar space (Figure 3C.b) and microabscesses composed of neutrophils. Only one third of the Ncf1 mutant mice survived up to 4 weeks and the latter results might represent a survivor effect, and not necessarily be representative for all Ncf1 mutant mice. In contrast, wild-type and Ncf1 rescue did not show massive infiltrate of inflammatory cells (Figures 3C.a–c and d–f).
In the absence of BCG infection, the organ weight indexes for lung, liver and spleen were comparable in all mouse strains (Figure 4A and Figure S1). After BCG infection, lung weight, as a surrogate measure of lung inflammation and edema, increased only moderately in wild-type and Ncf1 rescue mice, but massively in Ncf1 mutant mice (Figure 4A). At 4 weeks of BCG infection, the severity of lung pathology was also assessed by analysis of free alveolar space vs. occupied space. The occupied space was significantly increased in Ncf1 mutant as compared to wild-type and Ncf1 rescue lungs (Figure 4B). This quantification corroborates with the massive obstruction of alveolar space in Ncf1 mutant mice seen in histology. Similar as seen for Ncf1 mutant mice, BCG-infected Cybb-deficient mice showed severe inflammatory lesions with extended hemorrhagic lesions and decreased alveolar space (Figure S2.A and B.b). Microabscesses of neutrophils were also present in Cybb-deficient and Ncf1 mutant in C57Bl/6 background in the lung of both sacrificed and deceased mouse while mixed inflammatory cells were observed in Ncf1 rescue and wild-type lungs (Figure S2.B.d and C). The lung weight was also increased in Cybb-deficient and Ncf1 mutant in C57Bl/6 background mice compared to wild-type, 4 weeks after BCG infection (Figure S4.A). In summary, these data support that impaired ROS production by mononuclear phagocytes is associated with increased inflammatory response involving neutrophilic microabscess formation following BCG infection.
To evaluate mechanisms how ROS production protect from death by BCG infection, we assessed bacterial burden by colony forming unit quantification in different organs. Bacterial load in the lung of both the Ncf1 mutant and Ncf1 rescue mice were similar but higher than those in wild-type mice 3 days after infection (Figure 4C). Four weeks post-infection, no significant differences in bacterial load were observed in lung and liver. However, bacterial counts in the spleen of Ncf1mutant and Ncf1rescue mice were significantly increased compared to wild-type (Figure 4C). We further evaluated inducible nitric oxide synthase iNOS by western blot, which is crucial for clearance of BCG and mouse survival [34]. Expression of iNOS protein in the lung at 4 weeks post-infection was significantly increased in Ncf1 mutant as compared to wild-type mice (Figure 4D). NO and superoxide form peroxynitrite, a highly reactive molecule, are implicated in mycobacteria killing. Upon interaction with proteins, peroxynitrite produces nitrotyrosine, which are stable biological peroxynitrite markers [35]. Interestingly, despite the increase of iNOS protein, nitrotyrosine levels measured by ELISA in lungs of Ncf1 mutant were not different from wild-type (Figure 4D), presumably because Ncf1 mutant mice lack the second substrate required for peroxynitrite generation, namely superoxide. Thus, most likely CGD mice produce increased amounts of NO in response to mycobacteria, but given the lack of NOX2-generated superoxide, this is not accompanied by an increase in peroxynitrite. These results are compatible with the concept that peroxynitrite, rather than NO or ROS, is crucial for optimal mycobacterial killing.
We next measured levels of selected cytokines by ELISA in lung homogenates from BCG infected mice (Figures 5). Three days post infection, the increase of TNF levels in Ncf1 mutant lung was massive (3.6-fold compared to wild-type) and also observed at four weeks post-infection (Figure 5A). TNF levels in Ncf1 rescue lung were comparable to those observed in wild-type lung. Three days, but not 4 weeks, post infection, IL-17 lung levels were increased in Ncf1 mutant mice (Figure 5B). The same pattern was observed for IL-12p40 (Figure 5C). The pattern was slightly different for IFN-γ: there were increased in Ncf1 mutant mice at 3 days and 4 weeks post-infection, however Ncf1 rescue mice showed even higher IFN-γ levels 4 weeks post-infection (Figure 4D). We also assessed the levels of the chemokines CXCL1 (KC, the murine IL-8 homolog), and CCL5 (RANTES). We selected CXCL1 because it is a powerful neutrophil chemoattractant [36] and might explain the high number of neutrophils in the lung lesion in mutant mice and CCL5 because it is a leukocyte chemoattractant with a role in mycobacterial protection [37]. Ncf1 mutant mice showed a higher CXCL1 levels 3 days after BCG infection (Figure 5E). CCL5 levels were increased in Ncf1 mutant mice three days and 4 weeks after infection (Figure 5F). The general pattern was an increase in pulmonary cytokine and chemokine responses in Ncf1 mutant mice due to the infection which was controlled by Ncf1 rescue mice. Moreover, we also evaluated if Cybb-deficient mice would also respond with an exacerbated cytokine response using ex-vivo recall of spleen cells from BCG infected mice. Both re-infection of splenocytes or addition of BCG antigens resulted in enhanced TNF and nitrite, as an indicator of NO production, evaluated respectively by ELISA and Griess reagent, confirming that NOX2 deficiency leads to an increase response in TNF and immune mediators (Figure S3). Thus, at early time points a massive increase of pro-inflammatory cytokines was observed in BCG-infected CGD mice. At later time points, only TNF and CCL5 levels remained elevated, this had a probable importance for altered granuloma formation (see below).
We have previously demonstrated that ROS production in response to phorbol myristate acetate (PMA) or β-glucan was abolished in neutrophils, bone-marrow derived macrophages (BMDM) and dendritic cells (BMDC) in Ncf1 mutant mice [29]. We therefore investigated ROS production measured by amplex red in BMDM and BMDC exposed to BCG. Wild-type and Ncf1 rescue cells produced ROS in response to BCG, but not Ncf1 mutant cells (Figures 6A, B). Diphenylene iodonium (DPI), a non-specific NOX inhibitor, abolished the mycobacteria-induced ROS production in wild-type and Ncf1 rescue cells. The kinetic of ROS production was comparable in BMDM and BMDC from wild-type and Ncf1 rescue mice (Figures 6A and B).
We next investigated whether there are signs of ROS production in granulomas in vivo. For this purpose, liver sections from 4 weeks BCG-infected mice were stained with an antibody against 8-OHdG (8-hydroxydeoxyguanosine), a well-studied marker of DNA oxidation [38]. In wild-type and Ncf1 rescue liver, an important 8-OHdG staining was observed within granulomas (Figure 6C). Note that, to the best of our knowledge, this is the first demonstration of ROS generation during granuloma formation. Importantly, no 8-OHdG staining was observed in granulomas of Ncf1 mutant mice, demonstrating that the phagocyte NADPH oxidase is the major source of ROS during BCG infection.
Granuloma formation is a crucial mechanism to control mycobacterial infection. To determine the relationship between ROS production and granuloma formation, we next analyzed lung histology 3 days and 4 weeks after BCG infection using the following stainings: H/E (general morphology), Ziehl-Neelsen (mycobacteria), acidic phosphatase activity (activated macrophages). Three days post BCG infection, lung sections from wild-type and Ncf1 rescue mice showed clusters of macrophages (Figure 7A). In Ncf1 mutant mice, BCG infection induced abundant neutrophil abscesses, with a lack of macrophage clustering within restricted areas (Figure 7B). Mycobacteria appeared less abundant in lung section from wild-type as compared to Ncf1 mutant mice (Figure 7C). Interestingly, the Ziehl-Neelsen stain suggests a relatively high bacterial load in rescue mice, corroborating the quantitative bacterial load analysis (Figure 4C).
Granuloma formation is a crucial step in the mycobacterial containment and clearance. After 4 weeks of BCG infection, both wild-type and Ncf1 rescue mice had well differentiated granulomas containing multinucleated giant cells (Figures 7D–E). In these mice, granulomas enclosed the mycobacteria and virtually no mycobacteria were observed outside of granulomas (Figure 7F). In contrast, Ncf1 mutant mice presented large pyogranulomatous lesions with abundant neutrophil abscesses (Figure 7D) and diffusely distributed acid phosphatase-positive macrophages (Figure 7E). Importantly, no sequestration of mycobacteria was observed in Ncf1 mutant mice (Figure 7F). As seen for Ncf1 mutant mice, Cybb-deficient mice as well as Ncf1 mutant mice in a C57Bl/6 background showed larger granulomas without concise delimitations (Figure S4B). Disorganized granulomas with an abnormal presence of neutrophils were mainly in lung but there are also observed in the liver and spleen of Ncf1 mutant mice (data not shown). Thus, the presence of NADPH oxidase in mononuclear phagocytes is required for the formation of compact granulomas with concise delimitations and for sequestration of mycobacteria within granulomas.
In this study, we have analyzed BCG infection in several mouse model of phagocyte NADPH oxidase deficiency. CGD mice were highly susceptible to BCG infection. Our results suggest that the phagocyte NADPH oxidase limits the severity of mycobacterial infection by at least two mechanisms: i) block of overshooting cytokine release; and ii) contribution to mycobacterial sequestration in granulomas. For these two mechanisms, NADPH oxidase function in macrophages was essential. We also observed a modest increase in bacterial load in mice lacking NOX2 function.
Particularly interesting in this respect is the recent discovery of a family with a peculiar variant of CGD [32]. These patients lack ROS production in macrophages, but not in neutrophils and showed a high sensitivity to mycobacterial infection, in particular to BCG. The Ncf1 mutant mice used in this study, including a selective rescue in mononuclear phagocytes, provide a mirror image of the patient study [31]: selective rescue of NOX2 in macrophages protected CGD mice against BCG infection. Indeed, most of the enhanced mycobacterial pathology associated with NOX2-deficiency (morbidity, mortality, enhanced cytokine production, abnormal granuloma formation) could be attributed to macrophages. There is one exception to this: the moderately increased mycobacterial load, which is not reversed by Ncf1 rescue in macrophages and hence was not correlated to the outcome of infection. Thus, while our results in mice show that selective rescue of NOX2 in macrophages restores resistance BCG infection; in the above mentioned CGD patients a selective loss of NOX2 in macrophages establishes high susceptibility.
Hitherto, BCG infection has never been investigated in mouse models of CGD. However, infection of CGD mice with other types of mycobacteria led to discordant results: in some cases aggravation was observed, while in other studies no effect was observed. We wanted to assure that our results are not due to a specific choice of the CGD mutation or to the genetic background. We therefore tested two different CGD mutations (Ncf1, Cybb) as well as two different genetic backgrounds (C57/B10.Q, C57Bl/6). All results concur: CGD mice are highly susceptible to BCG infection.
Reconstitution of the phagocyte NADPH oxidase in mononuclear phagocytes completely reversed the neutrophil influx phenotype. Thus, it is not the lack of activity of NOX2 in neutrophils which leads to the increased number of neutrophils in inflammation. Most likely, NOX2 in mononuclear phagocytes regulates the number of invading neutrophils by controlling the release of neutrophil chemoattractants. These chemoattractants might be directly released from macrophages or possibly from other cells that depend on a macrophage signal. An alternative theory is the decreased uptake of apoptotic neutrophils by NADPH oxidase-deficient macrophages [39]. Enhanced neutrophil infiltration in CGD mice might be involved in the enhanced TNF production, thereby possibly contributing to the enhanced mortality in CGD mice.
Despite enhanced levels of iNOS, our results show a small, increase of mycobacterial load in Ncf1 mutants. This suggests that the well-documented bactericidal activity of iNOS-dependent NO production is several impaired in the absence of NOX2, compatible with the suggested role of peroxynitrite (i.e. the reaction product of NO and superoxide) in mycobacterial killing. Note however that NADPH-oxidase involvement in killing of mycobacteria does not necessarily signify a direct antibacterial action. For example, mycobacteria have been suggested to be sensitive to neutrophil extracellular trap (NET) [40] and NADPH oxidase-dependent NET formation [41] could also be a relevant mechanism limiting the multiplication of mycobacteria.
It has been suggested that the increased sensitivity of CGD patients to mycobacterial infection might be linked to a ROS activation of cytokine production, in particular IL-12 (which is secreted by macrophages to stimulate IFN-γ release by T lymphocytes [3]). In CGD patients, an hyperresponsiveness of neutrophils to different stimuli was usually observed [42]. In our study, we observed the opposite: CGD mice infected with BCG generated increased levels of cytokines. Interestingly, several of the cytokines increased in Ncf1 mutant mice (in particular TNFα, IL-12, IFN-γ, IL-17) are involved in the antimycobacterial defense. This might be a defense mechanism compensating for the lack of ROS generated by the NADPH oxidase. However, the high level of certain cytokines, in particular TNF, in CGD mice might also account for the high early mortality and absence of resolution of inflammation to mycobacterial infections.
Our results shed new light on granuloma formation in mycobacterial infection and the role of NOX2 in this process:
Taken together, our results provide strong evidence for a role of NADPH oxidase-dependent ROS generation in the fine tuning of granuloma formation. Thus, redox-sensitive signaling steps are involved in the coordinated genesis of granulomas, and the overshooting cytokine and chemokine productions observed in CGD mice probably destabilizes granulomas.
To which extend do results obtained in our study apply to CGD patients? Clearly, our analysis of the published literature demonstrates that, human CGD patients are sensitive to infection with the vaccinal BCG strain [6], [8]. Approximately 15% of CGD patient with BCG disease will develop a disseminated form, also referred to as BCGosis. At this point, it is not clear which are the factors precipitating such disseminated disease. Genetic modifiers, type of BCG strain, inoculum size of viable mycobacteria are among the possible culprits. Similar as observed in our mouse model of disseminated BCG infection, there was a substantial mortality associated with BCGosis in CGD patients.
Taken together, the results presented here not only shed new light on BCG infection in CGD, but also provide first evidence for a role of the macrophage NADPH oxidase in the coordination of granuloma formation. The vaccinal BCG strain is an important tool for the control of childhood tuberculosis in countries with a high incidence of the disease. In general, children are vaccinated at birth, because the major effect of BCG vaccination is prevention from tuberculous meningitis early in life. Thus, the vaccination occurs prior to first manifestations of immune deficiency. New algorithms need to be defined to assure vaccine protection of immunocompetent neonates, without putting immunodeficient neonates at risk.
Animal experiments complied with ethical standards of the University of Geneva and the Cantonal Veterinary Office (Authorization No. 1005/3715/2). Handling and manipulation of the animals complied with European Community guidelines.
Wild-type B10.Q, Ncf1 mutant and rescue mice, backcrossed into identical background were used (for details of backcross see [31], [43]). Ncf1 rescue mice are Ncf1 mutant animals which contain a transgenic wild-type Ncf1 gene under the control of a human CD68 promoter fragment. Ncf1 mutant with the same mutation on a C57Bl/6N background and its respective wild-type controls were used. Cybb-deficient mice and respective controls were backcrossed on C57Bl/6 background (Jackson Laboratories). For all experiments, mice aged 8–12 weeks were kept in a quiet room at 25°C with a 12 h light/dark cycle and food and water were supplied ad libitum.
Mice were infected intravenously with 107 living CFU of M. bovis BCG Connaught [44], [45]. Mortality and body weights were monitored during infection. Three days and 4 weeks post-infection, mice were sacrificed and lung, liver and spleen were weighted, fixed and frozen for subsequent analyses.
The number of viable bacteria recovered from frozen organs was evaluated as previously described [46], [47].
Bone marrow primary cells were obtained from mice by flushing both the femur and the tibia as previously described [29], [48]
BMDMs and BMDCs were stimulated with BCG (MOI 10). The production of ROS by NOX2 was measured using Amplex red (Invitrogen) fluorescence, as described previously [49].
Lung homogenates were prepared and western blot performed as previously described [50]. Nitrotyrosine, a stable end product of peroxynitrite oxidation, was assessed in serum by enzyme-linked immunosorbent assay (ELISA; Hycult biotechnology, Netherlands).
Histologic analyses of lung lesions were performed at 3 days and 4 weeks after infection. Lungs embedded in paraffin for hematoxylin/eosin (HE) and Ziehl-Neelsen stainings. For acid phosphatase staining, cryostat tissue sections from lung frozen in liquid nitrogen were used as previously described [51]. Signs of ROS production were evaluated by 8-hydroxy-2′-deoxyguanosine (8-OHdG) staining (1∶50, JaICA, Shizuoka, Japan) as previously described [52].
Evaluation of the histopathology was performed on three lung lobe sections per animal (n = 4/group). Lung sections were captured on Zeiss Mirax Scan microscope system. Virtual sections were subdivided and images covering lobe sections corresponding to a surface of 21.50±8.05 mm2 per mouse, were proceeded for quantification of free space and occupied lung tissue using a specific program designed in the Metamorph software identifying cellularity, hematoxilin-eosin stain and air spaces [53].
Mice were infected with BCG, sacrificed at day 17 and spleen cells were prepared as previously described [45]. Cells were stimulated with either medium alone, living BCG (103 CFU/well), or BCG culture protein extracts (17 µg/ml). After one, three and six days of treatment, medium was harvested for nitrite and TNF determination. Nitrite accumulation, as an indicator of NO production, was evaluated by Griess reagent (1% sulfanilamide and 0.1% naphtylethylenediamide in 2.5% phosphoric acid). TNF was determined in cell supernatants as described below.
Lungs were collected at different time points after BCG injection and tissue homogenate was prepared [54]. Cytokines and chemokines were measured by ELISA (Ready&D System).
Literature research on CGD and mycobacterial infections was done from PubMed and Google Scholar with no limitations in time.
Parametric (t -tests) and non-parametric (One-way analysis and Kruskal–Wallis) tests were used. In the case of multiple comparisons, a two-way ANOVA test with Bonferroni correction was used.
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10.1371/journal.pgen.0030062 | A Mammal-Specific Doublesex Homolog Associates with Male Sex Chromatin and Is Required for Male Meiosis | Gametogenesis is a sexually dimorphic process requiring profound differences in germ cell differentiation between the sexes. In mammals, the presence of heteromorphic sex chromosomes in males creates additional sex-specific challenges, including incomplete X and Y pairing during meiotic prophase. This triggers formation of a heterochromatin domain, the XY body. The XY body disassembles after prophase, but specialized sex chromatin persists, with further modification, through meiosis. Here, we investigate the function of DMRT7, a mammal-specific protein related to the invertebrate sexual regulators Doublesex and MAB-3. We find that DMRT7 preferentially localizes to the XY body in the pachytene stage of meiotic prophase and is required for male meiosis. In Dmrt7 mutants, meiotic pairing and recombination appear normal, and a transcriptionally silenced XY body with appropriate chromatin marks is formed, but most germ cells undergo apoptosis during pachynema. A minority of mutant cells can progress to diplonema, but many of these escaping cells have abnormal sex chromatin lacking histone H3K9 di- and trimethylation and heterochromatin protein 1β accumulation, modifications that normally occur between pachynema and diplonema. Based on the localization of DMRT7 to the XY body and the sex chromatin defects observed in Dmrt7 mutants, we conclude that DMRT7 plays a role in the sex chromatin transformation that occurs between pachynema and diplonema. We suggest that DMRT7 may help control the transition from meiotic sex chromosome inactivation to postmeiotic sex chromatin in males. In addition, because it is found in all branches of mammals, but not in other vertebrates, Dmrt7 may shed light on evolution of meiosis and of sex chromatin.
| Genes related to the sexual regulator Doublesex of Drosophila have been found to control sexual development in a wide variety of animals, ranging from roundworms to mammals. In this paper, we investigate the function of the Dmrt7 gene, one of seven related genes in the mouse. Female mammals are XX and males are XY, a chromosomal difference that presents specific challenges during the meiotic phase of male germ cell development. Some of these are thought to be overcome by incorporating the X and Y chromosomes into a specialized structure called the XY body. We find that DMRT7 protein is present in germ cells, localizes to the male XY body during meiosis, and is essential for male but not female fertility. The XY body normally is altered by recruitment of additional proteins and by specific modifications to histone proteins between the pachytene and diplotene stages of meiosis, but modification of the “sex chromatin” in Dmrt7 mutant cells is abnormal during this period. Because Dmrt7 is found in all branches of mammals, but not in other vertebrates, these results may indicate some commonality in regulation of sex chromatin among the mammals.
| Sexual differentiation generates anatomical, physiological, and behavioral dimorphisms that are essential for sexual reproduction. Many of these dimorphisms affect somatic cells, but the sexual dimorphisms that most directly mediate sexual reproduction are those of the gametes themselves. Gametes differ between the sexes in size and morphology, sometimes dramatically so, reflecting their very different roles in zygote formation. Indeed, the morphology of the gametes is what defines sex: females are the sex that produces the larger gametes and males produce the smaller ones.
Mammalian meiosis is regulated sex-specifically starting in embryogenesis and continuing through much of adult life (reviewed in [1]). For example, the timing and synchrony of meiosis are very different in the two sexes. In females, germ cells synchronously initiate meiosis in the embryo and arrest during meiotic prophase I. After puberty, oocytes are selectively recruited for ovulation, when they proceed to metaphase II and then complete meiosis after fertilization occurs [2]. In contrast, male meiosis occurs entirely postnatally, without the arrest periods found in females. In females, each meiosis can produce a single haploid oocyte (and two extruded polar bodies), whereas each male meiosis can produce four haploid spermatocytes.
Other meiotic processes, such as recombination and chromosome pairing (synapsis), occur in both sexes but operate somewhat differently. For example, there is a higher failure rate for meiosis in females, with human oocyte aneuploidy rates up to 25% versus about 2% in human sperm [3], and this may indicate that the checkpoints controlling and monitoring the events of meiotic progression in males are more stringent. Consistent with this idea, genetic analysis of a number of meiotic regulatory genes in the mouse has demonstrated a much stronger requirement in males than in females [1,4].
The existence of heteromorphic sex chromosomes, such as the XX/XY system of mammals, creates sex-specific challenges. One is the need for mechanisms to balance expression of sex-linked genes between the sexes, which in mammals is accomplished by X chromosome inactivation in females [5,6]. In male germ cells there is another sex-specific consideration during meiosis. In prophase I, when the homologous chromosomes synapse and homologous recombination occurs, X and Y chromosome pairing is limited to a region termed the pseudoautosomal region, leaving large portions of each chromosome unpaired. In eutherian and marsupial mammals, these unpaired chromosome regions are associated with a specialized chromatin domain termed the XY body or sex body. The function of the XY body is uncertain [7–11], but there is evidence that it is essential for male meiotic progression [12].
Several proteins are reported to localize to the XY body, including BRCA1, ATR, the histone variant H3.3, and modified histones such as ubiquitinated H2A (Ub-H2A) and phosphorylated H2AX (γH2AX) [12–15]. In the XY body, the sex chromosomes are transcriptionally silenced in a process termed meiotic sex chromosome inactivation (MSCI). The XY body disappears after pachynema; however, many sex-linked genes remain transcriptionally silent into spermiogenesis [16]. This maintenance of silencing is associated with a distinct set of chromatin marks that define a sex chromatin domain termed postmeiotic sex chromatin (PMSC) [16,17].
Regulators of sexual differentiation have been identified in a number of organisms, but in contrast to many other developmental processes, such as axial patterning or development of many body parts, the molecular mechanisms that regulate sexual differentiation are highly variable between phyla. A notable exception involves genes related to doublesex (dsx) of Drosophila, which share a Doublesex/MAB-3 DNA-binding motif called the DM domain [18,19]. DM domain–encoding genes have been shown to regulate various aspects of sexual differentiation in insects, nematodes, and mammals [20]. The mab-3 gene of Caenorhabditis elegans has been shown to function analogously to DSX in several respects and can be functionally replaced by the male isoform of DSX, suggesting that the similarity in the sequence of these genes may stem from conservation of an ancestral DM domain sexual regulator [18,21,22].
Vertebrates also have DM domain genes, and analysis to date, although limited, has shown that these genes also control sexual differentiation. Mammals have seven DM domain genes (Dmrt genes), several of which exhibit sexually dimorphic mRNA expression [23,24]. The best studied of these genes, Dmrt1, is expressed in the differentiating male genital ridges and adult testis of mammals, birds, fish, and reptiles, and a recently duplicated Dmrt1 gene functions as the Y-linked testis-determining gene in the Medaka fish [25–29]. Human DMRT1 maps to an autosomal locus, which, when hemizygous, is associated with defective testicular development and consequent XY feminization [30]. Similarly, mice homozygous for a null mutation in Dmrt1 have severe defects in testis differentiation involving both germ cells and Sertoli cells [31]. Female mice mutant in Dmrt4 have polyovular follicles, indicating that this gene also plays a role in gonadal development [32]. It appears from these studies that the involvement of DM domain genes in sexual differentiation is ancient and conserved. However, vertebrate Dmrt gene function is not limited to sexual differentiation: Dmrt2 is required in both sexes for segmentation in mice and fish [33–35].
Here, we have investigated the expression and function of the Dmrt7 gene in the mouse. Dmrt7 is expressed only in the gonad, and, unlike the other Dmrt genes, appears to be present exclusively in mammals and not in nonmammalian vertebrates [23,36]. We find that DMRT7 protein is expressed only in germ cells and is selectively localized to the XY body of male pachytene germ cells. To test its function, we generated a conditional null mutation of Dmrt7 in the mouse. We find that Dmrt7 is required in males for progression beyond the pachytene stage of meiotic prophase but is not required in females. In rare mutant cells that survive to diplonema, we observed sex chromatin abnormalities. Based on these observations, we suggest that Dmrt7 plays a critical role in a male-specific chromatin transition between pachynema and diplonema during meiotic prophase.
Our previous mRNA expression analysis suggested a possible meiotic function for Dmrt7, based on the expression of Dmrt7 mRNA in the fetal gonads of the two sexes [23]. In the fetal ovary, Dmrt7 mRNA was detected primarily from E13.5 to E15.5, the time during which meiosis progresses from pre-meiotic replication to the pachytene stage [4], whereas Dmrt7 expression in the non-meiotic fetal testis was very low. Because this earlier work did not examine adult Dmrt7 expression, we first performed reverse transcriptase (RT)-PCR on mRNA from ten adult organs and detected strong Dmrt7 mRNA expression in the testis and a trace of expression in heart, but not in any other tissue tested (Figure 1A). We examined the timing of Dmrt7 mRNA expression during postnatal testis development and detected strong expression beginning at 2 wk, which roughly coincides with the onset of the pachytene stage during the first synchronous wave of spermatogenesis (Figure 1B) [37].
To investigate DMRT7 protein expression, we generated an antibody against the C-terminal portion of the protein. The antibody was raised against a unique region lacking the DM domain in order to avoid cross-reaction with other DM domain proteins. Immunofluorescent staining with purified DMRT7 antisera showed that DMRT7 protein is expressed predominantly in mid- to late-pachytene spermatocytes (Figure 1C), as well as in sperm, and is not detectable in other germ cell types including spermatogonia and round spermatids. We did not detect DMRT7 protein in somatic cells such as Sertoli cells, peritubular myoid cells, or Leydig cells. To more precisely determine the pachytene stages of DMRT7 expression, we double-stained with an antibody to GATA1, which is expressed in Sertoli cells from stages VII to IX [38]. This confirmed that DMRT7 is expressed in mid- to late-pachytene spermatocytes, starting slightly earlier than stage VII and extending through stage IX (unpublished data).
Within pachytene spermatocytes, DMRT7 is concentrated in the XY body, or sex body, a densely staining chromatin domain that harbors the sex chromosomes. These undergo transcriptional inactivation and heterochromatinization as a result of their incomplete pairing during prophase of mammalian male meiosis [17]. To verify DMRT7 protein expression in the XY body, we double-stained mouse testis sections for DMRT7 and small ubiquitin-related modifier 1 (SUMO-1), which is concentrated in the XY body during pachynema [39,40]. DMRT7 and SUMO-1 were colocalized, confirming that DMRT7 protein is preferentially localized to the XY body (Figure 1D). We also confirmed XY body localization of DMRT7 by double staining for other markers including Ub-H2A and γH2AX (unpublished data). DMRT7 is not preferentially localized to the XY body at all stages but instead is dynamic. Based on epithelial staging, it appears that DMRT7 localizes to the XY body from mid- to late-pachynema, becomes diffusely distributed in late-pachynema, and disappears in diplonema (unpublished data). This localization was confirmed by staining of meiotic spreads (Figure S1). DMRT7 also is specifically localized in sperm, with antibody staining mainly in the perinuclear ring of the sperm head manchette. This staining coincided with the epithelial stages in which DMRT7 localizes to the XY body in spermatocytes (Figure 1C and 1D).
To establish the functional requirement for Dmrt7, we generated Dmrt7−/− mice by targeted disruption in embryonic stem (ES) cells using a strategy diagrammed in Figure S2A. The Dmrt7 gene has nine exons with the DM domain encoded by the second and third exons. Because the DM domain is essential for function of other genes, including mab-3, mab-23, and dsx [18,19,41], we generated a conditionally targeted “floxed” allele in which the DM domain–containing exons of Dmrt7 are flanked by recognition sites for the Cre recombinase (loxP sites). The targeting vector also contained a neomycin resistance cassette (neo) flanked by Flpe recognition sites. The removal of these sequences by Cre-mediated recombination eliminates the DM domain and the translational start site, thus generating a putative null allele. We identified three homologously targeted ES cell clones by Southern blotting (Figure S2B) and injected cells from two clones into C57BL/6 blastocysts. Chimeric animals from both cell lines transmitted the targeted allele through the germ line. Targeted animals were bred to β-actin Cre mice to delete the DM domain–encoding exons, generating the Dmrt7− allele, or to Flpe transgenic mice to delete the neo cassette, generating the Dmrt7flox allele. Dmrt7+/− mice were interbred to generate Dmrt7−/− mice. To confirm the lack of functional DMRT7 protein in Dmrt7−/−testes, we stained meiotic spreads from Dmrt7 mutants (Figure S1) and sections from mutant testes (Figure S2C) and carried out western blot analysis (unpublished data). In each case, we detected no DMRT7 protein in the mutant testes.
Breeding of Dmrt7 heterozygotes produced homozygous mutant progeny of both sexes at the expected frequency (63/264; 23%). Male and female homozygous mutants were viable, grew to adulthood normally, and exhibited normal sexual behavior. Female homozygotes were fertile, produced litters of normal size, and had no obvious ovarian abnormalities as judged by histological analysis (unpublished data). In contrast, Dmrt7 homozygous mutant males were completely infertile and had testes about one-third the weight of those of heterozygous or wild-type adult littermates (Figure 2). To determine when defective testis development begins in Dmrt7 mutants, we compared the testes of wild-type and mutant littermates during the first wave of spermatogenesis. Prior to postnatal day 14 (P14), mutant testes appeared histologically normal and the testis weights were similar to those of heterozygous and wild-type littermates, indicating that spermatogonia and early meiotic germ cells form normally (Figure 2B; unpublished data). Thereafter, the testes of the Dmrt7 mutant mice ceased to grow and the weight difference was significant. Microscopic examination of P21 and P42 Dmrt7 mutant testes revealed that germ cells arrest in pachynema, and later stages of germ cells are largely missing (Figure 2C and 2D). Dmrt7 mutant mice are deficient in postmeiotic spermatids and lack epididymal spermatozoa, although a few cells develop to the round spermatid stage. These meiotic defects are in agreement with a recent preliminary analysis of another Dmrt7 mutation [42]. While some Dmrt7 mutant tubules are highly vacuolated and contain primarily Sertoli cells and spermatogonia, others have abundant primary spermatocytes. In addition, some tubules contain multinucleated cells and cells with darkly stained nuclei that are typical of apoptotic cells (Figure 2D).
Since Dmrt7 mutant testes lack most post-pachytene cells, we used TUNEL analysis to test whether the missing cells are eliminated by apoptosis. At 3 wk, Dmrt7 mutant testes contain significantly more apoptotic cells than those of wild-type controls. The percentage of tubule sections with five or more apoptotic nuclei was about three times higher in Dmrt7 mutants compared with wild-type (20% versus 7%; Figure 2E). A similar elevation of apoptosis was apparent in mutant testes at 7 wk (Figure 2F). In mutants, many apoptotic cells were in the middle of the tubules, whereas the apoptotic cells in wild-type occur mainly near the seminiferous tubule periphery. The numbers of Sertoli cells were not significantly different between wild-type and mutant testes, and we observed no difference in somatic cell apoptosis in mutants (unpublished data). From these results, we conclude that loss of Dmrt7 causes a block in meiotic progression, mainly in pachynema, leading to the elimination, by apoptosis, of the arrested cells.
To better define the spermatogenic stage at which Dmrt7−/− male germ cells arrest and die, we used antibodies against several stage-specific germ cell markers. TRA98 is expressed in PGCs and spermatogonia [43]. In the wild-type adult testis, strongly staining TRA98-positive cells form a layer one cell deep; however, in the mutant TRA98, strongly positive cells were abnormally organized, and some tubules had a layer several cells deep (Figure 3A). The BC7 antibody recognizes spermatocytes in the leptotene to early-pachytene stages [44]. Dmrt7 mutant testes had BC7-positive cells in approximately normal numbers, but again abnormally organized, with many positive cells in the center rather than the periphery of the tubules (Figure 3B). The TRA369 antibody recognizes a calmegin protein expressed in pachytene spermatocytes through elongated spermatids [45]. In contrast to the earlier stages, far fewer TRA369-positive cells were present in mutant testes relative to wild-type (Figure 3C). We also quantitated the number of cells at each meiotic stage using spermatocyte spreads, assaying chromosome-pairing status by staining for SYCP3, a component of the synaptonemal complex (Figure 4). We found that Dmrt7 mutants accumulate pachytene cells but have greatly reduced numbers of cells in late-pachynema and beyond. Together, these results confirm that the meiotic arrest in Dmrt7 mutants occurs primarily during pachynema and results in efficient elimination of arrested cells.
Defects in chromosome pairing, synapsis, or recombination can trigger pachytene arrest and apoptosis [46]. We therefore examined these events in Dmrt7 mutant testes. To assess homolog synapsis, we used antibodies to SYCP1, a synaptonemal complex transverse element component, and SYCP3, a component of the axial element, which remains on the desynapsed axes during diplonema [47,48]. Formation of synaptonemal complexes in the mutant was indistinguishable from that in wild-type, as indicated by the proper accumulation of SYCP1 (unpublished data) and SYCP3 (Figure 5A). Likewise, the Dmrt7 mutant zygotene spermatocytes showed normal accumulation of the early recombination repair marker RAD51, suggesting that early meiotic recombination is not significantly affected (Figure 5B). Dmrt7 mutant spermatocytes exhibited the expected decline in the presence of RAD51 foci associated with the autosomal synaptonemal complexes (Figure 5B; unpublished data) [49]. The few surviving cells that progressed beyond pachynema also underwent apparently normal desynapsis during diplonema (Figure 5A). From these results, we conclude that chromosomal pairing, synapsis, recombination, and desynapsis during prophase I in Dmrt7 mutant males are grossly normal.
Sertoli cells interact with germ cells during spermatogenesis and the interaction is critical for germ cell maturation [50]. Although we did not detect DMRT7 expression in Sertoli cells by antibody staining, we nevertheless considered the possibility that Sertoli cell defects might contribute to the male-specific germ line failure of Dmrt7 mutants. To characterize Sertoli cell differentiation, we examined expression of the Sertoli cell markers GATA4 (a marker of immature postnatal Sertoli cells) and GATA1 (a mature Sertoli cell marker). The levels of these proteins appeared normal relative to wild-type at P14 and P42 (Figure 6A–6C), as did the androgen receptor (Figure S3; unpublished data). However, the organization of Sertoli cells in Dmrt7 mutant testes was abnormal: in some tubules GATA1-positive Sertoli cell nuclei were displaced from their usual close apposition with the basement membrane (Figure 6C). In such tubules, nuclei of pre-meiotic germ cells and spermatocytes were packed close to the basal membrane and few germ cells were found in the adlumenal region.
The aberrant Sertoli cell organization in Dmrt7 mutant testes raised the possibility that the germ cell phenotype might indirectly result from defects in Sertoli cell function. To test this possibility, we deleted Dmrt7 just in the Sertoli cell lineage by crossing mice carrying the floxed Dmrt7 allele with Dhh-Cre transgenic mice [51]. The Desert hedgehog (Dhh) promoter is active starting at about E12.5 in pre-Sertoli cells but not in germ cells, allowing deletion of Dmrt7 in Sertoli cells well before any likely requirement for its function [52]. Testicular size in Sertoli-targeted (SC-Dmrt7KO) animals was slightly reduced from that of wild-type, but histological analysis revealed no obvious difference between wild-type and SC-Dmrt7KO testes (Figure 6D). Spermatogenesis appeared normal, mature sperm were present, and SC-Dmrt7KO mice were fertile. In addition, GATA1 staining showed that Sertoli cell nuclei were located adjacent to the basement membrane as in wild-type (Figure 6E). These results suggest the germ cell defects of Dmrt7 mutants are not caused by lack of Dmrt7 in Sertoli cells. Rather, the abnormal organization of Sertoli cells appears to result from lack of Dmrt7 in the germ line.
The data presented so far indicate that Dmrt7 mutant germ cells undergo apparently normal early meiosis and then arrest during pachynema due to a strict requirement for Dmrt7 in the germ line. To better understand the basis of the meiotic arrest, we more closely examined meiotic germ cells in the mutant. We focused on the XY body, which is thought to be essential for meiotic progression and is the site of preferential DMRT7 localization. Condensation of the X and Y chromosomes begins in late-zygotene cells, and, by mid-pachynema (when homologous chromosome pairs are fully aligned) the sex chromatin forms a microscopically visible spherical structure near the nuclear periphery [53].
We first asked whether DMRT7 is required for XY body formation by evaluating several characteristic XY body chromatin features. First, we tested γH2AX expression by immunofluorescent staining. H2AX is a variant of H2A that is crucial for XY body formation and MSCI [12]. γH2AX localized normally to the XY body of DMRT7 mutant cells in meiotic spreads (Figure 7A), and many γH2AX-positive puncta were present in germ cells of Dmrt7 mutant testes (Figure 7B). Next, we examined SUMO-1 localization in the mutant testis. SUMO-1 expression normally increases in the XY body of early- to mid-pachytene spermatocytes at the time of sex chromosome condensation. Prior to the completion of the first meiotic division, SUMO-1 disappears from the XY body as the X and Y chromosomes desynapse [40]. Punctate SUMO-1 localization was present in Dmrt7 mutant germ cells, again consistent with formation of a correctly marked XY body (Figure 7C). However, some tubules in mutants had multiple layers of cells with SUMO-1-condensed spots (Figure 7C), rather than the normal single layer of cells. This accumulation of XY body–containing cells also was apparent with γH2AX staining and is consistent with a developmental arrest of mutant cells in mid- to late-pachytene. We also examined Ub-H2A localization in Dmrt7 mutant testes. In early-pachytene, Ub-H2A is concentrated in the XY body; by mid-pachytene Ub-H2A is observed throughout the entire nucleus, but it again becomes limited to the XY body in late-pachytene spermatocytes [13]. Analysis of nuclear spreads revealed that Ub-H2A localizes normally to the XY body in Dmrt7 mutants (Figure S4). Collectively, these results indicate that Dmrt7 mutant germ cells can establish an XY body with at least some of the normal chromatin marks.
Although the XY body can form during pachynema in Dmrt7 mutants, we considered the possibility that transcriptional silencing might not be properly established. This would be consistent with the Dmrt7 phenotype: pachytene cells that escape from MSCI normally are eliminated prior to late-pachytene [17]. Recently, MSCI has been shown to continue into meiosis II and spermiogenesis, apparently mediated by a distinct chromatin compartment termed postmeiotic sex chromatin (PMSC) that is established starting in diplonema [16]. We therefore asked whether the pachytene germ cell death in Dmrt7 mutants is associated with a failure either to initiate or to maintain sex chromosome inactivation.
First, we examined the mid-pachytene XY body. To examine XY transcriptional status, we carried out Cot-1 RNA fluorescence in situ hybridization (FISH) to detect nascent RNA polymerase II transcription, combined with DAPI staining to locate the XY body on spreads of seminiferous tubules (Figure 8A and 8B). In Dmrt7 mutants, the XY body was formed and excluded Cot-1 hybridization (Figure 8B), indicating that transcriptional silencing is established normally in mutant pachytene cells. We also examined expression of the Y-linked gene Rbmy, which normally is inactivated during pachytene and reactivated after secondary meiosis begins [54,55]. Rbmy was inactivated normally in pachytene cells of Dmrt7 mutants, based on immunofluorescent staining with an anti-RBMY antibody (Figure S5). We also examined heterochromatin protein 1 beta (HP1β), which normally localizes to the X centromere at mid-pachynema and then spreads through the XY body as it internalizes during diplonema [56]. We found that HP1β localization is normal in DMRT7 mutant cells in mid-pachynema (Figure 8C and 8D). These results suggest that XY body formation and initiation of MSCI both occur normally in Dmrt7 mutant germ cells.
We next considered the possibility that sex chromatin is established normally but is not properly modified as cells exit pachynema and begin to form PMSC. Although most Dmrt7 mutant cells are eliminated by apoptosis prior to diplonema, we were able to examine epigenetic markers of PMSC in rare Dmrt7 mutant spermatocytes that escaped pachytene arrest and progressed into diplonema. First, we examined nascent transcription by Cot-1 hybridization. Although heterochromatic regions generally showed lower Cot-1 signal than euchromatic regions (Figure 8E and 8F), in some mutant cells the sex chromatin appeared to be incompletely silenced relative to wild-type (Figure 8F). We also examined three epigenetic signatures of PMSC: histone H3 dimethylated or trimethylated at lysine-9 (H3-2meK9, H3-3meK9) and spreading of HP1β through the XY body [16,57,58] (S. H. Namekawa, unpublished data). We observed defects in sex chromatin localization of all three markers in Dmrt7 diplotene cells. Although HP1β localization to the X chromosome centromere initially appeared normal at mid-pachynema, we observed Dmrt7 mutant diplotene cells that failed to show spreading of HP1β to the entire XY body (Figure 8G and 8H). Similarly, we found Dmrt7 mutant diplotene cells lacking accumulation of H3-2meK9 and H3-3meK9 marks onto the sex chromatin (Figure 8I–8L).
Not all Dmrt7 mutant diplotene cells showed abnormal localization of HP1β to the sex chromatin (Figure 8M). In one experiment, 11/27 mutant cells in diplonema lacked HP1β on the XY body, as compared with 2/22 wild-type cells. We hypothesize that the mutant cells with normal HP1β may be those that can complete meiosis (Figures 4 and 5). Some of the mutant diplotene cells showing abnormal sex chromatin also had abnormal autosomal γH2AX staining (Figure 8L). γH2AX localizes to double-strand DNA breaks, so this staining may indicate that some diplotene mutant cells are approaching or entering apoptosis [59]. We did not observe sex chromatin defects prior to diplonema, but we cannot exclude the possibility that earlier defects exist and the affected cells are rapidly eliminated.
In the preceding experiments, we staged cells based on XY body internalization. Because this process could be abnormal in the mutant cells, we also staged mutant cells by chromosome morphology using an antibody to SYCP3 (Figure 9). This independently identified Dmrt7 mutant diplotene cells lacking HP1β accumulation in the XY body, such as the example in Figure 9B. From these results, we conclude that Dmrt7 mutant cells establish a normal XY body in mid-pachynema, but then have multiple epigenetic defects in the sex chromatin transition from pachynema to diplonema.
In this study, we find that the DM domain protein DMRT7 is required for male germ cells to complete meiotic prophase but is dispensable in the female germ line. In males, DMRT7 expression is highest in pachytene spermatocytes, and the protein preferentially localizes to the XY body. Consistent with this expression, we found that most mutant male germ cells arrest in pachynema and undergo apoptosis, although a small proportion can progress to diplonema and sometimes beyond. Examination of chromatin and nascent transcription in mutant cells that progressed to diplonema revealed sex chromatin abnormalities, as discussed below.
The pachytene stage of prophase involves tremendous chromosomal changes as the homologs align, synapse, and recombine. During this period, at least one pachytene surveillance system exists to monitor key events of meiotic progression. Cells in which any of these events is anomalous are efficiently eliminated by apoptosis [46]. Another key event of pachynema in male mammals is the packaging of the sex chromosomes into the XY body and the establishment of MSCI. Examination of male meiosis in XYY mice and mice carrying a sex-chromosome-to-autosome translocation showed that cells in which a sex chromosome escapes MSCI are eliminated prior to late-pachynema [17]. This indicates that the establishment of MSCI also is subject to surveillance.
Since the arrest and apoptosis of Dmrt7 mutant spermatocytes could result from perturbation of any of the critical pachytene events mentioned above, we tested whether they occur abnormally in the mutant cells. We found that chromosomal synapsis and recombination appear normal in Dmrt7 mutant cells. We therefore focused on the XY body, the most prominent site of DMRT7 accumulation. First, we tested whether the XY body forms and MSCI is established in Dmrt7 mutant cells. Surprisingly, we found that these cells form an XY body with normal morphology and proper accumulation of all the chromatin marks we examined. Moreover, Cot-1 hybridization and analysis of RBMY expression demonstrated that MSCI initiates normally in the XY body of mid-pachytene Dmrt7 mutant cells.
We did, however, observe three specific defects in the sex chromatin of Dmrt7 mutant germ cells that avoided arrest in pachynema and were able to enter diplonema. Normally cells accumulate H3-2meK9 and H3-3meK9 marks and HP1β protein on the sex chromatin as they progress to diplonema, but we observed mutant diplotene cells lacking these features. Thus, although a minority of Dmrt7 mutant germ cells can progress from pachynema to diplonema, there are defects in sex chromatin modification during the transition. A function in male sex chromatin can reconcile the findings that DMRT7 is required for meiosis, but only in males, and is present only in mammals. A proportion of mutant diplotene cells have apparently normal sex chromatin (for example, Figure 8M); these are likely to be the cells that can progress beyond diplonema.
Because most Dmrt7 mutant germ cells are eliminated by apoptosis around the time at which we observed sex chromatin defects, a simple model is that the apoptosis is a consequence of the sex chromatin defects. The reciprocal situation (sex chromatin defects caused by apoptosis) is possible, but seems unlikely, because we observed mutant cells with sex chromatin defects but no indications of apoptosis. Alternatively, apoptosis and abnormal sex chromatin may be two independent consequences of Dmrt7 loss. This question cannot be answered definitively until we know the detailed molecular mechanism of DMRT7.
A number of other proteins have been identified that interact with the XY body, including histone variants and modified histones, a testis-specific histone methyl transferase, chromobox proteins, an orphan receptor germ-cell nuclear factor, and recombination-related proteins [60]. A common feature of these proteins is involvement with heterochromatin and/or transcriptional repression. DMRT7 is unusual among XY body proteins in being related to highly site-specific transcriptional regulators. An attractive speculation is that DMRT7 may provide sequence specificity in recruiting other proteins, such as chromatin modifiers, to the XY body as part of the transition to PMSC. Chromatin regulation may be a common mechanism for DM domain proteins, as we find that other DM domain proteins associate with chromatin modifying complexes (M. W. Murphy, D. Zarkower, and V. J. Bardwell, unpublished data).
The finding that Dmrt7 is essential for mammalian meiosis expands the known functions of this gene family. Invertebrate DM domain genes so far have only been found to function in somatic cells. Two other DM domain genes, Dmrt1 and Dmrt4, do affect germ cell development in the mouse. Dmrt1 is required in pre-meiotic male germ cells for differentiation of gonocytes into spermatogonia, as well as in Sertoli cells, but it is not expressed in meiotic cells [31] (S. Kim and D. Zarkower, unpublished data). The requirement for DMRT1 in pre-meiotic germ cells and DMRT7 in meiotic germ cells demonstrates that DM domain proteins act at multiple critical points of male germ cell development. Ovaries of Dmrt4 mutant females have polyovular follicles (follicles containing multiple oocytes), but it is unknown whether this reflects a defect in the germ line or the soma. It is notable that at least three mammalian DM domain genes affect gonadal development only in one sex, given the similar roles of related proteins in directing sex-specific somatic development in other phyla.
Strikingly, Dmrt7 is present, not only in placental mammals, but also in marsupials and a monotreme (egg-laying mammal), the platypus, which has a clear Dmrt7 ortholog [36]. However, no close Dmrt7 ortholog has been reported in nonmammalian vertebrates, and our database searches did not reveal one. Thus, Dmrt7 likely arose, presumably by duplication and divergence of another Dmrt gene, shortly before or coincident with the mammalian radiation. Monotremes have five X and five Y chromosomes, which form an extended pairing chain during meiosis and appear unrelated to the sex chromosomes of the other mammals [61]. The presence of Dmrt7 in both lineages may support a common origin for either the sex chromosomes or the sex chromatin of monotremes and other mammals. A plausible model is that Dmrt7 evolved during the establishment of mammalian sex determination to help cope with ancestral differences in gene dosage, chromosome pairing, recombination, or other meiotic issues arising from sex chromosome heteromorphy. In this regard, we speculate that the recruitment of Dmrt7 during mammalian evolution may be analogous to the recruitment of chromatin regulatory complexes to achieve somatic dosage compensation during evolution of heteromorphic sex chromosomes in several phyla (reviewed in [62]). It will be of interest to determine whether DMRT7 localizes to sex chromosomes during monotreme meiosis.
In summary, we have found that the mammal-specific DM domain protein DMRT7 is essential for meiotic prophase progression in males. DMRT7 localizes to the sex chromosomes after they are assembled into specialized heterochromatin, and many Dmrt7 mutant cells have epigenetic defects in the modification of the sex chromatin between pachytene and diplotene. Although Dmrt7 belongs to an ancient and conserved gene family, it is found only in mammals, and to our knowledge DMRT7 is the only example of a mammal-specific protein that is essential for meiosis. It will be important to determine the precise mechanism by which DMRT7 affects sex chromatin regulation during male meiosis.
A mouse Dmrt7 cDNA fragment containing sequences from exon 8 was used to screen a mouse BAC library from the 129/SvJ strain (Stratagene, http://www.stratagene.com), and clones containing promoter sequences were isolated and sequenced to obtain Dmrt7 genomic sequence. The targeting vector pDZ169 (diagrammed in Figure S2) was constructed by the following scheme: The vector pDZ157 was used as a backbone vector [31]. 3′ to Pgk-neo and the loxP site, we inserted, as a XmaI/XmaI DNA fragment, the third intron of Dmrt7 (from 366 bp to 2,773 bp downstream of exon 3) generated by PCR. 5′ to Pgk-neo, we inserted an EcoRI/NotI PCR fragment extending from 4,107 bp to 336 bp 5′ of the Dmrt7 translational start. Finally, we inserted a loxP site and NotI site 336 bp 5′ of the Dmrt7 translational start. In the resulting vector, the second and third exons of Dmrt7 are flanked by loxP sites (floxed). The Dmrt7-containing portions of pDZ169 were completely sequenced.
pDZ169 was linearized with PmeI and electroporated into CJ7 ES cells (originally derived from the 129S1 strain). Three homologous recombinants were identified from 296 G418-resistant colonies by Southern hybridization by use of a DNA probe from the sequences upstream of exon 1 to screen genomic DNA digested with EcoRI. Homologous recombination was confirmed on both ends of the targeted region by Southern hybridization. Probes for Southern hybridization were made by PCR using primers DM5S10/DM5S11 (5′ probe) and DM5PR1/DM5PR2 (3′ probe), listed below. Two targeted ES cell clones containing the floxed allele Dmrt7neo were injected into C57Bl/6 blastocysts to generate chimeras. Chimeric males were bred with C57Bl/6 females to generate heterozygotes carrying Dmrt7neo. Dmrt7+/Dmrt7neo females were bred with male β-actin-Cre transgenic mice to delete the floxed sequences and generate heterozygous Dmrt7−/+ deletion mutants, which were interbred to generate homozygous Dmrt7−/− mutants.
For genotyping, tail-clip DNA was amplified for 35 cycles. Chromosomal sex was determined by PCR with primers to the Y chromosome gene Zfy (below). The wild-type Dmrt7 allele Dmrt7+ was detected by PCR with DM5S4/DM5S5, with an annealing temperature of 60 °C. The Dmrt7flox allele was detected by PCR with DM5S5F/DM7KO7R with an annealing temperature of 62 °C. The deleted Dmrt7 allele Dmrt7− was detected with DM5S3/DM7KO7R with an annealing temperature of 62 °C. RT-PCR for Dmrt7 expression analysis was as described [23] using primers SK111/SK112 with an annealing temperature of 62 °C.
Dissected testes were fixed in Bouin's fixative or phosphate-buffered formalin overnight at 4 °C, progressively dehydrated in a graded ethanol series, and embedded in paraffin wax. Sections (6 μm) were deparaffinized, rehydrated, and stained with hematoxylin and eosin. For TUNEL analyses, deparaffinized sections were treated with proteinase K for 15 min and quenched in 3.0% hydrogen peroxide in PBS for 5 min at room temperature. Subsequently, nuclear staining in apoptotic cells was detected using ApopTag kit (Chemicon, http://www.chemicon.com) according to the manufacturer's instructions.
Slides with paraffin sections were washed in PBT (0.1% Tween 20 in PBS) and autoclaved in 10 mM citric acid (pH 6.0) to retrieve antigenicity. Slides were blocked in 5% serum (matched to the species of the secondary antibody) in PBS for 1 h at room temperature and incubated with primary antibodies overnight at 4 °C prior to detection with secondary antibodies.
Rabbit polyclonal antibodies to DMRT7 were raised against a purified fusion protein containing glutathione-S-transferase (GST) fused to the C-terminal 279 amino acids of DMRT7. Antibodies to GST were removed by GST-affigel 10 chromatography and the antiserum was then purified by GST-DMRT7-affigel 15 chromatography. DMRT7 antibody was used at 1:200 dilution with a goat anti-rabbit secondary antibody (Molecular Probes, http://www.invitrogen.com) at 1:200 dilution. Other primary antibodies used for immunofluorescence were rat anti-GATA1 (1:200, Santa Cruz Biotechnology, http://www.scbt.com, sc-265), goat anti-GATA4 (1:200, Santa Cruz Biotechnology, sc-1237), rat anti-TRA98 (1:200, gift of H. Tanaka and Y. Nishimune), rat anti-BC7 (1:50, gift of H. Tanaka and Y. Nishimune), rat anti-TRA369 (1:200, gift of H. Tanaka and Y. Nishimune), rabbit anti-RAD51 (1:600 Calbiochem, http://www.calbiochem.com, PC130), mouse anti-GMP-1/SUMO-1 (1:200, Zymed, http://invitrogen.com, 33–2400), rabbit anti-phospho-H2AX (Ser139) (1:200, Upstate, http://www.millipore.com, 01–164), mouse anti-phospho-H2AX (1:200, Upstate, 05–636), mouse anti-SYCP3 (1:200, Abcam, http://www.abcam.com, ab12452), rabbit anti-HP1β (1:100, Abcam, ab10478), rabbit anti-H3-2meK9 (1:100, Upstate, 07–441), rabbit anti-H3-3meK9 (1:200, Upstate, 07–442), rabbit anti-AR (N-20) (1:200, Santa Cruz Biotechnology, sc-816), and mouse anti-αSMA clone 1A4 (1:800, Sigma, http://www.sigmaaldrich.com, A2547). Secondary antibodies used were goat anti-rabbit Alexa 488, goat anti-rabbit Alexa 594, goat anti-rat Alexa 594, and goat anti-mouse Alexa 488 (Molecular Probes) used at 1:250. Donkey anti-goat FITC (Jackson ImmunoResearch Laboratories, http://www.jacksonimmuno.com) and donkey anti-rabbit Texas Red (Jackson) were used at 1:50 according to the manufacturer's instructions.
Meiotic chromosome spread preparations were made from 3-wk-old mice, prepared as described by Reinholdt et al. [63]. For analysis of PMSC and Cot-1 RNA FISH, meiotic slides were prepared as previously described [16]. Slides containing chromosome spreads or meiotic spermatocytes were subjected to immunofluorescent staining or RNA FISH, as previously described [16,63]. For combined RNA FISH/immunostaining, we carried out RNA FISH first, followed by immunofluorescence. DNA FISH was performed using chromosome painting (Cambio, http://www.cambio.co.uk). Z-sections were captured by Zeiss Axioplan microscope (Zeiss, http://www.zeiss.com) and processed by Openlab (Improvision, http://www.improvision.com). |
10.1371/journal.pgen.1003489 | Female Bias in Rhox6 and 9 Regulation by the Histone Demethylase KDM6A | The Rhox cluster on the mouse X chromosome contains reproduction-related homeobox genes expressed in a sexually dimorphic manner. We report that two members of the Rhox cluster, Rhox6 and 9, are regulated by de-methylation of histone H3 at lysine 27 by KDM6A, a histone demethylase with female-biased expression. Consistent with other homeobox genes, Rhox6 and 9 are in bivalent domains prior to embryonic stem cell differentiation and thus poised for activation. In female mouse ES cells, KDM6A is specifically recruited to Rhox6 and 9 for gene activation, a process inhibited by Kdm6a knockdown in a dose-dependent manner. In contrast, KDM6A occupancy at Rhox6 and 9 is low in male ES cells and knockdown has no effect on expression. In mouse ovary where Rhox6 and 9 remain highly expressed, KDM6A occupancy strongly correlates with expression. Our study implicates Kdm6a, a gene that escapes X inactivation, in the regulation of genes important in reproduction, suggesting that KDM6A may play a role in the etiology of developmental and reproduction-related effects of X chromosome anomalies.
| Homeobox (HOX) genes are known to be under epigenetic control during development. Here, we report that two mouse X-linked homeobox genes implicated in reproduction, Rhox6 and 9, are activated by the histone demethylase KDM6A that removes methylation at lysine 27 of histone H3. Kdm6a is one in a small group of genes that escape X inactivation in mice and humans, and thus has female-biased expression. We found that knockdown of Kdm6a affects Rhox6 and 9 expression specifically in female ES cells. We also demonstrate that high expression of Rhox6 and 9 in mouse ovary is associated with recruitment of KDM6A to these genes, consistent with a role in a female-specific organ. Furthermore, we demonstrate paternal imprinting of Rhox6 and 9 in mouse ovary. The findings herein help to understand sex bias in the regulation of reproductive homeobox genes during early development and in ovary. Our findings provide clues into the sex-specific roles played by genes that escape from X inactivation, which may contribute to developmental defects and ovarian dysfunction in individuals with X chromosome abnormalities.
| Homeobox (HOX) genes are known for their ability to regulate embryogenesis and guide tissue differentiation. These genes encode transcription factors that specify cell identity and regulate many embryonic programs including axis formation, limb development, and organogenesis [1]. Control of HOX gene expression via epigenetic modifications that include DNA methylation and histone modifications is critical to this process. Notably, tri-methylation of lysine residue 27 of histone H3 (H3K27me3) plays a major role in repression of HOX genes [2]. The histone demethylase KDM6A (also known as UTX) removes H3K27me3 from HOX genes to restore their activity [3]. KDM6A contains a tetratricopeptide motif predicted to mediate protein-protein interactions [4], and is a member of a stable multi-protein complex that not only de-methylates H3K27me3 but also methylates lysine 4 at histone H3 to facilitate gene expression [5], [6]. Different protein partners modulate KDM6A recruitment to specific chromatin regions since ectopic KDM6A expression does not result in significant reduction of genome-wide H3K27me3 levels but rather targets specific genes [3], [7], [8], [9]. For example, KDM6A regulates muscle-specific genes during myogenesis and is necessary for proper cardiac cell differentiation [10], [11]. KDM6A mutations have been discovered in patients with Kabuki syndrome, a rare syndrome associated with distinct facial features, intellectual disability, growth retardation, and skeletal anomalies [12], [13]. Recent studies have also implicated KDM6A as a candidate tumor suppressor gene whereby ectopic expression leads to enhanced expression of the RB (retinoblastoma) and RBL2 (retinoblastoma-like 2) genes [14]. KDM6A inactivating mutations have been discovered in acute promyelocytic leukemia and multiple other cancer types [15], [16], [17].
A large set of homeobox genes clustered on the X chromosome has been implicated in male and female reproduction. In mouse, this cluster called Rhox (reproductive homeobox X-linked) contains 33 adjacent genes organized into three sub-clusters: α, β, and γ [18]. The Rhox cluster evolved at a rapid pace in mammals: the rat cluster contains 11 genes, and the human cluster, only 3 genes. In mouse, members of each paralog family have nearly identical sequences and are thus considered to be functional, although few members have been studied in detail [18]. Rhox genes are selectively expressed in male and female reproductive tissues, including testis, ovary, and placenta [19]. Similar to other homeobox genes, Rhox genes are also expressed during early embryonic development [19], [20], [21], [22], [23]. Little is known about the biological significance of individual paralogs.
Epigenetic regulation of the Rhox gene cluster has been mainly focused on DNA methylation and histone H1 control in placenta and during embryonic development [24], [25]. It is unknown whether other histone modifications control Rhox expression and what histone modifiers might be responsible. An important contender is KDM6A, which is known to regulate the HOX cluster [3]. Interestingly, KDM6A is encoded by an X-linked gene that escapes X inactivation in somatic tissues of human and mouse [26], [27], [28]. Expression of most X-linked genes in somatic tissues is equalized between males (XY) and females (XX) by random silencing of one X chromosome in early development [29]. Genes that escape X inactivation represent exceptional genes with higher expression in females versus males, suggesting that they may be important for female-specific functions [29], [30], [31].
To explore the potential role of KDM6A in the sex-specific regulation of Rhox genes, chromatin analyses were done to follow KDM6A recruitment to the Rhox cluster in male and female ES cells. We focused on Rhox6 and 9, two members of the Rhox cluster we discovered to be most affected by Kdm6a knockdown. KDM6A was specifically recruited to Rhox6 and 9 in female but not male ES cells, resulting in removal of the repressive histone mark H3K27me3 and in increased expression. KDM6A was also bound to Rhox6 and 9 in ovary where these genes are highly expressed. We conclude that KDM6A is important for removal of a repressive histone mark at the bivalent promoters of Rhox6 and 9 to facilitate their expression in female ES cells and in ovary.
Rhox6 and 9 expression levels were significantly higher in undifferentiated female versus male ES cells (Figure 1A). Expression was measured using quantitative RT-PCR (qRT-PCR) in two female (PGK12.1 and E8) and two male (WD44 and E14) ES cell lines before and after differentiation. ES cell differentiation and embryoid body formation were induced by removal of LIF (leukemia inhibitory factor). Rhox6 and 9 primers were verified to be gene-specific by cDNA sequencing (Figure S1). Sex-specific differences persisted to day 2 of ES cell differentiation (Figure 1B). Analyses of sexed 8-cell pre-implantation embryos confirmed higher female than male expression of Rhox6 and 9 in early development in vivo (Figure S2A). Furthermore, re-analyses of published microarray expression data [32] revealed a female bias in Rhox6 and 9 expression at later embryonic stages (11.5–13.5 dpc) in both germ cells (at all stages) and somatic cells (at 12.5–13.5 dpc) (Figure 1C). Note that expression was much higher in germ cells compared to somatic cells.
A female bias in Rhox6 and 9 expression in ES cells was unexpected because these genes are solely expressed from the maternal allele due to paternal imprinting [25]. Thus, the significantly higher expression we observed in undifferentiated female versus male ES cells (>6-fold for Rhox6 and >10-fold for Rhox9, respectively) must be due to another factor (Figure 1A). Interestingly, levels of the histone demethylase KDM6A known to play a role in HOX gene regulation were approximately two-fold higher in female compared to male ES cells as measured by qRT-PCR and western blot analyses (Figure 1D, 1E). This sex bias initially due to the presence of two active X chromosomes in undifferentiated female ES cells [33], [34] persisted throughout differentiation and at later embryonic stages, as expected for a gene that escapes X inactivation (Figure 1F, 1G). To determine whether KDM6A was involved in the sex-specific regulation of Rhox6 and 9 we measured occupancy using chromatin immunoprecipitation (ChIP) in male and female ES cells. KDM6A occupancy at the 5′ end of Rhox6 and 9 was greater in undifferentiated female than male ES cells as measured both by quantitative PCR (ChIP-qPCR) and by array analysis (ChIP-chip) (Figure 2A and Figure S2B). At day 2 of differentiation the female bias in KDM6A occupancy persisted, but following differentiation (day 15) KDM6A occupancy decreased (Figure 2C and S2B). These results are in agreement with the observed timing of changes in Rhox6 and 9 expression (Figure 1B). Furthermore, we observed corresponding changes in levels of H3K27me3, the histone modification removed by KDM6A. By ChIP-qPCR H3K27me3 enrichment mirrored changes in KDM6A occupancy at Rhox6 and 9 in female PGK12.1 ES cells during differentiation (Figure 2D). Quantitative analysis of H3K4me3 enrichment at Rhox6 and Rhox9 promoters revealed higher enrichment in female than male ES cells, as well as a decrease during differentiation correlating with expression changes (Figure 2B, Figure 1A and 1B).
During differentiation X inactivation initiates in female PGK12.1 ES cells, as confirmed by increased Xist expression and by the appearance of an Xist cloud detected by RNA-FISH in interphase nuclei of cells at day 15 (Figure S3) [35]. Concomitantly, H3K27me3 enrichment at Rhox6 and 9 increased almost 2–3-fold between day 0–2 and day 15 (Figure 2D). This increase was observed over the entire Rhox cluster, suggesting that the cluster is subject to silencing possibly by X inactivation (Figure S4) (see below) [29]. In male ES cells, H3K27me3 levels were very low at Rhox6 and 9 at all time points (Figure 2D). Taken together, our data indicate that KDM6A is specifically recruited to Rhox6 and 9 in undifferentiated female ES cells, which results in a 6–10-fold higher expression compared to male ES cells.
To directly assess the role of KDM6A in regulation of the Rhox cluster we performed knockdowns in two female and two male ES cell lines by RNAi. Using a pool of siRNAs to target multiple regions of Kdm6a RNA, we achieved a 60–80% knockdown in ES cells as shown by qRT-PCR and expression array analyses (Figure 3A). Immunoblots using two different antibodies confirmed a dramatic reduction (70–90%) in the amount of KDM6A protein after 48 h of knockdown (Figure 3A). Specificity of the siRNAs was confirmed using two individual siRNAs, each resulting in a ∼60% knockdown (Figure S5A).
Kdm6a knockdown caused a significant reduction in Rhox6 and 9 expression in the two female (PGK12.1 and E8) but not in the male (WD44 and E14) ES cell lines, indicating that the regulation of these genes by KDM6A is female-specific (Figure 3B). Rhox6 and 9 expression levels measured by qRT-PCR and by expression array analyses were diminished by 30–50% after Kdm6a knockdown whereas the control gene β-actin did not change (Figure 3A, 3B). By expression array analyses we found that among the Rhox genes, Rhox6 and 9 exhibited the highest expression decrease (>1.25 fold) (Table S1). The lesser decrease measured by expression arrays versus qRT-PCR can be attributed to the different methodologies; qRT-PCR was done using primers designed to be specific for either Rhox6 or Rhox9 (Figure S1), whereas expression changes measured by arrays may be dampened by cross-hybridization due to high sequence similarity between the genes. Importantly, Rhox6 and 9 expression depended on the amount of Kdm6a knockdown in a dose-sensitive manner, consistent with a sex-specific dosage effect (Figure 3C). Note that Rhox5 also showed a significant decrease after Kdm6a knockdown but its analysis was not pursued at this time. As expected, KDM6A occupancy was reduced at the 5′ end and gene body of Rhox6 and 9 after knockdown in female ES cells (Figure S5B). Conversely, H3K27me3 levels were significantly increased at the 5′ end, gene body, and 3′end of Rhox6 and 9, while there was no significant change in levels of H3K4me3 (Figure 3D and Figure S5C). Genes associated with pluripotency (e.g. Cd9, Nanog, Pou5f1, Stat3, Sox2) were not affected by Kdm6a knockdown in either female or male ES cells indicating no induction of differentiation (Figure S5D). We conclude that KDM6A plays a critical and dose-dependent role in regulating Rhox6 and 9 expression in female but not male ES cells.
Chromatin domains that contain both activating and inactivating histone marks in undifferentiated ES cells have been termed bivalent and are thought to be poised for activation during development [36], [37]. Notably, bivalent genes include homeobox genes, such as HOX genes, suggesting that Rhox genes are also candidates for bivalency. ChIP-chip profiles in both female and male undifferentiated ES cells demonstrated that both Rhox6 and 9 in cluster β were enriched in H3K27me3 and H3K4me3, indicating that these genes are bivalent and thus poised for activity during development (Figure 4 and Figure S6). Quantitative measurements showed higher H3K4me3 and H3K27me3 levels in female versus male ES cells (Figure 2B, 2D). H3K4me3 levels decreased and H3K27 levels increased between day 0 and 15 of differentiation in female ES cells, consistent with a decrease in Rhox6 and 9 expression (Figure 2B, 2D). In contrast, levels remained low in male ES cells. KDM6A binding being clearly female-biased would explain the female bias in gene expression at day 0–2 of differentiation, as described above (Figure 1 and Figure 2).
Other Rhox genes within the α- and γ-clusters did not appear to be bivalent but rather were enriched with the repressive mark H3K27me3, with no significant peaks of enrichment for the active mark H3K4me3, suggesting that these genes are mostly contained in a silenced chromatin domain in both female and male undifferentiated ES cells (Figure 4 and Figure S6). Inspection of representative genes from each cluster, Rhox6 and 9 in cluster β, Rhox3e in cluster α, and Rhox12 in cluster γ, confirmed these findings (Figure 4). Note that while Rhox1 and 7 also appeared bivalent at day 0, KDM6A was absent at their promoter, which may account for their low expression (Table S1).
Re-analyses of published expression array data confirmed that Rhox6 and 9 and Kdm6a are expressed at a higher level in ovary than in testis (Figure 5A) [18], [19]. This is consistent with measurements of expression in embryos, in which female germ cells have much higher expression than male germ cells (Figure 1C). As expected for a gene that escapes X inactivation, Kdm6a expression was higher in all female tissues examined in comparison to male tissues, including brain and sexual organs, as well as somatic and germ cells from embryos (Figure 5C and Figure 1G). To assess the in vivo binding of KDM6A to Rhox6 and 9 in reproductive tissues, chromatin extracted from adult mouse ovaries and testes was subjected to ChIP-qPCR. KDM6A occupancy was high at the promoters of Rhox6 and 9 in mouse ovary, consistent with high expression in this organ (Figure 5A, 5B) [38], [39]. In mouse testis where Rhox6 and 9 expression is lower, KDM6A was still bound but to a lesser extent (38% of occupancy in ovary), reflecting lower expression (Figure 5A, 5B). In mouse brain where the genes are not expressed [19], KDM6A occupancy was almost undetectable in females and completely undetectable in males (Figure 5B). Taken together, these data indicate that KDM6A occupancy is associated with Rhox6 and 9 expression in reproductive tissues, more significantly in females than in males.
To determine the allele-specific expression of Rhox6 and 9 in ovary we employed F1 mice derived from crosses between C57BL/6J females with (XistΔ) or without (XistΔ−) an Xist mutation and Mus spretus males. In F1 animals that carry the mutant Xist (XistΔ), X inactivation is completely skewed towards the M. spretus X chromosome. SNPs between the mouse species were used to distinguish alleles after RT-PCR and Sanger sequencing. In F1 mice with (XistΔ) or without (XistΔ−) the Xist mutation expression of Rhox6 and 9 was exclusively from the maternal C57BL/6J allele, with no evidence of the M. spretus allele, consistent with imprinting of the paternal allele (Figure 5D). This is similar to what has been reported in mouse ES cells and placenta [25]. Control genomic DNA amplification confirmed the presence of the SNPs in the F1 mice (Figure 5D). Our results suggest that imprinting has taken place in the germ cells from adult ovary, as we did not observe any evidence of paternal allele expression. By qRT-PCR Rhox6 and 9 expression was higher (1.7-fold and 3-fold, respectively) in ovaries of F1 mice carrying the Xist mutation (XistΔ) in which the maternal allele is expressed in all cells (due to skewing of X inactivation), compared to ovaries from non-mutant F1 mice (XistΔ−) in which the maternal allele is expressed in half of the cells (due to random X inactivation) (Figure 5E). The X inactivation effect on Rhox6 and 9 expression would only be pertinent in somatic cells, but not in germ cells in which the inactive X chromosome is reactivated. This complicates interpretation of our data because expression was measured in whole ovary containing both germ cells with very high Rhox6 and 9 expression and supporting somatic cells with lower expression (Figure 1C). Additional studies in germ cells and somatic cells of the ovary are needed to fully understand the developmental regulation of Rhox6 and 9 in this organ. Nonetheless, we conclude that female biased expression of Rhox6 and 9 in ovary is not due to bi-allelic expression in this tissue but rather to recruitment of KDM6A to activate Rhox6 and 9 on the active maternal X chromosome.
Rhox genes represent a set of X-linked homeobox genes specifically expressed in organs and cell types implicated in sexual development and reproduction [19], [20], [40], [41]. Here, we provide functional evidence identifying KDM6A, an enzyme that removes methylation at lysine 27 of histone H3, as an important regulator of a specific subset of Rhox genes, Rhox6 and 9, in female ES cells and in ovary. Interestingly, KDM6A is encoded by an X-linked gene that escapes X inactivation and has higher expression in females, which may indirectly facilitate its sex-specific role in enhancing Rhox6 and 9 expression [26], [27], [28]. Our knockdown experiments clearly support an important role for KDM6A in regulating Rhox6 and 9 in female but not male ES cells.
The 6–10 fold female bias in Rhox6 and 9 expression we measured in undifferentiated ES cells cannot be explained by the presence of two active X chromosomes in female ES cells prior to X inactivation since Rhox6 and 9 are paternally imprinted in these cells [25]. Rather, the female enhanced expression results from the specific recruitment of KDM6A at those genes to facilitate the transition from repressive to active histone modifications and to increase expression. A similar mechanism explains the female bias in Rhox6 and 9 expression in ovary where we demonstrate that the genes are imprinted as well. KDM6A is a member of a multi-protein complex that not only de-methylates H3K27me3 but also methylates lysine 4 at histone H3 to facilitate gene expression [5], [6]. KDM6A counterbalances polycomb activity by regulating H3K27me3 levels [9], which would help maintain Rhox6 and 9 expression in undifferentiated female ES cells and ovary. In differentiated female ES cells KDM6A occupancy decreases, which mirrors the accumulation of H3K27me3 at Rhox6 and 9, consistent with low expression in most somatic cell types as well as with the heavy DNA methylation reported for these genes during development [24]. Our analyses of 8-cell embryos suggest that a female bias in Rhox6 and 9 expression is already present at this early stage, prior to gonadal development. Somatic and germ cells from developing embryos still show a female bias in Rhox6 and 9 expression at later stages (11.5–13.5dpc) including those coincident with gonad differentiation, which confirms a previous study [20]. However, detailed analyses of sexed embryos at additional stages will be needed to fully follow developmental expression in multiple cell types.
Strikingly, within the Rhox cluster only Rhox6 and 9 show a marked increase in KDM6A in female ES cells. Tellingly, these genes share a high degree of sequence similarity but differ from the other Rhox genes in other sub-clusters [19], suggesting that they may contain a sequence motif to specifically recruit KDM6A. The question arises of which other histone demethylases would remove H3K27me3 at other Rhox genes to facilitate their expression in specific tissues. We determined that KDM6B, which also removes methylation at lysine 27 of histone H3, has low expression in undifferentiated male and female ES cells (data not shown). However, its expression increases after differentiation in both male and female ES cells, pointing towards a potential role for this enzyme in regulating expression of some of the other Rhox genes at later stages of development [42]. KDM6A binds to the promoter, gene body, and 3′end of Rhox6 and 9 in female ES cells, suggesting a mechanism of regulation at transcription initiation and elongation. Interestingly, recruitment of elongation factors to target genes has been demonstrated for KDM6B, in addition to its role in histone demethylation [43].
Epigenetic regulation of the Rhox cluster had been previously focused on DNA methylation [24], [44]. Rhox5 whose expression peaks at day 9 after ES cell differentiation is repressed by DNA methylation at later stages, while it remains unmethylated and highly expressed in extra-embryonic tissues. Similarly, Rhox6 and 9 are repressed following the establishment of CpG methylation by DNA methyltransferases DNMT3b and DNMT1 at their promoter regions in the embryo proper but not in extra-embryonic tissues [24]. Rhox5 is the only gene together with Xist known to be expressed from the paternal X chromosome (maternally imprinted) at early embryonic stages (until e6.5); surprisingly, it is expressed from the maternal X (paternally imprinted) in extra-embryonic tissues, like Rhox6 and 9 [18], [24], [25], [45]. Our results are consistent with paternal imprinting of Rhox6 and 9 in mouse ovary, in agreement with other studies in placenta and ES cells [25].
We found that Rhox6 and 9 are bivalently marked in undifferentiated ES cells as they are occupied by nucleosomes containing histone H3 methylated at both lysine 27 and lysine 4. Bivalent genes are usually silent while poised for expression [37]. However, Rhox6 and 9 are in fact expressed in undifferentiated ES cells, probably due to a specific recruitment of KDM6A in a portion of cells in which levels of H3K27me3 would be decreased. Bivalent modifications result from a dynamic equilibrium of negative and positive chromatin marks controlled by histone modifying enzymes such as KDM6A. Our findings of a H3K27me3 increase at Rhox6 and 9 after Kdm6a knockdown are in agreement with what has been reported for other bivalent genes and support a role for KDM6A in maintaining a balance between active and inactive marks at bivalent promoters [9]. Note that the extent of reduction in Rhox6 and 9 expression we measured in female ES cells is comparable to that reported for another HOX gene, HOXB1, after KDM6A knockdown in human cells [8]. Many homeobox genes important for specification of cell types and organs contain bivalent domains, suggesting that bivalency is an important part of stem cell differentiation and development [46], [47]. Except for Rhox6 and 9, most other Rhox genes are not occupied by bivalent marks in undifferentiated ES cells, thus Rhox6 and 9 may be specifically activated to influence lineage commitment. It will be interesting to determine which pathways and specific lineages are stimulated by RHOX6 and RHOX9 proteins. Rhox6 has been implicated in the determination of the germ cell lineage [48]. So far, only Rhox5 and 9 have been studied in vivo. Whereas Rhox5-null male mice exhibit increased germ cell apoptosis and sperm motility defects leading to sub-fertility, Rhox9-null male or female mice do not have any apparent phenotypes [19], [49]. It is possible that due to similarities in sequence, homeodomain, and expression patterns Rhox6 compensates for the loss of Rhox9 in these knockout mice [49], [50]. Additional evidence based on knockouts in mouse and rat epididymis, suggests that Rhox5 may act as a master regulator of many of its paralogs [51].
Both Rhox6 and 9 are highly expressed in ovary and to a lesser extent, in testis (this study) and [19]. An intriguing finding from our study is that KDM6A occupancy is high at Rhox6 and 9 in ovary and thus may serve to keep these two Rhox genes active. KDM6A is also bound to Rhox6 and 9 in testis, although at a lower level (1.7-fold and 2.5-fold lower in testis than in ovary, respectively), suggesting a threshold effect and/or another level of control in testis. Our knockdown experiments do indicate that KDM6A affects Rhox6 and 9 expression in a dose-dependent manner. In embryonic gonads the majority of Rhox genes are already expressed in a sexually dimorphic manner from an early stage. Specifically, Rhox6 and 9 are predominantly expressed in female versus male primordial germ cells at 12.5–15.5dpc (this study) and [19], [20], [38], [52]. In addition, Rhox6 and 9 are also expressed in somatic cumulus cells in ovary [53], [54]. Our findings indicate that Rhox6 and 9 are both imprinted on the paternal allele and subject to X inactivation. This implies the existence of a population of somatic cells without any Rhox6 and 9 expression, suggesting that cumulus cells tolerate such mosaicism. In contrast, all germ cells would express Rhox6 and 9 following X re-activation and subsequent imprinting of the paternal X chromosome. This is similar to what has been reported for some of the Xlr genes, a family of mouse genes also implicated in reproduction, some of which are also imprinted and subject to X inactivation [55].
In addition to removal of H3K27me3, KDM6A appears to have a demethylase-independent role in regulating chromatin structure [9], [56], [57]. Indeed, KDM6A and KDM6B regulate T-box family members through an interaction with SMARCA4-containing SWI/SNF complexes in T-cells [58]. Interestingly, Kdm6a knockout mice display a more severe phenotype at mid-gestation in female than male embryos [9], [59]. Thus, the Y-linked paralog Uty compensates for Kdm6a deficiency allowing survival of male embryos by a demethylase independent mechanism, since UTY does not have demethylase activity [9], [56], [59]. However, while some KDM6A-deficient male mice survive, most do not or are runted throughout adulthood, indicating that H3K27 demethylation remains an important function of KDM6A for survival and growth [56]. Additionally, histone demethylation appears to be the predominant mechanism required for activation of genes important in differentiation since mouse and human cells lacking KDM6A but retaining UTY fail to reprogram [60]. Furthermore, male primordial germ cells lacking KDM6A do not develop, as H3K27me3 levels are retained when compared to wild type [60]. Our knockdown experiments are consistent with a role for KDM6A in controlling levels of H3K27me3 and expression of Rhox6 and 9 in female ES cells. However, we cannot rule out the contribution of a demethylase independent mechanism since we did not test for one in the context of Rhox expression control.
It remains to be determined whether levels of KDM6A are critical for proper ovarian function. It is interesting that female mice with a single X chromosome, which would have a lower dose of KDM6A due to haploinsufficiency for Kdm6a, a gene that escapes X inactivation, have reduced fertility [26], [61], [62]. Furthermore, XO female mouse embryos are developmentally retarded when compared to XX littermates at early mid-gestation [63]. In human, the presence of a single X chromosome causes Turner syndrome associated with severe developmental defects and ovarian dysgenesis [62]. It will be important to determine whether any of the human RHOX genes are also regulated by KDM6A. Mutations of KDM6A in human cause Kabuki syndrome, associated with growth retardation, unique facial features, and severe intellectual disability. Both males with point mutations and females with complete heterozygous deletions have been reported [12], [13]. The Kabuki phenotype, present in females who have one deleted KDM6A copy but absent in Turner syndrome females, may be due to partial silencing of the normal copy by X inactivation in Kabuki females, while Turner females would have one expressed copy in all cells. It would be interesting to examine ovaries in these patients.
In summary, our study provides the first evidence that Rhox6 and 9 are regulated by the histone demethylase KDM6A in mouse ES cells and reproductive organs in a sex-specific manner. Our findings indicate that a gene that escapes X inactivation plays a sex-specific role in gene regulation in female ES cells and tissue. Higher female expression due to escape from X inactivation of Kdm6a may be favorable to Rhox6 and 9 expression in ovary.
Male ES cells WD44 (from C. Ware, University of Washington, US) and E14 (BL6/Cast) (from J. Gribnau, Erasmus MC Rotterdam, NL), and female ES cells PGK12.1 [64] and E8 (BL6/Cast) (from J. Gribnau, Erasmus MC Rotterdam, NL) were grown in high glucose DMEM media supplemented with 15% fetal bovine serum (FBS), 1% non-essential amino acids, 10 mg/ml APS, 0.1 mM 2-mercaptoethanol and 25 mM L-glutamine. ES cells were maintained in the presence of 1000 U/ml leukemia inhibitory factor (LIF) (Millipore) on a mono-layer of chemically inactivated mouse embryonic fibroblasts (MEF) and grown in a humidified incubator at 37°C and 5% CO2. Plates were enriched for ES cells by incubation on 1% gelatin coated dishes for 30 min to allow MEFs to attach, followed by transfer to fresh gelatin coated plates for overnight culture. Differentiation was achieved by removing LIF and culturing on non-adherent dishes to facilitate the formation of embryoid bodies. After 7 days, embryoid bodies were transferred to cell culture dishes for 8 days. Cells were harvested on days 0, 2, 4, and 15 after LIF removal. To follow X inactivation in PGK12.1 cells before and after differentiation, Xist expression was determined by RT-PCR and by RNA-FISH using standard protocols with a probe for Xist (Vysis) (Figure S3). Ovary, testis, and whole brain were collected from adult female and male C57BL/6J mice. Additionally, ovaries were collected from female F1 obtained by mating M. spretus males (Jackson Labs) with females that carry an Xist mutation (XistΔ) (B6.Cg-Xist<tm5Sado>) (from T. Sado, Kyushu University, available from RIKEN) [65]. Female progeny were genotyped to verify inheritance of the mutant Xist allele. Female progeny with a XistΔ fail to silence the BL6 X chromosome and thus have complete skewing of inactivation of the spretus X chromosome. All procedures involving animals were reviewed and approved by the University Institutional Animal Care and Use Committee (IACUC), and were performed in accordance with the Guiding Principles for the Care and Use of Laboratory Animals.
Stealth Select siRNAs (Invitrogen) were selected to target three different locations of the Kdm6a mRNA. The three siRNAs were transfected together or individually and oligonucleotides with no target were used as negative controls. For transfection, 5 µl of Lipofectamine RNAi Max Reagent (Invitrogen) mixed with 250 µl of Opti-MEM I Reduced Serum Medium (Invitrogen) containing 100 pmol of siRNAs were incubated for 30 min prior to addition to 6-well plates seeded with 1.5×105 ES cells in 2 ml of DMEM supplemented as stated above. Cells were harvested after 48 h of RNAi treatment. Knockdown was confirmed by qRT-PCR, expression arrays, and Western blotting using standard procedures. Immunoblot analysis was done using a KDM6A/UTX antibody either from K.Ge (NIDDK) or from Bethyl Labs. Three siRNAs were pooled and protein levels were measured after 48 h of treatment. Western blot band densities were measured using ImageJ software (http://rsbweb.nih.gov/ij/).
Tissues were homogenized using a glass homogenizer and ES cells were collected before, during, and after differentiation. Cells were incubated at room temperature for 15 min in 1% formaldehyde. Crosslinking was stopped by adding 50 µL glycine followed by a 5 min incubation at room temperature and cell lysis as described [66]. Chromatin was sonicated to yield fragments 300–1000 bp in length and was then pre-cleared with protein A agarose beads for 1 h at 4°C. An aliquot of 20 µL was kept to serve as the input fraction. Pre-cleared chromatin was incubated in immunoprecipitation buffer at 4°C overnight using the following antibodies: anti-KDM6A/UTX [7], anti-UTX (Bethyl Labs), anti-H3K27me3 (Millipore), and anti-H3K4me3 (Millipore). Samples were centrifuged at 1200 rpm for 1 min and a small portion of the suspension collected as the unbound fraction. Immunoprecipitated chromatin was collected and serially washed in increasingly stringent salt buffers. After elution, crosslinks were reversed in 5 M NaCl at 65°C overnight. DNA was purified using Qiaquick PCR purification kit (Qiagen) and subjected to PCR according to the following protocol: 95°C for 3 min followed by 35 cycles of 95°C for 30 sec, 56°C for 30 sec and 72°C 30 sec. Samples were incubated at 72°C for 10 min and analyzed by gel electrophoresis. Controls to assay the immunoprecipitation efficiency of KDM6A, H3K4me3, and H3K27me3 antibodies included an active gene (Kdm5c) and an inactive gene (Iqsec2) (Table S2).
For qRT-PCR, total RNA was prepared using the Qiagen RNeasy kit with on-column DNaseI digestion. For cDNA synthesis, 500 ng-1 µg of mRNA was reverse transcribed using the SuperScript First Strand Synthesis system (Invitrogen) according to manufacturer's protocol. Table S2 lists the RT-PCR primers specific for Rhox6, Rhox9, and Kdm6a. Quantitative PCR was performed using a SYBR green master mix (Roche) and a standard curve for each primer pair. Data normalized to the 18s housekeeping gene were averaged for 2 to 3 separate reactions each assayed in duplicate. For chromatin analyses, ChIP DNA was subjected to real-time PCR using primers listed in Table S2. Rhox6R1 and Rhox9R1 amplify regions upstream of the transcription start site of their respective gene. Rhox6/9R2 amplify regions in the 5′ gene body, and Rhox6/9R3 regions towards the 3′ end of both Rhox6 and Rhox9. After normalization to the input fraction, relative enrichment was calculated based on two separate immunoprecipitation reactions each assayed in duplicate. Following PCR amplification, melting curves were used to ensure only a single product was amplified. Western blots were done to confirm sexually dimorphic KDM6A protein levels in female and male ES cells using standard procedures. Briefly, nuclear protein was captured using an anti-KDM6A antibody (Bethyl Labs) using 1∶5000 dilution. Anti-β-ACTIN was used at 1∶10,000 dilution (Sigma) as a loading control. KDM6A protein was detected using HRP conjugated donkey anti-rabbit IgG, and β-ACTIN was detected using HRP conjugated goat anti-mouse IgG.
Allele-specific expression was determined by Sanger sequencing of RT-PCR products and control PCR of genomic DNA using primers listed in Table S2. For Rhox6, the SNP (T>G) that distinguishes between the maternal C57BL/6J (Xa) and paternal M. spretus (Xi) alleles is at nucleotide position 35180550 (NCBI37/mm9 build). For Rhox9, the SNP (T>G) that distinguishes between the maternal C57BL/6J (Xa) and paternal M. spretus (Xi) alleles is at nucleotide position 35254278 (NCBI37/mm9 build).
cDNA was hybridized to Affymetrix 1.0 ST and Affymetrix 430 2.0 mouse arrays. Array hybridizations were done at the Microarray Center or at the Center on Human Development and Disability (University of Washington, Seattle WA). The raw data files from the 430 2.0 arrays were analyzed by the Affymetrix software (GCOS 1.1) to produce the data in .CHP format (Excel), while raw data files from the 1.0 ST arrays were analyzed with Affymetrix Expression Console Software (http://www.affymetrix.com). Data was normalized with the RMA method as implemented in the Bioconductor Affymetrix package. Microarray quality control metrics were included according to the manufacturer's recommended guidelines. For analyses of published array data [32], [38], [39], spots were normalized by dividing the signal intensity against the average fluorescent intensity of each array [67]. Expression array data have been deposited in NCBI's GEO database [68] and are accessible through series accession number GSE45034 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE45034).”
Following ChIP, DNA was amplified by whole genome amplification using the GenomePlex Complete Whole Genome Amplification Kit (Sigma) with modifications previously described [69]. ChIP DNA was lyophilized and re-suspended in 10 µl of water. Library preparation buffer and stabilization buffer were added (2 µl and 1 µl, respectively), and samples incubated at 95°C for 2 min. After addition of library preparation enzyme, samples were incubated in a thermal cycler according to the following protocol: 16°C for 20 min, 24°C for 20 min, 37°C for 20 min, 75°C for 5 min. For amplification of the library, a master mix containing amplification master mix, water, and WGA DNA polymerase was added and samples subjected to 15 cycles of: 95°C for 3 min, 94°C for 15 sec, and 65°C for 5 min. Samples were purified using the Qiaquick PCR purification kit. ChIP and input fractions were labeled according to the standard Nimblegen sample labeling protocol prior to hybridization to HD2 Nimblegen tiling arrays for the entire mouse X chromosome (Roche). Enrichment profiles were generated (Genomics Resource Center, Fred Hutchinson Cancer Research Center, Seattle WA). Peak maps generated by the Nimblescan software consist of significant peaks (FDR score <0.05). Tiling array data have been deposited in NCBI's GEO database [68] and are accessible through series accession number GSE45390 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE45390).”
All p-values shown represent paired two-tailed Student's t-tests.
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10.1371/journal.pgen.1007533 | Metagenomic sequencing suggests a diversity of RNA interference-like responses to viruses across multicellular eukaryotes | RNA interference (RNAi)-related pathways target viruses and transposable element (TE) transcripts in plants, fungi, and ecdysozoans (nematodes and arthropods), giving protection against infection and transmission. In each case, this produces abundant TE and virus-derived 20-30nt small RNAs, which provide a characteristic signature of RNAi-mediated defence. The broad phylogenetic distribution of the Argonaute and Dicer-family genes that mediate these pathways suggests that defensive RNAi is ancient, and probably shared by most animal (metazoan) phyla. Indeed, while vertebrates had been thought an exception, it has recently been argued that mammals also possess an antiviral RNAi pathway, although its immunological relevance is currently uncertain and the viral small RNAs (viRNAs) are not easily detectable. Here we use a metagenomic approach to test for the presence of viRNAs in five species from divergent animal phyla (Porifera, Cnidaria, Echinodermata, Mollusca, and Annelida), and in a brown alga—which represents an independent origin of multicellularity from plants, fungi, and animals. We use metagenomic RNA sequencing to identify around 80 virus-like contigs in these lineages, and small RNA sequencing to identify viRNAs derived from those viruses. We identified 21U small RNAs derived from an RNA virus in the brown alga, reminiscent of plant and fungal viRNAs, despite the deep divergence between these lineages. However, contrary to our expectations, we were unable to identify canonical (i.e. Drosophila- or nematode-like) viRNAs in any of the animals, despite the widespread presence of abundant micro-RNAs, and somatic transposon-derived piwi-interacting RNAs. We did identify a distinctive group of small RNAs derived from RNA viruses in the mollusc. However, unlike ecdysozoan viRNAs, these had a piRNA-like length distribution but lacked key signatures of piRNA biogenesis. We also identified primary piRNAs derived from putatively endogenous copies of DNA viruses in the cnidarian and the echinoderm, and an endogenous RNA virus in the mollusc. The absence of canonical virus-derived small RNAs from our samples may suggest that the majority of animal phyla lack an antiviral RNAi response. Alternatively, these phyla could possess an antiviral RNAi response resembling that reported for vertebrates, with cryptic viRNAs not detectable through simple metagenomic sequencing of wild-type individuals. In either case, our findings show that the antiviral RNAi responses of arthropods and nematodes, which are highly divergent from each other and from that of plants and fungi, are also highly diverged from the most likely ancestral metazoan state.
| The presence of abundant virus-derived small RNAs in infected plants, fungi, nematodes, and arthropods suggests that Dicer-dependent antiviral RNAi is an ancient and conserved defence. Using metagenomic sequencing from wild-caught organisms we find that antiviral RNAi is variable across animals. We identify a distinctive group of virus-derived small RNAs in a mollusc, which have a piRNA-like length distribution but lack key signatures of piRNA biogenesis. We also report a group of 21U virus-derived small RNAs in a brown alga, which represents an origin of multicellularity separate from that of plants, fungi, and animals. The absence of virus-derived small RNAs from most of our animal samples may suggest either that the majority of animal phyla lack an antiviral RNAi response, or that these phyla possess an antiviral RNAi response resembling that reported for vertebrates, which is not detectable through simple metagenomic sequencing of wild-type individuals. In addition, we report abundant somatic piRNAs across anciently divergent animals suggesting that this is the ancestral state in Bilateria. Our study challenges a widely-held assumption that most invertebrates possess an antiviral RNAi pathway likely similar to that seen in Drosophila, other arthropods, and nematodes.
| RNA interference-related (RNAi) pathways provide an important line of defence against parasitic nucleic acids in plants, fungi, and most animals [1–5]. In plants and fungi, which lack a distinct germline, Dicer and Argonaute-dependent RNAi responses suppress the expression and replication of viruses and transposable elements (TEs) through a combination of target cleavage and/or heterochromatin induction [6,7]. This gives rise to a characteristic signature of short interfering RNAs (siRNAs) derived from both TEs and viruses [8–12]. In contrast, the best-studied animal (metazoan) lineages display two distinct signatures of defensive RNAi. First, reminiscent of plants and fungi, arthropods and nematodes exhibit a highly active Dicer-dependent antiviral pathway that is characterised by copious virus-derived siRNAs (viRNAs) peaking sharply in length between 20nt (e.g. Lepidoptera) and 22nt (e.g. Hymenoptera). These are cleaved from double-stranded viral RNA by Dicer, and loaded into an Argonaute-containing complex that targets virus genomes and transcripts via sequence complementarity [13,14]. Second, and in contrast to plants and fungi, animals also possess a Piwi-dependent (piRNA) pathway that provides a defence against TEs in germline (Drosophila and vertebrates) and/or somatic cells (e.g. [15–18]). This pathway is usually characterised by a broad peak of 26-30nt small RNAs bound by Piwi-family Argonaute proteins, and comprises both 5'U primary piRNAs cleaved from long ‘piRNA cluster’ transcripts by homologs of Drosophila Zucchini[19], and secondary piRNAs generated by ‘Ping-Pong’ amplification. This pathway is thought to target TE transcripts for cleavage and genomic copies for heterochromatin induction in most animals [20].
The presence of abundant viRNAs in infected plants, fungi, nematodes, and arthropods suggests that Dicer-dependent antiviral RNAi is an ancient and conserved defence [1,2]. However, RNAi has been entirely lost in lineages such as Plasmodium [21], some trypanosomes [22], and some Saccharomyces [23], and/or extensively modified in others. For example, antiviral RNAi was long thought to be absent from vertebrates [24,25], at least in part because their viRNAs cannot easily be detected by high-throughput sequencing of the total small-RNA pool from wild-type individuals [25–30]. Recently, it has been suggested that vertebrates also possess a functional virus-targeting RNAi pathway in tissues lacking an interferon response [31–33] and/or in the absence of viral suppressor of RNAi [32,34,35]. However, there is still debate as to whether this occurs under natural conditions, and whether or not it represents an immunologically relevant defence (compare [36,37]).
Despite this clear interest in the phylogenetic distribution of antiviral RNAi, comprehensive experimental studies of antiviral RNAi in animals are not available. Instead, studies have focussed on arthropods such as insects (reviewed in [38,39]), crustaceans ([40], and reviewed in [41]), chelicerates [42], and on nematodes [43–45] and vertebrates [25,26,28,29,31–35,46]. In particular, there have been few attempts to identify viRNAs in ‘early-branching’ animal lineages such as Porifera or Cnidaria, in divergent Deuterostome lineages such as Echinodermata or Urochordata, or in Lophotrochozoa (including the large phyla Annelida and Mollusca; See Fig 1 for the known distribution of RNAi-pathways across the Metazoa).
Broadly consistent with a wide distribution of antiviral RNAi, Argonaute and Dicer genes are detectable in most animal genomes (Fig 1; [47–50]). However, while Dicer and Argonaute genes would be necessary for an antiviral RNAi response, their presence is insufficient to demonstrate one, for two reasons. First, these genes also have non-defensive roles such as transcription regulation through miRNAs (see [51,52])—and a single gene can fulfil multiple roles. For example, whereas in Drosophila there is a distinction between the Dcr2-Ago2 antiviral pathway and the Ago1-Dcr1 micro-RNA (miRNA) pathway [53], in C. elegans a single Dicer is required for the biogenesis of both miRNAs and viRNAs [Fig 1; [43,54,55]). Second, RNAi pathways are labile over evolutionary timescales, with regular gene duplication, loss, and change of function [18,56–58]. For example, the Piwi-family Argonaute genes that mediate anti-TE defence in animals were ancestrally present in eukaryotes, but were lost independently in plants, fungi, brown algae, most nematodes, and dust mites [2,47,57–59]. In contrast, non-Piwi Argonautes were lost in many alveolates, excavates and Amoebozoa [59,60] while Piwi genes were retained in these lineages. At the same time, new RNAi mechanisms have arisen, such as the 22G RNAs of nematodes [57,61,62], the recent gain of an antiviral role for Piwi in Aedes mosquitoes [63,64], and the RNAi-mediated immune memory of some dipterans [65–67]. Taken together, the potential for multiple functions, and for gains and losses of function, make it challenging to confidently predict the phylogenetic distribution of antiviral RNAi from the distribution of the required genes alone (see [49]).
Thus, although antiviral RNAi is predicted to be shared by most extant eukaryotes (see [68,69]), in the absence of experimental studies, its distribution across animal phyla remains largely unknown (Fig 1). This contrasts sharply with our knowledge of other RNAi-related pathways, such as the miRNA mediated control of gene expression, which is conserved across plants, brown algae, fungi, and almost all animals [70], and the presence of TE-derived piRNAs in most animals: Porifera [15,71], Cnidaria [15,72], Ctenophora [73], Vertebrata [74,75], Arthropoda [76–79], some Nematoda [57,80], Platyhelminthes [81], but not Placozoa [15]. In eukaryotes that lack direct experimental evidence for viRNAs, the presence of an inducible RNAi response to experimentally applied long double-stranded RNA might indicate a potential for antiviral RNAi (Fig 1). This has been reported for Excavata [82], Heterkonta [83] Amoebozoa [84], trypanosomes [85], and among animals in Porifera [86], Cnidaria [87], Placozoa [88], Arthropoda [89], Nematoda [90], and several lineages of Lophotrochozoa including planarian flatworms [91], bivalve molluscs [92], rotifers [93] and annelids [94].
Thus, although circumstantial evidence suggests a near-universal potential for antiviral RNAi in animals, we still lack experimental evidence of exogenous viral processing. This knowledge gap is probably attributable, in part at least, to the challenges associated with isolating and culturing non-model animals and their natural viral pathogens in the lab. Here we seek to examine the phylogenetic distribution of viRNAs, and thus elucidate the phylogenetic distribution of a canonical (i.e. Drosophila-, nematode- or plant-like) antiviral RNAi response, through metagenomic sequencing. We combine rRNA-depleted RNA sequencing with small-RNA sequencing to detect both viruses and viRNAs in pooled samples of six deeply divergent lineages. This metagenomic approach circumvents the need to isolate and/or culture non-model organisms in the laboratory, and can capitalise on the high diversity of viruses naturally infecting individuals in the wild. It also avoids any artefactual outcomes that might result from non-native host-virus combinations or non-natural infection routes. First, we include two early branching animal species: a sponge (Halichondria panicea: Porifera, Demospongiae) and a sea anemone (Actinia equina: Cnidaria, Anthozoa) that branch basally to the divergence between deuterostomes and protostomes (Fig 1). Second, a starfish (Asterias rubens: Echinodermata, Asteroidea) that branches basally to vertebrates within the Deuterostomia. Third, two divergent species of Lophotrochozoa—the clade which forms the sister group to Ecdysozoa within the protostomes: a dog whelk (Nucella lapillus: Mollusca, Gastropoda) and earthworms (Annelida, Oligochaeta). Finally, to explore the deep history of antiviral RNAi within the eukaryotes, we included the brown alga Fucus serratus (Phaeophyceae, Heterokonta), which represents an origin of multicellularity separate from those of plants, fungi, and animals.
We detect 21nt 5’U small RNAs derived from both strands of an RNA virus in the brown alga, similar to virus-derived small RNAs seen in plants and fungi, despite the deep divergence among these multicellular eukaryotes. We also detect miRNAs and somatic TE-derived piRNAs in all the animal lineages, demonstrating that our small-RNA sequencing was successful, and suggesting that somatic piRNAs represent the ancestral state in Bilateria. However, although we find RNA viruses to be common and sometimes highly abundant, we do not find abundant viRNAs in most of the sampled animals. Specifically, we detect no viRNAs from RNA viruses infecting the earthworms, the sponge, or the sea anemone, suggesting that insect- or nematode-like antiviral RNAi is absent from these lineages, and thus potentially their common ancestor. In contrast, we do detect viRNAs from RNA viruses in a gastropod mollusc, the dog whelk. But, unlike the viRNAs of nematodes and arthropods, these peak broadly at 26-30nt, as would be expected of piRNAs—but they lack the 5'U or ‘ping-pong’ signature Finally, we identify primary piRNA-like 26-30nt 5'U small-RNAs derived from putatively endogenous copies of viruses in the sponge, the starfish, and the dog whelk, consistent with a piRNA-like response targeting endogenous virus copies. Together with the known differences among the antiviral RNAi response of plants, fungi, nematodes and arthropods, these findings imply that the true diversity of defensive RNAi strategies employed by eukaryotes may have been underestimated. They also suggest that antiviral RNAi may either be lacking from many animal phyla, or perhaps resembles the antiviral RNAi response reported for mammals.
Using the Illumina platform, we generated strand-specific 150 nt paired-end sequence reads from ribosome-depleted RNA extracted from metagenomic pools of each of six different multicellular eukaryotes: the breadcrumb sponge (Halichondria panacea, Porifera); the beadlet sea anemone (Actinia equina, Cnidaria); the common starfish (Asterias rubens, Echinodermata); the dog whelk (Nucella lapillus, Mollusca); mixed earthworm species (Amynthas and Lumbricus spp., Annelida), and a brown alga (the ‘serrated wrack’, Fucus serratus, Fucales, Phaeophyceae, Heterokonta). See S1 Table for collection data. Gut contents were excluded by dissection, and contaminating nematodes excluded by a PCR screen prior to pooling (Materials and methods; S1 Table). Reads were assembled separately for each species using Trinity v2.2.0 [95,96], resulting in between 104,000 contigs for the sponge and 235,000 contigs for the earthworms. Metagenomic analysis using Diamond v0.7.11.60 [97] and MEGAN6 [98] suggests the vast majority of these contigs derive for the intended host organism (S1 Fig). For each of the six species pools, the raw meta-transcriptomic contigs generated by Trinity are provided in compressed (gzipped) fasta format as unannotated contigs at https://doi.org/10.6084/m9.figshare.6803885.v1. To identify viruses, we used Diamond to search with translated open reading frames (ORFs) from our contigs against all virus proteins from the NCBI nr database, all predicted proteins from Repbase [99], and all proteins from the NCBI RefSeq_protein database (see Materials and methods). After excluding some low-quality matches to large DNA viruses and matches to phage, this identified nearly 900 potentially virus-like contigs (S1 Data). These matches were examined and manually curated to generate 85 high-confidence virus-like contigs between 0.5 and 12kbp (mean 3.7Kbp) that are the focus of this study. We have provided provisional names for these viruses following the model of Shi et al., [100] and the sequences have been submitted to GenBank under accession numbers MF189971-MF190055.
The majority of these virus-like contigs were related to positive sense RNA viruses (+ssRNA), including ca. 20 contigs from the Picornavirales, 10 Weivirus contigs, and around 5 contigs each from Hepeviruses, Nodaviruses, Sobemoviruses, and Tombusviruses. We also identified 18 putative dsRNA virus contigs (Narnaviruses, Partitiviruses and a Picobirnavirus) and 11 negative sense RNA virus (-ssRNA) contigs (5 bunya-like virus contigs, 3 chuvirus-like contigs, and two contigs each from Rhabdoviridae and Orthomyxoviridae). Our curated viruses included five DNA virus-like contigs, all of which were related to the single-stranded DNA Parvoviridae. Sequences very similar to our Caledonia Starfish parvo-like viruses 1, 2 and 3 are detectable in the publicly-available transcriptomes of Asterias starfish species (101; S2 Fig). Although some of the virus-like contigs are likely to be near-complete genomes, including several +ssRNA viruses represented by single contigs of >9kbp, many are partial genomes representing only the RNA polymerase, which tends to be highly conserved [102]. We identified virus-like contigs from all of the sampled taxa, although numbers varied from only three in the earthworm pool to around 40 in the sponge. This may represent differences in host species biology, but more likely reflects the different range of tissues sampled [103], and/or differences in sampling effort (S1 Text). A detailed description of each putative virus is provided in S2 Table.
After initially assigning viruses to potential taxonomic groups based on BLASTp similarity, we applied a maximum likelihood approach to protein sequences to infer their phylogenetic relationships. Many of the viruses derived from large poorly-studied clades recently identified by metagenomic sequencing [100,104], and most are related to viruses from other invertebrates. For example, five of the sponge picornavirales were distributed across the ‘Aquatic picorna-like viruses’ clade of Shi et al., [100] with closest known relatives that infect marine Lophotrochozoa and Crustacea. Associated with the breadcrumb sponge we identified sequences related to the recently described ‘Weivirus’ clade known from marine molluscs [100], and from the beadlet anemone we identified sequences related to chuviruses of arthropods [100,104]. Some of the virus-like sequences were closely-related to well-studied viruses, for example Millport beadlet anemone dicistro-like virus 1 and Caledonia beadlet anemone dicistro-like virus 2 are both very closely related to Drosophila C virus [105,106] and Cricket Paralysis virus [107]. Others are notable because they lack very close relatives, or because they fall closest to lineages not previously known to infect invertebrates. These include the Caledonia dog whelk rhabdo-like virus 2 sequence, which is represented by a nucleoprotein that falls between the Rabies/Lyssaviruses and other rhabdoviruses, and Barns Ness dog whelk orthomyxo-like virus 1—for which the PB2 polymerase subunit falls between Infectious Salmon Anaemia virus and the Influenza/Thogoto virus clade (Fig 2; the PA polymerase subunit shows similarity to the Thogoto viruses, but not other Orthomyxoviruses). Phylogenetic trees are presented with support values and GenBank sequence identifiers in S2 Fig, and the alignments used for phylogenetic inference and newick-format trees with support values are provided in S2 and S3 Data respectively.
In addition to avoiding gut content and/or nematode contamination, we sought to provide four lines of corroborating evidence that these virus-like sequences represent infections of the targeted hosts. First, we estimated the representation of potential hosts in each pool by mapping RNA-seq forward reads to the contigs of Cytochrome Oxidase 1 (COI, a highly expressed eukaryotic gene) that could be identified in our assemblies. COI reads that could not be matched to the target host animals amounted to less than 0.2% of the target’s own COI reads in every case, arguing against substantial contamination with non-target taxa such as parasites or commensals. Contamination was higher in the brown alga, perhaps reflecting the challenge of recovering RNA from this taxon (S1 Text). In this case we identified around 10 contaminating taxa, amounting to 5% of the COI reads, including taxa that we might expect to live as ectocommensals on seaweeds, such as a bryozoan with 3.6% and a tunicate with 1.2%. We also identified some cross-contamination and/or adapter-switching between libraries that shared an Illumina lane [108,109], with a mean of < 0.2% of COI reads deriving from the other libraries in the lane. Nevertheless, an average of 99.78% of the mapped COI reads in each invertebrate library derived from the targeted species (93% in the brown alga), suggesting that any viruses of contaminating species would need to be at a very high copy-number to be detected and erroneously attributed to the target host (read counts are provided in S3 Table).
Second, we remapped reads to the 85 focal virus contigs to measure the number of virus-derived reads relative to host COI. We reasoned that sequence reads from genuine infections are likely to appear in a single host species and to have high representation, whereas viruses present only as surface or sea-water contaminants would be present at low copy-number and seen in association with the multiple hosts that were collected together. We only identified one virus present at an appreciable copy-number in more than one host pool, suggesting that our virus-like sequences do not in general represent biological or experimental contaminants, and that the majority of viruses infected only one of the sampled host species. The exception was a 1.3 kbp partiti-like virus contig (Caledonia partiti-like virus 1), which displayed substantial numbers of reads in both the anemone and the sponge—perhaps indicative of closely related viruses infecting these highly divergent taxa. Four viruses were present at a very high level (>1% of COI in at least one library), including Caledonia beadlet anemone dicistro-like virus 2, Millport beadlet anemone dicistro-like virus, Lothian earthworm picorna-like virus 1, and in the brown alga, Barns Ness serrated wrack bunya/phlebo-like virus 1. In total, 18 of the 85 virus contigs were present at >0.1% of host COI in at least one library, and all but 8 were present at >0.01% of COI (S3 Table, S3 Fig).
Third, we recorded which strand each RNA sequencing read derived from, as actively replicating DNA viruses and -ssRNA and dsRNA viruses generate substantial numbers of positive sense RNAs (S4 Fig). As expected, all of the -ssRNA viruses in our sample (Orthomyxoviridae, Rhabdoviridae, Bunyaviridae/Arenaviridae-like, chuvirus-like) displayed substantial numbers of reads from both strands, consistent with active replication. We also detected negative-sense reads for many of the +ssRNA viruses, but not always at a substantially higher rate than seen for host mRNAs such as COI (S3 Table, S4 Fig. Nevertheless, although +ssRNA viruses also produce complementary (negative sense) RNA during replication, the positive to negative strand ratio is usually very high (e.g. 50:1 to 1000:1 in Drosophila C Virus), potentially making the negative strand hard to detect by metagenomic sequencing. These data provide strong evidence that all of the -ssRNA and dsRNA viruses we detected comprise active infections and are consistent with replication by the other viruses. Surprisingly, only one of the five DNA viruses (Millport starfish parvo-like virus 1) showed the strong positive sense bias expected of mRNAs, whereas the others displayed a negative sense bias. This suggests that these parvovirus-like sequences derived from expressed Endogenous Viral Elements (EVEs; [110]) rather than active viral infections.
Fourth, we selected 53 of the putative virus contigs for further verification by PCR (Materials and methods; S2 Table). For most of these, we confirmed that the template was detectable by RT-PCR but not by (RT-negative) PCR, confirming that the viruses were not present in DNA form, i.e. were not EVEs (Materials and methods; S2 Table). The exceptions were Caledonia dog whelk rhabdo-like virus 2 and (as expected) the DNA parvovirus-like contigs, which did appear in RT-negative PCR. We then estimated virus prevalence in the wild, using RT-PCR to survey all of our samples in pools of between 7 and 30 individuals. The majority of viruses had an estimated prevalence in the range 0.79–100% (S4 Table), with some virus-like sequences present in all sub-pools of the species. These ‘ubiquitous’ sequences included Caledonia dog whelk rhabdo-like virus 2, Caledonia starfish parvo-like virus 2, Caledonia starfish parvo-like virus 3, Caledonia beadlet anemone parvo-like virus 1, and thirteen of the sponge viruses. This suggests that these sequences are common or that they are ‘fixed’ in the population, which could be consistent with integration into the host genome (i.e. an EVE). However, given the sampling scheme, a sponge virus at >36% prevalence has a >95% chance of being indistinguishable from ubiquitous. In addition, with the exception of Caledonia dog whelk rhabdo-like virus 2, none of the RNA viruses could be amplified from a DNA template. Taken together, the use of tissue dissection in RNA preparation, the distribution of viruses across sequencing pools, the host distribution of related viruses, the abundance and strand specificity of virus reads, the absence of DNA copies (for all but one of the RNA viruses), and the variable prevalence in wild populations, support the majority of these sequences as bone fide active viral infections of the sampled species.
Virus and TE-derived small RNAs have been well characterised in plants, fungi, and some animals, but other major eukaryotic lineages such as Heterokonta, Alveolata, Excavata and Amoebozoa have received less attention. In principle, a metagenomic approach could also be applied to these lineages, but the difficulty of collecting large numbers of individuals of a single lineage makes this challenging for single-celled organisms. Here we have taken advantage of multicellularity in the brown algae (Phaeophyceae, Heterokonta) to test for the presence of viRNAs using the serrated wrack, Fucus serratus. Based on a single pooled sample of tissue from 100 individuals, we identified large numbers of small RNAs with a tight distribution between 19 and 23nt, peaking sharply at 21nt (S5 Fig). Almost all of the 21nt sRNAs were 5' U, as has been seen for sRNAs in diatoms (Bacillariophyceae, Heterokonta; [111]) and is seen for some small RNA classes in green plants [112,113] and fungi [114,115]. Although miRNAs have been described for two other brown algae, Ectocarpus siliculosus [116,117] and Saccharina japonica [118], we were unable to identify homologues of known miRbase miRNAs among these reads. This may reflect a lack of sensitivity, as the miRNA complements of the studied brown algae are highly divergent [118], and miRNAs of Fucus serratus may be sufficiently divergent again to be undetectable based on sequence similarity. In contrast, 1.8% of small RNAs corresponded to the subset of high-confidence TE contigs. These small RNAs were derived from both strands, but as expected given the absence of Piwi, displayed no evidence of ‘ping-pong’ amplification—with sRNAs from both strands showing a 5' U bias. Most interestingly, we also detected viRNAs corresponding to a -ssRNA bunya-like virus (Barns Ness serrated wrack bunya/phelobo-like virus 1; Fig 3E, S6 Fig). Although numbers were relatively small, comprising 0.01% of all small RNA reads, these derived from both strands along the full length of the virus-like contig, peaked sharply at 21nt, and were almost exclusively 5'U. We did not detect a viRNA signature from a further two -ssRNA or from four dsRNA virus-like contigs, although their copy-number was very low compared to Barns Ness serrated wrack bunya/phelobo-like virus 1 (S3 Fig, S3 Table).
Based on our knowledge of antiviral RNA interference in arthropods and nematodes, we expected viral infections in our animal samples to be associated with large numbers of Dicer-generated viRNAs, with a narrow size distribution peaking between 20nt (as seen in Lepidoptera; [119]) and 22nt (as seen in chelicerates, nematodes, and hymenopterans; [42,120,121]). Because animal piRNAs and viRNAs are generally modified by the addition of a 3' 2-O-methyl group, and some nematode small RNAs are generated by direct syntheses (resulting in a 5’ triphosphate group) our sequencing included small RNAs treated with 5' polyphosphatase (to remove 5’ triphosphates) and oxidised RNA (to increase the representation of small RNAs bearing a 3' 2-O-methyl group). Furthermore, to ensure that we did not exclude viRNAs that had been edited (e.g. by ADAR; [122]), or that contained untemplated bases (e.g. 3' adenylation or uridylation; [123]), our mapping approach permitted at least two high base-quality mismatches within a 21nt sRNA. We also confirmed that remapping with local alignment, which permits any number of contiguous mismatches at either end of the read, did not substantially alter our results.
We successfully recovered abundant miRNAs in all of the animal samples, with between 20% (sponge) and 80% (starfish) of 20-23nt RNAs from untreated libraries mapping to known miRbase miRNAs [124]. Consistent with the absence of a 3' 2-O-methyl group, these miRNA-like reads had much lower representation in the oxidised libraries, there comprising only 0.4% (earthworms) to 14% (dog whelk) of 20-23nt RNAs. We also identified characteristic peaks of small RNAs derived from ribosomal RNA at 12nt and 18nt in the sponge, at 12nt and 16nt in the sea anemone, and in oxidised libraries from all organisms. The only exception to this overall pattern was for the sea anemone, in which oxidation had no effect on the relative number of miRNAs, although did strongly affect the overall size distribution of rRNA-derived sRNAs. This suggests the presence of a 3' 2-O-methyl group in sea anemone miRNAs (S5 Fig). A few small RNAs also mapped to contigs nominally identified as bacterial in origin (S7 Fig) but numbers were small in the Sea Anemone, Dog Whelk, and Starfish, while those the brown alga were predominantly degradation products from bacterial rRNA, and manual inspection suggests the vast majority in the sponge and earthworm derived from miss-classified TE sequences.
Surprisingly, despite our identification of more than 40 RNA virus-like contigs associated with the sponge, 17 in the sea anemone, and three in the earthworms, we were unable to detect a signature of abundant viRNAs in any of these three organisms. On average, less than 0.002% of 17-35nt RNAs from these organisms mapped to the RNA virus contigs, and those that did map were enriched for shorter lengths (17-19nt), lacked a clearly defined size distribution, and were less common in the oxidised than non-oxidised libraries (S5 Fig, S3 Table)—features consistent with non-specific degradation products, rather than viRNAs. (Note that the starfish sample lacked detectable RNA viruses, precluding the identification of RNA-virus viRNAs).
The only metazoan sample to display a viRNA signature was the dog whelk (Nucella lapillus), with 0.14% of oxidised small RNAs derived from four of the seven RNA virus-like contigs. These included both contigs of Barns Ness dog whelk orthomyxo-like virus 1, Caledonia dog whelk rhabdo-like virus 1, and Caledonia dog whelk rhabdo-like virus 2. A Narnavirus-like contig and a very low copy-number Bunyavirus-like contig were not major sources of viRNAs. Given the absence of detectable viRNAs in the Sponge, Sea Anemone, and Earthworm, it is notable that the viRNA-producing viruses in the dog whelk were present at a much lower copy number than many viRNA-free viruses in those organisms (e.g. Lothians earthworm picorna-like virus 1, Barns Ness breadcrumb sponge hepe-like virus 1; S3 Fig). This suggests that, had viRNAs been present in those taxa, we were likely (for many viruses) to have been be able to detect them.
Nevertheless, the virus-derived small RNAs seen in the dog whelk did not show the expected size, strand, or 2nt overhang signature of canonical Dicer-generated viRNAs (Fig 3, S6 Fig). Instead, viRNA lengths formed a broad distribution from 26 to 30nt (peaking at 28nt), more consistent with piRNAs seen in the Drosophila and mammalian germlines. These small RNAs were derived almost entirely from the negative-sense (i.e. genomic) strand of Barns Ness dog whelk orthomyxo-like virus 1 (Fig 3A and 3B) and Caledonia dog whelk rhabdo-like virus 2 (Fig 3D), but from both stands of Caledonia dog whelk rhabdo-like virus 2 (Fig 3C and 3E). Although this size distribution is more consistent with the piRNA pathway, only those from Caledonia dog whelk rhabdo-like virus 2 (a suspected EVE, see above) displayed the strong 5'U bias expected of primary piRNAs (Fig 3D), and none showed any evidence of ping-pong amplification. In all three cases, the putative dog whelk viRNAs were derived from the whole length of the viral genome—albeit with strong hotspots in Caledonia dog whelk rhabdo-like virus 2. None of these findings were qualitatively altered by a requirement for perfect (zero mismatch) mappings, or by permitting local mapping. Relative to miRNAs, these RNA-virus derived viRNAs were much more strongly represented in the oxidised library than the untreated library, with the miRNA:viRNA ratio increasing 300-fold—consistent with the presence of a 3' 2-O-methyl group (S5 and S6 Figs).
DNA viruses are a source of Dicer-mediated viRNAs in arthropods and in plants, and antiviral RNAi pathways are important for antiviral immunity to DNA viruses in both groups (reviewed in [125,126]). Although our RNA sequencing strategy was intended to detect RNA viruses, we also identified four novel parvo/densovirus-like contigs (Parvoviridae; single-stranded DNA) in the starfish, and one in the sea anemone. These sequences constituted a substantial source of small RNAs in both organisms, particularly the starfish—contributing 0.3% of small RNAs in the untreated libraries and 3.4% of small RNAs in the oxidised library. In four of the five cases these small RNAs were almost exclusively negative sense, were 26 to 30nt in length (peaking at 28nt), and were very strongly biased toward U in the 5' position—resembling primary piRNAs (Fig 4). However, the high prevalence and/or negative strand RNAseq bias (S4 Fig) of these source contigs is consistent with expressed genomic integrations (EVEs) rather than active viral infections. In the case of Millport starfish parvo-like virus 1, both positive and negative sense reads were detectable, the negative sense reads again displayed a strong 5' U bias, but the positive sense reads displayed a postion-10 ‘A’ ping-pong signature (Fig 4B), as expected of piRNAs. Relative to miRNAs, these putative piRNAs were much more strongly represented in the oxidised library than the untreated library, consistent with the presence of a 3' 2-O-methyl group (S5 and S6 Figs).
Transposable elements and TE-derived transcripts represent a major source of piRNAs in the germlines of Drosophila [76], C. elegans [127,128], mice [129,130], and zebrafish [75], although the germline limitation seen in Drosophila is derived within the Arthropods [17]. Piwi-interacting RNAs are also detectable in Cnidaria and Porifera, although their tissue specificity is unclear [15]. Furthermore, TE transcripts in Drosophila and some other arthropods are also processed by Dicer to generate 21nt endo-siRNAs [17]. We therefore selected a total of 146 long, high-confidence, TE contigs from our assemblies to analyse TE-derived small RNAs (these contigs were selected on the basis of length and similarity to repBase entries, and to best illustrate small RNA properties; contigs are provided in S4 Data). We identified large numbers of TE-derived putative piRNAs in the somatic tissues of all the sampled organisms (Fig 5). In total, between 0.17% (starfish) and 1.7% (dog whelk) of untreated small RNA reads mapped to the 146 high-confidence TE contigs (S2 Data, S5 and S8 Figs). In every case except the anemone, the putative piRNAs were more highly represented in the oxidised library than in untreated or polyphosphatase-treated libraries (1.4–6% of oxidised reads), suggesting that they are 3' 2-O-methylated and result from cleavage rather than synthesis. Despite very large numbers of piRNAs for some TE contigs, we did not observe endo-siRNA -like small RNAs similar those observed in Drosophila and some other arthropods (e.g. [17,131]).
We observed putative piRNAs derived from one or both strands of the TEs (Fig 5). Where they derived predominantly from a single strand they were generally strongly 5'U-biased (consistent with primary piRNAs). Where they derived from both strands, those from the second strand presented evidence of ‘ping pong’ amplification (i.e. no 5' U bias, and a strong ‘A’ bias at position ten; Fig 5, S8 Fig). However, the piRNA size distribution varied substantially among organisms and TEs. In the sponge, the length of the 5' U-biased piRNAs either peaked at 23-24nt, or presented a broader bimodal distribution peaking at 23-24nt and 27-29nt. Where piRNAs derived from both strands, the strand with a ping-pong signature showed a shorter length distribution (22-23nt). In a few cases the putative sponge piRNAs from both strands showed a strong 5'U bias with no evidence of ping-pong amplification. In the sea anemone we consistently identified a strong peak of 5'-U biased sRNAs peaking at 28-29nt on one strand, but a generally bimodal distribution from the second ‘ping-pong’ strand (if piRNAs were present), peaking at around 23nt and 28nt. Again, both strands occasionally displayed a 5'-U bias and no evidence of ping-pong amplification. The patterns were again similar in the starfish and the earthworms, except that size distributions were unimodal, peaking at 29-30nt in the 5'-U biased strand and 25-26nt (starfish) and 26-27nt (earthworms) in the ‘ping-pong’ strand.
As with viRNAs, the only exception to this general pattern was seen in the dog whelk. In addition to TE-like contigs that displayed a classical piRNA-like signature (28nt 5'U RNAs from one strand; 26-28nt ‘ping-pong’ RNAs from the opposite strand), a small number of TE-like contigs in the dog whelk had an sRNA signature that resembled that of Barns Ness dog whelk orthomyxo-like virus 1 and Caledonia dog whelk rhabdo-like virus 1. In these TE-like contigs, the sRNAs were derived from one or both strands, peaked broadly at 26-30nt, and lacked any bias in base composition or evidence of ‘ping-pong’ (Fig 5E and 5F). This indicates that some TEs are processed in the same way as the identified RNA viruses, (e.g. Gypsy; S8D Fig). A minority of TE-like contigs displayed an intermediate pattern, with a weak 5'U-bias from one strand, and a broad peak that lacked a pong-pong signature from the other strand. Such an intermediate pattern could result either from a single TE targeted by two different mechanisms, or from cross-mapping of sRNAs derived from different copies of the same TE inserted in different locations/contexts. As before, our permissive mapping approach and re-mapping using local alignments reduces the possibility that a large category of sRNAs escaped detection, and a requirement for zero mismatches had no qualitative impact on our results.
We sought to examine whether the phylogenetic distribution and expression of RNAi pathway genes in our samples was consistent with the small RNAs we observed. As expected, based on the presence of abundant miRNAs and/or an antiviral pathway and given what is known for their close relatives [15,132–138], we identified two deeply divergent Dicer transcripts in the sea anemone, and a single Dicer transcript in each of the other animal species. The single Dicers seen in the starfish, dog whelk, and earthworms were more similar to Dicer-1 from the Drosophila miRNA pathway than to arthropod Dicer-2-like genes that mediate antiviral RNAi. Similarly consistent with an antiviral RNAi and/or a miRNA pathway, and with what is known for their close relatives [42,135,136,139–143], we identified two deeply divergent (non-Piwi) Argonaute transcripts in the sponge and in the anemone (S6 Table), and single Argonaute transcripts in the dog whelk and in the starfish. We identified three distinct Argonaute transcripts in the mixed-earthworm species pool, although these may represent the multiple earthworm species present. The dog whelk, starfish, and earthworm Argonautes were all more closely related to arthropod Ago-1 (which binds miRNAs but rarely viRNAs) and to vertebrate Argonautes, than to insect Ago2-like genes that mediate antiviral RNAi. It is likely that these genes mediate the miRNA pathway in these organisms, although it is possible that they may also mediate the production of novel viRNAs seen in the dog whelk. We also identified a single Dicer and Argonaute in the Fucus, which is consistent with what has been seen in other brown algae [116–118], and with the presence of both miRNAs and viRNAs.
Host-encoded RNA-dependent RNA polymerases (RdRp) play a key role in antiviral RNAi responses in plants [144] and nematodes [145,146], underlining the substantial diversity in antiviral RNAi pathways across eukaryotes. However, their role in RNAi in other animals is unknown, and they have an extremely patchy distribution across the animal phylogeny with multiple independent losses. For example, they are absent from Vertebrata and Pancrustacea, but are present in Porifera, Cnidaria, Chelicerata, Nematoda, Bivalvia, Brachiopoda, some Platyhelminthes, and non-vertebrate Deuterostomia. We identified three host RdRps in the Sea Anemone, each closely related to sequences from Exaiptasia pallida. We also identified a single RdRp sequence in the sponge and three in the Earthworm, although these did not cluster with their closest sequenced relatives. We were unable to identify any RdRp sequences in the dog whelk or the starfish, or in the brown alga, but it remains possible that they are present and expressed at a level too low for us to detect.
In animals, the piRNA pathway suppresses transposable element transcripts, and is mediated by homologs of the Drosophila nuclease ‘Zucchini’ and the Piwi-family Argonaute proteins Ago3 and Piwi/Aub. In mammals, fish, C. elegans and Drosophila, this pathway is primarily active in the germline and its associated somatic tissues [75,76,127–130], whereas in sponges and cnidarians—which lack a segregated germline—and many other arthropods, Piwi homologs are ubiquitously expressed [17,71,147]. Consistent with our finding of TE-derived piRNAs displaying a canonical ‘ping-pong’ signature, we identified single Zucchini, Ago3 and Piwi homologs in four of the five animals surveyed (S6 Table). The exception was the sea anemone, in which we could only identify a single Piwi (more similar to Drosophila Piwi/Aub than to Ago-3). Surprisingly, although we did not identify canonical piRNAs in the brown alga, we did identify a possible Piwi-like transcript. However, its relatively low expression and apparent similarity to Piwi genes from the Lophotrochozoa suggest it most likely derives from the contaminating bryozoan identified by COI reads (above). Finally, consistent with the altered small RNA profile associated with oxidation, we were able to identify a single homolog of the RNA methyl transferase Hen-1 in each of the animal species, but not in the brown alga. These sequences have been submitted to GenBank under accession numbers MF288049-MF288076.
Antiviral RNAi is an important defence mechanism in plants and many fungi, and in nematodes and arthropods, where it generates large numbers of easily detectable virus-derived small RNAs in wild-type individuals. Here we identified abundant viRNAs from RNA viruses in two of the six multicellular Eukaryotes we tested: from a bunya/phlebo-like virus in a brown alga (Fucus serratus) and from three different RNA virus-like contigs in the dog whelk (Nucella lapillus). The viRNAs from the brown alga strongly resembled other classes of small RNA from brown algae [117,118] and viRNAs from fungi [114,115] and some viRNAs from plants [113], consistent with an antiviral RNAi response in this species. The viRNAs from the dog whelk similarly displayed a distinct size distribution, derived from the full length of the viral sequence, and were over-represented after oxidation—implying the presence of a 3' 2-O-methyl group (Fig 3, S6 Fig). However, their broad length distribution around 28nt and the strong strand-bias were not consistent with Dicer processing, which is expected to generate sRNAs from both strands simultaneously and to result in a characteristic sequence length determined by the distance between the PAZ and RNaseIII domains [148]. We therefore suggest that these distinctive viRNA are consistent with an active, but divergent, antiviral RNAi pathway in this species.
We have also considered three alternative explanations for these data. First, it is possible that the result is artefactual, and that all of the virus-like reads derive from another unknown source, such as environmental contamination. However, the large number of complementary (mRNA) sequences show the -ssRNA viruses to be active, the sequences were not identified in any of the other co-collected taxa, and the COI read counts in the dog whelk show contamination rates to be low. Contamination was higher for the brown alga, but the virus would need to be at extremely high copy number in the contaminating taxon to achieve the observed 3% of brown alga COI expression. Second, it is possible that the virus-like contigs represent expressed host loci, such as EVEs. However, sequences were not detectable by PCR in the absence of reverse transcription, and in the dog whelk the low and variable population prevalence means that any putative EVE must be segregating and at very different frequencies in different samples—more consistent with an infectious agent. Moreover, in a previous analysis of insect viruses, expressed EVEs were found to be rare relative to active viral infections: zero of 20 viruses identified by metagenomic sequencing in Drosophila [149]. Third, even if the virus-like sequences do represent real infections, it is possible that the small RNAs do not represent an active RNAi-like response. However, their distinctive size distributions and the presence of a 3' 2-O-methyl group in the dog whelk and near 100% 5'U in the brown alga, argue strongly that these viRNAs are the result of active biogenesis rather than degradation.
In contrast, it seems probable that the shorter rhabdo-like virus fragment from the dog whelk (Caledonia dog whelk rhabdo-like virus 2; Fig 3D) is a host-encoded EVE. First, the only open reading frame is homologous to a nucleoprotein and we could not detect a polymerase—despite its close relationship with the nucleoprotein of Lyssaviruses (Fig 2A). Second, RNA sequencing was dominated by negative-sense reads, suggesting a lack of mRNA expression, but consistent with host-driven expression of an integrated locus. Third, the small RNAs were exclusively negative-sense and 5'U, as sometimes seen for primary piRNAs derived from EVEs in other taxa. Fourth, the sequence was ubiquitous in our population samples, consistent with fixation and thus genome integration. Fifth, we were able to PCR amplify a band from a DNA template. If this sequence is an EVE, this could represent an alternative antiviral RNAi mechanism, akin to the piRNA-generating EVEs seen in Aedes mosquitoes [150].
Despite the presence of more than 70 high-confidence RNA virus-like contigs, we were unable to identify an abundant or distinct population of viRNAs from RNA viruses in the sponge, sea anemone, or earthworm samples (the starfish sample lacked detectable RNA viruses). Whereas the -ssRNA viruses in the dog whelk produced 1–100 viRNA reads per RNAseq read (S9 Fig), and Barns Ness serrated wrack bunya/phelbo-like virus 1 in the brown alga produced ca. 0.1 viRNA reads per RNAseq read (S9 Fig), none of the other RNA viruses gave rise to ≥0.001 viRNA reads per RNAseq read. In contrast, in an equivalent analysis of Drosophila, all putative viruses produced viRNAs at approximately 10–1000 viRNAs per RNAseq read [149]. This represents a striking difference in the processing of RNA viruses between arthropods [17,38,39,42] and nematodes [44,45,120], and the processing of viruses by sponges (Porifera), anemones (Cnidaria), and earthworms (Annelida). Importantly, it suggests that these animal lineages either do not process RNA viruses into small RNAs in the way that plants, fungi, nematodes or insects do, or that they do so at a level that is undetectable through the bulk small RNA sequencing of wild-type organisms and viruses—as has been reported to be the case for mammals [31,33–35]. The broad distribution of dsRNA-inducible gene knockdown reported across the animals (Fig 1) may support the latter (cryptic small RNAs) explanation. However, it is also possible that these knockdowns function through the Dicer that mediates the miRNA pathway, as it does in C. elegans. In either case our data imply that the antiviral RNAi mechanisms seen in arthropods and nematodes are highly derived and unlikely to represent the ancestral state in Metazoa.
Nevertheless, it is necessarily hard to demonstrate that RNA viruses do not give rise to small RNAs in these lineages: an absence of evidence provides weak evidence of absence. For example, it is possible that small RNAs are abundant, but were not detected. This is highly unlikely as we were able to detect miRNAs, piRNAs, and small rRNAs, and we would also have detected viRNAs bearing a 5' triphosphate or 3' 2-O-methyl group, as well as viRNAs that had been edited or extended by untemplated bases at the 5' or 3' end. One alternative is that all of the RNA-virus like contigs that we identified from the sea anemone, sponge, and earthworm, were inactive and/or encapsidated at the time of collection, and thus not subject to Dicer processing. However, this is unlikely for three reasons. First, it can be ruled out for eight of the nine highest copy-number dsRNA viruses in the sponge, as these all showed a strong positive-strand RNAseq bias, consistent with gene expression. Second, it is not supported by the two -ssRNA virus contigs in the earthworms, which also displayed positive sense mRNA reads (although the virus copy-number was extremely low, such that that we had little power to identify either positive sense RNAseq reads or viRNAs). Finally, although the small number of negative sense reads resulting from +ssRNA virus replication makes it hard to exclude the possibility that they were inactive, it would be surprising if all of the -ssRNA viruses and dsRNA viruses (including those in the dog whelk and brown alga) were active, but none of the +ssRNA viruses were.
Perhaps a more plausible alternative is that the remaining viruses express viral suppressors of RNAi (VSRs) that completely eradicate the small RNA signature, such that it is undetectable through bulk sequencing of wild-type individuals. This appears to be the case for some mammalian viruses, where viruses genetically modified to remove their VSR do indeed form a much greater source of small RNAs [31,32,34,35]. However, it is not the case for the many insect and plant viruses that express well-characterised VSRs [151,152], and while it could certainly be true for some of the 80 different viruses we detect, it would be surprising if it were true for all of them.
It is also possible that abundant viRNAs are characteristic of a response against -ssRNA viruses in anemones, earthworms, and sponges, but are not characteristic of the response against +ssRNA or dsRNA viruses. This could also be consistent with our failure to detect viRNAs from putative dsRNA narnaviruses in the dog whelk and brown alga, and to a putative +ssRNA nodavirus in the brown alga. If so, then an apparent absence of antiviral RNAi in the sponge, sea anemone and earthworms may really reflect differences in the composition of the RNA virus community, with a preponderance of -ssRNA viruses in the dog whelk and their absence from the sponge or anemone. However, even if -ssRNA viruses, but not +ssRNA viruses or dsRNA viruses, give rise to viRNAs in most animal lineages, then this is still in striking contrast to the antiviral RNAi response in plants, fungi, nematodes and insects [9,38,153], and again suggests that antiviral RNAi mechanisms are highly variable among eukaryotic lineages. Finally, it also remains possible that the majority of sponge, sea anemone, and annelid species do possess an active antiviral RNAi mechanism that generates abundant viRNAs from RNA viruses, but that the particular species we examined here have lost the ability. It is certainly the case that RNAi mechanisms are occasionally lost, as in one clade of the yeast genus Saccharomyces [23,154]. However, unless antiviral RNAi is lost extremely frequently in these three animal phyla—which is not the case in arthropods or plants—it is extremely unlikely that we would by chance select three lineages that have lost the mechanism while others retained it.
We identified four parvo/denso-like virus contigs in the starfish, and one in the sea anemone. All of these sequences were detected as RNAseq reads and were associated with abundant 26-29nt piRNA-like small RNAs (Fig 4). However, RNAseq from three of the four starfish parvo/denso-like virus contigs, and the sea anemone contig, were dominated by negative sense reads. This is hard to reconcile with the normal functioning of ssDNA parvo/denso-like viruses, and may instead reflect host-driven transcription. For these four contigs, the small RNAs were also almost exclusively negative-sense and 5'U—as expected of primary piRNAs. In contrast, RNAseq and small RNAs reads from Millport starfish parvo-like virus 1 were almost exclusively positive (mRNA) sense, with the negative strand small RNAs showing a 5'U bias and positive strand sRNAs showing weak ‘ping-pong’ signature (S6 Fig). Together, these observations suggest that at least some of parvo/denso-like virus sequences represent expressed EVEs, but also that they are targeted by a piRNA pathway-related mechanism.
Unlike for RNA viruses, we were unable to test whether these sequences represent integrations into the host genome, as integrations are indistinguishable from viral genomic ssDNA by PCR, and both +ssDNA and -ssDNA sequences are usually encapsidated by densoviruses. However, Caledonia starfish parvo-like viruses 1, 2 and 3 are nearly identical to published starfish transcripts, and the two published sequences most similar to Caledonia beadlet anemone parvo-like virus 1 are from an anemone transcriptome and an anemone genome (S2 Fig). In addition, three of the five contigs (two in the starfish, and one in the anemone) appear to be ubiquitous in our wild sample. This ubiquitous distribution and close relationship to published sequences support the suggestion (above) that some of these sequences may be host integrations. The exceptions are Caledonia starfish parvo-like virus 1 and Millport starfish parvo-like virus 1, which both had an estimated prevalence of between 4% and 20% in the larger Millport collection. We were able to recover putatively near-complete genomes of 6.5 and 5.8 Kb, containing the full length structural (VP1) and non-structural (NS1) genes, from Millport starfish parvo-like virus 1 and Caledonia starfish parvo-like virus 1, respectively (S2 Table).
If these sequences are EVEs, as seems very likely for four of the five, then their expression and processing into piRNAs may reflect the location of integration—for example, into or near to a piRNA generating locus [155,156]. In contrast, if these sequences are not host EVEs, then the high expression of negative sense transcripts and the presence of primary piRNA-like small RNAs suggests an active Piwi-pathway response targeting DNA viruses in basally-branching animals. These are not mutually exclusive, and it is tempting to speculate that such integrations could provide an active defence against incoming virus infections in basal animals, as suggested for RNA-virus integrations in Aedes mosquitoes [150]. If so, the low-prevalence Millport starfish parvo-like virus 1 sequence, which shared 72% sequence identity with Caledonia starfish parvo-like virus 1, but displayed positive sense transcripts, positive and negative sense piRNAs and a ‘ping-pong’ signature, is a good candidate to represent an unintegrated infectious virus lineage.
The absence of detectable viRNAs in the sponge, sea anemone, or earthworm samples, combined with the presence of 26-29nt (non-piwi) viRNAs in the mollusc and 21nt 5'U viRNAs in the brown alga, reinforces the diversity of antiviral RNAi mechanisms in multicellular eukaryotes. Previously, the abundant viRNAs present in plants, fungi, nematodes and arthropods had implied that Dicer-based antiviral RNAi was ancestral to the eukaryotes and likely to be ancestral in animals, with a recent modification [or even loss; 24] in the vertebrates—perhaps associated with the evolution of interferons [157]. Our findings now suggest three alternative hypotheses. First, antiviral RNAi may have been absent from ancestral animals, and re-evolved on at least one occasion—giving rise to the distinctively different viRNA signatures seen in nematodes, arthropods, vertebrates, and now also a mollusc. Second, the ancestral state may have been more similar to current-day mammals, which do not produce abundant easily-detected viRNAs under natural conditions, but may still possess an antiviral RNAi response [31,33–35]. In this scenario, antiviral RNAi has been maintained as a defence—possibly since the origin of the eukaryotes—but has diversified substantially to give the distinctive viRNA signatures now seen in each lineage. Third, dsRNA, +ssRNA, -ssRNA, and DNA viruses may be targeted differently by RNAi pathways in divergent animal lineages, but arthropods have recently evolved a defence that gives rise to the same viRNA signature from each class. It is not possible to distinguish among these hypotheses without broader taxonomic sampling and experimental work in key lineages. For example, analyses of the Ago-bound viRNAs of Cnidaria and Porifera could help to distinguish between the first two hypotheses, and an identification of the nucleases and Argonautes and/or Piwis required for the 26-29nt mollusc viRNAs could establish whether this response is derived from a Dicer/Ago pathway or a Zucchini/Piwi like pathway. In each case, the limited taxonomic sampling and a lack of experimental data from these non-model taxa preclude any firm conclusions, and given the alternative possibilities outlined above, our interpretations should be treated as tentative. Nevertheless, the balance of evidence strongly suggests that the well-studied canonical antiviral RNAi responses of Drosophila and nematodes are likely to be derived compared to the ancestral state, and that there is substantial diversity across the antiviral RNAi mechanisms of multicellular eukaryotes.
The presence of piRNAs derived from transposable elements in the soma of all of the sampled animals also demonstrates a previously under-appreciated diversity of piRNA-like mechanisms. First, it argues strongly that the predominantly germline expression of the piRNA pathway in key model animals (vertebrates, Drosophila, and nematodes) is a derived state, and that “ping-pong” mediated TE-suppression in the soma is likely to be common in other animal phyla, as has been shown for arthropods [17], and has recently been confirmed in two other molluscs [158]. Second, it suggests that the TE-derived endo-siRNAs seen in Drosophila and mosquitoes [64,155,159–161] are absent from most phyla, and are therefore a relatively recent innovation. Third, the diversity of piRNA profiles we see among organisms—such as the bimodal length distributions of primary piRNAs in the sponge and in “ping-pong’ piRNAs in the sea anemone—suggests substantial variation among animals in the details of piRNA biogenesis. Finally, the large numbers of primary piRNAs derived from putative endogenous copies of parvo/denso-like viruses in the starfish and sea anemone, and from the putatively endogenous rhabdo-like virus 2 in the dog whelk, suggests that the piRNA processing of endogenous virus copies may be widespread across the animals, perhaps even representing an additional ancient defence mechanism.
We sampled six organisms: The breadcrumb sponge Halichondria panacea (Porifera: Demospongiae), the beadlet anenome Actinia equina (Cnidaria: Anthozoa), the common starfish Asterias rubens (Echinodermata: Asteroidea), the dog whelk Nucella lapillus (Mollusca: Gastropoda), mixed earthworm species (Amynthas spp. and Lumbricus spp.; Annelida: Oligochaeta), and the brown alga Fucus serratus (Heterokonta: Phaecophyceae: Fucales). Marine species were sampled from rocky shores at Barns Ness (July 2014; 56.00° N, 2.45° E), and from three sites near Millport on the island of Great Cumbrae (August 2014; 55.77° N, 4.92° E) in Scotland, UK (S1 Table, S1 Text). The terrestrial sample (mixed earthworms; Lumbricus spp., and Amythas spp.), were collected from The King’s Buildings campus, Edinburgh, UK (November 2015; 55.92° N, 3.17° E). To maximise the probability of incorporating infected hosts, we included multiple individuals for sequencing (minimum: 37 sponge colonies; maximum: 164 starfish; see S1 Table for sampling details, numbers). Marine organisms were stored separately in sea water at 4°C for up to 72 hours before dissection. After dissection, the selected tissues were immediately frozen in liquid nitrogen, pooled in groups of 5–30 individuals, and ground to a fine powder for RNA extraction under liquid nitrogen (see S1 Text for details of tissue processing). Except for the brown alga Fucus serratus, RNA was extracted using Trizol (Life Technologies) and DNase treated (Turbo DNA-free: Life Technologies) following manufacturer’s instructions. For Fucus, the extraction protocol was modified from Apt et al., [162]. Briefly, tissue was lysed in a CTAB extraction buffer, and RNA was repeatedly (re-)extracted using chloroform/isoamyl alchohol (24:1) and phenol-chloroform (pH 4.3), and (re-)precipitated using 100% ethanol, 12M LiCl, and 3M NaOAc (pH 5.2).
To avoid potential nematode contamination, an aliquot of RNA from each small (5–30 individual) pool was reverse transcribed using M-MLV reverse transcriptase (Promega) with random hexamer primers. These were screened by PCR with nematode-specific primers and conditions as described in [163] (Forward 5'-CGCGAATRGCTCATTACAACAGC; Reverse 5'-GGCGATCAGATACCGCCC). We excluded all sample pools that tested positive for nematodes from sequencing, although they were used to infer virus prevalence (below). For each host species, RNA from the nematode-free pools were combined to give final RNA-sequencing pools in which individuals were approximately equally represented. For the sponge, sea anemone, starfish, and dog whelk this pooling was subsequently replicated, using a subset of the original small pools, resulting in sequencing pools ‘A’ and ‘B’ (S1 and S2 Tables).
Total RNA was provided to Edinburgh Genomics (Edinburgh, UK) for paired-end sequencing using the Illumina platform. Following ribosomal RNA depletion using Ribo-Zero Gold (Illumina), TruSeq stranded total RNAseq libraries (Illumina) were prepared using standard barcodes, to be sequenced in three groups, each on a single lane. Lanes were: (i) sponge, sea anemone, starfish, and dog whelk ‘A’ libraries (HiSeq v4; 125nt paired-end reads; a Drosophila suzukii RNAseq library from an unrelated project was also included in this lane); (ii) sponge, sea anemone, starfish, and dog whelk ‘B’ libraries (HiSeq 4000; 150nt paired-end reads); (iii) Fucus and Earthworms (HiSeq 4000; 150nt paired-end reads). In total, this resulted in approximately 70M high quality read pairs (i.e. after trimming and quality control) from the sponge, 60M from the sea anemone, 70M from the starfish, 70M from the dog whelk, 130M from the earthworms, and 180M from the brown alga (S3 Table).
For small RNA sequencing, total RNA was provided to Edinburgh Genomics (Edinburgh, UK) for untreated libraries (A and B), or after treatment either with a polyphosphatase (“A: Polyphosphatase”) or with sodium periodate (“B: Oxidised”). In the first case, we used a RNA 5' Polyphosphatase (Epicentre) treatment to convert 5' triphosphate groups to a single phosphate. This permits the ligation of small RNAs that result from direct synthesis rather than Dicer-mediated cleavage, such as 22G-RNA sRNAs of nematodes. In the second case, we used a sodium periodate (NaIO4) treatment (S2 Text). Oxidation using NaIO4 reduces the relative ligation efficiency of animal miRNAs that lack 3′-Ribose 2′O-methylation, relative to canonical piRNAs and viRNAs. This permits identification of 3′- 2′O-methylated sRNA populations, and is expected to enrich small RNA library for canonical piRNAs and viRNAs. TruSeq stranded total RNAseq libraries (Illumina) were prepared from treated RNA by Edinburgh Genomics, and sequenced using the Illumina platform (HiSeq v4; 50nt single-end reads), with all ‘A’ libraries sequenced together in a single lane, and all ‘B’ libraries sequenced together with Fucus and earthworm small RNAs, across four lanes. In total, this resulted in between 46M and 150M adaptor-trimmed small RNAs (S3 Table). Raw reads from RNAseq and small RNA sequencing are available from the NCBI Sequence Read Archive under accession number SRP153010, within BioProject accession PRJNA394213.
For each organism, paired end RNAseq data were assembled de novo using Trinity 2.2.0 [95,96] as a paired end strand-specific library (—SS_lib_type RF), following automated trimming (—trimmomatic) and digital read normalisation (—normalize_reads). Where two RNAseq libraries (‘A’ and ‘B’) had been sequenced, these were combined for assembly. For the mixed earthworm assembly, which had a large number of reads, high complexity, and a high proportion of ribosomal sequences (18%), ribosomal sequences were identified by mapping to a preliminary build of rRNA derived from subsampled data and excluded from the subsequent final assembly. To provide a low-resolution overview of the taxonomic diversity in each sample, we used Diamond [97] and BLASTp [164] to search the NCBI nr database using translated contigs, and MEGAN6 [98] (long reads with the weighted lowest common ancestor assignment algorithm) to provide taxonomic classification. In addition, for a more sensitive and quantitative analysis of Eukaryotic contamination, we recorded the number of Cytochrome oxidase reads for each reconstructed COI sequence present. To identify cytochrome oxidase 1 (COI) sequences, all COI DNA sequences from GenBank nt were used to search all contigs using BLASTn [164], and the resulting matches examined and manually curated before read mapping. An analogous approach was taken to identify rRNA sequences, but using rRNA from related taxa for a BLASTn search.
To identify probable virus and transposable element (TE)-like contigs, all long open reading frames from each contig were translated and concatenated to provide a ‘bait’ sequence for similarity searches using Diamond [97] and BLASTp [164]. Only those contigs with an open reading frame of at least 200 codons were retained. To reduce computing time, we used a two-step search. First, a preliminary search was made using translations against a Diamond database comprising all of the virus protein sequences available in NCBI database ‘nr’ (mode ‘blastp’; e-value 0.01; maximum of one match). Second, we used the resulting (potentially virus-like) contigs to search a Diamond database that combined all virus proteins from NCBI ‘nr’, with all proteins from NCBI ‘RefSeq_protein’ (mode ‘blastp’; e-value 0.01; no maximum matches). Putatively virus-like matches from this search were retained for manual examination and curation (including assessment of coverage—see below), resulting in 85 high-confidence putative virus contigs. A similar (but single-step) approach was used to search translated sequences from Repbase [99], using an e-value of 1x10-10 to identify TE-like contigs.
Translated open reading frames from the 85 virus-like contigs were used to search the NCBI ‘RefSeq_protein’ blast database using BLASTp [164]. High confidence open reading frames were manually annotated based on similarity to predicted (or known) proteins from related viruses. Where unlinked fragments could be unambiguously associated based on similarity to a related sequence or via PCR (below), they were assigned to the same virus. These contigs were provisionally named based on the collection location, host species, and virus lineage. Where available, the polymerase (or a polymerase component) from each putative virus species was selected for phylogenetic analysis. Where the polymerase was not present, sequences for phylogenetic analysis were selected to maximise the number of published virus sequences available. For the Weiviruses, bunya-like viruses, and noda-like viruses, two different proteins were used for phylogenetic inference. Published viral taxa were selected for inclusion based on high sequence similarity (identifiable by BLASTp). Translated protein sequences were aligned using T-Coffee [165] mode ‘m_coffee’ [166] combining a consensus of alignments from ClustalW [167,168], T-coffee [165], POA [169], Muscle [170], Mafft [171], DIALIGN [172], PCMA [173] and Probcons [174]. Alignments were examined by eye, and regions of ambiguous alignment at either end were removed. Phylogenetic relationships were inferred by maximum-likelihood using PhyML (version 20120412); (version 20120412; [175]) with the LG substitution model, empirical amino-acid frequencies, and a four-category gamma distribution of rates with an inferred shape parameter. Searches started from a maximum parsimony tree, and used both nearest-neighbour interchange (NNI) and sub-tree prune and re-graft (SPR) algorithms, retaining the best result. Support was assessed using the Shimodaira-Hasegawa-like nonparametric version of an approximate likelihood ratio test. All trees are presented mid-point rooted.
To estimate virus prevalence in the five animal taxa, we used a PCR survey of the small sample pools (5–30 individuals) for 53 virus-like contigs. There was insufficient RNA to survey prevalence in the brown alga. Aliquots from each sample pool were reverse transcribed using M-MLV reverse transcriptase (Promega) with random hexamer primers, and 10-fold diluted cDNA screened by PCR with primers for virus-like contigs designed using Primer3 [176,177]. To confirm that primer combinations could successfully amplify the target virus sequences, and to provide robust assays, each of four PCR assays (employing pairwise combinations of two forward and two reverse primers) were tested using combined pools of cDNA for each host, with the combination that produced the clearest amplicon band chosen as the optimal assay. We took a single successful PCR amplification to indicate the presence of virus in a pool, whereas absence was confirmed through at least 2 PCRs that produced no product. PCR primers and conditions are provided in S7 Table. Prevalence was inferred by maximum likelihood, and 2 log-likelihood intervals are reported.
For 47 of the putative RNA virus contigs, we used PCR to verify that the sequences were not present as DNA in our sample, i.e. were not EVEs. We performed an RT-negative PCR survey of Trizol RNA extractions (which also contained DNA) using the primers and conditions provided in S7 Table. Where amplification was successful from cDNA synthesised from a DNAse-treated extraction, but not from 1:10, 1:100, or 1:0000-fold diluted RNA samples (serial dilution was necessary as excessive RNA interfered with PCR), we inferred that template DNA was absent. The remaining six (out of a total of 53 contigs for which designed PCR assays) were putative parvo/denso-like virus contigs, and were also tested as above. All six DNA virus contigs were detectable as DNA copies.
To identify the origin of RNA sequencing reads, quality trimmed forward-orientation RNAseq reads and adaptor-trimmed small-RNA reads between 17nt and 40nt in length (and trimmed using cutadapt and retaining adaptor triimed reads only; [178]) were mapped to potential source sequences. To provide approximate counts of rRNA and miRNA reads, reads were mapped to ribosomal contigs from the target host taxa and to all mature miRNA stem-loops represented in miRbase [124], using Bowtie2 [179] with the ‘—fast’ sensitivity option and retaining only one mapping (option ‘-k 1’). To identify the number and properties of virus and TE-derived reads, the remaining unmapped reads were then mapped to the 85 curated virus-like contigs, to COI-like contigs, and to 146 selected long TE-like contigs between 2kbp and 7.5kbp from out assemblies, using the ‘—sensitive’ option and default reporting (multiple alignments, report mapping quality). For small RNA mapping, the gap-opening and extension costs were set extremely high (‘—rdg 20,20—rfg 20,20’) to exclude maps that required an indel. The resulting read mappings were counted and analysed for the distribution of read lengths, base composition, and orientation. In an attempt to identify modified or edited small RNAs, we additionally mapped the small RNA reads to the virus-like and TE-like contigs using high sensitivity local mapping options equivalent to ‘—very-sensitive-local’ but additionally permitting a mismatch in the mapping seed region (‘-N 1’) and again preventing indels (‘—rdg 20,20—rfg 20,20’). We also reanalysed the data using only perfect (zero mismatch) mappings. Neither approach led to substantially different results.
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10.1371/journal.pcbi.1005952 | The role of spatial heterogeneity in the evolution of local and global infections of viruses | Viruses have two modes spread in a host body, one is to release infectious particles from infected cells (global infection) and the other is to infect directly from an infected cell to an adjacent cell (local infection). Since the mode of spread affects the evolution of life history traits, such as virulence, it is important to reveal what level of global and local infection is selected. Previous studies of the evolution of global and local infection have paid little attention to its dependency on the measures of spatial configuration. Here we show the evolutionarily stable proportion of global and local infection, and how it depends on the distribution of target cells. Using an epidemic model on a regular lattice, we consider the infection dynamics by pair approximation and check the evolutionarily stable strategy. We also conduct the Monte-Carlo simulation to observe evolutionary dynamics. We show that a higher local infection is selected as target cells become clustered. Surprisingly, the selected strategy depends not only on the degree of clustering but also the abundance of target cells per se.
| Viruses such as human immunodeficiency virus and measles virus can spread through physical contact between infected and susceptible cells (cell-to-cell infection), as well as normal cell-free infection through virions. Some experimental evidences support the possibility that high ability of cell-to-cell infection is selected in the host. Since the mode of spread affects the evolution of life history traits, it is important to reveal what condition favors high ability of cell-to-cell infection. Here we address what level of cell-to-cell infection is selected in different target cell distributions. Analysis of ordinary differential equations that keep track of dynamics for spatial configuration of infected cells and the Monte-Carlo simulations show that higher proportion of local infection is selected as target cells become clustered. The selected strategy depends not only on the degree of clustering but also the abundance of target cells per se. Our results suggest viruses have more chances to evolve the ability of local infection in a host body than previously thought. In particular, this may explain the emergence of measles virus strains that gained the ability to infect the central nervous system.
| Viruses have evolved various mechanisms to spread within a host body and between hosts. There are two modes for viral spread in a host body, one is to release infectious particles (virions) from the infected cells into the extracellular medium, and the other is to infect directly from an infected cell to an adjacent cell. The mode of viral spreading depends on the type of virus, their target cells and tissues. For example, viruses that lyse the host cell rely on the release of virions as the only way of spreading. In contrast, viruses that exit host cells by budding or some forms of exocytosis have a potential to spread directly from cell to cell. Conceptually the simplest mechanism of cell-to-cell spread is the fusion of infected and uninfected cells. To enter into a host cell, some viruses have proteins that cause membrane fusion, and these fusion proteins are expressed on the cell surface after viral replication is initiated. Thus, fusion proteins on the infected cell may cause membrane fusion to the adjacent uninfected cell, resulting in a single giant cell (syncytium). For instance, vaccinia virus forms two different forms specific to each mode: mature virus released after lysis of infected cells, and double membrane-enveloped extracellular virus that remains associated with the producer cell surface and spreads by cell-to-cell [1,2]. Influenza virus have the potential to spread in a cell-to-cell manner but inherently release virions [3,4]. Other more sophisticated mechanisms of cell-to-cell virus spread also exist (for more examples, see [5]).
Both cell-free and cell-to-cell modes of viral spread have their own advantages and disadvantages [5]. Since virions are much smaller and more resistant to environmental change than infected cells, they can disperse farther from the infected cell and even outside the infected host. However, cell-free infection takes a longer time for the virus to encounter a target cell and to engage attachment and entry receptors because the new infection event depends on diffusion and kinetic processes. This is particularly disadvantageous for viruses that bind to receptors that have low expression on host cells and/or those that must engage multiple receptors in order to enter the cell. Immunological barriers to free virions such as antibodies, complement, defensins and macrophages are also factors that discourage cell-free mode. In contrast, viruses that use cell-to-cell infection can avoid many of such obstacles. Another advantage of cell-to-cell infection is the efficiency of new infections: once an infection has occurred, the cell-to-cell mode of viral spread eliminates the rate-limiting step of diffusion. The disadvantage of cell-to-cell infection is a locality of new infection: as infections progress, "self-shading" occurs whereby host cells near infected cells decrease. Cell-mediated immunity, mainly caused by killer T cells, also has a large impact to cell-to-cell infection because infected cells are clustered.
The present study focuses on the intra-host evolution of cell-free and cell-to-cell infection, which we refer to as global and local infection respectively, with spatial structure of target cells. Since the mode of spread of a pathogen affects the evolution of its life history traits, such as virulence [6–12], it is important to reveal what level of global and local infection is selected. Previous studies assumed that there is a trade-off between global and local infection [6–12]. Our present study also uses this assumption that denotes the retention of virions on the infected cell surface and polarization to the side of cell-cell contact promote the efficient local infection but interfere with global infection [13]. Because virions could be transmitted to different susceptible cells during global infection, local infection can “waste” some virus particles by putting them all into one cells. As an example, previous study that addressed the evolution of global versus local spread, assuming spatial host dynamics whereby reproduction of host individuals is always local and, as a result, the host population is spatially structured [14]. They showed that the spatial structure is important for the evolution of local infection but this conclusion is limited to particular spatial structures generated by infection dynamics and host reproduction. Such self-organized structures are only a part of possible spatial structures; spatial distribution of target cells in a body, for example, have far more diversity than those self-organized by a particular epidemiological model. Another example of previous theoretical studies include local infection through virological synapses in retroviruses [15,16]. These studies considered viruses that spread via cell-free and cell-to-cell infection, and viral strategy is defined as the number of viruses passed per virological synapse. Since total number of virions produced before an infected cell dies is limited, putting virions all into one cell can “waste” those virus particles. The strategy selected in evolutionary competition can be an intermediate number of viruses passed per synapse (i.e. evolved viruses make use of local infection) depending on the viral kinetics. However the effect of spatial structure was only implicitly discussed. To date, the relationship between a measure of spatial structure and an evolutionary outcome remains unclear in the literature.
In the ecological context, our focus is interpreted as the evolution of short- vs. long-range dispersal. Harada [17] modeled population dynamics in a lattice-structured habitat and assumed a linear trade-off between global and local dispersal. In this model, the assumption that vacant sites are always available also causes the similar limitation as in Kamo and Boots [14]. Hiebeler [18,19] assumed the mixture of suitable and unsuitable habitats in the lattice space in which individuals reproduce globally and locally. In these works, pairwise invasibility was examined by Monte-Carlo simulation but the evolution of the proportion of global reproduction along an adaptive dynamics framework was not discussed.
The purpose of this paper is to analyze how the evolutionarily stable proportion of global (or local) infection is related to spatial heterogeneity. We model the evolution of the proportion of global and local infection in a spatially structured SIS model in which some sites are occupied by target cells and other sites are occupied by non-target cells on a lattice space. For simplicity, we assume that whether a site is occupied by a target cell or a non-target cell will never change. Although it may seem unrealistic to assume that an infected cell directly return to susceptible state (SIS model), this would be justified if a susceptible cell fills a blank immediately after an infected cell dies. The manner of target and non-target cell distribution over the lattice space was parameterized by the frequency of target cells and the pair frequency of target cells. We also assume viruses have an ability to establish persistent infection like human immunodeficiency viruses (HIV) that have enough time for within-host evolution and thus adaptive dynamics can be applied. The dependence of evolutionary outcomes on parameters that denote spatial structure is the main focus of our research, which has not been clearly shown in previous studies. At first, the infection dynamics are modeled by pair approximation and the endemic condition for a virus with a certain proportion of global infection is calculated. Next, the evolutionarily stable strategy is obtained by an adaptive dynamics framework. Finally, we conduct the Monte-Carlo simulation and compare results with analytical results.
For the first step, we checked whether a strain with a certain G value can be endemic or not by using the stability analysis of the disease free equilibrium. A similar analysis is done by Hiebeler [18] but the endemic condition was calculated for extreme cases (G = 0 or G = 1) in that study. Here we showed that the endemic condition is also obtained for a virus strain with intermediate G value. In addition to the stability analysis, we also obtained the next generation matrix [20] from the linearized dynamics and calculated basic reproductive number (R0), as,
R0=12α(gxC+lqC/C+(gxC+lqC/C)2+4gψpCC),
(6)
where g = βGG,l = βL(1−G)(1−θ), and ψ = βL(1−G)θ. The derivation is shown in Appendix A (S1 Text). When G = 1, R0 is βGxC/α ≡ ρ1, which is consistent with the result from the SIS model without spatial structure. In this case, the infection becomes endemic when ρ1 > 1 that means an infected cell infects more than one susceptible cell. When G = 0, R0 is βL(1−θ)qC/C/α ≡ ρ0. ρ0 > 1 is consistent with the "dyad heuristic" of Levin and Durrett [21], that is, a pair of infected cells will reproduce more than one pair of infected cells. For general values of G, the endemic condition is obtained by stability analysis of the disease free equilibrium,
α2−α(gxC+lqC/C)−gψpCC<0.
(7)
By solving (7) with respect to α, the result becomes consistent with R0 > 1. For the intermediate value of G, the condition (7) is rewritten by using ρ1 and ρ0,
[1−1ρ1G][1−1ρ0(1−G)]<11−θ.
Fig 1 shows the region of G that satisfies endemic condition (7) with changing the recovery rate α. The difference among Fig 1D–1F is the degree of spatial correlation, pCC/xC2 (but it is not exactly same as the spatial correlation (pCC−xC2)/(xC−xC2)). Of course, viruses cannot be endemic when the recovery rate α is too high. With increasing α, highly locally infecting strains drop out first when host cells distribute like CSR or when cells distribute more uniform than CSR. On the other hand, strains with intermediate G value are more resistant to the increase of α than other strains when host cells distribute with a positive correlation. Results of the simulation (Fig 1G–1I) indicate that the probability of survival in several trials show similar dependence as predicted by condition (7).
Using the invasibility analysis, we drew a pairwise invasibility plot (PIP); Fig 2A is an example of a single parameter set in which intermediate value of G is evolutionarily stable strategy (ESS). In all parameter region examined, there is a unique ESS and ESS strategy is any of completely global (G = 1), a mixture of local and global infections (an intermediate G), or completely local (G = 0). In the present model, there are no other patterns like the evolutionary branching, or more than two evolutionary singular points in the present model. The dependence of ESS on the parameters is shown in Fig 2B–2D. When target cells are relatively less clustered like CSR, ESS G is independent of α (red line in Fig 2B). In contrast, when target cells are relatively clustered, increase of α makes the ESS proportion of global infection higher (green and blue lines in Fig 2B). This is because high recovery rate increases the density of disease-free clusters, which makes global infection beneficial in accessing the isolated clusters. This dependence is also observed with βG < βL (S1 Fig). In Fig 2C and 2D, the dependence of ESS on the degree of clustering for target cells, pCC/xC2, is shown. In general, the higher the degree of target cell clustering becomes, the more local infection is optimal. When the rates of global and local infection are equal (βG = βL), the threshold below which the completely global infection becomes ESS is CSR, pCC/xC2=1 (Fig 2C). In addition to the dependence on pCC/xC2, the ESS proportion of global infection also depends on the fraction of cells xC alone (Fig 2C). If xC becomes higher with fixing pCC/xC2, the ESS level of global infection becomes lower. The reason is that in this alteration, the conditional probability that a randomly chosen target cell has a target cell at its nearest neighbor, qC/C = pCC/xC, becomes higher. Thus, local infection becomes more efficient in finding susceptible cells than global infection. The threshold point at which the completely global strain cannot be ESS does not change by altering xC, which is analytically shown in the next section. When the two infection rates differ, the ESS G value tends to prefer the infection mode of better efficiency (Fig 2D). It should be noted that the ESS proportion of global infection is not always a R0 maximizing strategy (Fig 2E). When pCC/xC2 is very high, the ESS proportion is much lower than the G value that maximizes R0. In most epidemiological or infection dynamics without structure, the ESS trait is to maximize R0 [22,23]. When cells are spatially clustered, however, the ESS is not always maximizing R0, which may be due to a “self-shading” problem as discussed in a previous study [8].
Here we consider the special case in which a mutant strain with a certain G′ (< 1) invades a completely global resident strain (G = 1). The endemic equilibrium of the completely global strain is obtained from Eq (4) (for the calculation, see equations (A9) in S1 Text),
x^I=xC−αβG,
p^SI=(1−αβGxC)αβGxCpCC,
p^IO=(1−αβGxC)(xC−pCC),
where x^I,p^SI and p^IO are pair densities at the equilibrium. In this case, we can analytically obtain the condition for a mutant strain to increase its density around the resident's endemic equilibrium (for derivation, see Appendix B in S1 Text),
βGβL(1−θ+G′θ)<pCCxC2.
(8)
The right-hand side of (8) denotes the degree of spatial correlation, and this is the reason why we choose pCC/xC2 as the horizontal axis of Fig 2C and 2D. The left-hand side of (8) represents an increasing function of the mutant’s proportion of global infection G’. Therefore if βG/βL>pCC/xC2 holds, the completely global strain prevents any kind of mutant from invading and it is an ESS. Especially when βG = βL, the threshold of the spatial configuration at which the completely global strain can be the ESS corresponds to complete spatial randomness (CSR). It means that when cells distribute with a negative correlation, the completely global strain is the ESS, but when cells distribute with a positive correlation, some degree of contact infection can be beneficial. As predicted in this section, the threshold point moves to βG/βL when βG ≠ βL (Fig 2D).
The mean G in the population quickly converges to a certain level, and fluctuates around that level (Fig 3A). As long as we use the same parameters, the evolutionary outcomes are similar to each other regardless of the initial condition and the number of trials. We found that evolutionary branching never occurs and the distribution of strains is always unimodal (see S2 Fig). Fig 3B and 3C shows evolutionary outcomes with changing the degree of spatial correlation pCC/xC2 like Fig 2C and 2D, where 20 trials are conducted for each parameter set. In general, the results are similar to the pair approximation, notably, 1) high pCC/xC2 prefers local infection, 2) evolutionary outcome depends on both pCC/xC2 and xC, and 3) increasing xC promotes the local infection.
The difference between the results from pair approximation (Fig 2C and 2D) and those from simulations (Fig 3B and 3C) is that the mean value of G in the simulations does not converge to extreme values (the completely global or the completely local). This may be because the population is always polymorphic as a result of mutations. In addition, there are two other reasons for the region in which the completely local is the ESS according to the pair approximation. The first reason is the effect of finite population size. Especially, since the completely local strain spreads only in a contiguous cluster, the number of available hosts is smaller than for other strains. Therefore, diffusing to other clusters becomes adaptive. The second one is the limitation of pair approximation. In this approximation, we approximate qσ/σ′σ″ the conditional probability that a randomly chosen nearest of a σ'σ'' pair has a σ site by qσ/σ′ the conditional probability that a randomly chosen nearest neighbor of σ' site is a σ site. When viruses are too biased toward local infection, infected cells tend to form a large cluster and the approximation does not work well. For these reasons, there is a discrepancy between pair approximation and simulation.
To check whether these results are specific to our method of generating a spatial structure, we also conducted the same evolutionary simulations on the several different deterministically generated structures each having the same global and pair densities (xC and pCC) as those in randomly generated structure. Fig 4 shows the comparison of results between randomly and deterministically generated structures. These results suggest the robustness of our results on the evolution of local and global infections based on the randomly generated spatial configurations for given singlet and doublet densities, xC and pCC.
In the above sections, we assumed a linear trade-off, that is, the proportion of local infection decreases at the same amount as the proportion of global infection G is increased. However, this should not be always the case in reality. When the local infection decreases in proportion to G0.5, the result is quite different (Fig 5). In this case, the PIP shows bistability in which the evolutionary outcome depends on an initial state (Fig 5B). This prediction by pair approximation is also confirmed by simulation (Fig 5C).
In this paper, we investigated the effect of population structure on an evolutionarily stable proportion of global infection. Before considering the effect, we have obtained the endemic condition of a virus strain in the SIS model on a lattice space using pair approximation. For the extreme cases (completely global or completely local strain), the endemic condition is consistent with previous studies [18,21]. For the intermediate global infection rate, the condition shows that the strain using both global and local infection can survive with the parameter sets with which the extreme strains cannot. For example, when the global density of target cells is too low to persist for completely global infection, high degree of clustering of target cells promotes the survival of a strain using local infection.
In fact, the invasibility analysis by pair approximation and the Monte-Carlo simulation show that evolutionarily stable strategies or evolutionary outcomes are strongly dependent on the global density of target cells and their degree of clustering. As target cells become aggregated, higher proportion of local infection is selected. This tendency can be seen in the negative slopes in the relationship between ESS G and the spatial aggregation measure pCC/xC2 in Fig 2. This effect seems stronger at high global density of target cells because the slope of high xC is steeper than that of low xC (Fig 2C). When the local density of target cells is high, spread through local infection is easier for finding the next susceptible cell compared to global infection. Thus, a virus that uses local infection may predominantly be efficient in spreading and that trait is selected. However, there is also a difference between invasibilty analysis and simulation, whereby the former predicts the completely local strain can be ESS in some range but the latter does not. This discrepancy may be attributed to the limitation of pair approximation. In the simulation of evolutionary dynamics, we can generate different spatial structures that have the same first and second moment (global and pair densities) but have different higher order moments. In spite of these variations in spatial structure, the evolutionary outcomes were very similar (Figs 2, 3 and 4). Therefore, we conclude that global and pair densities are enough to explain outcome of evolution of global or local infection, and that parameters which describe higher order configuration do not affect results.
We also checked the dependence of ESS on the parameters that define infection dynamics. When the ratio βG/βL is changed with fixing pCC/xC2, the ESS is adjusted to use more efficient pathway (for example, an ESS G for βG/βL > 1 is larger than that for βG = βL; Fig 2D). The analytical result show that the magnitude of βG/βL relative to pCC2/xC also affects whether or not the completely global strain can be an ESS. When the degree of clustering pCC/xC2 is smaller than βG/βL, the completely global infection becomes ESS. If βG = βL, the boundary is at complete spatial randomness (CSR). If βG < βL, using some local infection can be evolutionarily stable even if targets cells are more uniformly distributed than CSR. The effect of increasing recover rate α is against to favor local infection. This result is apparently counter-intuitive because high recovery rate would mitigate the self-shading effect [14] and hence local infection would be more efficient. However, high recovery rate increases the density of disease-free clusters, which makes global infection far beneficial in accessing the isolated clusters.
The evolution of global vs local spread is also addressed in some studies in ecology and epidemiology [14–17]. In the cited studies, spatial structure is either not considered at all or limited only to some patchy distribution. Self-organized spatial structures are not definitely controlled or parameterized during simulation. Thus, our model defines spatial structure at first, and examines the dependences of evolutionary outcomes on spatial parameters. The other difference is the effect of "self-shading". If infected individuals are clustered, the number of susceptible individuals available for local infection decreases. Kamo and Boots [14] assumed that infected individuals will die and vacant sites will be covered only by host local reproduction, which has a strong effect against local infection. In contrast, our model assumes SIS model in which an infected cell changes its state to susceptible and the spatial structure is mainly caused by definition. Therefore, self-shading effect is weaker than the previous studies and our analytical results show the suitable parameter region where the completely local strain can be an ESS. In terms of dynamics on artificially organized spatial structure, Hiebeler [18,19] modeled the competition between species using different proportion of global and local reproduction. In such models, the pairwise invasibility between resident and invader (or mutant) was checked, but the evolution of the trait after invasion was not considered. Here we used similar model for the within-host viral evolution and applied adaptive dynamics framework. It suggests that invasion has the possibility of replacement by mutants and this replacement drives the evolution of traits in a population. Applying adaptive dynamics is justified by the assumption that we focus on viruses that cause persistent infection. Such persistent viruses may have sufficient time for the within-host evolution and the adaptive dynamics can be applied. There should be conflicts between the aims for the short-term increase within a host and that for the long-term spreads between hosts. However, the combined effects of these conflicting selection processes is beyond the scope of the present paper.
As in the previous models [14–17], we assumed a trade-off between global and local infection. The cost of local infection in our model is superinfection caused by the retention of virions on the infected cell surface, which is also assumed in [15,16]. The importance of superinfection is inferred by the fact that various viruses have a mechanism to avoid superinfection promoted by local infection [24–29].
From an ecological point of view, our model can be applied for considering the evolution of long and short dispersal. Target cells correspond to habitats for animals or plants, with S sites and I sites corresponding to unoccupied and occupied sites, and non-target cells representing unsuitable sites to settle. According to the endemic condition (6), the persistence of a species with some local colonization rate depends on the spatial structure. Our results, if applied to a conservation biological setting, suggest that, even if we conserve the abundance of habitats for an endangered species, extinction might occur only due to the change in spatial arrangement of habitats. In terms of the evolution of dispersal distance, previous studies add other settings such as a trade-off between survivability and dispersal range [30], the population dynamics in local patches [31], kin selection [32], the existence of pests [33], or the disturbance structure [34,35]. However, the effect of spatial structure per se has not been clarified yet. We therefore checked how spatial structure affects the evolution of shot and long dispersal. Our results suggest that the ESS proportion of short dispersal depends not only on the degree of clustering but also on the density of habitats per se. Short dispersal is selected when habitats are clustered, and this tendency is strong when the abundance of habitats is high.
Our model is also regarded as a model of dispersal rate evolution that considers how much an offspring should disperse beyond its natal patch if we arrange habitats like patches. In this interpretation, local infection denotes staying in a natal patch and global infection represents outgoing from a natal patch. The infection rates βG and βL correspond to survival rate of outgoing and staying individuals, respectively. The conventional result in this situation suggests that even if the survival rate of an outgoing individual (βG) is much lower than that of a staying individual (βL), at least half of all offsprings should go out [36]. Pair approximation in the present study does not predict this result but this is clearly due to the limit of pair approximation. Actually, Monte-Carlo simulation shows that the completely local strategy is never selected. In addition to the previous result, we suggest that even if the survival rate of outgoing individual is higher than that of staying one (βG > βL), there is a parameter region in which some offspring should be left due to high pCC/xC2. Although a similar result has been suggested [37], the reasoning differs to our study. The previous result is due to the variation of patch quality, which means that an individual born in a good patch should leave its offspring in a natal patch. In our model, high clustering of habitats prefers leaving offsprings in a natal patch without assuming differences in patch quality. When the habitats are highly clustered (pCC/xC2>1), the probability of finding new habitat is higher for local dispersal (qC/C = pCC/xC) than for global dispersal (xC). Therefore, the advantage of finding a new habitat can outweigh the disadvantage of low survival rate.
Our model predicts the evolutionarily stable dispersal strategy only but some previous models suggest evolutional branching of dispersal rate [38,39] or evolutionary bistability in which the evolutionary outcome differs in initial state [40]. In general, branching or bistability may occur when the fitness of a phenotype depends on the frequencies of other existing phenotypes and possible phenotypes have a proper trade-off [41,42]. In our model, there is a trade-off between global and local infection. The reason why we only observe ESS is that the linear trade-off is not suitable for causing evolutionary branching or bistability. In fact, if we assume nonlinear trade-off between global and local infection is assumed, we can observe the evolutionary bistability (Fig 5).
Our model suggests that spatial structure has an important role, while it is not commonly considered in the field of virology. In in vitro experimental cases, two-dimensional cell culture is commonly used and this condition is similar to our model. Hence, there is a possibility that the evolution of global and local infection can occur. In an example of culture of measles viruses (MVs), a higher level of local infection was selected for in continuing passages [43], indicating the emergence of mutant viruses with a high ability to induce membrane fusion in vitro. When the evolution of global or local infection occur, other viral traits like virulence will evolve as shown in Boots and Sasaki [8]. They analytically showed that a lower virulence is predicted as infection becomes more local. The importance of local infection in the evolution of influenza viruses is shown experimentally [3,4]: cell-to-cell transmission promotes a faster expansion of the diversity of virus quasispecies and may facilitate viral evolution and adaptation when influenza viruses’ neuraminidases are inhibited and virus release from infected cells is suppressed [3,4]. Therefore, we emphasize the relationship between the spatial distribution of target cells and the evolution of viral infection mode when studying in vitro infectious dynamics. If we manipulate cell density and the efficiency of viral transmission by antibodies, viruses that have favorable level of global and local infection may be obtained.
In a host body, it is rare that cells distribute like two-dimensional cell culture systems except for epithelia. Epithelial cells form a continuous sheet and they may be different in susceptibility because of surface molecule expression, response to interferons and other immune cell activities. Therefore, epithelia can be a place where evolution of cell-to-cell infection can occur, and in fact, some viruses are known to have an ability for cell-to-cell infection in epithelia like MVs [44] (for other examples, see Table 1 in [5]). In contrast, influenza viruses can also infect epithelial cells but the evolution of local infection is not known. The reason may be the short length of infection period; influenza infection period can be as short as a week but some viruses like HIV survive in the host body for a long time. Such persistent viruses may have sufficient time for the within-host evolution and the adaptive dynamics framework can be applied. Since MVs also have an ability to establish persistent infection, these viruses may also have a chance to evolve efficient cell-to-cell infection in the host body.
It has been shown that cell-to-cell viral transmission through virological synapse occurs in retroviruses such as human T-lymphotropic virus type 1 (HTLV-1) [45] and HIV [46]. This process is thought to have important role because the contribution of cell-to-cell infection on HIV spread is estimated to be equal to or more that of cell-free infection by comparing static and shaking culture conditions [47,48]. Since infected cells can move in the lymphoid tissue and find a connection to susceptible cells, the spatial viscosity of the infected target cells in the lymph nodes should be weakened in these viruses. However, there would remain some non-random correlation of uninfected target cells (pCC/xC2>1) because of the locality of T cells in the lymph nodes, and this could favor cell-to-cell transmission over cell-free transmission in retroviruses too. Therefore, we suggest new conditions that evolutionarily promote cell-to-cell infection in those viruses: highly localized distribution of target cells in the lymphoid tissues (pCC/xC2>1). These points have not been suggested in the previous theoretical study [16].
Our model may explain the emergence of mutant MVs that are isolated from patients of subacute sclerosing panencephalitis (SSPE). These viruses have mutations that provide high ability of cell fusion (i.e. high level of local infection) [49] and can infect central nervous system cells, while wild type MVs cannot [43,49]. Since MVs can spread in a cell-to-cell manner between epithelial cells, epithelia is a candidate place in which the evolution of local infection occurs. Another possibility is lymph nodes because the main target cell of MVs is SLAM (signaling lymphocytic activation molecule, also known as CD150) expressing immune cells such as T and B cells etc. [50,51]. As discussed in the case of HIV, concentrating target cells in the lymph nodes satisfies the condition under which local infection is selected. Consequently, MVs are prone to evolve local infection in a host body and to gain the ability to infect cells of central nervous system but how MVs reach the central nervous system remains unknown.
In conclusion, our results suggest that the mode of viral spread, global or local infection, may undergo adaptive evolutionary change in vitro and in vivo. In the future, we can consider more realistic situation such as evolutionary dynamics in three-dimensional space or the repulsion of superinfecting virions that attenuates self-shading effect [29]. In order to examine the emergence of mutant virus or the evolution of virulence, we need to take into account the fact that the mode of infection itself is subject to selection.
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10.1371/journal.pcbi.1003313 | Computational Protein Design Quantifies Structural Constraints on Amino Acid Covariation | Amino acid covariation, where the identities of amino acids at different sequence positions are correlated, is a hallmark of naturally occurring proteins. This covariation can arise from multiple factors, including selective pressures for maintaining protein structure, requirements imposed by a specific function, or from phylogenetic sampling bias. Here we employed flexible backbone computational protein design to quantify the extent to which protein structure has constrained amino acid covariation for 40 diverse protein domains. We find significant similarities between the amino acid covariation in alignments of natural protein sequences and sequences optimized for their structures by computational protein design methods. These results indicate that the structural constraints imposed by protein architecture play a dominant role in shaping amino acid covariation and that computational protein design methods can capture these effects. We also find that the similarity between natural and designed covariation is sensitive to the magnitude and mechanism of backbone flexibility used in computational protein design. Our results thus highlight the necessity of including backbone flexibility to correctly model precise details of correlated amino acid changes and give insights into the pressures underlying these correlations.
| Proteins generally fold into specific three-dimensional structures to perform their cellular functions, and the presence of misfolded proteins is often deleterious for cellular and organismal fitness. For these reasons, maintenance of protein structure is thought to be one of the major fitness pressures acting on proteins. Consequently, the sequences of today's naturally occurring proteins contain signatures reflecting the constraints imposed by protein structure. Here we test the ability of computational protein design methods to recapitulate and explain these signatures. We focus on the physical basis of evolutionary pressures that act on interactions between amino acids in folded proteins, which are critical in determining protein structure and function. Such pressures can be observed from the appearance of amino acid covariation, where the amino acids at certain positions in protein sequences are correlated with each other. We find similar patterns of amino acid covariation in natural sequences and sequences optimized for their structures using computational protein design, demonstrating the importance of structural constraints in protein molecular evolution and providing insights into the structural mechanisms leading to covariation. In addition, these results characterize the ability of computational methods to model the precise details of correlated amino acid changes, which is critical for engineering new proteins with useful functions beyond those seen in nature.
| Evolutionary selective pressures on protein structure and function have shaped the sequences of today's naturally occurring proteins [1]–[3]. As a result of these pressures, sequences of natural proteins are close to optimal for their structures [4]. Natural protein sequences therefore provide an excellent test for computational protein design methods, where the goal is to predict protein sequences that are optimal for a desired protein structure and function [5]. It is often assumed that given a natural polypeptide backbone conformation, an accurate protein design algorithm should be able to predict sequences that are similar to the natural protein sequence. This test is commonly referred to as native sequence recovery [4] and it has been used extensively to evaluate various protein design sampling methods and energy functions [6]–[8].
Beyond simply recovering the native sequence, a further challenge in computational protein design is to predict the set of tolerated sequences that are compatible with a given protein fold and function [9]–[13]. Predicting sequence tolerance is important for applications such as characterizing mutational robustness [14], [15], predicting the specificity of molecular interactions [16]–[20], and designing libraries of proteins with altered functions [21], [22]. Recent methods developed for this goal involve generating an ensemble of backbone structures similar to the native structure and then designing low energy sequences for the different structures in the ensemble [9], [16], [19], [23]–[25]. These flexible backbone design methods can produce sequences that are highly divergent from the native sequence but may still fold into the desired structure, which makes simple native sequence recovery a poor indicator for the accuracy of these methods. A more useful computational test of these approaches involves comparing designed sequences with a set of reference sequences, either naturally occurring or experimentally derived, that share the desired protein fold. This comparison can be based on sequence profile similarity, which involves quantifying the difference between the frequencies of observing each amino acid at corresponding positions in the designed and reference sequences [16], [17], [19].
While high similarity between designed and reference sequence profiles can be informative to gauge the accuracy of a protein design method, it does not guarantee that the method will predict sequences that fold into the desired structure. This is because sequence profile comparisons evaluate amino acid positions independently from each other and therefore ignore the details of amino acid interactions that are critical for protein structure and function. Naturally occurring protein structures are formed cooperatively and each amino acid can physically interact with multiple neighboring amino acids. Evolutionary selective pressures have acted upon these interactions, resulting in the patterns of amino acid covariation that can be observed within today's naturally occurring protein families. Accordingly, previous studies have used information theoretic methods to detect amino acid covariation in multiple sequence alignments of many different protein families [26]–[28] and have used contact prediction based on covariation to dramatically improve the accuracy of protein structure modeling [29].
Despite the clear occurrence of amino acid covariation in natural protein sequences, the extent to which different selective pressures have shaped amino acid covariation in diverse protein families is unknown. Additionally, it is difficult to dissect to what extent phylogenetic bias has influenced the observations of amino acid covariation. Previous work has indicated that networks of covarying amino acids play a role in allosterically linking distant functional sites, suggesting that amino acid covariation is driven by protein functional constraints [30], [31]. However, other studies have shown in two test cases that computational protein design can recapitulate naturally occurring covariation in the cores of SH3 domains [4], [13], [32] and for two-component signaling systems [33]. These results indicate that constraints imposed by protein structure have played a role in producing the covariation in the studied examples, but it has not yet been shown that these observations are general.
In this paper, we use computational protein design to measure the extent to which protein structure has shaped amino acid covariation in a diverse set of 40 protein domains. Since computational protein design predicts sequences that are energetically optimal based on protein structure alone, we expect that pairs of amino acids that highly covary in both designed and natural sequences to have likely covaried to maintain protein structure. We find significant overlap in the sets of highly covarying amino acid pairs between designed and natural sequences for all 40 domains examined, suggesting that maintenance of protein structure is a dominant selective pressure that constrains the evolution of amino acid interactions in proteins. Our analysis furthermore quantifies the extent to which different types of interactions explain the observed covariation. Finally, we demonstrate the utility of amino acid covariation recapitulation as a sensitive test for evaluating different protein design methods. We find that flexible backbone design significantly improves covariation recapitulation relative to fixed backbone design and that recapitulation of amino acid covariation is exquisitely sensitive to both the magnitude and mechanism of backbone flexibility. Taken together, these results provide fundamental insights into the physical nature of amino acid co-evolution and, more practically, provide a new benchmark that may help improve the accuracy of computational protein design methods.
To compare amino acid covariation in natural and predicted designed protein sequences, we selected 40 protein domains that were diverse with respect to their secondary structure composition and fold class (Table 1). We then quantified natural amino acid covariation for each domain by creating a multiple sequence alignment for the domain, followed by computing covariation between every pair of columns in the multiple sequence alignment by using a mutual information based method [28] (see Methods). Pairs of amino acid positions with a covariation score that is two standard deviations above the mean or greater were considered to be highly covarying pairs.
We predicted designed protein sequences for each of the 40 domains using RosettaDesign [4], [34]. We first used the standard RosettaDesign fixed backbone protocol [34], which takes a crystal structure as input and runs Monte Carlo simulated annealing, to predict 500 designed sequences for each domain structure. We then quantified amino acid covariation in the designed sequences and compared it to natural amino acid covariation for each domain. We calculated the similarity between designed and natural covariation based on the percent overlap of the highly covarying pairs in each set (see Methods). We found this overlap to be significant (p<0.001) for all 40 domains (Table S1).
Given the observation that fixed backbone protein design can recapitulate a significant fraction of naturally covarying amino acid pairs, we next aimed to understand how incorporating backbone flexibility into the design protocol affects this recapitulation. To accomplish this, we generated a conformational ensemble of 500 backbone structures for each domain using the “backrub” method [35] in Rosetta [36], which iteratively applies local backbone perturbations throughout the protein structure combined with adjustments in side-chain conformations. We then used RosettaDesign to predict a low energy sequence for each backbone structure in the ensemble, resulting in 500 designed sequences. Figure 1 shows a flow chart of this approach applied to an SH3 domain.
To investigate the effect of the magnitude of backbone flexibility in the design protocol, we varied the temperature parameter in the Monte Carlo simulations used in the backrub protocol to generate conformational ensembles with different amounts of structural variation (Figure 2A). We designed sequences for each ensemble (kT = 0.3, 0.6, 0.9, 1.2, 1.8, 2.4) and quantified similarity to natural covariation for each set of sequences. We compared these results with sequences designed using the fixed backbone design protocol described above (“Fixed”). Figure 2B shows a significant increase in covariation similarity for the flexible backbone simulations relative to the fixed backbone simulation. Moreover, the distributions of covariation similarity for the 40 domains show that there is an optimal degree of structural variation, as low-temperature and high-temperature simulations perform significantly worse than mid-temperature simulations (Table S2). We observed this same trend when we repeated this analysis using a different method for quantifying covariation [37] (Figure S1), suggesting that our results are not dependent on the method used to quantify covariation.
To better understand the basis of this trend, we examined several other sequence and structural characteristics: sequence recovery, sequence profile similarity, sequence entropy and structural variation (see Methods). The resulting distributions for these characteristics are shown in Figure 2C. Sequence entropy and sequence profile similarity showed similar trends to covariation similarity (sequence entropy is most similar to natural sequences and profile similarity is highest at 0.9 kT), suggesting that backbone flexibility allows for sampling diverse sequences with native-like properties. These trends are consistent with the observation that sequence recovery decreases with increasing amounts of backbone flexibility. As diversity within a set of sequences increases, those sequences tend to become more dissimilar to any individual sequence, including the native sequence of the crystal structure used as input for design. Structural variation in the 0.3, 0.6, 0.9 and 1.2 kT simulations is less than the structural variation among naturally occurring protein structures with these domains, which could be due to the fact that natural proteins use additional mechanisms of generating structural variation that are not being modeled, such as the insertion or deletion of amino acids in loop regions. Taken together, these results suggest that a moderate degree of backbone flexibility allows for the accommodation of sequences that differ from the native sequence and yet are similar to naturally occurring sequences with respect to their sequence profiles, sequence entropies and patterns of amino acid covariation.
Next we examined whether or not these results were specific to the method used to generate the conformational ensembles for design. We tested two other Monte Carlo based methods that iteratively perform perturbations to the backbone. One method performed Kinematic Closure (“KIC”), which involves randomizing phi/psi torsions in a local region of the backbone while keeping the rest of the backbone fixed, thus introducing a chain break, and then using inverse kinematics to solve for the torsions that will close the chain [38]. The other method performs potentially non-local moves by perturbing the phi and psi torsions of residues by a random small angle (“Small”) [39]. We ran both of these methods for the same number of trials and for the same values of kT as the backrub protocol. The resulting distributions of covariation similarity show the same trend we observed previously with the backrub simulations, where mid-range temperature simulations result in an optimal degree of covariation similarity (Figure S2).
While the optimal simulation temperature parameter was comparable for each of the methods tested, the methods achieved a different optimum level of covariation similarity with the natural sequences. We found that the two local move simulations (KIC and Backrub) outperformed the non-local move simulation (Small). To test if this observation holds true more generally, we tested two additional methods of generating conformational ensembles that make non-local moves. These methods included FastRelax (“Relax”), which consists of multiple rounds of side-chain repacking and all-atom minimization while increasing the weight of the repulsive term in the Lennard–Jones (LJ) potential from 2% to 100% of its default value, and AbInitioRelax (“AbRelax”), which performs fragment-based ab initio structure prediction followed by FastRelax [40]. As an additional control, we also designed sequences using a fixed backbone structure with an energy function that dampens the weight of the repulsive LJ term (“Soft”). The resulting covariation similarity distributions show that recapitulation of natural amino acid covariation is sensitive to the method used to generate conformational ensembles (Figure 3A). Both local move simulations (KIC, Backrub) achieved higher median covariation similarities than the non-local move simulations (Small, AbRelax, Relax) and the fixed backbone simulations (Fixed, Soft) (see Table S3 for p-values).
We also evaluated each of these methods using the other metrics described above: native sequence recovery, sequence profile similarity, sequence entropy and structural variation (Figure 3B). Unexpectedly, the AbRelax method, which resulted in conformational ensembles with the greatest structural variation, achieved the highest sequence profile similarity with the natural sequences of any method tested. A possible explanation for this behavior is that local interactions are preserved in AbRelax generated structures, but the overall topology of the protein is incorrect. To test this hypothesis, we examined covariation similarity in the AbRelax sequences by splitting all covarying pairs into the following two sets: pairs separated by fewer than 10 residues in sequence (“Near”) and pairs separated by greater than 10 residues in sequence (“Far”). This analysis revealed that whereas AbRelax sequences have relatively high covariation similarity with natural sequences for pairs close in sequence, they have low covariation similarity for pairs that are distant in sequence (Figure 3C). In contrast, covariation similarity for “near” and “far” pairs were similar for simulations using backrub ensembles. These results suggest that AbRelax can model local interactions within a secondary structural element or between adjacent secondary structures, but it does not correctly capture non-local interactions that are likely critical for achieving a cooperatively folded, stable tertiary structure. This observation demonstrates the importance of using amino acid covariation to evaluate the accuracy of protein design methods, since it is possible to obtain deceptively high sequence profile similarity scores with highly divergent tertiary structures as long as local interactions are maintained. Of all the flexible backbone design methods tested, Backrub, kT = 0.9 resulted in sequences most similar to the natural sequences with respect to covariation similarity and sequence profile similarity. Using the assumption that a method that gives higher similarity to natural sequences will better capture the mechanisms underlying covariation, we used Backrub, kT = 0.9 as the representative flexible backbone sequences for the remainder of the study.
To understand how backbone flexibility influences the extent of covariation similarity between designed and natural sequences, we identified all pairs of amino acid positions that highly covaried in both the natural sequences and a set of flexible backbone sequences (Backrub, kT = 0.9) but did not highly covary in the fixed backbone sequences. We then took all pairs of amino acids at these positions that were not sampled in the fixed backbone simulation and designed them onto the crystal structure backbone using fixed backbone design. For each pair of these positions, we calculated mean interaction energies and compared these energies between fixed and flexible backbone design structures (Figure 4A). We calculated both one-body energies, which include the interaction of an amino acid residue with itself, and two-body energies, which include the interactions between two amino acid residues in the protein (see Text S1 for description of the components of Rosetta one-body and two-body energies). We found both the one-body and two-body energies of these pairs to be generally greater in the context of fixed backbones relative to flexible backbones. Splitting the energies into their component terms revealed that the backbone-dependent Dunbrack rotamer energy (fa_dun) and Lennard-Jones repulsive (fa_rep) terms resulted in greater energy increases in the one-body and two-body energies, respectively, than any other term in the energy function (Figure S3). These results suggest that amino acid pairs that covary in flexible backbone simulations but do not covary in fixed backbone simulations generally cannot be accommodated on fixed backbones without resulting in steric clashes or rotamers that are unfavorable for the given backbone. Simply modifying the energy function by using a “soft” repulsive potential that reduces the energy of clashes does not increase sequence diversity or covariation similarity (Figure 3B), suggesting that backbone movements are required to accommodate these amino acid interactions. Figure 4B shows representative cases where some degree of backbone flexibility is required to correctly model the precise interaction details of specific amino acid pairings.
We have thus far compared amino acid covariation between natural and predicted designed sequences based on the extent of overlap between the sets of highly covarying pairs. However, it is also important to consider the amino acid pair propensities at covarying positions to test whether the natural and designed covarying pairs utilize the same types of amino acid interactions. To accomplish this, we calculated amino acid propensities at pairs of positions that covary in both the natural and designed sequences (Figure 5A). Over-represented amino acid pairs in both designed and natural sequences included those with opposite charges, hydrophobic pairs and hydrogen-bonding pairs. Differences in the designed and natural amino acid pair propensities included the over-representation of cation-pi pairs in the natural sequences but not in the designed sequences (such as W-R). These differences highlight shortcomings of the energy function used for design, which does not currently account for cation-pi interactions.
To quantify the similarity between the natural and designed covarying pair propensities, we calculated the correlation coefficients between the natural and designed propensities for all sets of designed sequences. We found these correlations to be dependent on both the magnitude and mechanism of backbone flexibility, as we previously observed with the overlap in covarying pairs (Table S4). The comparison between natural and designed pair propensities for fixed backbone sequences and for a set of flexible backbone sequences (Backrub, kT = 0.9) are shown in Figure 5B, again supporting the conclusion that backbone flexibility improves recapitulation of amino acid covariation.
While similar pair propensities between natural and designed covarying pairs demonstrate that the same types of amino acid interactions occur in both natural and designed sequences, they do not show that the mechanisms underlying covariation are the same in both cases. To investigate this, we first classified the mechanism of covariation for all pairs that covary in both designed and natural sequences and then quantified how often the same mechanism is used. Figure 6A shows an illustration of three of the covariation mechanisms: size, hydrogen bonding and charge. Classifying each of these mechanisms requires examining the transition from one amino acid pair to another. For example, the transitions depicted in Figure 6A are IA–VV, AP–SS, RE–DR. Covariation due to size involves a decrease in the size of one amino acid and an increase in the size of the other (IA–VV). Covariation due to hydrogen bonding involves a hydrogen bond that exists in one pair but not the other (AP–SS). Covariation due to charge involves a pair of amino acids with opposite charges that either swap sign (RE–DR) or become uncharged amino acids. We also defined covariation mechanisms based on cation-pi interactions, pi-pi interactions, and other interactions not falling into any of the previous categories that we classify as hydrophobic, hydrophilic or mixed hydrophobic and hydrophilic (see Methods for a detailed definition).
For each pair of positions that covaried in both the designed and natural sequences, we computed the ten most significant transitions between amino acid pairs at those positions and classified each transition based on the mechanism of covariation. The resulting distributions of covariation mechanisms for the designed and natural pairs are shown in Figure 6B. The designed and natural covariation mechanisms distributions share similar properties, including covariation due to charge being the most common mechanism, whereas cation-pi, pi-pi and other (hydrophilic) covariation mechanisms are more rare. In both natural and designed distributions, hydrogen bonding and size covariation together account for approximately 30% of the total mechanisms. However, a number of quantitative differences exist in the distributions, including charge occurring more frequently in the designed pairs, suggesting that the design method may be over-predicting charged interactions. Additionally, in the natural pairs, size covariation is more common than hydrogen bonding covariation while the opposite is true in designed pairs. The “other” categories are also more common in the natural pairs than in the designed pairs. To better understand these differences, we split the pairs up based on the extent of their burial and compared the distributions of covariation mechanisms (Figure S4). This analysis revealed that covariation mechanism is dependent on the extent of pair burial and that buried pairs have the most significant differences between natural and designed covariation mechanisms. In natural buried pairs, the most common covariation mechanisms are size and other (hydrophobic), whereas the most common mechanisms in designed buried pairs are hydrogen bonding and size. This likely occurs due to insufficient penalization of buried polar groups during the design protocol, resulting in over-predicting polar amino acids at buried positions and therefore incorrect predictions of covariation mechanism.
To quantify how often the same covariation mechanism is used for specific pairs of positions in the designed and natural sequences, we calculated the percent of pairs sharing the same classification type in both the natural and designed sequences (percent overlap) for each type of covariation mechanism (Figure 6C). Covariation due to charge has the highest percent overlap between the designed and natural pairs, followed by hydrogen bonding, size, other (hydrophobic) and other (mixed), which have roughly equal percent overlaps. Covariation due to cation-pi and pi-pi interactions have relatively low percent overlaps between the designed and natural sequences, likely due to the fact that these types of interactions are not explicitly accounted for in the design energy function. We repeated this analysis using fixed backbone design sequences and found a decrease in the percent overlaps for size and other (hydrophobic) interactions, indicating that backbone flexibility may aid in modeling these types of covariation mechanisms (Figure S5). Taken together, this analysis provides insights into the mechanisms underlying amino acid covariation in naturally occurring proteins. Overall, the analysis shows considerable agreement between naturally occurring and designed covariation mechanisms. In some cases, it exposes pathologies in the design methods (such as the over-representation of polar amino acids in cores under-representation of cation-pi and pi-pi interactions) that can be addressed in future work using naturally occurring covariation as a reference point.
While computational protein design can model a significant fraction of naturally occurring covarying amino acid pairs, there remain pairs of amino acids that are highly covarying in the natural sequences but not in the designed sequences (nature-specific pairs). Moreover, there also exist pairs that highly covary in designed sequences but not in natural sequences (design-specific pairs). Figure 7A shows the classification of nature-specific, design-specific and overlap pairs for the SH3 domain. To understand the basis for these differences, we first compared these sets of pairs based on their distances in three-dimensional structure (Figure 7B). We found the design-specific and overlap covarying pairs to be significantly closer in structure than the nature-specific pairs. These results are consistent with the all-atom energy function used for generating the design sequences, which is most sensitive at short distances. The long distances in the nature-specific pairs could result from a number of factors, including interactions that bridge monomers in an oligomeric complex [37], interactions that exist in alternative conformations [37], long-range correlations in protein dynamics or from phylogenetic bias in the natural sequences. Another possibility is that in naturally occurring proteins, destabilizing substitutions (that occur in functional sites) co-vary with compensating stabilizing mutations in the protein that could be far away from the functional site.
In addition to analyzing design-specific and nature-specific pairs with respect to pair distance, we compared them based on extent of amino acid burial, the presence in interfaces or active sites, and amino acid pair propensity. We observed a slight decrease in the percent of exposed pairs in the designed-specific pairs relative to the nature-specific pairs (Figure S6), which may be due to the difficulty of accurately modeling solvent exposed interactions in protein design. We observed no difference in the design-specific and nature-specific pairs with respect to their presence in interfaces or active sites (Figure S7), suggesting that the constraints imposed by known functional sites are not responsible for the inability to model the nature-specific pairs. We observed that the amino acid pair propensities of nature-specific and overlap pairs were different, while the amino acid pair propensities of design-specific pairs were highly correlated to those of the overlap pairs (Figure 7C). The latter observation indicates that the energetic interactions leading to design-specific and overlap pairs may be similar to each other. A simple explanation may be that the design-specific pairs are equally compatible with the given protein structure, but may simply not have been sampled by nature. Such design-specific pairs may provide opportunities for engineering proteins with novel amino acid interactions, such as re-designing the specificity of protein-protein interactions.
Our study tested the hypothesis that the structural constraints imposed by protein architecture are a major determinant of amino acid covariation in naturally occurring proteins. If true, we reasoned that computational design methods that design sequences based on protein structure alone should be able to recapitulate amino acid covariation, provided that design predictions are sufficiently accurate. Confirming these ideas, we found a significant overlap between amino acid covariation in natural and designed protein sequences across a set of 40 diverse protein domains. These results quantify the influential role of the selective pressures for maintaining protein structure on shaping amino acid covariation. Therefore, even though correlated changes are undoubtedly important to evolve new activities and regulatory mechanisms [30], [31] the presence of covariation alone may not necessarily indicate a functional role.
Our study also illustrates how recapitulation of amino acid covariation serves as a stringent test for the ability of computational protein design methods to capture precise details of interactions between amino acids. We demonstrate that modeling backbone flexibility significantly increases the similarity between natural and designed covariation, and that this similarity is exquisitely sensitive to the mechanism used to model backbone changes. These findings indicate that protein backbone motions are required for allowing precise adjustments in amino acid interactions that enable covariation. Moreover, simulations that perform local backbone movements (Backrub and KIC) result in sequences with more natural-like covariation than simulations that perform non-local backbone movements (AbRelax, Relax, Small). Proteins may have undergone local motions similar to Backrub and KIC moves to accommodate new mutations and amino acid interactions during evolution [24], [35], [36], [41]. Such motions could have provided proteins with a mechanism to allow subtle, incremental changes to their structures without adversely affecting protein structure or protein function.
While local motions may be a common mechanism for proteins to accommodate point mutations, larger structural adjustments may be necessary for dealing with insertions or deletions. In this study, we found that a moderate degree of backbone flexibility best recapitulated natural amino acid covariation, however, the magnitude of structural variation produced by this degree of backbone flexibility was less than the structural variation among naturally occurring protein families.
This discrepancy is likely due to the assumption in the design method that the protein remains a fixed length. This is not true in naturally occurring sequences; in fact, all 40 domains in our benchmark include loop regions that have varying lengths. Mutations that change the length of a flexible loop could allow for secondary structure elements to re-orient themselves and slightly alter the tertiary structure. The accumulation of mutations in loop regions can produce significant structural diversity that cannot be modeled using a protein design method that keeps the number of amino acids in a protein constant. Future protein design methods, particularly those involving loop regions such as protein-protein interaction design or enzyme specificity design, could potentially benefit from incorporating moves that both change the conformation and length of the protein backbone.
In addition to observing significant similarity between the sets of natural and designed highly covarying amino acid pairs, we observed a high correlation in the amino acid propensities of these covarying pairs and showed that the structural mechanisms underlying covariation are similar for both natural and designed sequences. Differences between natural and designed covarying pairs highlight areas for improvement in the energy function used for protein design. For instance, cation-pi interactions, which are not explicitly accounted for in the energy function used in this study, have high propensities among naturally covarying pairs but not in designed covarying pairs. Similarly, polar amino acid pairs are more frequent in the cores of designed proteins than in naturally occurring proteins. Interestingly, we found differences in the pair propensities between nature-specific pairs and pairs that highly covary in both natural and design sequences. We also observed that nature-specific pairs tend to be more distant in three-dimensional structure. These results have implications for the field of contact prediction, as combining amino acid covariation with amino acid pair propensity information could improve the prediction of three-dimensional contacts in protein structures compared to using amino acid covariation alone. Improving methods of contact prediction would increase the accuracy of recent protein structure prediction algorithms that use amino acid covariation [29].
Unlike nature-specific pairs, design-specific pairs have amino acid propensities that are highly correlated with the amino acid propensities of pairs that covary in both natural and designed sequences. These design-specific pairs represent candidate positions for engineering amino acid interactions that have not been sampled by natural protein evolution. A practical application of this is the re-wiring of protein interaction specificity to design orthogonal protein-protein interactions for use in synthetic biology. Natural intermolecular covariation has previously been exploited to alter specificity in two component signaling systems [42]. Future work could exploit designed intermolecular covariation to re-engineer protein interactions with novel specificities that are orthogonal from naturally occurring protein-protein interactions [43] and therefore useful for synthetic applications.
The protein domains used in this study were selected from the Pfam database [44] based on the following criteria: 1) at least one crystal structure of a protein containing the domain was available from the Protein Data Bank (PDB) [45], 2) at least 500 sequences of proteins from the domain were available from Pfam and 3) the domain was equal to or less than 150 amino acids in length. We selected a total of 40 domains that represented a diverse set of protein folds (Table 1). The seed alignment and the full alignment for each domain were obtained from Pfam. In order to remove highly divergent sequences with uncommon insertions or deletions, we first removed sequences from the seed alignment if they had either of the following: 1) a gap in a position where 90% of the sequences in the seed alignment did not have a gap or 2) an amino acid in a position where 90% of the sequences in the seed alignment had a gap. Next, we aligned each sequence in the full alignment to the seed alignment using MUSCLE [46] and we discarded any sequences that resulted in the creation of gaps that were not in the seed alignment. This resulted in an alignment without sequences containing uncommon insertions or deletions. Finally, we used CD-HIT [47] to filter the sequence alignments by removing sequences with 80% redundancy or greater.
For each of the 40 protein domains, the highest resolution crystal structure of the domain was obtained from the PDB. This structure was used as a template for all the design simulations. The design method used in this study consisted of two steps: 1) the generation of a conformational ensemble and 2) the design of sequences onto each structure in the ensemble using RosettaDesign. For each of the 40 domains, 500 structures were generated for the conformational ensemble and 500 sequences were designed, one for each structure in the ensemble. Descriptions of each protocol used for generating conformational ensembles and for designing sequences are provided in Text S1 along with the corresponding Rosetta command lines.
Amino acid covariation was quantified using a mutual information based metric called Zpx [28]. First, the Shannon entropy is calculated at each position i as follows:where Px is the frequency of amino acid x at position i. The joint entropy is calculated between all pairs of positions as follows:where Px,y is the frequency of amino acid x and y and positions i and j, respectively. The mutual information (MI) between each pair of columns in a multiple sequence alignment, i and j, was calculated as the difference between the individual entropies and the joint entropy:Next, the background mutual information due to random noise and shared ancestry is subtracted to obtain the product corrected mutual information (MIp) [27]:where is the mean MI of position i with all other positions and is the overall mean. This value is converted to two Z-scores, one for each column, which are multiplied together:The final score, called Zpx, is the square root of the absolute value of . If is negative, then Zpx is multiplied by −1. This normalization of MIp was demonstrated to reduce the sensitivity to misaligned regions in multiple sequence alignments, which otherwise result in artificially high mutual information scores [28]. Calculation of Zpx was implemented in Python. Direct coupling analysis (DCA) was calculated using Matlab code provided by its authors [37].
To compare amino acid covariation between natural and designed multiple sequence alignments, Zpx was first computed for all pairs of ungapped positions in each alignment. The mean Zpx for each alignment was calculated and residue pairs with values greater than two standard deviations above the mean Zpx were considered to be covarying residue pairs. The covariation similarity between the natural and designed covarying amino acid pairs was calculated as the percent of overlap, 2C/(A+B), where A and B are the total numbers of natural and designed covarying pairs, respectively, and C is the number of pairs that covary in both natural and designed sequences. The same approach was used to calculate covariation similarity using DCA.
Sequence recovery was calculated as the mean percent identity of the designed sequences to the sequence of the crystal structure used as input for the design protocol. Sequence entropy was calculated for each position as defined above. Sequence profile similarity was calculated as the mean prof_sim score [48] between each position in the natural and designed alignments. Briefly, prof_sim is the product of two scores: 1) the estimated probability that two amino acid frequency distributions represent the same source distribution and 2) the a prior probability of the source distribution. Using this metric, positions in designed sequences receive high prof_sim scores if both 1) their amino acid distribution is similar to the amino acid distribution at the corresponding position in the natural alignment and 2) their amino acid distribution is different than the background amino acid distribution. Calculation of sequence recovery, entropy and profile similarity was implemented in Python.
Structural variation was calculated as the mean pair-wise RMSD between 10 randomly selected structures in each conformational ensemble. Natural structural variation was computed for all domains with at least 10 crystal structures in the PDB. The following 20 domains were used to compute natural structural variation: PF00013, PF00018, PF00041, PF00072, PF00076, PF00085, PF00111, PF00168, PF00169, PF00179, PF00254, PF00355, PF00439, PF00550, PF00581, PF00582, PF00595, PF01833, PF07679, PF07686. Structural alignments and RMSD calculations were performed using PyMol [49].
Amino acid pair propensities (PP) were calculated as the ratio between observed pair frequencies and the expected individual amino acid frequencies:To compare amino acid pair propensities between two sets of covarying pairs, we computed the Z-score for each pair amino acid pair x,y. The Pearson correlation coefficient r between the two sets of Z-scores was then calculated using R [50]. Cysteines were excluded from this analysis because they rarely appear in the designed sequences.
To classify the mechanisms of covariation for a pair of positions, we first computed a correlation coefficient for each amino acid pair x,y [32]. We then calculated a score for all possible amino acid pair transitions (PT) between one pair x,y and another pair a,b as follows:This pair transition score quantifies the significance of the transition between the amino acid pair x,y and the pair a,b. The most significant transitions are defined as those that highly favor pairs x,y and a,b but highly disfavor pairs x,b and a,y. For each pair of positions, ten pair transitions with the greatest scores were assigned one of eight classes in the following order: charge, cation-pi, pi-pi, size, hydrogen bonding, other (hydrophobic), other (hydrophilic) and other (mixed). Charge transitions involve a pair with opposite charges that either swap sign or become uncharged. A charge transition is also assigned to pair transitions that avoid like charges, for example, if x and b (or y and a) are like charges. Cation-pi transitions involve one pair with a potential cation-pi interaction but no cation-pi interaction in the other pair. Similarly, pi-pi transitions involve one pair with a potential pi-pi interaction but no pi-pi interaction in the other pair. Size transitions involve a decrease in the size of one amino acid by at least 18 Å3 (the volume of a methyl group) and an increase in the size of the other amino acid by at least 18 Å3. Hydrogen bonding transitions involve a potential hydrogen bonding interaction (hydrogen bond acceptor and donor) in one pair but not in the other pair. The three other classes are used to assign pair transitions that do not fit any of the above criteria. Other (hydrophobic) transitions are those where both pairs contain only hydrophobic amino acids, other (hydrophilic) transitions are those where both pairs contain only hydrophilic amino acids, and other (mixed) transitions are those with both hydrophobic and hydrophilic amino acids. Similarity between natural and designed was quantified using the percent overlap (defined above) for each covariation mechanism.
Amino acid burial was defined for each position based on the number of Cβ atoms within 8 Å of the Cβ atom of the given position as follows: exposed 0–8, intermediate 9–14 and buried >14. For the covariation mechanism analysis in Figure S4, we defined pairs of positions that were buried/buried or buried/intermediate as buried pairs, exposed/buried or intermediate/intermediate as intermediate pairs, and exposed/intermediate or exposed/exposed as exposed pairs.
For domains with known protein–ligand or protein–protein interface information, we defined all positions with a heavy-atom within 6 Å of any heavy-atom on the binding partner as an interface position. The domains with interface information were PF00013, PF00439, PF00498, PF00691, PF00072, PF00018, PF00076, PF00249, PF00327, PF01035, PF00169, PF00550 and PF00595. For domains with known active sites, we defined all positions with a heavy-atom within 6 Å of any heavy-atom on a catalytic residue as an active site position. The domains with active site information were PF00085, PF00111, PF00355, PF00708, PF00581, and PF01451.
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10.1371/journal.pntd.0004745 | We Remember… Elders’ Memories and Perceptions of Sleeping Sickness Control Interventions in West Nile, Uganda | The traditional role of African elders and their connection with the community make them important stakeholders in community-based disease control programmes. We explored elders’ memories related to interventions against sleeping sickness to assess whether or not past interventions created any trauma which might hamper future control operations. Using a qualitative research framework, we conducted and analysed twenty-four in-depth interviews with Lugbara elders from north-western Uganda. Participants were selected from the villages inside and outside known historical sleeping sickness foci. Elders’ memories ranged from examinations of lymph nodes conducted in colonial times to more recent active screening and treatment campaigns. Some negative memories dating from the 1990s were associated with diagnostic procedures, treatment duration and treatment side effects, and were combined with memories of negative impacts related to sleeping sickness epidemics particularly in HAT foci. More positive observations from the recent treatment campaigns were reported, especially improvements in treatment. Sleeping sickness interventions in our research area did not create any permanent traumatic memories, but memories remained flexible and open to change. This study however identified that details related to medical procedures can remain captured in a community’s collective memory for decades. We recommend more emphasis on communication between disease control programme planners and communities using detailed and transparent information distribution, which is not one directional but rather a dialogue between both parties.
| African elders are recognized by their communities as important traditional leaders. This role gives them an influential position, which is commonly overlooked by disease control programmes. We focused on sleeping sickness a disease which has a long history of control interventions in our study location in north-western Uganda. We interviewed elders to explore their memories of past interventions. This is important because negative perceptions of past interventions could influence how communities perceive control programmes today. Interviewed elders described sleeping sickness control interventions dating from the 1960s and more recent interventions from 1990s. Invasive diagnostic procedures, toxic side effects of treatment and long hospitalization were remembered from the later interventions. Despite these negative experiences, elders, however, observed recent improvements in treatment and had no negative perceptions of sleeping sickness control programmes. We conclude that community experience with control programmes remains in memories for decades, and we recommend the involvement of elders in planning of these interventions. This would be particularly beneficial because they are aware of the historical contexts of disease control in their environment, have insights into socio-cultural aspects of their communities and may serve as spokespersons between beneficiary community and programme implementers.
| All cultures around the world promote a normative respect for their elderly populations. Elders are often sought for their advice on life wisdoms in a rapidly changing world [1]. In addition, African elders have always been awarded with authoritative roles as leaders in social affairs, mediators in disputes, marriages, funerals and rites of passage rituals. Elders in traditional African societies have also been respected as agents possessing supernatural powers, manifested as links with ancestors’ spirits, and in healing and fortune telling skills [2–4].
This special role makes African elders influential agents in rural communities. In a rapidly changing African social and physical environment, they serve as a link between past and future and are therefore sometimes recognized as essential partners in health programmes. In Uganda, for instance, the UN liaised with local elders in efforts against female genital circumcision [5]. In Kenya, elders equipped with mobile phones contributed significantly towards the documentation of new-born birth weight [6]. There are also numerous studies reporting an impact of elders, who serve as guardians of HIV positive orphans, on management of paediatric HIV (for instance [7]).
Senior members of a community also act as a living memory of the past. An oral tradition is still a widely used way of preserving knowledge and cultural identity in rural Africa. The richness of this source has been recognized and documented in the context of historical- and development-oriented research [8–10], however this approach has seldom been used in disease control programmes. Exploring the collective memory of communities that participated in disease control programmes is especially insightful in cases of prolonged and potentially traumatic interventions.
One such example is sleeping sickness (human African trypanosomiasis; HAT), a parasitic disease, caused by trypanosomes and transmitted by tsetse flies (Glossina). There are two forms of HAT: the acute rhodesiense form occurs in south east Uganda; the second type is the slowly developing gambiense form, which is endemic in the north west of Uganda, where this study was conducted. Both forms in the initial stage (first stage) of disease cause symptoms such as headache, fevers, and general fatigue, but then progress to a second stage where neurological symptoms such as aggression, delirium, hallucinations and disturbed sleeping patterns may be exhibited. Due to these unique manifestations, there is clear historical documentation of the disease occurring in Africa, during the time of the slave trade, along with information on how patients were treated. Captured slaves, for instance, who exhibited the signs and symptoms of sleeping sickness were whipped for their “laziness” [11]. Furthermore colonial texts documented the imposition of numerous methods of sleeping sickness control measures on local African populations. In Uganda, these methods included establishment of check points and control of human migration in and out of sleeping sickness areas; treatment with toxic experimental drugs, such as the arsenic based atoxyl and strychnine; formation of segregated treatment camps [12, 13] and forced vegetation clearance along rivers to control tsetse [14]. The territory was strictly administered and penalties were imposed for those who failed to respect the rules [15].
The last major HAT epidemic in the history of Uganda was attributed to the influx and re-settlement of refugees from infected areas of South Sudan back into northern Uganda in the 1990s [16, 17]. After Uganda achieved independence in 1962, sleeping sickness epidemics were mostly controlled by international NGOs, such as Médecines sans Frontières (MSF), which in collaboration with the local governments established active screening campaigns in affected areas and treated patients in the local hospitals [18, 19]. No records, however, exist on how communities reacted to these interventions. Although communities in Uganda witnessed a number of HAT epidemics and were involved in numerous control programs, there is little attention in the literature to their perspectives. By examining elders’ memories in this study we aimed to explore what memories have been preserved in relation to colonial HAT control measures and more recent interventions implemented by MSF. We are not aware of any other attempt to document collective memory of HAT affected communities in Uganda. The study is especially relevant in the context of new diagnostic tools and treatment of HAT, which are expected to be introduced in the next couple of years [20–22]. Understanding of the community experiences of previous programs may greatly impact on how these new tools are accepted and utilized.
The main objectives of this study were to i) evaluate what experience is preserved in the memories of elders in relation to sleeping sickness; ii) assess if any of the memories are associated with collective trauma and; iii) determine if past trauma can be neutralized by more positive experiences with interventions against HAT. With this study we aimed to contribute towards better communication between providers and beneficiaries of HAT control programs and ultimately to support HAT elimination efforts.
The study protocol and procedures for obtaining participants’ consents were approved by the Research Ethics Committee of Liverpool School of Tropical Medicine (ref: 11.73) and Uganda National Council of Science and Technology (UNCST) Ethics Committee (ref: SS-2561). Local district and sub-county administrative authorities and village chiefs were informed about the study and their permission sought prior to data collection. All participants were informed about the study, and encouraged to ask questions; their voluntary participation and right to withdraw from the study were emphasised and their written consent was obtained. In case of illiterate participants a fingerprint was collected in front of a literate witness. Consent was also obtained for the use of photo materials. Signed or finger-printed consent forms are stored securely at the offices of the LSTM tsetse research project, Arua, Uganda.
Despite a decrease in the number of new HAT cases globally, 459 of cases of gambiense HAT were reported from Uganda between 2008 and 2012 [23] and West Nile still represents an important focus in efforts to eliminate HAT [24]. MSF-France, which responded to the last major HAT epidemic in West Nile in the 1990s, established the treatment centre in Omugo [18] which is still one of the main facilities in the region for diagnosis and treatment of HAT. Village and sub-county health centres and district hospitals are referral points for diagnosed or suspected HAT cases.
The study was conducted between July 2011 and March 2012 in rural areas of Arua and Maracha Districts (within the coordinates: 03°09’27.92”-03°12’16.57”N, 30°51’06.00”-30°55’01.68”E). Districts in Uganda are composed of sub-counties, which are organizational units joining several villages. The study villages were purposively selected based on their location in relation to known local HAT foci. Four villages were selected in an area where, in 2010 (i.e. within 12 months of the study) Médecins sans Frontières (MSF) conducted active screening and still detected HAT cases (HAT foci; HAT+). The other four villages were selected from an area where, based on medical records, no HAT cases have been reported prior to MSF campaigns (non-HAT foci; HAT-) and have therefore been excluded from active screening [19]. These villages were selected to compare if community memories of HAT and control interventions differ from those with more recent experiences with screening and treatment campaigns.
The predominant ethnic group in this area is Lugbara and main religious orientations are Christian (Roman Catholic and Protestant) and Muslim. Communities gain their livelihoods through small scale farming. They mostly plant food crops such as cassava, beans, maize and sweet potatoes and breed goats, cattle and sporadically pigs. Tobacco is planted in some areas as a cash crop. The structure of the villages is arranged in traditional household units with older and younger generations of relatives living in separate huts and sharing the same compound.
In-depth interviews were conducted face-to-face with 24 elders from eight villages in their homes. The interviews were conducted by the corresponding author (VK; PhD; medical anthropologist; female) and a Ugandan female interpreter. None of the participants were fluent in English; all interviews were therefore conducted in the local language (Lugbara) by directly translating questions posed by VK and answers provided by participants back to English by interpreter. The interpreter received training in interviewing and interpretation techniques prior to the data collection process.
Participants were selected using the snowball sampling technique [25]: starting with an initial small sample of elders in each location, we subsequently asked elders to identify others who may be willing to participate in interviews. This approach led us from one elder to another in each village and finally we interviewed eight women and fifteen men until we reached the data saturation point [25]. The selection criteria was not based on participants’ age but on communities’ own recognition of their members as ‘elders’, to ensure that the definition of ‘elder’ corresponded with community understanding of this role. The average reported age of the participants was 71 years; however most of them were unable to state their exact year of birth. Interviews were conducted using an interview guide (S1 Text) with several general open-ended questions, and probes to obtain further details on their memories of HAT and disease control interventions. The interview guide was pretested; questions and probes were discussed and adjusted with interpreter to obtain the closest interpretation to the original meaning of the questions. All discussions were recorded on a digital voice recorder and VK wrote down the field notes. After each interview the corresponding author and interpreter conducted a debriefing session to compare observations they obtained during the interviewing sessions and discuss data saturation.
Audio files were later transferred to a computer and transcribed into Word documents by the interpreter, supervised by the corresponding author. All transcripts were read several times by VK in order to develop codes. Maxqda software [26] was used for coding transcripts and organizing them into a matrix, consisting of an Excel spreadsheet with quotes corresponding to codes. Each sheet was then analysed and validated by VK and HS using a thematic analysis approach to identify main themes and describe differences between participants from HAT and non-HAT foci. The most commonly reported patterns of opinions, as well as opposing views, were identified in the process.
Six main themes were identified in the analysis of the interviews with participants, and each is discussed under a separate sub-heading. Participant quotes are used to illustrate the meaning of each theme (Boxes 1–6).
As soon as sleeping sickness was mentioned to the participants in relation to the past, many of them lifted their hands to the side of their necks, their gestures indicating examination of lymph nodes (Fig 1). Regardless of the location of the village in HAT-foci (HAT+) or non-HAT foci (HAT-), all participants remembered neck examination and many of them participated in this type of testing (box 1). These memories are drawn from the early 1940s to 1960s period, when most of them were children but some from the younger generation referred to the stories they heard from their parents. Mostly, they did not know at the time what disease they were tested for and indicated that they were informed about sleeping sickness when ‘whites [white foreigners] arrived in the area. Some mentioned that the disease was called ‘gland’ and one participant thought they were testing two different diseases: ‘gland’ and ‘sleeping sickness’.
Elders (male and female participants) from both areas (HAT+ and HAT-) had vivid memories of the severity of sleeping sickness and many remembered the last epidemic in the 1990s and attributed it to the migration of refugees from South Sudan. In contrast, participants who do not live in a current HAT focus (HAT-) reported that HAT had not been a major problem for them, even in the past.
Both male and female participants, but only from HAT+ areas, described how sleeping sickness was associated with traditional causes of disease. Some commented that this association was common before health information was distributed by foreigners. Other participants however, indicated that HAT symptoms were associated with traditional causes in recent times as well. Describing, for instance, treatment choices of their sick relatives, diagnosed for HAT in the 1990s, they explained, that both: biomedical and traditional treatment was used. HAT symptoms were interpreted as traditional disease after medical treatment, which was used as the first option, failed to improve health of their relatives. Two traditional categories of disease were mentioned in relation to HAT: ‘witchcraft’ and ‘poison’. Witchcraft was associated with social tensions within the clan and capacity of elders to ‘curse the person and bring sickness over them’. Sacrifice of a goat and sharing meat with the elder, identified as causative agent, was described as a way to ‘settle the matter’, by one participant. ‘Poison’ was described as an evil deed of somebody ‘ill wishing’. Participants explained that ‘poison’ can be diagnosed and treated by traditional healers who ‘wash the patient’ with engine oil to confirm traditional diagnoses and then treat them by traditional means.
The elders, from both areas (HAT+ and HAT-) recalled that mobilisation for ‘gland examination’ happened once a year and was carried out by medical teams who moved from parish to parish. Villagers were informed about the time and place of gathering by government representatives. Participants commented that testing was compulsory and the entire village, including children, gathered. Families lined up for neck examination, their names were called, and the sides of their necks checked manually. Participants recalled that positive cases were separated from negative ones. Those who were young children at the time or were not diagnosed as positive had no recollection as to what happened with the positive group. Most of the participants from non-HAT foci speculated that positive cases were injected on the spot and then sent home. In HAT foci some participants remembered a treatment camp where there were houses built of clay and where people were treated (injected) at the same site where they were examined (box 3). Most participants recalled that in recent times, patients were transferred to Arua hospital.
According to both, male and female participants, sleeping sickness testing was mandatory. In cases of non-compliance, there were different sanctions in place including interrogation, imprisonment, levy of fines and physical punishment. Despite these disciplinary measures, the vast majority from both areas reported that they willingly participated; they did not report or remember any fears associated with testing or treatment. Positive attitudes towards examination were also associated with the non-invasiveness of the neck examination which did not require collection of blood samples. They associated the examination with offers of help, also a positive association and perception. Some participants reported their childhood memories as being associated with fear of white foreigners and injections, but none of them reported their parents being fearful. Many participants remembered that the fear was not of examination but rather that the fear among adults was that they would get the disease and not be able to receive treatment.
Participants who were not from a HAT focus stated that after the period of ‘gland examination’, no more medical interventions related to HAT were carried out in their area. Thus, only memories from inhabitants living in HAT foci are reported. Participants remembered that after independence in 1962, there was a period without any intervention until MSF France started their campaign in the late 1980s and 1990s at the peak of the epidemic with active village-based screening and treatment of patients in regional hospitals. The organization of active screening and spread of information was in elders’ memories, which echoed those of earlier times. Village chiefs were involved in informing the village members and they ensured that villagers were present at the screening (box 4).
Both male and female elders reported that the use of new medical approaches created some reluctance among community members. At the time, this reluctance was associated with the length of treatment, side-effects of treatment and drug resistance, and high patient mortality. One participant was convinced that in the old days, people could live with HAT and it would just ‘make them dull’, but that once treatment was provided, the disease ‘changed form’ and caused people to die. Many negative associations were also related to diagnostic procedures, such as collection of blood samples and lumbar puncture and rumours associated with them that caused suspicion. Blood collected during diagnostic procedures, for instance, was believed to be collected and sold for transfusion purposes. One participant told of a rumour that the medical team was believed to be involved in the release of tsetse in order to gather people for testing bloods; then they could use the blood collected to sell it. However, despite these complaints, many of the elders reported that most of the community still participated in active screening.
The second medical campaign, carried out by MSF Spain (2010–2011) is remembered differently. Elders form HAT focus areas (HAT+) where MSF campaigns were carried out noticed improvements in treatment, which seemed to increase community trust and reduce general reluctance towards medical interventions (box 5). Particularly male participants noticed that durations of hospitalization were shorter, there were no relapse cases, treatment had fewer side-effects and that no deaths occurred in the process. The new treatment regime, i.e. Nifurtimox / Eflornitine combination therapy, was indeed introduced in the second MSF campaign [19]. Most participants declared that the community response to testing was better during this campaign, and also that fewer cases were diagnosed compared to the previous intervention period. They also reported that there were attitudinal changes within the community itself, having already been sensitized in previous campaigns. Also, many participants had personally witnessed development of the disease in their family members and were therefore aware of the importance of testing and treatment. In general, participants reported that negative associations and fear were lower during the last treatment campaign.
Only participants from the current HAT foci remembered organized activities related to tsetse control. Both male and female participants remembered cutting vegetation of the river banks—an activity which was organized from the 1950s until Ugandan independence (1962). Male participants remembered more details about these activities. Some of them reported that their parents or relatives participated in land clearance. Clearance of vegetation along river banks was first organized as forced voluntary labour, but was later transformed into paid employment. Re-settlement from the river banks to higher level sites in the village was also reported, but one of the elders commented that this was done willingly by community members, who were aware of the seriousness of the disease and also not concerned about the land ownership ‘since the land was free’. During this time, information about the link between tsetse and sleeping sickness started to spread within the community. Elders also commented that vegetation clearance significantly contributed towards control of sleeping sickness at the time, and so there appeared to be no negative associations with these interventions.
Elders’ memories, regardless of the location of their village (in or out of the local HAT focus) spanned from examination of lymph nodes in colonial times. More recent active screening and treatment campaigns were remembered only in the villages in HAT focus, which indicates that information about disease control interventions remains localized. Some negative memories dating back to the 1990s were associated with diagnostic procedures, treatment duration and treatment side effects. These memories, however, were combined with memories of the negative impacts related to sleeping sickness epidemics, particularly in HAT foci. More positive associations and memories from the recent treatment campaigns were reported, especially because of their observations regarding improvements in treatment. Similarly, no negative memories were associated with past tsetse control interventions.
This study showed that memories of the elders are well preserved. This is supported by other studies that demonstrated how oral traditions and historical narratives, which are strongly present across the African continent, are passed from generation to generation for several decades [27–29]. It is important to acknowledge that information stored in collective memory influences community responses to disease control programs. A recent study of barriers to HAT testing in DRC [30], for instance, revealed that memories of the previous interventions, particularly prohibitions after treatment, hampered community participation at current active screening. The imposed restrictions, communicated by health workers in the past, aimed to manage adverse effects of treatment with the toxic melarsoprol. This information, however, created taboos, which have been preserved despite the introduction of new drugs for the treatment of second-stage of gambiense-HAT.
On some occasions, community reactions to disease control programs are linked with memories of other, non-related events. One such example is related to Ebola crises in Liberia [31], where a community associated a curfew imposed by the government during the latest Ebola epidemic with the civil war. This caused additional fear and reluctance to adhere to control measures. Hence, careful examination of the context related to both current and past community experiences is needed before programs are implemented.
Our study, however, showed that previous negative memories, were neutralized with more recent positive experiences with HAT interventions, such as the improvements of treatment. This suggests that community attitudes towards disease control programs are prone to change if an opportunity for more positive experiences is provided. An example of positive community attitudes towards tsetse control traps was recorded in the study in West Nile [32]. High acceptance of traps in this study, was related to sufficient information received at the beginning of the intervention and long exposure to traps, which were introduced to the area a decade ago. Community attitudes in these villages were different to the villages in proximity, which were not exposed to the traps previously. When villagers noticed traps deployed during the study and without being informed about their purpose, this caused associations of traps with supernatural powers and fear among community members.
Thus, the assumption that community memory will adapt to the changes in a disease control program merely through observation and without dissemination of information, is unrealistic. The beneficiary community should be the first stakeholder to be informed about the changes occurring in global HAT control strategies, hence transparent communication and frequent dialogue is necessary not only for keeping all the information updated, but also to prevent future negative experience with disease control programs. As shown in our study, negative experiences will remain a part of the collective memory for a long time.
Effective communication with the host communities could be facilitated through engagement of elders and other traditional leaders. Elders, from our observation, are still perceived as authority in rural Uganda. They can therefore break barriers and act as a bridge between program implementers and beneficiary communities. One successful example of the benefit of involving traditional leaders is in HIV/AIDS prevention in Zimbabwe [33]. In this example leaders encouraged behavioural change related to harmful traditional marriage practices which reduced spread of HIV in their communities. Another example, from Botswana, showed how traditional leaders managed to defuse tensions among community members relating to the role of traditional and biomedical practices concerning male circumcision at the beginning of the program. Their initial collaboration, however, turned into resistance due to miscommunication by program staff and lack of leaders’ participatory involvement [34]. Another example of the failed engagement of leaders is a study on community participation and empowerment in an HIV/AIDS program [35], which showed that a single leader can make the difference between program success and failure. In this HIV/AIDS initiative, the village chief’s beliefs about community values and traditional roles of women completely de-stabilized the program, and as a result, the fundamental program objectives were not achieved. Hence engagement of traditional and other leaders at the beginning of intervention is crucial for successful implementation of disease control programs and their sustainability.
Finally, if disease control programs are not in line with what is acceptable to the beneficiary communities, they will find ways to overcome the obstacles imposed by the program. There is a series of documented evidence on how communities in West Nile rebelled against colonial HAT control measures [36]. The community, for instance, opposed forced re-settlement to HAT free zones and the evacuated people later moved back to their homes [36]. Imposed control in colonial times through a system of permits and passes to limit spread of HAT through human migration, resulted in people traveling through uncontrolled bush tracks. Furthermore, people moved away from water holes and washing places cleared of vegetation to control tsetse, and in their search for privacy started using parts of the river which were left out in the vegetation clearance efforts. Morris [36] suggests that these coercive measures and the responses they promoted from the local communities, served to reinforce the spread of infections rather than preventing the spread of HAT (ibid.).
Some more recent studies from other disease control programs show similar trends. When expectations from the program to control HIV/AIDS [37], for instance, were considered unrealistic by the HIV positive participants in Homa Bay (Kenya) they engaged in a series of different strategies to overcome obstacles. HIV positive patients managed to navigate between expectations of their social environment, which would have been challenged if they strictly followed the rules of the HIV control program and still appeared adherent to the program. The recent Ebola crisis in West Africa also provides examples of mismatch between local realities and imposed control measures resulting in communities rebelling against the rules and for instance hiding their sick relatives [38]. This misunderstanding and related lack of trust ultimately resulted in exacerbation of the epidemic instead of its control [39, 40]. Listening to the voices of beneficiary communities when planning disease control programs is an obvious but, surprisingly, commonly ignored fact.
In summary, disease control programs are often ignorant to the important and insightful position of African elders. Elders in leadership positions are treated as “gate keepers”, and besides seeking access to the populations they are in charge of, little attempt is made to consult them on other matters. Dialogue with elders and other traditional leaders could have a twofold benefit for disease control programmes: firstly, elders could help gain an understanding of any previous negative experiences with disease control which could hamper implementation of disease control programs; and secondly elders could act as active communicators between program implementers and beneficiary communities.
Elders’ memories may have been affected due to age, so recall bias may have occurred when discussing their memories. Only elders who had the ability to articulate their thoughts were interviewed, which means that those that did not, but who may have had thoughts and memories to discuss, were not included in the study.
Biases may also have occurred during direct interpretation from Lugbara to English which was conducted while carrying out interviews and focus group discussions. To minimize this bias, we carefully selected the interpreter based on their previous work experience as a translator and facilitator. A series of training sessions were organized (data collection methods, ethical principles of research, transcription and translation skills) for our research team before the data collection process was begun. Additional quality control on translation was ensured during the transcription process which was carried out by an independent member of the research team fluent in Lugbara and trained in social sciences.
Consulting elders was a useful framework for capturing collective memories about experiences with past control programs and it reflected current community attitudes towards participation in HAT control. Particularly elders from the villages that experienced more recent HAT control campaigns were knowledgeable about the improvements of treatment in the history of HAT control they witnessed in their villages. We recommend this research framework is used and memories documented before HAT or other community-based interventions are introduced. This will become extremely relevant in the context of introducing new diagnostic and treatment regimens which HAT affected communities are not familiar with.
Furthermore, research in contexts where community trauma is likely to have occurred in the past, such as in early HIV/AIDS interventions or in the recent Ebola crisis in West Africa, may use the same methodological framework with the following steps: i) exploring memories and/or perceptions of past control interventions, ii) acknowledgement, if any, of collective trauma having occurred and iii) discussion with elders and other village leaders on how to prevent potential trauma in the future. We recommend disease control planners to consider the historical context and community perceptions of disease control programs before they launch new disease control interventions.
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10.1371/journal.ppat.1007456 | Thymic expression of IL-4 and IL-15 after systemic inflammatory or infectious Th1 disease processes induce the acquisition of "innate" characteristics during CD8+ T cell development | Innate CD8+ T cells express a memory-like phenotype and demonstrate a strong cytotoxic capacity that is critical during the early phase of the host response to certain bacterial and viral infections. These cells arise in the thymus and depend on IL-4 and IL-15 for their development. Even though innate CD8+ T cells exist in the thymus of WT mice in low numbers, they are highly enriched in KO mice that lack certain kinases, leading to an increase in IL-4 production by thymic NKT cells. Our work describes that in C57BL/6 WT mice undergoing a Th1 biased infectious disease, the thymus experiences an enrichment of single positive CD8 (SP8) thymocytes that share all the established phenotypical and functional characteristics of innate CD8+ T cells. Moreover, through in vivo experiments, we demonstrate a significant increase in survival and a lower parasitemia in mice adoptively transferred with SP8 thymocytes from OT I—T. cruzi-infected mice, demonstrating that innate CD8+ thymocytes are able to protect against a lethal T. cruzi infection in an Ag-independent manner. Interestingly, we obtained similar results when using thymocytes from systemic IL-12 + IL-18-treated mice. This data indicates that cytokines triggered during the acute stage of a Th1 infectious process induce thymic production of IL-4 along with IL-15 expression resulting in an adequate niche for development of innate CD8+ T cells as early as the double positive (DP) stage. Our data demonstrate that the thymus can sense systemic inflammatory situations and alter its conventional CD8 developmental pathway when a rapid innate immune response is required to control different types of pathogens.
| Murine innate CD8+ T cells demonstrate strong cytotoxic capacity during the early phase of certain bacterial and viral infections. Such cells have been reported to be present in both mice and humans but many questions remain as to their differentiation and maturation process. Innate CD8+ T cells arise in the thymus and depend on IL-4 and IL-15 for their development. A description of the cellular and molecular mechanisms involved during their thymic development has been obtained from KO mice that lack kinases and transcription factors important for TCR signaling. In these mice, SP8 thymocytes with an innate phenotype are highly enriched over the conventional SP8 cells. Our work describes, for the first time, that in WT mice, thymic IL-4 and IL-15 expression triggered by Th1 infectious processes induce an adequate niche for development of innate rather than conventional CD8+ T cells. Our data show that the thymus is able to sense a systemic inflammatory response (probably mediated by systemic IL-12 and IL-18 production) and alter its ontogeny when pathogen control is needed.
| The thymus is the primary lymphoid organ where T cell development takes place in the host. In physiological conditions, several T cells lineages arise in the organ including conventional αβT cells, γδT cells, regulatory T cells and NKT cells. Most recently more lineages have been added to the list and these include several types of innate T cells[1–3].
The thymic cellular component not only consists of developing cells, but as reported by our group and other laboratories, a small number of mature peripheral B and T cells normally enter the thymus. Furthermore the number of these mature cells increase under inflammatory conditions[4–7]. In this context, our previous work described that during the acute stage of Th1 inflammatory/infectious processes, e.g. T. cruzi and C. albicans infections or systemic LPS treatment, a number of peripheral mature T cells with an activated/memory phenotype (CD44hi) are able to re-enter the thymus[7]. Moreover, we obtained similar data from mice that systemically express high levels of IL-12 + IL-18, demonstrating that T cells ingress the thymus in a non Ag-specific fashion depending upon a bystander cytokine storm triggered by the inflammatory process rather than to the pathogen itself[7]. However, the number of CD44hi T cells found in the thymus under these Th1 inflammatory conditions is too large to be solely explained by the ingress of peripheral T cells and is more evident in the SP8 subset that is enriched in CD44hi cells. Thus, we speculated that some of the SP8 CD44hi thymocytes might come from internal thymic development as it has been recently reported that SP8 cells with an activated/memory phenotype (CD44hi) normally arise in the thymus as an alternative lineage from conventional SP8 thymocytes[8–13]. These cells have been designated as “innate CD8+ T cells” and could represent up to 10% of total SP8 thymocytes in both C57BL/6 and BALB/c mice[9, 13, 14]. Over the years, innate CD8+ T cells have been further characterized based on their phenotypic and functional properties[15]. During their thymic maturation, innate CD8+ T cells up-regulate CD44 and CD122 expression and also acquire high cytotoxic and cytokine production capacities, while conventional memory T cells adopt these characteristic in secondary lymphoid organs (SLO)[16–18]. Other features of innate CD8+ T cells include 1: they exert their cytotoxic activity in an Ag-independent manner, 2: they highly depend upon IL-15 for proliferation and 3: they are able to rapidly produce interferon-gamma (IFNγ) when stimulated by IL-12 and IL-18 as response similar to that of NK cells[19–21].
Innate CD8+ T cells have been first described in the thymus of mice that lack certain Tec kinases that are important regulators of the TCR signaling cascade that include ITK[12], RLK[11] or the transcription factor KLF2[14]. Currently, they have been described in several other genetically modified mice where the common pathway leads to increased number of invariant NKT cells that express the transcription factor PLZF[22]. In all such models, IL-4 produced in the steady state by invariant NKT cells (or CD4 T cells) is required for SP8 cells to up-regulate the T-box transcription factor eomesodermin (Eomes), that represent one of the featuring markers of this lineage[9, 12, 14, 23].
In our report, we demonstrate a novel “cell developmental pathway” that occurs in the thymus of C57BL/6 WT mice undergoing an acute Th1 systemic infectious/inflammatory process. We provide strong evidence that after infection with 2 different strains of Trypanosoma cruzi, the thymus experiences an enrichment of SP8 with an “innate phenotype”. This phenomenon occurs from conversion of DP thymocytes to innate CD8+ cells and the generation of newly SP8 thymocytes with innate characteristics. Interestingly this effect can be reproduced after systemic induction of IL-12 and IL-18, cytokines both known to be expressed during the early phase of a Th1 infectious process[24–26] suggesting that this developmental change in the thymus could be driven by Th1 cytokines triggered during an infectious process rather than by the pathogens themselves.
Importantly, a human CD8+ T cell subset with similar characteristics to the murine innate CD8+ T cells has been recently described[27]. The fact that these cells are also found in cord blood suggests that in humans, innate CD8+ T cells might also develop in the thymus[27]. The authors hypothesize that human innate CD8+ T cells may play a role in immune defense during the neonatal to early childhood period until an adequate adaptive immune response is established[9, 27].
We have previously demonstrated that migration of mature T cells from SLO to the thymus, that occurs under inflammatory/infectious Th1 processes, is not necessary Ag-driven[7]. However, due to the capacity of T. cruzi to infect the thymus[28], we speculated that specific T cells might be recirculating to the organ as well. To confirm this hypothesis, we performed immunofluorescence staining, as it has been reported that intracellular amastigotes can be observed inside the infected cells[29]. As hypothesized, Fig 1A shows that T. cruzi can infect adherent cells from the thymi that are either CD11b+ cells (thymic Mϕ, Fig 1B) or CD11b- cells with large and oval-shaped features that resemble thymic fibroblasts (Fig 1C). The presence of the parasite in the thymi of infected mice suggests that Ag-specific T cells might be migrating to the organ. Using a tetramer linked to TSKB20, the most important and immunogenic antigen of the Tulahuen strain of T. cruzi[30], we evaluated by flow cytometry the presence of specific T cells in the thymi of T. cruzi-infected WT mice. When we analyzed the SP8 compartment, we observed that the percentage of Ag-specific T cells is higher in the CD44hi cell subset and is very low in the CD44lo cell subset (Fig 1D). This observation is expected as effector/memory T cells express high levels of CD44 after TCR activation[15] (Fig 1D).
TSKB20+ cells represent approximately 25% of the total SP8 CD44hi cell in T. cruzi-infected mice (Fig 1D) and TSKB20 specific T cells are the most abundant T cells during T. cruzi murine infection with Tulahuen strain[30]. Based on these findings, we investigated what may account for the remaining 75% of SP8 CD44hi cells. We hypothesized 2 different possibilities: 1) Non-Ag specific CD44hi CD8+ T cells are arriving to the thymus along with the Ag-specific cells, or 2) A different lineage from conventional T cells could arise in the thymus under these inflammatory Th1 conditions. The latter hypothesis is based on previous reports by several laboratories demonstrating that in mice lacking specific kinases involved in TCR signaling (ITK and RLK KO mice) or the transcription factor KLF2 (KLF2 KO mice), SP8 thymocytes alter their development from the “conventional” to the “innate” lineage[9, 11, 13, 22, 31].
To investigate both options, we first addressed if non-Ag specific cells could account for SP8 CD44hi cells found in the thymus. To avoid a significant alteration in the thymic environment (as we have not identified what signals or cells participate in this phenomenon), we utilized OT-I mice that were not RAG KO. However, we exclusively analyzed cells that were Vβ5+/ OVA-tetramer+ both in control and in T. cruzi-infected mice (Fig 1E and 1F, respectively). When we infected OT-I mice with T. cruzi, we observed an enrichment of CD44hi cells in the SP8 thymic compartment similar to what we detected in WT mice. Moreover, we determined that these cells were OVA specific but TSKB20neg both in uninfected control and T. cruzi-infected mice (Fig 1E and 1F, respectively).
Currently, there are no reports demonstrating that a change in the SP8 lineage commitment could occur in WT mice after an infection. In this context, we asked if SP8 CD44hi cells found in the thymus of T. cruzi-infected mice share characteristics of the innate CD8+ T lineage. To test this hypothesis, we performed a flow cytometry phenotypic analysis based on the consensus markers that are known to be expressed by these cells[10, 15, 32, 33]. A main characteristic of the innate CD8+ T cells is the expression of CD44. In the case of conventional T cells this marker is acquired by activated or memory T cells in SLO; but in contrast, innate CD8+ T cells up-regulate CD44 during their thymic maturation without Ag exposure[9, 13]. Upon gating on SP8 CD44hi cells in the thymi of WT uninfected control or T. cruzi-infected mice (Fig 2A), we evaluated the CD122 and CD49d surface expression markers. According to what has been previously reported for murine innate CD8+ T cells[15], we observed that the bulk population of SP8 CD44hi cells significantly up-regulated the expression of both molecules in T. cruzi-infected mice as compared to the equivalent population in control mice (Fig 2B). In addition to these two phenotypic markers, murine innate CD8+ T cells express high levels of the transcription factor Eomes and exhibit no alteration in Tbet levels. This is contrary to Ag-specific memory cells that up-regulate both factors upon TCR engagement[15, 22]. In our model, we observed that SP8 CD44hi thymocytes in T. cruzi-infected mice significantly increased Eomes but not Tbet expression compared to the same subset in control mice (Fig 2C). Thus, our results demonstrate that an enrichment of cells with characteristics of innate CD8+ T cells occurs in the thymic SP8 compartment after T. cruzi infection.
As the thymi of WT (B6) mice could also contain Ag-specific cells that share most of the markers of innate CD8+ T cells, we infected OT-I mice with T. cruzi and gated on the Vβ5+ cells (we have previously shown in Fig 1 that they all are OVA specific, TSKB20neg cells). Interestingly, we observed that SP8 CD44hi thymocytes express all the features of innate CD8+ T cells suggesting that the innate characteristics are acquired in an Ag-independent process (S1A Fig). Moreover, when we used control and T. cruzi-infected WT along with control and T. cruzi-infected OT-I mice to compare the percentage of thymic SP8 CD44hi and SP8 CD44lo cells, we observed a similar pattern both in the total cell number and percentage of cells. Additionally, and as expected, the absolute cell numbers were lower in OT-I mice when compared to WT mice (S1B Fig).
A fast screening of different Th1 infectious setting demonstrate an enrichment of SP8 CD44hi Eomeshi cells in the thymi of C. albicans-infected mice (S2A Fig) and also a large number of SP8 thymocytes with innate features after infection of mice with a different strain of T. cruzi (strain Y, S2B Fig). Based on these data, we hypothesized that appearance of thymic innate CD8+ T cells could be triggered by systemic levels of the cytokines IL-12 and IL-18. We based this hypothesis on knowledge that these infections induce a strong Th1 inflammatory process, resulting in elevated IL-12 and IL-18 production during the acute stage[24–26]. Furthermore, innate CD8+ T cells are known to constitutively express the receptor for these cytokines, and respond with high IFNγ production, as previously reported[21, 34]. To test this hypothesis, we induced IL-12+IL-18 systemic expression by cDNA hydrodynamic shear[7, 35, 36] and observed an enrichment of thymic SP8 CD44hi thymocytes with all the characteristics of innate CD8+ T cells (S2C Fig).
Next, to determine the origin of these cells, we performed in vivo experiments on T. cruzi-infected B6 mice treated with and without the immunosuppressant drug fingolimod (FTY720) (Fig 3). FTY720 arrests recirculation of T cells by blocking their exportation from SLO. This is due to internalization and degradation of S1P receptors[37]. Fig 3 shows similar percentages of CD44hi SP8 cells in FTY720 treated and untreated mice that share all innate CD8+ T cell markers. This data suggests that most of these cells are of thymic origin.
As previously mentioned, IL-4 is the key cytokine responsible for Eomes induction during thymic differentiation of innate CD8+ T cells[12, 14, 23]. However, another cytokine involved in proliferation and survival of this lineage is IL-15[15, 22]. In fact, it has been reported that innate CD8+ T cells expressed high levels of CD122, the IL2/IL-15 β chain receptor along with IL-4Rα[8–13, 16]. Based on this data, we speculated that SP8 CD44hi cells (CD122hi CD49dhi Eomeshi) found in T. cruzi-infected and IL-12 + IL-18 treatment models should express both IL2/IL-15 and IL-4 cytokine receptors. First, we analyzed IL-15, both cytokine and receptor expression. Fig 4A shows that CD122 is almost exclusively expressed by SP8 CD44hi cells compared to the SP8 CD44lo counterpart. We determined that the high affinity α chain of the IL-15 receptor is also expressed in the thymi of T. cruzi-infected mice (Fig 4B). Interestingly, thymi from IL-12+IL-18-treated mice also express IL-15Rα RNA (Fig 4C). A relevant finding is that IL-15 RNA is expressed in the thymi only in T. cruzi-infected (Fig 4B) and IL-12+IL-18-treated mice (Fig 4D) but not in control mice. Moreover, IL-15 can be further induced after rIL-12+rIL-18 in vitro stimulation of thymi of IL-12+IL-18 cDNA-treated mice but not in control mice (Fig 4E). Next, we evaluated the source of IL-15 in the thymus and found out that IL-15 is expressed in the double negative (DN) thymic compartment (Fig 4F). Moreover, further investigation determined that IL-15 RNA is highly expressed by thymic myeloid CD11b+/CD11c+ cells, a subset of DN cells (Fig 4G).
In the case of IL-4, we first determined that both SP8 CD44lo and SP8 CD44hi thymocytes from T. cruzi-infected mice express IL4Rα chain but at levels significantly higher in SP8 CD44hi cells (Fig 5A). When we analyzed the source of IL-4 in the thymus, we focus on NKT cell since it has been reported by several laboratories as the main source of thymic IL-4[22]. However, we did not want to miss other potential IL-4-producing cells like SP CD4 (SP4) CD44hi cells since it has been reported that innate CD8+ T cells can developed in the presence of PLZF+ CD4+ thymocytes[23]. In the case of SP4 CD44hi cells, we have previously reported that they are present in the thymi of T. cruzi-infected mice[7] and their absolute number are higher than in control mice (Fig 5B).
Then we decided to sort NKT and SP4 CD44hi cells from thymi of control and T. cruzi-infected mice and determine the functional capacity to produce IL-4 after PMA/Ionomycin in vitro stimulation (see gate strategy in S3 Fig). Interestingly, data from Fig 5C demonstrated that thymic NKT and SP4 CD44hi cells from T. cruzi-infected mice are much higher IL-4 producers than the same population from control mice. To determine if PLZF is involved in IL-4 production in these cells, we performed intracellular IL-4 and PLZF staining. Unfortunately, we could not stimulate the cells with PMA/Ionomycin due to TCR downregulation in NKT cells and the loss of CD1d tetramer detection after activation as previously reported[38]; however, we could confirm that in the thymi of control mice, the most important source of IL-4 was NKT PLZF+ cells as reported by several laboratories[22]. Surprisingly, in T. cruzi-infected mice, NKT PLZF+ cells cannot be detected (Fig 5D), although they were able to produce much larger amounts of IL-4 than NKT cells from control mice as shown in Fig 5C. Also, in T. cruzi-infected mice we detected a larger percentage of IL-4+ SP4 CD44hi that are also negative for PLZF (Fig 5D). These results demonstrated that induction of the two relevant cytokines involved in innate CD8+ T cell development/survival/proliferation occurred locally in the thymi of mice undergoing a systemic inflammatory/infectious Th1 process. Moreover, after the systemic infectious process, thymic IL-4 was produced by multiple sources that are different from the ones in control mice.
We next evaluated the biological effects of IL-4 and IL-15 in innate SP8 thymocytes. When we cultured thymocytes from T. cruzi-infected mice, we observed an overall survival of SP8 cells only when stimulated with recombinant IL-4 (rIL-4) or rIL-15 but not with rIL-12+rIL-18 (Fig 6A). Moreover, we observed a significant increase in the percentage of SP8 CD44hi cells after rIL-4 and rIL15 stimulation (Fig 6B). Even though SP8 CD44hi cells proliferate under non-stimulated (NS) conditions, the proliferative rate significantly increased in the presence of rIL-4 or rIL-15 in both control and T. cruzi-infected mice (Fig 6C). We next determined whether IL-4 and IL-15 are able to induce IFNγ production in this lineage, and we observed only a moderate increase in IFNγ in the bulk population of cells from T. cruzi-infected mice but not in control mice Fig 6D). As an additional experimental control, we stimulated the bulk population of the same thymocytes with IL-12+IL-18 and observed high levels of IFNγ largely due to the fact that they contain NKT, CD8+ and CD4+ CD44hi cell types known to be high producers of this cytokine (Fig 6D). When we performed a similar experiment using thymocytes from OT-I T. cruzi-infected mice, we determined that IL-4 and IL-15 are able to induced robust proliferation equal to the polyclonal population of thymocytes (Fig 6E).
It has been demonstrated that innate CD8+ T cells have a potent TCR-independent cytotoxic activity that involves granzymes and perforins release and NKG2D receptor-driven killing activity [19, 39–41]. Moreover, these cells play an important role during the early control of certain bacterial and viral infections[20, 21, 39, 42, 43]. In this context, we evaluated the expression of NKG2D in the SP8 compartment of control and T. cruzi-infected mice. Interestingly, NKG2D is highly up-regulated in SP8 CD44hi (CD122hi CD49dhi Eomeshi) thymocytes only after T. cruzi infection but its expression was detected in the equivalent population in control mice (Fig 7A). Similar results were observed with granzyme A as it was highly expressed only in SP8 CD44hi (CD122hi CD49dhi Eomeshi) thymic cells of T. cruzi-infected mice (Fig 7B). Moreover, SP8 CD44hi (CD122hi CD49dhi Eomeshi) cells from T. cruzi-infected mice demonstrated a high CD107a expression after PMA stimulation that correlates with a higher degranulation capacity than the CD44lo counterpart cells (Fig 7C, left panel). Similar results were observed when we measured CD107a expression on SP8 CD44hi vs SP8 CD44lo cells in OT-I T. cruzi-infected mice (Fig 7C, right panel). These results led us to speculate that thymic SP8 innate cells may exert a protective role during T. cruzi infection in a similar manner as has been reported for peripheral innate T cells in other murine infection models[20, 21, 39, 42, 43]. Production of IFNγ has been reported to be involved during protective immunity against Trypanosoma cruzi infection[44, 45]. Interestingly, IFNγ is a key cytokine that is highly produced by innate CD8+ cells[20–22]. In our T. cruzi infection model, we found that thymic SP8 CD44hi cells from T. cruzi-infected OT-I mice produce much higher amounts of IFNγ compared to SP8 CD44hi cells from the control group or SP8 CD44lo cells from both groups of mice(S4 Fig). Moreover, IFNγ+ cells correlated with Eomes expression (S4 Fig). This is consistent with the observation that Eomes was first reported to be the critical transcription factor promoting IFNγ expression in innate CD8+ T cells[13, 16].
Ag-specific CD8+ T are known to be crucially protective during the immune response against T. cruzi[46]. To investigate if Ag-independent CD8+ T cells are also able to exert protection in this infection model, we performed survival experiments using WT, CD8 KO and OT-I mice challenged with 5000 tripomastigotes. Survival was evaluated over 50 days post-infection utilizing a protocol described elsewhere[47]. As expected, CD8 KO mice died rapidly after infection compared to WT mice that carry Ag-specific and non-specific CD8+ T cells (Fig 8A). OT-I mice also died faster than WT mice but surprisingly survived better than CD8 KO mice, indicating that the CD8+ T cells present that are non-specific for this parasite could still induce some protection (Fig 8A). This result encouraged us to investigate if thymic innate CD8+ cells could also induce protection in this model. First we adoptively transferred (AT) a bulk population of thymocytes obtained from T. cruzi-infected mice (donors) to T. cruzi-infected mice (recipients) and observed 100% survival of recipient mice (Fig 8A). Since the bulk population of thymocytes obtained from T. cruzi-infected mice have both specific and non-specific CD8+ T cells (as demonstrated above, Fig 1) along with other cell types, we carried out survival experiments by transferring only SP8 thymocytes (>90% cells with innate CD8+ phenotype) from T. cruzi-infected OT-I mice to T. cruzi-infected mice. We observed a significant increase in survival along with a significant diminution of parasitemia in AT-OTI compared to non-AT recipient mice (Fig 8B and 8C, respectively). To evaluate if protection can be performed by innate CD8+ T cells from thymi of pathogen free mice, we adoptively transferred thymocytes obtained from IL-12+IL-18-treated mice and observed a significant increase in survival compared to non-AT mice (Fig 8D), although no changes in the parasitemia was observed (Fig 8E).
Thus far, our data have demonstrated that under systemic Th1 conditions, the thymus experiences changes in its cellular composition in a manner that accounts for an enrichment of innate SP8 cells over the conventional cell types. These cells share all the phenotypic and functional characteristics that identify innate CD8+ T cells and we have further demonstrated that adoptive transfer of these innate CD8+ thymocytes exerts protection during T. cruzi infection in an Ag-independent manner.
A relevant question that remains to be answered in this work is the origin of innate CD8+ cells found in the thymi of mice undergoing Th1 infectious/inflammatory processes. As a result of the FTY720 experiment (Fig 3) we hypothesized that these cells should be generated in the thymus. To address this question we utilized three different strategies. Our first approach was to stain the bulk population of thymocytes by performing intrathymic (i.t.) injections with the eFluor 670 (eF670) dye, in control and T. cruzi-infected mice at day 7 post-infection. We selected this time point as we already determined it to be the latest point when both groups of mice still contained the same proportion and phenotype of both SP8 CD44lo and SP8 CD44hi thymocytes (S5A Fig). Seven days later (day 14), when innate CD8+ cells were largely abundant in the thymus of T- cruzi-infected mice, we analyzed the thymi of both groups and observed that while SP8 eF670+ cells in control mice still maintained the original phenotype, SP8 eF670+ cells in T. cruzi-infected mice had significantly increased CD44 expression (S5B Fig). This data, along with the FTY720 experiments, represented the first indication that most innate SP8 cells found in the thymus may result from an endogenous conversion/expansion rather than migration from SLO. To confirm this hypothesis, we developed a second strategy by performing i.t. injections of CD45.2+ thymocytes from a control OT-I mouse (donor) into 2 different thymic environments: a CD45.1+ control mouse or a CD45.1+ T. cruzi-infected mouse (both B6 recipients). After 48h, we obtained the thymi and analyzed CD45.2+ Vβ5+ (OVA specific) cells in both groups of recipient mice. Fig 9A, 9B and 9C show the gate strategy used to analyze only CD45.2 expression of OT-I transferred donor cells. After the i.t. injections, we observed that the SP8 CD45.2+ cell numbers recovered in control recipient CD45.1+ mice were significantly lower than in T. cruzi-infected recipient mice (Fig 9B and 9C, respectively). When the phenotype of transferred donor CD45.2 cells was analyzed, we observed that except for Tbet, all innate CD8+ T cell markers were up-regulated only when injected into T. cruzi-infected recipient mice (Fig 9D). This data strongly demonstrated that during a systemic Th1 process like T. cruzi infection, the conventional SP8 thymic compartment becomes enriched in innate CD8+ cells. This led us to hypothesize that IL-4 and IL-15 could be responsible for this effect. Thus, we performed in vivo experiments with control or T. cruzi-infected mice in the absence of both cytokines. Interestingly, before the infection, the absolute number of SP8 CD44hi cells in IL-4KO mice was not changed as compared to B6 mice (Fig 10A). However, after T. cruzi infection, the outcome was totally different; while SP8 CD44hi cells were significantly increased in B6 mice, the cell number greatly dropped in IL-4KO mice due to an overall decrease in thymic cell viability in these mice (Fig 10B). Furthermore, expression of CD122, CD49d in SP8 CD44hi cells was diminished in IL-4KO T. cruzi-infected mice contrary to what was observed in B6 mice where these markers increased after infection (Fig 10C). It is worth to mention that while the total cell number in uninfected condition was similar between B6 and IL-4KO mice, both Eomes and CD122 expression was significantly lower in IL-4KO mice (Fig 10C). This is consistent with a previous report demonstrating that IL-4 up-regulates Eomes that, in turn, up-regulates CD122 expression[16]. These in vivo experiments confirmed that simultaneous neutralization of both IL-4 and IL-15 did not exacerbate the robust effects already triggered by the lack of IL-4 alone.
The in vivo neutralizing experiment provided substantial information especially about the role of IL-4 in innate CD8+ thymic development; however, it could not discern whether systemic or local (thymic) IL-4 and IL-15 were ultimately responsible for the generation of these cells in T. cruzi-infected mice. Another essential question that remains was whether innate CD8+ cells arise from pre-existing conventional SP8 cells or from earlier stages in the T cell development. To test this question, we developed an in vitro model based on a previous report[48]. We sorted DP cells from B6 (WT) or OT-I control CD45.2+ mice and co-cultured them with the bulk population of thymocytes from either CD45.1+ control or CD45.1+ T. cruzi-infected mice. We harvested the co-cultures 48h later and focused only on CD45.2+ cells (S6A Fig). Flow cytometry analysis demonstrated that SP8 cells but not SP4 cells arose from the DP cultures and, only in the presence of thymocytes from control or T. cruzi-infected mice but not when cultured alone from both WT or OT-I mice (S6B Fig). Moreover, our data demonstrated that OT-I DP cells either pre (CD69neg) or post-selection (CD69pos) generated equivalent large numbers of SP8 cells (S6B Fig).
When we analyzed the phenotype of sorted DP cells from OT-I mice, we observed that cells that were in contact in vitro with thymocytes from T. cruzi-infected mice were able to adopt innate CD8+ features (except for CD49d expression that remained unchanged) (Fig 11). In order to evaluate the role of local IL-4 and IL-15 in the development of thymic innate CD8+ T cells, we performed the same co-cultures with WT (B6) and IL-4KO (B6) mice treated with and without anti-IL-15 neutralizing antibody. Data shown in Fig 11 demonstrated that both cytokines are equally important in the acquisition of the innate phenotype. However, the simultaneous blocking of both cytokines did not show any additive effect. Interestingly, when we analyzed SP8 cells generated in the DP cultures, we observed once again, that the cells acquired an innate phenotype (except for CD49d) only when co-cultured with thymocytes from T. cruzi-infected mice that is inhibited in the absence of IL-4 or IL-15 (S7 Fig). The lack of expression of the commonly associated marker CD49d in the in vitro model may indicate that other signals are required for a full innate CD8+ phenotype that is acquired in the in vivo models but not in vitro. Finally, we evaluated sorted SP8 CD44lo thymocytes under the same co-culture conditions and observed only a slightly increase in CD44 and Eomes between control and T. cruzi co-cultures that did not revert in the absence of IL-4 and IL-15. This suggested that at this more mature stage of development, conversion to the innate phenotype and vice versa was not as flexible as in the DP stage (S8 Fig).
This data demonstrated that a systemic Th1 infection like T. cruzi was able to trigger thymic production of IL-4 and IL-15, that in turn, facilitated the appearance of SP8 thymocytes with an innate phenotype. This change in thymic development demonstrated greater flexibility when thymocytes were more immature (e.g. the DP stage) and not possible when the thymocytes acquired the more mature SP8 phenotype. Moreover, preliminary data with C. albicans and systemic IL-12+IL-18 encourages us to investigate whether this phenomenon is relevant to other infectious pathological processes that trigger a strong systemic Th1 cytokine response.
Development of CD8+ T cells in the thymus generates a predominant population of conventional naïve cells, along with minor populations of “innate” T cells that resemble memory cells. When analyzing the innate populations that arise in the thymus in a variety of KO mice that have an impaired TCR signaling pathway, several studies have demonstrated the presence of an increased number of IL-4-dependent innate CD8+ T cells (as compiled by Lee et al.[9]). These KO mouse models all converge on the fact that a population of thymic cells (PLZF+: NKT, γδ T cells or CD4 CD44hi cells) ultimately produces increased levels of IL-4 that drives innate CD8+ T cell development[9, 11, 12, 14]. However, the question remains as to whether a similar pathway regulates innate CD8+ T cell development in normal mice. Interestingly, inbred strains of mice were shown to vary in their frequency of IL-4-producing invariant NKT (iNKT) cells, with BALB/c mice on top of the spectrum and C57BL/6 mice on the low end. This data that correlates with higher percentages of SP8 CD44hi CD122hi Eomeshi innate cells in the thymus of BALB/c mice compared to C57BL/6 mice under physiological conditions[49]. Moreover, the importance of IL-4-producing iNKT cells in innate CD8+ cells development in the thymus is supported by the fact that no innate CD8+ T cells are found in BALB/c IL-4R KO and Cd1d KO mice[14].
Surprisingly, at the present time, there are no reports that address patho-physiological conditions that preferentially drive innate CD8+ T cell development over conventional CD8+ T cells in the thymus. In this context, our work presents strong evidence that an infectious process, like Trypanosoma cruzi infection, triggers a systemic Th1 response that leads to an enrichment of SP8 cells phenotypically expressing CD44hi, CD122hi CD49dhi Eomeshi Tbetint/lo[9, 10, 13, 15, 32, 33, 50] and with functional characteristics (NKG2Dhi, Granzymehi, CD107ahi)[19, 20, 39–42] of innate CD8+ T cells.
Interestingly, the acquisition of the innate phenotype occurs in the thymus environment and no from recirculation of mature peripheral T cells as demonstrated by the FYT720 and the co-culture experiments. Moreover, since no significant changes were observed in the number of SP8 CD44hi cells between FYT720-treated and untreated mice, we hypothesize that the large percentage of SP8 CD44hi TKSB20+ cells inside the thymus could result from migration of few mature Ag-specific cells from SLO that enter the thymus. However, when inside, they might proliferate and even acquire an innate phenotype due to local IL-4 and IL-15 expression. This data is supported by studies perform and reported by our laboratory indicating that small numbers of mature peripheral T cells are able to enter the thymus in infectious/inflammatory situations[7]. While it has been reported that T. cruzi infection in mice can alter multiple aspects of thymic biology[28, 51], we demonstrate here that the infectious conditions that trigger the appearance of thymic innate CD8+ cells are due to the bystander cytokine storm resulting from the systemic Th1 infectious processes. In support of this hypothesis, we observed similar results in three different experimental settings: 1) mice infected with two different T. cruzi strains (Tulahuen and Y), 2) in OT-I T. cruzi- infected mice (pathogen-independent model) and 3) in the absence of infection as in IL-12+IL-18 systemically treated mice.
Some reports describe that interaction of thymocytes with non-conventional MHC-Ib molecules expressed by thymic hematopoietic cells are important for innate CD8+ T cell development[23, 52]. Even though we cannot eliminate the possibility that cell-cell interactions are important for innate CD8+ T cell development, we demonstrate that innate CD8+ thymocytes are almost completely reverted to conventional SP8 thymocytes in the absence of both IL-4 and IL-15. Moreover, it appears that local production of IL-4 and IL-15 expression by different subsets of thymic cells plays a non-redundant role in innate CD8+ T cell development. In this context, it is important to emphasize that selection processes that occur in the thymus are not all TCR dependent. In fact, during lineage selection, some maturation events are strictly driven by cytokines but not by the TCR, especially by the γc chain-dependent cytokines (e.g. IL-7 is known to impose CD8 lineage fate)[53]. Thus, it is not unexpected that triggered expression of IL-4 and IL-15 in the thymus by these inflammatory situations could alter the normal/conventional lineage commitment of SP8 thymocytes. This is especially relevant since IL-4 is the extrinsic factor that induces Eomesodemin expression, the key transcription factor associated with innate CD8+ T cells[9, 12, 14, 23]. Furthermore, innate CD8+ T cells are also dependent on IL-15 signaling for their development and maintenance as innate CD8+ T cells, similar to NK cells, are largely absent in IL-15-deficient mice[54]. Moreover, a previous report indicates that one week after in vivo administration of an IL-15 blocking antibody, there is a significant reduction in the percentage of innate CD8+ T cells in ITK-deficient mice[55].
The in vitro experiments also reinforce the finding that the innate phenotype does not occur by homeostatic expansion of resident SP8 CD44hi cells due to available space resulting from the death of DP cells that occurs after the infection. This is demonstrated by our co-culture experiments where we seeded the same numbers of WT or OT-I thymocytes in the plate and obtained similar outcomes as in the in vivo intrathymic experiments. Moreover, we determined that less mature thymocytes (e.g. DP cells) are more “flexible” in their ability to adopt the innate CD8+ T cell features than the already pre-existing mature SP8 thymocytes. However, from the in vitro model we demonstrated that SP8 thymocytes that develop from DP cells tend to easily adopt the innate rather than the conventional SP8 phenotype. Interestingly, our findings are supported by other reports demonstrating that following T. cruzi infection in BALB/c mice, high levels of IL-4 are produced in the thymus and this finding correlates with the appearance of CD44hi DP cells[28, 56].
Conversion of a naïve CD8+ T cells to the innate phenotype may also occur in SLO in the presence of IL-4 as reported by others[13, 18, 57, 58]. Moreover, it has been demonstrated that even conventional αβ memory CD8 T cells are able to exert innate-like functions in response to heterologous challenge (e.g. infections) and are independent of cognate antigen recognition[58]. This cytokine-driven phenomenon occurs when memory CD8 T cells are spatially positioned close to pathogen-activated macrophages and phagocytes in lymph nodes (LNs) and efficiently receive IL-12, IL-15 along with inflammasome-generated IL-18 signals. This cytokine interaction induces a rapid Ag-independent IFNγ expression and effector functions by conventional αβ memory CD8+ T cells[58]. These observations not only blur the strict distinction between the innate and adaptive immune compartments but also challenge the established paradigm that innate and adaptive immune responses are undertaken by different type of cells.
Innate CD8+ T cells acquire effector function during their maturation process in the thymus rather than by interaction with specific antigens in SLO[13, 32]. It has been postulated that they exert their cytotoxic capacity in a TCR-independent manner by mechanisms that involve strong and rapid production of IFNγ, killing activity through receptors like NKGD2 and degranulation of granzymes and perforins[19, 20, 39–42]. Moreover, it has been reported that peripheral innate CD8+ T cells play an important role during the early stages of certain bacterial and viral infections[20, 21, 39, 42, 43]. Our data show that innate CD8+ cells that develop in the thymus of T. cruzi-infected mice not only up-regulate those receptors but also, when adoptively transferred to T. cruzi-infected mice, exert protection in an Ag-independent manner. This was demonstrated by the survival experiments with adoptive transferred SP8 cells from OT-I T. cruzi-infected mice or from IL-12+IL-18-treated mice. Our work provides new and unique data about the role of these cells during this parasite infection suggesting that they might effectively operate during the early control of several types of infections, a role previously reported in certain bacterial and viral infections models[20, 21, 39, 42, 43].
Currently, we are performing experiments designed to demonstrate that the innate CD8+ T cells that develop under these systemic inflammatory/infectious processes are able to exit the thymus and induce protection in SLO. Preliminary data suggest that, exportation of CD8+ T cells with an innate phenotype is observed in these models albeit in lower numbers than in control mice. However, reduction in the exportation of cells from the thymus to SLO is not a concern since it has been extensively reported that this is a common phenomenon during systemic infections[59]. Even though those experiments suggest that innate CD8+ T cells might reach SLO under these conditions, at present we are addressing the question as to whether upon cessation of the inflammatory/infectious process, is the thymus able to recover its normal anatomical and cellular components and return to the development of conventional CD8+ T cells.
An important aspect to take into account is that CD8+ T cells that share a similar phenotype with their innate murine counterpart have been recently described in humans[27]. This report describes a subset of CD8+ T cells KIR/NKG2A+ CD8+ T cells in healthy human adults with increased Eomes expression, prompt IFNγ production in response to innate-like stimulation by IL-12+IL-18, and a potent Ag-independent cytotoxicity[27]. The investigators also identified this cell type in human cord blood, suggesting that development did not depend on cognate antigens and likely arises from the thymus as well[27]. Furthermore, another recent report demonstrates that a higher number of peripheral innate CD8+ T cells correlates with a better outcome in certain cancer patients[15, 50].
Overall, our work contributes to the understanding that the thymus is not an isolated and immune privileged organ, but rather has the capacity to sense peripheral stimuli and adapt its developmental program to meet the real time immunological needs. Furthermore, studies to demonstrate that this phenomenon also occurs in humans need to be pursued in order to better understand immune developmental mechanisms and to develop approaches to harness immune responses to fight infections and cancer.
The experimental protocols were approved by the Institutional Animal Care and Use Committee (IACUC) from Facultad de Ciencias Químicas, Univesidad Nacional de Córdoba, (authorization no. 2016–249). This committee follows the guidelines for animal care in the “Guide to the care and use of experimental animals” (Canadian Council on Animal Care, 1993) and the “Institutional Animal Care and Use Committee Guidebook” (ARENA/OLAW IACUC Guidebook, Nacional Institutes of Health, 2002). Our animal facility also has obtained NIH animal welfare assurance (assurance number A5802-01, OLAW, NIH, USA).
Female and male WT C57BL/6 CD45.2+, WT C57BL/6 CD45.1+, OT-I (RAG-sufficient, B6 background), and CD8KO mice (B6 background, Jackson Laboratory) used in this study were 6–10 week old and maintained under specific pathogen-free conditions.
Trypanosoma cruzi trypomastigotes (Tulahuen) were maintained by serial passages in WT mice. WT mice were i.p. infected with 5 × 105 trypomastigotes from T. cruzi diluted in PBS. Mice were euthanized between days 14 and 16 post-infection. Trypanosoma cruzi parasites (Y-Br strain) were cultured in NIH3T3 mouse fibroblasts and were collected as described[60]. Mice 7–9 weeks of age were infected by intraperitoneal injection of 1 × 104 trypomastigotes, diluted in a solution of 1% glucose in PBS[60].
Yeast cells of C. albicans were grown on Sabouraud glucose agar slopes at 28°C, and maintained by weekly subculture. B6 mice were i.p. injected with 3 × 107 viable yeast diluted in PBS. Mice were sacrificed 5 days after the infection.
The hydrodynamic gene transfer procedure was described previously by our laboratory[7, 35, 36]. The designated amount of each DNA was dissolved in 1.6 mL of sterile 0.9% sodium chloride solution. Animals were injected in the tail vein with the cDNAs in less than 8 s and separated in two groups, control: 15 μg of ORF empty vector control cDNA and IL-12 + IL-18: 1 μg of IL-12 cDNA (pscIL-12, p40-p35 fusion gene) plus 10 μg of IL-18 cDNA (pDEF pro-IL-18). All the expression plasmids utilize the human elongation 1-α promoter to drive transcription.
For multicolor staining, fluorochrome-conjugated Abs (BDPharmingen, Immuno tools, Ligatis, Miltenyi Biotec) were used in various combinations. Briefly, cells were stained for surface markers (CD4, CD8, CD11b, CD11c, CD44, NK1.1, CD45.1, CD45.2, NKG2D, CD122, CD124 (IL4R), CD49d for 30 min at 4°C and washed twice. To detect intracellular expression of cytokines or granzyme production, cells were cultured with PMA (50ng/ml) and Ionomycin (1μg/ml) for 4 h and 5 μg/ml Brefeldin A (Sigma) was added during the last 3 h. Cells were then stained for surface markers, washed, and fixed with Cytofix/Cytoperm buffer (BD Pharmingen) for 30 min at 4°C. Cells were washed with Perm Wash buffer (BD Pharmingen) and incubated with the anti-mouse IFNγ Ab or isotype-matched Ab for 30 min at 4°C. Following two washings, cells were analyzed in the flow cytometer. To detect intracellular IL-4 and intranuclear EOMES, Tbet or PLZF expression, cells were stained for surface markers, washed, and fixed with IC Fixation Buffer (eBioscience) for 90 minutes at 4°C. Cells were washed with Permeabilization Buffer (eBioscience) and incubated for 30 minutes with the same buffer. Cells were centrifuged and incubated with the Eomes PE anti-mouse Ab, Tbet PCP-Cy5.5 or PECy7 anti-mouse Ab, PLZF PECy7 anti-mouse Ab or isotype-matched Ab (BD-Pharmingen) for 45 min at 4°C and then analyzed by flow cytometry in a BD FACS CantoTM II cytometer or BD LSR Fortessa X-20 cytometer (BD Biosciences, San José, CA, USA). For cell sorting, cells were stained with monoclonal Abs and separated in a Becton Dickinson FACSAria II cytometer (BD Biosciences, San José, CA, USA) as SP4 CD44+ cells and NKT cells (See gate strategy in S3 Fig). Then, cells (10 x 105 cells/100μl/well) were in vitro stimulated with PMA (50ng/ml) and Ionomycin (1μg/ml) for 5 h. Supernatant were harvested and IL-4 production was measured by ELISA following the manufactures´ instructions.
T. cruzi-specific CD8+ T cells were detected using H-2K(b) T. cruzi trans-sialidase amino acids 567–574 ANYKFTLV (TSKB20) APC-labeled Tetramer (NIH Tetramer Core Facility). OVA-specific CD8+ T cells were detected using H-2K(b) chicken ova amino acids 257–264 SIINFEKL APC-labeled Tetramer (NIH Tetramer Core Facility). NKT cells were detected using CD1d APC-labeled Tetramer (NIH Tetramer Core Facility).
For FTY720 in vivo treatment, control or T. cruzi-infected B6 mice (Tulahuen) were i.p. injected with 25μg of FTY720 (Sigma-Aldrich) resuspended in 200μl sterile 0.9% sodium chloride solution on days 8, 10 and 13 post-infection (pi) based on a previous report[37]. Mice were euthanized on day 14 pi.
To visualize T. cruzi parasites by immunofluorescence, thymi from WT mice were harvested 14–16 days post infection and a bulk suspension of thymocytes was resuspended in complete medium (RPMI 1640 supplemented with 10% heat-inactivated FBS, 100 U/ml penicillin G sodium, 100 μg/ml streptomycin sulfate, 2 mM L-glutamine, 1 mM sodium pyruvate, 1x essential amino acids, and 10 mM 2-ME). The bulk population of thymocytes were then placed into 24 well plates with glass slides inside and cultured for 72h at 37°C, 5% CO2. After the incubation period, adherent cells were enriched on the glass slides by washing the non-adherent cells with warm supplemented media. Slices were then stained with a serum from a chagasic patient along with a primary rat IgG antibody anti-CD11b[61]. Subsequently, the samples were incubated with a secondary anti-human IgG conjugated with FITC and anti-rat IgG conjugated to Alexa Fluor 546. Finally, the cells were stained with DAPI (300 ng/ml) to distinguish cell nuclei. The images were taken on a confocal microscope Olympus-1000 spectral Fluoview.
Culture supernatants were assayed for mIL-4 production by ELISA (BD-Pharmingen, La Jolla, CA) according to the manufacturers’ instructions.
Thymi were mechanically disrupted, washed, and resuspended in supplemented medium (RPMI 1640 supplemented with 10% heat-inactivated FBS, 100 U/ml penicillin G sodium, 100 μg/ml streptomycin sulfate, 2 mM L-glutamine, 1 mM sodium pyruvate, 1 x essential amino acids, and 10 mM 2-ME). Cells were counted and stained with 4mM CFSE dye and then cultured at 1x106 cells/ml at 37°C with medium alone or in the presence of IL-15 (150 ng/ml), IL-4 (20ng/ml), IL-12 (100 ng/ml) plus IL-18 (50 ng/ml), for 72 h, in triplicate in 96-well flat-bottom plates. Cells were stained for surface markers for 30 min at 4°C and washed twice and then gated for CSFE dilution analysis by flow cytometry in a BD FACS Canto TM II cytometer (BD Biosciences, San Jose, CA, USA).
Thymi were mechanically disrupted, washed, and resuspended in supplemented medium (RPMI 1640 supplemented with 10% heat-inactivated FBS, 100 U/ml penicillin G sodium, 100 μg/ml streptomycin sulfate, 2 mM L-glutamine, 1 mM sodium pyruvate, 1 x essential amino acids, and 10 mM 2-ME). Cells were counted and cultured at 2x106 cells/ml at 37°C with medium in the presence of PMA (50 ng/ml), ionomycin (1μg/ml), GolgiStop (BD Biosciences) and anti-CD107a antibody (0,002 μg/μl) for 5 hours in 96-well flat-bottom plates. Cells were stained for surface markers for 30 min at 4°C and washed twice and then analyzed by flow cytometry in a BD FACS CantoTM II cytometer (BD Biosciences, San Jose, CA, USA).
Intrathymic injections were performed in 8-wk-old C57BL6 CD45.2+ or CD45.1+ WT mice. Briefly, mice were anesthetized by i.p. injection of ketamine (0,05mg/g) and xylazine (0,01mg/g) (both from Richmond Vet Pharma) in saline. An incision was opened to expose the thymus, and 10 μl of eFluor 670 (eF670) dye (0,5mM, BD Biosciences) or 10 × 106 thymocytes from OT-I mice (97–99% Vβ5+) resuspended in 20 μl PBS, were injected into CD45.1+ or CD45.2+ B6 recipient mice. The wound was closed with instant adhesive, and the mice were placed in a warm environment until they recovered. Mice were analyzed 6 days after eF670 injection or 48h after cell injections.
For IL-15 in vivo neutralization, IL-4 KO T. cruzi-infected (Tulahuen) mice were i.p. injected with 25μg of an anti-IL15 (eBioscience) resuspended in 200μl sterile 0.9% sodium chloride solution on days 10 and 12 post-infection (pi). Mice were euthanized on day 14 pi.
Thymi from T. cruzi-infected (Tulahuen) WT mice or IL-12 + IL-18-injected mice were obtained and cell suspensions were prepared. Approximately 5–6 × 106 total thymocytes were resuspended in 0.2 mL of sterile 0.9% sodium chloride solution and injected i.v into the B6 recipient’s retrorbital sinus. For AT of OT-I (97–99% OVA-tetramer+ Vβ5+) thymocytes from T. cruzi-infected mice, SP8 cells were sorted and approximately 5–6 x 106 cells were AT to recipient B6 mice as described above. Three hours post-adoptive transfer, recipient mice were infected i.p. with 5000 trypomastigotes from T. cruzi (Tulahuen) diluted in 200 μl PBS and monitored for the number of parasites (per ml of blood) in peripheral blood at 10, 13 and 16 days post infection (dpi) with survival measured during 50 days. The control group was B6 mice infected i.p. with 5000 parasites (Tulahuen) without the cell transfer.
Thymi from CD45.1+ control WT mice, CD45.2+ T. cruzi-infected (Tulahuen) WT mice and CD45.2+ T. cruzi-infected (Tulahuen) IL-4KO mice were obtained and thymocytes isolated and resuspended in PBS + 5% FBS. Cells were counted and then stimulated with PMA (50 ng/ml) and Ionomycin (1 μg/ml) for 2 hours in 24-well culture plates by placing 1x 106 cells in each well in 1 ml of complete RPMI (10% 1/100 Glutamine, 1/1000 Gentamycin). Stimulated cells were then washed twice and in the corresponding reservoirs, an anti-IL15 mAb (100 μg/ml, eBioscience) was added to block IL-15. Secondly, 1 x 106 cells from the bulk population of thymocytes or, bulk DP (CD4+CD8+ cells), DP (CD4+CD8+ CD69neg cells), DP (CD4+CD8+ CD69pos cells) or SP8 CD44low sorted cells from CD45.2+ OT-I control mice were extracted and co-cultured with the previously mentioned cells in a 1:1 ratio. The co-cultures were maintained at 37°C under a constant atmosphere of 5% CO2. After 48h, cells were removed from the plate and analyzed by flow cytometry.
Total RNA was isolated using a single-step phenol/chloroform extraction procedure (TRIzol; Invitrogen Life Technologies). Real-time (RT) PCR was performed with 100 ng of total RNA for each sample (Super Script III one step RT-PCR with platinum Taq, Invitrogen), utilizing the following program: 15 min reverse transcription at 45°C, 40 cycles of denaturing at 94°C (15 s), annealing at 55°C (30 s), and extension at 68°C (60 s), with a final extension for 5 min at 68°C. Primers used were: IL-15Ra S: 5'-CCCACAGTTCCAAAATGACGA-3'; AS: 5'-GCTGCCTTGATTTGATGTACCAG-3'. IL-15 S: 5'-ACATCCATCTCGTGCTACTTGT-3'; AS: 5'-GCCTCTGTTTTAGGGAGACCT-3'. Briefly, RNAs were treated with DNase I prior to reverse transcription. Reverse transcription was performed on 1 μg of RNA using random hexamers as primers. Semiquantitative real time PCR was performed on cDNAs using TaqMan R expression assays (Life Technologies) specific for each target gene. All reactions were run on a 96-well, 7300 Real Time PCR System (Life Technologies). Expression of all target genes was normalized using HPRT or GAPDH as the control housekeeping gene. For IL-15 expression, 5 μg of total cytoplasmic RNA was analyzed using the RiboQuan kit mCK.1 (BD Pharmingen) and [33P]UTP-labeled riboprobes by the RNase protection assay (RPA).
Data were compared in all cases between each treated-mice group with its own control group. Results are expressed as means ± SEM. Data were analyzed using one-way analysis of variance (ANOVA) with a Bonferroni post-test to compare different columns (p < 0.05). In all cases, the assumptions of ANOVA (homogeneity of variance and normal distribution) were attained. When indicated, significant differences were performed using Student’s t test for paired or grouped samples. For survival analysis the statistic test applied was Gehan-Brelow-Wilcoxon test. In all statistical analyses, p < 0.05 was considered to represent a significant difference between groups.
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10.1371/journal.pgen.1001194 | ATM Limits Incorrect End Utilization during Non-Homologous End Joining of Multiple Chromosome Breaks | Chromosome rearrangements can form when incorrect ends are matched during end joining (EJ) repair of multiple chromosomal double-strand breaks (DSBs). We tested whether the ATM kinase limits chromosome rearrangements via suppressing incorrect end utilization during EJ repair of multiple DSBs. For this, we developed a system for monitoring EJ of two tandem DSBs that can be repaired using correct ends (Proximal-EJ) or incorrect ends (Distal-EJ, which causes loss of the DNA between the DSBs). In this system, two DSBs are induced in a chromosomal reporter by the meganuclease I-SceI. These DSBs are processed into non-cohesive ends by the exonuclease Trex2, which leads to the formation of I-SceI–resistant EJ products during both Proximal-EJ and Distal-EJ. Using this method, we find that genetic or chemical disruption of ATM causes a substantial increase in Distal-EJ, but not Proximal-EJ. We also find that the increase in Distal-EJ caused by ATM disruption is dependent on classical non-homologous end joining (c-NHEJ) factors, specifically DNA-PKcs, Xrcc4, and XLF. We present evidence that Nbs1-deficiency also causes elevated Distal-EJ, but not Proximal-EJ, to a similar degree as ATM-deficiency. In addition, to evaluate the roles of these factors on end processing, we examined Distal-EJ repair junctions. We found that ATM and Xrcc4 limit the length of deletions, whereas Nbs1 and DNA-PKcs promote short deletions. Thus, the regulation of end processing appears distinct from that of end utilization. In summary, we suggest that ATM is important to limit incorrect end utilization during c-NHEJ.
| When a chromosome is fragmented by multiple double-strand breaks (DSBs), each set of DSB ends needs to be matched correctly during repair to avoid chromosomal rearrangements. Considering the case of two tandem DSBs, if the ends of different breaks (incorrect ends) are used for repair, loss of the intervening DNA can occur. Alternatively, when the ends of a single DSB (correct ends) are used for repair, the original order of the chromosome is restored. Deficiencies in the factors ATM and Nbs1, as seen in patients with Ataxia Telangiectasia and Nijmegen Breakage Syndrome, respectively, have been associated with elevated chromosome rearrangements and cancer predisposition. Hence, we examined the possibility that these factors may be important for the usage of correct ends during repair of multiple DSBs. For this, we developed a reporter system to examine end usage during repair of two tandem DSBs in mammalian chromosomes and found that disruption of ATM or Nbs1 leads to elevated usage of incorrect ends. Furthermore, we found that the role of ATM during end usage depends on a repair pathway called classical non-homologous end joining (c-NHEJ). We suggest that ATM suppresses genome rearrangements via limiting incorrect end utilization during c-NHEJ.
| Recent sequencing of cancer genomes has revealed a prevalence of chromosome rearrangements, including interchromosomal translocations and intrachromosomal rearrangements [1]. These rearrangements could arise from end joining (EJ) of incorrect ends of multiple chromosomal double-strand breaks (DSBs). Such EJ could be performed by classical non-homologous end joining (c-NHEJ) factors that mediate V(D)J Recombination (e.g. Ku70/Ku80, XLF, DNA-PKcs, and Xrcc4/Lig4), or by Alternative-EJ (alt-EJ) pathways that are independent of these factors [2], [3]. We suggest that identifying the mechanisms that are important for the fidelity of end utilization during c-NHEJ and/or alt-EJ will provide insight into maintenance of chromosome stability and tumor suppression.
Factors that reduce incorrect end utilization during EJ are likely to be important for suppressing chromosome rearrangements. Mutations in the ATM kinase, found in patients with the genetic disorder Ataxia-Telangiectasia (A-T), cause elevated levels of chromosomal abnormalities, along with a predisposition for cancer [4]. Part of the role of ATM in suppressing chromosomal abnormalities is likely related to its key function during the DNA Damage Response (DDR). Without the DDR, cells fail to activate cell cycle checkpoints following DNA damage, and are more likely to undergo DNA replication and/or mitosis with broken chromosomes, which could lead to rearrangements [5]. Also important for the DDR is Nbs1, which is a member of the Mre11-complex (Mre11-Rad50-Nbs1) and is important for ATM activation [6], [7]. Patients with mutations in the Nbs1 gene (Nijmegen Breakage Syndrome), like A-T patients, show cancer predisposition associated with elevated chromosomal abnormalities [8].
ATM and Nbs1 localize to sites of DSBs, and are important for their repair [6], [9]. Both ATM and Nbs1 are important for cell survival following ionizing radiation (IR)-induced DSBs [4], [8], and promote homologous recombination [10]. Also consistent with a role in repair, a subset of IR-induced DSBs persist in ATM-deficient cells [11]. Persistent breaks have also been observed in ATM-deficient lymphocytes during V(D)J recombination and class switch recombination (CSR) [12]–[15]. ATM and Nbs1 affect the repair fidelity of certain V(D)J recombination substrates, in which the Rag1/2 nuclease forms two types of DSB ends: hairpin coding-ends and blunt signal-ends [13], [16], [17]. Correct end utilization in this context involves pairing coding-coding and signal-signal ends during NHEJ. When Rag1/2 cleavage sites are placed in an inverted orientation, both ATM and Nbs1 have been shown to suppress hybrid signal-coding EJ products [13], [16]–[18]. Thus, these factors are important for faithful repair of Rag1/2-induced DSBs. Consistent with this notion, ATM-deficient lymphocytes show elevated chromosome rearrangements resulting from V(D)J recombination [19], [20]. ATM and Nbs1 also promote efficient CSR and suppress translocations between IgH and c-myc during this process [9], [12], [21]–[25]. Similarly, the ATM orthologue in yeast (TEL1) is important to suppress translocations in favor of intrachromosomal EJ [26].
Thus, we considered the possibility that ATM and/or Nbs1 play a role in correct end utilization during EJ repair of multiple chromosomal DSBs in mammalian cells, outside of the programmed rearrangements during lymphocyte development. For this, we monitored EJ products following the induction of two tandem DSBs, which can be repaired using either correct ends (Proximal-EJ) or incorrect ends (Distal-EJ). We find that disruption of ATM or Nbs1 causes elevated Distal-EJ, but not Proximal-EJ. Furthermore, the elevation of Distal-EJ caused by ATM-disruption is dependent on the c-NHEJ factors DNA-PKcs, Xrcc4, and XLF. In addition, to examine the role of these factors on end processing, we analyzed Distal-EJ repair junctions. We find that ATM and Xrcc4 limit extensive deletions during EJ, whereas Nbs1 and DNA-PKcs promote short deletions. Thus, the role of individual factors during end processing does not directly correlate with their roles during end utilization. In summary, we suggest that ATM is important to limit incorrect end utilization during repair by c-NHEJ.
We investigated incorrect and correct end utilization during EJ repair of two tandem DSBs. In this context, incorrect end utilization involves the joining of distal DSB ends (Distal-EJ), as this repair event leads to an intrachromosomal deletion between the two DSBs. In contrast, correct end utilization maintains proximal ends during repair (Proximal-EJ). We developed a method to measure Proximal-EJ and Distal-EJ repair of two tandem chromosome breaks generated by the meganuclease I-SceI, using the reporter EJ5-GFP (Figure 1) [27], [28]. In this reporter, a promoter is separated from the rest of a GFP expression cassette by 1.7 kb (puro cassette) that is flanked by two tandem I-SceI sites. Following I-SceI expression, Distal-EJ places the promoter adjacent to the rest of the GFP-expression cassette, such that Distal-EJ can be quantified as the percentage of GFP+ cells [27], [28].
Proximal-EJ is difficult to measure with I-SceI expression alone, since EJ that restores the I-SceI site cannot be differentiated from the uncut reporter [27]. Thus, we adapted this reporter system to enable the quantification of I-SceI-resistant Proximal-EJ products by co-expressing I-SceI with a non-processive 3′ exonuclease (Trex2). As described previously, expression of Trex2 appears to cause partial degradation of the 4 nt. 3′ cohesive ends generated by I-SceI, such that co-expression of I-SceI with Trex2 leads to a high level of I-SceI-resistant Proximal-EJ products [27]. Thus, Proximal-EJ can be quantified by loss of the I-SceI site through PCR amplification across the 3′ I-SceI site, and subsequent I-SceI digestion analysis (Figure 1, primers p1, p2). In this assay we determine the percentage of I-SceI-resistant events by quantifying the relative intensity of the I-SceI-resistant and I-SceI-sensitive products within the same sample. This approach has been described previously for other I-SceI assays [29], and confirmed here to be quantitative within at least two-fold (Figure S1A).
Using this assay system, Proximal-EJ has been shown to be substantially more efficient than Distal-EJ [27]. To confirm this finding, we co-expressed I-SceI with Trex2 in a WT mouse ES cell line with a chromosomally integrated copy of EJ5-GFP [28], and analyzed the EJ repair products, as described above (Figure 1). From these experiments, we observed low levels of Distal-EJ (0.2% GFP+ cells, Figure 2A), and much higher levels of Proximal-EJ (13% I-SceI-resistant p1,p2 amplification products, Figure 2B). In contrast, following transfection of I-SceI without Trex2, we found no detectable I-SceI-resistant Proximal-EJ products (Figure 2B). These results indicate that Proximal-EJ predominates following Trex2 and I-SceI co-expression, such that this experimental approach may uncover factors important for correct end utilization that are otherwise masked from experiments using I-SceI expression alone.
Importantly, the Distal-EJ products resulting from co-expression of I-SceI and Trex2 are also completely I-SceI-resistant [27]. We have confirmed this notion here, using GFP+ sorted samples from the aforementioned transfection experiment (Figure 2C, Figure S1B). Therefore, this method can be used to measure the frequency of two different I-SceI-resistant products (Distal-EJ and Proximal-EJ) from a single sample (Figure 1).
We considered the possibility that ATM may affect end utilization during EJ, as this factor is important for chromosome stability [4]. To test this hypothesis, EJ5-GFP was chromosomally integrated into ATM−/− mouse ES cells [30], and analyzed in parallel with the WT ES cell line described above. Furthermore, during co-expression of I-SceI and Trex2, we treated the cells with either a highly-specific ATM kinase inhibitor (ATMi) [31] or vehicle (DMSO). Subsequently, the percentage of GFP+ cells (Distal-EJ) was measured by FACS analysis. From these experiments, we found that ATMi treatment of WT cells caused an increase in Distal-EJ, as compared to DMSO treated cells (3.4-fold, p<0.0001, Figure 2A). Similarly, ATM−/− cells exhibited higher levels of Distal-EJ as compared to WT cells (7.4-fold, p<0.0001, Figure 2A). Finally, treatment of the ATM−/− cells with ATMi had no effect on Distal-EJ, which is consistent with the high-specificity of ATMi [31]. These data indicate that ATM kinase activity is important for the suppression of Distal-EJ.
To measure Proximal-EJ, we isolated genomic DNA from the same samples used in the FACS analysis, and determined the percentage of I-SceI-resistant amplification products, as described above (Figure 1, primers p1, p2). For both WT and ATM-deficient cells, we found that co-expression of I-SceI and Trex2 results in a significant level of I-SceI-resistant Proximal-EJ products, which are not detectable from expression of I-SceI alone (S+Trex2 versus S+EV, respectively, Figure 2B, Figure S1C). Regarding frequencies, we found that WT and ATM−/− cells exhibited equivalent levels of Proximal-EJ (Figure 2B). ATMi treatment caused a modest reduction in Proximal-EJ in WT cells (Figure 2B, 1.5-fold, p = 0.0007), but not in ATM−/− cells. Thus, loss of ATM kinase activity, but not complete disruption of ATM, appears to modestly reduce Proximal-EJ. Importantly, neither ATMi nor genetic disruption of ATM caused an increase in Proximal-EJ.
We then directly compared Distal-EJ and Proximal-EJ values to determine the relative frequency of incorrect end utilization (Distal End Utilization). First, we confirmed that the Distal-EJ products (GFP+) formed following I-SceI and Trex2 co-expression were I-SceI-resistant for all cell types. We sorted GFP+ cells, isolated genomic DNA, amplified the Distal-EJ products (Figure 1, primers p2, p3), and performed I-SceI digestion analysis. We found that Distal-EJ products were completely I-SceI-resistant for both WT and ATM-deficient cells following I-SceI and Trex2 co-expression, unlike Distal-EJ products resulting from transfection with I-SceI alone (Figure 2C, Figure S1B). We then quantified Distal End Utilization by calculating the ratio of Distal-EJ versus Proximal-EJ for individual samples. From this analysis, we found that ATMi-treatment of WT cells led to a substantial increase in Distal End Utilization in comparison to DMSO treated cells (Figure 2D, 5.3-fold, p<0.0001). Similarly, ATM−/− cells exhibited a striking increase in Distal End Utilization, in comparison to WT cells (Figure 2D, 8.7-fold, p<0.0001). Last, ATMi-treatment of ATM−/− cells did not affect Distal End Utilization.
In the above experiments, ATM appears to suppress Distal-EJ without promoting Proximal-EJ to a similar degree. However, in all conditions, Proximal-EJ is predominant (e.g. 13% for WT, 11.7% for ATM−/−) over the minor Distal-EJ product (e.g. 0.2% for WT, 1.6% for ATM−/−). Thus, Distal-EJ is relatively infrequent, as compared to Proximal-EJ. Accordingly, the fold-increase in Distal-EJ caused by ATM-disruption would not necessarily be matched by a similar fold-decrease in Proximal-EJ. Considering one other detail of these experiments, we note that determining the effect of ATMi on Distal End Utilization after 6 days of culturing post-transfection was not statistically different from the 3 days protocol described in Figure 2 (Figure S1D). This finding indicates that 3 days is a reasonable end-point for these experiments.
We next considered the possibility that ATM might inhibit the formation of both I-SceI-induced DSBs, which would limit Distal-EJ. For this, we used clonal analysis to determine the frequency of I-SceI-resistant Proximal-EJ products at both tandem I-SceI sites. Specifically, we expressed I-SceI and Trex2 in WT ES cells treated with ATMi or DMSO. Following the usual 3 days of culturing, we plated transfected cells at low density to isolate single clones. For individual clones, we determined whether the 5′ and/or 3′ I-SceI recognition sites had been lost, by performing PCR amplification and I-SceI digestion analysis. From this experiment, we found that clones with loss of either the 5′ or 3′ I-SceI site frequently lost the second site (Figure S2A; WT DMSO treated cells: 19 clones lost both 5′ and 3′ sites, 18 clones lost only one of the sites). Thus, cutting at two tandem I-SceI sites, followed by EJ that leads to I-SceI-resistant products at both sites, appears efficient in WT cells. Furthermore, ATMi treatment did not cause an increase in clones that lost both I-SceI sites (Figure S2A; WT ATMi treated cells: 9 clones lost both 5′ and 3′ sites, 25 clones lost only one of the sites). These results indicate that ATM does not suppress the formation of tandem I-SceI-induced DSBs. However, we note that these experiments do not address potential effects of ATM on the probability that both DSBs persist simultaneously (see break persistence model in Discussion).
We also performed the ATMi analysis in a distinct cell type: a transformed human embryonic kidney cell line (HEK293, Figure S2B) that contains a chromosomally integrated EJ5-GFP reporter [28]. Using this HEK293-EJ5-GFP cell line for the same transfection experiment described for ES cells, we found that ATMi treatment caused elevated levels of Distal-EJ (2.1-fold, p<0.0001), but did not affect Proximal-EJ, leading to an increase in Distal End Utilization (Figure S2B, 2.2-fold, p<0.0001). Combined, these findings indicate that ATM is important for limiting Distal End Utilization during EJ repair of multiple DSBs in both mouse ES and human HEK293 cells.
We next tested whether the increase in Distal-EJ that is caused by ATM-disruption involves c-NHEJ factors, specifically DNA-PKcs, Xrcc4, and/or XLF. DNA-PKcs is recruited to DSBs by the Ku70/Ku80 heterodimer, and can stabilize the two ends of a DSB prior to ligation by the Xrcc4/Lig4 complex, which is promoted by XLF [2]. We integrated EJ5-GFP into DNA-PKcs−/− [32], Xrcc4−/− [33], and XLF−/− [34] ES cells, and analyzed EJ efficiency in these cell lines following expression of I-SceI and Trex2, along with ATMi or DMSO treatment, as described above for WT cells. From these experiments (Figure 3A), we found that ATMi treatment did not affect Distal-EJ in DNA-PKcs−/− and XLF−/− cells, and caused a decrease in Distal-EJ in Xrcc4−/− cells (1.9-fold, p<0.0001), all of which are distinct from the 3.4-fold increase observed in WT cells. Importantly, these results indicate that DNA-PKcs, Xrcc4, and XLF are essential for the increase in Distal-EJ caused by ATM-disruption.
Regarding overall frequencies of EJ in ATM-proficient cells, we observed some differences between WT versus DNA-PKcs−/−, Xrcc4−/−, and XLF−/− cells. For instance, we found that I-SceI-resistant Proximal-EJ products were below the level of detection for both the DNA-PKcs−/− and XLF−/− cells (<2%, Figure 3B), similar to previous findings in Xrcc4−/− cells [27] that we have repeated here (Figure 3B). These results indicate that DNA-PKcs, Xrcc4, and XLF are essential for significant levels of Proximal-EJ of DSB ends processed by Trex2. Consistent with these results, DNA-PKcs, Xrcc4/Ligase IV, and XLF were previously shown to promote NHEJ of non-cohesive DSB ends in vitro [35]. Regarding Distal-EJ frequencies compared to WT cells, DNA-PKcs−/− and Xrcc4−/− cells showed a reduction (1.6-fold, p<0.0001, and 1.3-fold, p = 0.0041, respectively, Figure 3A), whereas XLF−/− cells exhibited an increase in Distal-EJ (1.8-fold, p = 0.0014, Figure 3A). Unfortunately, since Proximal-EJ is below the limit of detection in the c-NHEJ-deficient cells, it is not possible to quantify Distal End Utilization for these cell lines. Nevertheless, Proximal-EJ is substantially reduced in these cells (<2%) compared to WT cells (13%), whereas Distal-EJ levels in these cells are within 2-fold of WT cells. These results indicate that DNA-PKcs, Xrcc4, and XLF are important for correct end utilization.
We then sought to determine whether the EJ events measured with EJ5-GFP may be mechanistically distinct from c-NHEJ during V(D)J recombination. For this, we examined the c-NHEJ factor Artemis: a nuclease that is important for hairpin opening during V(D)J recombination [2]. We integrated the EJ5-GFP reporter into Artemis−/− ES cells [36], and performed the transfection analysis described above. In contrast to the above c-NHEJ factors, Artemis−/− cells showed no clear distinction from WT cells on the frequencies of Distal-EJ, Proximal-EJ, or Distal End Utilization, nor on the effect of ATMi treatment on these EJ events (Figure 3). These results indicate that Artemis is not involved in these EJ processes, which provides a contrast to the findings with DNA-PKcs, Xrcc4, and XLF. These findings also confirm the notion that repair of the EJ events measured here show mechanistic distinctions from the hybrid coding-signal joints of V(D)J recombination substrates, which are also elevated in ATM-deficient cells, yet require Artemis [2], [18], [36].
As Nbs1 is important for activation of ATM following DSBs [6], [7], we considered that this factor might also affect end utilization. To test this hypothesis, we used an Nbs1-hypomorphic mouse ES cell line (Nbs1n/h), in which both alleles of the Nbs1 gene are targeted [37], causing a 5-fold decrease in the level of Nbs1 protein [27]. The Nbs1n/h cell line containing EJ5-GFP was described previously, and shown to exhibit an elevated level of Distal-EJ, compared to WT cells [27]. To determine the role of Nbs1 on the relative efficiency of Proximal-EJ and Distal-EJ, as well as the effect of ATMi treatment on these EJ events, we performed the aforementioned I-SceI/Trex2 experiment using the Nbs1n/h-EJ5-GFP cell line. We found that Nbs1n/h cells exhibited a substantial increase in Distal-EJ repair relative to WT cells (5.1-fold, p<0.0001, Figure 4A). ATMi treatment of the Nbs1n/h cells caused a modest increase in Distal-EJ (1.4-fold, p = 0.0053, Figure 4A). Proximal-EJ was equally efficient in WT and Nbs1n/h cells, and ATMi treatment of the Nbs1n/h cells casued a slight reduction in the frequency of Proximal-EJ (1.3-fold, p = 0.0156, Figure 4B). Lastly, Distal End Utilization in Nbs1n/h cells was higher than in WT cells (4.9-fold, p<0.0001, Figure 4C), and was enhanced by ATMi treatment (1.8-fold, p = 0.0011, Figure 4C). Notably, the effect of ATMi on Distal End Utilization in Nbs1n/h cells (1.8-fold, Figure 4C) is substantially reduced as compared to the effect in WT cells (5.3-fold, Figure 2D, Figure 4C). In summary, these data indicate that Nbs1 is important to limit Distal End Utilization to a similar degree as ATM.
Apart from suppressing Distal End Utilization, we considered whether ATM and/or Nbs1 might also affect the degree of end processing during EJ. For this, we cloned Distal-EJ amplification products from GFP+ sorted cells following I-SceI and Trex2 co-expression of WT (DMSO and ATMi treated), ATM−/−, and Nbs1n/h cells (DMSO and ATMi treated), (p3, p2 products shown in Figure 2C, Figure S1B). For each condition, 30 independent clones were sequenced to determine the Distal-EJ repair junctions.
As compared to an I-SceI+ Distal-EJ product, we classified the sequences into five groups: +1 insertion, 1 to 5 nt. deletions, 6 to 9 nt. deletions, 10 to 19 nt. deletions, and ≥20 nt. deletions (Figure 5 and Table S1). For WT cells, we found that Distal-EJ products showed mostly deletions of the I-SceI overhang region (17/30 with 1 to 5 nt. deletions), and the remaining clones showed only slightly larger deletions (12/30 with 6 to 9 nt. deletions, 1/30 with a 10 nt. deletion). For both ATMi treated WT cells and ATM−/− cells, we found an increase in the frequency of deletions greater than 9 nt., as compared to DMSO treated WT cells (12/30 for WT+ATMi, p = 0.0011; 17/30 for ATM−/−, p<0.0001; compared to 1/30 for WT). In contrast, for Nbs1n/h cells we found a reduction in clones showing deletions greater than 6 nt., in comparison to WT cells (1/30 for Nbs1n/h cells, p = 0.0004; compared to 13/30 for WT). In summary, while ATM and Nbs1 both suppress Distal End Utilization, these factors appear to show divergent effects on end processing, with ATM suppressing longer deletions, and Nbs1 promoting short deletions. Notably, for ATMi treated Nbs1n/h cells we found an increase in clones showing deletions greater than 9 nts., as compared to either WT or Nbs1n/h cells (29/30 for Nbs1n/h with ATMi, p<0.0001; compared to 1/30 for WT and 1/30 for Nbs1n/h). This latter result indicates that the increase in longer deletions caused by ATMi is dominant over the decrease in short deletions caused by the Nbs1n/h alleles.
The finding that Distal-EJ events in ATMi-treated cells show longer deletions, yet are promoted by c-NHEJ factors, indicates that deletion size may not necessarily be predictive of the involvement of the c-NHEJ pathway. This result is consistent with previous studies showing that while Xrcc4-deficiency causes longer deletion EJ products, DNA-PKcs-deficiency does not lead to elevated deletion sizes [38]–[41]. To confirm this distinction in our experiments, we performed the above sequence analysis using the DNA-PKcs−/− and Xrcc4−/− cell lines. Namely, we cloned Distal-EJ amplification products from GFP+ sorted cells following co-expression of I-SceI and Trex2 in these cell lines (p2, p3 products shown in Figure S1B), and subsequently sequenced 30 clones each (Figure 5, Table S1).
From this analysis, we found that junctions from DNA-PKcs−/− cells showed fewer deletions greater than 6 nts. in comparison to WT cells (3/30 for DNA-PKcs−/−, p = 0.0074; compared to 13/30 for WT), along with a number of 1–5 nt. deletions (16/30). Thus, DNA-PKcs−/− cells showed a shift towards shorter deletions as compared to WT cells, similar to the findings of Nbs1n/h cells. The rest of the DNA-PKcs−/− junctions were +1 insertions (11/30), which were not observed in WT cells, but were found in ATM−/− (9/30) and Nbs1n/h cells (12/30). In contrast, Xrcc4−/− cells show much more extensive deletions, with all clones showing ≥20 nt. deletions (30/30), compared to none with WT cells (p<0.0001). These results indicate that Xrcc4 is important to limit extensive deletions during Distal-EJ, whereas DNA-PKcs promotes short deletions.
Apart from variations in deletion size, use of microhomology and templated nucleotides are distinct between individual repair events, but these characteristics also are not necessarily predictive of the involvement of c-NHEJ. For instance, only clones from Xrcc4−/− cells showed any evidence of microhomology greater than 4 nt. (19/30 show a junction with 6 nt. of microhomology). The rest of the repair junctions observed in our experiments showed 0–4 nt. of microhomology, without any clear distinction between the cell lines. As the mechanistic requirements for limited microhomology during different EJ pathways is still unclear [42], [43], EJ events with 0–4 nts. of microhomology could be mediated by c-NHEJ factors or alt-EJ. Similarly, the +1 insertion events likely involve Family X DNA Polymerases (Pol X), which also could function during c-NHEJ or alt-EJ events [44]. Though, in this case, we observe an increase in +1 insertion events in cells deficient for Nbs1, ATM, and DNA-PKcs, which could reflect an improved recruitment of Pol X polymerases during EJ.
To summarize the junction analysis, we found that ATM and Xrcc4 limit the length of deletions, whereas Nbs1 and DNA-PKcs promote short deletions. In contrast, we found that Distal-EJ is suppressed via ATM and Nbs1, and that the elevated level of Distal-EJ caused by ATM-disruption requires both Xrcc4 and DNA-PKcs. These findings indicate that the role of individual factors during end processing is not predictive of their role during end utilization.
Limiting the use of incorrect ends during EJ repair of multiple chromosome breaks is likely an important aspect of genome maintenance, and hence tumor suppression. Using a method for quantifying end utilization during repair of two tandem DSBs, we present evidence that ATM and Nbs1 are important to limit Distal End Utilization. We also present evidence that the increase in Distal-EJ that is caused by ATM-disruption is dependent on c-NHEJ factors (DNA-PKcs, Xrcc4, and XLF). We suggest that ATM and Nbs1 may suppress genome rearrangements not only through activating the DDR, but also via promoting faithful end utilization during c-NHEJ. This notion is consistent with important previous studies showing that ATM supports correct utilization of hairpin coding ends during V(D)J recombination via c-NHEJ factors [13], [16]–[20]. Our findings indicate that such a role for ATM is not limited to Artemis-dependent c-NHEJ of hairpin ends generated by the Rag1/2 endonuclease, but is also important for Artemis-independent c-NHEJ repair of multiple DSBs with open ends. In summary, we suggest that cells that are deficient in ATM or Nbs1 are more prone to chromosome rearrangements during c-NHEJ of multiple DSBs.
In addition, we find that c-NHEJ-deficiency does not cause a substantial effect on Distal-EJ levels in ATM-proficient cells (within 2-fold of WT, Figure 3A). This finding is consistent with other studies showing that neither Ku70 nor Xrcc4 are required for chromosomal translocations that result from repair of multiple I-SceI-induced DSBs [45]–[48]. These studies have raised the possibility that c-NHEJ factors may not play a role in promoting chromosome rearrangements outside the programmed rearrangements during lymphocyte development. Rather, these studies suggested that alt-EJ mechanisms might be responsible for such chromosome rearrangements. However, we have presented evidence that c-NHEJ factors (Xrcc4, DNA-PKcs, and XLF) can promote genome rearrangements caused by ATM-deficiency. Thus, we suggest that c-NHEJ may indeed play a role during genome rearrangements, but specifically under conditions that enable incorrect end utilization (e.g. deficient in ATM or Nbs1).
The increase in Distal-EJ versus Proximal-EJ caused by disruption of ATM (and/or Nbs1) could be due to at least two mechanisms: increased break persistence and/or defective end tethering (Figure 6). Considering the former, ATM-disruption could enhance the persistence of each DSB, thereby increasing the probability of both DSBs existing simultaneously, leading to more Distal End Utilization. This model is supported by findings that DSBs formed during V(D)J recombination in ATM-deficient cells persist longer, even through multiple cell doublings [12]–[15]. However, c-NHEJ-deficiency also causes an increase in break persistence [49], but does not lead to a substantial increase in Distal-EJ. Thus, not all conditions that lead to elevated break persistence appear to cause an increase in Distal-EJ. To summarize the break persistence model, ATM (and/or Nbs1) could be important to limit the persistence of DSBs, and thereby reduce the probability that multiple DSBs occur simultaneously, which would limit the frequency of chromosome rearrangements.
Alternatively, disruption of ATM and/or Nbs1 could cause defective end tethering, thereby increasing the probability of distal end synapsis, and hence Distal End Utilization. A role for Nbs1 during end synapsis is consistent with the DNA tethering activity of the Mre11-complex (Mre11-Rad50-Nbs1) [50]–[52]. Such tethering could be important not only for recruitment of the sister chromatid during homologous recombination, but also for end synapsis during EJ. ATM could support this tethering function of the Mre11-complex, as Nbs1 is a target of ATM kinase activity [6], [7]. Alternatively, since ATM is important for recruitment of a number of factors to chromatin to activate the DDR, such factors could stabilize damaged chromatin [53], and thereby support faithful end tethering during repair. In a related model, ATM could regulate the end tethering functions of c-NHEJ factors, since ATM can phosphorylate XLF [54] and DNA-PKcs [55], the latter of which can tether DNA molecules in vitro [56]. A role for ATM during these events is supported by findings that combined loss of ATM with DNA-PKcs or Lig4 caused substantially elevated levels of broken mitotic chromosomes, as compared to either single mutant [15], [57]. Nbs1 could also be important for such ATM-dependent mechanisms of end tethering, since Nbs1 activates ATM kinase activity following DSBs [7]. Thus, ATM and/or Nbs1 could support the end tethering functions of either the Mre11 complex and/or c-NHEJ factors themselves, and thereby limit incorrect end utilization during EJ.
Of course, these two aspects of repair need not be mutually exclusive, as defects in end tethering could delay EJ causing increased break persistence, and vice versa. However, we suggest that even in situations of elevated DSB persistence, incorrect end tethering is still essential for generation of Distal-EJ products. In summary, we suggest that disruption of ATM (and/or Nbs1) leads to defective end tethering and/or elevated break persistence in a manner that results in a substantial elevation of incorrect end utilization during c-NHEJ repair of multiple DSBs (Figure 6).
We also find that individual factors show distinct effects on end processing during EJ. The end processing observed in these experiments could be influenced by 5′ to 3′ end resection, and/or other mechanisms of DSB end degradation. Since Nbs1 appears to promote 5′ to 3′ end resection during in vitro EJ assays [58], this mechanism likely contributes to its role in promoting short deletions during EJ.
In contrast to Nbs1, we find that ATM appears to suppress deletions, which is supported by recent findings that ATM limits terminal end processing of DNA ends in vitro, and during plasmid EJ in vivo [59], [60]. Furthermore, we find that ATMi causes longer deletions even in the Nbs1-deficient cells. This result indicates that loss of ATM-mediated end protection may enable the low level of Nbs1 in these cells (5-fold reduced relative to WT [27]) to facilitate end resection. Alternatively, loss of ATM-mediated end protection could lead to an Nbs1-independent mechanism of end degradation. The former model is supported by a recent study showing that Mre11 promotes the terminal end processing caused by ATM-disruption [60]. Somewhat paradoxical to these findings of ATM-mediated end protection, ATM has been shown to promote end resection as measured by recruitment of ssDNA binding protein (RPA) to DSBs [61], although apparently not in all circumstances [62]. Perhaps ATM may limit terminal end resection, but promote extensive end resection [61], [63]. Such a model is consistent with studies in yeast that support a two-step end resection process [64].
Notably, ATM and Nbs1 affect end processing in different directions, while both suppress Distal End Utilization. We also find a distinction between Xrcc4 versus DNA-PKcs. Namely, we find that Xrcc4 is important to limit the extent of deletions during EJ, while DNA-PKcs promotes short deletions. This distinction is consistent with other reports [38]–[41], as well as the notion that c-NHEJ is a modular and flexible process that can result in a variety of products [42]. In contrast, we find that both Xrcc4 and DNA-PKcs are important for the elevated level of Distal-EJ caused by ATM-disruption. In summary, these studies of ATM, Nbs1, Xrcc4, and DNA-PKcs indicate that the regulation of end processing appears to be distinct from that of end utilization.
In conclusion, correct end utilization is likely an important mechanism for limiting chromosome rearrangements that can lead to cancer development. While disruption of ATM kinase activity may be beneficial for promoting tumor cell death via clastogenic agents [31], [65], such a therapeutic strategy may also disrupt faithful end utilization in non-tumor cells, which could lead to therapy-related malignancies. Conversely, developing therapeutic strategies to enhance faithful end utilization in non-tumor cells could have the potential to reduce therapy-related malignancies. As well, since meganucleases are being developed as potential genome engineering tools [66], we suggest that Trex2 expression could enhance mutagenesis around the DSB site of meganucleases. However, as such nucleases may form DSBs at multiple sites, we also suggest that functional ATM would be critical for limiting genome rearrangements during such a therapeutic approach.
XLF−/− [34], DNA-PKcs−/− [32], and Artemis−/− [36] ES cells were generously provided by Dr. Frederick Alt, and ATM−/− ES cells [30] were generously provided by Dr. Yang Xu. Cells (107 in 0.8 ml Optimem, Invitrogen) were electroporated with 70µg of XhoI digested pim-EJ5-GFP at 710–720V/10µF. Hygromycin B selection (0.12 mg/ml) was used to select for targeting to the pim1 locus in XLF−/−, DNA-PKcs−/− and Artemis−/− cells, as confirmed by PCR analysis [28]. Puromycin selection (1.2 µg/ml) was used to select random integrants of EJ5-GFP in ATM−/− cells. Integration of an intact copy of the reporter in ATM−/− cells was confirmed by Southern blot analysis, as described previously [28]. Other cell lines with chromosomally integrated EJ5-GFP were described previously: WT ES (AB2.2), Nbs1n/h ES, Xrcc4−/− ES, and HEK293 [27], [28].
Mouse ES and HEK293 cells were cultured as described previously [28], and 105 cells were plated the day before an incubation with a mixture of 0.8µg of pCBASce, 0.4µg of pCAGGS-Trex2, and 3.6µL of Lipofectamine 2000 (Invitrogen), in 1ml of antibiotic-free media [27]. After 3 hr, the transfection media was removed and replaced with complete media containing either 10µM ATMi [31](EMD Biosciences) or DMSO (vehicle). Subsequently (3 days), half of each transfection sample was analyzed by FACS (CyAN ADP, Dako) to determine %GFP+ cells (Distal-EJ), and the other half was used to isolate genomic DNA for determination of Proximal-EJ, as described previously [27]. Briefly, genomic DNA was amplified using EJ5purF (p1, 5′ agcggatcgaaattgatgat) and KNDRR (p2, 5′ aagtcgtgctgcttcatgtg). The amplification products were purified (GFX, GE), and digested with I-SceI (NEB), separated on agarose gels, and detected with ethidium bromide, where complete digestion was confirmed with parallel samples from untransfected cells. The percentage of I-SceI-resistant product was calculated from the relative staining intensity of I-SceI+ versus I-SceI-resistant bands within the same lane, as described previously [27], [29].
For single clone analysis, we performed the same transfection protocol, except we included 0.4µg of dsRED-N1 (Clontech) and a total of 4.8µL of Lipofectamine 2000. Three days after transfection, we enriched for transfected cells by sorting dsRED+ cells, which we plated at low density to isolate single clones. For each clone, we determined whether the 5′ and 3′ I-SceI sites had been disrupted, using the Proximal-EJ assay described above, where the 5′ I-SceI site was analyzed using the primers KNDRF (p3, 5′ ctgctaaccatgttcatgcc) and EJ5purR (p4, 5′ cttttgaagcgtgcagaatg) [27].
To calculate Distal End Utilization for individual samples, the percentage of GFP+ cells was divided by the percentage of I-SceI-resistant amplification products. To facilitate comparison to WT, each individual Distal End Utilization value was divided by the mean value for WT DMSO treated cells. We amplified Distal-EJ products from GFP+ sorted cells from representative transfections, using KNDRF (p3) and KNDRR (p2). The amplification products were digested with I-SceI, as above. I-SceI-resistant bands were isolated and cloned into TA vectors (Invitrogen) for sequencing with the M13R primer.
For comparison of EJ frequencies, we used Student's unpaired t-test. For comparison of Distal-EJ breakpoint junctions, we used Fisher's Exact Test.
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10.1371/journal.pntd.0007689 | Paradoxical reactions in Buruli ulcer after initiation of antibiotic therapy: Relationship to bacterial load | We investigated the relationship between bacterial load in Buruli ulcer (BU) lesions and the development of paradoxical reaction following initiation of antibiotic treatment.
This was a longitudinal study involving BU patients from June 2013 to June 2017. Fine needle aspirates (FNA) and swab samples were obtained to establish the diagnosis of BU by PCR. Additional samples were obtained at baseline, during and after treatment (if the lesion had not healed) for microscopy, culture and combined 16S rRNA reverse transcriptase/ IS2404 qPCR assay. Patients were followed up at regular intervals until complete healing.
Forty-seven of 354 patients (13%) with PCR confirmed BU had a PR, occurring between 2 and 42 (median 6) weeks after treatment initiation. The bacterial load, the proportion of patients with positive M. ulcerans culture (15/34 (44%) vs 29/119 (24%), p = 0.025) and the proportion with positive microscopy results (19/31 (61%) vs 28/90 (31%), p = 0.003) before initiation of treatment were significantly higher in the PR compared to the no PR group. Plaques (OR 5.12; 95% CI 2.26–11.61; p<0.001), oedematous (OR 4.23; 95% CI 1.43–12.5; p = 0.009) and category II lesions (OR 2.26; 95% CI 1.14–4.48; p = 0.02) were strongly associated with the occurrence of PR. The median time to complete healing (28 vs 13 weeks, p <0.001) was significantly longer in the PR group.
Buruli ulcer patients who develop PR are characterized by high bacterial load in lesion samples taken at baseline and a higher rate of positive M. ulcerans culture. Occurrence of a PR was associated with delayed healing.
ClinicalTrials.gov NCT02153034.
| Buruli ulcer is a neglected tropical skin disease caused by the third most common pathogenic mycobacterium: Mycobacterium ulcerans. Paradoxical reaction, a phenomenon observed in some patients is characterised by worsening of existing lesion(s) with attendant pain and occurrence of new lesions during or after antibiotic therapy following an initial period of clinical improvement. This significantly affects treatment outcomes. In this clinical study, tissue samples obtained from patients were subjected to 16S rRNA/ IS2404 qPCR to measure bacterial load. This was to identify a link between bacterial load in BU lesions and the development of paradoxical reactions following initiation of antibiotic treatment. We found that 13% of participants developed PR. Patients who developed PR had higher baseline bacterial load; a higher rate of positive M. ulcerans culture and persistently positive culture during antibiotic treatment. Occurrence of a paradoxical reaction was associated with delayed healing.
| Buruli ulcer (BU) is a neglected tropical disease caused by infection with Mycobacterium ulcerans (M. ulcerans) which is common in rural parts of West African countries including Ghana. It causes large, disfiguring skin ulcers mainly in children aged 5 to 15 years although persons of any age can be affected [1]. Access to treatment in rural areas is limited and many patients present with late stage disease because of fear, suspicion about conventional medicine and the economic consequences for poor families [2]. The incidence of the disease is highly focal, and in Ghana for example, most cases occur in particular parts of the Ashanti Region [3]. The mode of transmission remains unknown but there have been major advances in understanding the mechanism of disease since the establishment of the WHO Buruli ulcer Initiative in 1998 together with improved diagnosis and management. The initial BU lesion is a subcutaneous painless nodule tethered to the skin or an intradermal plaque. These enlarge over a period of days to weeks and ulcerate in the centre. Ulcers are usually painless and have a necrotic base and irregular undermined edges [4, 5].
The mainstay of treatment is the combination of rifampicin and streptomycin or clarithromycin but additional treatment such as debridement and skin grafting, and early basic management with appropriate dressings and physiotherapy when an ulcer is close to a joint can minimize complications [6, 7]. The most common complication is paradoxical reaction occurring during or after treatment in 8–12%[5, 8] of patients in Africa. In an Australian population the phenomenon has been reported to occur more frequently (21%) in elderly patients [9]. Another study in Africa, also reported similar frequency of paradoxical reaction occurrence (22%) associated with trunk localization, larger lesions and genetic factors [10]. The time to development of paradoxical reaction varies widely between patients from antibiotic initiation, from few weeks in some patients to several months in others [5, 8, 9].
Paradoxical reactions cause anxiety to both patient and carer with the possibility that it represents uncontrolled or recurrent infection and indeed it is likely that earlier perceptions that antibiotics were ineffective for management of Buruli ulcer may have been influenced by such reactions. Culture of samples from the lesions are usually negative if the reaction occurs after completion of antibiotics but this does not exclude persistent infection since the sensitivity of culture for Mu is only 35–60%[11, 12]. Paradoxical reaction is thought to be due to an immunological response to residual M. ulcerans antigens which are known to persist for many months after successful treatment[9]. The immunological mechanism underpinning paradoxical reactions requires clearer elucidation in order to design appropriate evidence-based interventions for this important clinical phenomenon. Even though, several studies have associated paradoxical reactions with larger lesions, its relation to bacterial load has not been demonstrated. The aim of the present study was to investigate the clinical forms of paradoxical reactions in relation to their time of occurrence, the lesion type and bacterial load as potential risk factors for their occurrence.
From June 2013 to June 2017, patients with clinically suspected Buruli ulcer were screened at Agogo Presbyterian hospital, Tepa, Dunkwa and Nkawie-Toase Government hospitals. The diagnosis of Buruli ulcer was confirmed by M. ulcerans IS2404 PCR. Patients who had already started antibiotic therapy or refused to participate were excluded. All categories of BU lesions were included.
Demographic data of all participants, details of the timing and nature of paradoxical reactions were collected prospectively using WHO BU01 and study designed laboratory forms. The dimensions of lesions were documented using Silhouette (ARANZ Medical, Christchurch, New Zealand), a 3-dimensional imaging and documentation system together with digital photographs[13]. Patients were reviewed by an experienced clinician every 2 weeks up to 8 weeks and thereafter every month up to one year after completion of treatment. The time of complete healing was documented for all patients. All the patients recruited into the study were given combination antibiotic therapy of either rifampicin and clarithromycin or rifampicin and streptomycin for 56 days as recommended [7]. Two fine needle aspirates (FNA) were taken from non-ulcerated lesions; for patients with ulcerated lesions, two swabs from the undermined edges of ulcers were taken to confirm the diagnosis of BU by microscopy and PCR. The presence of viable bacteria was determined by taking samples for culture and 16S rRNA reverse transcriptase/IS2404 qPCR assay. Samples were collected at baseline and at weeks 4, 8, 12 and 16 (only if lesions remained unhealed). When a paradoxical reaction occurred, samples were taken from those lesions for culture and 16S rRNA reverse transcriptase/ IS2404 qPCR assay. Paradoxical reactions were defined by the presence of one or both of the following features as previously described: (i) an initial improvement in the clinical appearance of an M. ulcerans lesion during or after antibiotic treatment, followed by an episode of new inflammation, with or without pus formation, with significant enlargement of a healing lesion or its surrounding tissues or (ii) the appearance of a new lesion(s)[14].
Clinical samples were transported to the laboratory in appropriate transport media and processed immediately upon arrival at the laboratory. All routine laboratory tests and molecular assays were conducted at Kumasi Centre for Collaborative Research in Tropical Medicine (KCCR).
For laboratory confirmation of Buruli ulcer disease, smear microscopy for acid-fast bacilli, culture on Lowenstein-Jensen medium and IS2404 qPCR were performed by well-established methods as previously described[15–17]. A final diagnosis of Buruli ulcer was based on the IS2404 qPCR result which was the most sensitive test.
FNA and swab samples were transported from the study site to the KCCR laboratory stabilized in 500 μl RNA protect (Qiagen, UK). Whole transcriptome RNA and whole genome DNA were extracted separately from the same clinical sample. The RNA and DNA isolation was carried out within 5 hours of sample collection using the AllPrep DNA/RNA Micro kit (Qiagen, UK). RNA extracts were reverse transcribed into cDNA using Quantitect kit as described elsewhere[13]. The cDNA prepared was subjected to qPCR for detection of human glyceraldehyde-3-phosphate dehydrogenase (GAPDH) mRNA [17]. The detection of the GAPDH was for quality assurance purposes to confirm correct sample collection and to exclude false negative 16S rRNA RT qPCR results. All whole transcriptome RNA extracts from Buruli ulcer lesions tested positive when subjected to GAPDH mRNA RT qPCR at baseline.
The cDNA was then subjected to 16S rRNA qPCR and DNA to IS2404 qPCR to increase the specificity for M. ulcerans and for quantification of the bacterial load as previously described[16]. Ten-fold serial dilutions of known amounts of a plasmid standard of IS2404 (99 bp) and 16S rRNA (147 bp) (Eurofins MWG Operon, Ebersberg, Germany) were included with PCR amplification for preparation of a standard curve. M. ulcerans bacillary loads in original clinical samples were calculated based on threshold cycle values per template of IS2404 qPCR (standard curve method) adjusted to the whole amount of DNA extract and the known copy number of 207 IS2404 copies per M. ulcerans genome on average.
The raw data generated from the study were entered in Microsoft Excel (Microsoft Corporation, Redmond, WA) and analyzed using GraphPad Prism version 5.0 (GraphPad Software, Inc., La Jolla, CA) STATA statistical package (StataCorp). Continuous variables such as age, IS2404 copies and M. ulcerans 16SrRNA copies were compared using the Mann-Whitney U test. Chi-square test was used to compare the frequencies of all categorical variables, except the location of lesions which was compared using the Fisher’s exact test.
The frequency and percentage of missing values for each variable were collected, analysed and reported. When there were missing values for the variables of interest/outcome, exclusion of observations with missing values for the variables of interest/outcome was considered. Highly incomplete covariates (>33% of observations missing) were excluded from analyses.
The Kaplan-Meier survival analysis was used to determine the effect of developing paradoxical reaction on time to healing. Simple proportions of positive AFB and culture among the paradoxical reaction and the non-paradoxical reaction participants were also calculated. Logistic regression was performed to assess incidence rates and association of variables with PR. Univariate analysis was done to determine crude rate ratios and a multivariate analysis performed adjusting for age, gender and location of lesion to test associations with characteristics assessed at pretreatment. P value < 0.05 was considered statistically significant in all the analyses.
Verbal and written informed consent was obtained from all eligible participants and from parents or legal representatives of participants aged 18 years or younger. Ethical approval was obtained from the Committee of Human Research Publication and Ethics, School of Medical Sciences, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana (CHRPE/AP/229/12) and registered with ClinicalTrials.gov identifier NCT02153034.
A flowchart indicating the recruitment of patients is shown as Fig 1. Forty-seven (13%) out of 354 patients in the study developed paradoxical reactions. Of the 32 lesions that enlarged, 24 (51%) were just warm and enlarged, and 8 (17%) were also warm and pus filled, with or without pain. When enlargement happened, it was always during antibiotic therapy. Fifteen patients (32%) developed new lesion(s): 14 of them had a new lesion developing close to the original site and one had multiple new lesions around the existing one (Fig 2). Thirty-two (68%) paradoxical reactions occurred during antibiotic treatment. The median (IQR) time from the start of antibiotic administration to development of paradoxical reaction was 6 (4–11) weeks but the time to occurrence of an enlarged lesion (6; 4–8 weeks) was shorter compared to that for a new lesion (10; 5–28 weeks: p<0.01). A higher proportion (26/29 = 90%) of patients with nodules and plaques with paradoxical reaction had an enlarged lesion in comparison to patients with oedematous lesions and ulcers (6/18 = 33%: p<0.001). The majority of paradoxical reactions manifested as new lesions occurred in patients with oedematous lesions and ulcers (12/18 = 66%). No other variables significantly influenced the type of reaction that occurred.
Patients who developed a paradoxical reaction had a significantly higher bacterial load both in terms of IS2404 copy numbers, median cps/ml (IQR) [500 (500–8000) vs 500 (500–500), p = 0.020] and higher viable organisms measured by Mu 16SrRNA, median cps/ml (IQR) [500 (500–4875) vs 500 (0–1000), p = 0.014] at baseline than patients with no paradoxical reaction (Fig 3). This was supported by the finding that a larger proportion of patients who developed a paradoxical reaction had positive AFB microscopy (61%) compared to those who did not (31%; p = 0.003). Similarly, the proportion of patients with positive culture results was significantly higher in those who developed paradoxical reaction (44% vs 24%; p = 0.025) (Table 1).
Paradoxical reactions were related to the clinical form of the initial lesion. They were more common in patients with a plaque (27%) or oedematous lesion (23%) than in those with nodule (15%) or ulcer (7%) (p <0.001). Their incidence was significantly related to lesion category at presentation; 9% in category I and 10% in category III had a paradoxical reaction compared to 19% in category II (p = 0.04) (Table 2). Paradoxical reaction was equally common in patients who received streptomycin or clarithromycin combined with rifampicin. Using a logistic regression model, multivariate analysis showed that plaque (OR 5.42; 95% CI 2.25–13.04); p<0.001), oedematous lesion (OR 4.13; 95% CI 1.37–12.42; p = 0.012), nodular lesion (OR 2.63; 95% CI 1.12–6.17; p = 0.026) and category II lesions (OR 2.37; 95% CI 1.19–4.71; p = 0.014) were strongly associated with development of paradoxical reaction adjusting for age, gender and location of lesion.
Positive cultures for M. ulcerans and/or positive 16S rRNA results were found in two out of fifteen patients (A and B in Fig 4) who had a paradoxical reaction after completion of antibiotic treatment. In patient A, a new lesion appeared on the right knee at week 10 at the same time as the original lesion on the right thigh re-ulcerated. Culture was positive from the new lesion. No additional antibiotics were administered and both lesions healed by week 24. Patient B, a 16-year-old girl with an ulcer on the left upper arm, developed a new lesion close to the initial lesion at week 11 which tested positive to combined 16S rRNA/IS2404 qPCR assay. Both lesions healed completely at week 20 without further antibiotic therapy. All other paradoxical reactions before or after treatment had negative culture and 16S rRNA/IS2404 qPCR results. In 23 patients who had positive M. ulcerans 16S rRNA or culture results at week 4, the median time to developing a PR was 6 weeks (IQR 4–8) compared with 13 weeks (IQR 6–26, p = 0.015) in 10 patients with negative week 4 M. ulcerans 16s rRNA/culture results.
The median time to complete healing for patients with paradoxical reaction was 28 weeks compared to 16 weeks for those with no PR (p <0.001) (Fig 5). By the end of antibiotic treatment at 8 weeks only 2 of 47 (4%) PR group patients had completely healed compared to 42 of 307 (12%) in the no PR group. Patients with PR had a 1.58-fold increase (95% CI 1.23–2.10) in the time to complete healing of Buruli ulcer lesions compared to those who did not develop PR.
Paradoxical reactions have been reported previously in 8–12% of Buruli ulcer patients in Africa during or after treatment with antibiotics [5, 8]. In the present study the overall incidence was similar at 13% but lower than that which was reported in an Australian cohort [9], possibly because of the younger age distribution in this study compared to the elderly population in the Australian study; older age is a known risk of developing PR. We also recognised some distinctions in the clinical presentation. During antibiotic treatment for 8 weeks the common form of paradoxical reaction was re-enlargement of the lesion after healing had begun. This occurred more than 4 weeks after initiation of treatment by which time the necrotic tissue around the lesion had cleared by auto-debridement and it was usually associated with new inflammation, sometimes severe with pus formation. After completion of antibiotic treatment, paradoxical reactions consisted mainly of new inflammatory lesions adjacent to the original one, with or without new ulceration in the original lesion. This was more common when the initial lesion was an ulcer or oedematous lesion, possibly because bacteria were more widely disseminated around such lesion. It takes a longer time to kill the bacteria and clear mycolactone in the skin before inflammatory process due to dead bacteria in the skin starts which results in delayed PR even at distant sites occurring as new lesions.
It is difficult to make a distinction between paradoxical reaction and treatment failure when viable M. ulcerans can still be detected by culture or by the 16S rRNA assay in lesions during or after antibiotic treatment [13]. This was the case in 2 of 11 patients who developed new inflammatory lesions after completion of antibiotics but they were included as paradoxical reactions because they had clinical features of inflammation such as pain and/or pus formation and in each case the inflammation settled without further antibiotic treatment or any other additional therapy. The diagnosis of paradoxical reactions is usually clinical but investigations such as AFBs, mycobacterial culture, PCR for IS2404 repeat sequence and histopathology may be done. Viable organisms may also be detected using the 16S rRNA assay. In the present study, 2 patients were culture and 16S rRNA positive at the time of development of PR. Other studies have reported negative mycobacteria cultures at the diagnosis of PR [5, 9, 18]. In our study, no additional treatment (antibiotics, surgery or steroids) was given when PR was detected. However, other treatments including aspiration of pus without additional antibiotics[18], surgical excision [5] and administration of steroids [9] have been reported.
It is impossible to estimate accurately the total M. ulcerans bacterial load in a Buruli ulcer lesion but using a simple sampling method and estimating bacterial load by the number of copies of IS2404 or 16S rRNA, we found an association between paradoxical reaction and high baseline bacterial load. This was supported by the finding that AFB detection and M. ulcerans culture were also more likely to be positive in these patients. The pathogenesis of paradoxical reaction in M. ulcerans disease is unknown but one hypothesis is that it is caused by an inflammatory reaction that, prior to antibiotic treatment, is suppressed by mycolactone, the M. ulcerans toxin. As the organisms are killed by antibiotics, mycolactone production ceases and its suppressive effect is lost causing a rebound of inflammation. This would be analogous to paradoxical reactions in M. tuberculosis and HIV co-infected patients when anti-retroviral treatment restores the immune response. In immune reconstitution inflammatory syndrome (IRIS) associated with HIV and M. tuberculosis or cryptococcal co-infection it has been postulated that antigens of the co-infecting pathogen accumulate before the immune response recovers leading to an excessive acute inflammatory response during anti-retroviral treatment[19].
There is a considerable disparity in the time to development of the paradoxical reaction. In this study it occurred from 4 to 28 weeks following initiation of therapy but 2 to 58 weeks is reported in other studies[9, 20]. We predicted that a paradoxical response would happen earlier in patients in whom M. ulcerans was cleared rapidly from their lesion. In fact, the opposite was the case; when the M. ulcerans 16S rRNA assay and culture were negative at 4 weeks, the paradoxical reaction occurred later than in those whose tests were still positive at 4 weeks. Unfortunately, we were unable to measure mycolactone in this study so the observation remains difficult to explain. Further studies of the pattern of cytokine secretion during treatment may shed some light on the problem.
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10.1371/journal.ppat.1000578 | Quantitation of Human Seroresponsiveness to Merkel Cell Polyomavirus | Merkel cell carcinoma (MCC) is a relatively uncommon but highly lethal form of skin cancer. A majority of MCC tumors carry DNA sequences derived from a newly identified virus called Merkel cell polyomavirus (MCV or MCPyV), a candidate etiologic agent underlying the development of MCC. To further investigate the role of MCV infection in the development of MCC, we developed a reporter vector-based neutralization assay to quantitate MCV-specific serum antibody responses in human subjects. Our results showed that 21 MCC patients whose tumors harbored MCV DNA all displayed vigorous MCV-specific antibody responses. Although 88% (42/48) of adult subjects without MCC were MCV seropositive, the geometric mean titer of the control group was 59-fold lower than the MCC patient group (p<0.0001). Only 4% (2/48) of control subjects displayed neutralizing titers greater than the mean titer of the MCV-positive MCC patient population. MCC tumors were found not to express detectable amounts of MCV VP1 capsid protein, suggesting that the strong humoral responses observed in MCC patients were primed by an unusually immunogenic MCV infection, and not by viral antigen expressed by the MCC tumor itself. The occurrence of highly immunogenic MCV infection in MCC patients is unlikely to reflect a failure to control polyomavirus infections in general, as seroreactivity to BK polyomavirus was similar among MCC patients and control subjects. The results support the concept that MCV infection is a causative factor in the development of most cases of MCC. Although MCC tumorigenesis can evidently proceed in the face of effective MCV-specific antibody responses, a small pilot animal immunization study revealed that a candidate vaccine based on MCV virus-like particles (VLPs) elicits antibody responses that robustly neutralize MCV reporter vectors in vitro. This suggests that a VLP-based vaccine could be effective for preventing the initial establishment of MCV infection.
| For more than 50 years it has been known that some polyomavirus types can induce cancer in experimental animals. However, associations between the various polyomaviruses known to chronically infect most humans and the development of cancer have been difficult to uncover. Last year, DNA from a new human polyomavirus, called Merkel cell polyomavirus (MCV), was found embedded in an uncommon form of skin cancer called Merkel cell carcinoma. Emerging evidence indicates that most adults display detectable immune responses to MCV, suggesting that most individuals eventually become infected with the virus. In this study, we investigate antibodies that directly bind the protein coat of MCV, thereby obstructing its ability to penetrate cultured cells. We found that the magnitude of antibody responses against MCV varies dramatically among normal adults. Interestingly, patients suffering from MCV-associated Merkel cell carcinoma display uniformly strong antibody responses against the virus. This suggests that the development of Merkel cell carcinoma is preceded by an unusually robust MCV infection. It is currently unclear whether MCV infection may also be associated with additional diseases aside from Merkel cell carcinoma. Quantitation of immune responsiveness to the virus, using techniques reported here, could help identify such links.
| The Polyomaviridae are a diverse family of non-enveloped DNA viruses named for some family members' ability to cause various types of tumors in experimentally challenged animals. Although BK and JC polyomaviruses (BKV and JCV) are highly prevalent in human populations, neither virus has been clearly shown to cause cancer in humans (reviewed in [1]). A previously unidentified polyomavirus was recently found associated with Merkel cell carcinoma (MCC), a relatively unusual form of skin cancer that tends to strike elderly or immunocompromised individuals ([2], reviewed in [3],[4]). Sequences from this new virus, called Merkel cell polyomavirus (MCV or MCPyV), have been confirmed to be present in a majority of MCC tumors [5]–[8]. The viral DNA is maintained as a circular episome during productive infection but is clonally integrated into the cellular DNA of MCV-positive MCC tumors. Integrated viral genomes carry a characteristic pattern of mutations of the large T antigen gene that produce truncating deletions of the T antigen protein [9]. The mutations abrogate the protein's ability to drive replication of the viral DNA but preserve regions with predicted oncogenic potential. In some integrated viral genomes, deletions also occur in the late region of the virus encoding the viral capsid proteins [5],[10]. Taken together, the available evidence suggests that nonproductive integration of MCV genomic DNA into the host cell's DNA is an etiologic factor underlying the development of most cases of MCC.
Recent serological studies using recombinant MCV capsid proteins have shown that about 50–80% of adults display detectable MCV-specific antibody responses [11],[12]. This suggests that MCV infection is common, but only rarely leads to MCC. Although a majority of adults are seropositive for MCV, our initial serological studies suggest that some individuals display stronger humoral responses to MCV than others. To more accurately quantitate MCV-specific serum antibody responses in human subjects, we developed an assay for measuring antibody-mediated neutralization of cellular transduction with an MCV-based reporter vector. The assay employs very low viral particle doses, allowing improved accuracy and reproducibility compared to previously-reported MCV serological methods.
Unlike enzyme-linked immunosorbent assays (EIAs), which simultaneously measure both neutralizing and non-neutralizing antibodies, viral neutralization assays have the useful feature of measuring only the subset of antibodies that are likely to confer protection against infection. Neutralization assays have therefore been used for characterizing candidate vaccines [13]. Although VLP-based vaccines against viruses such as human papillomavirus (HPV) and hepatitis B virus are highly immunogenic, it appears that VLPs based on some polyomavirus types can be poorly immunogenic in animal model systems [14]. Using the MCV reporter vector-based neutralization assay, we show that MCV VLPs elicit robust functional antibody responses and thus could potentially be employed in vaccines aimed at preventing MCV infection.
Isolation of infectious MCV virions has not yet been reported. To simulate MCV infection in vitro, we generated gene delivery vectors employing the VP1 and VP2 capsid proteins of MCV. The MCV reporter vectors were produced by transfecting human embryonic kidney-derived 293TT cells [15] with expression plasmids carrying codon-modified versions of MCV VP1 and VP2 genes of MCV isolate 339 [2],[12]. For initial optimization experiments, the VP1 and VP2 expression plasmids were co-transfected with a reporter plasmid encoding GFP. The transfected cells produced high yields of capsids with a VP1∶VP2 ratio of about 6∶1 [12]. A fraction of the particles encapsidated the GFP reporter plasmid. The GFP transducing potential of the MCV-based reporter vector particles was titered on HeLa cells, which were found to be permissive for transduction with the GFP reporter gene.
Previously-identified polyomaviruses encode a minor capsid protein, VP3, whose translation initiates from an in-frame methionine (Met) codon within the VP2 open reading frame. However, MCV lacks the conserved Met-Ala-Leu motif that forms the amino-terminus of all previously described polyomavirus VP3 proteins. We generated expression plasmids encoding possible alternative VP3 proteins initiated from MCV VP2 Met46 or Met129 codons. While inclusion of VP2 improved the infectivity of the MCV reporter vector by about five-fold, compared with using VP1 alone, inclusion of the candidate VP3 expression constructs either slightly reduced or did not affect reporter vector infectivity (data not shown). The results suggest that, in contrast to other polyomaviruses, MCV may not encode a functional VP3 protein.
It has recently been shown that bacterially-expressed VP1 capsomers based on MCV isolate 350 are serologically distinct from MCV339 capsomers [11]. Like MCV339, MCV350 was isolated from an MCC tumor. We attempted to generate reporter vectors based on the MCV350 VP1 protein. However, the VP1 protein of MCV350 was rapidly degraded to undetectable levels in 293TT cell lysates (Figure S1). Attempts to purify MCV350 capsids by ultracentrifugation were similarly unsuccessful (data not shown). The results indicate that MCV350 encodes a structurally defective VP1 protein, possibly due to mutations arising during tumorigenesis. This concept is consistent with the fact that MCV350 VP1 residues His288, Ile316 and Asn366 differ from the consensus Asp, Arg or Asp residues (respectively) that are highly or absolutely conserved among all known polyomaviruses, including MCV339 and a variety of more recently described MCV VP1 isolates [16].
The transducing potential of a viral vector can typically be blocked by antibodies capable of neutralizing the virus on which the vector is based. To develop a reporter vector-based MCV neutralization assay, we employed a highly sensitive Gaussia luciferase (Gluc) reporter gene. 293TT cells [15], which stably express SV40 large T antigen, were used as an infection target. Successful transduction of 293TT cells results in T antigen-mediated amplification of the transduced Gluc reporter plasmid, which carries the SV40 origin of replication. The MCV-Gluc/293TT assay is highly sensitive, with MCV-Gluc reporter vector doses of 80 pg of VP1 per well (roughly 8 pM with respect to VP1 or roughly 100 virions per cell) yielding signal to noise ratios of 1000∶1.
A pooled human serum sample was serially diluted and tested for the ability to neutralize MCV vector-mediated transduction of the Gluc gene into cells. 50% neutralizing titer (EC50) was calculated by fitting a sigmoidal dose-response curve to luminometric values for the dilution series. The calculated EC50 for the pooled serum occurred at a 17,900±2500-fold serum dilution (Figure 1). Serum from a rabbit inoculated with MCV VLPs (see below) also robustly neutralized the infectivity of the MCV-Gluc reporter vector, while preimmune serum from the rabbit was less than 50% neutralizing at the 1∶100 serum dilution. Since the pre-immune rabbit serum showed non-specific neutralizing effects at dilutions less than 1∶100, this dilution was chosen as a cutoff for subsequent work.
A control experiment using IgG purified out of the pooled human serum gave a neutralization curve that overlapped that of the original serum (EC50 = 14,900±2200). Conversely, stripping the pooled serum of immunoglobulins reduced the EC50 by nearly 40-fold (data not shown). The results demonstrate that the MCV vector-neutralizing activity of serum diluted 1∶100 or greater is entirely or almost entirely attributable to antibodies.
Serological cross-reactivity between BKV and SV40, which occupy a phylogenetic cluster that also includes JCV, has previously been documented (reviewed in [1] ). MCV is part of a different phylogenetic cluster that includes African green monkey B-lymphotropic polyomavirus (LPV) and murine polyomavirus (MPyV). It has long been suspected that an LPV-like virus infects humans [17]. Kean and colleagues have recently confirmed that 10–20% of human subjects display LPV-specific antibody responses in a capsomer-based EIA. The report further demonstrated that antibodies specific for MCV do not cross-react with LPV [11]. To verify that the vector-based MCV neutralization assay is specific for MCV, we developed a neutralization assay based on MPyV, which, in contrast to LPV, is not thought to infect humans. Neither the pooled human serum nor the MCV-specific rabbit serum inhibited transduction of 293TT cells by the MPyV reporter vector (Figure 1). In contrast, the MPyV reporter vector was neutralized by control serum from a rabbit immunized with MPyV VP1 [18]. Similar results were observed when the MPyV reporter vector and sera were applied to murine NIH-3T3 cells (data not shown). We also developed an LPV reporter vector and confirmed the observations of Kean and colleagues that 10% of serum samples from paid donors had very low neutralizing titers to LPV reporter vectors (data not shown). The majority of donors with neutralizing LPV titers did not have significant MCV neutralizing titers, although other sera did (see below). The results demonstrate that neutralizing antibodies in human sera are specific for MCV and not one of MCV's known near relatives.
Under ideal circumstances, the EC50 values observed in neutralization assays and VLP-based EIAs reflect the affinity of relevant antibodies for the viral capsid. This requires that the assay conditions satisfy the assumptions of the law of mass action. This concept was first put forward in 1933 by Andrewes and Elford as the “percentage law,” which states that the virus-neutralizing titer of an antibody preparation is not affected by the amount of virus, so long as the antibody is in excess over the virus [19]–[21]. In other words, if the concentration of antigen in the assay approaches or is in excess of the affinity constants of the antibody/antigen interactions being measured, antibody is stripped from solution before affinity-driven equilibrium between bound and unbound antibody can be reached. As a consequence, the EC50 begins to reflect the dose of antigen, rather than the affinity of the interaction. A straightforward strategy for testing whether a seroassay complies with the percentage law is to examine EC50 values for various antigen doses [22]–[24]. Under compliant conditions, the EC50 is insensitive to antigen dose.
Neutralization assays of the pooled human serum using MCV-Gluc doses ranging from 16 to 240 pg of VP1 per well gave neutralization curves that were not significantly different, with EC50 values ranging from 15,600 to 17,900 (Figure 2). In contrast, the use of MCV-Gluc doses of 800 pg or 1.2 ng of VP1 per well resulted in lower EC50 values (7600 and 2800, respectively). VLP-based EIAs using VP1 doses of 100 or 33 ng per well gave dramatically lower EC50 values (230 and 460, respectively, Figure 2). The results indicate that, using standard antigen doses, the neutralization assay complies with the percentage law and the EIA does not. Optimized polyomavirus VLP EIA methods use VP1 doses ranging from 6 to 200 ng per well [25]–[27], suggesting that polyomavirus VLP EIA could not be adapted to the <240 pg/well doses required to comply with the percentage law. The data indicate that the neutralization assay offers a more accurate and sensitive measurement of serological responsiveness to MCV than the EIA. The fact that the neutralization assay is insensitive to virion dose would also be expected to make it more reproducible than the EIA.
To further explore the relative accuracy of the neutralization assay, we tested serial dilutions of sera from a selected set of 10 blood donors whose EIA reactivity was robust enough to allow calculation of an EC50 value [12]. The blood donors were compared to 12 MCC patients whose tumors were found to harbor MCV DNA sequences. As seen in Figure 3, the neutralization assay allowed improved discrimination between the two groups' seroresponsiveness to MCV. While the EIA suggested a 4-fold difference between the geometric mean titers (GMT) of the blood donor and MCC patient groups (GMT of 876 and 3,390, respectively), the neutralization assay revealed a >10-fold difference (GMT of 21,500 and 222,000, respectively) between the two groups, with correspondingly stronger p values (Figure 3). Furthermore, EIA EC50 values for individual subjects were an average of 50-fold lower than their neutralizing EC50 values (Figure S2), confirming the greater accuracy of the neutralization assay.
It was striking that subjects with MCC displayed significantly higher neutralizing titers than a selected group of strongly seropositive blood donors (Figure 3). To better characterize this apparent difference; we tested a set of 48 sera from older adults (age range 47–75 years) without diagnosed MCC. The control subject sera were compared to a total of 21 MCV-positive MCC patients (age range 14–95 years). As seen in Figure 3, MCV+ MCC patients invariably displayed high titer MCV-neutralizing responses, with a GMT of 160,000. Control subjects, in contrast, showed a broad, continuous distribution of neutralizing titers, with a significantly lower GMT of 2700 (p<0.0001). Only 7/48 (15%) of control subjects displayed titers within or above the interquartile range of the MCV+ MCC patient population.
The prevalence of MCV-neutralizing activity in the control subject population was high, with 88% (42/48) of the subjects displaying EC50 values falling within the tested range of serum dilutions. It is not clear whether sera with titers below 100 are weakly MCV seropositive or rather contain non-specific neutralizing activity, as was observed for the pre-immune rabbit serum (Figure 1). The neutralization assay results confirm recent findings showing that MCV-specific seroprevalence is common among older adults and suggests that the 67% EIA-based seroprevalence observed in this same group of subjects [12] may have been a slight underestimate.
The presence of high MCV-specific titers in all the MCV-positive MCC patients could, in theory, reflect an immunocompromised state in which latent polyomavirus infections are allowed to resurface, triggering strong virus-specific antibody responses. To test this hypothesis we evaluated sera from the same set of control subjects and MCC patients for the presence of anti-BKV antibodies using a BKV-based reporter vector [28]. There was no apparent correlation between BKV and MCV titers in individual subjects (data not shown), suggesting a lack of general reactivation of polyomaviruses as well as a lack of cross-reactivity between the two virus types in the neutralization assays. The BKV GMT was 5,100 for control subjects and 2,300 for MCV-positive MCC patients (Figure 3). This slight difference in titer was not statistically significant.
Sera from a set of six MCC patients whose tumors did not contain detectable amounts of MCV DNA were also tested in the neutralization assay. 4/6 of the MCV− MCC patients displayed very low titers in the neutralization assay (Figure S3).
The incidence of MCV seroresponsiveness has been shown to increase with subject age, reaching an apparent maximum prevalence in late adulthood [11],[12]. Age-specific trends in the MCV-neutralizing titers of the control subjects shown in Figure 4 were not evident, perhaps in part because the distribution of ages is clustered about the mean (56±5.7 years, Figure S3). Interestingly, adult MCV+ MCC patients displayed a marginally significant inverse correlation between subject age and MCV-neutralizing titer (p = 0.0497, Spearman r = −0.4443, Figure S3). The trend is reminiscent of the gradual age-related decline in BKV-specific antibody responses observed in cross-sectional studies of adults [29]. The data indicate that the higher MCV-specific titers of the MCC patients are unlikely to be attributable simply to their more advanced average age relative to the control subjects.
One possible explanation for the higher MCV-specific antibody titers of MCV+ MCC subjects could be that the MCC tumor itself serves as a source of MCV capsid protein immunogen. One previous report has documented an MCC tumor that carries a VP1 gene with a large internal deletion that would presumably render the protein incapable of forming intact capsids [5]. The current study suggests that the MCC350 tumor would likewise be genetically incapable of expressing conformationally intact capsids or of making stable protein. However, it remains conceivable that other MCC tumors might produce MCV capsid protein. To address this question, we performed immunohistochemical staining of MCC tumor sections. Since sections of the tumors from subjects on whom the serological studies were performed were unavailable, we selected 10 MCC tumors that had previously scored positive for expression of MCV T antigen [10]. Unfortunately, matched sera for this set of tumors were unavailable.
MCC tumor sections were co-stained with MCV VLP-specific rabbit serum and antibody CM2B4, which is specific for MCV T antigen [10]. To generate positive controls, HeLa cells were transfected with expression constructs encoding either MCV VP1 or MCV T antigen. The transfected cells were paraffin-embedded and sectioned in a manner analogous to the preparation of the MCC tumor sections. As seen in Figure 5, T antigen and VP1 were readily detectable in the appropriate HeLa control cells. MCC tumor cells stained positive for MCV T antigen but negative for MCV VP1. Some MCC tumor sections were co-stained with antibody to cytokeratin-20 (CK20, a histological marker of MCC tumor cells) instead of CM2B4. While CK20 was readily visualized in MCC tumor cells, VP1 was again not detected in the tumor cells (Figure 5). 10/10 MCV T antigen-positive MCC tumors analyzed displayed an absence of VP1 staining. The results indicate that most MCC tumors produce little or no MCV VP1.
To investigate the functional immunogenicity of MCV VLPs, serum from a rabbit inoculated with purified MCV VP1/VP2 VLPs was tested using the reporter vector neutralization assay. Hyperimmune serum from the animal displayed a neutralizing titer of 1.9 million±0.4 million (Figure 1, top panel).
Five mice were also administered MCV VLPs. Two of the mice received an initial prime of VLPs without adjuvant, while three other mice received the VLP prime in complete Freund's adjuvant. All the mice received a booster dose of VLPs in incomplete Freund's adjuvant. Mice receiving the unadjuvanted prime displayed neutralizing EC50 titers of 0.9 and 3.2 million, while the three mice receiving the priming dose with adjuvant displayed titers of 1.1, 1.1 and 1.6 million. The results show that MCV VP1/VP2 VLPs can elicit potent MCV vector-neutralizing antibody responses in a vaccine setting.
The results show that, while a majority of older adults are exposed to MCV, the magnitude of serological responsiveness to the viral capsid proteins varies continuously across a 10,000-fold range. Compared to control subjects, all MCV+ MCC patients in the study displayed unusually high-titer humoral responses to MCV. In an initial EIA-based study establishing the prevalence of serological responsiveness to MCV in human subjects, we found that sera from MCV+ MCC patients contained MCV-specific antibodies at levels that appeared to saturate the EIA at the tested 1∶500 serum dilution [12]. EIA-saturating responses were less common among various groups of control subjects. In the current report we extend these observations, providing accurate scalar measurements of human seroresponsiveness to MCV.
The human polyomaviruses BKV and JCV are thought to establish latent infections that persist for decades [30]. For these virus types, reactivation from latency and active shedding of virions, which can occur under conditions of immunosuppresion, is positively correlated with serum antibody responses to the viral capsid proteins [27],[31],[32]. Thus, strong seroresponsiveness against MCV may record a history at some point of relatively uncontrolled MCV infection. Although it seems paradoxical that MCV infection would not be controlled by antibody responses expected to neutralize the infectivity of the virus, it is possible to imagine that MCV, like BKV and JCV, is able to establish a reservoir of latently infected cells. Such latent infections might be resistant to clearance by neutralizing antibodies and thus could serve as a durable source of immunogenic virions, even in the face of effective neutralizing antibody responses. Alternatively, a putative delayed immune response might have resulted in a high viral load that ultimately did induce high antibody levels. Since responses to BKV were similar in MCC patients and control subjects, it appears that MCC is associated with a specific failure to control MCV infection, as opposed to a more generalized failure to control all polyomavirus infections.
It is important to note that about a third of control subjects we studied displayed MCV responsiveness in the same range as MCV+ MCC patients (Figure 4). In light of the rarity of MCC (roughly 1500 cases per year in the United States, [33] reviewed in [34]), the results imply that most individuals who mount strong serological responses against MCV will not ultimately develop MCC. This is reminiscent of data indicating that exposure to ultraviolet light correlates with (but obviously does not guarantee) the development of MCC (reviewed in [4],[6]). Taken together, the results suggest a model in which uncontrolled MCV infection is one of multiple carcinogenic insults underlying the development of most cases of MCC.
Although MCV DNA has been detected in skin, bowel, lymph node, and respiratory tract samples [2], [35]–[37] the normal site or sites of productive MCV replication and the character of actively replicating MCV strains remains unclear. It is also unclear whether MCV infection may be a factor in other forms of disease in addition to MCC. MCV DNA sequences have recently been detected in a fraction of non-melanoma, non-MCC skin cancers, but a causal link between MCV and these forms of cancer has not yet been clearly established [38],[39]. While it is formally possible that neutralization of authentic MCV in the bona fide cellular target might differ with neutralization in 293TT cells, our results suggest that the current assay provides quantitative analysis of seroreactivity to a large subset of MCV neutralizing antibodies as reflected by the high rates of seropositivity detected in both MCC patients and in the general population. This assay can reveal potential links between the immunogenic infection with the virus and a disease state, such as MCC. Compared to VLP-based EIAs, the neutralization assay presented in this work demands less operator hands-on time and provides substantially more accurate results. Thus, the neutralization assay should become a preferred technique for investigating possible correlations between highly immunogenic MCV exposure and other disease states, including non-MCC cancers. It may be possible to increase the throughput of the assay by initially identifying high-titer subjects using a single serum dilution point. For example, a cutoff of 90% neutralization at the 1,600-fold serum dilution would have correctly identified all subjects with EC50 titer values greater than 20,000.
The apparent absence or very low level of VP1 protein expression we have observed in MCC tumors confirms previous suggestions that the virus does not actively replicate in MCC tumors. The finding is also consistent with the concept that the tumors are under immunological pressure favoring reduced expression of capsid proteins. This reduced expression could be due either to mutations in VP1, as appears to be the case for MCV350, or due to control of VP1 expression at transcriptional, RNA processing or translational levels. In any event, it appears to be unlikely that robust MCV capsid-specific antibody responses are directly primed by the MCC tumor, suggesting that strong seroresponsiveness to MCV indicates a prior history of active MCV infection of non-tumor or (pre-tumorous) tissues.
In human papillomavirus (HPV) infections, the virally induced cellular changes that lead to development of cancer occur in the absence of a productive viral infection and in the presence of existing neutralizing antibodies. A prophylactic HPV VLP-based vaccine that generates neutralizing antibodies seems to be sufficient to block the development of cancer by preventing the initial establishment of infection [40]. Development of MCC likewise seems to occur in the presence of effective humoral responses, but a prophylactic vaccine might nevertheless be effective for preventing the initial establishment or dissemination of MCV infection. In addition, more research might unveil MCV as a causative agent in more common public health threats, if so, a prophylactic vaccine might be beneficial. To begin to explore the idea that a VLP-based vaccine against MCV might be effective, we immunized animals with a candidate MCV vaccine composed of MCV VP1/VP2 VLPs. All the vaccinated animals displayed strong MCV vector-neutralizing antibody responses, with 50% neutralizing titers of roughly one million-fold serum dilution. This is comparable to the titers of animals administered HPV VLP-based vaccines [41], and higher than titers observed in animals receiving JCV VLPs, particularly when the JCV VLPs were administered without adjuvant [14]. Thus, it appears that MCV VLPs are relatively potent immunogens that could, in principle, be incorporated into existing VLP-based preventive vaccine regimens.
Cell culture and small animal models for MCV replication are not yet available and little is known about the infectious tropism of the virus beyond the clinical inference that it can enter Merkel cells or their precursors. To the extent that MCV reporter vector-mediated transduction may faithfully recapitulate the MCV infectious entry pathway, the vectors could be useful for exploring the entry tropism of the virus in vitro and in vivo. The vectors should also be useful for investigation of MCV virion assembly and structure, as well as for high-yield production of infectious virions containing MCV genomic DNA.
This study was conducted according to the principles expressed in the Declaration of Helsinki. All samples and data for MCC patients were collected after written consent under study protocols approved by the institutional review boards of the University of Pittsburgh Cancer Institute and the University Clinic of Würzberg. For control individuals consent was not obtained, instead samples were de-identified and analyzed anonymously.
All animal experiments were performed at Lampire (Pipersville, PA) commercial facilities. Protocols at this facility are reviewed and approved for use by the Lampire Institutional Animal Care and Use Committee (IACUC) as mandated for a USDA regulated research institution.
MCV reporter vector stocks were produced by transfecting human embryonic kidney cells engineered to stably express the cDNA of SV40 T antigen (293TT) [15]. The cells were transfected using Lipofectamine2000 (Invitrogen) according to previously-reported methods [42]. In initial studies, plasmids pwM and ph2m [12] expressing, respectively, codon-modified versions of the VP1 and VP2 genes of MCV strain 339, were co-transfected with a GFP reporter plasmid, pEGFP-N1 (Clontech). Neutralization assay stocks employed phGluc, which encodes a Gaussia luciferase reporter gene (NEB), as a reporter plasmid. Forty-eight hours after transfection, the cells were harvested and lysed at high density (108 cells per ml) in Dulbecco's phosphate buffered saline (DPBS, Invitrogen) supplemented with 9.5 mM MgCl2, 0.4% Triton X-100 (Pierce), 0.1% RNase A/T1 cocktail (Ambion) and antibiotic-antimycotic (Invitrogen). The cell lysate was incubated at 37°C overnight with the goal of promoting capsid maturation [43]. Lysates containing mature capsids were clarified by centrifugation for 10 min at 5000×g. The clarified supernatant was loaded onto a 27–33–39% iodixanol (Optiprep, Sigma) step gradient prepared in DPBS with a total of 0.8 M NaCl. The gradients were ultracentrifuged 3.5 hours in an SW55 rotor at 50,000 rpm (234,000×g). Gradient fractions were screened for the presence of encapsidated DNA using Quant-iT Picogreen dsDNA Reagent (Invitrogen). VP1 protein concentration was determined by comparing vector stock to bovine serum albumin standard (BioRad) in SYPRO Ruby-stained Nupage gels (Invitrogen). Vector stock yields were typically several µg of purified VP1 per 225 cm2 flask of transfected cells.
Vector stocks based on murine polyomavirus (MPyV) or BKV were produced using a similar scheme. For MPyV cells were co-transfected with plasmids pwP and ph2p [12] (carrying codon-modified MPyV VP1 and VP2, respectively) together with phGluc. An additional plasmid, ph3p, encoding the MPyV minor capsid protein VP3, was also included in the co-transfection mixture. For BKV vector stocks, plasmid pCAG-BKV (a generous gift from Dr. Akira Nakanishi (NCGG, Japan) [28]) encoding the capsid protein genes was co-transfected with phGluc.
In some virion production systems, capsids containing linear fragments of cellular DNA can substantially outnumber capsids containing the viral genome or desired reporter plasmid [43],[44]. In the vector harvest procedure detailed above, unwanted capsids associated with large segments of cellular DNA (as opposed to reporter plasmid DNA) tend to sediment away during the 5000×g clarification step and tend to be retained toward the top of the Optiprep gradient ([42] and unpublished results). For production of VLPs, recovery of capsids containing cellular DNA is desirable and was achieved by adding Benzonase (Sigma) and Plasmid Safe (Epicentre) nucleases to the lysis buffer (0.1% each) and adjusting the lysate to 0.8 M NaCl immediately prior to clarification. These modifications to the harvest protocol increased VLP yield to roughly 1 mg of VP1 per transfected 225 cm2 flask.
Maps of plasmids used in this work and detailed virus production protocols are available from our laboratory website <http://home.ccr.cancer.gov/LCO/>.
Neutralization assays were performed using a 96-well plate format. Sera and virus stocks were diluted in cell culture medium (DMEM without phenol red and supplemented with 25 mM HEPES, 10% heat-inactivated fetal bovine serum, 1% MEM non-essential amino acids, 1% Glutamax and 1% antibiotic-antimycotic, all from Invitrogen). Test sera were subjected to a series of ten four-fold dilutions (range 1∶100 to 1∶2.6×107). 24 µl of the diluted serum sample were added to 96 µl of diluted reporter vector stock. The virus/diluted serum mixture was gently agitated then placed on ice for 1 hour. 293TT cells were seeded in 100 µl of culture medium at a density of 3×104 cells/well in 96-well flat bottom plates for 3–5 hours prior to addition of 100 µl of the virus/serum mixture. Each plate also contained eight wells of cells receiving vector stock without test serum (no serum control) and 2 wells with cells that received only culture medium (no virus control). To minimize plate edge effects, the outer wells of the plate were not used for the assay and were instead filled with culture medium. Three days after virus inoculation, the plates were thoroughly agitated and 25 µl samples of conditioned culture supernatant were transferred to a white 96-well luminometry plate (Perkin Elmer). A BMG Labtech Polarstar Optima luminometer was used to inject 50 µl of Gaussia Luciferase Assay Kit substrate (NEB), and light emission (in relative light units, RLUs) was measured according to manufacturer instructions. Typical assay conditions resulted in a “no serum” signal of roughly 500,000 RLUs with a “no virus” noise of <500 RLUs.
To calculate effective concentration 50% (EC50) values, Prism software (GraphPad) was used to fit a variable slope sigmoidal dose-response curve to RLU values for each serum dilution series. Curves were constrained to average no serum and no virus control values. Each serum sample was tested in at least two independent neutralization assay runs. A small subset of sera whose repeat EC50 values differed by more than three-fold were re-tested until their EC50 values stabilized. For all sera, the results of the final round of testing are shown. Although the sera used in this work were not heat-inactivated prior to testing, analysis of a subset of human sera showed that the assay is compatible with a 30 minute 56°C heat-inactivation of test sera (data not shown).
BKV neutralization assays were performed using 293TT cells with a dose of less than 50 pg of VP1 per well. The MPyV neutralization assay was performed using 293TT cells in a similar fashion except that sera were tested at a single dilution (1∶500) against an MPyV-Gluc vector. The MPyV-Gluc vector transduced 293TT cells and murine NIH-3T3 cells much less efficiently than the MCV-Gluc vector and it was therefore necessary to use a dose of 2 ng of MPyV VP1 per well. The MPyV neutralization assay was carried out in the presence of 100 nM trichostatin A (EMD Biosciences), a histone deacetylase inhibitor that has previously been shown to enhance MPyV vector-mediated transduction [45].
EIAs were performed using Immulon HB2 plates (Thermo) coated overnight with VLPs at 100 ng/well in PBS. The wells were blocked with PBS+0.5% nonfat dry milk (blotto). Serum samples were diluted in blotto and incubated in EIA wells at room temperature with orbital shaking for 45 minutes. The plates were then washed with PBS and bound antibody was detected using horseradish peroxidase-conjugated donkey anti-human IgG (Jackson) diluted 1∶7500 in blotto. ABTS substrate (Roche) development was monitored by absorbance at 405 nm with a reference read at 490 nm.
Merkel Cell carcinoma tissue sections were cut from formalin-fixed paraffin embedded biopsies collected under a University of Pittsburgh IRB approved protocol. Staining was performed as described by Robertson et al. [46] with some modifications. Briefly, slides with the formalin fixed paraffin embedded tissues were baked for 1 hour at 60°C. Deparaffinization was performed by rinsing twice in xylenes for 5 min, once for 30 seconds in each of the following solutions: 100% Ethanol, 90% ethanol, 70% Ethanol, and twice in deionized water for 30 seconds. Slides were then placed in a jar containing 1× Target Retrieval Solution (Dako # S6199) in a 95 degree water bath for 30 minutes. The jar was then incubated at room temperature for 20 minutes and the slides rinsed 3 times for 1 min in water. Sections were blocked for 10 min at 37 degrees in Protein Block solution (Dako #x0909), incubated in primary antibody for 2 hours at 37 degrees, rinsed 3 times in PBS, and incubated in Alexa-488 or 594 conjugated secondary antibodies at a 1∶1000 dilution (Invitrogen) followed by 3 rinses in PBS. Prolong Gold Antifade with Dapi (Invitrogen) was used as the mounting medium and slides were visualized by Confocal microscopy using a Zeiss NLO 510 instrument. The primary antibodies were Mouse anti-cytokeratin (Dako) used at 1∶50, Purified anti-MCV T antigen monoclonal CM2B4 [10] at 1∶300, and rabbit anti-MCV (VP1/VP2) generated as described in “Candidate MCV Vaccine” section used at 1∶2000. Images of Hela controls (T Antigen and VP1/2 transfections) and MCC samples had identical gain and pinhole settings, however the gain was lowered by 30% on Hela control cells transfected with T antigen to remain in the linear range of pixel saturation.
A pool of human sera from male U.S. AB plasma donors was purchased from Sigma (cat# H4522). IgG was purified out of the pooled sera using a Pierce NAb Protein G Kit, according to manufacturer's instructions. To generate a neutralization curve, the purified IgG (1.1 mg/ml) was standardized to the IgG content of the original serum (8.1 mg/ml). Serum was stripped of immunoglobulins by passage over a mixture of protein L and protein A/G resins (Pierce).
De-identified blood donor sera were obtained from the Columbia University and New York City Blood Banks. Individual serum samples from paid donors visiting U.S. plasma donation centers were purchased from Equitech-Bio and Innovative Research. The paid donors were 69% male, 42% Caucasian, 56% African American, and had a mean age of 56 years (range 47–75). All sera were tested for antibodies against HIV, HCV, HBV and syphilis and were found to be negative. 21 MCV positive cases (age 14–95 years) were obtained from persons with histologically-confirmed MCC [12]. MCV status was determined by qPCR as previously described [2].
To generate MCV-specific serum, a rabbit was immunized with two 300 µg doses of MCV VP1/VP2 VLPs, according to a standard immunization schedule offered by Lampire, Inc. The first dose was prepared in complete Freund's adjuvant. A booster dose was administered 3 weeks later in incomplete Freund's adjuvant. Immune serum was collected 10 days after the boost. Mice were immunized twice with 80 µg of MCV VP1/VP2 VLPs. For three mice, the first dose was prepared in complete Freund's adjuvant. Another two mice were primed with VLPs in PBS without adjuvant. For all mice, the boost (4 weeks post-prime) was administered in incomplete Freund's adjuvant. Sera were collected for testing 10 days after boosting. Rabbit serum specific for MPyV VP1 was a generous gift from the lab of Dr. Thomas L. Benjamin (Harvard) [18].
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10.1371/journal.pntd.0003147 | Molecular Characterization of Human Pathogenic Bunyaviruses of the Nyando and Bwamba/Pongola Virus Groups Leads to the Genetic Identification of Mojuí dos Campos and Kaeng Khoi Virus | Human infection with Bwamba virus (BWAV) and the closely related Pongola virus (PGAV), as well as Nyando virus (NDV), are important causes of febrile illness in Africa. However, despite seroprevalence studies that indicate high rates of infection in many countries, these viruses remain relatively unknown and unstudied. In addition, a number of unclassified bunyaviruses have been isolated over the years often with uncertain relationships to human disease.
In order to better understand the genetic and evolutionary relationships among orthobunyaviruses associated with human disease, we have sequenced the complete genomes for all 3 segments of multiple strains of BWAV (n = 2), PGAV (n = 2) and NDV (n = 4), as well as the previously unclassified Mojuí dos Campos (MDCV) and Kaeng Khoi viruses (KKV). Based on phylogenetic analysis, we show that these viruses populate 2 distinct branches, one made up of BWAV and PGAV and the other composed of NDV, MDCV and KKV. Interestingly, the NDV strains analyzed form two distinct clades which differed by >10% on the amino acid level across all protein products. In addition, the assignment of two bat-associated bunyaviruses into the NDV group, which is clearly associated with mosquito-borne infection, led us to analyze the ability of these different viruses to grow in bat (RE05 and Tb 1 Lu) and mosquito (C6/36) cell lines, and indeed all the viruses tested were capable of efficient growth in these cell types.
On the basis of our analyses, it is proposed to reclassify the NDV strains ERET147 and YM176-66 as a new virus species. Further, our analysis definitively identifies the previously unclassified bunyaviruses MDCV and KKV as distinct species within the NDV group and suggests that these viruses may have a broader host range than is currently appreciated.
| Bunyavirus infections cause febrile illnesses of varying severity worldwide; however, despite their public health importance most remain relatively unstudied. In order to clarify the genetic relationships among African orthobunyaviruses associated with human infection, we have sequenced multiple strains of Bwamba (BWAV), Pongola (PGAV) and Nyando virus (NDV). Based on genetic analysis we showed that, while different BWAV and PGAV virus strains are closely related, NDV strains were highly variable and warrant classification as two distinct virus species. In addition, sequencing of the previously unclassified Mojuí dos Campos (MDCV) and Kaeng Khoi (KKV) viruses showed that both are closely related to NDV. This was unexpected considering that these viruses were isolated in South America and Southeast Asia, respectively, and are mainly associated with bats. Further, our experiments also indicated that BWAV and PGAV, as well as NDV, MDCV and KKV, are able to infect both bat and mosquito cell lines, suggesting that ecological studies focusing on these potential host and vector species are warranted. In the future, the availability of complete genetic information for these viruses, together with an understanding of their genetic relationships, will aid in better defining the distribution and public health impact of these viruses.
| The Bunyaviridae are a large, diverse group of more than 350 viruses divided into 5 genera, of which more than 150 belong to the genus Orthobunyavirus [1]. Importantly, these viruses represent a significant cause of arthropod-borne human disease worldwide, with infection often associated with a febrile and/or encephalitic illness, and in rare cases also hemorrhagic manifestations [2]. However, despite their importance for public health, from both a genetic and an evolutionary standpoint the family has been only poorly characterized.
In addition to the well-known agents of human bunyavirus disease in Africa, such as Rift Valley Fever virus (RVFV; genus Phlebovirus) and Crimean-Congo Hemorrhagic fever virus (CCHFV; genus Nairovirus), there are a number of viruses in the genus Orthobunyavirus that are also agents of human disease, with many of them being highly prevalent within their endemic areas. Among these viruses, by far the most prevalent appears to be Bwamba virus (BWAV), which has been reported to be among the most common arthropod-borne diseases in Africa [3]. Infection is associated with a relatively non-specific febrile illness that, while usually self-limiting, is frequently associated with exanthema and can include meningeal involvement [4]. However, recently a group of 14 fatal cases of BWAV infection with hemorrhagic complications, particularly bleeding from the oral mucosa and into the gastrointestinal tract, were reported during an outbreak among Rwandan refugees [4], indicating that BWAV infection can also be associated with very severe disease manifestations. To date a total of only 21 human cases of BWAV infection have been reported from various countries (including Uganda [4], [5], Central African Republic [6], Kenya [7] and Tanzania [4]). However, virus isolation and/or serological studies suggest that this virus circulates in several additional countries (i.e. Mozambique, South Africa and Nigeria) (Figure 1A) and show that seropositivity exceeds 90% in some populations [8]–[11]. Thus, these data clearly suggest that a lack of concerted surveillance efforts, together with the frequency of infections with other pathogens causing febrile illness in the affected areas, have contributed to under-reporting, and as a result an under-appreciation of the disease burden imposed by BWAV infection.
Interestingly, a closely related virus, Pongola virus (PGAV), has been isolated from mosquitoes (Aedes circumluteolus) in South Africa [9] and appears to have been responsible for a single reported case of human infection associated with febrile illness in Uganda [12]. While serological studies also indicate a high prevalence (9–26%) of PGAV infection in several countries (i.e. South Africa, Mozambique, Namibia, Botswana, Angola [8], [13]–[15]), it must be noted this virus is highly cross-reactive with BWAV in many serological tests [5], [9], [16], and that it has historically been difficult to distinguish the distributions and relative abundance of these two viruses; particularly since the affected geographical regions appear to substantially overlap (Figure 1A and B). As a consequence of this high degree of serological cross-reactivity, these viruses are classified together into a single serogroup.
Similar to BWAV and PGAV, Nyando virus (NDV) is also capable of causing moderate-severe febrile disease in humans. Although to date only a single human case with multiphasic fever, myalgia and vomiting has been reported from the Central African Republic [6], [17], again serological studies indicate a high level of seroprevalence in many countries, including Kenya, Uganda and Senegal [18], [19] (Figure 1C). Together with repeated isolations from mosquito pools [17], [18], [20] these findings indicate that, as with many of the African orthobunyaviruses, NDV virus might also be much more prevalent than is currently appreciated. Intriguingly, the limited sequencing data previously available for this virus suggests that Nyando virus (strain ArB16055; GenBank Accession AM709781) is closely related to Bunyamwera virus [21], a finding that appears to be at odds with the lack of serological cross-reactivity between these viruses. Indeed Nyando virus is classified into a distinct serogroup (Nyando serogroup), of which it is presently the only member [22].
Until now, bunyavirus classification has relied almost exclusively on serological testing, which may include complement fixation, hemagglutination inhibition, immunofluorescence assay and/or viral neutralization assays, and on the basis of these assays, the genus Orthobunyavirus is presently divided into 18 serogroups [22]. However, cross-reactivity between viruses is common and often limits our ability to make definitive identifications based on these methods [23], [24]. Further, many other bunyaviruses remain uncharacterized as a result of an inability to assign them a position within this serological classification [25]. Examples of such unclassified bunyaviruses include Mojuí dos Campos virus (MDCV), which was isolated from an unknown bat species in Brazil [26] (Figure 1D), and Kaeng Khoi virus (KKV), which has been isolated from bats (Tadarida plicata and Taphozous theobaldi) in Thailand and Cambodia [27]–[29] as well as from bedbugs (Stricticimex parvus and Cimex insuetus) [27] (Figure 1E). While no informative serological relationships could be established for KKV [29], MDCV was originally observed to show some cross-reactivity with Nyando virus as well as San Angelo virus (California encephalitis serogroup) [25], raising the possibility that it may be related to one or both of these viruses.
While genome sequencing is known to be both a rapid and accurate means of identifying viruses, and is indeed the standard for the identification of most other virus families, it is dependent on the availability of sufficient pre-existing sequence data, something that is presently lacking for bunyaviruses. Indeed, where genetic analysis of these viruses has been performed it is often extremely limited and focuses almost exclusively on the S-segment. However, this issue has been increasingly recognized within the field and is beginning to be addressed, particularly as a result of large-scale de novo sequencing efforts [30]–[32]. In light of the tri-segmented genome structure of these viruses, which allows them to evolve by both antigenic drift and antigenic shift (i.e. reassortment), this leaves us with an incomplete understanding of the exact identity of many bunyaviruses, and the relationships and diversity that exists among them. However, without substantial improvements in the availability of genetic information for these viruses, such determinations will be difficult, if not impossible.
In order to improve our understanding of the evolutionary relationships and genetic diversity among orthobunyaviruses causing human disease in Africa, and particularly the viruses of the Bwamba/Pongola virus and Nyando virus serogroups, we have undertaken the complete genome sequencing of multiple strains of each of these viruses. In addition, we have determined the first complete sequences of MDCV and KKV, which has allowed their definitive identification, and based on the genetic relationships identified in our analyses, we have begun to explore the possibility that additional host and vector species may be involved in the ecology of these viruses.
Vero E6 (African green monkey kidney, ATCC CRL-1586), Tb 1 Lu (Tadarida brasiliensis lung, ATCC CCL-88) and RE05 (Rousettus aegyptiacus fetus; kindly provided by Ingo Jordan, ProBioGen AG [33]) cells were grown in Dulbecco's modified Eagle's medium (DMEM; Sigma-Aldrich), supplemented with 10% fetal calf serum (FCS; Life Technologies), 2 mM L-glutamine, 50 U/ml penicillin and 50 µg/ml streptomycin (Life Technologies) at 37°C in the presence of 5% CO2. C6/36 (Aedes albopictus larva, ATCC CRL-1660) were grown in Eagle's Minimum Essential Medium (EMEM; Life Technologies) supplemented with 10% fetal calf serum (FCS; Life Technologies), 2 mM L-glutamine, 50 U/ml penicillin and 50 µg/ml streptomycin (Life Technologies) at 28°C in the presence of 5% CO2.
The virus strains used in this study were kindly provided by the Centers for Disease Control and Prevention (CDC), Division of Vector-Borne Diseases (DVBD) arbovirus reference collection and the World Reference Center for Emerging Viruses and Arboviruses (WRCEVA) arthropod-borne virus reference collection. Information regarding their origin is summarized in Table 1.
Virus stocks were grown in Vero E6 cells in DMEM supplemented with 2% FCS, 2 mM L-glutamine, 50 U/ml penicillin and 50 µg/ml streptomycin and 10 µg/ml Mycokill AB (GE Healthcare). Virus growth was monitored based on the appearance and progression of cytopathic effect (CPE) in cells. When advanced CPE was observed (50–75% of cells detached), the culture supernatants were harvested for RNA isolation.
Cell culture supernatants from infected cells were spun twice at 1,000× g for 5 min at 4°C to pellet cell debris. For Sanger sequencing, RNA was then extracted using the QIAamp viral RNA extraction kit (Qiagen) according to the manufacturer's directions. Alternatively, for Next Generation sequencing (NGS), samples were further purified and concentrated through a centrifugal filtration device (Millipore) prior to RNA extraction, as previously described [34].
cDNA for NGS was synthesized using a previously described modification [34] of the protocol described by Palacios et al. [35]. Briefly, first strand cDNA was synthesized using the Superscript III Reverse Transcriptase system (Life Technologies) with 100–1,000 ng of total RNA using a random octamer linked to a defined 17-mer primer (5′-GTT TCC CAG TAG GTC TCN NNN NNN N-3′). RNA was then hydrolyzed in NaOH and the single-stranded cDNA (ss-cDNA) products purified using the QIAquick system (Qiagen). The resulting ss-cDNAs were randomly amplified using a 1∶9 mixture of the arbitrary 17-octamer primer and a primer targeting a specific 17-mer sequence (5′- CGC CGT TTC CCA GTA GGT CTC-3′). The resulting ss-cDNA templates were used as template for PCR using Platinum Taq polymerase. PCR products were purified using the QIAquick kit following the manufacturer's protocol (Qiagen) and used as template for sequencing on the 454 Titanium FLX sequencer (454/Roche Life Sciences).
cDNA samples were quantitated using Picogreen reagent (Life Technologies) and prepared according to the Rapid Library Preparation Method Manual – GS FLX Titanium Series October 2009 (454 Life Sciences). A multiplex was titrated in medium volume emulsion (MVE) format to determine the optimal copy-per-bead ratio (CPB) which produced the best sequencing quality. A 454 Titanium sequencing run was then performed using 1 CPB.
Genomic viral sequences were produced on the 454 FLX sequencer and de novo assembled using GS De Novo Assembler v2.6 (454 Life Sciences) and CLC Genomics Workbench 4.0 (CLC Bio). Translated BLAST (blastx) was performed to remove non-viral contaminants and the initial assembly was performed using Sequencher v5.0 (Gene Codes). Assembled contigs were then verified, refined or corrected by mapping the 454 reads using GS Reference Mapper v2.6 (454 Life Sciences). Where needed, several rounds of manual assembly and trimming were performed in Sequencher with verification done using GS Reference Mapper to eliminate discrepancies or errors discovered during the prior reference mapping procedure.
Based on the assembled data obtained from NGS, primers for reverse transcription PCR (RT-PCR) and Sanger sequencing were designed (primer sequences available upon request). RT-PCR was performed with Superscript III reverse transcriptase (Life Technologies) and iProof DNA polymerase (Bio-Rad). The 3′ and 5′ termini of each genome RNA segment were amplified using both a 3′ and 5′ RACE approach based on ligation-anchored PCR, as previously described [36]–[38], with some sequences additionally confirmed using a commercially available 5′ RACE System (Life Technologies) according to the manufacturer's instructions.
The nucleotide sequences obtained for each genome segment, or the deduced amino acid sequences of each of the open reading frames, were aligned with the representative sequences of other known members of the genus Orthobunyavirus from GenBank (Table S1). Sequences were aligned using the MUSCLE algorithm and the evolutionary history for each tree construction was inferred using the neighbor-joining (NJ; [39]) and maximum likelihood methods (ML; [40]), as implemented in MEGA 5 [41]. For the NJ analyses, the evolutionary distances were computed using the Maximum Composite Likelihood method [42]. Statistical support for the tree topology obtained with all methods was evaluated based on bootstrap re-sampling [43] with values calculated based on 1,000 replicates.
RE05, Tb 1 Lu and C6/36 cells were grown for 80–90% confluence in 6 well plates, and the various virus strains indicated were used to infect these cells at an MOI of 0.1. The formation of CPE was monitored daily from 24–72 h and supernatants were harvested at 72 h post-infection for titration via plaque assay. Briefly, a 10-fold dilution series of supernatants were prepared in DMEM without FCS or supplements and 500 ul per well was applied to 12 well plates. Following incubation for 1 h at 37°C virus dilutions were removed and wells were overlaid with 0.9% agarose in 1× MEM containing 2% FCS, 2 mM L-glutamine, 50 U/ml penicillin and 50 µg/ml streptomycin. Once solidified plates were incubated at 37°C for 3 d (NDV(MP401), NDV (ERET147), MDCV and KKV) or 5 d (BWAV and PGAV) prior to fixation overnight in 10% formalin containing 0.1% crystal violet (Sigma-Aldrich).
The genome sequences determined in this study were deposited in GenBank under the following accession numbers (S segment, M segment, and L segment): KJ867176, KJ867177 and KJ867178 (PGAV, strain SA Ar1); KJ867179, KJ867180 and KJ867181 (PGAV, strain 191B-07); KJ867182, KJ867183 and KJ867184 (BWAV, strain M459); KJ867185, KJ867186 and KJ867187 (BWAV, strain UgAr1888); KJ867188, KJ867189 and KJ867190 (NDV, strain MP401); KJ867191, KJ867192 and KJ867193 (NDV, strain UgAr1712); KJ867194, KJ867195 and KJ867196 (NDV, strain ERET147); KJ867197, KJ867198 and KJ867199 (NDV, strain YM176-66); KJ867200, KJ867201, and KJ867202 (MDCV, strain BeAn 276121); KJ867203, KJ867204 and KJ867205 (KKV, strain PSC-19).
In order to better understand orthobunyavirus evolution as it relates to the relationships between and degree of diversity among BWAVs, PGAVs and NDVs, we have determined the complete genome sequences for all 3 viral RNA segments from multiple strains of each of these viruses. In addition, we have determined the complete sequences of a single strain of each of the “uncharacterized” bunyaviruses MDCV and KKV (listed in Table 1).
Based on phylogenetic analysis of these viruses in relation to previously published data obtained from GenBank for other members of the genus Orthobunyavirus (Table S1), it is apparent that BWAV and PGAV form a distinct virus clade, separate from that formed by the NDV strains (Figure 2), consistent with their assignment to distinct serogroups. For BWAV and PGAV, all three segments form a lineage that diverged from a common ancestor shared with the California encephalitis virus (CEV) group, indicating that these two groups are closely related at the genetic level. The NDV clade is less closely related to any currently recognized orthobunyavirus group forming a very distinct genetic grouping branching ancestrally to both the CEV and BWAV/PGAV lineages. However, closer analysis of the NDV group also shows considerable variation between NDV strains, such that the NDV group actually forms two distinct clades: one composed of strains MP401 and UgAr1712, while the other is composed of strains ERET147 and YM176-66. The groupings and relative positions of these viruses were well-preserved regardless of whether a criterion-based method (ML; Figure 2) or a clustering method (NJ; Figure S1) was used for construction of the phylogenetic trees. Further, the same relationships are observed regardless of whether complete genome segment nucleotide sequence, coding region nucleotide sequence or amino acid sequence datasets are used for the analysis (data not shown).
Early serological data for MDCV had indicated a possible distant relationship to both CEV and/or bunyamwera group viruses. Further, a preliminary phylogenetic analysis of very short sequence data fragments available for KKV in GenBank (accession numbers JN010801 and AY843028–AY843038) indicated a possible, but weakly supported, evolutionary relationship to the NDV group (data not shown). On this basis we additionally undertook full-length sequencing of a single strain each of MDCV and KKV using de novo sequencing. Based on these complete genome data we found that both viruses clearly grouped together with the NDV viruses we had sequenced (Figure 2). However, despite NDV being the closest known relative of both of these viruses, at both the nucleotide and amino acid levels these viruses were considerably divergent from all NDV strains examined, demonstrating only 53–73% nucleotide identity and 39–70% amino acid identity for MDCV, and 53–75% nucleotide identity and 39–72% amino acid identity for KKV, clearly indicating that these should be considered as distinct virus species (Tables S2–S4).
Analysis of the genome structures for all of these viruses indicates that they are consistent with what is known for previously analyzed orthobunyavirus genomes (Figure 3). The S segments for these viruses ranged from 902 [NDV (MP401)] to 1061 (MDCV) nucleotides and encoded both a nucleoprotein of 233 (NDV, MDCV, KKV) to 235 (PGAV) amino acids in length and an NS protein of 92 (BWAV, PGAV, NDV, MDCV) to 106 (KKV) amino acids derived from an alternate downstream ATG. The M segment and L segment were found to be between 4395 (MDCV) and 4568 (KKV) nucleotides, and 6866 (KKV) and 6994 (MDCV) nucleotides in length, respectively, and encoded only one long open reading frame (ORF) each, corresponding to the glycoprotein precursor (GPC) and the RNA-dependent RNA polymerase (L). The GPC protein of the various viruses were found to be between 1415 (MDCV) and 1447 (BWAV) nucleotides in length, while the L proteins were between 2249 (KKV) and 2268 (NDV) amino acids in length.
In order to apply rational criteria to the taxonomic assignments within these virus groups, we next analyzed the levels of sequence divergence between the different strains of BWAV, PGAV and NDV (Tables S2, S3, S4, S5, S6, S7). Based on these analyses we found 95–99% nucleotide identity among BWAV strains, while values for amino acid sequence conservation were between 98–99%. Among PGAV strains even higher levels of sequence identity were observed with 98%–99.6% sequence conservation at the nucleotide level and 99%–100% at the amino acid level. Despite the relationship between strains of either virus, sequence conservation between these two groups decreased to 67%–89% at the nucleotide level and 63%–86% at the amino acid level across all three segments. This clearly supports the classification of BWAV and PGAV as distinct virus species despite previous reports of strong serological cross-reactivity, which renders them nearly indistinguishable in some tests [5], [9], [16].
Among the NDVs, the situation observed was rather different. While the NDV clade is clearly highly divergent from all other recognized virus groups, it also demonstrated much more divergence between strains. Analysis showed that the MP401 and UgAr1712 strains exhibit 92%–99% identity at the nucleotide level and 96%–100% identity at the amino acid level, consistent with the levels of conservation observed between BWAV and PGAV strains. Similarly, the ERET147 and YM176-66 strains showed the expected high levels of sequence conservation, with 80%–98% identity at the nucleotide level and 90%–98% at the amino acid level. However, between these groups much more substantial differences in sequence were observed and identity levels dropped to 61–89% at the nucleotide level and 57%–86% at the amino acid level, respectively. These levels are then very similar to those seen when comparing BWAV and PGAV, members of two different virus species, and suggest that these two NDV clades should also be recognized as distinct orthobunyavirus species.
Given the paucity of full-length genome sequences available for bunyaviruses, including the orthobunyaviruses, little is known about the sequence and/or arrangement of their terminal untranslated regions (UTRs) (Figure S2). Based on a comparison of our full-length BWAV/PGAV and NDV/MDCV/KKV group sequences we noted that all sequences determined for these virus groups contained the well-conserved terminal sequences believed to be characteristic of all orthobunyavirus genome sequences (3′-AGTAGTGTAC…GCACACTACT-5′). In addition we found that the downstream 5 nt fit well to the sequences determined for Jamestown Canyon virus (JTCV), a member of the closely related California Encephalitis virus group. Further, where deviations from this prototype sequence were observed, compensatory mutations are found in the other UTR, which would then maintain base pairing at these positions. Such deviations from the established sequences of CEV group members were seen for the MDCV and KKV S segment UTRs and the BWAV and PGAV M segment UTRs (Figure S2). This observation supports previously proposed base pairing models, based on in vitro work, which have suggested direct interactions between the 3′ and 5′ UTR sequences that need to be maintained for functionality [44].
Beyond these well conserved terminal sequences we found that the UTR sequences exhibit a general lack of conservation in both sequence and length between different virus species. The 3′ UTRs varied in length from 24–85 nt (S segment: 38–85 nt, M segment: 24–49 nt, L segment: 27–45 nt), with the PGAV M segment 3′UTR being uncommonly long in comparison to other 3′ UTR sequences. Compared to the 3′ UTRs, the 5′ UTR sequences were generally much longer, ranging from 85–310 nt, and with sequences in excess of 200 nt determined for the BWAV, MDCV and KKV S segment 5′ UTRs and the KKV M segment 5′ UTR. Interestingly, despite the marked variability of these sequences between virus species, within a single species these sequences are in fact highly conserved. This can be clearly seen when examining the NDV(MP401) and NDV(UgAr1712) UTR sequences in comparison to those of NDV(ERET147) and NDV (YM176-66) (Figure S2). As such, both UTR sequence and length may provide useful additional criteria/markers for species delineation.
The genetic assignment of MDCV and KKV to the NDV clade was surprising, since neither of these viruses has been previously associated with transmission from mosquitos, which are the sole established vector for the transmission of BWAV, PGAV and NDV. In contrast, MDCV and KKV were both originally isolated from bats, which is rather unusual for orthobunyaviruses and has not been reported for BWAV, PGAV or NDV. However, it raises the possibility that these viruses might have a broader host/vector range than is currently appreciated.
In order to establish the feasibility of a role for these additional vector and host species in nature, we assessed the ability of representative viruses from the BWAV, PGAV, NDV, MDCV and KKV groups to productively infect cells from African (Rousettus aegyptiacus) and South American (Tadarida brasiliensis) bat species, as well as an Aedes albopictus mosquito cell line. Our data clearly indicate that all of these viruses have the ability to grow in both bat and mosquito cell types with titre increases in infected cells of between 1.5–4 logs between 0 and 72 hours post-infection (Figure 4). During the same time frame all of these viruses showed ∼3 logs of growth in VeroE6 cells, which are highly permissive for infection with a broad range of orthobunyavirus. There were no identifiable trends observed regarding which viruses (i.e. mosquito associated African orthobunyaviruses [BWAV, PGAV, NDV] or bat associated orthobunyaviruses from other regions [MDCV, KKV]) showed more efficient growth in any of these cell types. During infection, all of the viruses tested showed prominent cytopathic changes (CPE) in each of the mammalian cell types examined (i.e. VeroE6, RE05 and Tb 1 Lu cells; data not shown). In contrast, none of the viruses produced clear CPE in C6/36 cells. This lack of CPE in C6/36 cells occurred despite all viruses showing 3–4 logs of virus growth in these cells, comparable to what is seen, for instance, with VeroE6 cells where strong CPE formation is observed. Thus, it appears that this lack of CPE formation is a feature of infection in C6/36 cells rather than being influenced by the different viruses tested. Overall, based on these data it appears at least possible for KKV and MDCV to productively infect mosquito cells. Similarly infection of bat cells with BWAV, PGAV and NDV is also possible and leads to productive infection associated with cytopathological changes.
In this study we have determined the first full-length sequences for all three segments of multiple strains of BWAV and PGAV, as well as NDV, and for single strains of the related MDCV and KKV. As a result we have been able to definitively establish the relationships among these viruses, as well as their relationship to other orthobunyavirus groups. This work has not only clarified previously uncertain assessments about their relationships based on serology but has also led to the identification of two previously unclassified bunyaviruses, MDCV and KKV, as close relatives of NDV, and the identification of existing NDV strains as highly diverse, warranting classification into distinct virus species.
Our complete genome-based analysis of all three segments of BWAV and PGAV confirmed their placement within the orthobunyavirus genus, consistent with what has been previously reported based on S-segment analysis alone [45]. In particular, the position of these groups, branching immediately ancestral to the CEV group viruses for all three segments, explains previous reports of cross-reactivity of BWAV to members of the CEV group. Interestingly, despite their high degree of serological cross-reactivity in many assays, BWAV and PGAV display amino acid divergence values for all viral proteins that clearly support their classification as separate virus species. Indeed, the high degree of serological cross-reactivity between these viruses, including in neutralization assays [5], [9], [16], is surprising given that they exhibit only 64% amino acid identity in GPC. This suggests that there may be an unusually high degree of conservation among neutralizing epitopes in these viruses, in relation to the overall levels of sequence conservation, and/or that there may be a marked immunodominance of a few well conserved epitopes.
Unlike for BWAV and PGAV, the relative position identified by our analysis for NDV was not consistent with a previous report examining the S segment sequence of NDV (strain ArB16055) [21]. This previous study had suggested a close genetic relationship to Bunyamwera virus, an observation that was apparently at odds with serological evidence assigning NDV to a distinct serogroup [19]. In contrast, we clearly observed that all four of our Nyando virus isolates fell into a distinct clade ancestral to those formed by BWAV/PGAV and CEV and that this position was consistent for all three segments. We did not see any evidence for Nyando virus strains that were genetically related to bunyamwera virus on the S segment, suggesting that the identity of NDV (strain ArB16055) needs to be closely re-examined, as it may either have been misidentified or may be reassortant in nature. However, based on the currently available data, there does not appear to be any evidence for reassortment involving either the BWAV/PGAV or NDV groups.
In our study, very little genetic divergence was noted among different strains of BWAV and PGAV, despite substantial differences in the location and/or time at which they were collected, indicating that molecular detection methods based on information from one or a few virus strains may be adequate to detect all of the genetic diversity present for BWAV and PGAV. However, given the small number of isolates available for each of these virus species, we cannot exclude the possibility that other strains showing much more substantial sequence divergence also exists. In contrast to the situation for BWAV and PGAV, for NDV a large degree of genetic divergence was noted among the strains analyzed. Indeed, this genetic divergence was to such an extent that, based on a cut-off value of 10% amino acid divergence, a division of the existing NDV strains into two distinct virus species would be warranted. Given the prevailing naming conventions among bunyaviruses (i.e. naming based on the location of initial isolation) we would, therefore, propose that the prototype NDV (MP401), isolated from the Nyando river valley [18], and the closely related NDV (UgAr1712) continue to be referred to as “Nyando virus”, while NDV (ERET147), could be reclassified as “Manéra virus”, reflecting its original isolation from the Manéra forest in Ethiopia, along with the closely related NDV (YM176-66). Based on their close serological relatedness, it is also likely that other ERET and YM series viruses (e.g. ERET124, YM120-68 and YM259-68) isolated as part of the same studies [46], [47] will also fall into this genetic group. Our observation that NDV forms two highly distinct genetic groups supports early findings indicating that these viruses are serologically quite distinct [19]. Further, the closer genetic grouping of the ERET147 and YM176-66 strains is also supported by serological findings obtained during the initial isolation of YM176-66 [48].
The placement of MDCV within a larger NDV clade is also supported by early serological data which indicated reactivity by immunofluorescence assay (IFA) and complement fixation (CF), but not neutralization (NT), to both NDV and CEV group viruses [25]. Thus this is consistent with its genetic placement close to, but distinct from, both of these groups, as shown in this study. Initially, the existence of such a close relationship of these bat-associated bunyaviruses to what have been, until now, strictly mosquito-borne viruses was surprising. However, this finding appears to only contribute to the increasing number of bunyaviruses shown to be associated with bats. Interestingly, while for MDCV the virus has only been isolated from a single live bat with unreported health status [26], for KKV, infection in bats appears to be detrimental in a significant proportion of infected animals, as shown by frequent and consistent virus isolations from dead bats [28], [29]. Indeed, our in vitro data also indicate that bats should be more closely considered in future ecological and epidemiological investigations looking at BWAV, PGAV and NDV, as all of these viruses display at least a fundamental ability to infect cells derived from various bat species. Further, we cannot currently exclude that productive infection of bat cells may in fact be a feature of a wide range of other orthobunyavirus species as well, opening up the possibility that a broader host range than is currently appreciated might generally exist for orthobunyaviruses, and specifically that consideration of bats as a potential host for other orthobunyaviruses may also be warranted.
While for MDCV no arthropod vector has yet been established, for KKV, until now only bedbugs have been identified as a potential vector species [27]. On this basis, the classification of KKV in the NDV group was particularly surprising, since orthobunyavirus transmission appears to be almost exclusively mosquito-borne, with the exception of the Tete virus group, which is mainly transmitted by ticks, and a few specific instances of culicoid fly vectored viruses [1]. No other example of an orthobunyavirus associated with infection of and transmission via bedbugs has been reported. Interestingly, we also observed that both KKV and MDCV were able to infect mosquito cells in vitro, and while these data only show that infection of mosquitos is fundamentally possible at a cellular level, when taken together with the close genetic relationship of these viruses to a number of well-documented mosquito-borne viruses, this clearly indicates that the possibility of mosquito-borne transmission should be considered in future field studies. Such studies will be particularly important given that evidence exists supporting the relevance of KKV for human infection. In particular, high levels of seroprevalence among Guano miners working in caves known to have infected bats and/or bedbugs have been reported [28], [29], and anecdotal reports suggest that working in such caves is associated with a mild generalized febrile illness in new workers [27], [28].
The inclusion of MDCV and KKV in the NDV clade was also surprising given their geographical distribution, having been isolated exclusively from South America and Southeast Asia, respectively. The existence of such a highly genetically diverse clade, which spans three continents, suggests on the one hand that additional related but unrecognized virus species likely exist, and also that transmission between these non-contiguous geographical locations has most likely been facilitated by an unknown host species common to all of these virus groups. In particular, transmission of KKV and MDCV via migratory bird routes through various flyways would appear possible (i.e. via the American Flyways<>Black Sea and Mediterranean flyways<>Asia Flyways<>American Flyways), and indeed infection of bird species has been shown to be possible for several other orthobunyaviruses, including Turlock virus and Mermet virus [49], [50]. Alternatively, we must also consider that transmission between Africa and Asia may also have been facilitated by bats species endemic to these regions, some of which also populate broad geographical areas. In future the availability of comprehensive genome sequencing datasets, such as that determined in this study, will be important not only for molecular-based detection of virus infection (i.e. in infected mosquito samples, acutely infected humans, etc.) but will facilitate the development of recombinant antigen-based detection systems, which will be necessary for undertaking broader serological surveillance/screening efforts aimed at defining the geographic areas affected by these viruses, as well as estimating seroprevalence in animal species and/or larger segments of the human population to better define the public health impact of these viruses in the endemic areas. In addition, our study, which was restricted to only a small number of available isolates, also highlights the need for increased sample collection for these and other neglected tropical disease agents, and particularly the collection of human isolates, in order to develop a clearer picture of the actual extent of virus genetic diversity.
Overall the data contained in this study have not only led to the genetic identification of two previously uncharacterized viruses, but in doing so, has considerably expanded our knowledge of virus diversity along the BWAV/PGAV and NDV genetic lineages. On this basis we have also presented arguments for a more refined and evidence based approach to the taxonomic classification of the viruses in these groups, something that is increasingly appreciated as being sorely needed within the Orthobunyavirus genus. Further, our in vitro data, informed by the genetic relationships established as part of our sequencing efforts, have identified the possibility of infection with these viruses in an expanded range of host and vector species. The availability of complete genetic information for these viruses, as well as a better understanding of their genetic relationships, will be instrumental in assisting future surveillance efforts aimed at determining the distribution and public health impact of these viruses, as well as efforts in identifying the contributions of various potential host and vector species.
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10.1371/journal.pntd.0007726 | Field effectiveness of new visceral leishmaniasis regimens after 1 year following treatment within public health facilities in Bihar, India | An earlier open label, prospective, non-randomized, non-comparative, multi-centric study conducted within public health facilities in Bihar, India (CTRI/2012/08/002891) measured the field effectiveness of three new treatment regimens for visceral leishmaniasis (VL): single dose AmBisome (SDA), and combination therapies of AmBisome and miltefosine (AmB+Milt) and miltefosine and paromomycin (Milt+PM) up to 6 months follow-up. The National Vector Borne Disease Control Program (NVBDCP) recommended an extended follow up at 12 months post-treatment of the original study cohort to quantify late relapses.
The 1,761 patients enrolled in the original study with the three new regimens were contacted and traced between 10 and 36 months following completion of treatment to determine their health status and any occurrence of VL relapse.
Of 1,761 patients enrolled in the original study, 1,368 were traced at the extended follow-up visit: 711 (80.5%), 295 (83.2%) and 362 (71.5%) patients treated with SDA, AmB+Milt and Milt+PM respectively. Of those traced, a total of 75 patients were reported to have relapsed by the extended follow-up; 45 (6.3%) in the SDA, 25 (8.5%) in the AmB+Milt and 5 (1.4%) in the Milt+PM arms. Of the 75 relapse cases, 55 had already been identified in the 6-months follow-up and 20 were identified as new cases of relapse at extended follow-up; 7 in the SDA, 10 in the AmB+Milt and 3 in the Milt+PM arms.
Extending follow-up beyond the standard 6 months identified additional relapses, suggesting that 12-month sentinel follow-up may be useful as a programmatic tool to better identify and quantify relapses. With limited drug options, there remains an urgent need to develop effective new chemical entities (NCEs) for VL.
| In 2010, the WHO Expert Committee recommended liposomal amphotericin B (in single or multiple doses) along with three short combination treatment regimens containing liposomal amphotericin B (LAmB), miltefosine (Milt) and/or paromomycin (PM) as preferred options to replace the existing miltefosine monotherapy for kala-azar treatment in South Asia. The Drugs for Neglected Diseases initiative (DNDi) in partnership with Rajendra Memorial Research Institute of Medical Science (RMRI-Regional ICMR institute), State Health Society Bihar, and Médecins Sans Frontières (MSF) conducted a phase 4 field effectiveness study to determine the effectiveness and to assess the safety and feasibility of using single dose LAmB, and the combination therapies of LAmB+Milt and Milt+PM for the treatment of VL at public healthcare facility settings in India. Based on the provisional results of this effectiveness study at 6 months follow-up, the Indian government revised the national policy in August 2014, introducing SDA as first option and Milt+PM as second option to replace miltefosine monotherapy in the kala-azar elimination initiative. National Vector Borne Disease Control Programme (NVBDCP) expert committee recommended to follow up this large cohort of patients for one year to identify relapses yielding further evidence on new treatment regimens.
| Leishmaniasis is a disease caused by infection of protozoa parasites Leishmania, transmitted through the bite of phlebotomine sand flies. The visceral leishmaniasis form affects the reticuloendothelial system, presenting insidious clinical manifestations of fever, hepato-and splenomegaly, anemia and weight loss. Visceral leishmaniasis is fatal if not treated. In Asia, visceral leishmaniasis, also named kala-azar, affects poor populations mainly in India, Bangladesh and Nepal. Effective treatment is key to improving patient outcomes and reducing disease transmission [1]. Current treatment recommendations in Asia include liposomal Amphotericin B (LAmB), or combination therapies containing Amphotericin B (LAmB), miltefosine (MF) and/or paromomycin (PM) [1]. Shorter regimens appear to be more affordable, safer, easier to administer and with better compliance, and combinations may protect the lifespan of the individual drugs [2–4]. Based on effectiveness study results [5], the Indian government has adopted single dose LAmB as first option for VL treatment, and Milt+PM as second option to replace miltefosine monotherapy in the kala-azar elimination initiative since 2014.
The majority of phase 3 clinical trials of treatments for VL use 6 months following completion of treatment as the endpoint for efficacy assessment, which has also been adopted by the WHO as the programmatic indicator of final cure rates in routine national programmes. However, studies in India have shown that a longer follow-up period of up to 12 months may identify further cases of relapse [6,7]. The occurrence of Post Kala-azar Dermal Leishmaniasis (PKDL), which is also a VL sequel of interest, is usually observed at an average of 2 years after VL treatment in India; thus, continued follow-up beyond 12 months and a future analysis are planned to ascertain incidence of PKDL [8]. In order to determine the incidence of relapse at 12-months following completion of treatment in these new treatment regimens, the National Vector Borne Disease Control Programme (NVBDCP) expert committee recommended conducting an additional follow up of the cohort of patients from the original study (N = 1,761) at one-year post VL treatment. This manuscript presents the results of this extended follow-up.
The original study has been described in a previous publication (5). All the 1,761 patients who were treated in the original non-randomized, non-comparative study with single dose AmBisome (SDA), a combination of AmBisome and miltefosine (AmB+Milt) or a combination of miltefosine and paromomycin (Milt+PM), were contacted and invited to attend one of the original study centres at 12 months post-treatment. Details of inclusion and exclusion criteria, VL treatment regimens, study sites, etc. are described in Goyal et al., 2018 [5].
Patients who had received VL treatment with any of the three treatment regimens were contacted by the Information, Education and Communication (IEC) team by telephone to attend to a 12-months follow-up visit. If a patient did not come for the visit, the IEC team would contact them by telephone again and if unable to make contact, conduct a home visit with the support of ASHA (Accredited Social Health Activists) workers. Follow-up visits were performed between January and September 2015. Follow-up was intended to take place 12 months after completion of treatment. However, due to the long patient recruitment period of the original study (August 2012 to October 2014), by the time of the request by the NVBCP to extend follow- up and given the shorter time frame in which to complete the follow-up work, many of the patients had already passed the 12 month post-treatment time point. As a result, the 12-month follow-up visits were performed between a range of 10 and 36 months post treatment completion (S1 Table).
During the extended follow-up visit, patients were assessed clinically by medical history, physical exam and haemoglobin analysis. Parasitological diagnosis (bone marrow or spleen aspiration) was indicated if VL signs and symptoms were present.
The study was approved by the Institutional Ethics Committee of RMRI, Patna. Patients from the original study were contacted by telephone and verbally consented to participate in the study while attending the secondary follow up visit. Those who attended were consented in writing by treating physician and those who agreed were included in the analysis. For children, consent of parents or of a legal representative was obtained.
Analyses were performed (excluding treatment interruptions / defaults in the original study, PKDL and those lost to follow-up at the extended follow up). Analyses were also stratified by age (≤ 12 and > 12 years).
Effectiveness outcomes were characterized as cured, relapse, death or lost to follow-up (if no contact was made).
Factors associated with VL relapse any time up to the extended follow-up were analysed. These analyses compared only those with extended follow-up data and excluded patients with earlier default, treatment interruption or PKDL.
Analyses were conducted in SAS 9.320 (SAS Institute, Cary, NC, USA). Statistical differences were tested in univariate analyses using Chi Square test, Fisher Exact test, Wilcoxon Rank Sum, or Kruskal-Wallis tests as appropriate. Multivariable logistic regression models were constructed using stepwise backwards variable elimination, and model fit tested using the Hosmer and Lemeshow Goodness-of-Fit Test.
Time to relapse was used to compute survival or Kaplan Meier curves by treatment arm. Kaplan Meier curves (Fig 1) were compared using both Wilcoxon Gehan statistics (which emphasize early differences in hazard rates), and the Log Rank test (which is more sensitive to later deviations in hazard rates). Log-log plots of the survival distribution function were produced that indicated the proportional hazards assumption was reasonable. Accordingly, proportional hazards models, using the time until relapse as an outcome, were constructed using stepwise backwards variable elimination.
Baseline characteristics of the population was presented in Goyal et al. 2018 [5]. Main findings were severe anaemia was more common in the SDA treatment arm. ALT levels were higher in the AmB+Milt arm, whereas AST levels were higher in the SDA and AmB+Milt arms than in the Milt+PM arm. Patients treated with SDA and Milt+PM (a majority of whom were treated at district hospitals) tended to be younger, more likely to be female, and to present with severe wasting than those treated with AmB+Milt, but these differences did not reach statistical significance. The limited number of patients treated at the RMRIMS had a significantly longer reported duration of illness (median of 8 weeks as compared to 4 weeks in other sites).
A total of 1,368 patients were successfully traced and attended the extended follow-up visit. Of these, 15 patients had developed PKDL, these cases were excluded from subsequent analyses. Accordingly 1353 patients were included in the current analyses: 710 treated with SDA (79.7% of the original 891), 294 treated with AmB+Milt (82.1% of the original 358) and 349 treated with Milt+PM (68.1% of the original 512). Successful tracing at extended follow up was significantly lower in the Milt+PM arm compared to the other two arms. Median time to extended follow-up was 12.6 months (IQR 12.1–14.5) for the SDA group, 13.1 months (IQR 12.3–16.0) for the AmB+Milt group and 10.3 months (IQR 7.1–11.1) for the Milt+PM group.
Of 1353 patients with extended follow-up, 75 reported a history of relapse; 45 (6.3%) of 710 in the SDA arm, 25 (8.5%) of 294 in the AmB+Milt arm and 5 (1.4%) of 349 patients in the Milt+PM arm. Of the 75 relapse cases, 55 had already been identified in the 6-months follow-up and 20 were identified as new cases of relapse at extended follow-up; 7 in the SDA, 10 in the AmB+Milt and 3 in the Milt+PM arms.
Interestingly, at 6 months follow up, cure rate by complete case analysis of SDA was 95.5%, AmB+Milt was 95.5% and Milt+PM 99.6% [Goyal et al, 2018], meanwhile at 12 months, the cure rate decreased in all arms to 93.7%, 91.5% and 98.6%, respectively.
In univariate, only the drug regimen used, age ≤ 12 years, and length of illness less than 8 weeks were significantly associated with risk of relapse (Table 1). Gender, anaemia, wasting, abnormal liver/renal function tests and a previous history of VL were not associated with relapse. S2 Table shows proportion of relapses between 0–6 months, 6–12 months and > 12 months.
In multivariable logistic regression models, these same three variables remained after stepwise elimination (Table 2). A proportional hazards model yielded similar results.
Kaplan Meier plots showed highly significant differences in relapse rates between study arms for both the Wilcoxon test (emphasizing early differences) and the Log Rank test (giving more weight to later differences) (p<0.0001 in each case), confirming the observations from the multivariate logistic regression analysis.
There are consistent statistically significant differences in follow up / relapse time by study arm for both cured and relapsed patients. The median relapse time is much later for Milt+PM patients, but subjects that were cured in the Milt+PM arm were also followed up for a longer period. In addition, there are significant differences in follow-up time by site. Fortunately, relapse times are shorter than follow-up times for cured patients.
To attempt to address this issue, survival times of over 500 days were recoded to 500 days (essentially right truncation). The resulting Kaplan Meier plots by study arm are much closer to each other, since relapse rates for all three arms were less than 10% at 500 days, which corresponds more closely to the observed cure rates. The differences were still highly significant between arms (p<0.0001 for both Wilcoxon and Log Rank tests). Since the recoding does not change the ranking of survival (that is, relapse-free follow-up) times, the results for the un-recoded and recoded survival times were virtually identical, despite having Kaplan Meier curves that appeared to be quite different. However, given the differences between follow-up time by drug arm, the differences in survival time are difficult to interpret. It may be difficult to interpret whether these differences are due to follow-up procedures or to actual differences in survival by drug arm.
In the original study, 55 relapses were identified by 6 months follow-up; complete case cure rates were 95.5% for SDA (95% CI 93.9–96.8), 95.5% for AmB+Milt (95% CI 92.7–97.5) and 99.6% for Milt+PM (95% CI 98.6–99.9) [5]. The extension of follow-up in this study allowed for the identification of a further 20 cases of relapse. The cure rates at the extended 12 months follow-up was 93.7% for SDA, 91.5% for AmB+Milt and 98.6% for Milt+PM in the complete case analysis. The possibility of late relapses with all drugs or combinations, should be explained to the patients when discharged.
The efficacy of SDA at 6 months in a study conducted in Bangladesh by local doctors was similar (ITT efficacy 97%) [9]. Earlier, a DNDi phase-3 clinical trial in Bangladesh assessing the safety and efficacy of short course combination regimens in field conditions at upazila (subdistrict) level documented excellent efficacy outcomes (≥95%) at 6 months with very good safety profiles [10].
Similarly, an earlier study showed that following the treatment with 20mg/kg AmBisome in 4 divided doses, the relapse rate was 0.3% at 6 months after treatment and 3.7% by 12 months (70% of all relapses), with the mean point of relapse at 9.6 months [7].
Considering the relatively low proportion of relapses at extended follow up seen in this study, the resources required, cost-effectiveness and feasibility of implementation at programme level may need to be considered when recommending additional routine follow-up of all patients of up to 1 year (in addition to follow up at 6-months). However, a recent study in Nepal showed that the failure rate for miltefosine was 10.8% at 6 months rising to 20% by 12 months, with the age survival analysis consistent with the pharmacokinetics of allometric dosing, which has been shown to result in suboptimal levels in children [6, 11]. Without extended follow up, the increasing failure rates of this regimen may not have been detected. In the present study, the extended follow-up period varied from 10 to 36 months, with 90% of the relapses captured by 20 months of follow-up. As such, it may be that 12-month failure rates may provide a signal of reduced effectiveness when monitoring new drugs, however this requires further research.
In India, PKDL cases could be reservoir of the Leishmania parasite and may play major role in anthroponotic transmission of VL [12]. Development of resistance to antimonial monotherapy in South Asia has been well described [13]. Apart from a few case descriptions, there is no strong evidence of resistance to miltefosine from clinical isolates in immunocompetent patients [6]. Nevertheless, the efficacy of miltefosine has decreased from 94% to 90% within a decade of use in India [14] leading to a strong recommendation to be used only in combination with other medicines [15]. The fact that late relapses were found in this extensive cohort, even when used in combination, underlines the importance of monitoring effectiveness over time. There is therefore a need to strengthen pharmacovigilance within the national program for reporting efficacy and adverse drug reactions of various VL treatment regimens under the national road map in endemic regions.
The limitations of the original study have been described elsewhere [5]. The limitations of the extended follow-up were mainly related to the fact that this additional assessment was not planned in the original study [5]. Rather, the recommendation from the Indian national program to further substantiate treatment outcomes based on long-term risk of relapse for the new VL therapies came after a substantial number of patients had already completed 12 months post-treatment. The long recruitment period of the original study and the limited time available to reach the patients, resulted in a low tracing yield at extended follow up, and the wide variation of loss to follow-up between arms, making the results of this study difficult to interpret.
The late relapses found in this extensive cohort, including combination treatments, suggest that there are still deficiencies in the currently available treatment regimens for VL. There is an urgent need to improve monitoring and early signal detection of mechanisms of resistance development for existing treatments, while a sustained focus on developing new chemical entities for visceral leishmaniasis is critical.
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10.1371/journal.pcbi.1001078 | Power-Law Input-Output Transfer Functions Explain the Contrast-Response and Tuning Properties of Neurons in Visual Cortex | We develop a unified model accounting simultaneously for the contrast invariance of the width of the orientation tuning curves (OT) and for the sigmoidal shape of the contrast response function (CRF) of neurons in the primary visual cortex (V1). We determine analytically the conditions for the structure of the afferent LGN and recurrent V1 inputs that lead to these properties for a hypercolumn composed of rate based neurons with a power-law transfer function. We investigate what are the relative contributions of single neuron and network properties in shaping the OT and the CRF. We test these results with numerical simulations of a network of conductance-based model (CBM) neurons and we demonstrate that they are valid and more robust here than in the rate model. The results indicate that because of the acceleration in the transfer function, described here by a power-law, the orientation tuning curves of V1 neurons are more tuned, and their CRF is steeper than those of their inputs. Last, we show that it is possible to account for the diversity in the measured CRFs by introducing heterogeneities either in single neuron properties or in the input to the neurons. We show how correlations among the parameters that characterize the CRF depend on these sources of heterogeneities. Comparison with experimental data suggests that both sources contribute nearly equally to the diversity of CRF shapes observed in V1 neurons.
| Both the response and membrane potential of neurons in the primary visual cortex (V1) are selective to the orientation of elongated stimuli. The widths of the tuning curves, which characterize this selectivity, hardly depend on stimulus contrast whereas their amplitude does. The contrast dependence of this amplitude, the contrast response function (CRF), has a sigmoidal shape. Saturation of the spike response is substantially lower than the neurons' maximal firing rate. These well established facts constrain the possible mechanisms for orientation selectivity in V1. Furthermore, the single neuron CRFs in V1 display a broad diversity in their shape. This adds other constraints. Many theoretical works have tried to elaborate mechanisms of orientation selectivity that are compatible with the contrast invariant tuning widths. However, these mechanisms are usually incompatible with sigmoidal CRFs. We propose a mechanism which accounts simultaneously for contrast invariant tuning width for both rate and voltage response and for the shape and diversity of the CRFs. This mechanism relies on the interplay between power-law frequency-current transfer functions of single neurons, as measured in vivo in cortex, and on the recurrent interactions in the cortical circuit.
| The dependence of the neuronal response amplitude on stimulus contrast, the contrast-response function (CRF), typically displays a sigmoidal shape in the visual cortex: it accelerates at low contrast and saturates at high contrast [1]–[8]. This major nonlinearity appears to be accentuated in cortex, as ganglion cells in the retina and relay cells in the LGN saturate at higher contrast and show shallower slopes [3], [7], [9]–[15]. In the extreme, some parvocellular neurons in primate LGN display a quasi-linear contrast-response function [13], [14], [16].
A large fraction of neurons in the primary visual cortex (V1) respond in a manner that is selective to the stimulus orientation [17], [18]. The dependence of the spike rate on stimulus orientation (the orientation tuning curve) is well described by a Gaussian whose amplitude varies substantially with stimulus contrast. Although this might be less true in primate [19], [20], it has been shown in carnivore and rodents that the width of the tuning curves does not change when the contrast is modified [2], [7], [15], [21]–[25] This property is referred to as “contrast invariance” of orientation tuning. The membrane potential response of cortical neurons displays an orientation tuning width that is typically 1.5 times larger than that of the spiking response [15], [26]–[30]. This tuning width is also contrast invariant [23]. These contrast-invariant properties constitute strong constraints for understanding the mechanisms underlying the response of V1 neurons to visual stimuli.
The models that have been proposed to explain orientation selectivity in V1 can be broadly classified in two groups (reviewed in [31], [32]): feedforward models, in which orientation selectivity emerges mainly from the spatial arrangement of ON and OFF receptive fields of the LGN cells that form the input to V1 neurons, and recurrent network models in which the orientation selectivity emerges mainly from the recurrent connectivity within V1. Both classes of models have limitations. Although recurrent models can account for contrast invariance in the spiking response [33]–[37], they appear incompatible with the fact that V1 recurrent inputs seem to have, at best, a very weak effect on the voltage tuning width [38], [39]. Recurrent models have further been questioned given the peculiar responses they generate in the presence of pairs of oriented contours [40] and given strong interaction between spatial frequency and orientation selectivity [36]. Furthermore, in contradiction to the experimental results, the response of neurons in such models either display contrast invariance of orientation selectivity, or CRFs saturation, but not both simultaneously [41], [42].
The feedforward model, in its original formulation [17], cannot account simultaneously for the fact that orientation tuning of the spike response is sharper than the tuning of the voltage and for the contrast invariance of the spike response tuning width. Nevertheless, including feedforward anti-phase inhibition [43] or broadly tuned inhibition [44], [45] in the feedforward model permits contrast-invariance of orientation tuning in the membrane potential response. Anderson et al. [23] further showed that contrast invariance of orientation tuning for the spiking response, in addition to that of the membrane potential response, can be achieved in the feedforward model if membrane potential fluctuations (“synaptic noise”) are taken into account. This is because these fluctuations smooth the threshold non-linearity [15], [46], [47]. This smoothing effectively transforms the transfer function of the neurons to a power-law voltage-rate relationship. This is exactly what is needed to obtain contrast invariance for both the voltage and the firing rate, provided membrane potential fluctuations amplitude scales with contrast [15]. However, the feedforward model may account for sigmoidal CRFs only if the LGN input saturates sufficiently strongly. Yet the contrast at which saturation occurs in V1 is lower than for the LGN input. This implies that additional mechanisms are required to account for the co-occurrence of contrast-invariant orientation tuning and of CRFs typical of V1 neurons.
Some of the models used to examine the mechanisms responsible for the saturation of the CRF also display contrast-invariant stimulus selectivity. In the “normalization model” [48]–[51], saturation results from feedback shunting inhibition from a pool of inhibitory neurons. Because this pooling includes inhibitory neurons with a wide range of preferences, this model also accounts for cross-orientation inhibition as well as for contrast-invariance of orientation tuning. However, this model has been questioned due to membrane time constants requirements [32].
Alternatively, synaptic depression has been proposed as one mechanism to explain saturation at high contrast [52], [53]. In these models however, contrast-invariance of orientation tuning does not depend on synaptic depression but depends on the push-pull arrangement of inhibition and excitation, as in the model proposed by Troyer et al. [43]. In another recent model, Banitt et al. [54] examined how contrast-invariance of orientation tuning may depend on thalamocortical synaptic depression, but they did not explore the mechanisms underlying contrast saturation. Models based on synaptic depression are able to explain not only the static properties of the behavior of V1 neurons, but also dynamical aspects, such as contrast-dependent phase advances and frequency-dependent contrast saturation. Nevertheless, recent experimental studies showed that synaptic depression in the thalamocortical pathway may be rather weak in vivo, especially in the presence of spontaneous activity that generates a steady state of synaptic depression [55]–[57].
Thus the question is: can one formulate, without resorting to synaptic depression, a model in which cortical neurons display contrast-invariant tuning-width for both membrane potential and spike responses, as in the feedforward model in the presence of synaptic noise, while at the same time intracortical interactions induce a saturation of the CRF of cortical neurons at lower contrast than their LGN afferents ? To examine this question, we investigated a rate model of a hypercolumn in the visual cortex with neurons whose transfer function nonlinearity was described by a power-law. This allowed us to find conditions for getting both a sigmoidally shaped CRF and contrast invariant orientation tuning width when both feedforward and feedback inputs were included.
We then tested whether our results hold in a less idealized network model made of conductance-based (CBM) neurons. Using numerical simulations in this later model, we investigated the robustness of the results obtained in our rate model. We analyzed the respective contributions of the feedforward input, of the recurrent intra-cortical input, and of neuronal intrinsic properties in shaping the CRF. In particular, we studied the differences between inhibitory and excitatory neurons, and how these relate to differences in their intrinsic properties.
Finally, we explored possible explanations for the broad diversity of CRFs shapes observed in V1 neurons: although typically sigmoidal, CRFs are characterized by parameter values that vary widely at the population level [1], [3], [8]. For this purpose, we compared the predictions from our model with experimental data obtained in area V1 of the marmoset monkey. Our results suggest that substantial heterogeneities in the intrinsic properties of the neurons as well as heterogeneities in the CRFs of LGN neurons are required to account for the diversity of CRFs shapes observed in the primary visual cortex.
Part of this work has been presented at the 34th and 36th annual meeting of the Society for Neuroscience (San-Diego, Oct 2004; Atlanta, Oct 2006).
In this section, we investigate to what extent the results we have obtained in our simplified rate model still hold in a more realistic conductance-based model, in which neuronal dynamics is governed by voltage-dependent conductance channels and synaptic interactions are mediated by conductance changes (see Eqn. 23). We also investigate in this framework how far diversity in the intrinsic cell properties or in the connectivity can account for the heterogeneity in the CRFs observed experimentally (present study and [1]–[6], [8]).
We simulated a network model of V1 made of these conductance-based neurons. The effect of a visual stimulus is modeled by adding an input to the neurons. We take the connection widths such that they satisfy the condition: (see Eqns. (7, 8)) where are given by the best fit of the f-I curves (see above and Fig. 5). The maximal LGN input, , depends on the contrast :(11)The width of the LGN input to the excitatory and inhibitory populations is and respectively.
Fig. 6 shows the orientation tuning curves for the firing rate and average voltage of both neuron types. The firing rate tuning curve is well fitted by a Gaussian for both types of neurons. The width of the optimal Gaussian changes by less than 10% when the contrast increases from 1 to 64%. For this contrast range, the effective leak conductance, , increases from 0 to 0.19 mS/cm for the excitatory neurons and from 0 to 0.13 mS/cm for the inhibitory ones.
We have also plotted in Fig. 6 the predictions given by the effective rate model. For the excitatory population, the simulations results differ substantially from the prediction of the rate model; the rate model underestimates the peak of the tuning curve of the excitatory neurons by as much as 30% (Fig. 6A). The discrepancy is less substantial for the inhibitory population (Fig. 6B). It may be surprising that the discrepancy is larger for the excitatory neurons than for the inhibitory neurons, whereas the deviations from a power-law in Fig. 5 is bigger for the former than for the later. However, this can be explained as follows.
According to Fig. 5, the inhibitory rate should be lower in the spiking network than in the effective rate model. This, however, also decreases the inhibitory feedback to the population. This decreased inhibitory feedback cancels the effect of the deviation from power law of the f-I curve to a large extent. For the excitatory neurons the fit to a power-law is good for the whole input range, but the population also receives less inhibitory feedback than predicted from the effective rate model. This leads to a substantial increase in the firing rate of the excitatory neurons, compared to what one would expect from the effective rate model (Fig. 6A).
In contrast to the height of the tuning curves, there is surprisingly little discrepancy between the numerical simulations and the predictions of the effective rate model for what concerns the width of the tuning curves. This also stems from the corrective effect of the inhibitory feedback. The inhibitory feedback to the inhibitory populations suppresses the broadening of the output tuning curve implied by the deviation of the power-law. As a result, the width of the inhibitory feedback to the excitatory cells is close to that predicted by the effective rate model. Hence the excitatory tuning width is also close to the predicted one.
Because in the CBM the average voltage varies almost linearly with the input, the tuning curve of the voltage follows the tuning curve of the net input. Since the latter is close to a Gaussian with a contrast independent width, the voltage tuning curves are well approximated by Gaussians and have a close to contrast-invariant tuning width, as shown in Fig. 6C,D. Note that voltage tuning width is substantially broader than the tuning width of the spike response.
Fits to the H-ratio function of the CRFs of V1 neurons reveal a large diversity in the parameters , and [1]–[6], [8]. Can this diversity be accounted for in the framework of our model ?
Many theoretical studies have previously investigated possible mechanisms explaining the contrast invariance of the width of the orientation tuning curves measured in neurons in primary visual cortex [15], [23], [33]–[37], [42]–[44], [46], [47], [49]–[51], [54]. Some studies have provided theoretical explanations for the contrast-response functions of these neurons [42], [48]–[50], [52], [53]. However, only a few of them have examined both features together, either unsuccessfully [42] or using parameter regimes that may not be relevant to the in vivo situation (membrane time constants: [49], [50]; synaptic depressions: [52], [53]).
All the findings of the present paper rely on the fact that, in the presence of noise, the effective input-output transfer function is accelerating and can be fitted by a power-law over the physiological range of neuronal responses to visual stimuli [15], [23], [46], [47], [68], [69]. The noise in the input influences the neuron's transfer function by effectively smoothing the effect of the spiking threshold. The mean input current and voltage are also non-linearly related, such that the rate-voltage transfer function is well fitted by a power-law, but with an exponent that is larger than the one of the rate-current transfer function. In the present model, the exponent, , of the input-output transfer functions of the neurons must be larger than 1 to insure spike tuning curves sharper than the tuning curves of the LGN input. For neurons in vivo, the transfer function for voltage vs. firing rate is well approximated by a power-law, with an exponent, ranging between 2 and 5 [15], [23], [68].
Under the assumption that the input noise is on the same order for different neuron types, the input-output transfer function of our model inhibitory neurons accelerate more than that of excitatory neurons. This is because inhibitory neurons have higher gain and show less firing rate adaptation (e. g., [63], [70]). Thus, the fit of the spiking rate to a power-law reveals different exponents , for the different neuron types in our model. That the exponent tends to be higher in the inhibitory cells than in the excitatory ones has been reported in recent experimental studies [69].
A major difference between the rate model and the conductance-based model is that, in the later, synaptic inputs increase the effective leak conductance, an effect that was not taken into account in the former. Nevertheless, we have shown here that an increase, , of the leak conductance, if not too large (increasing the effective up a factor of 2) has the same effect on the transfer function as an additional negative current, . This current is proportional to , . This is similar to what was found by Shriki et al. [59] for the transfer of conductance based neurons in the absence of noise. As we have shown, this allows for the derivation of an effective rate model, which replicates the steady state behavior of the CBM.
Noise, as inferred from voltage traces, has been reported to be independent of stimuli contrast and orientation [23] (but see [15], [71]). Such a noise in the input current effectively results in a power law transfer function [46], [47]. It has been shown that, in the absence of recurrent cortical interactions and with feedforward inputs alone, the power-law transfer function leads to an approximate contrast invariance of the orientation tuning curve width [46], given contrast invariant input width, as they emerge from the spatial arrangement of LGN ON and OFF cells [17], [61]. Due to the nonlinearity of the transfer function the outputs are more tuned than the inputs by the factor . Here we extended these results to take into account recurrent cortical interactions. We showed that they remain true provided that the synaptic distributions have an appropriate spatial extent, namely that the conditions expressed by Eqns. (7, 8) are satisfied.
When the conditions for the width of the feedback, expressed by Eqns. (7, 8) are satisfied, the feedback interactions do not contribute to the sharpening of the tuning. The latter is determined by the tuning of the LGN input, together with the sharpening effect of the power-law transfer function. This is in sharp contrast to the role of recurrent interactions in network models of V1 studied previously [33]–[37], [72]. Recurrent interactions, however, appear essential for explaining the shape of the CRFs (see below).
In the absence of recurrent interactions, the CRF of the cortical neurons is shifted toward higher contrast compared to the CRF of their feedforward inputs. This means that to achieve a reasonably large response at low contrast the parameter of the LGN input must be quite large. This implies that, at maximum contrast, the response of the cortical neurons is large too. However, beyond a critical value, the response amplitude would fall in a range where the transfer function of the neurons deviates substantially from a power-law. In our conductance-based model, this deviation becomes appreciable above . In turn, this deviation from power-law implies substantial deviations from contrast-invariance of the tuning-width at high contrast. Therefore, the strong inhibitory feedback in the recurrent network model we have studied plays a crucial role, which is to regulate the high contrast responses, relative to the responses at intermediate and low contrast. As a result, both feedforward and excitatory recurrent inputs can be relatively strong, resulting in a consistent response for both low and intermediate contrast, yet the response at high contrast does not reach values beyond which contrast invariance is lost. We have demonstrated this role in our conductance-based model. The saturation due to the feedback which keeps the response within the power-law range for high contrast also causes a decrease of the and an increase in the slope of the CRF relative to the LGN input.
We have modeled the LGN input as a Gaussian, with a width that is independent of contrast. This represents a simplification, which is nevertheless justified given previous theoretical studies on contrast invariance of orientation tuning in simple cells.
A well known problem in this context [31], [43] is that, in simple cells, the LGN input generates an untuned DC component in the membrane potential response, which grows faster with contrast that the tuned AC component. A solution to this problem consists in canceling this DC component by including either anti-phase or broadly tuned inhibition in the models [43], [45], [72]. This was not explicitly incorporated in our model. We rather simplified it with a tuned LGN input that one should view as a net input into the cells which combines both the actual LGN input and the feedforward inhibition.
The conditions expressed by Eqns. (7, 8) imply specific range for the synaptic connections between sub-populations of neurons. They show that, if the orientation tuning width of inhibitory neurons is broader than that of excitatory neurons as reported experimentally [28], [30] the synaptic projection from inhibitory to excitatory neurons should be narrower than the projection width from excitatory to excitatory neurons. This is compatible with anatomical data, which show that the spatial extent of inhibitory connections is usually less than that of excitatory connections [73], [74]. Note that these conditions were obtained under the assumptions of Gaussian inputs and outputs, which are in line with experimental data (e. g., [75]).
Here an important caveat should be made. We showed that contrast invariance of the tuning width is robust to violations of conditions Eqns. (7, 8). If the range of the synaptic feedback, both excitatory and inhibitory, is changed by as much as 50%, contrast invariance is still nearly achieved with a relative error of less than 10%. Thus the model predictions about the relative extent of the excitatory and inhibitory feedback should not necessarily be taken as quantitative.
The parameters we used generated relatively narrow tuning curves (see Results), in accordance with the tuning width reported for layer 4 simple cells in some studies (e. g., [15], [69]). However, others studies reported a large heterogeneity of tuning width, including broadly tuned cells and cells showing a non-negligible response at the orthogonal orientation [7], [19], [20], [28], [30], [76]. We therefore checked whether our results were valid for parameter regimes different from the one we initially used. We simulated networks with broader tuning curves (), for which the response at the orthogonal orientation was approximately one tenth of that at the preferred orientation. For such networks, we found that the orientation tuning width did not change significantly with contrast. However, the ratio of the response at the orthogonal orientation versus the preferred orientation decreased slightly with contrast. Interestingly, this departure from strict contrast-invariant orientation tuning has been observed experimentally for broadly tuned cells in some studies [7], [19]; but see [20]. However, this should not be taken too seriously because, as Fig. 8 shows, deviations from Eqns. (7, 8) for the feedback width can have a substantial effect on the response at the orthogonal orientation, which could result in the reverse effect.
The CRF of the spike response can be well fitted by an H-ratio function in a large fraction of V1 neurons. However, the parameters of the function are highly diverse across neurons [1]–[4], [7]. Most studies that aim to explain contrast invariance or the shape of the CRF ignore this heterogeneity and usually do not indicate whether the proposed mechanism can accommodate a large diversity of responses.
Whether the excitatory neurons saturate or not is determined by the strength of the feedback connections, particularly from the inhibitory cells. This implies that some degree of fine-tuning of these strengths is necessary if we impose that the average excitatory CRF saturates at 100% contrast. Because of this sensitivity, relatively small variability in the feedback strengths for individual neurons leads to rather large changes in the CRFs. This can contribute to the large variability in CRFs, with non-saturating, saturating and super-saturating cells observed in the primary visual cortex of the same animal.
Here we have investigated other possible sources for this diversity, focusing on the contribution of variability in single neuron intrinsic properties, and on the contribution of heterogeneities in the CRFs of LGN neurons. We have demonstrated that these two sources of variability can both account for the diversity observed in experiments. In addition, our model predicts a correlation between the parameters and , which is either negative or positive, depending on the source of heterogeneities. The strength of the correlation is further predicted to be reduced when both sources are mixed, in proportion to the relative contribution of each.
We examined CRFs for neurons in the primary visual cortex of marmoset monkeys. The parameters and obtained in these experimental data were at best weakly negatively correlated. This suggests that heterogeneity in the LGN input may contribute slightly more than the neurons' intrinsic properties to the diversity of CRFs shape.
Another possible source of heterogeneity we did not examine is heterogeneity in the recurrent feedback inputs. We assumed that these are uncorrelated. Then, given their large number comparatively to LGN inputs, heterogeneities in feedback inputs would cancel each others and this would result in an “averaged” CRF input to all neurons. However, some studies showed that subset of excitatory and inhibitory neurons may form specific connections with other neurons [77]–[80], and in many cases the connections are not reciprocal. This would lead to heterogeneity in the feedback input, that we expect to have the same effect on the correlations between and as the diversity in the feedforward input. Other studies [81], [82], however, suggest that inhibitory fast spiking cells establish a dense network with other neurons, as assumed in the present study.
Two major weaknesses of our model is that we have to add external noise to the system to obtain voltage fluctuations that are biologically plausible and that it does not exhibit heterogeneity in the orientation tuning curves.
One way to obtain input fluctuations intrinsically is to use a model that operates in the balanced regime [83], [84]. In this regime, heterogeneity in the response naturally arises from the strongly amplified effect of randomness in the connectivity. However, in their current formulation, balanced network models cannot explain the shape of the CRF as observed experimentally. This is because in such networks the population averaged response should scale linearly with the external input [83], [84], so that on average the of both the excitatory and inhibitory populations should be the same as the of the LGN input, in contrast to what is observed experimentally. It is our hope that development of such models, in which recurrent connections are responsible for the synaptic noise which is so essential to contrast-invariance of tuning width, will help further integration of feedforward and feedback models for a better understanding of the mechanisms at work in cortical processing.
The protocol for the experiments which are reported here is in accordance with guidelines of the French ministry of agriculture (décret 87/848) and the European Union (directive 87/609).
Our rate model consists of excitatory and inhibitory neurons. The firing rate of excitatory neuron and inhibitory neuron , denoted by and respectively satisfy(12)where is the membrane time constant for population , is the total, noise averaged, input into the neuron, and is the effective, noise averaged, transfer function. Following recent experiments [23], [68] and theoretical studies [46], [47], we assume that the transfer function is a threshold power-law function, . Here denotes the half rectified linear function, for and for . The exponent of the power law function is and sets its scale.
Our model network represent a hypercolumn in V1 and has the geometry of a ring [33]. Neuron in population is characterized by an angle , defined as the orientation of the visual stimulus for which the LGN input it receives is maximum. We model this input as(13)where is the orientation of the stimulus, is the -periodic Gaussian with width , defined as . gives the overall strength of the LGN input and depends on the stimulus contrast. As we will see, for , not only the LGN input to neuron is maximum but so is also of its spike response. Therefore, is also the preferred orientation of the neuron.
We assume that varies with the contrast, , of the visual stimulus as where is in percents. This logarithmic dependence, which does not saturate, was chosen to facilitate the analysis of the cortical network.
The preferred orientations of the neurons are uniformly distributed over the interval . The feedback input from the network to neuron , , is given by(14)where the synaptic strengths, , depend on the difference in preferred orientations between neurons and and falls off with this difference as a periodic Gaussian with width (15)Note that we have scaled the synaptic strength by the density of neurons. The number of neurons in population with preferred orientation between and is equal to , which explains the factor in Eqn. (14).
In the limit of large , we can replace by , and by , where is a continuous variable. The rates satisfy the dynamics(16)Due to the rotation symmetry of the network, the response of the neurons depends on the stimulus orientation only through the difference, , between this orientation and the neurons preferred orientation, . Thus we need only to consider the case where .
In the conductance-based network, neurons are point-like and the dynamics of their membrane potential, , is:(21)where . The first term on the right-hand side of Eqn. (21) is the leak current . The next five terms correspond to a sodium current, , a delayed rectifier potassium current, , responsible for the up and down-stroke of the action potential respectively, a slow potassium current, , inducing spike adaptation, an A-type potassium current, , which becomes active during the hyper-polarization period and affects the length of the inter-spike interval, and a persistent sodium current, , which tends to amplify small depolarizations.
The gating variables , , , follow the dynamics:(22)For , the functions and , with the parameters and as given in Table 1 and for the functions and are given in Table 2. The maximal conductances of the ionic channels of the excitatory and inhibitory neurons are given in Table 3. They are chosen to reproduce qualitatively the frequency-current transfer functions of regular spiking excitatory neurons and fast spiking inhibitory neurons, such that excitatory neurons have a lower threshold [85] and stronger spike frequency adaptation than inhibitory neurons (e.g., [63], [70]).
The terms left on the right-hand side of Eqn. (21) are the synaptic inputs, , the neuron receives because its recurrent interactions with the other neurons in the network, a current, , representing the feedforward inputs from the LGN to V1, and the noise .
The synaptic current received by neuron in population , is(23)where mV and mV are the reversal potentials of excitatory and inhibitory synapses respectively. The strength of a synapse connecting the presynaptic neuron in population , to postsynaptic neuron in population , is characterized by , where is given by Table 4. Note the normalization to the neuronal density . The term describes the contribution of the th spike of neuron in population , which occurred at time , to the synaptic conductance at time . We take(24)with rise time constant msec and decay time constant msec for excitatory as well as for inhibitory synapses. The current, , is a Gaussian white noise with zero mean. Its standard deviation, , is chosen such that the standard deviation of the membrane potential of the neurons is approximately 3–4 mV, as measured experimentally in V1 [23].
The LGN input is modeled as in Eqn. (13) with , where the values of and are taken in accordance with experimental data for magnocellular cells [3] and is such that the activity of the neurons are similar to those measured in V1 during visual stimulation [3], [6].
In the rate model we simulated networks with 100 neurons for each of the populations, using a second order Runge Kutta integration scheme with a time step of 1 msec. After verifying that this discretization was sufficiently fine, we used these simulations to find the fixed points in the rate equations and to verify the stability of steady state.
The conductance-based model dynamics of networks consisting of 400 excitatory and 400 inhibitory neurons was simulated using a second order Runge-Kutta integration scheme with a time step msec. For each contrast, ten trials with different noise realizations were simulated and the responses were averaged over a time window of 1.5 sec after elimination of a transient.
The orientation tuning curves of the neurons were fitted with Gaussians parametrized as: . For the rate model, we set the offset, , to zero. For the CBM, was in general non-zero because the noise induced a non-zero activity at cross-orientation. The peak amplitude of these Gaussians estimated for different contrast, , yielded the CRFs of the neurons, which were subsequently fitted with the H-ratio function [1]:(27)where is the maximum firing rate, is the contrast (in ) for and the exponent, , is a measurement of the function's steepness. In the case of the CBM, we additionally computed the relative error of the estimated values of the CRF parameters (the relative error on is its SD divided by its mean, ). Good fits were defined as those with relative errors smaller than 0.15 for all the parameters.
Experimental data for the CRF was obtained from marmoset monkeys (Callithrix Jacchus, ). Details about the experimental protocol can be found in [20]. One half hour before anesthesia induction, the animals were tranquilized with diazepam (Valium, Roche) (i. m., 3 mg/kg) and atropine (0.05 mg/kg) was given at the same time to reduce secretions and to prevent bradycardia. Anesthesia was induced with Alphadalone/Alphaxalone acetate (Saffan, Essex Pharma, 1.2 ml/kg) injected intramuscularly and maintained during surgery by i. v. injection (0.17 ml/kg every 10–15 minutes). Synthetic corticoids were given to prevent brain edema. Animal's body temperature was maintained at C using a heating pad controlled by a rectal thermistor. EKG recording was performed through metallic pliers.
The surgical procedure consisted first in placing a catheter in the femoral vein. Next, a tracheotomy was performed to allow artificial ventilation. The marmoset was then set in a stereotaxic frame. Two holes were drilled over the frontal cortex and Ag wires inserted for epidural EEG recording. A craniotomy was made to gain access to area V1. A head post was sealed to the skull and fixed to the stereotaxic apparatus.
Following surgery, the animal was artificially ventilated with / (50%/50%). Anesthesia and analgesia were supplemented by a continuous infusion of sufentanil citrate (Sufenta, Janssen, 4–6 g/kg/hr) after a loading dose of 1 g/kg. The infusion vehicle was made of the mixture of 2 ml glucose 30%, 15 ml of amino-acid perfusion solution (Totamin, Baxter) and included synthetic corticoids (0.4 mg/kg/hr); NaCl was added to a final volume of 50 ml. We waited for 1–2 hours of infusion with this solution to ensure adequate depth of anesthesia. The animal was then paralyzed by adding pancuronium bromide (Pavulon, Organon, 0.1 mg/kg/hr) to the solution described above. Mydriasis and cycloplegia were induced with ophthalmic atropine sulfate (1%, Alcon). Gas permeable contact lenses were used to protect the eyes. The heart rate, rectal temperature and expiratory concentration were monitored throughout the experiment and maintained at 250–350 bpm, 37–C and 3–5%, respectively. The EEG and the absence of reaction to noxious stimuli were regularly checked.
Action-potentials were recorded extracellularly in area V1 using tungsten-in-glass microelectrodes. Spike-sorting was performed using Spike2 (Cambridge Electronic Design, Cambridge, UK) system. Appropriateness of single-unit isolation was based on the refractory period of the neuron. Visual stimuli were presented onto a computer monitor placed at 114 cm from the animal's eyes. We first determined the preferred orientation using square-wave drifting gratings. Optimal spatial frequency was then determined using sine-wave drifting grating. The CRF was then established using sine-wave drifting grating with optimal orientation and spatial frequency, presented at 12 different levels of contrast increasing geometrically for 2 to 90%. All visual stimuli were presented in a circular patch of 2–6 degrees diameter, centered on the receptive field. Drift velocity was between 0.5 and 2 cycles/sec. To avoid transient responses, the contrast was incremented in a 1 sec duration ramp, maintained at steady level for 3 or 4 sec, then decreased back to 0% in a 1 sec duration ramp, then maintained at 0% contrast for 1 sec. The measurement of mean firing rates was restricted to the 3–4 sec plateau period. The fits of the CRF to a H-ratio function was performed as with the simulations data (see above). The quality of the fit was good, () except one supersaturating cell () but there was no good reason to exclude this cell. The mean was (S.D.) and the median (interquartile).
Receptive fields were classified as “simple” or “complex” on the basis of the relative modulation (F1/F0 [86]) in their response to gratings at the optimal spatial frequency. In our data set, the distribution of F1/F0 was bimodal, with a gap at 1. Cells were considered as simple when the relative modulation was and complex when it was [86].
A cell was considered to display saturating response when the response extrapolated to 100% contrast was equal to . It was considered as non-saturating when the extrapolated response was less than 0.95 and as super-saturating if the response to at least one of the test contrast below 90% was larger than 1.05 .
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10.1371/journal.ppat.1000712 | Nutrient Availability as a Mechanism for Selection of Antibiotic Tolerant Pseudomonas aeruginosa within the CF Airway | Microbes are subjected to selective pressures during chronic infections of host tissues. Pseudomonas aeruginosa isolates with inactivating mutations in the transcriptional regulator LasR are frequently selected within the airways of people with cystic fibrosis (CF), and infection with these isolates has been associated with poorer lung function outcomes. The mechanisms underlying selection for lasR mutation are unknown but have been postulated to involve the abundance of specific nutrients within CF airway secretions. We characterized lasR mutant P. aeruginosa strains and isolates to identify conditions found in CF airways that select for growth of lasR mutants. Relative to wild-type P. aeruginosa, lasR mutants exhibited a dramatic metabolic shift, including decreased oxygen consumption and increased nitrate utilization, that is predicted to confer increased fitness within the nutrient conditions known to occur in CF airways. This metabolic shift exhibited by lasR mutants conferred resistance to two antibiotics used frequently in CF care, tobramycin and ciprofloxacin, even under oxygen-dependent growth conditions, yet selection for these mutants in vitro did not require preceding antibiotic exposure. The selection for loss of LasR function in vivo, and the associated adverse clinical impact, could be due to increased bacterial growth in the oxygen-poor and nitrate-rich CF airway, and from the resulting resistance to therapeutic antibiotics. The metabolic similarities among diverse chronic infection-adapted bacteria suggest a common mode of adaptation and antibiotic resistance during chronic infection that is primarily driven by bacterial metabolic shifts in response to nutrient availability within host tissues.
| Chronic infections are distinguished from many other infections in that they are difficult to eradicate with antibiotics. Thus, the microbes that cause chronic infections persist within host tissues for long periods despite our best treatment efforts. During the course of these chronic infections, the causative microbes often change genetically. For example, a bacterium that commonly infects the lungs of people with the genetic disease cystic fibrosis (CF) undergoes several known changes that affect the growth of this pathogen. However, the causes and clinical impact of the changes undergone by this and other chronically infecting microbes are unclear. We show that a common, early mutation found in bacteria isolated from chronically infected CF airways renders these bacteria better able to grow in the nutrients found in CF lung secretions. Interestingly, these same changes also confer resistance to several antibiotics used commonly to treat CF patients. Many of the characteristics conferred by this mutation are exhibited by other microbes found in chronic infections, suggesting that adaptation of these microbes to host tissue nutrient environments may be a common mechanism of antibiotic resistance in chronic infections.
| Microbes are subjected to selection in host environments during the course of chronic infections [1],[2],[3]. The characteristics selected may have profound impacts on disease outcomes, particularly if they confer increased microbial fitness or resistance to therapy. One example of this phenomenon is the adaptation of Pseudomonas aeruginosa within the airways of people with cystic fibrosis (CF). Diverse phenotypic changes have been observed among CF chronic P. aeruginosa infection isolates, including changes in several surface antigens [4],[5], altered antibiotic susceptibilities [6], and overproduction of the mucoid exopolysaccharide alginate [3]. P. aeruginosa CF adaptive changes have been associated with poor clinical outcomes [7],[8] and, in the case of mucoidy, a diminished likelihood of eradication by antibiotics [9].
Recently, several groups have described P. aeruginosa CF isolates with inactivating mutations in the gene lasR [2],[8],[10],[11],[12]. Genetic analyses demonstrated that these mutants emerged from existing, chronically-infecting lineages, as opposed to representing new infections, and that multiple lineages with independent lasR mutations occurred within individual patients, indicative of strong selective pressure against LasR function [2],[11]. lasR encodes a central regulator of the bacterial intercellular signaling system known as quorum sensing that requires the synthesis and recognition of P. aeruginosa small molecule products, including acyl-homoserine lactones (AHL). lasR mutant isolates occur in at least one-third of P. aeruginosa culture-positive individuals younger than 15 years attending CF clinics in Seattle [2],[8]. Among this population, lasR mutant isolates emerged relatively early during CF airway infection (on average 2 years before mucoidy), and were associated with worse lung function [8]. LasR inactivation conferred distinct phenotypic consequences, including distinctive colony morphology (autolysis and surface iridescent sheen) that facilitates the identification of mutant isolates, a growth advantage in specific amino acids abundant in CF secretions [13], and increased β-lactamase enzyme activity [2],[11]. These growth phenotypes suggested that selection may be due to exposure to antibiotics and nutrient availability within the CF airway. The latter possibility was further indicated by altered growth in specific nitrogen sources by lasR mutants compared with their wild-type counterparts [11].
AHL signaling was shown previously by transcriptional microarray [14] and enzymatic analyses [15],[16] to regulate the P. aeruginosa nitrogen metabolic pathway known as denitrification (Fig. 1A). However, the las system comprises only a portion of the complex AHL regulon, and the metabolic consequences of LasR inactivation were not defined previously. Previous evidence from a global physiological analysis of clinical isolates indicated that lasR mutation could confer a growth advantage in the denitrification substrates nitrate (NO3−) and nitrite (NO2−) [11], suggesting that lasR mutant P. aeruginosa cells may exhibit increased utilization of NO3− and NO2− as electron acceptors. Conversely, LasR inactivation conferred sensitivity to high concentrations of NO2− among these isolates [11], as would be predicted if lasR mutant cells avidly metabolize NO2− to nitric oxide (NO·), the chief toxic metabolic side-product of denitrification (Fig. 1A).
The airways of people with CF are known to contain abundant concentrations of NO3− and other nitrogen species [17],[18], while the concentrations of NO· (an important antimicrobial component of host innate immunity) are usually significantly lower than in people without CF for as yet unknown reasons [19]. In addition, CF secretions infected with P. aeruginosa include areas with very low molecular oxygen tensions [20]. These conditions would tend to favor the use among infecting microbes of nitrogen oxides as electron acceptors at the expense of oxygen utilization [16],[21]. Thus, we hypothesized that lasR mutant P. aeruginosa cells have respiratory alterations favoring growth in the nitrogen and oxygen conditions characteristic of CF airways. Since many antibiotics work best under aerobic conditions [22],[23], such a metabolic shift could adversely affect susceptibility (and thus clinical response) to antibiotics. To test these hypotheses, we defined the consequences of lasR mutation with respect to nitrate and oxygen metabolism, as well as antibiotic susceptibility, in laboratory strains and CF clinical isolates of P. aeruginosa.
Given the evidence that lasR mutants may have a growth advantage in NO3− [11], we compared the growth of P. aeruginosa laboratory strain PA14 and clinical isolates carrying wild-type lasR alleles with their derived lasR mutant strains in the presence of various concentrations of NO3−. lasR mutants exhibited a substantial growth advantage in minimal medium with added NO3−. As shown in Fig. 1B for a lasR mutant with a gentamicin insertion cassette derived from PA14 (PA14-lasR::Gm), a growth advantage was detected in NO3− concentrations as low as 125 µM, well below the average NO3− concentrations recently measured in CF airway secretions [17],[18], and the advantage was more pronounced at higher NO3− concentrations (not shown). The average rate of lasR mutant growth (calculated as the slopes of lines fit to the datasets shown between 8 and 16 minutes) in 50 mM NO3− was increased ∼5-fold relative to wild-type. Similar results were obtained using Luria Broth, with PA14 with an unmarked lasR deletion (PA14ΔlasR), and with paired lasR wild-type and mutant clinical isolates (not shown). This analysis confirms and extends our previous finding that lasR mutations confer a growth advantage with nitrogen sources that are abundant in the CF airway, including NO3−, as well as with aromatic amino acids [11].
lasR mutant strains and isolates converted NO3− to NO2−, and degraded NO2−, at significantly higher rates than did wild-type strains and isolates (Fig. 1C). For example, the average rate of NO2− production by lasR mutant strains was ∼4.4-fold greater than by wild-type. In contrast, lasR mutant strains and isolates demonstrated a relatively modest spontaneous increase in NO· reduction relative to wild-type (Fig. 1D); slopes for lines fit to each dataset in Fig. 1D between 25 and 100 seconds demonstrated that lasR mutant cells had an NO· degradation rate only ∼1.8-fold greater than wild-type cells. These activities resulted in dramatically higher levels of NO· (Fig. 1E) in lasR mutant cultures that could not be explained by any concurrent difference in growth rates between lasR mutants and wild-type in added NO3− (compare Figs. 1B and 1E). The accumulation of NO·, a potent microbicide [24], in lasR mutant cultures would be predicted to result in cell death at very high cell densities (as observed with P. aeruginosa cells with mutations in the quorum sensing regulator rhlR [16]) and in increased susceptibility to exogenous NO· sources.
Because lasR mutant P. aeruginosa produces elevated levels of endogenous NO·, and bacterial cells possess a finite capacity for detoxifying NO· that can be exceeded by exposure to exogenous reactive nitrogen species (RNS) [25], we predicted that lasR mutants would also be more susceptible to the exogenous nitrosative stress presented by either NO· donors (which have relatively short aqueous half-lives [26]) or acidified NO2− (with substantially greater aqueous half-life [27]). Therefore, lasR mutant strains and isolates were tested for these susceptibility phenotypes. lasR mutants were more susceptible to growth inhibition by the addition of NO· donor compounds to liquid cultures (Fig. 2A), and by NO2− disks during growth on acidified agar medium (Fig. 2B). These results are in agreement with our previous phenotype array findings that lasR inactivation in clinical P. aeruginosa isolates confers increased susceptibility to high concentrations of NO2− in unbuffered liquid minimal medium [11]. Furthermore, analysis of clinical isolate pairs demonstrated that the impact of lasR mutation on NO2− susceptibility was similar to the effect demonstrated previously for mucoidy [27], as shown in Fig. 2B for one isolate pair (NCAMT0101-2 and -3).
P. aeruginosa encodes two NO3− reductases, one in the bacterial inner membrane and the other in the periplasm. It was found previously that, of these two, only the membrane-bound enzyme was required for anaerobic growth of P. aeruginosa [17]. Interestingly, we found that spontaneous lasR mutants did not emerge during extended growth on agar medium from strains with transposon insertions in genes encoding subunits of the membrane-bound NO3− reductase (narJ and narK2), while sectors displaying the characteristic lasR phenotype arose frequently among strains with similar mutations in genes encoding the periplasmic enzyme (PA1173 and napA) (Fig. 2C). Furthermore, the growth advantage in NO3− conferred by lasR mutation (Fig. 1B) was not observed in the absence of narK genes (narK1narK2lasR, data not shown). Thus, the membrane-bound NO3− reductase was required for both the growth advantage of lasR mutants in added NO3− and for rapid lasR mutant emergence in vitro. These results functionally link the growth advantage in NO3− conferred by a lasR mutation with the selection of these mutants, at least in vitro, and perhaps also in the NO3− -rich CF airway [17],[18].
The findings that lasR mutants overproduce the potent microbicide NO· (Fig. 1E), that they undergo autolysis at high cell density [11], and that lasR mutants exhibit increased growth inhibition by exogenous sources of NO· (Fig. 2A) suggested that factors that detoxify NO· could enrich for lasR mutant growth. This hypothesis is supported by the observation that the cell death observed when RhlR mutants are grown anaerobically as biofilms can be prevented by addition of an NO· scavenger [16]. Similarly, we found that P. aeruginosa lasR mutants growing near disks containing hemoglobin, which scavenges NO· stoichiometrically, also grew better than did cells farther away from the disk (Fig. 3A). These results suggest that the presence of an NO· “sink” such as hemoglobin increased growth of lasR mutant P. aeruginosa.
Some bacteria, including the gram-positive CF bacterial pathogen Staphylococcus aureus, are known to be relatively resistant to the effects of NO· as a result of efficient cellular detoxification mechanisms [24]. Furthermore, we found previously that the presence of live, but not dead, S. aureus decreased expression of a P. aeruginosa gene (fhp [25]) involved in NO· degradation [28], suggesting that S. aureus may detoxify NO· produced by P. aeruginosa. The catalytic effect of growing S. aureus cells would be predicted to be even more robust than that of the stoichiometric agent hemoglobin. Therefore, we compared the growth of lasR mutants and wild type bacteria in the presence and absence of S. aureus.
When grown near S. aureus, lasR mutants exhibited wild-type growth phenotypes, as manifested by thicker colonies, using either clinical isolates or laboratory strains of each species (Figs. 3B–C). This phenotypic change did not require contact with S. aureus. Cell-free culture medium, cell sonicates, and organic extracts of S. aureus cultures did not exhibit the activity of S. aureus colonies, suggesting that S. aureus cell activity was required for this phenotypic change.
To further characterize the growth of lasR mutants and its modification by S. aureus, we inoculated static, liquid cultures with equal numbers of P. aeruginosa wild-type and lasR mutant cells in the presence or absence of wild-type or mutant S. aureus partially defective for NO· degradation (hmp mutants [24]), and measured the growth of each strain after incubation. As in previous experiments (e.g., Fig. 2A) [11], P. aeruginosa lasR mutants grown alone did not have a growth defect relative to wild-type strains and isolates in these nutrient conditions (not shown). We found that lasR mutant growth was enhanced by co-culture with wild-type S. aureus, but not by hmp mutant S. aureus (Fig. 3D). In addition, lasR mutant colonies growing on LB agar near colonies of hmp mutant S. aureus displayed substantially more autolysis than did lasR mutants growing near wild-type S. aureus (not shown), supporting the notion that S. aureus NO· detoxification is required to impede lasR P. aeruginosa colony autolysis. These results suggest that the presence of S. aureus, which commonly co-infects CF airways with P. aeruginosa [29], encourages the growth of lasR mutant P. aeruginosa by detoxifying NO·. This effect of S. aureus and other microbes could contribute to the relatively low tensions of NO· observed within CF airways [19], which would be predicted to further encourage the growth of lasR mutant P. aeruginosa by providing a mechanism to mitigate the toxic effects resulting from the shift to nitrate metabolism.
Low molecular oxygen tension and abundant nitrogen oxides have been observed in CF secretions [18],[20]. Furthermore, deficiency in las signaling has been shown to result in decreased expression of cytochromes central to oxygen utilization [30]. Therefore, P. aeruginosa lasR mutants could have decreased utilization of oxygen as an electron acceptor. To test this hypothesis, we examined rates of oxygen utilization in liquid (Fig. 4A) and agar-grown (not shown) P. aeruginosa cultures. lasR mutant cultures exhibited oxygen consumption rates at approximately 40–50% those of wild-type cultures (determined by comparing slopes of lines fit to each dataset from 1–5 minutes in Fig. 4A). Aerobic metabolism generates toxic reactive oxygen species (ROS), including superoxide (O2−·) [31]. As lasR mutant cells exhibit decreased rates of oxygen utilization relative to wild-type cells (Fig. 4A), lasR mutant cells could consequently contain lower endogenous levels of ROS. Hydroethidine is a specific fluorescent indicator of intracellular O2−· [32]. Hydroethidine addition to air-grown agar (Fig. 4B) or liquid (not shown) cultures of lasR mutants yielded much lower cell fluorescence than did its addition to wild-type cultures. We demonstrated that cell permeability was equivalent in wild-type PA14 and lasR mutant cells using two established methods: one measuring uptake of ethidium bromide during efflux pump chemical blockade [33] and another based on the uptake of the fluorescent molecule NPN without efflux pump inactivation [34] (data not shown). These results indicate that intracellular O2−· concentrations are lower in lasR mutant cells than in wild-type cells. By analogy to the increased susceptibility of lasR mutants to exogenous nitrosative stress associated with higher endogenous NO· production (Figs. 1E and 2A–B), these results suggest that lasR mutant cells would be more resistant to exogenous sources of oxidative stress, including redox-cycling agents [31],[35].
To test this hypothesis, we measured the response of P. aeruginosa cultures to the redox-cycling agent paraquat, which reacts with intracellular oxygen to generate O2−· [35]. As shown in Fig. 4C, cultures of lasR mutants (in both laboratory strain and clinical isolate backgrounds) were more resistant to paraquat and, as with nitrite susceptibility (Fig. 2B, far right), this effect was present in lasR mutant clinical isolates after several years of infection (Fig. 4C, far right). Differences in susceptibility to exogenous hydrogen peroxide exhibited the same trend, but to a lesser extent (not shown). Thus, the susceptibility of lasR mutant P. aeruginosa to exogenous oxidative stress is altered, apparently due to lower endogenous production of ROS and higher residual capacity for detoxification. Polyacrylamide gel enzymatic activity assays [31] demonstrated that lasR mutant cells and their wild-type counterparts exhibited similar activities of superoxide dismutases, enzymes that degrade O2−· (data not shown), supporting the concept that the differences in endogenous O2−· levels, and susceptibility to paraquat, were due to differences in O2−· production rather than differences in O2−· degradation.
The lower endogenous O2−· concentrations of lasR mutants indicate that they might have a growth advantage compared with wild-type cells when grown under conditions of oxidative stress. To test this hypothesis, agar-suspended cultures were inoculated with equal numbers of lasR and wild-type cells in the presence of paraquat, and then the density of each strain was determined in serial, thin culture slices. Using an oxygen microprobe, oxygen concentration within these cultures became undetectable within approximately 2 mm of depth below the surface after 24 hours of incubation (data not shown). This growth medium is a viscous gel, limiting the motility and sedimentation of cells and thus preserving two-dimensional culture structure, resulting in the establishment of a stable oxygen gradient. In this way, this culture may reproduce some aspects of CF respiratory secretions, which are relatively viscous compared to liquid cultures and exhibit oxygen gradients [20]. Furthermore, as ROS such as O2−· are side-products of oxygen-based respiration, ROS are produced at decreasing amounts with increased depth within the cultures. As shown in Fig. 5A, lasR cells greatly outcompeted wild-type cells at more superficial depths, where oxygen was detectable and O2−· could be produced upon paraquat exposure. This effect diminished with increasing depth and, therefore, with lower oxygen concentration. Thus, under growth conditions in which superoxide is generated, lasR mutants have a relative fitness advantage.
One condition under which ROS are generated within bacterial cells is upon exposure to bactericidal antibiotics, including fluoroquinolones and aminoglycosides, under aerobic conditions [22],[36]. Bacterial killing by both classes of antibiotics has been shown to be attributable in part to induction of superoxide production [37]. In addition, efficient aminoglycoside uptake (and thus bacterial killing) requires aerobic electron transport [23]. Furthermore, a las-regulated P. aeruginosa exoproduct, the Pseudomonas quinolone signal, induces an oxidative stress response, increased cellular ROS, and increased susceptibility to fluoroquinolones in P. aeruginosa [38], functionally linking response to oxidative stress and susceptibility to fluoroquinolones. A relationship between fluoroquinolone susceptibility and oxidative stress is supported by work in other bacterial species [39], including the observation that spontaneous mutants in superoxide response regulators have been selected by exposure of both Escherichia coli and Salmonella enteritidis to fluoroquinolones [40]. Thus, we predicted that the lower oxygen utilization rates and increased resistance to sources of superoxide exhibited by lasR mutants would result in decreased susceptibility to the fluoroquinolone ciprofloxacin and the aminoglycoside tobramycin, both of which are used frequently to treat CF patients [41]. As shown in Fig. 4D, surface cultures on nitrate-containing agar medium of lasR mutants were less susceptible to disks containing these drugs. Agar-suspended cultures in the same medium demonstrated that these differences were oxygen-dependent (Figs. 5B–C), as with paraquat (Fig. 5A). These results suggest that, under these culture conditions, inactivating lasR mutation confers resistance to two of the antibiotics used most frequently in CF care, tobramycin and ciprofloxacin. To further investigate the relationship between oxidative stress and antibiotic resistance, we compared the susceptibilities to oxidative stress and antibiotics of strains of P. aeruginosa carrying the double mutation oxyRkatA, or the triple mutation oxyRkatAlasR. Strains null for oxyR and katA are defective for the defensive response to oxidative stress [36]; accordingly, the oxyRkatA mutant exhibited increased susceptibility to paraquat compared with wild-type (Fig. S1). However, the oxyRkatAlasR triple mutant was even more resistant to paraquat than was wild-type, confirming and extending the observation that a lasR mutation confers resistance to ROS (not shown). Similarly, the oxyRkatA mutant was more susceptible to tobramycin (Fig. 4E) (as shown for the oxyR single mutant and the aminoglycoside gentamicin [36]) and to ciprofloxacin (not shown) than was wild-type; as with paraquat, the oxyRkatAlasR triple mutant exhibited resistance to each of these drugs, an effect that was reversed by complementation with a wild-type copy of lasR on a plasmid (Fig. 4E and data not shown). These results indicate that lasR mutation confers resistance to these two antibiotics through its effects on respiratory activity and oxidative stress response. As lasR mutant strains and isolates also exhibit increased tolerance to some β-lactams due to increased β-lactamase activity [11], these results suggest that the emergence of lasR mutant isolates during chronic infections could adversely impact the clinical response to all three of the antibiotic classes used most commonly during standard CF treatment (β-lactams, fluoroquinolones, and aminoglycosides). The recent discovery [42] that increased bacterial production of NO· (which is increased by LasR inactivation, Fig. 1E) confers additional protection against a wide variety of antibiotics, including β-lactams, quinolones, and aminoglycosides, further supports this possibility.
In this work, P. aeruginosa isolates with inactivating mutations in the AHL-responsive transcriptional regulator LasR exhibited a profound growth advantage with nitrogen substrates found in the CF airway. These differences are attributable to lasR-dependent increased utilization of nitrogen oxides and decreased utilization of oxygen. This metabolic shift results in an increase in the production of the RNS NO·, and a corresponding decrease in the ROS O2−·, the latter of which is associated with decreased susceptibility in our conditions to at least two antibiotics used frequently in treating CF lung infections. This growth advantage in conditions characteristic of CF airways, and the resulting antibiotic resistance, may explain the observed high prevalence of LasR mutants and the associated worse lung function of CF patients whose airways contain these mutants [8].
The metabolic changes that occur upon lasR inactivation would be predicted to favor growth of lasR mutants arising spontaneously in the CF airway due to the confluence of selective forces encountered in this environment. For example, the abundant NO3− and NO2− [17],[18] and low oxygen tensions [20] found in CF secretions, as well as the relatively low NO· levels [18], would provide optimal metabolic conditions for lasR mutant selection. As suggested previously [43], P. aeruginosa likely adapts to a continuum of different oxygen tensions, with variation in the relative ratio of oxygen and nitrate utilization. Inactivating mutations in lasR may confer advantages in a variety of these microenvironments found in the CF lung. Also contributing to the beneficial nature of this environment for lasR mutant growth is the presence of NO·-detoxifying microbes, such as S. aureus and perhaps anaerobic bacteria, the latter of which were recently found to occupy CF secretions at high densities [44]; it should be noted that, while contact of lasR mutant P. aeruginosa with wild-type P. aeruginosa was also shown previously to reverse autolysis and sheen [11], it is not yet clear whether the mechanism of this effect is similar to that of S. aureus. The availability of amino acids as nutrient sources in CF secretions [13] would provide an additional selective pressure for lasR mutant growth [11]. Similarly, lasR mutants are relatively resistant to sources of oxidative stress, including tobramycin and ciprofloxacin (Figs. 4D–E), two antibiotics that, along with ceftazidime (to which lasR mutants are also relatively tolerant due to augmented β-lactamase activity [11]), are among the antibiotics used most commonly in CF treatment [41]. Although other sources of ROS are present in CF airways, such as H2O2 from host cells [36], whether exogenously adding these molecules to P. aeruginosa effectively confers intracellular oxidative stress is not as clear as is is the effect of the above antibiotics [22],[23]. While the results presented here demonstrate that nutrient conditions (particularly relating to oxygen and nitrogen oxides) are sufficient to enrich for lasR mutant growth in vitro, the frequent treatment of CF patients with the above antibiotics likely provides additional selection for these mutants, resulting in a complex dynamic between the CF airway nutrient environment, P. aeruginosa adaptation, therapy, and pathophysiology. These ideas are summarized in the model in Fig. 6.
There are multiple therapeutic and pathophysiologic implications of the model in Fig. 6. For example, assuming that P. aeruginosa infection leads to airway inflammation, and thus to obstructive lung disease, as suggested by current models of CF pathogenesis [41], the growth advantage of lasR mutant cells within the CF airway would be predicted to render such mutants more pathogenic to CF patients by virtue of higher cell density and greater consequent inflammation. (It should be noted that while lasR mutant P. aeruginosa strains were shown to be less pathogenic in animal models of short-term respiratory infection [45], those models may not accurately reflect the pathogenic mechanisms of chronic CF airway infection, during which many “acute” virulence factors are not expressed [46]). This effect may contribute to the observed association between lasR mutant CF airway infection and worse lung function [8]. Furthermore, the clinical response to standard antibiotic therapy in patients infected with lasR mutants would be predicted to be poor relative to patients with wild type isolates, perhaps further contributing to the clinical impact and rendering eradication increasingly difficult [8]. Thus, the presence of lasR mutants in CF respiratory cultures may be of prognostic value, and aggressive, directed treatment of these mutants upon isolation (i.e., through the expanded use of monobactams, tetracyclines, or polymyxin in the case of lasR mutant infection) or with regimens that do not select for their growth may lead to improved outcomes.
While recent publications have shown that quorum sensing regulates the expression of denitrification genes [14],[15],[16] and oxygen metabolic genes [30] at the transcriptional level, the mechanism of the distinct metabolic behaviors of lasR mutant and wild-type cells is likely to be as complex as the quorum sensing system itself. In P. aeruginosa, quorum sensing involves at least three parallel signaling systems, at least four different signal receptors, and regulation by diverse environmental cues [14],[47],[48]. However, some mechanistic clues are evident from our results. Previously, we showed that the two-component metabolic regulatory system CbrAB contributes to the metabolic phenotypes of lasR mutant clinical isolates of P. aeruginosa [11]; mutants in this system have decreased capacities to use amino acids as nitrogen sources [49], and lasR mutant isolates have upregulated expression of the transcriptional metabolism regulator cbrB [11]. The current results also suggest an additional mechanism for the growth advantage of lasR mutant P. aeruginosa in specific amino acids (most markedly with phenylalanine, but also with other aromatic and branched-chain amino acids [11]). Many enzymes that metabolize amino acids are inactivated by reactive oxygen species (ROS), including the first enzyme in the phenylalanine catabolic pathway, phenylalanine hydroxylase [50],[51]. Therefore, cells with lower intracellular concentrations of ROS, such as lasR mutants (Fig. 4B), would be predicted to be better able to utilize amino acids such as phenylalanine as nutrient sources. Additionally, the las system is involved in regulating the levels and timing of production of a family of hydroxyalkylquinoline (HAQ) molecules [52], including the compounds 4-hydroxy-2-heptylquinoline (HHQ), the overproduction of which generates the sheen characteristic of lasR mutant colonies [11]; its N-oxide HQNO, which is a redox-cycling agent [29]; and the Pseudomonas quinolone signal (PQS) [52]. Exposure to PQS was shown to modify P. aeruginosa responses to reactive oxygen species and ciprofloxacin [38], suggesting a functional linkage between HAQs, oxidative stress responses, and susceptibility to fluoroquinolones. Therefore, these quinolines may regulate metabolic properties in both source and neighboring cells, and temporal differences in their production resulting from LasR inactivation may contribute to the observed metabolic changes.
Numerous explanations have been offered for the identification of lasR mutant P. aeruginosa in diverse clinical and experimental conditions [10],[11],[12],[53],[54],[55],[56],[57],[58],[59],[60]. For example, in experimental growth medium in which P. aeruginosa growth requires the production of lasR-regulated protease, lasR mutants emerge that “cheat” from the protease produced by wild-type strains [59],[60]. However, CF sputum is abundant in free amino acids [13] (upon which P. aeruginosa lasR mutants can grow without requiring protease [11],[59]), and it has been shown that both laboratory strains [61] and clinical isolates [12] may produce protease in the absence of a functional las system. Furthermore, lasR mutants are frequently isolated from CF sputum without detectable wild-type co-isolates [2],[11]. These findings suggest that cheating alone does not explain the high prevalence of lasR mutants among people with CF. Alternatively, it has been suggested that lasR mutants emerge due to physiological characteristics that confer relative fitness advantages in specific growth conditions [11],[56]. The current results support the hypothesis that lasR mutant P. aeruginosa have a growth advantage in nutrient and antibiotic conditions found in the CF airway (as summarized in Fig. 6). While it is unclear which of these forces, antibiotics or nutrients, predominates in vivo in selecting for inactivating lasR mutations, their combination would be predicted to exert powerful pressure against LasR function.
The hypothesis that P. aeruginosa adaptation to the CF airway is driven in large part by metabolic forces found in CF airway secretions is supported by findings with other adapted mutants from chronic infections. For example, mucoid P. aeruginosa strains and isolates also exhibit upregulated NO3− metabolism relative to non-mucoid P. aeruginosa and, as a result, are more susceptible than nonmucoid isolates to acidified NO2− [27]. Furthermore, the mucoid phenotype is promoted by hypoxia [20]. Similarly, P. aeruginosa isolates with another CF adaptation, mutations that upregulate the glucose-6-phosphate dehydrogenase gene zwf, confer resistance to oxidative stress and paraquat [62],[63]. The enrichment for lasR mutant P. aeruginosa, with growth advantages in CF airway conditions, by S. aureus is also reminiscent of the reverse interaction: the selection of S. aureus metabolic mutants, known as small-colony variants (SCVs), due to co-culture with wild-type P. aeruginosa [29]. S. aureus SCVs are defective for aerobic growth, are resistant to aminoglycoside antibiotics such as tobramycin, and frequently exhibit both increased expression of denitrification genes [64] and associated increased susceptibility to NO2− [65], much like lasR mutant P. aeruginosa. The symmetry of this S. aureus-P. aeruginosa relationship, in each direction favoring the growth of antibiotic-resistant, metabolic mutants with decreased aerobic activity, further suggests a common mechanism for selection during chronic CF infections, and perhaps during many other chronic infections, driven by the metabolic forces present in host tissues. In support of this hypothesis, the likelihood of persistent, latent infection by the respiratory pathogen Mycobacterium tuberculosis is thought to be determined in large part by the lung metabolic milieu, particularly the relative ambient concentrations of nitrogen and oxygen species [66]. Similarly, the pathogenic fungus Cryptococcus neoformans exhibits early metabolic adaptations in animal models of chronic pulmonary infection, including altered responses to nitrosative stress and superoxide [67]. As with M. tuberculosis [68], these findings support the concept that chronic CF airway infections with P. aeruginosa could be amenable to therapies that increase airway nitrosative stress. Such therapies could include inhaled NO2− [27] or L-arginine [19], two treatments already being examined as candidate CF treatments. Our results support the utility of these treatments both in preventing P. aeruginosa adaptive changes associated with advanced lung function decline [7],[8] and that may be attributable to current antibiotic regimens (Fig. 6), as well as in treating patients with advanced infection in which these adaptations have already occurred.
In summary, the nutrient conditions characteristic of the CF airway select for growth of lasR mutant P. aeruginosa, resulting in decreased susceptibility to antibiotics without the need for antibiotic exposure. Adaptation of many microbes to new environments during chronic infections may commonly result in metabolic changes that impact response to antibiotics. This scenario may be particularly relevant for opportunistic pathogens such as P. aeruginosa, many of which naturally occupy competitive and nutrient-poor environmental niches like soil and water, as they adapt to the specific nutrient conditions found in host environments such as the nitrogen-rich CF airway.
Table 1 lists the bacterial strains and isolates used in this work, except for the strains carrying transposon insertion mutations in nitrate metabolic genes, which were obtained from the PA14 transposon insertion mutant library [69]. The origins of all strains and isolates are described in the references provided in Table 1, except for the narK1K2 and narK1K2lasR mutants, described below.
Each deletion in the lasR, narK1K2 and narK1K2lasR mutants was generated using allelic exchange with sacB-containing counterselectable gene replacement vectors using sucrose counterselection essentially as described [70]. Briefly, the lasR gene was entirely deleted from the chromosome except for the start and stop codon, using the plasmid sacB-based pEX18Gm for integration and excision. The narK1-narK2 genes, which are organized tandemly as an operon, were deleted as a one continuous stretch of DNA using identical methods, both in wild-type PA14 as well as the lasR mutant background. The deletion removed the narK coding sequence beginning from the 30th codon of narK1 until the 462nd codon of narK2, leaving the first 29 codons of narK1 and the last 7 codons of narK2 intact.
The plasmid pUCPSK-lasR was the kind gift of Eric Déziel and was used for complementation of lasR deficient strains and isolates as described [61].
Except where indicated, all cultures were inoculated from LB overnight cultures of bacteria or cells suspended from LB agar cultures. Liquid static cultures were grown in LB with 400 µM KNO3 (Sigma) except where indicated otherwise. Phosphate buffered LB agar was prepared as described [27]. Chemically defined PN medium was prepared as previously described [24], and consists of a phosphate buffer supplemented with a carbon source (glucose), nitrogen and sulfur sources [(NH4)2SO4 and MgSO4], amino acids, nucleic acid bases, and vitamins (thiamine, niacin, biotin, and pantothenic acid).
Hemoglobin, hydroethidine, tobramycin, paraquat (methylviologen dichloride hydrate), potassium nitrate, and sodium nitrite were obtained from Sigma. NO donors DEANO (DEA-NONOate) and ProliNO (Proli-NONOate) were purchased from AG Scientific (San Diego, CA) and SperNO was obtained from CalBiochem (San Diego, CA). Ciprofloxacin was from Biochemika/Sigma. Prepared antibiotic disks with tobramycin, kanamycin, gentamicin, carbenicillin, tetracycline, aztreonam, ceftazidime, and polymyxin B were from Becton Dickinson. Growth media and agar were from Becton Dickinson & Co.
Growth of cells in the indicated liquid media was measured optically using a BioScreen C Microbiology Microplate reader (Growth Curves USA, Piscataway, NJ) without shaking (except immediately prior to readings), a condition that limits oxygen mass-transfer. Assays to look for mutant sectors were performed by inoculating 10 µl drops of 1∶10-diluted overnight cultures on LB with 400 µM KNO3, followed by incubation at 37°C for 24 hours and then at room temperature for up to approximately 1 month thereafter.
NO· was quantified using an ISO-NOPMC Mark II electrode (WPI Instruments, Fl) and dissolved oxygen was measured in parallel using a Clark-type electrode MLT1120 (ADI Instruments) with standard curves as per manufacturer instruction. Data from both probes were analyzed through an Analog Adapter MLT1122 (ADI Instruments). NO2− disk diffusion on acidified, buffered LB agar was performed as described [27], except that all incubations were performed with aerobic growth.
Respiration rates in liquid cultures were measured by resuspending PBS-washed cells in prewarmed LB with 400 µM KNO3 in a microrespiration system (Unisense AS, Denmark). Calibrations were performed according to manufacturer's instructions using air-purged and argon-purged growth medium.
Fluorescence after hydroethidine addition to lawns of cells during growth on LB agar (similar results were obtained with and without added NO3−) was measured using excitation/emission wavelengths of 396/570 nm [32], followed by photography and quantitation using NIH ImageJ software (NIH, Bethesda, Md, http://rsb.info.nih.gov/ij/).
Agar-suspended cultures were grown in 0.9% LB agar inoculated with equal cell numbers of all cell types- approximately 105 CFU of the indicated strains (resulting in a final cell density of approximately 2×103 CFU/mL), except when indicated otherwise- and with chemicals and antibiotics added as indicated. In each case, the prepared agar was inoculated with bacteria when the medium had cooled after autoclaving to approximately 37°C but before gelling. The medium was then poured into 10 mL syringes from which the port ends had been removed, leaving an open end, which was loosely covered for incubation. After incubation, the plunger of the syringe was depressed slowly, ejecting a cylinder of culture. Serial, 1.5 mm slices of culture were removed and added to 1 mL each of sterile PBS, and vortexed for 30 seconds before enumeration of cells from the resulting solution by plating.
NO2− production was measured using the Griess Reagent System kit (Promega, Madison WI). Nitrate was quantified enzymatically using a commercially available reagent set (R-Biopharm, Marshall, MI). Rates of NO· degradation were determined as previously described [24]; briefly, five milliliter cultures in PN medium were grown by shaking at 37°C to an OD660≈0.4. Cells were then resuspended to 1×108 cfu ml−1 in 8 ml final volume. A two-hole rubber stopper sealed with Parafilm enclosed the cell suspension in an 8 ml glass vial with no gaseous headspace. Cells were stirred vigorously at 37°C as ProliNO was added through one open port to 1 µM. The resulting immediate release of approximately 2 µM NO· followed by the gradual decay of detectible signal was recorded and normalized to the fraction of initial [NO·]. Measurements were performed in triplicate for each strain tested. NO· susceptibility was determined by measuring the lag in growth after bacterial cultures in LB medium were supplemented with 0.5 or 2.5 mM SperNO (t1/2 = 39 min at 37°C).
Deep-agar cultures inoculated with serial dilutions of P. aeruginosa lasR and wild-type cells in LB-0.9% agar with and without 400 µM KNO3 and with and without paraquat were grown overnight at 37°C. Oxygen concentrations were subsequently recorded using a microsensor setup (OX 10 oxygen microsensor, PA 2000 picoammeter, both from Unisense AS, Denmark) at 37°C in a preconditioned water bath. Data were recorded using SensorTrace Basic software (Unisense). The probe was advanced into the agar, and measurements taken, in 50 µm increments.
Differences between experimental measurements were computed using unpaired, two-tailed Student's t-tests.
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10.1371/journal.pcbi.1007041 | Exploring E-cadherin-peptidomimetics interaction using NMR and computational studies | Cadherins are homophilic cell-cell adhesion molecules whose aberrant expression has often been shown to correlate with different stages of tumor progression. In this work, we investigate the interaction of two peptidomimetic ligands with the extracellular portion of human E-cadherin using a combination of NMR and computational techniques. Both ligands have been previously developed as mimics of the tetrapeptide sequence Asp1-Trp2-Val3-Ile4 of the cadherin adhesion arm, and have been shown to inhibit E-cadherin-mediated adhesion in epithelial ovarian cancer cells with millimolar potency. To sample a set of possible interactions of these ligands with the E-cadherin extracellular portion, STD-NMR experiments in the presence of two slightly different constructs, the wild type E-cadherin-EC1-EC2 fragment and the truncated E-cadherin-(Val3)-EC1-EC2 fragment, were carried out at three temperatures. Depending on the protein construct, a different binding epitope of the ligand and also a different temperature effect on STD signals were observed, both suggesting an involvement of the Asp1-Trp2 protein sequence among all the possible binding events. To interpret the experimental results at the atomic level and to probe the role of the cadherin adhesion arm in the dynamic interaction with the peptidomimetic ligand, a computational protocol based on docking calculations and molecular dynamics simulations was applied. In agreement with NMR data, the simulations at different temperatures unveil high variability/dynamism in ligand-cadherin binding, thus explaining the differences in ligand binding epitopes. In particular, the modulation of the signals seems to be dependent on the protein flexibility, especially at the level of the adhesive arm, which appears to participate in the interaction with the ligand. Overall, these results will help the design of novel cadherin inhibitors that might prevent the swap dimer formation by targeting both the Trp2 binding pocket and the adhesive arm residues.
| Classical cadherins are the main adhesive proteins at the intercellular junctions and play an essential role in tissue morphogenesis and homeostasis. A large number of studies have shown that cadherin aberrant expression and/or dysregulation often correlate with pathological processes, such as tumor development and progression. Notwithstanding the emerging role played by cadherins in a number of solid tumors, the rational design of small inhibitors targeting these proteins is still in its infancy, likely due to the challenges posed by the development of small drug-like molecules that modulate protein-protein interactions and to the structural complexity of the various cadherin dimerization interfaces that constantly form and disappear as the protein moves along its highly dynamic and reversible homo-dimerization trajectory. In this work, we study the interaction of two small molecules with the extracellular portion of human E-cadherin using a combination of spectroscopic and computational techniques. The availability of molecules interfering in the cadherin homophilic interactions could provide a useful tool for the investigation of cadherin function in tumors, and potentially pave the way to the development of novel alternative diagnostic and therapeutic interventions in cadherin-expressing solid tumors.
| Classical cadherins constitute a subfamily of calcium-dependent cell–cell adhesion proteins that belong to the large and phylogenetically diverse cadherin superfamily. The various members of the classical cadherin subfamily show a tissue-dependent expression profile as well as a high sequence and structure homology. In the different tissues, they are mostly localized at the adherens junctions, where they promote cell–cell adhesion through the homodimeric engagement of ectodomains protruding from neighbouring cells [1,2]. This process, which involves cadherin clusterization on the cell membrane, results in the formation of tight cell-cell adhesion interfaces. The extracellular portion of classical cadherins features five tandemly arranged immunoglobulin-like domains (EC1-EC5), whose relative orientation is controlled and rigidified through the coordination of calcium ions at the interdomain level. The interaction of their cytoplasmic tail with catenins allows a number of cell signalling and trafficking processes, providing also a physical link between cadherins and the actin cytoskeleton machinery [3]. The dynamic adhesive interface of the extracellular portion of E-cadherin and other classical cadherins has been revealed by several crystal structures, which so far have captured only some of the numerous conformational states of the protein [4–8]. In essence, dimerization has been shown to involve mainly the two most membrane-distal domains, EC1 and EC2. In order to form the so-called ‘strand-swapped dimer’, two E-cadherin molecules mutually exchange their N-terminal sequence (the A*-strand or adhesion arm), anchoring the aromatic side chain of their Trp2 residue into each other’s binding pocket. Interestingly, to reach the strand swap dimer conformation, which is the reversible endpoint of the dimerization trajectory, two monomeric cadherins must first go through an X-dimer conformation, which lowers the energy of the strand exchange process by firmly placing the two adhesion arms in close physical proximity [9–10].
Beside its broad-ranging effects on physiological tissue organization, classical cadherin dysfunction is often correlated with cancer progression and metastasis [11]. Despite Epithelial (E)- to Neural (N-) cadherin switching being considered a molecular hallmark of epithelial to mesenchymal transition of cancer cells, carcinomas and distal metastases often retain E-cadherin expression [12], as observed for instance in late stage tumors in epithelial ovarian cancer (EOC) [13–15]. In this context, targeting the E-cadherin adhesive interface with small molecules could have a therapeutic and diagnostic value. Linear and cyclic peptides based on the His79-Ala80-Val81 conserved sequence of the EC1 domain (not belonging to the swap dimer interface) have been previously developed to inhibit N-cadherin mediated processes. The most studied cyclic peptide is ADH-1 (Ac-CHAVC-NH2), which has been shown to be an anti-angiogenic agent able to cause cell apoptosis [16]. Peptidomimetics of ADH-1 have also been identified [17].
Recently [18], we reported the first library of small peptidomimetic molecules targeting the strand-swapped dimer interfaces of E- and N-cadherin. These compounds are mimics of the tetrapeptide sequence Asp1-Trp2-Val3-Ile4 (DWVI) of the adhesion arm, modified by replacing the central dipeptide unit (WV) with various scaffolds, all of them bearing a benzyl group that is intended to mimic the indole moiety of Trp2. The most promising compounds identified by a docking protocol were synthesized and were shown to inhibit cadherin homophilic adhesion in EOC cells at low millimolar concentrations. Of these, compound 1 (a.k.a. FR159) (Fig 1), was co-crystallized with a mutant of the E-cadherin EC1-EC2 fragment lacking the first two residues of the adhesion arm (Asp1 and Trp2), while attempts to co-crystallize both compounds 1 and 2 with intact E-cadherin-EC1-EC2 did not yield results [19]. In the X-ray complex structure (PDB code: 4ZTE), compound 1 binds to a dimeric conformation of E-cadherin, the so-called ‘X-dimer’, which is a key intermediate along the E-cadherin dimerization trajectory leading from the monomeric to the strand swap dimer conformation. Unexpectedly, the ligand was found to occupy a hydrophobic pocket that is formed at the X-dimer interface and not in the Trp2 cavity for which it had been designed. This novel hydrophobic pocket formed by the side chains of residues Ile4, Pro5, Ile7, and Val22 from both cadherin molecules does not overlap with the swap dimer interface. However, in the course of the complex cadherin dimerization mechanism the protein undergoes large conformational changes (such as for instance the opening of the adhesion arm that leads to strand swap dimer formation) and goes through different intermediate steps. Hence, it is conceivable that during the process the ligand may bind transiently and via variable moieties also to different surface areas of the protein.
Here, to investigate the extent by which variable ligand-cadherin interactions may form over time in solution and to provide a possible dynamic picture of the binding event, we have applied a combination of NMR (Nuclear Magnetic Resonance) and computational techniques to the complexes of two peptidomimetic inhibitors from our library (Fig 1) and human E-cadherin-EC1-EC2.
We used ligand-based NMR techniques (Saturation Transfer Difference, STD, and transferred NOE, tr-NOESY) [20,21] to assess binding occurrence as well as to identify the binding epitope of the ligands and to estimate the dissociation constant of the complexes. The experiments were performed using wild type (wt) human E-cadherin-EC1-EC2 at three different temperatures (283, 290 and 298 K). Furthermore, we analysed both ligands in the presence of the EC1-EC2 mutant that was used for the X-ray study and that lacks the first two N-terminal residues (E-cadherin-(Val3)-EC1-EC2).
Then, in order to rationalize the atomic details of peptidomimetic-cadherin interaction, the NMR data obtained in the presence of wt E-cadherin-EC1-EC2 were analyzed computationally. Indeed, based on NMR data, docking calculations into the EC1 domain of E-cadherin were carried out first. Then, to take into account the temperature factor and introduce protein flexibility, Molecular Dynamics (MD) simulations were also performed. Two temperatures, 300 K and 320 K, were used starting from the different docking poses of 1 and 2 into the E-cadherin model. Of note, ligands binding seems to be highly dependent on the protein flexibility, particularly of the adhesive arm. Overall, the dynamic data described herein will help the design of novel cadherin inhibitors that may bind more efficiently and more selectively into the Trp2 binding pocket.
STD-NMR is a consolidated technique [20,21] based on Overhauser effect that is used to study the interactions between small ligands and macromolecules [22–24]. The method relies on the selective irradiation of the protein, which allows magnetization to be transferred to the bound ligand (which is in great excess in solution compared to the protein concentration). The saturated ligand is displaced in solution due to the binding equilibrium and the observation of the ligand signals in the NMR spectrum provides an indication of the interaction. Those ligand protons that are nearest to the protein are more likely to become highly saturated, and therefore show the strongest signal in the mono-dimensional STD spectrum. Owing to the efficiency of the saturation process, the modulation of the ligand signal intensity is used as an epitope-mapping method to describe the target-ligand interactions. In fact, the intensity of the STD signal (expressed as absolute STD percentage) reflects the proximity of the ligand to the protein surface. Therefore, the group epitope mapping obtained provides information about the nature of the chemical moieties of the ligand that are crucial for molecular recognition in the binding site. We also used the STD amplification factors (STD-AF) to derive the dissociation constant (KD) of the ligand-protein complexes [25–27]. Moreover, we performed tr-NOESY experiments in order to determine the preferred bound conformation of the ligands.
In solution, the EC1-EC2 domain fragment can adopt several conformations depending on protein and Ca2+ ion concentration. At 1 mM calcium concentration and at less than 40 μM protein concentration, a monomeric species is observed predominantly, while dimeric forms or even oligomers are present at higher protein concentrations in solution (600 μM) [28]. The calcium ions provide a rigidification of the linker region connecting the EC1 and EC2 domains in the monomer, thus making the dimerization surface available.
STD–NMR and tr-NOESY spectra were acquired in 20 mM phosphate buffer at pH 7.4 (with 150 mM NaCl and 1 mM CaCl2) and 40 μM EC1-EC2 fragments of E-cadherin. In this condition, a monomeric form is predominant in solution [28]. We performed STD-NMR experiments at 283K, 290K and 298K since lowering the temperature helps to shorten the rotational correlation time of the receptor and to increase the effective magnetization transfer. Interestingly, for both compounds we observed different binding epitopes in relation to temperature variations. Furthermore, we also studied the binding modes of the ligands in the presence of the truncated mutant (E-cadherin-(Val3)-EC1-EC2).
The NMR interaction data of peptidomimetics 1 and 2 with the EC1 domain of E-cadherin were further investigated computationally. In the X-ray structure of compound 1 bound to the deleted E-cadherin-(Val3)-EC1-EC2 construct missing the Asp1-Trp2 N-terminal residues, the ligand fits into a novel site at the interface of the X-dimer assembly rather than occupying the Trp2 hydrophobic pocket as initially assumed based on their design strategy. However, according to the NMR results discussed above, the ligands show different binding epitopes depending on the protein construct used, suggesting that the adhesive arm may indeed also be involved in stabilizing the ligand-cadherin binding. To clarify this further, we performed MD simulations starting from the docking poses of 1 and 2 generated into the Trp2 binding pocket of wt E-cadherin model. To take into account temperature modulation, the MD runs were carried out at 300 K and 320 K. For each ligand, a detailed analysis of ligand protonation states and conformational geometries was carried out (see SI) in order to select the most relevant conformations and ionization forms for docking calculations.
In this study, we evaluated the binding properties of two peptidomimetic ligands to the extracellular portion of E-cadherin using NMR spectroscopy combined to molecular docking and molecular dynamics calculations. Both ligands have been previously shown to act as mM inhibitors of E-cadherin-mediated cell adhesion [18] and to the best of our knowledge, they are the first peptidomimetics developed from the N-terminal DWVI sequence of the E-cadherin EC1 domain. In these compounds, the central dipeptide unit (Trp2-Val3) of the tetrapeptide motif have been replaced by two different scaffolds bearing an aromatic group that, in our docking model, inserts into the hydrophobic cavity of Trp2, thus preventing swap dimer formation. In accordance with this hypothesis, a reconstruction of the free energy profile of the conformational transition of the E-cadherin monomer from its closed inactive state (with the Trp2 indole moiety intramolecularly docked) to its open form (indole moiety solvent exposed), has shown that in solution the monomer could significantly populate both the open and closed states, which are almost iso-energetic [34]. However, the recent crystallographic structure of 1 in complex with a deleted form of E-cadherin EC1-EC2 domain (lacking the N-terminal Asp1-Trp2 residues) showed also a novel possible mechanism of action based on a different adhesive interface [19]. Indeed, in the X-ray structure the peptidomimetic compound binds to the X-dimer conformation of the protein, a crucial kinetic intermediate of the cadherin dimerization pathway, by inserting into a novel hydrophobic cavity that is formed at the interface of the two interacting cadherins.
Clearly, owing to the complexity of the E-cadherin homo-dimerization process and to the dynamic behaviour of the target itself, which undergoes major conformational changes as part of its substrate recognition mechanism, further investigations of ligand-cadherin binding are needed. In this work, to sample a set of possible interactions of compound 1 and 2 with E-cadherin, we carried out STD-NMR experiments in the presence of two slightly different E-cadherin species, the wt-E-cadherin-EC1-EC2 and the E-cadherin-(Val3)-EC1-EC2 fragments. Depending on the protein constructs, a different binding epitope of the ligand and also a different temperature effect on STD signals were observed, both suggesting an involvement of the Asp1-Trp2 sequence over time and among the set of possible binding events.
Prompted by these considerations, the ligand binding mode proposed by the docking model that places the compound in the Trp2 cavity making interactions with the adhesive arm portion was selected for further investigations. MD simulations were performed starting from the corresponding docking poses of 1 and 2 into a wt-E-cadherin EC1 model. To assess the temperature modulation, MD runs were performed at 300 K and 320 K and the results compared to the STD spectra. The stability of the docking poses and also the interactions with the E-cadherin pocket, including the adhesive arm, were evaluated and compared at the different temperatures. In the input structures, both ligands insert the aromatic group into the Trp2 pocket. The aromatic moiety remains docked into the hydrophobic pocket also during MD runs at 300 K and 320 K, supporting the presence of the aromatic hydrogens STD signal (detected at all temperatures). In general, the starting ligand binding mode is quite conserved during MD run while the protein displays major fluctuations. In particular, for compound 1 protein flexibility increases with temperature, especially for the adhesive arm residues. As a consequence, at 320 K the contacts and the hydrogen bonds of the STD amide protons with the adhesive arm are not present and only the percentage of NHIle contacts with the pocket residues is enhanced compared to 300 K (Table 1). At 300 K, NH1 resulted to be more engaged by the adhesive arm residues, followed by NH10 and NHIle and this behavior agrees with the STD spectrum at 283 K. At 320 K the population of the ligand NHIle contacts with the protein increased while the other amide protons lost their contacts with the adhesive arm (too flexible) and also reduced the interactions with the residues of the binding site. This trend is in good agreement with the STD spectrum at 298 K where the NHIle signal replaces those of NH1 and NH10.
During MD simulations of compound 2, only the binding mode type A is stable and the ligand remains bound to the receptor. At 300 K, the interaction of NH19 proton with E-cadherin is stronger than NH2 and this behaviour is in agreement with the STD spectra at 283 K and 290 K, showing higher intensity of NH19 signals compared to NH2. At 320 K, we observed a slight decrease of NH19 contacts (Asp1 not present) and, despite losing the interaction with Trp2, an increase of NH2 contacts population with the pocket residues. This trend is consistent with the intensity variation observed in the corresponding STD spectra.
In conclusion, our MD results on ligand-cadherin binding are in agreement with STD spectra. The simulations at different temperatures could also explain the different ligand binding epitope observed in the presence of wt E-cadherin. In particular, the modulation of the signals seems to be dependent on the protein flexibility, especially the adhesive arm, an active part of the binding site that participate in the interaction with the ligand. Based on these results, the design of novel cadherin inhibitors to target more efficiently and selectively the Trp2 binding pocket, will be focused on the stabilization of ligand interactions towards both the hydrophobic cavity and the adhesive arm residues. Moreover, further NMR studies with labelled E-cadherin constructs (wt vs. deleted forms) will be carried out to map the protein residues involved in the interaction with the inhibitors and clarify their mechanism of action.
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10.1371/journal.pntd.0005453 | Murine models of scrub typhus associated with host control of Orientia tsutsugamushi infection | Scrub typhus, a febrile illness of substantial incidence and mortality, is caused by infection with the obligately intracellular bacterium Orientia tsutsugamushi. It is estimated that there are more than one million cases annually transmitted by the parasitic larval stage of trombiculid mites in the Asia-Pacific region. The antigenic and genetic diversity of the multiple strains of O. tsutsugamushi hinders the advancement of laboratory diagnosis, development of long-lasting vaccine-induced protection, and interpretation of clinical infection. Despite the life-threatening severity of the illness in hundreds of thousands of cases annually, 85–93% of patients survive, often without anti-rickettsial treatment. To more completely understand the disease caused by Orientia infection, animal models which closely correlate with the clinical manifestations, target cells, organ involvement, and histopathologic lesions of human cases of scrub typhus should be employed. Previously, our laboratory has extensively characterized two relevant C57BL/6 mouse models using O. tsutsugamushi Karp strain: a route-specific intradermal model of infection and persistence and a hematogenously disseminated dose-dependent lethal model.
To complement the lethal model, here we illustrate a sublethal model in the same mouse strain using the O. tsutsugamushi Gilliam strain, which resulted in dose-dependent severity of illness, weight loss, and systemic dissemination to endothelial cells of the microcirculation and mononuclear phagocytic cells. Histopathologic lesions included expansion of the pulmonary interstitium by inflammatory cell infiltrates and multifocal hepatic lesions with mononuclear cellular infiltrates, renal interstitial lymphohistiocytic inflammation, mild meningoencephalitis, and characteristic typhus nodules.
These models parallel characteristics of human cases of scrub typhus, and will be used in concert to understand differences in severity which lead to lethality or host control of the infection and to address the explanation for short duration of heterologous immunity in Orientia infection.
| Scrub typhus is an acute febrile illness with considerable mortality, and no available vaccine, caused by the obligately intracellular bacterium, Orientia tsutsugamushi. Despite the life-threatening severity of the illness in approximately one million cases annually, 85–93% of patients survive. The lack of appropriate animal models of scrub typhus has left a void in the fundamental knowledge necessary to develop a vaccine, such as mechanisms which contribute to disease severity and immunity. Here, we report a sublethal inbred murine model for scrub typhus using the intradermal and intravenous routes of inoculation, which are comparable to the natural route of chigger-bite transmission and subsequent hematogenous spread. This model, infection of mice with O. tsutsugamushi Gilliam strain, can be employed in conjunction with the lethal model of O. tsutsugamushi Karp strain to perform in-depth mechanistic studies related to strain cross-protection, lethality, pathogenesis and specific immunological investigations of the host immune response.
| Scrub typhus is a potentially fatal febrile illness caused by infection with the obligately intracellular bacterium Orientia tsutsugamushi. The geographic range of confirmed cases includes Asia, islands of the Pacific and Indian Ocean, and northern Australia; areas home to more than one-third of the world’s population [1]. Moreover, growing evidence implicates a range of Orientia infection outside of the known endemic region, including a case transmitted in the United Arab Emirates, serological and molecular data from Africa and South America and molecular evidence which has suggested Orientia species are present in Europe [2,3,4,5,6,7,8,9,10]. Individuals are infected with the bacteria transmitted to humans during feeding by infected larval trombiculid mites. Foci of transmission correspond to the distribution of the chigger mite vectors whose habitat consists of secondary or transitional forms of vegetation that exist after environmental modification such as removal of primary forests, practice of shifting cultivation, abandonment of fields, plantations and village sites during conflict, and neglect of urban and suburban garden plots [11,12,13,14]. The prospect of increasing vector habitat and the wide geographic distribution stress the importance and widespread impact of this disease, emphasizing the need for an effective vaccine.
Scrub typhus presents one to two weeks after exposure with a not-always-observed bite-site eschar and regional lymphadenopathy, followed by fever and rash accompanied by non-specific flu-like symptoms, requiring empirical treatment based on presumptive etiology. If prompt and appropriate antibiotic therapy is not administered, multi-organ failure and death can follow [15,16,17,18,19,20,21,22]. Fatal scrub typhus is characterized by disseminated endothelial infection, diffuse interstitial pneumonia, hepatic lesions, acute renal failure, and meningoencephalitis [23,24,25,26,27,28,29,30,31]. In scrub typhus autopsy or eschar samples, Orientia have been observed intracellularly in endothelial cells, macrophages, dendritic cells, and cardiac myocytes [24,28,32]. Understanding the systemic immune and pathophysiological mechanisms of scrub typhus in humans early in the course or in non-fatal cases is limited by sample size, diagnostic acuity, and invasiveness of sampling. Employing an appropriate animal model, which produces disease severity, pathology and systemic endothelial infection resembling human infection, may be used to overcome this impediment to understanding scrub typhus disease progression and the host immune mechanisms necessary for effective vaccine development.
The adaptive immune response against O. tsutsugamushi is not well characterized, is short-lived, complicated by strain diversity, and does not afford sterile protection. Studies of naturally acquired O. tsutsugamushi and vaccine studies in humans using live organisms have provided evidence of strain-specific protection, persistent infection with the immunizing strain, short-lived heterologous immunity, and simultaneous infection with multiple strains of Orientia [33,34,35,36,37,38,39,40,41,42,43]. Numerous animal models have shown that inoculation with live-, fixed-, or replication-deficient-O. tsutsugamushi have afforded partial, time-dependent protection against the homologous strain and poor, waning protection against heterologous strains of O. tsutsugamushi [44,45,46,47,48,49,50,51]. The mouse models used for these protection studies have employed intraperitoneal challenge, resulting in severe and lethal peritoneal infection and inflammation, which are not a characteristic of natural infection [52,53,54,55]. Although the data from previous murine studies confer valuable information, conclusions about the events of long-term, cell-mediated immunity against O. tsutsugamushi still need to be elucidated from more appropriate animal models. This well-characterized model of O. tsutsugamushi Gilliam strain infection is a necessary addition to the murine model repertoire for future studies of immunity to scrub typhus.
Herein, we report the dose- and route-specific kinetics of bacterial dissemination and disease progression of a model of sublethal scrub typhus utilizing the O. tsutsugamushi Gilliam strain. Recent advances to utilizing inbred murine models with more relevant routes and characterization of the pathogenic features of human scrub typhus have been achieved using the Karp strain [55,56,57]. This sublethal model of disseminated Gilliam strain infection is a crucial addition to research efforts to understand host-pathogen interactions influencing sublethal versus lethal outcomes and heterologous strain immunity dynamics.
All experiments and procedures were approved by the Institutional Animal Care and Use Committee (protocol # 1302003) of the University of Texas Medical Branch-Galveston. Mice were used according to the guidelines in the Guide for the Care and Use of Laboratory Animals and comply with the USDA Animal Welfare Act (Public Law 89–544), the Health Research Extension Act of 1985 (Public Law 99–158), the Public Health Service Policy on Humane Care and Use of Laboratory Animals, and the NAS Guide for the Care and Use of Laboratory Animals (ISBN-13).
L929 and Vero cells (ATCC, Manassas VA) were maintained in Dulbecco’s Modified Eagle Medium (DMEM, Gibco Life Technologies, Grand Island, NY) supplemented with 5% fetal bovine serum (FBS, HyClone Laboratories, Logan UT) and 1% HEPES buffer (Cellgro, Manassas, VA) at 37°C with 5% CO2 in a humidified incubator.
Orientia tsutsugamushi Gilliam strain (unknown passage history) was obtained from the Rickettsial and Ehrlichial Species Collection at the University of Texas Medical Branch. Identification of the strain was confirmed by sequencing of the Orientia 47 kDa gene (accession number L31933). Orientia was propagated 3 passages in L929 cells from yolk sac seed stock and stored at -80°C in sucrose-phosphate-glutamate (SPG) buffer (218 mM sucrose, 3.8 mM KH2PO4, 7.1 mM K2HPO4, 4.9 mM monosodium L-glutamic acid, pH 7.0) until further use.
An Orientia quantitative viability assay was utilized to enumerate viable Orientia as previously described [56]. Briefly, confluent 6-well plates of Vero cells were inoculated in triplicate with serial 10-fold dilutions of Orientia stocks prepared in Dulbecco’s phosphate buffered saline (DPBS, Cellgro, Manassas, VA). The plates were centrifuged for 5 minutes at 700 x g to enhance oriential contact with cells and incubated for two hours at 34°C with 5% CO2. After two hours, the wells were triple rinsed with warm DPBS with calcium and magnesium to remove extracellular bacteria. DNA was extracted from each well using a DNeasy Blood and Tissue Kit (Qiagen, Valencia, CA) according to the manufacturer’s instructions, and the bacterial load determined by quantitative real-time PCR (qPCR) to determine the quantity of viable Orientia that had attached and actively entered Vero cells.
The single copy gene for the 56-kDa protein was amplified with primers [OtG729 (5′- TCGTGATGTGGGGGTTGATAC-3′) and OtG873 (5′- ATTCTGAGGATCTGGGACCATATAG-3′) (IDT, Coralville, IA)] to determine Orientia copy numbers. qPCR was accomplished using iQ SYBR-green supermix (Bio-Rad, Hercules CA) with a Bio-Rad CFX96 thermal cycler according to the protocol: one cycle at 94°C for 5 minutes followed by 40 cycles of two-step amplification at 94°C for 5 seconds and 61.8°C for 30 seconds. Serial 10-fold dilutions of a known concentration of a plasmid that contained a single copy of the 56-kDa gene were utilized to produce a standard curve to determine copy numbers. Bacterial loads and dissemination to selected organs were assessed by qPCR. DNA was extracted using a DNeasy Blood and Tissue Kit (Qiagen, Valencia, CA) from bead homogenized tissue samples according to the manufacturer’s instructions. Tissues samples were normalized using tissue wet weight and were expressed as the number of O. tsutsugamushi Gilliam strain 56 kDa copies per milligram (mg) of tissue.
Female C57BL/6 (B6) mice, 6–8 weeks of age, purchased from Harlan Laboratories (Indianapolis, IN) were housed in an animal biosafety level 3 facility (ABSL3) under specific pathogen-free conditions. The mice were allowed to acclimate for 7 days prior to experimental use and then were inoculated i.v. by the tail-vein with 3 doses: high (7.5 x 106), mid (7.5 x 105), or low (7.5 x 103) or intradermally in the lateral ear with 2.5 x 105 O. tsutsugamushi organisms as determined by viability assay and monitored twice daily for signs of illness. For the i.v. infected animals, one group (N = 5) of mice from each dose was sacrificed every three days for a period of fifteen days, and one group (N = 5) of i.d. inoculated mice was sacrificed every six days for a period of 30 days. Mice were necropsied, and their tissues were tested for bacterial burden and prepared for histology. The remaining animals were observed for veterinary-approved signs of illness (ruffled fur, hunched posture, erythema, lethargy, conjunctivitis, and weight loss). All animal experiments were conducted twice.
At the designated sacrifice time points, blood samples (500 μL) were collected in K2EDTA-coated BD microtainer tubes (Becton, Dickinson and Company, Franklin Lakes, NJ) and blood cell counts performed using a calibrated 950FS HemaVet apparatus (Drew Scientific, Waterbury CT). The blood samples were analyzed using the FS-Pak reagent kit and were measured for the following parameters: white blood cell count (WBC), differential leukocyte (%) count, hemoglobin concentration (HGB), hematocrit (HCT), red blood cell count (RBC), mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), red cell distribution width (RDW), platelet count (PLT), and mean platelet volume (MPV).
Orientia tsutsugamushi Gilliam strain antigen-coated, acetone-permeabilized 12-well slides were equilibrated to room temperature in phosphate buffered saline (PBS) and then blocked in PBS containing 1% bovine serum albumin (BSA) and 0.01% sodium azide for 10 minutes at room temperature. Sera were diluted 2-fold starting at 1:64 and, if reactive, extended to final end-point titers in a solution of PBS containing 1% BSA, 0.1% Tween 20, and 0.01% sodium azide. Dilutions of sera were added to individual antigen-coated wells and incubated at 37°C for 30 minutes in a humidified chamber. Slides were rinsed and washed twice for 10 minutes with PBS containing 0.1% Tween-20. Secondary antibody, either DyLight 488-conjugated anti-mouse IgG (1:15000), Fluorescein (FITC)-conjugated AffiniPure anti-mouse IgG Fcγ subclass 1specific (1:600), FITC-conjugated AffiniPure anti-mouse IgG, Fcγ subclass 2c specific (1:1000, Jackson Immunoresearch, West Grove, PA) or FITC-conjugated anti-mouse IgM antibody, mu chain specific (1:500, Vector Labs, Burlingame, CA), was incubated for 30 minutes at 37°C in a humidified chamber. Slides were subsequently rinsed and washed twice as before with the final wash containing 1% Evans blue solution, mounted with DAPI fluoromount-G (SouthernBiotech, Birmingham, AL) and coverslipped. Slides were observed under a fluorescence microscope at 400X magnification (Olympus Scientific, Waltham, MA). Serum was unavailable for one mouse from the i.d. route group on 18 dpi (n = 4), otherwise n = 5 for all groups and time points. Mice that had a positive IFA result at a 1:64 dilution were considered to have seroconverted, whereas mice with non-reactive serum at this titer were assigned a value of zero.
Tissue samples were fixed in 10% neutral buffered formalin (NBF) and embedded in paraffin. Tissue sections (5 μm thickness) were stained with hematoxylin and eosin or processed for immunohistochemistry (IHC). For IHC, all reagents were from Vector Laboratories (Burlingame, CA) unless specified otherwise. Slides were deparaffinized, rehydrated and processed. Antigen retrieval was performed by incubation in 1x citrate buffer (Labvision, Fremont, CA). Sections were sequentially blocked with 1x casein, BLOXALL blocking solution, avidin and biotin solution and 5% normal goat serum. Sections were incubated with polyclonal rabbit anti-O. tsutsugamushi antibody (1:12000, produced in-house) at 4°C overnight, followed by incubation with biotinylated anti-rabbit IgG (1:200) for 30 minutes. Signals were developed with Vector Red Alkaline Phosphatase substrate kit. Slides were counterstained with hematoxylin, dehydrated, mounted and cover slipped with VectaMount and examined with an Olympus BX51 microscope (Olympus Scientific, Waltham, MA).
Sections were examined to assess the histopathology and establish grading scales. The slides were then examined blindly, without knowledge of dpi or bacterial loads, and scores were determined independently based on the grading systems described below. The grading scale for the lung histopathology was based on the spectrum of lesions throughout the entire course of infection (S1 Fig). Grade 1 was defined as scattered inflammatory cells in focal areas of pulmonary parenchyma and around bronchovascular bundles. A score of 2 was assigned to tissues with widening of alveolar septa and inflammatory cell infiltrates present multifocally in the pulmonary parenchyma and around bronchovascular bundles. Grade 3 was defined as similar to grade 2 but present more diffusely in the pulmonary parenchyma and around bronchovascular bundles and Grade 4 was assigned to tissues presenting with the description of Grade 3 plus areas of atelectasis. The diameters of hepatic clusters of inflammatory infiltrates were measured, and the average lesion size and number of lesions per four typical (100X) fields of liver were determined for each time point. The liver inflammatory index was calculated as number of lesions per four medium-power fields (MPF) multiplied by the mean diameter (μm) of mononuclear cellular infiltrative clusters. Quantitative assessment of the renal histopathology was based on the extent of mononuclear cellular infiltrate. Digital images of four to six randomly selected medium power fields (100X) of the renal cortex were captured using Olympus DP controller software. Semi-automated counting was performed using ImageJ (National Institutes of Health, Bethesda, MD, USA) after converting the image to 8-bit grayscale. Cells contributing to the total mononuclear cell count were identified using a manual threshold and pixel-based area measurement. The number of pixels was counted and presented as a proportion of the total number of pixels in the area under analysis.
Values are reported as mean ± standard deviation (SD). The data were analyzed using an one-way ANOVA with Tukey’s multiple comparison as post-hoc analysis (GraphPad Prism, San Diego, CA) at a statistical significance level of *, p < 0.05; **, p < 0.01, ***, p < 0.001.
The dose responses of C57BL/6 mice to intravenous inoculation with O. tsutsugamushi Gilliam strain were observed as differences in incubation period prior to onset, duration of illness, magnitude of signs of illness and percent body weight loss. Mice infected intravenously (i.v.) with a high dose of O. tsutsugamushi Gilliam strain developed decreased activity (7–12 dpi), ruffled fur and erythema (6–12 dpi), labored breathing (9–11 dpi), conjunctivitis (11–12 dpi) and began to lose weight at day 8 pi, with a nadir mean percent body weight (16% loss) by day 12 pi (Fig 1A). The animals inoculated i.v. with the mid-dose developed signs of illness including decreased activity, ruffled fur labored, breathing and skin erythema on 10–12 dpi followed by weight loss delayed by 3 days with mean nadir percent body weight loss (11%) observed at 13 dpi. The mean weights for the group receiving the low dose i.v. did not decrease below the mean starting weight; however, it was below the mean weight of the uninfected controls at 14 dpi followed by perceptible labored breathing at 15 dpi. After intradermal inoculation, mice were monitored through 30 dpi, during which mean percent body weight did not deviate below uninfected controls. However, decreased activity was observed from 12–16 dpi, ruffled fur during 13–18 dpi, and conjunctivitis on 13 dpi. Onset and duration of splenomegaly, significant increase of whole spleen wet weight above uninfected controls, was observed in a dose- and route-dependent manner. Splenomegaly was observed in mice inoculated i.v. with the high dose from 9 dpi until the end of study of these animals on day 15, in mice inoculated i.v. with the mid-dose from 12 dpi to the end of the observations on day 15, and in mice inoculated i.v. with the low dose at 15 dpi (Fig 1B). Intradermal inoculation resulted in splenomegaly during 12–24 dpi, with the peak at 18 dpi comparable to that of high dose i.v. inoculation.
Peripheral blood parameters of infected mice were compared to uninfected controls and veterinary accepted normal ranges for mice. While circulating lymphocyte counts remained within the normal range, the mean lymphocyte count for mice infected with high dose i.v. was elevated above the mean of uninfected controls (1.74 K/μL) at 12–15 dpi (3.09–4.37 K/μL) and was elevated on 15 dpi (4.01 K/μL) for i.v. mid-dose (Fig 2A). A steady mean increase in absolute neutrophil concentrations was observed during 9–15 dpi for high dose i.v. infected mice, reaching statistical significance by 15 dpi (Fig 2B). A significant increase in neutrophil concentration was also observed at 15 dpi in mice infected with the mid-dose i.v. Less consistent elevation of neutrophils occurred after low dose i.v. inoculation or after i.d. inoculation. Decreases in hematocrit and platelet count below the normal range were observed regardless of dose or route of inoculation (Fig 2C and 2D).
Seroconversion was first observed at day 3 after high dose (Fig 3A) i.v. inoculation with 80% and 40% (n = 5) having IgM and IgG anti-O. tsutsugamushi antibody reactivity, respectively, followed by 100% IgM and IgG seroconversion on 6 (Table 1, Fig 3A). Mid-dose i.v. infection induced 80% IgM and 40% IgG seroconversion by 3 dpi followed by 100% seroconversion at 6 dpi and 9 dpi for IgM and IgG, respectively (Table 1, Fig 3B). The mice which received low dose i.v. inoculation (Fig 3C) had seroconversion of 40% IgM at 3 dpi, followed by 40% of animals seroreactive for IgM and IgG at 6 dpi, and while 100% had IgM antibodies, only 80% had IgG seroconversion by 15 dpi, the last experimental time point (Table 1, Fig 3C). Animals infected by intradermal inoculation had IgM seroconversion in 80% of mice and IgG seroconversion in 40% of mice at 6 dpi with all mice seroconverted by 18 dpi and a continual increase in antibody titer through the final time point, 30 dpi (Table 1, Fig 3D). At the final experimental time point, the reciprocal endpoint titer of IgG2c antibodies was higher than IgG1 for high dose i.v. (medians 2048 vs. 512), mid-dose i.v. (medians 4096 vs. 1024), low dose i.v. (medians 1024 vs. 0), and i.d. route (medians 16384 vs. 512) (Fig 3E and 3F).
Intravenous inoculation resulted in dose-dependent, self-limited systemic infection. The earliest peak of bacterial burden after i.v. inoculation was observed in the spleen on 6 dpi after high dose, 9 dpi after mid-dose, and from 12–15 dpi after low dose inoculation (Fig 4A). Of the tissues examined, the lungs had the highest bacterial load starting at 3 dpi and reached a peak at 9 dpi for the high dose i.v. group (Fig 4B), which was the day of onset of weight loss (Fig 1A). The peak of bacterial load for the i.v. mid-dose recipient mice was observed later, at 12 dpi (Fig 4B). In contrast, a sustained peak was observed at 12 and 15 dpi in the i.v. low dose inoculated mice. The same trend was observed for hepatic bacterial loads but with a lower bacterial load per milligram of tissue (Fig 4C). Intravenous inoculation resulted in higher bacterial loads in the kidney than i.d. inoculation (Fig 4D). A steady increase of bacterial loads was observed in the kidney after i.v. high and mid-dose inoculation, reaching a sustained peak at 9–15 dpi with the high dose and a peak at 12 dpi with the mid-dose. Renal bacterial loads after i.v. low dose inoculation were detected inconsistently.
The histopathology of the liver in this model was characterized by multifocal oriential vascular infection and persistent multifocal lesions typified by multifocal lymphohistiocytic and polymorphonuclear cellular infiltrates, vasculitis, and dose-dependent portal triaditis (S2C Fig). Initially, the magnitude of lymphohistiocytic cellular infiltration coincided with increased bacterial loads; however, it continued to intensify as the bacterial loads were controlled. Intravenous high dose inoculation resulted in periportal lymphohistiocytic infiltrates at 3 dpi and focal lesions in the sinusoids at 6 dpi. The lobular lesions were more numerous and encompassed greater tissue area by 6 dpi, as indicated by the increased liver inflammatory index, and were unresolved at the experimental endpoint of 15 dpi (Fig 5A). The peak liver inflammatory index occurred at 12–15 dpi for mid and low dose i.v. route (Fig 5B and 5C). Following i.d. inoculation, the peak liver inflammatory index occurred at 12–18 dpi, and although the lesions decreased in diameter and quantity, they were still present at the experimental endpoint of 30 dpi (Fig 5D).
Nephritis, characterized by interstitial cellular infiltrates in the renal cortical parenchyma, which were localized between renal tubules and commonly at the corticomedullary boundary, was observed in the kidneys of mice inoculated with O. tsutsugamushi Gilliam strain regardless of route or dose of inoculum (Fig 6A). The kidney inflammatory ratio continued to intensify through the experimental endpoint, 15 dpi after i.v. high and mid-dose inoculation (Fig 6B). After i.v. low dose inoculation, the kidney inflammatory ratio plateaued at 6 dpi and remained elevated through 15 dpi. The peak of the kidney inflammatory ratio after i.d. inoculation occurred at 18 dpi and was unresolved at day 30 pi.
Lung pathology during O. tsutsugamushi Gilliam strain infection progressed throughout the time course observed, and the onset of significant pathology scores correlated with the route of inoculation. Mild, isolated peribronchovascular inflammation was observed at 3 dpi in i.v. high dose infected mice followed by widening of the alveolar septa by 6 dpi. Beginning at 9 dpi patchy frank interstitial cellular inflammation was observed with mild vasculitis. At subsequent time points after high dose i.v. inoculation, from 9 dpi through day 15 pi, peribronchial infiltrates continued to be evident, and the interstitial inflammation was more diffuse and encompassed larger portions of the tissue. After mid-dose inoculation, the peak of pulmonary inflammation occurred at 9–15 dpi and was significantly elevated, and greater than after i.v. low dose inoculation at those time points. Pulmonary inflammation after i.d. inoculation was statistically significant at 12 dpi with the peak lung pathology score observed at 18–30 dpi (Fig 6C).
Mild myocarditis was observed after infection (S2A Fig). Mononuclear cellular infiltrate was observed in the pericardium and between cardiac myocytes. Mild meningoencephalitis and characteristic typhus nodules (clusters of perivascular microglial cells, macrophages and lymphocytes) were observed in mice inoculated both i.v. and i.d. (Fig 7D, S2D Fig). Expansion of the splenic marginal zone and lymphoid activation in periarteriolar lymphoid sheaths were observed after Gilliam strain inoculation (S2B Fig).
Orientia identified by IHC on 9 dpi, the peak of bacterial burden after high dose i.v. inoculation, was located in endothelial cells of the alveolar capillaries (Fig 7A), and in splenic and hepatic mononuclear phagocytes (Fig 7B and 7C). Orientia antigen was also observed in the endothelial cells of cardiac microcapillaries between bundles of cardiac myocytes. At the peak of bacterial load after i.d. inoculation at 18 dpi, Orientia antigen was identified in pulmonary and renal endothelial cells as well as in characteristic typhus nodules (Fig 7D).
Several mouse models of O. tsutsugamushi infection have been developed, but few are extensively characterized in an inbred mouse model to allow for elucidation of the mechanisms of pathogenesis and immunity correlating with human infection [58,59,60]. The use of inbred C57BL/6 mice to characterize O. tsutsugamushi infection will allow for consistent animal-to-animal responses required for the further testing of hypotheses and vaccine evaluation to a level of statistical significance necessary for interpretation of results. Utilizing the abundance of conditional and gene knockout mice available on this background will enable elucidation of the mechanisms of immunity to O. tsutsugamushi. Studies exploring inbred mouse susceptibility to O. tsutsugamushi Gilliam strain have reported resistance of C57BL/6 and C57BL/6J to high inoculum i.p. challenges [61,62]. We have characterized, in more detail, scrub typhus animal models employing the more relevant i.v. and i.d. routes of inoculation of O. tsutsugamushi Gilliam strain in C57BL/6 mice with hematogenous dissemination to pulmonary and systemic endothelial cells of the microcirculation, which results in disseminated self-limited disease mimicking the mite-transmitted infection in many persons. This mouse strain, in contrast, succumbs to challenge with O. tsutsugamushi Karp strain inoculated i.v. and i.p [55,61]. Lethal and sublethal endothelial cell target models using i.v. inoculation or i.d. inoculation, respectively, of O. tsutsugamushi Karp strain in C57BL/6 mouse strain have already been extensively characterized [55,56]. These well characterized scrub typhus animal models with divergent lethality utilizing the same inbred mouse strain may be employed for studies on virulence mechanisms of the bacterial strains and the protective host immune mechanisms. Although more than 70 strains of O. tsutsugamushi genetic variants have been identified, strains genetically related to the Karp and Gilliam strains have been reported with overlapping geographical distribution and are implicated as prevalent types causing scrub typhus illness, important considerations for designing experiments relevant to human disease and immunity [63].
To model scrub typhus, it would be ideal to study bacterial dissemination characteristics and host-pathogen interactions after natural chigger-bite infection. However, limited access to the infected Leptotrombidium vector colonies, the burden of colony maintenance, and inability to standardize the dose of bacterial transmission by the vector favors the utility of needle-inoculated animal models. Intradermal inoculation is the needle inoculation route which most closely mimics the natural vector transmission during cutaneous feeding. The i.v. route of infection results in a hematogenously disseminated systemic infection as occurs in human scrub typhus, but bypasses the events of early cutaneous infection and the initial spreading steps. The kinetics of infection following the i.v. and i.d. administration routes allows for optimized experimental design focusing on critical disease course time points. In contrast to what has been reported after subcutaneous footpad inoculation of O. tsutsugamushi Karp strain, characteristics of infection of i.v. and i.d. infection in these models including bacteremia, target organs and infected cell types are analogous with only varying kinetics [57,64].
In these models, we characterized the clinical signs including splenomegaly and the degree of weight loss, as well as kinetics of bacterial spread. We also analyzed the hematologic response to infection, rate of IgM and IgG antibody production, and antibody isotype. The histopathologic events in response to Gilliam strain challenge included development of interstitial pneumonitis, interstitial nephritis, mild meningoencephalitis, cerebral typhus nodules, and perivascular lymphohistiocytic inflammation in the lung, liver, heart, and kidney. Statistically significant lung inflammation was observed concurrently irrespective of dose or route and was sustained after the peak of infection, suggesting elicitation by a host-mediated inflammatory response. This model recapitulates the pathology that has been observed in humans with scrub typhus, which may be correlated with documented clinical manifestations including labored breathing, pulmonary edema, cardiac dysfunction, hepatomegaly, elevations of serum hepatic aminotransferases, and acute renal failure [22,23,24,28,65,66]. The cellular tropism demonstrated by antigen location in pulmonary and systemic endothelial cells, splenic mononuclear phagocytes and cardiac myocytes has been described in scrub typhus autopsies [28]. Relevant animal models, including this sublethal mouse model, advance the understanding of scrub typhus disease kinetics and cellular tropism, which cannot be achieved solely from the limited availability of human patient samples from sublethal infections and lethal outcomes. The ability to study various stages of infection confirms observations of multi-organ involvement and systemic endothelial infection during the acute phase in a sublethal model, substantiating the principal features of scrub typhus disease, which are independent of a lethal outcome. These clinical and pathologic features of scrub typhus should be considered for interpretation of experiments exploring immunological and pathogenic mechanisms of the disease.
Infection of C57Bl/6 mice with O. tsutsugamushi Gilliam strain, including high, 7.5x106, and mid-dose, 7.5x105, both in excess of the i.v. LD50 of 1.25x105 of O. tsutsugamushi Karp strain, results in sublethal disease evidenced by weight loss and replication of the bacteria in target organs [55]. In the i.v. model, onset of weight loss was preceded by the peak in splenic bacterial burden and coincides with the peak of lung bacterial burden, and these observations were dose-dependent. The peak of lung bacterial load after i.d. inoculation of mice reached the same magnitude as that of the i.v. high and mid-dose; however, it occurred nine days later in the infection at 18 dpi.
Hematologic responses to infection were characterized by increased circulating lymphocytes and neutrophils as well as thrombocytopenia, which have been described in human clinical disease presentation and progression [18,67,68]. IgG antibody titers increase substantially during the first two weeks after onset in human cases [69]. In these murine models, detection of IgG seroconversion by IFA was earliest and most consistent in high dose i.v. infected mice; however, at later time points in the i.d. inoculation model, mice had continually increasing titers. The rate of seroconversion after low dose i.v. challenge was not as consistent as after the higher doses, with a delayed response reaching 100% IgM reactivity by 12 dpi and only 80% IgG seroconversion by 15 dpi. It is unknown whether the i.v. inoculation would trend the same way as the i.d. route, since only the acute disease was characterized after i.v. inoculation.
The antibody response was dominated by the IgG2c isotype, suggesting the importance of Th1 responses in this model. However, IgG1 was detected in all groups except after low dose i.v. infection, which is indicative of Th2 immune response involvement as well. Further experimental time points beyond 15 dpi would need to be assessed after low dose i.v. infection to understand whether a lack of IgG1 reactivity is a difference in immune responses or merely delayed kinetics. We have previously observed a balanced Th1/Th2 response in our sublethal i.d. model of O. tsutsugamushi Karp infection [56]. In contrast, our lethal i.v. model of O. tsutsugamushi Karp revealed impairment of select Th2 related immune response molecules [70]. These studies combined suggest that contributions by Th2 responses improve immune homeostasis and result in sublethal outcomes after Orientia infection.
In human cases, Orientia infects endothelial cells, macrophages and cardiac myocytes in disseminated lethal infection and dendritic cells and macrophages in the human mite inoculation site cutaneous eschar [24,28,29,32]. In contrast to models utilizing non-human primates, no eschar was formed after i.d. inoculation in our murine model [71,72,73]. Location of Orientia antigen in the i.v. Gilliam model recapitulates many hallmarks of the human scrub typhus cases. At the peak of bacterial burden after high dose i.v. inoculation, Orientia antigen colocalized with endothelial cells of the pulmonary and cardiac microcapillaries, and in splenic and hepatic mononuclear phagocytes. At 18 dpi, the peak of bacterial load after i.d. inoculation, Orientia antigen was observed in pulmonary and renal endothelial cells and colocalized with typhus nodules in the brain. The distribution of Orientia is directly influenced by the route of inoculation. It has been shown that intramuscular and subcutaneous inoculation results in a disseminated infection involving Kupffer cells and macrophages [57,58]. Intracerebral inoculation and intraperitoneal inoculation have been shown to result in infections initially limited to the central nervous system and peritoneal lining, respectively [55,58]. Dissemination characteristics and the subsequent target cells are relevant to accurately interpret immunologic conclusions, and therefore route is an important consideration in experimental design.
In summary, we present the first comprehensive murine model of consistently sublethal scrub typhus in C57/BL6 mice utilizing the O. tsutsugamushi Gilliam strain. Infection with O. tsutsugamushi Gilliam in this model results in dose- and route-dependent kinetics, perceptible clinical signs, and measurable histopathologic lesions, and recapitulates human scrub typhus. These models can be utilized to elucidate the progression and pathogenesis of the majority of scrub typhus cases, which result in untreated non-lethal outcomes [74]. Although the magnitude and persistence of lymphohistiocytic infiltrates during this sublethal infection were unforeseen, we hypothesize that this is indicative of a robust immune response and its capacity to control the infection. This feature of these models highlights the necessity to study the host immune regulation involved in sublethal infection and how it differs from the dysregulation we have reported in the lethal model utilizing O. tsutsugamushi Karp strain [70]. We will focus future studies on the contributions of immune cell subsets to protection to establish the immunologic foundation necessary for development of an effective vaccine. This model complements the available lethal murine model of scrub typhus and will allow for in-depth mechanistic studies related to cross-protection, lethality, and pathogenesis.
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10.1371/journal.pbio.1000109 | Neo-Lymphoid Aggregates in the Adult Liver Can Initiate Potent Cell-Mediated Immunity | Subcutaneous immunization delivers antigen (Ag) to local Ag-presenting cells that subsequently migrate into draining lymph nodes (LNs). There, they initiate the activation and expansion of lymphocytes specific for their cognate Ag. In mammals, the structural environment of secondary lymphoid tissues (SLTs) is considered essential for the initiation of adaptive immunity. Nevertheless, cold-blooded vertebrates can initiate potent systemic immune responses even though they lack conventional SLTs. The emergence of lymph nodes provided mammals with drastically improved affinity maturation of B cells. Here, we combine the use of different strains of alymphoplastic mice and T cell migration mutants with an experimental paradigm in which the site of Ag delivery is distant from the site of priming and inflammation. We demonstrate that in mammals, SLTs serve primarily B cell priming and affinity maturation, whereas the induction of T cell-driven immune responses can occur outside of SLTs. We found that mice lacking conventional SLTs generate productive systemic CD4- as well as CD8-mediated responses, even under conditions in which draining LNs are considered compulsory for the initiation of adaptive immunity. We describe an alternative pathway for the induction of cell-mediated immunity (CMI), in which Ag-presenting cells sample Ag and migrate into the liver where they induce neo-lymphoid aggregates. These structures are insufficient to support antibody affinity maturation and class switching, but provide a novel surrogate environment for the initiation of CMI.
| Lymph nodes (LNs) are believed to be the most important tissues initiating immune responses by facilitating the activation of T and B lymphocytes. Mice lacking such LNs (called alymphoplastic) are severely immune compromised and resistant to immunizations. We discovered that the immune-deficiency of such alymphoplastic mice is actually not caused by the loss of LNs, but rather by the underlying genetic lesion. Surprisingly, mice lacking all lymph nodes can still mount potent T cell-mediated immune responses. We also discovered that T and B cells have completely different structural requirements for their activation/maturation. Whereas B cells rely on LNs to become efficient antibody-producing cells, T cells can be activated successfully outside of such dedicated tissues. So—in the absence of LNs—antigens delivered by immunization are actively transported into the liver where cellular immunity is initiated. The mammalian fetal liver is responsible for the early formation of blood and immune cells, and we propose that the adult liver can still provide a niche for T cell–antigen encounters. During evolution, T and B cells emerged simultaneously, allowing cold-blooded vertebrates (which lack LNs) to launch adaptive immune responses. The development of LNs in mammals coincided with a drastic improvement in antibody affinity maturation, whereas T cells remain LN-independent to this day.
| Secondary lymphoid tissues (SLTs) are highly organized structures with defined compartments consisting of B and T cell areas. These distinct locations support the rapid circulation and concentration of Ag and the interaction of Ag-presenting cells (APCs) with lymphocytes. Prevailing dogma dictates that only if competent APCs transport Ag into SLTs, an adaptive immune response is initiated; otherwise, the Ag is ignored by the immune system [1]. For the initiation of humoral antibody (Ab)-mediated immunity in mammals, the formation of B cell follicles and germinal centers (GCs) appears to be a prerequisite. The dynamic nature of such GCs, including the interaction of follicular dendritic cells (FDCs) with B cells and Ag, was recently elegantly demonstrated by others [2]. However, in contrast to the B cell-dominated cortex, T cell areas, where T cells encounter mature APCs and their cognate Ag, are structurally ill defined. Whereas intravital confocal microscopy has provided compelling evidence for the capacity of SLTs to host T cell priming [3], definitive data supporting their absolute requirement for the initiation of T cell-mediated immunity (CMI) do not exist. In addition, cold-blooded vertebrates lacking conventional SLTs generate potent immune responses upon immunization. However, in the mammalian system, the apparent immunodeficiency of mice that lack SLTs strongly supports the notion that the initiation of effective immune responses requires the dedicated structures provided by SLTs [4]–[8]. Alymphoplasia (aly/aly) mice are characterized by a complete lack of lymph nodes (LNs) and Peyer's patches, and structural alterations of the spleen and thymus due to a point mutation in the NFκB-inducing kinase (NIK) [9]. NIK is vital for the initiation of the noncanonical NFκB cascade, which appears to play a discrete role, for instance, in the function of CD40 and lymphotoxin-β receptor (LTβR) signaling in some cell types [10]–[12]. Aly/aly mice display impaired Ab responses and loss of CMI, demonstrated by their inability to reject allogeneic grafts or tumors [4],[13],[14]. The developmental deficits in aly/aly mutants are readily explained by the requirement of NIK in LTβR signaling. LTβR is vital for the development of SLTs, and LTβR−/− mice display similar developmental defects as do aly/aly mice or NIK−/− mice [12],[15].
In this study, we describe that the immunodeficiency of aly/aly mice is not due to the absence of SLTs, but due to the impact of the underlying genetic defect on cellular immunity. Using different strains of alymphoplastic mice and T cell migration mutants in an experimental paradigm in which the site of Ag-delivery is distant from the site of priming and again distant from the site of inflammation, we can detect both TH cell-driven autoimmune disease as well as systemic CTL-mediated antitumor immunity initiated through classical subcutaneous (s.c.) immunization/vaccination independent of SLTs. APCs present at the site of immunization migrate to and select the liver as a natural extra-lymphoid tissue for the initiation of CMI, which we propose to be an evolutionary hard-wired pathway already found in cold-blooded vertebrates. This alternative pathway, undescribed to this day, can potently drive CMI but fails to elicit B cell immunity, indicating that the immunization-induced T cell accumulation within conventional lymphoid organs mainly serves humoral immunity but that CMI can be initiated elsewhere.
We first sought to determine whether LNs are an absolute requirement for the induction of a complex TH cell-driven autoimmune response initiated by the s.c. delivery of auto-Ag. Experimental autoimmune encephalomyelitis (EAE) is a B cell-independent, TH cell-mediated demyelinating autoimmune disease of the central nervous system (CNS) and serves as the animal model for multiple sclerosis (MS). The conversion of TH cells from the naive to effector state is vitally dependent on the structures provided by LNs [5],[6]. Cervical LNs are widely held to constitute the predominant intrinsic priming site for encephalitogenic T cells, based on the observation that these LNs support the expansion of PLP-TcR transgenic (Tg) T cells [16],[17]. However, draining inguinal LNs drive the polyclonal, endogenous T cell population after s.c. immunization with encephalitogenic peptides. To assess the role of SLTs in the transition of TH cells from a naive to effector state (T cell priming), we induced EAE in aly/aly or aly/+ mice by s.c. immunization with myelin oligodendrocyte glycoprotein peptide and complete Freud adjuvant (MOG35–55/CFA). Figure 1A shows that aly/aly mice are completely resistant to EAE compared to aly/+ control mice (the latter developing normal SLTs as NIK is haplosufficient). To verify the notion that pathogenic T cells cannot be raised in aly/aly mice, they were immunized s.c. with MOG35–55. Eleven days postimmunization (dpi), splenocytes were harvested, and MOG35–55-reactive cells were expanded in vitro and subsequently transferred into aly/aly as well as aly/+ recipients. Figure 1B shows that only cells derived from aly/+ donors were able to induce disease regardless of whether the recipients had SLTs (aly/+) or not (aly/aly). In contrast, MOG35–55-reactive T cells derived from aly/aly donors were not pathogenic and did not mediate CNS inflammation.
To assess the capacity of LN-less mice to initiate T cell expansion in response to s.c.-delivered Ag, CFSE-labeled TcR Tg T cells (2D2) specific for the encephalitogenic MOG35–55 peptide [18] were adoptively transferred into either aly/aly or aly/+ mice prior to immunization with their cognate Ag. After 4 d, splenocytes were analyzed for T cell expansion by flow cytometry (Figure 1C). Ag-specific T cell proliferation can be observed in aly/aly mice; however, they display slightly delayed kinetics in comparison to aly/+ mice. Similar results were obtained with Ovalbumin (OVA) TcR Tg T cells (OTII) transferred into aly/aly and aly/+ mice (unpublished data), indicating that T cell expansion can be initiated independent of SLTs, whereas efficient effector function is dependent on the microenvironment provided by SLTs.
The fact that aly/aly mice do not develop T cell-driven autoimmune disease could be explained by their inability to prime self-reactive T cells (a) due to the lack of dedicated draining LNs [5],[6], or (b) due to a direct impact of the NIK mutation on immune cells [19],[20]. In order to define whether their EAE resistance is due to the lack of LNs or an intrinsic defect of aly/aly mice to prime T cells, we generated a series of bone marrow (BM)-chimeric mice. To restrict the NIK mutation to the hematopoietic system, lethally irradiated aly/+ mice were injected with BM cells from aly/aly donor mice (aly/aly→aly/+). Conversely, to conserve the developmental structural defects, without the NIK lesion of the hematopoietic compartment, aly/aly mice were reconstituted with BM cells of normal aly/+ donors (aly/+→aly/aly). As previously reported, spontaneous development of lymphoid tissues in aly/aly recipients upon reconstitution was expectedly not detected [21].
Surprisingly, we discovered that aly/+→aly/aly BM-chimeras were fully susceptible to EAE after s.c. immunization with MOG35–55 (Figure 2A), clearly demonstrating that s.c. immunization can mount a productive T cell-driven autoimmune response even in the absence of draining LNs. Using the reciprocal approach, by generating aly/+→aly/+ (WT-NIK immune system and normal SLTs) as well as aly/aly→aly/+ BM-chimeras (NIK-deficient immune system and normal SLTs), we found that the NIK mutation lead to EAE resistance, even when the lymphoreticular compartment is unperturbed (Figure 2B). This finding clearly demonstrates that the reported immunodeficiency of aly/aly mice can largely be explained by the requirement of NIK for the initiation of immunity rather than the lack of LNs. In support of this, we found that unmanipulated LTβR−/− mice, which also lack all LNs but have normal NIK function, are also fully susceptible to EAE (Figure 2C).
The formation of IFNγ and IL-17–secreting autoreactive T cells has been demonstrated to be a prerequisite for the development of autoimmunity [22]. In aly/aly→aly/+ mice we observed a substantial reduction in IL-17– and IFNγ-producing cells compared to the control mice aly/+→aly/+ (Figure 2D), indicating that the resistance to EAE in the absence of NIK could be related to the function of NIK in T cell polarization. The mechanistic underpinnings of this phenomenon are currently being investigated, but it is clear that the loss of NIK signaling impairs the capacity of aly/aly mice to generate pathogenic TH cells regardless of their structural defects.
Given the dogma that in mammals, CMI initiated by s.c. or intramuscular Ag-delivery requires the presence of SLTs, it is feasible that the remaining SLT (i.e., the spleen) in aly/+→aly/aly BM-chimeras compensates for the absence of LNs. In order to test this notion, we splenectomized aly/+→aly/aly BM-chimeras (aly/+→aly/alyspl) 14 d prior to the induction of EAE. Upon immunization, aly/+→aly/alyspl mice developed EAE with the same disease severity as control mice (Table 1). We noted a slight delay in disease onset when all SLTs are absent, while histopathological analysis of diseased mice revealed no difference between aly/+→aly/+ and aly/+→aly/alyspl mice (Figure S1).
In contrast to T cell activation, we found that B cell activation requires the structural environment provided by SLTs. To investigate the impact of immunization on T versus B cell responses, we used Keyhole limpet hemocyanin (KLH) as a model of foreign Ag to elicit delayed-type hypersensitivity (DTH) responses. Aly/aly as well as aly/+ mice were immunized with KLH, and 11 dpi, they were challenged by intradermal injection with KLH into the ear. As illustrated in Figure 2E, both groups were able to mount a solid DTH reaction measured by ear swelling, which was only marginally lower in aly/aly than in aly/+ mice. However, in contrast to ear swelling, which is indicative of CMI, aly/aly mice did not mount Abs against KLH when compared to aly/+ mice, demonstrating that the development of a humoral immune response is ablated in the absence of lymphoreticular structures (Figure 2F). We could reproduce functional DTH responses using other Ags including OVA and MOG35–55 (unpublished data). Similarly, in our EAE paradigm using BM-chimeras, whereas control mice (aly/+→aly/+ and aly/+→aly/+spl) elicit high Ab titers, anti-MOG Abs are virtually absent in mice without LNs (either aly/+→aly/aly or aly/+→aly/alyspl) (Figure 3A). Analysis of isotype subtypes revealed that in splenectomized alymphoplastic mice, elevated anti-MOG IgM could be detected, which has previously been reported [7],[23],[24], whereas class switching to IgG could not be observed (Figure 3B).
Taken together, and in agreement with the notion that SLTs are vital for B cell activation, highly organized SLTs are obligatory for the generation of high-affinity Igs and class switching, whereas potent cellular immunity can be induced successfully upon s.c. immunization even in the absence of SLTs.
Since the loss of SLTs in aly BM-chimeric mice does not hinder the development of T cell immunity, we wanted to determine at which alternative site T cell priming could take place and to which organ the Ag travels from the site of immunization (s.c.). Therefore, aly BM-chimeras were injected s.c. with yellow green (YG) carboxylate microspheres emulsified in CFA. At 7 dpi, various organs were isolated and analyzed for the presence of fluorescent cells by flow cytometry. Figure 4A shows that in control mice (aly/+→aly/+), fluorescently labeled APCs were exclusively detected in LNs upon s.c. immunization. It was previously shown that the BM has the capacity to drive an enriched population of high-affinity TcR Tg T cells in response to blood-borne Ag [25]. As expected, upon intravenous (i.v.) delivery of Ag, the vast majority of it accumulates in the spleen, BM, and liver, regardless of the presence of SLTs (Figure S2). However, after (s.c.) immunization of aly/+→aly/alyspl BM-chimeras lacking SLTs, APCs carrying fluorescent microspheres migrate primarily to the liver and not the other organs analyzed (thymus, CNS, and gut; unpublished data) (Figure 4A). Only a small amount of Ag reaches the liver when draining SLTs are present. Next, we wanted to determine the means of the Ag transport from the s.c. reservoir to the liver. To determine whether the Ag diffuses to the liver or is actively transported by APCs, aly/+→aly/+ and aly/+→aly/alyspl chimeric mice were separated into two groups. One received YG microspheres/CFA in the left flank and polychromatic red (PR) microspheres/CFA in the right flank. The other group received a mixture of YG- and PR-coupled beads in both flanks (see scheme in Figure 4B). After 7 d, mice were sacrificed, perfused, and a single-cell suspension of livers, LNs, and spleens was generated for cytofluorometric analysis. We found that the mixture of PR/YG-coupled beads generated a large proportion of dual-labeled CD11b as well as CD11c-positive APCs. Conversely, the injection of either PR- or YG-coupled microspheres into each flank revealed merely single-labeled APCs in the liver. The presence of single-labeled cells within the liver strongly suggests that the Ag is delivered to the liver by the migration of APCs initially present at the site of immunization. Passive diffusion of the Ag from the site of immunization via the bloodstream to the liver cannot be fully excluded, but is evidently not the dominant means of Ag delivery. In addition, only a negligible amount of Ag reaches the liver when dedicated SLTs are present (Figure 4). We could also confirm these findings by using soluble FITC painted on shaved flanks (without the adjuvant CFA). Twenty-four hours after FITC skin painting, we found FITC+ APCs primarily in the liver, again supporting the notion that the liver can serve as an alternative Ag-presenting site when draining LNs are not available (Figure 4C).
In order to determine whether lymphoid-like structures can be found in the liver, we analyzed the livers of BM-chimeric mice immunized s.c. with MOG35–55/CFA by histology (7 dpi). Livers of aly/+→aly/alyspl BM-chimeras showed massive infiltration of leukocytes in comparison to aly/+→aly/+ control mice (Figure 5). Histological analysis displays dendritic cells (DCs) in close proximity to T cells in the infiltrated periportal areas of the liver, indicative of T cell priming by Ag-laden APCs (Figure 5B). In spite of the stroma's inability to respond to LTα/β, detailed histological analysis revealed the presence of VCAM and ICAM in the infiltrates as well as B cells (Figure S3) and even the presence of CXCL13 transcripts indicative of aggregates ability to recruit B cells (unpublished data). However, no evidence for GC formation could be obtained (Figure S3).
We also transferred TcR Tg T cells from Luciferase-2D2 (Luc-2D2) mice into recipient BM-chimeras and observed the accumulation of Ag-responsive T cells in the liver 2 dpi with MOG35–55/CFA by bioluminescence imaging (Figure 6A). Figure 6B shows that the number of DCs (CD11c+) and adoptively transferred 2D2 T cells (CD4+/Vβ11+) is drastically increased in the liver in mice lacking SLTs.
In order to demonstrate that the observed lymphocyte accumulations in the liver can support cell expansion, we injected naive (CD62L+) CD4+ T cells derived from 2D2 Tg mice into aly BM-chimeras and subsequently immunized them with MOG35–55/CFA. At 5 dpi, livers were analyzed for Ag-specific CD4+ T cell proliferation. Even in normal mice, we find a large number of expanded T cells within the liver (Figure 6C), but one could argue that they have immigrated from their initial priming site, the draining LN. However, in the absence of SLTs, the livers of aly/+→aly/alyspl BM-chimeric mice are sufficient to propagate Ag-driven T cell expansion and accumulation. In order to confirm that Ag-specific T cell proliferation occurs in situ in the liver, we administered BrdU intraperitoneally (i.p.) into aly BM-chimeras 7 dpi with MOG35–55/CFA. Thirty minutes after BrdU injections, the mice were sacrificed, and livers were analyzed for proliferating (BrdU+) CD4+ T cells by flow cytometry. Figure 6D and 6E reveal the presence of BrdU+ cells in the livers of both aly/+→aly/+ and aly/+→aly/alyspl BM-chimeras. The number of BrdU+ T cells in the liver is increased in aly/+→aly/alyspl BM-chimeras compared to the controls. The fact that we found such a rapid (30 min) emergence of proliferating T cells even in normal mice in which SLTs are present, indicates that some degree of liver-initiated CMI occurs simultaneously to the priming within draining LNs.
In contrast to our findings, which show that mice lacking SLTs do not generate high-affinity Ab-responses, intranasal influenza infection of splenectomized LTα−/− mice reconstituted with wild-type (wt) stem cells, for instance, can initiate the formation of extra-lymphoid follicles within the lung, which support some degree of B cell maturation and Ab secretion [24],[26]. One possible explanation for these contrasting observations regarding Ab production is that in our case, stroma cells such as FDCs cannot signal through LTβR due to the mutation within NIK and that this could be the reason for our inability to observe GC formation and Ab secretion, whereas Moyron-Quiroz et al. [24],[26] used mice in which the stroma compartment can be engaged by LTα/β. To definitively address whether the stroma's inability to signal through NIK is the reason for the weak B cell response, we obtained LN-deficient LTα−/− mice and reconstituted their hematapoietic system with wt stem cells. The resulting chimeras were splenectomized and lacked all peripheral SLTs (analogous to the aly/+→aly/alyspl). Yet in contrast to aly/+→aly/alyspl, wt→LTα−/−spl chimeras have normal stromal cell function, and FDCs are capable of responding to LTα/β. These mice were immunized s.c., and the formation of B cell maturation and Ab production was analyzed. Figure 7A demonstrates that these wt→LTα−/−spl chimeras behave exactly like aly/+→aly/alyspl in regards to their inability to generate high Ab titers and to class switch.
In a comparative fashion, we analyzed the histological parameters of wt→wt, aly/+→aly/alyspl, and wt→LTα−/−spl chimeras (Figure 7B). Although only alymphoplastic mutants revealed the presence of lymphoid aggregates surrounding periportal areas of the liver, neither FDCs nor PNA-positive clusters could be found, again supporting the notion that the surrogate structures in the liver support T cell function but fail to initiate the formation of GCs needed for Ab-affinity cell maturation and class switching. Lastly, the large number of Ki67+ cells within the liver aggregates again support our conclusion, that active proliferation within the liver can be induced by s.c. immunization (Figure 7B).
Although we have demonstrated the development of TH cell-driven autoimmune disease in mice lacking SLTs, we wanted to elucidate whether these mice are also capable of inducing successful CTL immunity. We used the B16.F10 murine melanoma model, which represents a lethal and poorly immunogenic cancer. Irradiated GM-CSF expressing B16.F10 cells are used as s.c. vaccine to initiate potent CD8+-antitumor immunity against live parental B16.F10 tumor cells [27]. We injected irradiated B16.F10-GM-CSF cells s.c. into one flank of aly/+→aly/+ and aly/+→aly/alyspl chimeric mice. At 12 dpi, mice were challenged with parental B16.F10 cells injected into the opposite flank. Figure 8A shows that aly/+→aly/alyspl chimeric mice can elicit potent antitumor CTL responses revealed by the inhibition of tumor growth. Next, we transferred CFSE-labeled MHC class I-restricted OVA-TcR Tg OTI T cells into aly/+→aly/alyspl and aly/+→aly/+ BM-chimeric mice and subsequently injected irradiated B16.F10 cells expressing OVA. At 12 dpi, livers and, in control animals, also spleen and LNs were analyzed by FACS for Ag-specific CD8+ T cell expansion. As demonstrated in Figure 8B, proliferation of CD8+ OTI cells was detected in the liver of mice lacking SLTs. Hence, even under conditions in which the draining LNs are considered a compulsory site hosting the encounter of captured Ag and infiltrating CD8+ T cells, we can detect potent T cell responses, which originate in the liver when SLTs are absent.
We next wanted to address the relevance of the liver to serve as an alternative priming site in a setting where LNs are present but T cell migration into LNs is defective. To this end, we analyzed plt/plt (paucity of LN T cells) mice, which display undisturbed B cell zones but severely abrogated T cell zones due to the loss of CCL19 and CCL21, which results in the inhibition of both naive T cell and DC homing into SLTs [28]. We found that plt/plt mice also developed delayed but fulminant EAE after s.c. immunization with MOG35–55/CFA (Figure 8C). Examination of liver sections of immunized plt/plt mice again revealed lymphocyte aggregates consisting mainly of CD4+ T cells and DCs within the liver (Figure 8D).
S.c. immunization instigates a situation in which draining LNs are widely held to be absolutely obligatory for the initiation of adaptive immunity. In the absence of such draining LNs, we found however, that APCs take up the Ag at the site of immunization and subsequently select the liver as an extra-lymphoid environment for the initiation of CMI. These findings are consistent with the propensity of alymphoplastic mice (NIK−/−, LTα−/−, and LTβR−/−) to develop abnormal lymphocytic infiltrates primarily in the liver [15],[29]. The lymphocyte accumulation seen in the liver of naive alymphoplastic mice does not coincide with any overt tissue damage, nor do they develop any secondary sign of hepatic injury (M. Heikenwaelder, Zurich, Switzerland, personal correspondence). Such surrogate structures are evidently not as sophisticated as true SLTs and fail to support B cell priming, but are clearly sufficient to support CMI. Such neo-lymphoid structures in the liver are not restricted to alymphoplastic mouse strains, but can be reproduced in mice in which T cells do not migrate into the LNs (plt/plt). The fact that we observe the rapid emergence of immunization-induced T cell expansion in the liver of normal mice supports the notion that the adult liver provides an efficient niche for the initiation of CMI. Moyron-Quiroz et al. [26] elegantly demonstrated that the lymphoid tissue in the lung (BALT) is sufficient to generate immunity against an infectious agent attacking the lung. In their experimental paradigm, peripheral SLTs are not compulsory for the initiation of protective immunity, and they could even observe some degree of B cell maturation. In our report, however, after s.c. immunization, the local APCs must sample the Ag and then actively migrate to and select the liver as a site for T cell priming, which then is even capable of driving autoimmune responses within the CNS. In our experimental paradigm, the site of Ag deposition, priming, and inflammation are distinct. The liver is thus not like the BALT or the NALT, a site where local immune responses can be initiated, but represents a niche for systemic T cell priming under conditions in which the draining LNs are widely held to be absolutely compulsory. The fact that Ag-laden APCs migrate from the site of immunization to the periportal areas in the liver could be explained by the presence of chemoattractive factors in the liver aggregates observed in SLT mutants. Alternatively, the extensive lymphatic network of the liver makes it an ideal niche for the accumulation of leukocytes as a reservoir when regular SLTs are inaccessible.
Although the induction of CMI is not a function traditionally attributed to the adult liver, the fetal liver is a primary lymphoid organ hosting early hematopoiesis. Our findings suggest that the liver has the potential to “remember” its lymphoid function. The phenomenon, that, for instance, food allergies can be transferred by the transplantation of livers from an allergic donor to a previously nonallergic recipient [30], can be explained by our findings. Such transplant-acquired food allergy has only been described for the liver and not for other transplanted organs of the same donor [30]. It has been hypothesized that this occurrence is due to donor-derived allergen-specific lymphocytes residing in the liver. In support of this, Klein and Crispe [31] reported recently that after liver transplantation in a mouse in which Ag presentation was restricted to resident cells of the liver grafts, efficient CD8+ T cell priming can be induced locally in the transplanted liver.
The situation also is reminiscent of the effect of immunizations on some cold-blooded vertebrates that are much more primitive than mammals in their SLT organization (i.e., lacking GCs and showing only minimal affinity maturation). Frog tadpoles (Alytes obstetricans) immunized with rabbit serum in CFA developed a large accumulation of lymphocytes in the liver visible 2–3 wk after injection (L. Dupasquier, Basel, Switzerland, personal correspondence). Interestingly, during evolution, the emergence of RAG was permissive for the development of adaptive immunity in jawed fish [32]. RAG mediates somatic recombination and is required for the formation of both B and T cell receptors, which appear to have emerged simultaneously during evolution. However, whereas the adaptive immune system is well developed in the oldest jawed vertebrates (cartilaginous fish, e.g., sharks), potent affinity maturation, Ig-class switching, and GC formation are lacking. Class switching only appeared at the time of the divergence of amphibians [33]. The fact that CMI evolved earlier than modern humoral immune responses corroborates our discovery that T cells can function outside of dedicated lymphoreticular structures.
In summary, we demonstrate that the structural requirements for the initiation of B and T cell responses differ significantly. We found that B cells are dependent on the topography of dedicated lymphoid tissues, whereas CD4+ as well as CD8+ T lymphocytes retain the capacity to recognize Ag in a structure-independent fashion. This finding has obvious implications for our understanding of adaptive immunity and vaccination. As for the development of autoimmune diseases, our findings show that self-reactive T cells may not need to be primed in tissue-draining LNs, but could occur at the inflammatory site or even in organs distant to the target tissue.
C57BL/6 mice were purchased from Janvier Laboratories. Alymphoplasia (aly/aly) mice were obtained from Clea Laboratories and bred in-house under specific pathogen-free (SPF) conditions. Heterozygous aly (aly/+) mice were used as controls for homozygous aly mice (aly/aly); 2D2 (MOG-TCR Tg) mice were provided by V. Kuchroo (Harvard Medical School, Boston, Massachusetts); LTβR−/− and LTα−/− mice were provided by A. Aguzzi and M. Heikenwalder (University Hospital Zurich, Zurich, Switzerland); and OTII and OTI mice were purchased from Jackson Laboratories. Luciferase (pbActin-Luciferase) Tg mice were obtained from C. Contag (UCSF) and crossed to the 2D2 mice (Luc-2D2). Plt/plt mice were obtained from B. Ludewig (Kantonsspital St. Gallen, Switzerland). All mice were bred in-house under SPF conditions. BM-chimeras were generated as described previously [34]. Mice were splenectomized as described previously [35]. Animal experiments were approved by the Swiss veterinary Office (68/2003, 70/2003, 10/2006, and 13/2006).
MOG35–55 peptide (MEVGWYRSPFSRVVHLYRNGK) was obtained from GenScript. EAE was induced as described previously [34] with the modification that BM-chimeras were generally not boosted with pertussis toxin. For adoptive transfer, MOG-reactive lymphocytes were generated as described [34]. Each time point shown is the average disease score of each group±the standard error of the mean (SEM).
Mice were euthanized with CO2, and various organs were removed to isolate leukocytes: For isolating lung cells, lungs were incubated with DNase (0.5 mg)/Liberase (1 mg/ml) (Roche) for 30 min at 37°C. Spleen, LNs, thymus, and lung were homogenized, and BM cells were isolated by flushing the bones with PBS. Cells were strained through a 100-µm nylon filter (Fisher) and washed. Erythrocytes of whole blood, BM, and spleen were lysed. For isolating hepatic nonparenchymal cells, the liver was incubated with DNase/Liberase for 30 min at 37°C, homogenized, and then centrifuged at room temperature (RT) for 2 min at 50g. The supernatant was then centrifuged at 1,500 rpm for 10 min, and the pellet was resuspended in 30% Percoll (Pharmacia) and centrifuged at 12,000 rpm for 30 min at 4°C. The interphase cells were collected and washed. For isolating intestinal lymphocytes, intestines were opened longitudinally, washed, and then cut into small pieces. Tissues were then incubated with DNase/Liberase and leukocytes were isolated using a percoll gradient as described above. Isolation of CNS lymphocytes has been described previously [35].
Mice were injected i.v. with 20×106 CFSE (carbofluorescein diacetate succinimidyl ester)-labeled (Invitrogen/Molecular Probes) (10 µM) splenocytes obtained from either 2D2, OT-II, or OT-I TcR Tg mice or with 8×106 CFSE-labeled naive CD4+ 2D2 Tg T cells (isolated with CD4+CD62L+ isolation kit from Miltenyi). Mice were subsequently immunized s.c. with 200 µg of MOG35–55./CFA (Adjuvant complete H37 Ra..; DIFCO) (for 2D2), OVA323–339/CFA (for OT-II), or with a 1∶1 mix of irradiated 2×106 B16.F10-GM-CSF/B16.F10-OVA cells (for OT-I). At 4 or 5 dpi (12 dpi for OT-I), mice were sacrificed, and spleen, LNs (if present), and livers were analyzed by fluorescence-activated cell sorting (FACS) for the proliferation of CD4+ T cells using the clonotypic TcR and CFSE fluorescence (2D2: TCR Vα3.2 Ab; OT-II and OT1: Vα2 Ab).
Tissues were freshly snap-frozen in liquid nitrogen. To determine infiltration of inflammatory cells, tissue sections were stained with hematoxylin and eosin (H&E) or with the following mouse-specific Abs as previously described [34]: anti-CD11c (Jackson ImmunoResearch Labs), anti-CD11b (BMA Biomedicals), anti-CD3, anti-CD4, anti-CD19, anti-FDC M1, and anti-Thy1.1 (BD-Pharmingen), anti-ICAM, anti-VCAM, and anti-CD8 (Serotec). GC cells were stained with peanut agglutinin (PNA; Vector Laboratories).
For FACS analysis, the following Abs were used: anti-CD11c, anti-CD4, anti-CD8, anti-CD11b, anti-Vα3.2, anti-Vα3, and anti-Thy1.1 (BD-Pharmingen). The cells were analyzed using a FACS-Canto (BD) with Cell-Diva software. Postacquisition analysis was performed using FLOWJO software. To trace the distribution of Ag after immunization, mice were injected s.c. with 200 µl of yellow-green (YG) or polychromatic red (PR) 1.0-µm microspheres (Polysciences) emulsified in CFA. At 7 dpi, mice were euthanized with CO2, and organs were removed to isolate lymphocytes as described above. Single-cell suspensions were analyzed by FACS for the presence of fluorescein isothiocyanate (FITC+) or PE+ cells.
For FITC skin painting, mice were painted on the shaved flanks with 100 µl of 5 mg/ml FITC (Molecular Probes) dissolved in 1∶1 acetone:dibutylphtalate. On day 1, mice were euthanized with CO2, and organs removed and analyzed by FACS as described above.
Mice were immunized s.c. with 100 µg/flank of KLH (Sigma) emulsified in CFA. At 11 dpi, mice were challenged by injecting 10 µg/10 µl KLH, PBS into the dorsal surface of the ear. DTH responses were determined by measuring the ear thickness using a caliper micrometer (Mitutoyo) 24 h after challenge, and Δ ear swelling was established by the increase in ear thickness over baseline (prechallenge ear thickness).
Plates were coated with 10 µg of rMOG1–121 in 0.1 M NaHCO3 (pH 9.6) at 4°C overnight or KLH (Sigma), and blocked with 1% (w/v) bovine serum albumin (BSA). Diluted sera were incubated for 2 h at RT. After washing, peroxidase-conjugated antibodies to mouse immunoglobulins, IgG, IgA, and IgM (Sigma) were added (1∶1,000 diluted) and incubated for 1 h at RT. Plates were washed, and chromogen (Biosource) was added. Absorbance was measured on a microplate reader (450 nm) (Bio-Rad).
A total of 2×105 cells were plated in medium containing 10% FCS and 50 µg/ml of MOG35–55 in 96-well plates (Millipore) coated with the capture Ab against either IFNγ or IL-17A [36]. Elispots were revealed as described previously [36] and subsequently analyzed on an Elispot reader (CTL immunospot).
To visualize Luc-2D2 cells, mice were injected i.p. with 3 mg of luciferin (Xenogen) prior to bioluminescence imaging using an IVIS100 imaging station (Xenogen). The luminescent image was overlaid on the photographic image.
Mice were immunized s.c. with MOG35–55/CFA. At 7 dpi, BrdU (BD Pharmingen) (2.5 mg) was injected i.p. 30 min before the mice were sacrificed and analyzed for proliferating (BrdU+) CD4+ T cells by flow cytometry with anti-BrdU Ab (eBioscience).
Mice were s.c. vaccinated into one flank with irradiated (6,000 rads) 1×106 B16.F10-GM-CSF cells. At day 12 after vaccination, mice were injected with live 2×105 B16.F10-Luc cells into the opposite flank. Each time point shown is the average tumor size of each group±SEM, measured using a caliper.
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10.1371/journal.pbio.0050288 | An Information Theoretic Characterisation of Auditory Encoding | The entropy metric derived from information theory provides a means to quantify the amount of information transmitted in acoustic streams like speech or music. By systematically varying the entropy of pitch sequences, we sought brain areas where neural activity and energetic demands increase as a function of entropy. Such a relationship is predicted to occur in an efficient encoding mechanism that uses less computational resource when less information is present in the signal: we specifically tested the hypothesis that such a relationship is present in the planum temporale (PT). In two convergent functional MRI studies, we demonstrated this relationship in PT for encoding, while furthermore showing that a distributed fronto-parietal network for retrieval of acoustic information is independent of entropy. The results establish PT as an efficient neural engine that demands less computational resource to encode redundant signals than those with high information content.
| Understanding how the brain makes sense of our acoustic environment remains a major challenge. One way to describe the complexity of our acoustic environment is in terms of information entropy: acoustic signals with high entropy convey large amounts of information, whereas low entropy signifies redundancy. To investigate how the brain processes this information, we controlled the amount of entropy in the signal by using pitch sequences. Participants listened to pitch sequences with varying amounts of entropy while we measured their brain activity using functional magnetic resonance imaging (fMRI). We show that the planum temporale (PT), a region of auditory association cortex, is sensitive to the entropy in pitch sequences. In two convergent fMRI studies, activity in PT increases as the entropy in the pitch sequence increases. The results establish PT as an important “computational hub” that requires less resource to encode redundant signals than it does to encode signals with high information content.
| We are constantly required to perceive, distinguish, and identify signals in our acoustic environment. A critical first stage of these processes is the encoding of the information into a robust neural code that allows efficient subsequent processing in the auditory system [1]. We investigated the properties of such a robust neural code at the level of the cortex by varying the amount of information—or entropy—in the acoustic signal.
In the context of information theory [2,3], entropy (H) denotes the uncertainty associated with an event and thus provides a metric to quantify information content: a rare—or uncertain—event carries more information than a common—or predictable—event. The properties of many information transmitting systems can be characterised in terms of entropy. Indeed, Shannon originally applied information entropy to describe transitional probabilities in language [2]: in English, less common letters (e.g., “k”) have a lower probability (or higher uncertainty) than more common letters (e.g., “e”), and therefore carry higher information and entropy. Similarly, entropy can be used to characterise pitch transition probabilities in simple musical melodies [4,5]. We used entropy to quantify the information content of pitch sequences.
“Fractal” pitch sequences based on inverse Fourier transforms of f–n power spectra [6,7] provide a means to control directly the entropy of the sequence via the exponent n (Figure 1). For n = 0, the excursion of the pitch sequence is equivalent to fixed-amplitude, random-phase noise and thus is completely random (high entropy). In the context of information theory, the high degree of randomness in this signal does not correspond to noise that must be removed by the system, but rather to a low predictability of the stimulus that results in each individual element of the sequence making a high degree of contribution to the information in the sequence. As n increases, a single stream gradually dominates the local pitch fluctuations and successive pitches become increasingly predictable (low entropy). Such stimuli are more predictable so that each element of the sequence makes little contribution to the overall information in the stimulus. These families of pitch sequences with different values of n are statistical “fractals” [8] in the sense that their statistical properties are scale-independent [7]. For present purposes, the critical property of these pitch sequences that we exploit here is not their fractal behaviour, but the variation of entropy that is produced as n varies, whilst pitch range, tempo, and pitch probability remain largely constant (however, it is inherent to the system that for large exponents n > 4, the pitch distribution approaches a sinusoid and consequently is tilted toward the extremes of the pitch range and also that the average interval size between successive pitches decreases for increasing exponents n).
Entropy for pitch sequences generated with a given value of exponent n can be determined by computing the sample entropy (HSampEn) [9]. Intuitively, HSampEn is based on the conditional probability that two subsequences of length m that match within a tolerance of r standard deviations remain within a tolerance r of each other at the next point m + 1. Explicitly, for a signal or time series of length N, HSampEn is defined as:
where Ar(m) (or Ar(m + 1)) denotes the probability that two subsequences of length m (or m + 1) match within a tolerance r. Two sequences “match” if their maximum absolute point-by-point difference is within a tolerance of r standard deviations. That is, sample entropy is essentially a measure of self-similarity, where highly self-similar time series signify high redundancy and therefore low entropy, and time series with low self-similarity represent a high degree of uncertainty and therefore high entropy. Furthermore, sample entropy is a nonparametric measure in the sense that it does not require a priori knowledge of the true probability density function of the underlying time series. In the present case, the parameters were chosen as m = 2, r = 0.5, and N represents the number of tones of the pitch sequence.
By varying information theoretic properties of pitch sequences, we address encoding mechanisms applied to sounds at a level of generic processing that is not specific to any semantic category. Even before such encoding mechanisms are engaged, the auditory system must represent spectrotemporal features of the stimulus in sufficient detail such that a number of different aspects of the stimulus can be encoded, in order to allow different types of subsequent categorical and semantic processing. In the current context, encoding constitutes the stage of analysis between the detailed representation of the spectrotemporal structure of the stimulus and the subsequent categorical analysis of abstracted acoustic forms. A single sound may be associated with more than one abstracted form: for example, we might obtain vowel, speaker, and position from a single sound, where each feature can undergo subsequent categorical and semantic processing. Here we use information theory to demonstrate encoding mechanisms in the brain that result in the abstraction of a form of the stimulus.
We hypothesise that if such encoding mechanisms are efficient, they will use less computational resource for stimuli that have a low information content compared with stimuli that have high information content. This hypothesis is tested by measuring the functional MRI (fMRI) blood oxygenation level–dependent (BOLD) signal as an estimate of neural activity and computational resource during the encoding of auditory stimuli in which the information content is systematically varied. We further hypothesise that processing in primary auditory cortex in the Heschl's Gyrus (HG) corresponds to a stage at which the detailed spectrotemporal structure of sounds is represented [10–12] and where such a relationship will not be observed. Instead, such a relationship is expected to be observed in distinct auditory association cortex in the planum temporale (PT), which we have previously characterised as a “computational hub” [13] that is required to convert spectrotemporal representations into “templates”—sparse symbolic neural representations that are the basis for categorical, semantic, and spatial processing. For example, the spectral envelope of a sound would represent such a template for vowel processing [14]. The model was developed to account for the involvement of PT in the analysis of a variety of complex sounds that can be processed categorically (speech, music, and environmental sounds) as well as different spatial attributes (for a review, see [13]).
Here we investigate the encoding of pitch sequences that can be like melodies in their structure, but in which the structure and information content is determined by statistical rules. We sought brain areas that display a positive relationship between the information content or entropy of pitch sequences and neural activity as assessed by the BOLD signal during encoding. Specifically, we hypothesised that such a relationship exists in PT but not in earlier auditory areas.
Participants were presented with pure-tone pitch sequences that were based on f–n power spectra with n ranging from n = 0–1.5 in five steps of 0.3. In a behavioural experiment before scanning, we acquired full psychometric functions demonstrating that all of the 22 participants could reliably distinguish a nonrandom pitch sequence from a random (n = 0) reference in a two-interval, two-alternative, forced-choice (2I2AFC) paradigm (see Materials and Methods). Perceptual thresholds for discriminating nonrandom from a random pitch sequence lay between n = 0.6 and n = 0.9 for the majority of participants.
In a sparse fMRI paradigm [15,16], participants listened to pitch sequences of a given value for n and indicated whether it was random or not. A parametric regressor based on the mean sample entropy [9] value at each of the six levels of n (Table 1) was used to probe for cortical areas that increased their activity with increasing entropy. The fMRI analysis revealed a BOLD signal increase in PT as a function of increasing entropy at a significance level of p < 0.001 (uncorrected for multiple comparisons, see Figure 2 and Table 2) and using a small volume correction for the anterior part of PT at a significance level of p < 0.05 (see Materials and Methods). No area increased its activity as a function of decreasing entropy, i.e., increasing predictability or redundancy.
These results suggest a greater computational and energetic demand for encoding in PT as the information content of acoustic sequences (as assessed by entropy) increases. However, the present study has three potential confounds, which we addressed in a second study. First, we considered whether the effect of entropy in PT might reflect adaptation of the sensory cortical representation of frequency, as the pitch sequences were based on pure tones: for low values of exponent n, the frequency excursions are greater on average, so that the signal moves more between specific frequency representations, and PT might adapt less and thus produce a greater local activity. Such a mechanism would also be expected to occur in primary and secondary auditory cortex within HG. We therefore explored the specific relationship between fractal exponent and local activity in HG and PT by extracting the first eigenvariate of the BOLD signal in left and right HG as well as the local maxima in PT (see Materials and Methods). No significant difference across entropy levels was demonstrated in HG (2 Hemisphere (left, right) × 6 Entropy Level (1–6) repeated measures analysis of variance (ANOVA): no main effect of Entropy Level (F(5,17) = 1.11, p > 0.1); Figure 2). Furthermore, a 2 Area (PT, HG) × 6 Entropy Level (1–6) × 2 Hemisphere (left,right) repeated measures ANOVA demonstrated a significant difference in the relationship between BOLD signal across entropy levels in PT versus HG: Area × Entropy Level interaction (F(5,17) = 4.86, p < 0.001).
The existence of the effect in auditory association cortex in PT, the absence of an effect in HG, and a significant interaction between effects in the two areas are indirect evidence against an explanation of the results based on sensory adaptation. Nevertheless, we addressed a putative sensory explanation in a second study by using regular-interval noise, where sounds have identical passband regardless of their pitch [17–19].
Second, we also considered whether the effect of entropy might reflect perceptual adaptation at the level of the representation of pitch. Again, such an effect would not be expected in association cortex, but in a proposed “pitch centre” in lateral HG [20–22]. The second study therefore incorporated a more suitable design to detect a potential differential response to the entropy of the acoustic stimuli in cytoarchitectonic [23] and functional [20] subdivisions of HG in medial, central, and lateral HG.
Finally, we controlled for the fact that, in the first study, participants were explicitly required to assess whether the sequences were random or not. This made it possible that the results reflected a category judgment rather than a fundamental encoding mechanism. To test this, the second study differentially examined encoding and retrieval components as a function of entropy but independent of any other stimulus-related classification task.
In a sparse fMRI paradigm [15,16], participants were presented with fractal pitch sequences based on f–n power spectra, with n ranging from n = 0–1.2 in four steps of 0.3. The separate pitches corresponded to regular-interval noise [17–19] (see Materials and Methods). By using broadband stimuli and an increased number of silent trials, the second study used a more suitable design to allow disambiguation of the medial functional area in HG that corresponds to the primary auditory cortex and areas in lateral HG that correspond to secondary cortices, including the area within which activity corresponds to pitch salience [20,21]. The second paradigm also enabled the disambiguation of encoding and retrieval mechanisms. Participants were scanned (1) after being required to encode a pitch sequence with a particular entropy value and (2) after listening to a second pitch sequence that was either identical to the first sequence or different from the first sequence but with the same entropy value. Activity during the first scan reflects the energetic demands of encoding the first sequence, whereas activity during the second scan reflects encoding of the second sequence, retrieval of the first, and comparison of the two. In order to decorrelate the two scans [24], we introduced a delay of one, two, or three scans between the pitch sequences (see Material and Methods and Figure 3). In contrast to the first study, participants were not informed about the nature of the pitch sequences and instead were only told that they would hear pairs of pitch sequences and that their task would be to say whether the second was same or different.
Participants' behavioural performance in the scanner was assessed via hits (hit) and correct rejections (cr) percent scores (see also Figure S2). Both mean hit (74.25% ± 3.14 standard error of the mean [SEM]) and mean cr (73.42% ± 3.31 SEM) scores were significantly above chance (50%) (one-sample t-test, hit: t23 = 7.73; cr: t23 = 7.08, both p < 0.001). Furthermore, a 2 Response (hit, cr) × 5 Entropy Level (1–5) × 3 Delay(1–3) repeated measures ANOVA showed no main effect in any of the three factors (F(23,1) = 0.33; F(20,4) = 1.1; F(22,2) = 0.53; all p > 0.05, for Response, Entropy Level and Delay, respectively). There was no Response × Entropy Level interaction (F(20,4) = 1.01, p > 0.05), indicating that participants' performance was not influenced by the entropy level of the pitch sequences. Participants had higher cr than hit scores for delay 3, whereas there were more hits than cr for delays 1 and 2 (Response × Delay interaction; F(22,2) = 7.91, p = 0.001). An Entropy Level × Delay interaction (F(16,8) = 2.14, p < 0.05) showed a performance increase for delay 1 from entropy level 1 to entropy level 5, but there was no such systematic effect for delay 2 or delay 3. There was no Response × Entropy Level × Delay interaction (F(16,8) = 0.45, p > 0.1).
The imaging results replicate the findings of the first study, demonstrating that activity in PT for encoding (as assessed by both the first and second scan of each pair) increased significantly as a function of entropy for the same significance thresholds as in the first study (Figure 4 and Table 2). We examined in detail the effect at the level of primary and secondary auditory cortex by extracting the BOLD signal in medial, central, and lateral HG [20,23] (Figure 4 and Figure S1): three separate 5 Entropy Level (1–5) × 2 Hemisphere (left, right) repeated measures ANOVAs showed no main effect of Entropy Level (F(4,20) = 0.85, F(4,20) = 0.77, F(4,20) = 1.83, all p > 0.1, for medial, central, and lateral HG, respectively).
Furthermore, the relationship between entropy and BOLD signal was significantly different between PT and all three subdivisions of HG: three separate 2 Area (PT, (medial, central, or lateral) HG) × 5 Entropy Level (1–5) × 2 Hemisphere (left, right) repeated measures ANOVAs carried out for medial, central, or lateral HG showed an Area × Entropy Level interaction (F(4,20) = 2.61, p < 0.05; F(4,20) = 3.31, p < 0.05; F(4,20) = 5.55, p < 0.001, for medial, central, and lateral HG, respectively).
The cardiac gated image acquisition in Study 2 furthermore allowed an examination of a potential effect of stimulus entropy in subcortical auditory structures. We examined the relationship between entropy and the activity in the medial geniculate body (MGB) and inferior colliculus (IC) using a smaller smoothing kernel (4 mm full width at half maximum [FWHM]) that is appropriate for these subcortical structures (Figure 5). This analysis showed no main effect of entropy on the BOLD response in these areas (two separate 5 Entropy Level (1–5) × 2 Hemisphere (left, right) repeated measures ANOVAs: F(4,20) = 0.35, p > 0.1, for IC; F(4,20) = 1.32, p > 0.1, for MGB). Due to the different spatial smoothing, no meaningful interaction with the response in cortical structures can be computed.
A second analysis based on the contrast between the second and first scans sought areas involved in retrieval and comparison, but not encoding. This contrast highlighted activity within a bilateral fronto-parietal network, including the anterior insulae and frontal opercula, inferior parietal sulci, medial superior frontal gyri, and dorsolateral prefrontal cortex (p < 0.05, family-wise error (FWE) corrected for multiple comparisons; Figure 6 and Table S1). A further contrast was carried out to identify an effect of entropy on retrieval and comparison, but not encoding. No effect of entropy on retrieval and comparison was demonstrated.
We have demonstrated an increase in the local neural activity as a function of the entropy of encoded pitch sequences in PT but not in HG. The results are consistent with a computational process in PT that requires increasing resource and energetic demands during encoding as the entropy of the sound stimulus increases.
In the first study, the use of pure tones could not exclude a possible alternate explanation of the data in terms of sensory adaptation within cortical frequency representations. The existence of the relationship in PT, but not in HG, was indirect evidence against such sensory adaptation. However, in the second study we used broadband stimuli that continually activate a broad range of cortical frequency representations irrespective of pitch, rendering explanations based on sensory adaptation untenable.
Another interpretation of these results could be based on perceptual adaptation within cortical correlates of pitch (as opposed to sensory adaptation of the stimulus representation). Previous studies have demonstrated mapping of activity within secondary auditory cortex in lateral HG as a correlate of the perceived pitch salience, whether the stimulus mapping was in the temporal domain [20] or frequency domain [21]. An explanation of the results of either study might therefore be based on adaptation within the pitch centre in lateral HG for pitch sequences with higher fractal exponent n. In the second study, we were able to identify separate activations in medial, central, and lateral HG. Contrary to an interpretation based on adaptation in pitch-sensitive channels, there was no relationship between the entropy and local activity in any of the subregions of HG that would have supported such an explanation. Furthermore, the interaction between HG and PT provides additional evidence for an effect of entropy that is specific to PT.
The most compelling explanation of these results is in terms of greater computational activity (and therefore local synaptic activity and BOLD signal [25]) as a function of the information content or entropy of the encoded sound. This is the first explicit demonstration of such a relationship. The results suggest an efficient form of encoding within PT, whereby sequences are encoded by a mechanism that demands less computational resource for sequences carrying low information content and high redundancy (due to the predictability of the sequence) than that required to encode sequences with little or no redundancy. “Sparse” [26–28] and “predictive” [29–31] coding both constitute such mechanisms and bases for PT acting as a computational hub [13].
In contrast, retrieval and comparison do not depend on entropy in the same way, which we propose reflects the decreased computational and energetic demands of retrieving and comparing stimuli at symbolic levels beyond stimulus encoding. The initial encoding process depends on a computationally expensive process that must abstract features from a complex spectrotemporal structure. Beyond this stage, the subsequent categorical retrieval and comparison mechanism does not depend on the detailed spectrotemporal structure. Indeed, the computational hub model [13] states that PT gates its output towards higher-order cortical areas that perform analysis at a symbolic and semantic level. We suggest that at least part of the function of PT is to compress the neural code corresponding to the initial acoustic signal (e.g., via sparse or predictive coding), and that subsequent processing is not dependent on stimulus entropy.
That PT might even perform this type of analysis in more general or supra-modal terms is suggested by work in the visual domain [32], demonstrating activation in Wernicke's area and its right-hemisphere homologue as a function of the entropy within a sequence of visually presented squares, irrespective of whether or not participants were aware of an underlying sequence. However, later studies using similar visual stimuli did not replicate this finding [33,34].
The retrieval and comparison phase highlighted a fronto-parietal network consisting of the anterior insulae and frontal opercula, inferior parietal sulci, medial superior frontal cortex, and dorsolateral prefrontal cortex. This activation pattern is common in the retrieval and comparison phase of (auditory) delayed match-to-sample tasks (e.g., [35,36]). The anterior insula in particular has been proposed as an additional auditory processing centre that allocates auditory attention, specifically with respect to sound sequences (see [37] for a review). Similarly, the parietal cortex is generally regarded as being important for attention to and binding of sensory information [38], whereas activity in the prefrontal cortex is often associated with response preparation and selection [39].
Our main aim was to study generic neural mechanisms of sound encoding as a function of entropy, and the range of pitch sequences we used included those approximating f−1 (“one-over-f”) power spectra, which resemble many naturally occurring acoustic phenomena [40]. Notably, music and speech display f−1 power spectra characteristics, reflecting the relative balance of “surprises” (e.g., musical transitions) and predictability in such signals [41,42]. Pertaining specifically to the signals used here falling in the range of f−1, two recent electrophysiological studies demonstrated preference within primary sensory cortices for f−1 signals [43,44]. We did not demonstrate any “tuning” to particular values of exponent in HG (no main effect of Entropy Level; Figures 2 and 4 and Figure S1). Although we do not dismiss the possibility of neuronal preference for particular natural sequence categories at the level of HG in humans, the current studies addressed the computational and energetic demands of the perceptual encoding of sounds, rather than their sensory representation.
We have used entropy to characterise pitch sequences, but the information theoretic approach could be used to characterise sequences containing rhythm or more complex natural sound sequences. The hypothesised mechanism in PT is not a specific pitch mechanism and also predicts a similar relationship between information content and the encoding of more natural stimuli. In summary, the present data implicate PT as a neural engine within which the computational and energetic demands of encoding are determined by the entropy of the acoustic signal.
Participants. 30 right-handed human participants (aged 18–43 y, mean age = 24.9 y; 19 females) with normal hearing and no history of audiological or neurological disorders provided written consent prior to the experiment. None of the participants was a professional musician. The experiment was approved by the Institute of Neurology Ethics Committee, London. Eight participants had to be excluded due to excessive head movements (more than 5 mm translation or 5° rotation within one session) or not meeting the psychophysical assessment criteria (see below), leaving a total of 22 participants (aged 18–40 y, mean age = 24.2 y; 12 females).
Stimuli. All stimuli were created digitally in the frequency domain using Matlab (http://www.mathworks.com). Stimuli were fractal sine tone sequences based on inverse Fourier transforms of f–n power spectra [6,7] for six levels of n (0, 0.3, 0.6, 0.9, 1.2, and 1.5), where pitch sequences ranged from totally random (n = 0; high entropy) to more coherent or predictable (n = 1.5; low entropy). By randomising the phase spectrum, each exemplar is unique while at the same time displaying the same characteristic correlational properties of a given level. The pitch range spanned two octaves from 300–1,200 Hz, with each octave split into ten discrete equidistant pitches. Pitch sequences were presented at a tempo of five notes per second, with a total duration of 7.6 s for each pitch sequence (38 notes per sequence). There were 60 exemplars for n = 0 and 30 exemplars for the remaining five levels of n.
We calculated the mean entropy for each level of exponent n using the sample entropy HSampEn [9] measure, as described in the Introduction:
Ar(m) denotes the probability that two subsequences of length m match within a tolerance r, i.e., Ar(m) is the ratio of [all pairs of subsequences of length m that match] divided by [all possible pairs of subsequences of length m]; the same applies to Ar(m + 1). Guided by Lake and colleagues [45], we chose tolerance r = 0.5 and length of subsequence m = 2 as parameter values. As Eke et al. [8] point out, taking a subset of data points from a fractal time series essentially introduces noise into the resulting time series, leading to lower n and consequently higher entropy estimates relative to the original values. Table 1 therefore lists the mean sample entropy values for the time series of the 38 notes in each pitch sequence.
Experimental design. In a behavioural experiment prior to scanning, we acquired full psychometric functions from participants discriminating the nonrandom pitch sequence against a random reference (n = 0) in a 2I2AFC paradigm. Participants were not given feedback. Stimuli were not the same as in the subsequent imaging paradigm and there were 72 trials (12 trials per level). Psychometric functions and 75% correct thresholds were estimated via a Weibull boot-strapping procedure [46]. Participants who did not reach at least 80% performance for levels 5 or 6 were not included in the fMRI analysis. In the functional imaging paradigm, participants were asked to categorise whether or not the pitch sequence was random by pressing the corresponding button at the end of each pitch sequence, bearing in mind that pitch sequences of intermediate levels (n = 0.6–0.9) are neither completely random nor completely coherent (in these cases, participants should nevertheless indicate their predominant percept). Stimuli were presented via custom-built electrostatic headphones at 70 dB sound pressure level (SPL) using Cogent software (http://www.vislab.ucl.ac.uk/Cogent/).
Gradient weighted echo planar images (EPI) were acquired with a 3-T Siemens Allegra MRI system (Erlangen, Germany), using a sparse temporal sampling technique [15,16] (time to repeat/time to echo, TR/TE = 10,530/30 ms). A total of 246 volumes (42 slices, 3 × 3 × 3 mm voxel resolution) were acquired over three sessions (82 per session), including 60 volumes for n = 0 and 30 volumes for the other levels of n, as well as 30 silent control trials (the first two volumes of each session were discarded to allow for saturation effects). To correct for geometric distortions in the EPI images due to B0 field variations, Siemens fieldmaps were acquired for each participant [47,48]. A structural T1 weighted scan was acquired for each participant [49].
Image analysis. Imaging data were analysed using statistical parametric mapping software (SPM2, http://www.fil.ion.ucl.ac.uk/spm). Volumes were realigned and unwarped using the fieldmap parameters, spatially normalised [50] to standard stereotactic space, and smoothed with an isotropic Gaussian kernel of 8 mm FWHM. Statistical parametric maps were generated using a finite impulse response (FIR) box-car function in the context of the general linear model [51]. The six conditions were parametrically modulated based on the average sample entropy [9] value for each level of n (Table 1), statistically evaluated using a random-effects model and thresholded at p < 0.001 (uncorrected for multiple comparisons across the brain) for areas where we had an a priori hypothesis, i.e., in auditory cortex and specifically in PT. In addition, we carried out a volume-of-interest analysis controlling for multiple comparisons within PT by centering a sphere with 1-cm radius around the centroid of the triangular anterior part of PT that is situated within the superior temporal plane as opposed to the more posterior part that abuts the parietal lobe (Montreal Neurological Institute (MNI) [x, y, z] coordinates [–56, –28, 6] and [58, –24, 8] for left and right PT, respectively). Our choice of volume was based on the identification of the anterior part of PT in the studies that suggested the computational hub model [13]. For areas that were not predicted a priori, we adopted a statistical threshold of p < 0.05 after FWE correction.
We investigated in detail a potential effect of adaptation in frequency bands at an earlier sensory level. Study 1 did not allow disambiguation of the three cytoarchitectonically [23] and functionally [20] distinct areas in HG, namely medial, central, and lateral HG (see Study 2 below for further discussion). Therefore, we identified single coordinates based on local maxima of a sound minus silence contrast for left [–46, −24, 6] and right [50, –24, 8] HG that are most similar to central HG as defined by references [20,23] and extracted the first eigenvariate of the BOLD signal at these coordinates (see Figure 2).
The BOLD signal was extracted using a standard procedure in SPM: the time series of a given voxel (e.g., the peak activation voxel for the entropy effect) is provided by SPM via a voxel-of-interest (VOI) routine. At the second-level statistical analysis, this results in a time series for each contrast where each data point corresponds to a participant. The routine is executed for each contrast, in the current case either six (Study 1) or five (Study 2) [Level–Silence] contrasts, resulting in a 22 × 6 or 24 × 5 matrix (22 or 24 participants, respectively), where each row corresponds to a participant and each column to a contrast. The threshold at which the BOLD signal was extracted was p < 0.05 (uncorrected for multiple comparisons). The values are then normalised to the maximum value.
Note that the interaction described here between the BOLD signal in HG and PT across levels assumes that the coupling between neuronal response and the haemodynamic BOLD signal is identical in the two brain regions. While we have no reason to assume the contrary, it has also not been proven that this is indeed the case.
Participants. 30 right-handed participants (aged 20–44 y, mean age = 28.0 y; 16 females) with normal hearing and no history of audiological or neurological disorders provided written consent prior to the experiment. The experiment was approved by the Institute of Neurology Ethics Committee, London. Six participants had to be excluded because of excessive head movements (more than 5-mm translation or 5° rotation within one session), leaving a total of 24 participants (aged 20–44 y, mean age = 28.58 y; 12 females).
Stimuli. Similar to Study 1, pitch sequences were again based on f–n power spectra for five levels of n (0, 0.3, 0.6, 0.9, and 1.2). Each pitch was based on regular-interval noise [17–19] with 16 iterations. The pitch range spanned two octaves from 150–600 Hz, with each octave split into ten discrete equidistant pitches. Pitch sequences were presented at a tempo of four notes per second, with a total duration of 6 s for each pitch sequence (24 notes per sequence). The mean entropy values for each level of n are depicted in Table 1 and are slightly different from Study 1, because each pitch sequence had 24 notes instead of 38. There were 30 exemplars for each level of n, and stimuli were presented via custom-built electrostatic headphones at 70 dB SPL using Cogent software (http://www.vislab.ucl.ac.uk/Cogent/).
Experimental design. In a sparse imaging paradigm [15,16], participants were scanned (1) after being required to encode a pitch sequence with a particular entropy value and (2) after listening to a second pitch sequence that was either the same sequence or a different sequence from the same entropy level and indicating whether this was the same pitch sequence or different (see also Figure 3). To de-correlate [24] activations due to the first and second pitch sequence, the second pitch sequence followed the first pitch sequence either immediately in the next TR, or with two or three TR's delay (within-trial delay). Similarly, the first pitch sequence of the next pair could follow the second pitch sequence of the previous pair immediately, or with one or two TR's delay (between-trial delay). There were 20 pitch sequence pairs for each level, amounting to 100 encoding and 100 retrieval stimuli across the five levels of exponent n. In addition, there were a total of 100 within-trial volumes and 100 between-trial rest volumes. For each level of exponent n, 10 out of 20 pairs were identical, and 10 were different. Stimuli were counterbalanced between participants.
To guide participants, a “1” was displayed at the centre of the screen from the start of the first pitch sequence until the start of the second pitch sequence, when a “2” was displayed. At the end of the second pitch sequence, participants briefly saw a “?” to indicate they should now give their response as to whether they thought the second pitch sequence was the same as or different from the first pitch sequence. Participants received immediate feedback. During the rest period between trials, participants saw a fixation cross “+” at the centre of the screen and were instructed to relax.
Gradient-weighted EPIs were acquired with a 3-T Siemens Allegra MRI system (Erlangen, Germany), using a sparse temporal sampling technique [15,16], where each volume was cardiac gated to reduce motion artefacts (TR/TE = ∼8,800/30 ms). A total of 404 volumes (42 slices, 3 × 3 × 3 mm voxel resolution) were acquired over two sessions (the first two volumes of each session were discarded to allow for saturation effects). Subsequent to the functional paradigm, a structural T1 weighted scan was acquired for each participant [49].
Image analysis. Imaging data were analysed using statistical parametric mapping software (SPM5, http://www.fil.ion.ucl.ac.uk/spm). Volumes were realigned and unwarped, spatially normalised [50] to MNI standard stereotactic space, and smoothed with an isotropic Gaussian kernel of 8-mm FWHM. Statistical parametric maps were generated by modelling the evoked haemodynamic response to the stimuli and the delay period in the context of the general linear model [51].
To probe for an effect of entropy on encoding, a contrast was carried out to identify areas in which the BOLD signal in the first and second scans increased as a function of a parametric regressor based on the mean sample entropy value at each level (see Table 1). A second contrast investigated the effect of retrieval and comparison independent of encoding by subtracting the effect of encoding of the first stimulus only (corresponding to the first scan) from that to encoding of the second stimulus, retrieval of the first, and comparison of the two (corresponding to the second scan). A third contrast examined the effect of entropy on retrieval by subtracting [first scan entropy increase] from [second scan entropy increase]. Statistical results are based on a random-effects model and thresholded at p < 0.001 (uncorrected for multiple comparisons across the brain) for areas where we had an a priori prediction, i.e., PT, in addition to the same small volume correction (p < 0.05 corrected for multiple comparisons) as in Study 1. For areas that were not predicted a priori, we adopted a more conservative statistical threshold of p < 0.05 after FWE correction.
The second study was better suited to identify the three cytoarchitectonically [23] and functionally [20] distinct areas within HG based on the sound minus silence contrast because of (1) the greater number of silent trials and (2) the use of broadband stimuli. Three activations were identified in HG in either hemisphere, primarily to locate the lateral area previously implicated in perceptual pitch analysis [20,21] and to allow a comparison of the effect of entropy on activity here with that in PT (for individual coordinates see Table 2 for PT, Figure 2 for central and Figure S1 for medial and lateral HG).
Cardiac gating in Study 2 produced a reliable signal in subcortical structures IC and MGB (Figure 5). We reanalysed the data with a 4-mm FWHM smoothing kernel that is appropriate to these structures. Local maxima based on a sound minus silence contrast were identified in left IC ([–6, −34, −12]) and right IC ([6, –34, –10]) and left MGB ([–14, −26, −8]) and right MGB ([12, –24, –8]).
For further analysis considerations see Text S1, Figures S3 and S4, and Table S2.
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10.1371/journal.pbio.2000487 | Inefficiencies and Patient Burdens in the Development of the Targeted Cancer Drug Sorafenib: A Systematic Review | Failure in cancer drug development exacts heavy burdens on patients and research systems. To investigate inefficiencies and burdens in targeted drug development in cancer, we conducted a systematic review of all prelicensure trials for the anticancer drug, sorafenib (Bayer/Onyx Pharmaceuticals). We searched Embase and MEDLINE databases on October 14, 2014, for prelicensure clinical trials testing sorafenib against cancers. We measured risk by serious adverse event rates, benefit by objective response rates and survival, and trial success by prespecified primary endpoint attainment with acceptable toxicity. The first two clinically useful applications of sorafenib were discovered in the first 2 efficacy trials, after five drug-related deaths (4.6% of 108 total) and 93 total patient-years of involvement (2.4% of 3,928 total). Thereafter, sorafenib was tested in 26 indications and 67 drug combinations, leading to one additional licensure. Drug developers tested 5 indications in over 5 trials each, comprising 56 drug-related deaths (51.8% of 108 total) and 1,155 patient-years (29.4% of 3,928 total) of burden in unsuccessful attempts to discover utility against these malignancies. Overall, 32 Phase II trials (26% of Phase II activity) were duplicative, lacked appropriate follow-up, or were uninformative because of accrual failure, constituting 1,773 patients (15.6% of 11,355 total) participating in prelicensure sorafenib trials. The clinical utility of sorafenib was established early in development, with low burden on patients and resources. However, these early successes were followed by rapid and exhaustive testing against various malignancies and combination regimens, leading to excess patient burden. Our evaluation of sorafenib development suggests many opportunities for reducing costs and unnecessary patient burden in cancer drug development.
| Numerous research subjects are exposed to unsafe and/or ineffective treatments in unsuccessful drug development programs. Yet, even successful drug development programs can involve heavy burdens for research subjects. In this manuscript, we measure risks and benefits for research subjects participating in the successful development of the anticancer drug sorafenib (first approved by the United States Food and Drug Administration in 2005). After discovering the first two cancer types responding to sorafenib, drug developers and researchers tested sorafenib against many other cancer types and in combination with many other drugs. We find that researchers were able to discover the utility of sorafenib for the first two cancer types quickly and with very little patient burden. Thereafter, attempts to extend the clinical application of sorafenib to other cancers and drug combinations involved many patients and adverse events and were mostly fruitless. We also find that many studies pursued after the first approval of sorafenib returned limited scientific information because they were duplicative or insufficiently informative. Our findings suggest that even successful drug development programs can entail substantial patient burden; they also point to ways that regulators, researchers, and policymakers can improve the risk-benefit ratio for research subjects.
| In cancer, only 1 in 20 new drugs introduced to clinical development receives approval from the United States Food and Drug Administration (FDA) [1]. This high rate of attrition imposes burdens and opportunity costs on research subjects. It also consumes scarce human and material resources. Numerous studies have identified various sources of inefficiency in research, including poor priority setting, [2] biased study design, [3] underpowering, [4] and incomplete reporting [5]. Eliminating such inefficiencies holds promise for improving human protections and the social return on research investments.
Targeted therapies offer great promise for improving efficiencies and reducing burdens in cancer drug development. Indeed, targeted drugs like imatinib, sunitinib, or crizotinib have been approved for marketing on the basis of a small number of trials. Yet, little is known about total research activities and burdens for targeted drugs—especially those occurring after a drug receives its first regulatory approval. To quantify the patient burden and examine inefficiencies in cancer drug development, we undertook a systematic review of all published clinical trials for the drug sorafenib for which there was no FDA label at the time of trial launch (hereafter called “prelicensure trials”).
Sorafenib (Bayer/Onyx Pharmaceuticals) is the first multikinase inhibitor targeting RAF serine/threonine kinases and tumour vasculature [6]. Sorafenib was approved by the FDA for renal cell carcinoma (RCC) in 2005, [7] hepatocellular carcinoma (HCC) in 2007, [8] and radioactive iodine-refractory differentiated thyroid cancer in 2013 [9] and remains a current standard of care for each. In 2013, total sales were approximately €771 million [10]. Sorafenib has also frequently been used off-label, especially in patients whose tumours show alterations in relevant gene targets [11,12]. Our selection of sorafenib enabled us to capture trial activities for a novel targeted drug over 15 y, including almost a decade since initial FDA approval.
We searched Embase and MEDLINE databases on October 14, 2014, using the following search terms: “sorafenib” or “Nexavar” or variations of “BAY 43–9006,” and MeSH terms including variations of “clinical trial” or “randomized controlled trial” or other keywords associated with clinical trial design. No date restrictions were applied. Our complete search strategy can be found in the supplementary materials (see S1 Text).
We applied the following inclusion criteria: (1) primary data, (2) full-text publication, (3) English language, (4) final report, (5) interventional trials, and (6) administered sorafenib as monotherapy or in a combination treatment regimen in (7) patients with a cancer diagnosis. We excluded articles that reported the following: (1) laboratory study of ex vivo human tissues, (2) case reports, (3) expansion cohorts of previously published trials, (4) adjuvant or maintenance therapy trials (these were excluded because the main measure of benefit used in our study, objective tumour response, is inapplicable), (5) expanded access, and (6) FDA on-label studies in which enrolment began after regulatory approval for the same indication and treatment regimen. For the purposes of this study, “on label” was defined on the basis of cancer site.
We extracted articles using a previously described template [13] that captures the following fields: (1) purpose and investigator conclusions, (2) study design and funding, (3) patient characteristics, (4) treatment procedures and duration, and (5) measures of patient risk and benefit—including response, survival, and adverse events. Trials were extracted independently by two coders using Numbat meta-analysis management software, [14] and disagreements were resolved by discussion. When data were not reported, we emailed corresponding authors. The response rate for missing data queries was 58% (19/33).
The duration of treatment was determined by the period between first drug exposure and the primary endpoint. For example, if a study used progression-free survival at 6 mo (PFS6) for the primary endpoint, duration of treatment was scored as 6 mo. For all other endpoints, we imputed duration of treatment by multiplying the median number of cycles administered by cycle length. The objective response rate (ORR) was defined as the proportion of confirmed complete and partial responses according to Response Evaluation Criteria In Solid Tumours (RECIST) or alternative response criteria if RECIST response was absent. We excluded haematological malignancies from all response analyses, since response evaluation criteria are noncomparable with solid tumours.
Treatment-related grade 3 to 5 adverse events (G3–5 AEs) according to the National Cancer Institute Common Terminology Criteria for Adverse Events (CTCAE) were captured. Safety reporting was highly variable from one trial to another. To standardize the safety data across trials, we captured the most frequent treatment-related grade 3–4 adverse event reported. Grade 5 events were captured separately. Safety data were expressed as a percentage of patients with a grade 3–5 adverse event who received at least one dose of sorafenib.
A trial was categorized as “positive” if the primary efficacy endpoint demonstrated a statistically significant positive effect and the authors reported acceptable toxicity. When primary endpoints were not declared, or if results were inconclusive according to prespecified criteria or a trial had multiple primary efficacy endpoints with conflicting outcomes, trials were scored as inconclusive. A trial was deemed “negative” if it failed to reach its primary endpoint with statistical significance (i.e., null result) or had an author assessment of unacceptable toxicity. For hybrid trials (e.g., Phase I–II trials), the primary endpoint was extracted and the trial outcome was scored from the higher phase component.
We deemed trials to have “limited value” when they were (1) duplicative Phase II, (2) not appropriately followed up, or (3) had recruitment failure. All criteria for limited value were specified before analysis. We considered a Phase II trial to be potentially duplicative if it matched a previous Phase II trial in its phase, patient characteristics (based on indication, histopathology/subtype, biomarker eligibility, number of prior therapies, types of prior therapies, response to prior therapies, and disease stage), and treatment regime (dose and combination drug). We restricted this to Phase II trials only, since these trials are aimed primarily at generating hypotheses that can be tested in more rigorous randomized trials, and they do not generally use clinical endpoints (hence, pooling of multiple small studies would not enable a more precise estimate of clinical utility). Our criteria for a trial that lacked follow-up included any Phase II trial that met its primary efficacy endpoint with a recommendation from the authors to pursue further testing that we were unable to identify a follow-up randomized trial using survival endpoints in the ClinicalTrials.gov or World Health Organization (WHO) clinical trial registries as of at least 3 y since trial close. We considered these trials to have limited value because they have delivered a signal of clinical promise but their hypotheses regarding clinical value remain unresolved and thus are inadequate for guiding practice. Recruitment failure was defined as any trial (Phase I through III) that failed to reach at least 85% of its targeted enrolment for reasons other than futility or benefit, based on trial report or registration. All trials were assessed independently by two coders for “limited value” status; raw agreement was 88%. All differences were resolved by discussion [15].
The above methods were published in a previous report [13]. However, the following methodological refinements were made in analysis stages because we believed they better captured the most relevant dynamics of efficiency and patient burden: (a) all figures involving time were presented by enrolment date rather than publication date, (b) criteria for studies of limited value were more stringent, (c) we included combination trials in our analysis, (d) we probed for trends in risk and benefit of trials using cumulative meta-analysis.
Objective response rate and serious adverse event proportions with 95% confidence intervals were calculated with R version 3.2.3 [16]. Pooling of objective response rates and serious adverse event rates was done using the meta package for R, [17] using the DerSimonian and Laird random-effects method [18].
The differences in our analysis of risk (grade 3–5 adverse event rate) and benefit (objective response rate) between industry and nonindustry trial subgroups and between Phase I and Phase II trial subgroups were analysed in R version 3.2.3 [16] using a weighted regression analysis. We tested whether industry-funded studies had more favourable outcomes on the hypothesis that companies have more complete and timely access to preclinical and clinical evidence and that companies might be motivated to fund only those studies likely to produce a license. The individual trial results were weighted by the inverse variance of the rate estimate. For all inferential testing, we defined p < 0.05 to be statistically significant. As all inferential testing was exploratory, we did not adjust for statistical multiplicity.
To show how risks and benefits evolved during sorafenib development, we plotted cumulative rates of grade 3–5 adverse events and overall responses by trial phase. Cumulative effect estimates were calculated using the DerSimonian and Laird random-effects method.
To determine where burdens are most concentrated and to probe for research inefficiencies, we plotted Accumulation of Evidence and Research Organization (AERO) diagrams representing indication in the vertical axis and year of first patient enrolment in the horizontal. Each node represents a clinical trial categorized by trial phase (node shape) and primary endpoint attainment (node colour).
Survival data were collected from experiments with any nonsorafenib comparator arm in which survival endpoints included all patients. Hazard ratios were either reported in the study or were calculated from summary time-to-event data using formulas from Tierney et al [19].
No patients were involved in setting the research question or the outcome measures, nor were they involved in the design or implementation of the study. Since we used data from previous clinical trials, we are unable to disseminate the results of the research to study participants directly.
Our search captured 203 clinical trials, of which 74 administered sorafenib as monotherapy [20–93] and 132 tested sorafenib in combination with another anticancer therapy; [43,55,66,94–190] 3 trials tested sorafenib as both monotherapy and in a combination regimen [43,55,66]. As a whole, 11,355 patients were exposed to sorafenib in prelicensure trials in 26 malignancies, with an estimated 3,928 patient-years of involvement over a period of 13.2 y. A total of 4,988 patients received sorafenib as monotherapy, and 6,367 patients received sorafenib in combination therapy. The properties of trials in our sample are listed in Table 1, and a PRISMA flow diagram can be found in S1 Fig.
In total, 1,486 patients receiving sorafenib experienced objective tumour response (12.1%, 95% CI 10.4% to 14.1%), and 108 died from drug-related toxicities (2.2%, 95% CI 1.9% to 2.5%). A minimum of 2,618 patients experienced grade 3–4 drug-related serious adverse events (24.1%, 95% CI 21.7% to 26.6%).
For monotherapy, 281 patients showed objective tumour response (6.8%, 95% CI 5.2% to 8.7%), and 23 patients died from drug-related toxicities (1.4%, 95% CI 1.1% to 1.8%); a minimum of 748 patients experienced grade 3–4 drug-related toxicities (15.3%, 95% CI 13% to 17.9%). In combination therapy, 1,205 patients experienced objective tumour response (17.0%, 95% CI 14.5% to 19.8%), and 85 patients died from treatment-related toxicity (2.6%, 95% CI 2.2% to 3.1%). A minimum of 1,870 patients experienced grade 3–4 drug-related toxicities (30.5%, 95% CI 27.2% to 34.0%).
To show how risks and benefits evolved during sorafenib development, we plotted cumulative rates of grade 3–5 adverse events and objective responses for monotherapy (Fig 1) and combination therapy (Fig 2), by trial phase. Risk-benefit ratios remained relatively stable over the course of development in both monotherapy and combination therapy. We plotted AERO diagrams to show trial activities as a function of time and malignancy for monotherapy (Fig 3) and combination therapy (Fig 4). The apparent tapering of activities after year 2009 actually reflects that many trials now initiated have not yet published results and thus do not appear on the AERO diagram (on average, the interval between trial initiation and publication was 5 y). Optimal dose and schedule were identified in the earliest Phase I trials initiated in 2000 [20–23]. Initiation of two Phase II monotherapy trials occurred in 2002, each identifying an indication (renal cell carcinoma [40] and hepatocellular carcinoma [33]) that went on to receive licensure. At this time, sorafenib testing had accrued 5 drug-related deaths (4.6% of 108 total) and 93 total patient-years of involvement (2.4% of 3,928 total).
With the pivotal renal cell carcinoma trial underway, the exploration of new indications surged in 2004 and 2005—with 27 Phase II trials launched across 14 new indications (Figs 3 and 4)—and diminished thereafter. Two of these “exploratory” studies achieved statistical significance on their primary efficacy endpoint [53,61], one of which recommended further investigation of sorafenib monotherapy in non-small-cell lung carcinoma (NSCLC) [61]. From 2006 onwards, the majority of new trials investigated sorafenib in a combination regimen, with especially intensive investigation (>5 efficacy trials) of 5 indications: melanoma, ovarian cancer, breast cancer, non-small-cell lung carcinoma, and pancreatic cancer. Testing in these 5 indications comprised 56 drug-related deaths (51.8% of 108 total) and 1,155 patient-years (29.4% of 3,928 total) of burden in unsuccessful attempts to discover clinical utility. In total, sorafenib was tested in 26 indications and 67 drug combinations. Currently, sorafenib has not received an FDA label for any combination therapy, consistent with the unsuccessful Phase III trials in Fig 4. Fig 5 shows adverse events and number of patients enrolled as a function of time with landmark events.
The pivotal RCC Phase III trial [41] did not demonstrate a statistically significant advantage on its prespecified primary endpoint of overall survival (OS). This might have been due to early crossover; sorafenib was licensed on the basis of a significant progression-free survival advantage.
In HCC, the pivotal trial [35] identified coprimary endpoints of overall survival and time to symptomatic progression (TTSP). However, only overall median survival met the prespecified criteria for statistical significance, and the trial was represented as inconclusive with respect to the primary endpoint in Fig 3. The parallel Phase III study, designed for regulatory submissions in Asia [36], explicitly excluded a primary endpoint from the protocol and was also scored inconclusive on the AERO diagram (Fig 3). This study also reached statistical significance for overall survival but not time to symptomatic progression.
The most recent FDA approval occurred in November 2013 for metastatic differentiated thyroid cancer refractory to radioactive iodine treatment [9]. This was on the basis of a successful Phase III trial showing improved progression-free survival over placebo [74]; this trial did not demonstrate a statistically significant survival advantage.
Analysis of overall survival revealed significant benefit against comparators in hepatocellular carcinoma in one monotherapy [35] and one combination trial [126] (Fig 6). For all other indications and combinations, exposure to sorafenib was neither demonstrably advantageous nor disadvantageous for patients, according to prespecified thresholds of significance. There was no obvious trend towards sorafenib disadvantage.
There was a significant improvement in objective response rate between Phase I and Phase III trials testing sorafenib in a combination regimen (15.5% and 22.4%, respectively; p < 0.05). Phase III trials testing sorafenib monotherapy reported a significantly lower grade 3–5 adverse event rate than Phase II trials (10.4% versus 16.7%, respectively; p < 0.05). All other analyses of objective response rate and grade 3–5 adverse events by funding source or trial phase were not significantly different between subgroups (Fig 7).
In total, 32 Phase II trials of limited value were identified (26% of Phase II trials), representing 1,773 patients and 701 y of patient involvement (15.6% and 17.8% of total prelicensure trial activity, respectively). In these trials, there were 367 grade 3–4 sorafenib-related adverse events and 9 patient deaths (14.0% of 2,618 adverse events and 8.3% of 108 deaths in sorafenib testing, respectively). Among the 1,717 patients in these studies evaluable for response in solid tumours, 269 achieved objective tumour response (18.1% of 1,486 responses in sorafenib testing).
Out of 124 Phase II studies, 10 were deemed potentially duplicative (8.1%) (S2 Table). In 14 instances, Phase II trials with a positive primary efficacy endpoint and author recommendation for further testing were not followed up in randomized trials testing survival (11.3%). Ten Phase II studies failed to accrue a sample sufficient to address the primary objectives of the study (8.1%; see S2 Text for details).
In this study, we extend our previous work exploring efficiency and burden in drug development. Consistent with our earlier reports looking at the anticancer drug sunitinib [13], we find that drug developers identified useful dosing, scheduling, and responding indications in a short amount of time with only a minimal number of trials, adverse events, or patient years. This suggests that drug developers can be highly effective at detecting signals of clinical promise using preclinical and preliminary clinical evidence—especially when there is a strong market incentive for efficiency.
However, once indications responding to sorafenib were identified, many indications and combination regimes were tested in rapid succession, leading to an accumulation of burden with greatly diminished return, as measured by FDA label revisions or confirmed clinical value. Cumulative risk and benefit analyses showed that in over a decade of drug development, the risk-benefit ratio remained relatively stable. This suggests that patients were not necessarily exposed to more unfavourable conditions with persistent testing but also that drug developers and clinical research groups were not able to mitigate risk or hone their ability to unlock greater clinical utility. Our analysis also found that patients allocated to sorafenib arms in randomized trials were generally neither advantaged nor disadvantaged in terms of survival (note that our pooled estimate may somewhat underestimate survival advantage because of crossover in HCC and RCC studies). This suggests that patient burdens entailed by this pattern of rapid-fire exploration are at least not associated with survival disadvantage for patients receiving sorafenib.
Our results also suggest that, while drug developers are adept at identifying and confirming correct hypotheses about clinical utility, researchers are slow to curtail attempts to discover new responding indications or to abandon hypotheses that flag in testing. Regarding the former, attempting to extend the label of a drug seems appropriate—especially where a drug has shown activity against two discrete malignancies. Elsewhere, we have argued that translation trajectories must contain at least some negative trials in order to furnish health care systems with adequate evidence [223]; not testing sorafenib against other plausible malignancies would have left oncologists uncertain about additional applications of the drug. However, our findings suggest that researchers were unable to join preclinical evidence together with a rapidly expanding clinical evidence base to extend the label of sorafenib. For a systematic review of preclinical evidence relating to monotherapy trials in this paper, see Mattina et al. 2016 [224]. It is possible that new adaptive trial designs might offer more efficient ways of ruling out nonresponding indications.
Reluctance to abandon hypotheses is illustrated by the highly perseverant and, until now, futile attempts to apply sorafenib to indications like non-small-cell lung carcinoma, breast cancer, and glioma. One potential explanation for such perseverance is clinical demand. Treatment of glioma has improved little in the last decade, and researchers might be willing to launch new glioma trials even if their confidence in promise is meagre. A second possibility is market demand: if the potential commercial returns are sufficiently large, drug companies may be willing to pursue trials that have marginal prospects of success. Third, seeming perseverance may reflect that many researchers test nearly identical hypotheses simultaneously, thus limiting the ability of researchers to build on insights. Favouring this interpretation is the fact that the periods of clinical testing for all trials deemed duplicative in our analysis overlapped. On the other hand, many trial activities for highly tested indications such as breast cancer did not overlap in terms of time period. Last, researchers may be prone to cognitive biases by which evidence confirming a molecular hypothesis is given greater weight than evidence disconfirming it. Sorafenib was tested against melanoma, generally on the conviction that sorafenib can interrupt disease progression through the MAPK pathway. However, seven trials that were founded on this premise had negative outcomes, resulting in potentially excess exposures [46,48,160,161,165,168,169]. The pattern of testing we observed is consistent with a dynamic in which researchers embrace findings when they support a pathophysiological premise but doubt their veracity when results conflict with favoured pathophysiological premises. The possible effects of cognitive biases on clinical development decisions deserves further exploration.
Our analysis raises a natural question: at what point should researchers discontinue attempts to extend a drug’s label to other indications and drug combinations? Reasonable people might disagree. The fact that cancer is a life-threatening illness would favour the kind of exhaustive testing we observed, as would the prospect that a novel multikinase inhibitor like sorafenib might show activity against a variety of malignancies. Under conditions of high uncertainty, it could make sense to launch many “hypothesis-generating” Phase II trials [225]. In our view, however, several points would caution against the extensive and perseverant testing we observed after licensure. First, the nontrivial toxicities associated with sorafenib—especially when combined with other drugs—would demand grounding any new trial in a good evidentiary rationale. Second, the fact that sorafenib is a targeted drug and that drug developers were so adept at selecting viable hypotheses early on should favour a higher proportion of successes in extending the label of sorafenib. Third, further testing of a drug ties up human and material resources that might be applied to other research programs. In a realm like cancer, where there is a dense pipeline of novel drug candidates and pressing clinical need, these opportunity costs should encourage judicious launch of new trials [225].
Our findings identify other opportunities to decrease patient burden and improve research efficiency. According to the criteria we used, 26% of prelicensure Phase II trials of sorafenib had limited value because they were duplicative, not followed up as appropriate, or unable to recruit a critical mass of subjects. To be sure, that a study is destined to have “limited value” is not always knowable at trial outset. Yet, knowing the volume of studies that—in retrospect—had limited value can motivate development of mechanisms and practices that reduce their occurrence. For instance, duplicative trials were always conducted during overlapping time periods; researchers, ethics committees, and data safety monitoring bodies should monitor trial registries for identical studies that have already started recruitment, and reevaluate risk and benefit when there is duplication. Others have observed that many positive, exploratory studies are not followed up with rigorous testing—particularly in combination trials [226]. It may seem counterintuitive to label any “positive” trial as having “limited value.” However, a large percentage of drug prescriptions in cancer are off-label, [227,228] and in numerous cases, positive surrogate responses that have not been promptly followed up with rigorous testing have led to adoption of treatments that were later shown ineffective and harmful [229–231]. Less duplication and better follow up of promising findings might be achieved by improving the flow of information and coordination among research teams. Trials with unsuccessful accrual are less likely to meet the statistical power needed to answer their primary question and impose unnecessary burden on patients and resources in the process. Ethics committees, investigators, and data safety monitoring bodies should closely monitor the design, recruitment, and feasibility of meeting accrual goals throughout a trial to mitigate risks from slow accrual [15].
Our findings should be interpreted in light of the following limitations. First, this analysis focused solely on published trial reports and is thus susceptible to the effects of publication bias. Because of labour limitations, for example, we did not search clinical trial registries for completed but unpublished studies. However, incorporating unpublished studies would not be expected to improve the favourability of risk-benefit ratios or to alter the temporal dynamics we observed. Second, this analysis used objective response rate in solid tumours as a proxy for benefit and treatment-related serious adverse events as a proxy for patient burden. We used response because it allows comparison of benefit across solid tumours and trial phases. However, response is a surrogate endpoint. Moreover, sorafenib is cytostatic in many malignancies, and clinical benefit may not be reflected in objective response rate. Treatment-related adverse events were also inconsistently reported—many studies only reported adverse events occurring in over a certain percentage of patients. Our measure of risk likely underestimates the real estimates. Third, some might question our criteria for “limited value” trials. Our definition of duplication probably excluded trials that many would consider duplicative or overlapping or that a meta-analyst would consider similar enough to aggregate. Indeed, several studies we classified as distinct using our criteria would almost certainly be viewed by many oncologists as substantially overlapping if not identical (see S2 Text). On the other hand, there may be grounds for believing some studies we classified as duplicative are distinct because they enrolled different demographic groups. As well, we note that even studies deemed to have “limited value” yield some information. For example, studies that are underpowered because of recruitment failure might be combined with similar studies in a meta-analysis. However, this places the onus of deriving scientific insights on meta-analyses that might or might not materialize; the important question is whether the value of this information is sufficient to purchase their patient burden and opportunity costs. Also, studies that have “limited value” might sometimes be beyond the control of sponsors or research teams. If the standard of care shifts radically, studies might close for low recruitment or positive Phase II studies might not have follow-up. It is therefore impossible to exclude that some studies classified as having limited value reflect dynamism elsewhere in cancer drug development. Last, the ethical implications of our findings need to be interpreted against the fact that the vast majority of trials in our sample enrolled patients with refractory disease. Nothing in our analysis would suggest such patients were deprived of standard of care or that enrolment was associated with survival disadvantage. Nevertheless, even when patients have advanced disease, the imposition of burdens like side effects and time commitment should be grounded in a compelling biological rationale.
Our findings—when interpreted alongside the other drug development trajectory we analysed—suggest that drug developers can marshal preclinical and early trial evidence to discover a drug’s utility very efficiently. Inefficiencies, costs, and burdens accumulate later in drug development, and the majority of patient burden—however measured—accumulates after successful interventional strategies have been discovered. The patterns we observe suggest that a combination of commercial considerations, poor coordination, time pressures, and cognitive biases may distort trial decision making, resulting in substantial excess burdens and resource demands.
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10.1371/journal.pntd.0003607 | Predominant Leptospiral Serogroups Circulating among Humans, Livestock and Wildlife in Katavi-Rukwa Ecosystem, Tanzania | Leptospirosis is a worldwide zoonotic disease and a serious, under-reported public health problem, particularly in rural areas of Tanzania. In the Katavi-Rukwa ecosystem, humans, livestock and wildlife live in close proximity, which exposes them to the risk of a number of zoonotic infectious diseases, including leptospirosis.
A cross-sectional epidemiological study was carried out in the Katavi region, South-west Tanzania, to determine the seroprevalence of Leptospira spp in humans, domestic ruminants and wildlife. Blood samples were collected from humans (n = 267), cattle (n = 1,103), goats (n = 248), buffaloes (n = 38), zebra (n = 2), lions (n = 2), rodents (n = 207) and shrews (n = 11). Decanted sera were tested using the Microscopic Agglutination Test (MAT) for antibodies against six live serogroups belonging to the Leptospira spp, with a cutoff point of ≥ 1:160. The prevalence of leptospiral antibodies was 29.96% in humans, 30.37% in cattle, 8.47% in goats, 28.95% in buffaloes, 20.29% in rodents and 9.09% in shrews. Additionally, one of the two samples in lions was seropositive. A significant difference in the prevalence P<0.05 was observed between cattle and goats. No significant difference in prevalence was observed with respect to age and sex in humans or any of the sampled animal species. The most prevalent serogroups with antibodies of Leptospira spp were Sejroe, Hebdomadis, Grippotyphosa, Icterohaemorrhagie and Australis, which were detected in humans, cattle, goats and buffaloes; Sejroe and Grippotyphosa, which were detected in a lion; Australis, Icterohaemorrhagie and Grippotyphosa, which were detected in rodents; and Australis, which was detected in shrews. Antibodies to serogroup Ballum were detected only in humans.
The results of this study demonstrate that leptospiral antibodies are widely prevalent in humans, livestock and wildlife from the Katavi-Rukwa ecosystem. The disease poses a serious economic and public health threat in the study area. This epidemiological study provides information on circulating serogroups, which will be essential in designing intervention measures to reduce the risk of disease transmission.
| Leptospirosis is a disease of worldwide significance, and it is also an important zoonotic disease, particularly in developing countries. Subclinically infected rodents maintain leptospires in nature, and some that recover from the primary leptospiral infection may release the bacterium in their urine for the rest of their lives. These rodents serve as a potential source of leptospiral infection to animals and humans. Non-rodent mammals can also be reservoirs of leptospiral infection to animals and humans. Globally, animal and human leptospirosis has been attributed to rodents. There is limited knowledge on the occurrence of the disease in domestic animals, humans, wildlife and rodents in many parts of Tanzania, including the Katavi-Rukwa ecosystem. Serological examination of cattle, goats, humans, buffaloes, zebra, lions, rodents and shrews in the ecosystem revealed the presence of antibodies to serogroups Sejroe, Hebdomadis, Grippotyphosa, Icterohaemorrhagie, Australis and Ballum. These serogroups infect not only their usual hosts but also other animal species, which can in turn act as reservoirs of these serogroups to other animals and humans. This study demonstrates the distribution of leptospiral serogroups in domestic animals, humans, wildlife, rodents and shrews in the Katavi-Rukwa ecosystem. The results of the current study will help in developing appropriate interventions for preventing or mitigating the impacts of infections in domestic animals, humans, wildlife, rodents and shrews. Our results also suggest that human and animal populations are at risk of contracting the infection.
| Leptospirosis is an emerging/re-emerging, worldwide, contagious, bacterial zoonotic disease that affects all mammals, including humans, livestock and wildlife [1, 2]. The disease is caused by different serovars of pathogenic species of the genus Leptospira [1, 2], which is common in tropical and subtropical regions, wherever environmental conditions favour the survival and transmission of the bacterium [3, 4]. Leptospirosis was first identified by Weil (1886) and Inada (1916) [5]. In the East and Central African regions, the disease was reported three decades ago [6]. The sources of infection for humans and other incidental hosts, such as cattle, pigs, horses, and companion animals, are subclinically infected wild and domestic animals, which are the reservoirs for over 250 known serovars of Leptospira [7]. Rodents are the most important source of infection for humans and animals [8, 9]. The role of rodents as carriers and the main source of leptospiral infection in human has been investigated in some countries. Moreover, different species of rodents, such as Rattus, R. norvegicus, Mus musculus, Bandicota bengalensis, Bandicota indica and Cricetomys gambianus, are known to carry different pathogenic leptospiral serovars [8, 9]. Leptospira spp lives for a long time in the kidney tubules of an infected animal host, from where they are excreted through the urine [10]. Humans become infected through either direct contact with the urine or other biological materials from the infected animals or indirect contact with water, soil and vegetation polluted with urine from animals harbouring pathogenic leptospires [11]. Leptospirosis is also an occupational disease affecting veterinarians, abattoir workers, sewer workers and other groups of people whose job exposes them constantly to contaminated materials [12]. A serological assay, the Microscopic Agglutination Test (MAT), is considered as the gold standard for the diagnosis of leptospiral infection [12]. The test is used to detect antibodies against different Leptospiral serovars.
Previous reports from Tanzania have indicated that leptospiral infection is widely prevalent in humans, livestock, and rodents in some parts of the country [13, 5, 14, 15, 16, 17, 18, 7]. However, a study on leptospirosis in the Katavi region has not been conducted, suggesting that the role of animals in the transmission and maintenance of the infection is not well understood. Hence, the objective of this study was to establish the seroepidemiology of Leptospira spp and to identify the most prevalent leptospiral serogroups in humans and animals using the Microscopic Agglutination Test (MAT).
The study was carried out between September 2012 and April 2013 in the Katavi region, southwest Tanzania, which is an agro-pastoral community with a wide range of domestic animals and wildlife. The Katavi region is located approximately 6° 30’S and 31° 30’E. All the districts in the Katavi region, namely Mpanda, Nsimbo, and Mlele, (Fig. 1) were involved in this study. Katavi has a tropical climate, with a rainy season from November to April and a dry season from May to October. During the rainy season, rainfall can be extremely high, with a mean annual rainfall greater than 100 mm. The economic activities of the people in the Katavi region are mainly livestock keeping and small-scale farming. For livestock keeping, Katavi residents practice free-range grazing, and for small-scale farming, they cultivate both food and cash crops. During the dry season, the agro-pastoralist graze animals on crop residues, and thereafter, they shift these animals to distant grazing land, commonly known as grazing camps. Different habitats are selected for trapping rodents, such as, plough fields, tiny bushes around homes, marshy areas for the cultivation of rice and sugar, vegetable gardens, and areas with garbage close to homes and within homes.
Katavi contains national parks, such as Katavi National Park, which is composed of seasonally flooded grassland plains, miombo woodlands, small lakes, and swampy wetlands [19]. Wild animals commonly found in the park include African buffaloes (Syncerus caffer), elephants (Loxondata africana), zebras (Equus burchelli), impalas (Aepyceros melampus), giraffes (Giraffa Camelopardalis), elands (Taurotragus oryx), baboons (Papio anubis), hippopotamuses (Hippopotamus amphibious), and predators, such as lions (Panthera leo) and other small carnivores [20].
The ethical clearance for conducting this study was granted by the Institutional Review Board of Sokoine University of Agriculture (SUA/FVM/R.1/9), Medical Research Coordinating Committee of the National Institute for Medical Research, reference number NIMR/HQ/R8a/Vol.IX/1627, and the Tanzania Wildlife Research Institute (TAWIRI). Additionally, permission was requested and granted from all local authorities in the study area, including TANAPA and the Local Government Authority. Verbal consents were obtained from all the study participants. To safeguard the wellbeing of animals, this study adhered to Animal Welfare Act [21], as well as the guidelines adopted from the Australian government [22].
A cross-sectional epidemiological study was carried out, in which a multistage cluster sampling was conducted. Villages were randomly selected as the primary unit, from which a total of 138 households were chosen from the list of agro-pastoralists using random numbers. Members of the selected households were subjected to a random selection to obtain a total of 267 humans who were readily available, regardless of their health status. Our target was households with domestic animals. Thus, using cluster sampling, a total of 1351 apparently healthly livestock were selected from the same households where humans were sampled, as described above [23]. Calves and kids below three months and children below the age of two years were not sampled. Wild animals were also targeted for this study, and a total of 42 of these animals were sampled opportunistically. Rodents and shrews were trapped from the sites located near human settlements and near human activities, including homes and crop stores in open fields where a large number of rodent burrows were observed. A total of 207 live rodents and 11 shrews were captured using Sherman LFA Live traps (7.5 × 9.0 × 23.0 cm; HB Sherman Traps, Inc., Tallahassee, FL). The traps were placed inside and surrounding the selected houses early in the evenings, and peanut butter mixed with concentrates was used as bait. The traps were inspected every morning, and the captured rodents were anaesthetized using ether in cotton swabs before taking samples. The sex, species, weight, age, and location of the trappings of the captured rodents and shrews were identified and recorded. The rodents and shrews were grouped into two classes based on age (juvenile and adult), as previously described [24].
Human blood samples were collected from the brachial vein using 5 ml plain vacutainer tubes. Cattle and goats were manually restrained, and blood samples were collected from the jugular vein using 10 ml plain vacutainer tubes. For wild animals, buffaloes were captured by darting using a combination of 5–8 mg etorphine hydrochloride (M99 9.8 mg/ml) (Novartis, Kempton Park, South Africa) and 50–80 mg azaperone tartarate, and zebras were immobilized using a combination of 6–7 mg etorphine hydrochloride (M99) and 80 mg azaperone. Lions were immobilized using a combination of 2.5 mg/kg ketamine hydrochloride and 0.1 mg/kg medetomidine hydrochloride (Kyron, Pty, SA). The drug was remotely injected using a darting gun. The antidote, diprenorphine hydrochloride (M5050) (Novartis, Kempton Park, South Africa), was used to revive the buffaloes and zebras after the collection of the blood samples. Lions were revived using antisedan (atipemazole hydrochloride). The blood from these animals was collected from the jugular vein using 10 ml plain vacutainer tubes. The blood samples from both domestic and wild animals were allowed to clot in a slanted position, and serum samples were harvested after 24 hours.
For rodents and shrews, blood was collected from the retro orbital sinus using sterile capillary tubes and then transferred to eppendorf tubes. The samples were centrifuged, and sera were immediately harvested.
The sera harvested from domestic animals, wildlife, rodents and shrews were dispensed into appropriately labelled 1.5 ml cryovials and stored in liquid nitrogen (−78°C) before being transferred to the Faculty of Veterinary Medicine, Sokoine University of Agriculture laboratories and stored in an ultra-deep freezer (−80°C) until a subsequent MAT was performed.
Seven leptospiral serogroups, including local isolates, Icterohaemorrhagie (Leptospira interrogans serovar Sokoine), Australis (Leptospira interrogans serovar Lora), Ballum (Leptospira borgpetersenii serovar Kenya) and Grippotyphosa (Leptospira kirschneri serovar Grippotyphosa), and reference serogroups, Sejroe (Leptospira interrogans serovar Hardjo), Hebdomadis (Leptospira santarosai serovar Hebdomadis) and Canicola (Leptospira interrogans serovar Canicola), which are commonly found in Tanzania, were used in the study.
All sera were tested for antibodies against live antigens suspensions of Leptospira spp serogroups Icterohaemorrhagie (Sokoine), Australis (Lora), Ballum (Kenya), Gripotyphosa (Grippotyphosa), Sejroe (Hardjo), Hebdomadis (Hebdomadis) and Canicola (Canicola) by MAT, as previously described by [25] and [26]. Briefly, the sera (10 μl) were diluted with phosphate buffered saline (PBS) to obtain 100 μl of diluted sera in ‘U’ microtitration plates to obtain an initial dilution range of 1:20–1:160. Then, 50 μl of the full-grown antigens in Ellighausen—mcCoullough/Johnson-Harris (EMJH) with an approximate density of 3*108 leptospires/ml on the MacFarland scale was added to all microtiter plate wells and mixed thoroughly on a microshaker. The microtitration plates were then incubated at 30°C for two hours. The serum antigen mixture was visualized under dark field microscopy for the presence of agglutination/clearance, and the titers were then determined. A serum was considered positive if 50% or more of the microorganisms in the microtiter well were agglutinated at the titer ≥ 1: 160. This was determined by comparing 50% of spirochaetes, which remained free cells with a control culture diluted 1:2 in phosphate-buffered saline [26]. In this study, we examined the positive and negative controls and selected the samples that agglutinated more than halfway through, as previously described by International Committee on Systematic Bacteriology [27]. The samples that agglutinated were identified during the screening of 1:160 dilutions, the numbers were recorded, and the sera were further diluted to determine the end point titer for each sample. The agglutinating sera were tested again at dilutions of 1:20, 1:40, 1:80, 1:160, 1:320, 1:640, 1:1,280, 1:2,560, 1:5,120, 1: 10,240 and 1:20,480. Negative and positive controls were included in each test. Phosphate-buffered saline (PBS) was used as a negative control. As a negative control, an equal (50 μl) volume of PBS was mixed with the different antigens. The positive control used in this study was rabbit antiserum of each specific serogroup. The positive control antiserum was supplied by the WHO Reference laboratory at the Royal Institute of Hygiene (KIT), Amsterdam, Netherlands. We used seven different positive control antisera from rabbit to test samples from animals and humans, regardless of the species tested.
Microsoft Office Excel® 2007 (Microsoft Corporation, Redmond, 98052-7329, USA) was used for storing data and drawing graphs. The prevalence of leptospiral antibodies was computed using Epi-Info version 7 (CDC Atlanta, USA). The proportions were compared using MedCalc® version 13.0.2 (MedCalc software, Acacialaan 22, B-8400, Ostend, Belgium).
The overall prevalences of leptospiral antibodies in human, domestic ruminants, wildlife, rodents and shrews were 29.96%, 26.35%, 28.57%, 20.29% and 9.09%, respectively. The specific prevalences of leptospiral antibodies in human and in different animal species are indicated in Fig. 2. One of the two sampled lions was seropositive. Statistical analysis of the results for cattle and goats demonstrated a significant difference in seroprevalence between the two species (difference 21.90%, 95% CI = 16.93–26.12, P<0.0001). No leptospiral antibodies were detected in zebra (n = 2). The association of leptospiral infection with sex and age in humans, cattle, goats, rodents, and shrews was not statistically significant (P > 0.05).
In this study, leptospira antibodies were detected in 42 (20.29%) out of 207 apparently healthy rodents tested. Additionally, 11 shrews were tested, and one (9.09%) was found positive. One shrew and seven rodent species were found positive, with varying prevalence among species. The prevalences of leptospira antibodies among different rodent and shrew species are shown in Table 1. The geographical distribution of leptospiral antibodies were based on the MAT results and are shown in Fig. 1.
The proportions of seropositive individuals exposed to different serogroups, for humans, domestic ruminants and rodents, are presented in Table 2. Australis was the only serogroup exposed to shrews. In buffaloes, the detected antibodies specific for the serogroups were Sejroe (7.89%), Hebdomadis (7.89%), Australis (5.26%), Grippotyphosa (5.26%), and Icterohaemorrhagie (5.26%), and antibodies against Sejroe and Grippotyphosa were detected in one of the two lions. The results also showed that samples from 13 humans, 63 cattle, two goats, and two rodents reacted to more than one serogroups (Table 3). In buffaloes, the three positive samples showed serological cross-reactions with two serogroups, specifically, Icterohaemorrhagie and Sejroe, Australis and Grippotyphosa and Hebdomadis and Icterohaemorrhagie. With regards to the lion, the positive sample showed cross-reactions of serogroups Sejroe and Grippotyphosa. The distributions of the different serogroups among the seropositive humans, domestic ruminants, wildlife, rodents and shrews are shown in Fig. 3.
The findings from this study indicate that leptospiral antibodies are prevalent in the Katavi-Rukwa ecosystem, as the antibodies were detected in humans, cattle, goats, buffaloes, lions, rodents and shrews. This is the first report of leptospira seroprevalence linking humans and animal infections in Tanzania. The demonstration of the exposure of these animals and humans at the same time provides a significant and important epidemiological picture and increases our understanding of infection patterns of leptospiral serogroups at the interface areas. Previous studies demonstrated that the seroprevalence of leptospira in healthy animals suggests levels of local exposure [28]. Animals with low prevalence of leptospira antibodies might be a significant cause of infection in humans, and high seroprevalence may signify exposure pressure from different animals and thus a high infection risk in humans as well [28].
In domestic animals, the highest seroprevalence was observed in cattle, as opposed to goats, and the difference was statistically significant (P<0.0001). The observed difference can be attributed to the feeding behaviour of goats, specifically, grazing on the top end of grasses, browsing on shrubs and staying in less wet areas, as opposed to cattle. As such, they have less exposure to leptospires [29]. In the present study, no significant difference in seroprevalence according to age was established in humans and in other animal species. The study results demonstrate that leptospirosis is endemic in the study area. This implies that all age groups face equal risk of being infected by leptospires. This finding is in agreement with the observation made by other researchers [30].
In humans, the serogroup Sejroe (serovar Hardjo) was the predominant serogroup, followed by Icterohaemorrhagie. Other prevalent serogroups were Grippotyphosa, Hebdomadis, Ballum and Australis. The predominance of serogroup Sejroe (serovar Hardjo) in humans can be attributed to the high contact rate with cattle, which are widespread in areas where human subjects were sampled for this serosurvey. Cattle are known to be natural hosts for serovar Hardjo, and the spirochete can survive in cattle for years [31]. Interactions between humans and cattle can lead to the interspecies transmission of serovar Hardjo. The seropositivity of serogroup Icterohaemorrhagie (serovar Sokoine) and Grippotyphosa in humans can be attributed to the abundance of rodents in the study area, as rodents are the natural carriers of these serogroups [31].
Antibodies to serogroup Ballum (serovar Kenya) were detected in humans but not in the sampled animal species in the ecosystem. The serogroup was previously isolated from urine of African giant pouched rat (Cricetomys gambianus) from Morogoro, Tanzania [14]. The seropositivity of the serogroup Ballum in humans may be due to the presence of African giant pouched rats in the study area, which may serve as a potential source of the serogroup to humans due to contamination of the environment with urine.
The main serogroup identified in cattle was Sejroe (serovar Hardjo). This finding corresponds well with findings in previous published reports that showed that cattle are the maintenance host of this serogroup [5, 18]. However, studies conducted in different areas of Tanzania have reported significant lower seroprevalence (5.6%) than what was observed in the current study [5]. The observed difference in the results between the current and the previous studies is likely due to variations in ecological factors, such as humidity, climate, and environmental factors [32, 33], as well as a variation in the level of interaction with other animals in the study area. Serovar hardjo is considered to be an important cause of bovine leptospirosis, which, in most cases, has been associated with abortion in cattle and has also been the most common cause of leptospiral infection in humans, due to the possibility of high rates of interaction between cattle and humans [31]. Other serogroups detected in cattle, such as Hebdomadis, Australis, Grippotyphosa and Icterohaemorrhagie, are accidental infections that are carried by other domestic and free range animals, and which are dependent on farm management practices, as described elsewhere [34].
Icterohaemorrhagie, Sejroe (serovar Hardjo) and Grippotyphosa were the most prevalent serogroups observed in goats. Similarly, rodents are known to be the natural reservoir hosts for the serogroups, Icterohaemorrhagie and Grippotyphosa [31]. Therefore, the high prevalence of these two serogroups in goats implies that there is probably high rate of interaction between goats and rodents in the study area. Furthermore, the results suggest interaction between these animals and humans, as the same serogroups were detected in human samples in the same interface (Table 2). The serogroups Icterohaemorrhagie (serovar Sokoine) and Sejroe (serovar Hardjo) have previously been reported to be among the most important occupational diseases in and around Tanga city, eastern Tanzania [35].
Antibodies to different leptospiral serogroups were detected in seven different species of rodents (Table 1) trapped in various areas of the Katavi-Rukwa ecosystem, suggesting that rodents are probably the carriers of different leptospiral serogroups, therefore exacerbating transmission of leptospiral infection to humans and animals in the ecosystem. Serogroup Australis had the highest seroprevalence (18.84%), followed by Icterohaemorrhagie (1.93%) and Grippotyphosa (0.48%), in the tested rodents in the study area. These findings are in agreement with the findings in previous studies conducted in different parts of Tanzania [11, 7]. Interactions among rodents, humans, domestic ruminants and wildlife occur frequently in the study area, as rodents share the same habitat with these animals and humans. These interactive activities of rodents in the study area create a favourable environment for leptospiral transmission from rodents to humans, domestic animals and wildlife. Australis was the only serogroup exposed to shrews, with a seroprevalence of 9.09%. Exposure to the serogroup was also detected in both rodents and shrews (insectivores). This study was not able to demonstrate whether shrews were the maintenance host for this serogroup or if the serogroup was transmitted to shrews from rodents. This lack of clear understanding of the maintenance hosts may require further studies in this area, as it poses a major public health risk.
In buffaloes, the predominant serogroups were Sejroe (serovar Hardjo), Hebdomadis, Australis, Grippotyphosa and Icterohaemorrhagie. The seropositivity against serogroup Serjoe (serovar Hardjo) as a predominant serogroup is probably due to high interaction with cattle, which are the maintenance host of the serogroup [36]. Buffaloes are also a reservoir for Hardjo. Hence, the possibility of transmission of the serovar from cattle to the buffaloes and vice versa is very high. The prevalence of serogroup Grippotyphosa and Icterohaemorrhagie in buffaloes may be attributed to the high rate of interaction between buffaloes and rodents in the area because rodents are the carriers of the serogroups [33]. As noted earlier, the leptospiral serogroups found in the sampled buffaloes were similar to the serogroups detected in cattle. The presence of a wide range of buffaloes in Katavi allows cattle and buffaloes to share grazing grounds and watering points. Hence, the possibility of transmission or spillover of serogroups from cattle to buffaloes and vice versa is very high. A similar observation was also reported in Turkey, where researchers found a similar leptospirosis seroprevalence in buffaloes as that observed in cattle [36]. In this study, Sejroe (serovar Hardjo) and Grippotyphosa were the only leptospira serogroups detected in lions (only two lions were sampled and only one was seropositive). These same serogroups were also found in buffaloes and domestic ruminants. This may be attributed to the feeding behaviour of lions that prey on wild and domestic ruminants.
The current study identified similar leptospiral serogroups circulating in humans, domestic ruminants, wildlife, rodents and shrews sharing the same ecosystem (Fig. 3). This may be attributed to the intense overlap of these species, bush meat handling, and environmental and seasonal drivers, such as drought and floods [37]. Katavi residents are mainly agro-pastoralists, who frequently come into contact with livestock, as well as wildlife and their excreta, in the ecosystem. Furthermore, the majority of the communities in the study area slaughter animals at home, and some of the people consume raw kidney and liver, as was reported in the interviews conducted. It is believed that direct contact between humans and animals is an important risk factor for human Leptospirosis [38, 5, 39, 40]. The results of this study indicate that livestock share the same serogroup with humans, and this implies a public health risk, particularly among those involved in animal handling. Similar findings were observed in Italy, where patients were infected through direct contact with infected animals or through contaminated urine [41].
In Katavi National Park, wild ungulates are found in high densities around lake Chada, the Kitusunga flood plains, and around lake Katavi, especially during the dry season, due to an influx of wildlife in search of pasture and water [20]. The large influx of animals might easily contaminate the area with urine and increase the chances of the spillover of infections to other animal species. During the rainy season, rivers flood, which increases the risks of leptospirosis outbreaks due to runoff soil contaminated with urine from domestic animals, wildlife, rodents and shrews flowing into common water sources. This is an important driver of leptospiral transmission.
In the Katavi-Rukwa ecosystem, bush meat consumption is common, and the main species hunted are impala, common duiker, warthog, buffalo and bushbuck [19]. Leptospiral infection in humans can occur through the direct contact with the blood, tissues, organs and urine of infected animals [39, 40]. Therefore, slaughtering and handling of bush meat from infected animals may pose a great risk of leptospiral transmission to humans in areas where consumption of bush meat is practiced. A study carried out in South America showed that human leptospirosis was associated with men who captured, slaughtered, and consumed large rodents [42].
This study observed serum agglutination to more than one serogroup in humans and all animal species tested. This may reflect a mixed or two different past infections, which most frequently reflect serological cross-reactions. These cross-reactions are mostly seen in acute or early convalescent sera, whereby the host, previously infected with one serogroup, may subsequently become infected by another serogroup, and the recently acquired serogroup may cross-react to the previous one, leading to activation of the memory response against the subsequent serogroup [43]. The titer of antibodies relating to the previous serogroup could be higher than antibodies specific to the new infecting serogroup. This may also reflect an infection caused by a serogroup not included in the MAT panel, as the MAT panel used was not very wide.
In conclusion, the present study demonstrates the possible interaction between livestock, wildlife, and humans, as similar serogroups were detected among these species. This may have a very serious implication on the public health of the communities, as the capacity to diagnose leptospirosis is not available in any of the surveyed villages, including the district hospital. Therefore, human leptospirosis should be included in the differential diagnosis of febrile illnesses in humans in the study area. Furthermore, the results from this study demonstrate common serogroups circulating among humans, domestic ruminants and wildlife, which will help in planning for interventions for the control or mitigation of the impact of infections in domestic animals and in humans. Thus, we recommend further studies on the molecular typing of leptospiral isolates from humans and from different animal species in the Katavi- Rukwa ecosystem.
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10.1371/journal.ppat.1005669 | Leishmania amazonensis Engages CD36 to Drive Parasitophorous Vacuole Maturation | Leishmania amastigotes manipulate the activity of macrophages to favor their own success. However, very little is known about the role of innate recognition and signaling triggered by amastigotes in this host-parasite interaction. In this work we developed a new infection model in adult Drosophila to take advantage of its superior genetic resources to identify novel host factors limiting Leishmania amazonensis infection. The model is based on the capacity of macrophage-like cells, plasmatocytes, to phagocytose and control the proliferation of parasites injected into adult flies. Using this model, we screened a collection of RNAi-expressing flies for anti-Leishmania defense factors. Notably, we found three CD36-like scavenger receptors that were important for defending against Leishmania infection. Mechanistic studies in mouse macrophages showed that CD36 accumulates specifically at sites where the parasite contacts the parasitophorous vacuole membrane. Furthermore, CD36-deficient macrophages were defective in the formation of the large parasitophorous vacuole typical of L. amazonensis infection, a phenotype caused by inefficient fusion with late endosomes and/or lysosomes. These data identify an unprecedented role for CD36 in the biogenesis of the parasitophorous vacuole and further highlight the utility of Drosophila as a model system for dissecting innate immune responses to infection.
| Leishmaniasis is caused by Leishmania parasites and transmitted to humans by sandflies. After the establishment of infection, the intracellular parasite form, known as an amastigote, preferentially infects and replicates in macrophages, cells otherwise specialized for killing microbes. To overcome macrophage lethality, Leishmania possesses a sophisticated evasion strategy that sabotages macrophage defenses. The cell biology of Leishmania-macrophage interactions is not completely understood, because of the complexity of the host-parasite relationship and limited technical resources available in the classical mouse infection model. In this study we created a model of leishmaniasis in fruit flies, which have advantages of genetic tractability, low cost, and high conservation. By screening a collection of genetically modified flies, CD36-like receptors were identified as factors involved in the Leishmania-phagocyte interaction. Further testing in CD36-deficient mouse macrophages showed that they did not support parasite proliferation due to the inability of parasites to enlarge the parasitophorous vacuole, a strategy used to avoid toxicity of reactive nitrogen species. The participation of CD36 in the control of the parasitophorous vacuole, which is an altered phagosome, has further implications for diseases such as Alzheimer’s disease, atherosclerosis, and certain bacterial infections where CD36 is a known phagocytic receptor.
| Leishmaniasis affects 12 million people in over 98 tropical and subtropical countries or territories [1, 2]. The disease is caused by protozoan parasites of the genus Leishmania, which are transmitted by sandflies. In humans, an infection starts with the interaction between promastigote forms of the parasite, delivered by a sandfly bite, and host phagocytes. In the case of L. major infection, neutrophils in particular are recruited to the bite site and serve as the first host for these parasites, where they differentiate into amastigote forms (3). However, this relationship is temporary because the infection induces apoptotic death of these neutrophils, which stimulates their phagocytosis by dendritic cells and macrophages. The delivery of amastigotes from apoptotic neutrophils to macrophages and dendritic cells as well as the capacity of neutrophils to reduce the recruitment of dendritic cells to the sandfly bite site have been shown to be an important step for the establishment of L. major infection in the vertebrate host [3–5]. In all types of leishmaniasis, infection is perpetuated in macrophages and the symptoms associated with leishmaniasis are promoted through sequential cycles of intracellular amastigote replication, lysis, and infection of naïve cells.
Host cell receptors engaged during Leishmania infection modulate the innate and acquired immune responses. Multiple macrophage receptors are implicated in Leishmania recognition, making the study of individual components challenging. The Fcγ receptor was the first of these receptors to be studied in depth; Fcγ receptors bind to antibody-opsonized amastigotes to promote their internalization. This interaction favors infection by inducing the secretion of the anti-inflammatory cytokine IL-10 [6–10]. Similar to Fcγ receptors, complement receptor 3 increases the efficiency of infection and reduces the cellular response against promastigote forms [11, 12]. Parasite recognition by the phosphatidylserine receptor and DC-SIGN are also implicated in down modulating cellular responses while fibronectin and mannose-fucose receptors are examples of receptors that bind to parasites and favor phagocytosis [13–17]. On the other hand, it has been shown that Toll-like receptors (TLR) are required to induce a protective cellular response observed in resistant mouse strains [18–21]. However, the inventory of host factors that are engaged during Leishmania infection is still incomplete, limiting our knowledge about host cell signaling pathways essential for leishmaniasis.
Following receptor mediated phagocytosis, the Leishmania-containing phagosome rapidly matures into a phagolysosome-like organelle known as a parasitophorous vacuole (PV). The PV size varies among Leishmania species: very large PVs that can harbor multiple amastigotes are typical with Leishmania mexicana complex (L. amazonensis, L. mexicana, L. pifanoi, L. venezuelensis) infections. On the other hand, small and tight fitting PVs, which accommodate single amastigotes and split when the parasite replicate, are observed with other Leishmania species, such as L. major. Yet the mechanisms involved in the maturation and maintenance of PVs are largely unknown.
To date, studies have shown that the coordinated fusion of vesicles from both endocytic and secretory pathways, similar to classical phagolysosome maturation, is required for formation of these large PVs. In particular, the PV gradually acidifies and acquires markers of early endosomes (Rab5), late endosomes and lysosomes (Rab7a, MHC class II, LAMP1, LAMP2, M6PR and hydrolases), as well as endoplasmic reticulum markers such as calnexin [22–28]. However, unlike the degradative pathway of classical phagosomes, where the content is degraded and the organelle is recycled, L. amazonensis PVs quickly enlarge and maintain this enlarged size throughout the life of the infected cell [29]. PV enlargement has been shown to be associated with the high fusogenicity with secondary lysosomes and, to a lesser extent, with the endoplasmic reticulum, however the relative contribution of these organelles for PV formation and the mechanisms that control their fusion to PVs remain unclear [28, 30, 31].
The customization of the PV by Leishmania is essential for the acquisition of nutrients and to avoid immune responses. In a series of experiments studying the role of the protein LYST/Beige, Wilson et al. (2008) demonstrated that large PVs are essential for L. amazonensis replication. Cells from mice mutant in LYST/Beige have oversized L. amazonensis PVs with higher parasite replication, while overexpression of LYST/Beige inhibited the expansion of PVs and blocked parasite proliferation. Furthermore, large PVs were shown to dilute the intravacuolar concentration of nitric oxide, lowering it to concentrations tolerated by the parasite. The association of PV size with parasite survival has also been related to the fusion of PVs with the endoplasmic reticulum, where interference with SNARE protein functions caused a discrete but significant decrease in PV size and parasite proliferation [28]. These studies highlight the importance of proper enlargement of the PV for L. amazonensis proliferation.
Here we developed an experimental Drosophila model of Leishmania infection and used a forward genetic screen to identify novel factors that modulate parasite infection. With this approach, 6 scavenger receptors required for resistance to infection were identified, three of which shared homology with mammalian CD36. We further analyzed the role of CD36 in mammalian models of L. amazonensis infection and found that CD36 is concentrated in the PV membrane juxtaposed to the posterior end of amastigotes. Moreover, the PV in CD36-/- mouse macrophages was reduced in size, as a consequence of reduced fusion of late endosome and/or lysosomes with the PV, and did not support amastigote replication. Collectively, these studies identify an essential role for CD36 in the maturation of the PV and Leishmania survival, and demonstrate that alternative models of infection, such as Drosophila, are advantageous for discovery of new host factors involved in the Leishmania-host interaction.
Given the important role of amastigotes in perpetuating Leishmania infection and promoting disease, we focused on analyzing the innate interactions between this parasite form and the immune system of the host. As Drosophila has proven to be an excellent model system to study such interactions [32], we developed a Drosophila model of L. amazonensis infection, in which opsonin-free amastigotes were isolated from mouse bone marrow-derived macrophage (BMDM) cultures and used to infect adult flies by microinjection. This infection caused only mild lethality, with the death of approximately ~25% of flies between days 3 and 6 post-infection (Fig 1, compare black and blue curves).
The robust survival of ~75% of the Leishmania-infected flies suggests the presence of defense mechanisms in Drosophila to control these parasites. To identify key defense pathways for host protection, we next infected mutant Drosophila that were deficient in known immune defense mechanisms, including humoral immune responses, melanization, or phagocytosis.
The Drosophila humoral immune response plays a crucial role in the defense against bacterial and fungal infections through the production of a diverse set of antimicrobial peptides and other factors circulating in the hemolymph. Two NF-κB dependent innate immune pathways control this response, the Toll and Imd (immune deficiency) pathways. We infected flies mutant for either Dif or the receptors PGRP-LC/LE, which are essential components of the Toll or Imd pathways, respectively. These mutants had similar survival rates compared to WT flies (Fig 1A and 1B) suggesting that humoral immune signaling, and its ensuing antimicrobial peptide response, does not participate in the defense against Leishmania parasites.
Melanization is classically described as a defense mechanism used by arthropods to encapsulate and kill some pathogens such as eggs of parasitoid wasps and the ookinetes of the malarial parasite Plasmodium berghei [33, 34]. Melanization is also linked to the effective response against certain bacterial infections [35, 36]. We examined the potential role of the melanization cascade in controlling Leishmania infection. Flies deficient in serine protease 7 (CG3066), which are unable to cleave and activate prophenoloxidase and fail to trigger the melanization cascade [37], displayed normal resistance against Leishmania infection indicating that this pathway is also not critical for defense against Leishmania (Fig 1C).
In flies, approximately 95% of hemocytes are plasmatocytes, professional phagocytes responsible for detecting and clearing invading microbes as well as unwanted cells [38]. To evaluate whether plasmatocytes participate in Leishmania defense, we ablated the phagocytic ability of plasmatocytes by injecting polystyrene beads into the hemocoel [39]. Polystyrene beads were co-injected with L. amazonensis amastigotes and fly viability was observed for ten days (Fig 1D). Bead-injected flies were significantly (p<0.0001) more susceptible to Leishmania infection, with 72% lethality over the course of the 10-day study. This was confirmed using a second approach in which all hemocytes were genetically ablated with the targeted expression of the proapototic protein Bax in plasmatocytes and other blood cells, generating the “phagoless” strain [40]. Similar to the bead-injected animals, this fly strain also showed dramatically reduced survival (p<0.0001) following amastigote infection (Fig 1E).
These results suggest that plasmatocytes and phagocytosis play a critical role in the control of L. amazonensis infection in Drosophila. We hypothesized that plasmatocytes control infection by killing parasites following their phagocytosis. The phagocytosis of parasites was confirmed by microscopic studies of GFP-expressing hemocytes from flies infected with dsRed-expressing L. amazonensis amastigotes. As expected, flies co-injected with polystyrene beads and amastigotes had plasmatocytes saturated with polystyrene beads but few intracellular parasites at 24 h (Fig 2A); at this same time point, flies injected with amastigotes but not beads had plasmatocytes loaded with numerous parasites (Fig 2B). After 72 h of infection, plasmatocytes contained numerous round amastigotes as well as occasional elongated promastigote forms (Fig 2C). In mammalian macrophages, L. amazonensis amastigotes proliferate within enlarged PVs, with multiple parasites per vacuole as they replicate. On the other hand, amastigotes in Drosophila plasmatocytes remain enclosed in tight fitting phagosomes which rarely contain more than one parasite (Fig 2B and 2C). This suggests that amastigotes cannot effectively manipulate the Drosophila phagocytic machinery to create their preferred niche, and instead these hemocytes control the infection through phagocytosis and parasite killing.
We next evaluated whether the high mortality of flies correlated with increased parasite proliferation. The amount of parasites surviving within the host was estimated by limiting dilution of individual flies. Bead-treated flies contained significantly more parasites than untreated flies (Fig 2D). Additionally, while the control flies showed a trend of reduced parasite load over the course of 12 days of infection, the flies defective in phagocytosis had a parasite load that increased over three days post-infection and remained elevated until day 12 (Fig 2D). These data are consistent with the high death rate observed between the 3rd and 6th day of infection of phagocytosis-deficient flies (Fig 1D and 1E). In summary, these data demonstrate that phagocytic activity, and plasmatocytes in general, are critical for controlling parasite proliferation and protecting the fly from death.
Given the importance of phagocytosis in controlling Leishmania infection, the Drosophila model was then used to screen for hemocytes genes required for anti-parasite defense. We analyzed a collection of flies carrying RNAi constructs targeting a curated set of potential phagocytic genes. Targeted factors included the core phagocytic machinery and hemocyte-specifying factors, such as Rac2 or FTZ-F1, as well as a collection of 25 receptors linked to phagocytosis, including members of the Scavenger Receptors Class B (i.e., CD36-like), Class C (an insect-specific group) and Class F (rich in EGF repeats such as Nimrods, Eater, Draper and Slow down (Table 1)). The knockdown of these genes was targeted specifically to hemocytes using the Hml(Δ)-Gal4 driver. Consistent with the role of phagocytosis in the Drosophila defense against Leishmania infection, this approach identified several essential phagocytic components, including Rac2, Coat Protein (coatomer) ß’ and the ftz transcription factor 1 (Fig 3A–3C, Table 1). In addition, multiple scavenger receptors were found to be important for defense against L. amazonensis infection: SR-CIII and SR-CIV, Nimrod C3, and three CD36-like receptors (or SR-Bs), namely CG10345, CG31741, and Croquemort (Fig 3D–3J and Table 1).
Because CD36-deficient flies were more susceptible to Leishmania infection (Fig 3), we next investigated if this susceptibility was linked to an increase in parasite burden and defects in phagocytosis. While the parasite load of control flies did not increase during infection, a sharp increase in parasites was observed in flies expressing RNAi for CD36-like receptors (Fig 4). Next, to investigate the role of CD36 scavenger receptors in phagocytosis, the number of parasites associated with hemocytes was scored 1 h post-infection. Notably, hemocytes from RNA flies for Crq, CG31741, and CG10345 contained significantly less intracellular parasites, compared to WT hemocytes (Fig 4B). These data support our hypothesis that the expression of each of these three CD36-like scavenger receptors in Drosophila hemocytes is critical for control of Leishmania infection by phagocytosis.
Next we evaluated which parasite stage is proliferating in the susceptible flies. At day one of infection all parasites are in the amastigote form, in all genotypes. However, in control flies by day three 15% of parasites are promastigotes and this increases to 20% by day eight. With knockdown of CG10345 or crq, promastigote levels significantly increased to 40% or 60%, respectively (Fig 4C). However, total parasite load did not correlate with the degree of promastigote differentiation/replication.
Given the results with knockdown of CD36 homologs in the Drosophila model, we investigated the role of this scavenger receptor in the interaction between L. amazonensis and mammalian cells. The localization of mCerulean3-tagged CD36 during amastigote infection of human 293T cells was analyzed. By one hour post-infection, CD36 was widely distributed on the cell membrane and co-localized with mCherry-Rab7a at numerous cytoplasmic vesicles and at the PV membrane (Fig 5A). Notably, at 6 h post-infection, when the PV is expanding, CD36 concentrated at the PV membrane juxtaposed to the parasite point of contact (Fig 5B). This suggests that localization of CD36 on the PV membrane is regulated in response to the amastigote.
To further demonstrate the close association of CD36-positive PV membranes and parasites, we studied the association of CD36 with amastigotes harvested from mCherry-CD36 expressing 293T cells infected with GFP-expressing parasites. The intracellular parasites were isolated free of host cells 24 h after infection by mechanical disruption and centrifugation. These amastigotes retained a strong mCherry-CD36 signal at their posterior end (Fig 5C), suggesting a persistent CD36-parasite interaction in infected cells.
A time-lapse confocal micrograph of amastigote-infected mCherry-CD36 293T cells (S1 Movie) showed the dynamic fusion of the late endosomes and lysosome in cells infected for 6 h and analyzed after a 15 min pulse of pHrodo-Dextran. When endocytosed, pHrodo serves as a marker of late endosomes and lysosomes because it becomes highly fluorescent in the acidic environment of these organelles. PV regions juxtaposed to parasites and rich in CD36 were associated with numerous acidic (pHrodo positive), mCerulean3-CD36 positive organelles. Although it was not possible to differentiate between vesicles fusing with PV from those simply moving out of the focus plane, the gradual decrease in the number of endocytic vesicles along with the increase of pHrodo signal into the PV is a clear indication of fusion.
The intriguing recruitment and localization of CD36 on the PV of 293T cells, led us to examine the phenotype of amastigote infected CD36-/- mouse bone marrow derived macrophages (BMDMs), a more relevant cell type for Leishmania studies. L. amazonensis parasites, as well as other members of the L. mexicana subgenus, invade macrophages by receptor-mediated phagocytosis and induce the formation of an enlarged PV in WT cells (Fig 6). We infected BMDMs derived from WT (C57BL/6) or CD36-/- mice with a multiplicity of infection (MOI) of three parasites per macrophage and observed that the size of the PVs was similar between CD36-/- and WT at 8 h post-infection but by 24 h the PVs of WT macrophages were significantly larger (Fig 6A and 6B). The difference peaked at 48 h when the area of PVs from WT macrophages reached 62 μm2 while the area of PVs from CD36-/- macrophages was only 16 μm2. From 48 h to 72 h the size of PVs did not change significantly (Fig 6B).
At an MOI = 3, PV expansion occurs slowly and this may contribute to the delay in the appearance of clear differences between WT and CD36-deficient BMDMs. Therefore, BMDMs were infected at an MOI = 10, which induces a quicker expansion of the PV. Using this condition, WT cells had enlarged PVs by 3 h post infection, while the PVs in the CD36-deficient macrophages remained small (Fig 6C and 6D), suggesting that CD36 is directly involved in the expansion and maintenance of L. amazonensis PVs.
We next evaluated if parasites were able to establish a replicative niche in the small PV of CD36-/- macrophages. The percentage of infected cells and the average number of parasites per infected cell were similar in WT and CD36-/- macrophages at 4 h post-infection, indicating that CD36 is not essential for parasite phagocytosis (Fig 7). However, while the replication rate of the parasite was similar in CD36-/- and WT macrophages during the first 48 h of infection, by 72 h parasite proliferation in CD36-/- macrophages was significantly reduced compared to WT suggesting a key role for CD36 in amastigote proliferation (Fig 7A).
Wilson et al. (2008) reported that the large PV of L. amazonensis-infected cells dilutes the concentration and toxicity of nitric oxide (NO) inside the PV, thereby allowing parasite replication [41]. To evaluate whether the impaired proliferation of parasites in the small PV of CD36-/- macrophages was related to the NO toxicity, macrophages were treated with N-nitro-L-arginine methyl ester (L-NAME), an inhibitor of NO synthase, prior to amastigote infection. Notably, the inhibitor rescued the number of parasites in CD36-/- macrophages at 72 h post-infection (Fig 7A), confirming the role of NO in restraining parasite proliferation in the small PVs of CD36-/- macrophages. On the other hand, L-NAME had no effect on PV size (Fig 7B) and significantly reduced the NO levels of infected macrophages stimulated with IFN-γ (Fig 7C). However without IFN-γ stimulation, the levels of NO remained low and the effect of L-NAME on this low level of NO was not significant (Fig 7C). Therefore, L-NAME treatment likely protects parasites from the local NO production that is thought to occur at the PV surface [42].
Intriguingly, while local NO impaired parasite replication in the small PV of CD36-deficient macrophages, GFP-expressing parasites were observed for at least 4 days in these cells, suggesting that they could not mount a sterilizing anti-parasitic response. The production of reactive nitrogen species in IFN-γ/LPS-stimulated macrophages is important to the clearance of intracellular parasites [43]. Uninfected CD36-/- macrophages produced WT levels of NO following IFN-γ and LPS (Fig 7C). Consistent with this finding, IFN-γ/LPS treatment 24 h post-infection caused a nearly complete clearance of intracellular parasites in both WT and CD36-/- macrophages by 96 h (Fig 7D).
Together, these data confirm that CD36-/- macrophages maintain IFN-γ/LPS-inducible anti-parasitic activity and argue that the large PV of L. amazonensis protects amastigotes from the local production of NO, but not against the high concentrations generated by fully activated macrophages.
CD36 is involved in the transport of long chain fatty acids and cholesterol [44, 45]. Therefore, we investigated if CD36-/- macrophages exhibited any defects in the transport of these lipids to the PV or to the parasites that might relate to the formation of PVs. The incorporation of BODIPY FL C12 and CholEsteryl BODIPY was measured by flow cytometry at 2 h of continuous presence of the probes in the culture. The incorporation for both probes was similar in uninfected WT and CD36-/- macrophages, and both probes were also similarly incorporated into L. amazonensis amastigotes within the PV of WT and CD36-/- macrophages (S1 Fig), indicating that these lipids are trafficking normally in the absence of CD36.
In a previous study, Beige was shown to regulate the L. amazonensis PV size [41]. In particular, these authors concluded that the expression of Beige is triggered by Leishmania infection, as a defense mechanism that reduces the PV size and increases the intravacuolar NO concentration for more effective parasite killing. Beige mRNA levels were quantified by qPCR in WT and CD36-deficient macrophages after infection with L. amazonensis amastigotes. If Beige was related to the undersized PV in CD36-/- macrophages we would expect higher expression of this gene, however we did not detect significant differences compared to WT (S2A Fig).
CD36 has also been implicated as a co-receptor for TLR2/6 and TLR4/6 heterodimers, which are involved in triggering the inflammatory response following stimulation with Staphylococcus aureus [46] or oxidized low-density lipoprotein [47, 48]. To determine if the small PVs in CD36-deficient macrophages were caused by an inefficient CD36-TLR signaling axis, the PV size was measured in immortalized macrophages (iMO) from mutant mice lacking the adaptor protein MyD88 or both MyD88 and TRIF. As expected, CD36-deficient iMOs had smaller PVs, but MyD88 single or MyD88/TRIF double knockout iMOs contained PVs with areas comparable to WT iMOs (S2B Fig), demonstrating that the undersized PVs of CD36-deficient macrophages is not related to TLR signaling.
To test whether overexpression of CD36 could rescue the undersized PV phenotype of CD36-/- macrophages infected with L. amazonensis, CD36-deficient iMOs were engineered to express mCherry-tagged CD36 and infected with L. amazonensis amastigotes. As shown in Fig 8A, CD36-/- iMOs exhibited small PVs, consistent with our observations in primary macrophages. The expression of mCherry-CD36, via a stable transduction of a lentiviral expression vector, restored the PV size of amastigote-infected CD36-/- iMO to WT levels.
While infection of 293T cells induced the aggregation of CD36 in the PV within a few hours of infection, mCherry-CD36 expressing iMOs exhibited much more rapid CD36 clustering. Immediately following amastigote contact with the iMO, CD36 was observed clustering on the plasma membrane at the amastigote contact site, as part of the phagocytic cup (Fig 8B). These CD36 clusters were then incorporated, along with some of the plasma membrane, into the developing PV during amastigote entry (Fig 8B). During the first 6 h of infection, the CD36 clusters were not associated with a specific pole of the parasite, but were always localized at the point of contact. Later, with the enlargement of the PV, the posterior pole of most parasites aligns to contact the PV membrane, reorienting the CD36 clusters to this region of the parasite (Fig 8C). The attachment and polarization of amastigotes inside PV is typical for L. amazonensis [23, 26, 29].
We next asked whether the amastigote actively induces the recruitment of CD36 to the cell membrane surface by secretion of virulence factors or by passive receptor-ligand interaction. Formaldehyde-fixed amastigotes also induced CD36 clustering, demonstrating that molecules on the surface of the parasite are responsible for CD36 recruitment (Fig 8D). However, the formaldehyde-fixed amastigotes were degraded by macrophages within 6 h of infection and the CD36 aggregates were dispersed.
Promastigotes are the initial infective form of the parasite and are also internalized by macrophages. To test whether promastigotes induce CD36 recruitment, we infected iMOs expressing mCherry-CD36. Notably, unlike amastigotes, promastigote cell contact and internalization did not recruit CD36 within the first 6 h of infection (Fig 9A). However, starting at 6 h, PV containing promastigotes (or differentiating amastigotes) gradually acquired CD36, which increased up to 24 h (Fig 9). Note, the promastigote PVs were still small and tight fitting, indicating a delay in PV enlargement compared to infections initiated with amastigotes.
To further investigate the role of CD36 clustering in Leishmania infection, we studied CD36 dynamics in response to L. major, which proliferate in small single parasite PVs. Amastigotes were harvested from mouse footpad lesions and used immediately to infect iMOs expressing mCherry-CD36. Confocal microscopy demonstrated that CD36 was not enriched in the small PVs of L. major at either 6 h or 24 h post-infection (S4A and S4B Fig).
The absence of CD36 clustering in L. major tight fitting PVs suggests that the receptor is not involved in this infection. However, to further evaluate the participation of CD36 in L. major proliferation, the parasite loads of WT and CD36-/- macrophages were monitored during a time course of infection. The number of parasites recovered in WT and CD36-/- macrophage cultures was similar throughout the infection (S4C and S4D Fig). The initial parasite load at 3 h of infection was relatively high because both infective and non-infective parasites were recovered. The following 3 days, both WT and CD36-/- macrophages had low parasite levels because only a small proportion of infective promastigotes survived. Parasite proliferation was detected by 96 h post-infection in both genotypes, indicating that lack of CD36 does not impair L. major proliferation.
CD36 has been reported to induce cell membrane fusion through binding to phosphatidylserine [49]. In this model, the interaction of CD36 from one cell with phosphatidylserine on the surface of another, promotes the fusion of macrophages and generates giant cells similar to those observed in granulomas. These results suggest that the small PV phenotype observed with L. amazonensis could be caused by CD36-mediated phosphatidylserine recruitment. If true, we would expect the enrichment of phosphatidylserine on the PV in a CD36-dependent manner. However, PS was not observed associated with the PV of WT or CD36-/- macrophages, as monitored by Annexin V staining (S5 Fig). These results do not support the model of CD36-mediated recruitment of PS to the PV membrane.
To study the maturation of the PV, which is an altered phagosome, we next characterized the acidification of PV in WT and CD36-/- macrophages. We monitored the acidification of PV during the first 2 h post-infection using live L. amazonensis amastigotes that were uniformly immunolabeled with antibodies conjugated to Oregon Green 488 (pH-sensitive) or Alexafluor 647 (pH-stable). The fluorescence intensity of the infected macrophages was determined by flow cytometry. As shown in Fig 10A, CD36-/- and WT BMDMs similarly acidify the PV, which reaches a pH of 4.5 within 15 min post-infection and gradually stabilizes by 1 hour post-infection to an approximate pH of 4.0. Note, the opsonized parasites probably engaged the macrophage Fc receptors during internalization, however, this interaction did not interfere with the small PV phenotype of CD36-/- macrophages (S3 Fig).
As PV acidification appeared normal in CD36-deficient cells, we also characterized endosome to PV fusion in Leishmania-infected BMDMs. Twenty four hours post-infection, macrophages were loaded with pHrodo Dextran and the amount of dye that accumulated in the PV was measured 30 min later. Notably, the total pHrodo fluorescence intensity per PV was higher in WT than in CD36-deficient macrophages, indicating that CD36 contributes to the fusion of PV with endocytic vesicles (Fig 10B).
This assay, however, does not discriminate which endocytic organelle delivers pHrodo Dextran to PVs. In particular, lysosomes are well known as an important source of material for PV enlargement [22, 24, 30, 31, 41]. Therefore, we hypothesized that the underdevelopment of PVs in CD36-/- macrophages could be caused by a dysfunction of lysosome biogenesis and/or fusion to the PV. To assess this, we measured the accumulation of the lysosomal markers LAMP1 and LAMP2 in the PV membrane of infected macrophages by immunostaining. Fluorescence microscopy imaging of WT macrophages showed that ~40% of PVs stained strongly for LAMP1/2 at 8 h post-infection, and this increased to ~80% by 72 h (Fig 10C and 10D). In CD36-/- macrophages, the percentage of LAMP positive PVs was similar to WT at earlier time points (8 h and 24 h), whereas there was significantly lower association of LAMP proteins with PV by 48 h and 72 h post-infection (Fig 10C and 10D).
The lower concentration of LAMP1 and LAMP2 in the CD36-/- PVs could be caused by lower quantity and/or reduced fusogenicity of lysosomes. To investigate this possibility further, two approaches were used to quantify the lysosomal content. The quantification of total fluorescence intensity of LAMP1 and LAMP2 in the macrophages, and the measurement of the activity of the lysosomal protease cathepsin B in total lysates. On average, the total fluorescence from LAMP1 and LAMP2 staining was similar in CD36-/- and WT macrophages (Fig 10E). Also, the cathepsin B activity was similar in lysates of uninfected CD36-/- and WT macrophages (Fig 10F). Together, these assays indicate that lysosomal biogenesis was normal in CD36-deficient macrophages, and therefore the lower rate of lysosomal fusion is the most likely explanation for reduced LAMP staining and small PV size.
In order to reinforce these findings, WT and CD36-/- BMDMs were stained with the lysotropic dye Lysotracker prior to and 24 h after infection. Cells were imaged by confocal microscopy and highly fluorescent organelles were scored through a complete Z-stack (Fig 10G). As expected, the number of Lysotracker-positive organelles in WT macrophages decreased 24 h post-infection, indicative of lysosomal fusion to PVs. However, the number of these organelles did not change in infected CD36-/- macrophages (Fig 10H) indicating a defect in lysosomal-PV fusion in the absence of CD36. While the CD36-deficient cells also showed a reduced overall level of lysotracker-positive vesicles, and this may contribute to the defect in PV enlargement, these vesicles were still detectable yet did not reduce after infection, as observed in WT macrophages. This strongly suggests that lower fusogenicity of late endolysosomal vesicles is the main cause of PV maturation dysfunction in the absence of CD36. In fact, this reduced fusogenicity may contribute to the overall reduction in large endolysosomal compartments observed prior to infection.
In this work, we developed an in vivo L. amazonensis infection model using Drosophila melanogaster to exploit the genetic tools available in this system. Characterization of this Drosophila infection model revealed that phagocytosis plays a pivotal role in parasite resistance. The central role of phagocytic plasmatocytes in Leishmania defense enabled an in vivo screening approach, in which RNAi was expressed specifically in these macrophage-like cells to evaluate the role of different genes in cellular anti-parasitic defense. Using the Drosophila infection model in a small scale screen, we identified six scavenger receptors, from three different families, that are involved in the defense of flies against Leishmania, suggesting that mammalian scavenger receptors could be more important to Leishmania infection than previously realized. Importantly, three of the scavenger receptors identified in our Drosophila screen were CD36-like (also known as the SR-B family). These Drosophila CD36s are critical for efficient phagocytosis of parasites and to control the infection. One of these CD36-like receptors, Crq was first identified as a plasmatocyte phagocytic receptor for apoptotic cells [50, 51], and was later linked to S. aureus recognition, which led to the discovery that mammalian CD36 is the co-receptor for the presentation of bacterial lipopeptides to TLR 2/6 [46]. The other two CD36-like receptors identified in our screen are uncharacterized.
Our most striking finding was that in mammalian systems CD36 plays a key role in the innate immune response to L. amazonensis infection by promoting PV expansion and parasite proliferation. Interestingly, CD36 is intensely recruited to the macrophage membrane upon amastigote contact, and then internalized within the phagocytic cup. Within a few hours after entry, CD36 is clustered at the anchoring site of the parasite, suggesting that the recruitment and fusion of endosomal vesicles is induced by parasites at this site. In the absence of CD36, the enlarged PV, typical of L. amazonensis infection, does not form. Although macrophages do not generate massive NO production upon amastigote infection [52, 53] the local NO environment was enough to arrest parasite proliferation in the small PVs of CD36-/- macrophages, in agreement with previous work [41]. This implies that the toxicity of the local NO environment is diluted in large PVs, but high concentrations of NO produced by IFN-γ/LPS-activated macrophages is sufficient to kill parasites.
The formation of similar clusters of CD36 upon contact with heat killed or fixed amastigotes indicates that CD36 aggregates upon binding to ligands found on the parasite surface, rather than through the action of some secreted virulence factors. Therefore, promastigotes probably do not recruit CD36 because they do not express CD36 ligands on their surface. Indeed promastigotes and amastigotes of Leishmania parasites have very different cell surface compositions. Promastigotes have a thick glycocalyx composed of lipophosphoglycan (LPG), proteophosphoglycan, gp63 and glycophosphatidylinositol lipids. In contrast, the amastigote glycocalyx has a low abundance of LPG and is rich in glycoinositolphospholipids (reviewed in [54]).
In agreement with the hypothesis of differential CD36 ligand exposure of promastigotes and amastigotes of L. amazonensis, the accumulation of CD36 following promastigote infection started to appear around 12 h post-infection, when amastigotes begin to form inside the PV. In this situation, the delay in CD36 accrual may be caused by expression of LPG and contribute to the delayed PV enlargement often observed in infections started with promastigotes [55, 56].
Collectively, the observation of the focal aggregation of CD36 in the large PV of L. amazonensis amastigotes, and not in the small PV of L. amazonensis promastigotes or L. major amastigotes is consistent with the finding that CD36 is essential for PV enlargement and maturation. Data presented here show that CD36 is critical for fusion of late endolysosomes with growing PVs. These data are in agreement with previous reports highlighting the importance of lysosomes for the enlargement of PVs observed in L. mexicana group infections [22, 24, 30, 31, 41].
While analysis of LAMP levels and cathepsin B activity were comparable between WT and CD36-/- macrophages, the total level of large lysotracker-positive organelles was decreased in CD36-/- macrophages. This apparent discrepancy might be associated with the methodology, which considered only a fraction of total lysosomes, the ones with a strong and large Lysotracker signal; and therefore did not account for the numerous smaller and dimmer lysosomes [29]. This decrease in larger lysosomal vesicles suggests a fundamental defect in vesicle fusion in the absence of CD36.
Other members of the scavenger receptor B class have been linked to the maturation of phagolysosomes. The best studied example is the mammalian LIMP-2, which binds β-glucocerebrosidase at the endoplasmic reticulum and transports this enzyme into lysosomes [57, 58]. In addition, two other Drosophila Class B-related scavenger receptors, Crq and Debris buster (Dsb), were recently shown to be involved in the late stages of phagosome maturation necessary to degrade dendrite fragments phagocytosed by epithelial cells [59]. Thus, given the high degree of structural similarity amongst members of this class, the role of CD36 in maturation of PV is not unprecedented [60].
The underlying molecular mechanisms involved in the active fusion of PVs with endolysosomal vesicles and the link to CD36 requires further investigation. One possibility is that the CD36 aggregation observed on the L. amazonensis PV triggers CD36-signal transduction via receptor multimerization [61–64] or via interaction with other factors localized to the parasite anchoring site, such as MHC Class II, LAMP1 and LAMP2 [23, 26, 65]. Alternatively, CD36 may function as a channel to exchange lipids to and/or from the PV, including virulence factors that could increase fusogenicity of the PV. Such a channel has been demonstrated in the crystal structure solution of LIMP-2, and modeled in other members of the CD36 family [60]. For SR-BI this channel has been related to the bidirectional transport of cholesterol(esters) [66–68]. Another hypothesis is that CD36 on the PV membrane mediates the interaction with anionic lipids present in the incoming vesicle and promotes the fusion of these two organelles. Although we did not observe enrichment of phosphatidylserine in the PV, as suggested for the formation of giant granuloma cells [49], further studies will investigate if other lipids are involved in the CD36-dependent PV expansion.
It is important to emphasize that our work is focused on the study of amastigote forms and to further investigate the role of CD36 in the infection initiated by promastigotes, future studies should consider the infection of neutrophils, which are the primary cell type infected by Leishmania promastigotes during natural infection by sandfly bites [3, 69]. The neutrophilic delivery of amastigotes to macrophages was shown to be essential to establish the infection of L. major in the ear pinna of mice following a sand fly bite. Although the role of neutrophils in in vivo infection with parasites of the L. mexicana group is not completely understood, one interesting connection is that macrophage CD36, a known receptor for apoptotic cells, could be used to phagocytose apoptotic infected neutrophils, enabling a Trojan horse infection.
In conclusion, here we described the development of a novel Drosophila infection model of Leishmania which can be used to efficiently explore the innate immune pathways involved in interaction between phagocytes and Leishmania parasites. Studies in this model demonstrated that CD36-like receptors are essential for the fly to defend against Leishmania infection. In mammalian macrophages, we showed that L. amazonensis parasites exploit host CD36 function to favor their intracellular survival: amastigotes expose CD36 ligands to stimulate fusion with endolysosomal vesicles and promote PV enlargement, thereby reducing NO levels in the local environment and enhancing replication. On the other hand, promastigotes do not expose CD36 ligands and PV enlargement is delayed until these parasites differentiate into the intracellular adapted form, the amastigote. These findings open a new avenue of investigation to study the CD36 signaling pathways and cell biology involved in vesicular trafficking, phagosome maturation and host defense in leishmaniasis as well as in other diseases.
All experiments were conducted according to the guidelines of the American Association for Laboratory Animal Science and approved by the Institutional Animal Care and Use Committee at the University of Massachusetts Medical School (Docket#: A-2056-13).
CD36 mutant mice were generated by targeted gene disruption in embryonic stem cells [70]. C57BL/6 and Balb/c mice were obtained from The Jackson Laboratory. All animals used in this work were 7 to 12-weeks-old and were maintained under pathogen-free conditions at the University of Massachusetts Medical School animal facilities.
Femurs and tibia were dissected from mice and bone marrow was flushed with PBS using a 30G needle connected to a 10 mL syringe. The cells were cultivated in RPMI supplemented with 30% L929 cell-conditioned medium and 20% FBS and maintained in bacteriological petri dishes at 37°C and 5% CO2 [71]. The cultures were fed on day 3 and used on day 7. Immortalized macrophages were generated using J2 recombinant retrovirus carrying v-myc and v-raf oncogenes as described in Halle [72]. MyD88-/- and TRIF-/- immortalized macrophages were kindly provided by Dr Egil Lien (University of Massachusetts) and were generated from mutant mice BMDMs [73, 74].
GFP-transfected L. amazonensis (MHOM/BR/1973/M2269), and dsRed-transfected (RAT/BA/LV78)[75] were generously donated by Dr. Silvia R. Uliana (ICB-USP, Brazil) and Dr. Kwang-Poo Chang (Rosalind Franklin University of Medicine and Science), respectively. Leishmania major was of the MHOM/IL8/Friedlin strain. Promastigotes were cultivated in vitro in M199 medium supplemented with 10% FBS and 30 μg/mL Hygromycin B (GFP transfected) or 5 μg/mL of Tunicamycin (dsRed transfected) at 26°C.
Promastigote forms were differentiated axenically to amastigote forms by transferring the parasites to 199 media supplemented with 0.25% glucose, 0.5% trypticase, 40 mM sodium succinate (pH 5.4) and 20% FBS, at 1x10ˆ7 cells/mL at 32°C for 3 days. Axenic amastigotes were then used to infect Balb/c BMDM cultivated in 175 cm2 T-flasks (1.5x10ˆ7 cells/flask) at a multiplicity of infection of 10 parasites/BMDM. One week after infection cells were harvested from T-flasks and BMDMs were disrupted in PBS at 4°C using a glass dounce tissue grinder with teflon rod followed by two centrifugations at 210g for 8 min to remove intact BMDMs and cell debris, and one at 675g for 12 min to harvest the amastigotes.
The full length mouse CD36 cDNA (NM_007643.4) and mCherry were inserted in the retroviral transfer plasmid pCX4 Puro. 293T cells were transfected with the pCX4 mCherry-CD36, MLV gag-pol, and VSVG using GeneJuice transfection reagent (EMD Millipore) following the manufacturer’s recommendations. For the viral production, supernatants were refreshed 24 h and collected 48 h after transfection, filtered through a 0.45 μm pore filter and stored at -80°C until use. Immortalized macrophages were cultured in the presence of virus particles and 8 μg/ml Polybrene for 24 h, and then the transduced cells were selected with 3 μg/ml of puromycin for 4 days.
Fly lines used in this work are listed in the S1 Table. Hml(Δ)GAL4 was used as a hemocyte-specific driver, expressed from the 1st instar larvae [40, 76]. Phagoless flies were generated by crossing virgin hml(Δ)-GAL4,UAS-eGFP female flies [31] with UAS-bax/CyO-actin-eGFP males. The progeny were maintained at 29°C after eclosion to optimize the function of the UAS/Gal4 system. Seven to 10 day old male flies were used in all experiments. A Nanoject (Drumond) equipped with a capillary needle was used for microinjections of 32 ƞL of a suspension of parasites and/or polystyrene beads in the abdomen. Polystyrene beads (0.2 μm diameter, FluoSpheres, Invitrogen F8805) were washed with PBS three times followed by centrifugation and suspended in PBS at concentration of 2% for injections. Forty thousand BMDM-derived amastigotes resuspended in PBS were injected per fly immediately after purification. We used at least 60 flies per sample for survival experiments that were monitored daily for at least 10 days. The survival curves of experimental flies were compared to flies injected with PBS and to control flies carrying the driver alone injected with the same preparation. The statistics of the survival curves were analyzed using Log-rank (Mantel-Cox) test. Susceptible flies were defined as those that reproducibly showed statistically significant decreases in survival with parasite but not with PBS injection.
The relative amount of parasites in adult flies was determined by limiting dilution. Infected flies were quickly washed in ethanol and individually ground with a tissue glass-teflon dounce homogenizer in 5 mL of Schneider media supplemented with 10% FBS, and 50 U/mL of a penicillin and streptomycin solution. The homogenates were filtered through a 70 μm mesh cell strainer to remove debris, and 100 μL of homogenate was serially diluted 24 times by a factor of 2 in triplicates. The cultures were analyzed for the presence of live promastigotes in the wells after 7 days of culture at 27°C, a technique modified from the limiting dilution method [77]. Alternatively, the flies were gently ground in 40 μL of Schneider media using a plastic pestle and the cell suspension loaded in an improved Neubauer chamber to count the dsRed parasites using CellProfiler cell image analysis software [78]. The same sample was used to determine the percentage of promastigotes in the flies by visual inspection.
hml(Δ)Gal4-GFP adult flies expressing GFP in hemocytes were injected with parasites and/or beads and dissected in 20 μL of Schneider media on a glass slide on ice to drain the hemolymph. Samples from 5 flies were pooled in a coverglass bottom petri dish and centrifuged for 3 min at 200g to settle down the hemocytes in the bottom of the plate. The cells were visualized using a Leica SP8 AOBS laser-scanning microscope equipped with a 63X objective. The parasite load in hemocytes was determined in RNAi fly lines by counting the number of dsRed parasites in each GFP-expressing hemocyte using a fluorescence microscope. Each sample was a pool of 3 flies and 4 samples were scored per strain at 1 h post-infection.
Bone marrow derived macrophages were seeded (1x10ˆ5 cells/well) in 24 well plates containing round 12 mm glass coverslips and allowed to adhere overnight at 37°C and 5% CO2 in RPMI 1640 medium supplemented with 10% FBS and 5% L929 cell conditioned medium. Cultures were infected with amastigotes for 2 h and the coverslips were washed 3 times with RPMI media and transferred to a new plate. At the indicated times infected BMDMs were fixed with 4% paraformaldehyde for 15 min and mounted over a glass slide for microscopic imaging in a fluorescence microscope. The parasite load was determined by counting the intracellular GFP or dsRed-expressing amastigotes in each cell by microsocopy. The borders of PVs that contained GFP expressing parasites were delineated manually using ImageJ software to calculate the PV area.
293T cells were cultured in coverglass bottom 35mm dishes (Mattek Corporation), and transfected with mCerulean3-CD36-C-10, mCherry-CD36-C-10 and/or mCherry-Rab7A, gift from Michael Davidson, Addgene plasmid # 55405 [79], #55011, #55127 using GeneJuice (EMD Chemicals) according to the manufacturer’s recommendations. Cells were infected with amastigotes between 24 h and 48 h after transfection. For time-lapse live cell imaging, cells were infected for 24 h and incubated with pHrodo Red Dextran, 10,000 MW (Molecular Probes) for 15 min, washed and images were taken at intervals of 3 s at 34°C using Leica SP8 AOBS laser-scanning microscope equipped with a 63X oil immersion objective. The imaging conditions were optimized to use the minimum laser power to reduce phototoxicity.
To determine the parasite load of L. major in macrophages, the cultures were grown in 96 well plates, infected with metacyclic promastigotes for 3 h, then extensively washed to remove free parasites and cultured at 34°C. At indicated times post infection the media was removed and switched to Schneider media containing 10% FBS and cultured for 6 days at 25°C. These conditions induce macrophage death and release of parasites that proliferate in promastigote forms. At the end of 6 days in culture the promastigotes were counted using a hemacytometer to estimate the relative parasite load in each sample.
Cells were cultivated in coverslips and nitric oxide synthase was inhibited by addition of N-nitro-L-arginine methyl ester (L-NAME) at 100 μM in the culture media for 1 h and washed prior to infection. For parasite killing assays, bone marrow derived macrophages were infected for 24 h and activated with 150 U/mL of IFNγ (eBioscience) and 50 ng/ml of ultra pure LPS (Invivogen) in the culture media. Infection was measured by counting the intracellular parasites and the percentage of infected cells.
The fusion of endocytic vesicles with PV in infected macrophages was measured using a fluorescence-labeled dextran dye (pHrodo red dextran, MW 10000, Molecular Probes). This marker enters cells through endocytosis and increases fluorescence in low pH compartments like late endosomes and endosomes. Twenty four hours after infection, BMDM cultures were incubated with 50 μg/mL of pHrodo dye for 15 min, washed with PBS and incubated for 15 min in 20 mM Hepes buffer [pH = 7.4] containing 140 mM NaCl, 2.5 mM KCl, 1.8 mM CaCl2, and 1 mM MgCl2. The cultures were washed and the cells were imaged in a confocal microscope at the level of the parasite center for the next 20 min. The area of PV of the images was delineated manually and the intensity of fluorescence was measured using ImageJ.
The pH of PVs was determined by dual fluorescence flow cytometry [80]. For the dual fluorescence staining, amastigotes were incubated on ice for 30 min with Balb/c infected mouse serum (1:1000), washed once and immunostained with the secondary antibodies conjugated to Alexafluor 647 or Oregon green 488 (Invitrogen) for another 30 min on ice and then washed once before using for infections. BMDMs were seeded in non-tissue culture treated 24 well plates one day before infection. The cultures were incubated on ice for 10 min to inhibit phagocytosis, and then the labeled parasites were added at an MOI = 4 and centrifuged 300g for 3 min to increase interactions between the BMDM and parasites. The plates were immediately incubated at 37°C in a water bath for 15, 30, 60 or 120 min. To detach the cells from the plate, the media was replaced by calcium and magnesium free PBS, kept on ice for 10 min and pipetted up and down until most of the cells were in suspension. Cell fluorescence was measured by flow cytometry and the pH was determined by the ratio of fluorescence of Oregon Green 488 to Alexafluor 647. Standard curves for each sample were prepared by equilibrating the cells in solutions with defined pH, 80mM potassium chloride, 30mM sodium chloride, 30 mM potassium phosphate, 5.5 mM glucose, 0.8 mM magnesium sulfate,1.3 mM calcium chloride, and 20 μM Nigericin to equilibrate the pH of the phagosomes to the pH of the media [81].
Infected BMDMs were prepared as described above, and after fixation the cells were permeabilized with 0.05% Triton X-100 in PBS for 10 min, blocked with PBS 1% bovine serum albumin and stained with the monoclonal antibodies 1D4B (LAMP-1) and ABL-93 (LAMP2) from developmental studies hybridoma Bank (Iowa City, IA) overnight at 4°C followed by secondary antibody conjugated to Alexafluor 488 (Invitrogen) for 1 h at 37°C. The coverslips were washed and mounted with 50% glycerol on microscopic glass slides and imaged in a fluorescence microscope. For quantification assays we set the images to a high threshold and counted only PV that presented more than half of extension of the membrane enriched with LAMP1/2.
Uninfected cells or cells infected for 24 h were stained with 100ηM Lysotracker for 2 h at 34°C. The images were taken in the continuous presence of Lysotraker using a Leica SP8 AOBS laser-scanning microscope equipped with a 63X objective, in z-stacks of 0.5 μm to cover the entire cells. The number of late endocytic compartments was determined using the 3D image counter plugin in ImageJ, and the counter was adjusted to consider only objects with intense fluorescence to avoid counting false objects from the background signal, and areas less than 8 μm3 to avoid counting PVs.
Cathepsin B activity assays were performed using the chromogenic substrate Z-RR-pNA (Enzo Life Sciences). 200 μM of substrate was diluted in 50 mM sodium acetate (pH = 5), 2.5 mM EDTA, and 0.1% Triton X-100. Cells were lysed in the assay buffer plus 1μM pepstatin A, 0.75μM aprotinin and 1mM PMSF (2x10ˆ7 cells/ml), and 10 μL of lysate was used per reaction in triplicate. The cleavage of substrate was monitored at 410 nm for 45 min at 15 min intervals and the amount of pNA released by Cathepsin B activity was determined using a pNA standard curve.
Total RNA from BMDMs was isolated using the TRIzol reagent (Invitrogen) following the manufacturer’s recommendations. The RNA was then treated with DNase and re-extracted by phenol:chloroform method. cDNA was synthesized using iScript cDNA synthesis kit (BioRad) and quantitative PCR analysis was performed using SYBR Green (BioRad). The specificity of amplification was assessed for each sample by melting curve analysis and relative quantification was performed using a standard curve with dilutions of a standard. Oligonucleotide primers 5’-AGCAGAAGGTGATAGACCAGAA-3’ and 5’CCCACACTTGGATCATCAATGC-3’ were used to amplify the Beige/LYST and 5’-TCAGTCAACGGGGGACATAAA-3’ and 5’-GGGGCTGTACTGCTTAACCAG-3’ to amplify the loading control standard cDNA HPRT1 [41].
The statistical analyses were performed using GraphPad Prism version 6.00 for Mac OS X, GraphPad Software, La Jolla California USA, www.graphpad.com. The tests and the criteria used for each comparison are reported in the Figure legends.
To quantify the incorporation of lipids, C57BL/6 and CD36-/- macrophages were cultured for 2 h in the presence of the fluorescent fatty acid and cholesterol analogs BODIPY FL C12 and CholEsteryl BODIPY 542/563 C11 (Molecular Probes) (5 μM final concentration). The cells were harvested from the culture dishes and the incorporation of the probes was measured by flow cytometry. The localization of the lipids was analyzed by confocal microscopy in paraformaldehyde macrophages infected for 4 h and incubated for 2 h with the probes.
Infected BMDMs were fixed with 4% formaldehyde in PBS, permeabilized with 0.05% Triton X-100 in PBS for 10 min, blocked with PBS 1% bovine serum albumin and stained with Annexin V FITC (Apoptosis detection kit, eBiosciences). The coverslips were washed and mounted with 50% glycerol on microscopic glass slides and imaged in a confocal microscope.
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10.1371/journal.pgen.1007512 | Discovery of a dual protease mechanism that promotes DNA damage checkpoint recovery | The DNA damage response is a signaling pathway found throughout biology. In many bacteria the DNA damage checkpoint is enforced by inducing expression of a small, membrane bound inhibitor that delays cell division providing time to repair damaged chromosomes. How cells promote checkpoint recovery after sensing successful repair is unknown. By using a high-throughput, forward genetic screen, we identified two unrelated proteases, YlbL and CtpA, that promote DNA damage checkpoint recovery in Bacillus subtilis. Deletion of both proteases leads to accumulation of the checkpoint protein YneA. We show that DNA damage sensitivity and increased cell elongation in protease mutants depends on yneA. Further, expression of YneA in protease mutants was sufficient to inhibit cell proliferation. Finally, we show that both proteases interact with YneA and that one of the two proteases, CtpA, directly cleaves YneA in vitro. With these results, we report the mechanism for DNA damage checkpoint recovery in bacteria that use membrane bound cell division inhibitors.
| Prokaryotes and eukaryotes coordinate cell division to genome integrity using DNA damage checkpoints. Many bacteria express a small, membrane binding protein to slow cell division when obstacles to DNA replication are encountered. Cell division inhibitors of this class have been identified in several bacterial species, yet the mechanism used to alleviate inhibition has remained unknown. Using forward genetics, we identified two unstudied genes, coding for the proteases YlbL and CtpA, that when deleted result in sensitivity to drugs that directly damage DNA. We show that sensitivity to DNA damage in protease mutants is a result of accumulation of the cell division inhibitor. Further, we show that YlbL and CtpA are responsible for degrading the cell division inhibitor allowing for cell division to resume. Importantly, these two proteases are not homologs, demonstrating a striking example of a bacterium using non-homologous enzymes to degrade a single substrate. Our investigation uncovers the previously unknown mechanism used to remove a cell division inhibitor, while also illuminating a potential strategy that bacteria can use to regulate signaling pathways. The use of multiple, unrelated proteins to perform a single function may represent a strategy employed throughout biological systems.
| The DNA damage response (DDR, SOS response in bacteria) is an important pathway for maintaining genome integrity in all domains of life. Misregulation of the DDR in humans can result in various disease conditions [1, 2], and in bacteria the SOS response has been found to be important for survival under many stressors [3–5]. The DNA damage response in all organisms results in three principle outcomes: a transcriptional response, which can vary depending on the type of DNA damage incurred, DNA repair, and activation of a DNA damage checkpoint [6–9]. In eukaryotes, the G1/S and G2/M checkpoints are established by checkpoint kinases, which transduce the signal of DNA damage through inactivation of the phosphatase Cdc25 [7]. Checkpoint kinase dependent inhibition of Cdc25 leads to accumulation of phosphorylated cyclin dependent kinases, which prevents cell cycle progression [7]. In bacteria, the SOS-dependent DNA damage checkpoint relies on expression of a cell division inhibitor, though the type of inhibitor varies between bacterial species.
In Escherichia coli, the SOS-dependent DNA damage checkpoint is the best understood bacterial checkpoint [6]. Upon activation of the SOS response, the cytoplasmic cell division inhibitor SulA is expressed [10]. SulA accumulation leads to a block in septum formation by preventing the assembly of the cytokinetic ring by FtsZ, a homolog of eukaryotic tubulin [11, 12]. SulA binds directly to FtsZ [13] and inhibits FtsZ polymerization [14, 15]. Recovery from the SulA-induced checkpoint occurs through proteolysis of SulA. Lon is the primary protease responsible for clearing SulA [16–18], although ClpYQ (HslUV) were found to contribute to SulA degradation in the absence of Lon [19–21]. Thus, the mechanisms of DNA damage checkpoint activation by the cytoplasmic protein SulA and subsequent recovery are well understood in E. coli. The SulA-dependent checkpoint, however, is restricted to E. coli and a subset of closely related bacteria. It is becoming increasingly clear that most other bacteria use a DNA damage checkpoint with an entirely different mechanism of enforcement and recovery.
An evolutionarily broad group of bacterial organisms have been shown to use a notably different DNA damage checkpoint mechanism [22–25]. In these Gram-positive and Gram-negative organisms, a small protein with a transmembrane domain is expressed that inhibits cell division without targeting FtsZ. One example is in the Gram-negative bacterium Caulobacter crescentus, where the SidA and DidA proteins bind to the essential membrane bound divisome components, FtsW/N that contribute to peptidoglycan remodeling [22, 26]. Another example is the Gram-positive bacterium Bacillus subtilis in which the SOS-dependent cell division inhibitor is YneA [23]. YneA contains an N-terminal transmembrane domain with the majority of the protein found in the extracellular space [27]. Upon SOS activation, LexA-dependent repression of yneA is relieved and yneA is expressed [23]. Increased expression of yneA results in cell elongation, though FtsZ ring formation still occurs [27], suggesting YneA inhibits cell division through a mechanism distinct from that of SulA. Further investigation found that overexpressed YneA is released into the medium, and that full length YneA is likely the active form of the protein [27]. The mechanism(s) responsible for YneA inactivation is unknown. Therefore, although the use of a small, membrane bound cell division inhibitor is wide-spread among bacteria, in all cases studied the mechanism of checkpoint recovery remains unknown [22–26].
We report a set of forward genetic screens to three different classes of DNA damaging agents using transposon mutagenesis followed by deep sequencing (Tn-seq). Our screen identified two proteases, YlbL and CtpA, that are important for growth in the presence of DNA damage. Mechanistic investigation demonstrates that YlbL and CtpA have overlapping functions, and in the absence of these two proteases, DNA damage-dependent cell elongation is increased and checkpoint recovery is slowed. A proteomic analysis identified accumulation of YneA in the double protease mutant. We also found that DNA damage sensitivity of protease mutants depends solely on yneA. Further, we show that both proteases interact with full length YneA in a bacterial two-hybrid assay, and that CtpA is able to digest YneA in a purified system. With these results, we present a model of DNA damage checkpoint recovery for bacteria that use the more wide-spread mechanism employing a small, membrane bound cell division inhibitor.
In order to better understand the DNA damage response in bacteria, we performed three forward genetic screens using B. subtilis. We generated a transposon insertion library consisting of more than 120,000 distinct insertions (S1 Table). The coverage of each transposon mutant in the library was plotted against the genome coordinates, which showed that the distribution of insertions was approximately uniform across the chromosome in the population of mutants (Fig 1A). Two small exceptions were detected where coverage decreased. Decreased coverage corresponds to regions where many essential genes are clustered (Fig 1A, arrow heads). With the goal of identifying mutants important for the DNA damage response, we grew parallel cultures of either control or DNA damage treatment over three growth periods, modelling our experimental design after a previous report [28; Fig 1B]. Mitomycin C (MMC), methyl methane sulfonate (MMS), and phleomycin (Phleo) were chosen for screening because these agents represent three different classes of antibiotics that damage DNA directly. MMC causes inter- and intra-strand crosslinks and larger adducts [29, 30], MMS causes smaller adducts consisting of DNA methylation [31], and Phleo results in single and double stranded breaks [32, 33]. As a result, we reasoned that the combined data would provide a collection of genes that are generally important for the DNA damage response.
After sequencing, we performed quality control analysis. First, given that sequencing data are count data, the distribution of the coverage should be log-normal [34]. Indeed, the distribution of each replicate for the initial library and starter culture samples is approximately log-normal (S1A Fig). We also found that the distributions for the remaining time points of the pooled replicates followed an approximate log-normal distribution (S1B Fig). The sequencing data and viable cell count data (S1 Table) were used to calculate the fitness of each insertion mutant in each condition [Fig 1C; 35, 36]. The relative fitness of each insertion was calculated by taking the ratio of treatment to control (Fig 1C), thereby isolating fitness effects of the treatments. The relative fitness of each gene was determined by averaging the relative fitness calculated for each insertion within a gene (Fig 1C). To verify that a t-test would be appropriate for determining relative fitness deviating significantly from one, we plotted the distribution of insertion relative fitness. All the distributions were normal with a mean close to one (S1C Fig). We determined the relative fitness for every gene with sufficient data (see supplemental methods), and report the relative fitness values and the adjusted p-values [37] in S2 Table.
Initial inspection found that several genes known to be involved in DNA repair (recA, ruvAB, recN, and recOR [38]) had decreased relative fitness in growth period one of all experiments (S2 Table). A closer analysis of recN, addA, and polA, three genes that are found toward the top of the lists in all treatments, showed that relative fitness is less than one in most cases, though in the Phleo experiment, it appears that the cultures were adapting to the treatment by growth period three (Fig 1D). For comparison, we also plotted the relative fitness of thrC, a gene involved in threonine biosynthesis, and found the relative fitness to be about one in all conditions examined (Fig 1D). Importantly, insertion in uvrA, a component of the nucleotide excision repair machinery [38, 39] which helps repair MMC adducts but not MMS or Phleo related damage [40, 41], decreased relative fitness in growth periods 2 and 3 with MMC, but did not significantly decrease relative fitness in MMS or Phleo (Fig 1D and S2 Table). Taken together, these results validate the approach by demonstrating that we were able to identify genes known to be involved in DNA repair.
We also wondered whether our results contained false positives. To test this, we decided to experimentally validate the genes with the forty lowest relative fitness values from growth period two in the MMC experiment. We found that eight of the forty gene deletions were not sensitive to MMC in a spot titer assay (Table 1). Several genes that were false positives are located in the genome near genes with validated phenotypes, suggesting that polar effects explain some of the false positives (Table 1; see supplemental results for detailed analysis). To identify genes required generally as part of the DNA damage response, we examined the 200 genes from growth period two with the lowest relative fitness and an adjusted p-value less than 0.01 from all three experiments (S3 Table). We found that 21 genes overlapped for all three experiments (Fig 1E), some of which are known to be involved in DNA repair (recN, addB, polA, radA), while several genes have no known function (e.g., ylbL and ctpA) (S3 Table).
Among the genes important for growth in the presence of DNA damage, we focused on two putative proteases YlbL and CtpA. YlbL is predicted to have three domains: a transmembrane domain, a Lon protease domain, and a PDZ domain (Fig 2A). CtpA is predicted to have four domains: a transmembrane domain, a S41 peptidase domain, a PDZ domain, and a C-terminal peptidoglycan (PG) binding domain (Fig 2A). In all three Tn-seq experiments, the relative fitness of insertions in either ylbL or ctpA was significantly less than one in the second and third growth periods (Fig 2B), suggesting that absence of either protease results in sensitivity to DNA damage. In contrast, a control gene amyE, which is involved in starch utilization, had a relative fitness of approximately one in all conditions examined (Fig 2B). To verify the Tn-seq results, we constructed clean deletions of ylbL and ctpA and found both mutants to be sensitive to DNA damage in a spot titer assay (Fig 2C). Each phenotype was also complemented by ectopic expression of each protease in its respective mutant background (Fig 2C). To identify putative catalytic residues, we aligned the protease domain of YlbL to LonA and LonB from B. subtilis and Lon from E. coli. The sequence alignment revealed that YlbL contains a putative catalytic dyad consisting of a serine (S234) and a lysine (K279) (S2A Fig). Similarly, we aligned CtpA to its homologs CtpB from B. subtilis and Prc from E. coli, which identified a putative catalytic triad consisting of a serine (S297), a lysine (K322), and a glutamine (Q326) (S2B Fig). To test whether these putative catalytic residues were required for function, we attempted to complement the DNA damage sensitivity phenotype via ectopic expression of serine and lysine mutants. Both the serine and lysine mutants of YlbL and CtpA failed to complement the deletion phenotypes (Fig 2C). The variants and the wild-type proteases were ectopically expressed to the same level in vivo (Fig 2D), suggesting that the lack of complementation is not due to instability caused by the amino acid changes. With these results, we conclude that protease activity is required for YlbL and CtpA to function in response to DNA damage.
The similarity in phenotypes led us to hypothesize that YlbL and CtpA have overlapping functions. To test this, we performed a cross-complementation experiment using spot titer assays for MMC sensitivity. Over-expression of YlbL, but not YlbL-S234A, complemented a ctpA deletion (Fig 3A & 3B). Similarly, over-expression of CtpA, but not CtpA-S297A, complemented a ylbL deletion (Fig 3A & 3B). In addition, deletion of both proteases rendered B. subtilis hypersensitive to MMC, even more so than loss of uvrA, which codes for the protein responsible for recognizing MMC adducts as part of nucleotide excision repair [Fig 3C; 38, 39]. To further test the hypothesis that YlbL and CtpA have overlapping functions, we over-expressed each of the proteases separately in the double protease mutant background and observed a complete rescue of MMC sensitivity upon expression of the wild type (WT), but not the serine variants (Fig 3D & 3E).
The experiments performed thus far cannot distinguish between sensitivity to MMC resulting from cell death, growth inhibition or both. To determine whether sensitivity arises from cell death, we performed a survival assay using an acute treatment of MMC. We detected a slight decrease in percent survival as MMC concentration increased in the ΔylbL and the double mutant strain (S4A Fig). We compared the decrease in percent survival in single and double protease mutants to a ΔuvrA strain, which has been shown previously to be acutely sensitive to MMC [40]. The strain lacking uvrA was very sensitive to an acute treatment of MMC (S4A Fig), whereas, the double protease deletion strain was significantly less sensitive to acute exposure compared with ΔuvrA (compare Figs 3C & S4A). Taken together, we conclude that MMC sensitivity of the protease mutants observed in spot titer assays is primarily caused by growth inhibition.
We hypothesized that sensitivity to DNA damage resulting from growth inhibition could also be explained by inhibiting cell proliferation, or by inhibiting cell division rather than cell growth. To distinguish between these two possibilities, we measured cell length, because inhibition of proliferation should be observed as an increase in cell length, consistent with a failure in checkpoint recovery. Thus, we designed a MMC recovery assay, reasoning that following treatment with MMC, cells lacking YlbL, CtpA, or both, would remain elongated showing slower checkpoint recovery relative to the WT strain. We grew cultures either in a vehicle control or in the presence of MMC. After a two-hour treatment, the MMC containing media was removed and cells were washed. Cells were then transferred to fresh media without MMC and allowed to continue growth to assay for checkpoint recovery. Although cells appeared to be elongated in the ΔylbL and double mutant strains, there was heterogeneity in the population (Fig 4A). As a result, we measured the cell length of at least 900 cells for each genotype and each condition and plotted the cell length distributions as histograms (Fig 4B). There was no difference in the vehicle control cell length distributions (Fig 4B). The MMC treatment of all strains resulted in a rightward shift in the distribution for all strains (Fig 4B, compare upper panels). When comparing the protease deletions to the WT, the difference in distribution could be visualized by considering the percentage of cells greater than 6.75 μm in length, which is about three cell lengths of 2.25 μm each. We found that deletion of ylbL resulted in an increase in the percentage of cells longer than 6.75 μm in MMC treated cultures and after both two hours and four hours of recovery (Fig 4C). Deletion of ctpA, however, resulted in a very slight, though significant (p-value = 0.0142 for one-tailed Z-test), increased percentage of cells longer than 6.75 μm after 4 hours of recovery (Fig 4C). The double mutant resulted in a percentage of cells slightly greater than ΔylbL alone after both two hours (p-value = 0.0001 for one-tailed Z-test) and four hours (p-value = 0.0088 for one-tailed Z-test) of recovery (Fig 4C).
Taken together, we conclude that YlbL is the primary protease under these conditions, with CtpA also contributing. We also conclude that cells lacking YlbL or both YlbL and CtpA take longer to divide following exposure to MMC, which is consistent with DNA damage sensitivity resulting from inhibition of cell proliferation. Further, the observation of inhibition of cell proliferation suggests that YlbL and CtpA proteases could be important for DNA damage checkpoint recovery (see below).
A potential model to regulate YlbL and CtpA in response to DNA damage is to increase protein levels following exposure to DNA damage. Increased protease levels in response to DNA damage could promote the DNA damage checkpoint recovery when needed. To test this model, we monitored YlbL and CtpA protein levels via Western blotting over the course of the MMC recovery assay. YlbL and CtpA protein levels did not change relative to the loading control DnaN throughout the course of the experiment (S4B & S4C Fig). As a positive control, we performed the same experiment and monitored RecA protein levels and found that, indeed, RecA protein levels increased (S4B & S4C Fig), as expected because recA is induced as part of the SOS response [42, 43]. We conclude that YlbL and CtpA protein levels are not regulated by DNA damage.
The data presented thus far led us to hypothesize that in the absence of YlbL and CtpA, a protein accumulates which results in inhibition of cell division (Fig 5A). To identify the accumulating protein, we performed an analysis of the entire proteome of WT and double protease mutant cell extracts. We chose to analyze the proteomes of cells after two hours of recovery, because the cell length distributions differed most between WT and the double protease mutant (Fig 4B). We found that the normalized spectral count data had similar distributions for both WT and the double mutant, which were approximately log normal (S5A Fig). We verified that the distribution of the test statistic (the difference in double mutant average and WT average) was normally distributed (S5B Fig), thus allowing a t-test to be used. We also performed a principle component analysis and found that WT replicates and double mutant replicates each clustered together (S5C Fig).
In total, 2329 proteins were detected (S4 Table), and 183 proteins were found to be differentially represented (p-value < 0.05) in the double mutant relative to WT (S5 Table). Of the proteins differentially represented in the double mutant, 104 had a fold change greater than one (Fig 5B, red points). There are three major mechanisms that have been reported in B. subtilis to inhibit cell division: 1) Noc dependent nucleoid occlusion [44], 2) FtsL depletion [45, 46], and 3) expression of YneA [23]. One possibility was that Noc protein levels were higher in the double mutant, but we observed no difference in Noc levels (S5D Fig). Another possibility was that FtsL or the protease RasP, which degrades FtsL, was affected in the protease mutant background [46]. We found no difference in relative protein abundance of FtsL or RasP (S5D Fig), ruling out the FtsL/RasP pathway. Among the top 10 proteins that were more abundant in the double mutant was YneA, the SOS-dependent cell division inhibitor (S5 Table). We asked if the enrichment of YneA was simply because it is SOS induced. We analyzed the relative abundance of several other proteins that are known to be SOS induced, including RecA, UvrA, UvrB, DinB, and YneB [42], which is in an operon with YneA [23]. We found that none of these other proteins were enriched in the double mutant (S5E Fig). These results suggest that YneA accumulation is not a result of increased SOS activation, and regulation of YneA accumulation is likely to be post translational, because the protein levels of another member of the operon, YneB were unchanged. Taken together, our proteomics data suggest that YlbL and CtpA promote DNA damage checkpoint recovery through regulating YneA protein abundance.
We directly tested for YneA accumulation in protease mutants throughout the MMC recovery assay using Western blotting. YneA accumulated in all protease deletion strains after 2 hours and 4 hours of recovery, though YneA accumulation in ΔctpA was slight (Fig 5C). In the double mutant, YneA accumulated in the MMC treatment condition in addition to both recovery time points (Fig 5C). In the double mutant we observed multiple YneA species, which we hypothesize to be the result of unnaturally high YneA protein levels resulting in non-specific cleavage by other proteases. With these results, we suggest that YneA is a substrate of YlbL and CtpA, both of which degrade YneA allowing for checkpoint recovery.
Although accumulation of YneA fit our data well, we considered that the other proteins enriched greater than five-fold in the double mutant may have contributed to the DNA damage sensitivity phenotype. To test this, we constructed deletions of each gene in WT and the double mutant and tested for MMC sensitivity. We found that no single deletion of each of the 10 genes resulted in sensitivity to MMC (S6A Fig). In the double mutant, only deletion of yneA was able to rescue the sensitivity to MMC (S6A Fig). We verified that deletion of yneA could rescue MMC sensitivity in all protease mutant backgrounds (Fig 6A). We examined cell length in the DNA damage recovery assay. As expected, deletion of yneA resulted in less severe cell elongation relative to WT (compare WT in Fig 4B and ΔyneA∷loxP in Fig 6B). In addition, deletion of ylbL, ctpA, or both no longer changed the cell length distribution in the absence of yneA at the two-hour recovery time point (Figs 6B, 6C and S6B). In the MMC treatment, we did observe a slight increase (p-value = 0.0004 for one-tailed Z-test) in the percentage of cells greater than 6.75 μm in the double protease deletion strain compared to WT (Fig 6C). Given that MMC sensitivity and most cell elongation in protease mutants depends on yneA, we hypothesized that expression of YneA alone would be sufficient to inhibit growth to a greater extent in the protease mutants. Indeed, strains lacking YlbL, CtpA or both were more sensitive to over-expression of yneA from an IPTG inducible promoter than WT (Fig 6D). Further, we show that YneA accumulated in the protease mutant strains following yneA ectopic expression (Fig 6E). We conclude that YneA accumulation results in severe growth inhibition in cells lacking YlbL and CtpA.
To test the hypothesis that YneA is a direct substrate of the proteases we purified YneA (a.a. 28–103), CtpA (a.a 38–466), and YlbL (a.a 36–341) lacking their N-terminal transmembrane domains to allow for isolation. We were unable to detect protease activity from YlbL using YneA, lysozyme, or casein as substrates (S7 Fig; see discussion). When purified CtpA was incubated with YneA, we observed digestion of YneA over time, but no digestion was observed using CtpA-S297A (Fig 7A). To test if CtpA activity against YneA was specific we completed the same reaction using lysozyme as a substrate and detected no activity (Fig 7B). We conclude that YneA is a direct and specific substrate of CtpA.
Although we could not detect YlbL protease activity in vitro we asked if YlbL could interact with YneA. To test this, we used a bacterial two-hybrid assay [47, 48]. We used this assay because it is effective at detecting interactions between membrane proteins [22, 26, 49]. We tested YlbL-S234A and CtpA-S297A to prevent digestion of YneA, and assayed for interaction with full length YneA or YneA without its transmembrane domain (YneAΔN) as a control. We found that YlbL and CtpA both interacted with full length YneA (Fig 7C), but no interaction was detected with YneAΔN (Fig 7C), likely due to YneA failing to localize to the membrane. Given that YlbL did not have activity in vitro and we detected an interaction with YneA in the bacterial two-hybrid assay, we suggest that YneA is a direct substrate of YlbL and that YlbL requires full length YneA for interaction.
How do YlbL and CtpA recognize YneA as a substrate? An intriguing facet of this checkpoint recovery mechanism is the use of unrelated proteases. YlbL and CtpA both have transmembrane domains and PDZ domains, but the peptidase domains are very different. CtpA has a S41 peptidase domain and is homologous to Tail-specific protease or Tsp (also Prc), which recognizes the C-terminus of its substrate through its PDZ domain [50, 51]. We suggest that CtpA also recognizes YneA through the PDZ domain and that this mechanism explains how CtpA recognizes its other cognate substrates. In fact, the study by Mo and Burkholder identified a residue at the C-terminus of YneA (D97) which when mutated to alanine stabilizes YneA [27]. It is tempting to speculate that D97 in YneA is important for CtpA to recognize YneA. The mechanism by which YlbL recognizes YneA is less clear. YlbL has a unique domain organization not found in other studied proteases. YlbL does not have the AAA+ ATPase domain common in other Lon proteases, which is logical given that YlbL likely resides extracellularly in order to degrade YneA. Instead of an ATPase domain, YlbL has a PDZ domain, which could act as a substrate recognition domain or as an inhibitory domain similar to the PDZ domain of DegS [52–54]. The bacterial two hybrid assay suggests that YlbL does recognize YneA directly (Fig 7C), though we cannot rule out the possibility that there is an adaptor protein that recognizes YneA when these proteins are tethered to the membrane. We also did not identify a potential adaptor in the Tn-seq data further suggesting a direct interaction does occur between YlbL and YneA. A final possibility is that YlbL recognizes YneA through the transmembrane domain, which then activates the Lon peptidase domain to degrade or cleave YneA. This would also explain the reason we were unable to detect activity using YlbL lacking its transmembrane domain. In any case, further experiments are necessary to elucidate the mechanism by which YlbL recognizes YneA.
All organisms control cellular processes through regulated signaling. To regulate a cellular process a signaling pathway must have mechanisms of activation and inactivation. Many bacteria use a small membrane protein as an SOS-induced DNA damage checkpoint protein [22–25]. The mechanism of checkpoint recovery, however, for organisms using membrane protein checkpoints has remained unclear. Our comprehensive study identified a dual protease mechanism of DNA damage checkpoint recovery (Fig 7D). Proteases YlbL and CtpA are constitutively present in the plasma membrane of cells even in the absence of DNA damage. After encountering DNA damage, YneA expression is induced. We hypothesize that YlbL and CtpA activities become saturated by increased YneA expression, which results in a delay of cell division. Following DNA repair, expression of YneA decreases and YlbL and CtpA clear any remaining YneA allowing cell division to resume.
DNA damage checkpoints are of fundamental importance to biology, and we have discovered the pathway responsible for checkpoint inactivation and cell cycle re-entry in B. subtilis. These findings represent an important advance in identifying how checkpoint recovery occurs in bacteria. The membrane bound cell division inhibitors identified to date [22–25], are not homologs of YneA, and in fact the only unifying feature is that they are small membrane bound proteins [22–25]. This poses a great challenge because the components of checkpoint enforcement and recovery need to be experimentally identified. Our study serves as a model to identify the checkpoint recovery proteases through forward genetics, which in turn could be used to identify the enforcement protein through proteomics. The critical feature of our approach was the use of several growth periods in Tn-seq, which allowed us to identify both proteases. Thus, we propose a strategy using the combined approaches of forward genetics and targeted proteomics to identify the DNA damage checkpoint pathways in genetically tractable bacterial pathogens.
Recovery from a DNA damage checkpoint is a critical process for all organisms. One theme found throughout biology is the use of multiple proteins with overlapping functions. In eukaryotes, the phosphorylation events that establish the checkpoint are removed by multiple phosphatases [55, 56]. In E. coli, there are two cytoplasmic proteases, Lon and ClpYQ, that have been found to degrade the cell division inhibitor SulA [16, 18–20]. Our study further extends the use of multiple factors in regulating checkpoint recovery to B. subtilis, by describing a mechanism using two proteases. In eukaryotes, multiple proteins with overlapping functions often exist due to spatial or temporal restrictions, which appears to at least partially explain the use of multiple factors in checkpoint recovery [55, 56]. In E. coli, ClpYQ was found to be important at higher temperatures in the absence of Lon [19], again suggesting that each protease functions under specific conditions. In the case of YlbL and CtpA, however, there appears to be a shared responsibility in rich media. Deletion of each protease results in DNA damage sensitivity and the double mutant has a more severe sensitivity. In contrast, during growth in minimal media, YlbL appears to be the primary protease, as the cell elongation phenotype is more pronounced in cells lacking ylbL. Still it is unclear how or when each protease functions. Why isn’t one protease sufficient to degrade YneA? Do the proteases occupy distinct loci in the cell, requiring that each protease degrades a specific YneA pool? Another possibility is that protease levels are constrained by another evolutionary pressure, such as substrates unique to each protease. Thus, the cell cannot maintain the individual proteases at levels required to titrate YneA as part of the DDR, because the levels of another substrate would be too low. Another explanation is that using multiple factors is an evolutionary strategy that increases the fitness of an organism. It is clear that checkpoint recovery is crucial, because the fitness of cells lacking ylbL or ctpA is significantly decreased in the presence of DNA damage (S2 Table).
Although the dual protease mechanism described here resolves an important step in the DDR, our data also reveal the complexity of the system. After we exposed cells to MMC the cells elongated. We noticed however, that not all elongation depended on yneA (see Fig 6B), suggesting another mechanism for cell cycle control. In B. subtilis, there have been reports of yneA-independent control of cell division following replication stress [45, 57, 58]. The essential cell division component FtsL has been reported to be unstable and depletion leads to inhibition of cell division [58]. Further, ftsL transcript levels were reported to decrease following replication stress independent of the SOS response [45], thus linking depletion of the unstable FtsL protein to cell division control following replication stress. A study using a replication block consisting of the Tet-repressor bound to a Tet-operator array, observed cell division inhibition independent of yneA, noc, and FtsL [57]. Interestingly, recent studies of Caulobacter crescentus uncovered two cell division inhibitors that are expressed in response to DNA damage, with one inhibitor SOS-dependent and the other SOS-independent [22, 26]. In B. megaterium, a recent study found that the transcript of yneA is unstable following exposure to DNA damage [59], suggesting yet another layer of regulation. No factor was identified to regulate yneA transcripts in the previous study, though it is possible that one of the genes of unknown function identified in our screens could regulate yneA mRNA. Together, these studies highlight the complexity of regulating the DNA damage checkpoint in bacteria.
Bacterial strains, plasmids, and oligonucleotides used in this study are listed in S6 Table and the construction of strains and plasmids is detailed in the supplemental methods. All Bacillus subtilis strains are isogenic derivatives of PY79 [60]. Bacillus subtilis strains were grown in LB (10 g/L NaCl, 10 g/L tryptone, and 5 g/L yeast extract) or S750 minimal media with 2% glucose (1x S750 salts (diluted from 10x S750 salts: 104.7g/L MOPS, 13.2 g/L, ammonium sulfate, 6.8 g/L monobasic potassium phosphate, pH 7.0 adjusted with potassium hydroxide), 1x metals (diluted from 100x metals: 0.2 M MgCl2, 70 mM CaCl2, 5 mM MnCl2, 0.1 mM ZnCl2, 100 μg/mL thiamine-HCl, 2 mM HCl, 0.5 mM FeCl3), 0.1% potassium glutamate, 2% glucose, 40 μg/mL phenylalanine, 40 μg/mL tryptophan) at 30°C with shaking (200 rpm). Mitomycin C (MMC), methyl methane sulfonate (MMS), and phleomycin were used at the concentrations indicated in the figures. The following antibiotics were used for selection in B. subtilis as indicated in the method details: spectinomycin (100 μg/mL), chloramphenicol (5 μg/mL), and erythromycin (0.5 μg/mL). Selection of Escherichia coli (MC1061 or TOP10 cells for cloning or BL21 for protein expression) transformants was performed using the following antibiotics: spectinomycin (100 μg/mL) or kanamycin (50 μg/mL).
A transposon insertion library was constructed similar to [61] with modifications described in the supplemental methods. Tn-seq experiments were designed with multiple growth periods similar to a prior description [28], with a detailed description in the supplemental methods. Sequencing library construction and data analysis were performed as described previously [35, 61] with modifications described in the supplemental methods. Sequencing data were deposited into the GEO database with accession number GSE109366.
B. subtilis strains were struck out on LB agar and incubated at 30°C overnight. The next day, a single colony was used to inoculate a 2 mL LB culture in a 14 mL round bottom culture tube, which was incubated at 37°C on a rolling rack until OD600 was 0.5–1. Cultures were normalized to OD600 = 0.5 and serial diluted. The serial dilutions were spotted (4 μL) on the agar media indicated in the figures and the plates were incubated at 30°C overnight (16–20 hours). All spot titer assays were performed at least twice.
Survival assays using an acute treatment of mitomycin C were performed as previously described [62]. Cultures were grown to an OD600 of about 1, and triplicate samples of 0.6 mL of an OD600 = 1 equivalent were taken and cells were pelleted via centrifugation: 10,000 g for 5 minutes at room temperature (all subsequent centrifugation steps were identical). Cells were washed with 0.6 mL 0.85% NaCl (saline) and pelleted via centrifugation. Cell pellets were re-suspended in 0.6 mL saline, and 100 μL aliquots were distributed for each MMC concentration. MMC was added to each tube to yield the final concentration stated in the figure in a total volume of 200 μL, and cells were incubated at 37°C for 30 minutes. Cells were pelleted via centrifugation to remove MMC, re-suspended in saline, and a serial dilution yielding a scorable number of cells (about 30–300) was plated on LB agar to determine the surviving fraction of cells. Each experiment was performed three times in triplicate for each strain.
Purified proteins (see supplemental methods for purification protocols) were submitted to Covance for antibody production using rabbits. Two rabbits were used in the 77 day protocol, and the serum with the least background was used for Western blots.
For YlbL, CtpA, RecA, and DnaN Western blots, a cell pellet equivalent of 1 mL OD600 = 1 was re-suspended in 100 μL 1x SMM buffer (0.5 M sucrose, 0.02 M maleic acid, 0.02 M MgCl2, adjusted to pH 6.5) containing 1 mg/mL lysozyme and 2x Roche protease inhibitors at room temperature for 1 or 2 hours. Samples were then lysed by addition of 6x SDS loading dye (0.35 M Tris, pH 6.8, 30% glycerol, 10% SDS, 0.6 M DTT, and 0.012% bromophenol blue) to 1x. Samples (12 μL) were separated via 10% SDS-PAGE, and transferred to nitrocellulose using a Trans-Blot Turbo (BioRad) according to the manufacturer’s directions. Membranes were blocked in 5% milk in TBST (25 mM Tris, pH 7.5, 150 mM NaCl, and 0.1% Tween 20) at room temperature for 1 hour or at 4°C overnight. Blocking buffer was removed, and primary antibodies were added in 2% milk in TBST (αYlbL, 1:5000 or 1:8000; αCtpA, 1:5000; αRecA, 1:4000; αDnaN, 1:4000). Primary antibody incubation was performed at room temperature for 1 hour or overnight at 4°C. Primary antibodies were removed and membranes were washed three times with TBST for 5 minutes at room temperature. Secondary antibodies (Licor; 1:15000) were added in 2% milk in TBST and incubated at room temperature for 1 hour. Membranes were washed three times as above and imaged using the Li-COR Odyssey imaging system. All Western blot experiments were performed at least twice with independent samples. Molecular weight markers were used in the YlbL and CtpA blots. YlbL migrates to a location between the 30 and 40 kDa markers, consistent with its predicted molecular weight of 37.6 kDa. CtpA migrated to a location between the 40 and 80 kDa markers consistent with its molecular weight of 51.1 kDa.
For YneA Western blots, cell pellets, 10 mL OD600 = 1 for MMC recovery assay and 25 mL OD600 = 1 for over-expression, were re-suspended in 400 or 500 μL, respectively, of sonication buffer (50 mM Tris, pH 8.0, 10 mM EDTA, 20% glycerol, 2x Roche protease inhibitors, and 5 mM PMSF), and lysed via sonication. SDS loading dye was added to 2x and samples were incubated at 100°C for 7 minutes. Samples (10 μL) were separated using 16.5% Tris-Tricine-SDS-PAGE (BioRad) and transferred to a nitrocellulose membrane using a Trans-blot Turbo (BioRad) according to the manufacturer’s directions. All subsequent steps were performed as above with a 1:3000 primary antibody dilution.
An LB agar plate grown at 30°C overnight was washed with pre-warmed S750 minimal media and used to inoculate a culture of S750 minimal media at an OD600 = 0.1. The cultures were incubated at 30°C until an OD600 of about 0.2 (2–2.5 hours). MMC was added to 100 ng/mL and cultures were incubated at 30°C for 2 hours. Cells were pelleted via centrifugation (4,696 g for 7 minutes) and the media was removed. Cell pellets were washed in an equal volume of 1x PBS, pH 7.4, and pelleted again via centrifugation as above. Cell pellets were re-suspended in an equal volume of pre-warmed S750 minimal media and incubated at 30°C for four hours. Samples for microscopy and Western blot analysis were taken after the two hour MMC treatment and at two and four hours following recovery, as indicated in the figures. The vehicle control samples were treated for 2 hours with an equivalent volume of the vehicle in which MMC was suspended (25% (v/v) DMSO).
A 500 μL sample from the MMC recovery assay above was taken and FM4-64 was added to 2 μg/mL and incubated at room temperature for 5 minutes. Samples were then transferred to 1% agarose pads made of 1x Spizizen’s salts. Images were captured using an Olympus BX61 microscope.
Cells were scored for cell length using the measuring tool in ImageJ software. For each image scored, all cells that were in focus were measured. The number of cells scored for each strain/condition is stated in the figures (n = cells measured). The histograms were generated using ggplot2 in R. All scoring was done using unadjusted images. Representative images shown in the figures were modified in ImageJ by subtracting the background (rolling ball radius method) and adjusting the brightness and contrast. Any adjustments made were applied to the entire image.
Samples (5 mL OD600 = 1) were harvested from cultures grown as described in the MMC recovery assay section at 2 hours recovery via centrifugation: 4,696 g at room temperature for 10 minutes. Samples were washed twice with 500 μL 1x PBS, pH 7.4 and pelleted via centrifugation: 10,000 g at room temperature for 5 minutes. Samples were frozen in liquid nitrogen and stored at -80°C. Samples were submitted for mass-spectrometry analysis to MS Bioworks. Further sample processing and data analysis was performed by MS Bioworks as described in the supplemental methods. The raw data files are available upon request and the processed data tables are provided as supplemental tables (S4 and S5 Tables).
YneA digestion reactions were prepared as a 20 μL reaction in 20 mM Tris pH 7.5, 20 mM NaCl, and 20% glycerol containing 150 μM YneA, and 2 μM CtpA. Reactions were incubated at 30°C for the time indicated in the figure. Reactions were stopped by addition of 6x SDS-dye to 1x and incubating at 100°C for 5 minutes. Reaction products were separated via 16.5% Tris-Tricine SDS-PAGE. Proteins were detected by staining with coomassie blue. Lysozyme digestion assays were performed as for YneA using 2 mg/mL lysozyme and reactions were incubated at 30°C for 3 hours.
Bacterial two hybrid assays were performed as previously described [47, 48, 63]. Briefly, T18 and T25 fusion plasmids were co-transformed into BTH101 cells and co-transformants were selected on LB agar + 100 μg/mL ampicillin + 50 μg/mL kanamycin at 37°C overnight. Cultures of LB + 100 μg/mL ampicillin + 50 μg/mL kanamycin were inoculated using several colonies and incubated at 37°C for 90 minutes. Cultures were diluted 250-fold and 4 μL were spotted on LB agar + 100 μg/mL ampicillin + 50 μg/mL kanamycin + 0.5 mM IPTG + 40 μg/mL X-gal and incubated at 30°C for 48 hours, then at room temperature for 24 hours. The brightness and contrast of the images were adjusted using Adobe Photoshop with changes applied to the entire image. All bacterial two-hybrid assays were performed at least twice.
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10.1371/journal.pbio.1000445 | Auditory Cortex Tracks Both Auditory and Visual Stimulus Dynamics Using Low-Frequency Neuronal Phase Modulation | Integrating information across sensory domains to construct a unified representation of multi-sensory signals is a fundamental characteristic of perception in ecological contexts. One provocative hypothesis deriving from neurophysiology suggests that there exists early and direct cross-modal phase modulation. We provide evidence, based on magnetoencephalography (MEG) recordings from participants viewing audiovisual movies, that low-frequency neuronal information lies at the basis of the synergistic coordination of information across auditory and visual streams. In particular, the phase of the 2–7 Hz delta and theta band responses carries robust (in single trials) and usable information (for parsing the temporal structure) about stimulus dynamics in both sensory modalities concurrently. These experiments are the first to show in humans that a particular cortical mechanism, delta-theta phase modulation across early sensory areas, plays an important “active” role in continuously tracking naturalistic audio-visual streams, carrying dynamic multi-sensory information, and reflecting cross-sensory interaction in real time.
| When faced with ecologically relevant stimuli in natural scenes, our brains need to coordinate information from multiple sensory systems in order to create accurate internal representations of the outside world. Unfortunately, we currently have little information about the neuronal mechanisms for this cross-modal processing during online sensory perception under natural conditions. Neurophysiological and human imaging studies are increasingly exploring the response properties elicited by natural scenes. In this study, we recorded magnetoencephalography (MEG) data from participants viewing audiovisual movie clips. We developed a phase coherence analysis technique that captures—in single trials of watching a movie—how the phase of cortical responses is tightly coupled to key aspects of stimulus dynamics. Remarkably, auditory cortex not only tracks auditory stimulus dynamics but also reflects dynamic aspects of the visual signal. Similarly, visual cortex mainly follows the visual properties of a stimulus, but also shows sensitivity to the auditory aspects of a scene. The critical finding is that cross-modal phase modulation appears to lie at the basis of this integrative processing. Continuous cross-modal phase modulation may permit the internal construction of behaviorally relevant stimuli. Our work therefore contributes to the understanding of how multi-sensory information is analyzed and represented in the human brain.
| We do not experience the world as parallel sensory streams; rather, the information extracted from different modalities fuses to form a seamlessly unified multi-sensory percept dynamically evolving over time. There is a compelling benefit to multimodal information: behavioral studies show that combining information across sensory domains enhances unimodal detection ability—and can even induce new, integrated percepts [1]–[4]. The relevant neuronal mechanisms have been widely investigated. One typical view posits that multisensory integration occurs at later stages of cortical processing, subsequent to unisensory analysis. This view has been supported by studies showing that higher, “association” areas in temporal, parietal, and frontal cortices receive inputs from multiple unimodal areas [5]–[8] and respond to stimulation in manner that reflects multisensory convergence, for example with amplified or suppressed responses for multimodal over unimodal stimuli [9]–[12].
A growing body of evidence provides a complementary view, suggesting that cross-modal interaction is not restricted to association areas and can occur at early, putatively unisensory cortical processing stages [11],[13]. For example, non-auditory stimulation (visual and somatosensory) has been found to drive auditory cortical activity, as observed in both humans and animals [4],[14]–[23]. Similarly, visual cortical responses are modulated by inputs from other modalities [24],[25]. Importantly, independent anatomical evidence also reveals direct connections among early sensory areas [26],[27]. Therefore, multisensory integration may operate through lateral cross-sensory modulation, and there exist multiple integration pathways beyond purely hierarchical convergence [12],[28],[29].
How is early cortical activity coordinated? Beyond the classical examination of cross-modal influences on neuronal firing rate, recent studies suggest temporal coherence [30],[31] to underlie multisensory integration [28],[32]. This view posits that oscillations synchronous across different brain areas might serve an essential role in multisensory binding, similarly as that for feature binding and attentional selection [30],[33]–[36]. Several EEG/MEG studies in humans implicate oscillations and cross-area coherence in multisensory integration [29],[37]–[42]. However, most of the studies employed short, transient multisensory stimuli and focused on the evoked transient oscillatory power instead of examining sustained cross-modal modulation for long, naturalistic audiovisual streams.
Importantly, with regard to the cross-area modulation mechanism, it has recently been suggested that cross-sensory phase modulation may underlie this interaction [28],[32],[43],[44]. For example, non-auditory inputs (re)set the phase of ongoing local neuronal activity in auditory cortex to a high-excitability state (reflected in phase angle), effectively “selecting” or amplifying the response to subsequent auditory inputs [11],[13],[20],[22],[45]. Whether such a mechanism is implemented in populations of neurons and could mediate the perception of audiovisual speech in human viewers/listeners is completely unknown.
In order to test directly the proposal of cross-modal phase modulation of oscillatory neural activity, we investigate online audiovisual interaction, in auditory and visual cortices simultaneously, by recording magnetoencephalography (MEG) responses from human participants presented with 30-s-long natural movie clips from the movie “Dumb and Dumber” (1994, New Line Platinum Series). These video segments had either “matched” (congruent audio-visual combinations, V1A1, V2A2, V3A3) or “mixed” streams (incongruent audio-visual, V1A3, V2A1, V3A2). Building on our previous results showing that the theta-band phase pattern in human auditory cortex reflects the dynamic structure of spoken sentences [46], we employed a new trial-by-trial phase tracking analysis to explore multi-sensory integration. We conjectured that, in response to naturalistic audio-visual streams (movies), the low-frequency phase of auditory and visual sensory activity in single trials (i) will robustly track and discriminate (in a classification analysis) the sensory stream dynamics in each modality (“within-modality tracking”; i.e. auditory channel tracks auditory, visual tracks visual dynamics), (ii) may carry information about stimulus dynamics in the other modality (“cross-modality tracking”; e.g. an auditory channel can reflect visual dynamics), and (iii) that the efficacy of such cross-sensory phase modulation (trial-to-trial phase variance) depends on the relative audiovisual timing, such that a temporally matched audio-visual stream will enhance phase tracking reliability, compared to unmatched (mixed) pairs. Our data support these predictions, highlighting the critical role of cross-sensory phase modulation of oscillations in multisensory integration, commensurate with the hypothesis [28],[44]. We thus argue that multi-sensory integration may use cross-modal phase modulation as a basic mechanism to construct temporally aligned representations that facilitate perceptual decoding of audiovisual speech.
We first assessed whether MEG responses in single trials can reliably track the six movie clips we presented to participants (three Matched, three Mixed). The phase and power pattern of MEG responses to the movies (see illustration of cross-trial phase coherence analysis in Figure 1a) and the corresponding discrimination ability were calculated as a function of frequency of the brain response (0–50 Hz) using previously developed methods [46]. We quantified stimulus-specific trial-by-trial phase and power pattern coherence in 20 auditory and 20 visual channels, which were defined in separate auditory (1 kHz tone pip) and visual (alternating checkerboard) localizer pretests for each subject (see Figure S2). As illustrated in Figure 2a, both auditory and visual cortical responses showed good discrimination ability in the delta-theta-band (2–7 Hz) phase pattern (above zero discrimination score, 2-way ANOVA, main effect of frequency, F(24, 840) = 7.94, p<0.0001; post-hoc one-sample t test in delta-theta band (2∼7 Hz), Auditory: t = 11.57, df = 35, p<0.0001, Visual: t = 11.16, df = 35, p<0.0001). Critically, phase tracking was not accompanied by comparable power pattern tracking (Figure 2b, 2-way ANOVA, main effect of frequency, F(24, 840) = 0.517, p = 0.97; t test in delta-theta band (2∼7 Hz), Auditory: t = 0.913, p = 0.368; Visual: t = 0.698, p = 0.49). These results demonstrate that the phase of ongoing auditory and visual cortical low-frequency oscillations is reliably modulated by the audio-visual stimuli, and thus conveys information about the rich naturalistic dynamics of these multi-sensory movies.
Having established the sensitivity of the low-frequency phase pattern to different audiovisual movie streams using the cross-trial phase coherence (Figure 1a), we next evaluated its modality specificity in auditory and visual cortical responses, by employing a cross-movie coherence analysis (Figure 1b; Figure S3 schematizes the logic). Given the predominantly unisensory characteristics of cortical responses early in the cortical processing hierarchy, the low-frequency phase pattern should be mainly driven by the stimulus in the corresponding sensory modality. We thus tested a double dissociation hypothesis, namely that in auditory channels, movie clips sharing the same auditory input regardless of visual input (stimuli we call “SameAud”) should induce a more similar low-frequency phase pattern response (and display higher cross-movie delta-theta phase coherence) than those containing the same visual but different auditory input (stimuli called “SameVis”); analogously, in visual channels, SameVis movies should yield higher cross-movie delta-theta phase coherence compared to SameAud movie pairs.
For the three matched clips (V1A1, V2A2, V3A3), we selected the corresponding SameVis and SameAud stimuli (see Figure 1b and Figure S3 for visualization of the design; e.g., for matched clip V1A1, its SameVis counterpart is V1A3, its SameAud is V2A1); we then calculated the similarity or coherence between the responses to matched clips and the corresponding SameAud or SameVis mixed clips (), separately for auditory and visual areas. The cross-movie low-frequency phase coherence results (, ) show a double dissociation (Figure 3a; condition×place interaction, F(1, 5) = 10.44, p = 0.023). This confirms the efficacy of the auditory and visual “functional channel localizers”; more importantly, though, this analysis suggests, plausibly, that the phase patterns over auditory and visual areas are predominantly driven by the sensory stimulus structure in the corresponding modality. Critically, the corresponding power coherence (, ) did not show the double dissociation pattern (Figure 3b; condition×place interaction, F(1, 5) = 0.077, p = 0.79), confirming that precise timing—as reflected in the phase of delta and theta activity—plays a dominant role in sensory stream representation.
The modality-dependent characteristics of the delta-theta phase pattern in all 157 recorded channels were verified by comparing the spatial distribution maps of the cross-movie delta-theta phase coherence (, ). We observed a lateral temporal origin of and an occipital origin of in every subject (Figure 4). The spatial distribution results thus confirm the finding that in response to a multi-sensory audiovisual stream, the low-frequency phase of the auditory and visual cortical activities principally and concurrently tracks the respective sensory stimulus dynamics.
We then examined the critical hypothesized cross-modality modulation effects in the low-frequency phase pattern, by studying whether naturalistic visual input can affect the phase of auditory cortical oscillations (as previously only observed using artificial stimuli and in animal data), and similarly whether the auditory dynamic structure influences the phase of ongoing rhythmic activities in visual cortex, to some extent. A cross-movie coherence analysis was again performed (Figure 1b; Figure S3 schematizes the logic), by calculating the coherence or similarity between the responses to matched clips and the corresponding NoSame mixed clips, i.e. movie clip differing in both auditory and visual input (e.g., for matched clip V1A1, V2A2, V3A3, their respective NoSame counterpart is V3A2, V1A3, V2A1), in auditory and visual areas separately.
The logic of this analysis is as follows: If the low-frequency phase pattern in one sensory modality is systematically influenced by the other modality, movies sharing same visual input (SameVis) should show more similar low-frequency phase pattern in auditory cortex, compared to movies differing in both visual and auditory inputs (NoSame); similarly, in visual cortex, the SameAud movies should show higher cross-movie coherence than NoSame movies. Figure 3a shows that the NoSame pair manifested the smallest cross-movie phase coherence (), supporting our hypothesis (3-way ANOVA, condition main effect, F(2, 10) = 36.394, p<0.0001; post-hoc analysis, NoSame versus SameVis, p<0.0001, NoSame versus SameAud, p<0.0001; condition×place interaction, F(2, 10) = 8.467, p = 0.007). The delta-theta power pattern reflects no such effect (Figure 3b). This suggests that in response to an audio-visual stream (e.g., V1A1), the phase of the cortical activity is driven and modulated not only by the input in the corresponding modality (double dissociation result discussed above) but also by input from another modality (cross-sensory phase modulation).
The above cross-movie coherence results demonstrate that the phase pattern in response to an audiovisual stream carries information about both auditory and visual stimulus structure. We next ask whether multisensory tracking is simply a mixture of passive following responses to unisensory stimuli, or—more interestingly—whether phase-tracking plays an active role in multisensory integration, by establishing a cross-modal temporal context in which a unisensory stimulus unfolds and merges into a coherent perceptual representation. We first examined the similarity in the elicited phase pattern response in auditory and visual areas. Given the congruent temporal structure in matched audiovisual stimuli, together with the observed within-modality phase tracking, we predict that both auditory and visual areas show higher similarity in low-frequency phase responses for the matched conditions. The cross-movie analysis results support the hypothesis (Figure 5c, paired t test, t(9) = 2.31, p = 0.046); the corresponding power coherence revealed no statistical difference (Figure 5d, paired t test, t(9) = 1.93, p = 0.086).
In light of the observed similarity between the phase response in the two modalities, we next conjecture that the cross-modality phase modulation will occur in a manner “temporally commensurate” to within-modality phase modulation, leading to more temporally reliable integration and consequently achieving a more robust low-frequency-based representation of audio-visual naturalistic stimuli (enhanced trial-to-trial response reliability) in both sensory areas (not between areas). Importantly, the cross-trial reliability enhancement hypothesis cannot be derived from a passive following response interpretation.
We compared the delta-theta cross-trial phase coherence for the three matched and three mixed movies separately, noting that the three movies in the mixed group contained exactly the same auditory and visual inputs as the matched one—but in incongruent audio-visual combinations (Figure 1a). We observed stronger trial-by-trial delta-theta phase pattern coherence in the matched group than in the mixed group (2-way ANOVA, significant main effect of condition, F(1, 9) = 7.33, p = 0.024), in both auditory and visual areas (Figure 5a). The cross-trial power coherence revealed no significant difference between the two conditions (Figure 5b, condition main effect, 2-way ANOVA, F(1, 9) = 3.64, p = 0.09). The result that the trial-by-trial phase reliability depends on the relative audiovisual temporal relationship thus supports the “active cross-modal phase modulation” hypothesis for multisensory integration. In our view, sensory cortical activity builds a more efficient and robust continuous representation for a temporally congruent multi-sensory stream by mutually modulating the low-frequency phase of ongoing oscillatory activity in an active manner, perhaps facilitating temporal packaging of information that can then act “predicatively” across modalities.
To apply a unified analysis framework to our data, a classification analysis was employed based on the low-frequency (2–7 Hz) phase pattern in single response trials across all six movies. For each of the six movie clips, the delta-theta phase pattern as a function of time for one single trial response under one stimulus condition was arbitrarily chosen as a template response for that movie. The delta-theta phase pattern of the remaining trials of all stimulus conditions was calculated, and their similarity to each of the six templates was defined as the distance to the templates. Responses were then classified to the closest movie template. The classification was computed 100 times for each of the 20 auditory and 20 visual channels in each subject, by randomly choosing template combinations. This classifier analysis shows that the delta-theta phase pattern successfully discriminates among movies. The individual trial data for each condition were predominantly classified as belonging to that condition, for both auditory (Figure 6a) and visual (Figure 6b) areas. Second, the classification results support the tracking hypothesis for matched versus mixed conditions, revealing higher “self”-classification for matched than mixed movies. Third, the modality-specific characteristics of phase tracking were manifested in the classification in that in auditory areas, each of the six movies was categorized to the movie stimulus sharing the same auditory input (SameAud) with larger proportion than to SameVis input, and vice versa for visual areas. Finally, the classification results also support the elevated response reliability by congruent audiovisual stimuli. The response to each movie clip was primarily classified to itself, secondly to the clip sharing the same modality (e.g., SameAud for auditory channels), and thirdly to the movies sharing the same input in the other modality (e.g., SameVis in auditory area), which has a significantly better classification proportion than stimuli differing in both inputs (NoSame). A statistical analysis and summary of the classification data (Figure 6c) underscores the effect of this cross-sensory phase modulation. The results demonstrate that the low-frequency phase pattern in sensory cortices can be relied on for audiovisual stream discrimination in single trial responses, and that it is modulated by input from multiple sensory domains, reflecting an active cross-sensory integration, dynamically evolving in time.
Neurophysiological work in animal preparations suggests that non-auditory inputs can modulate auditory responses towards a preferred excitability state, by aligning the phase of ongoing low-frequency auditory activity with a specific phase angle known to elicit maximal stimulus-driven responses, resulting in the cross-sensory response amplification [20],[22]. We hypothesize that stimulus-induced temporal regularization leads to robust phase tracking, by resetting the phase of the intrinsic low-frequency rhythmic activity to a preferred phase. We thus expect (i) that the cross-trial delta-theta phase coherence is phase dependent, and the phase values corresponding to high cross-trial phase coherence values are non-uniformly distributed and centered on a preferred phase angle, and (ii) that the matched movie elicits a larger fraction of optimal phase compared to the mixed condition, since a temporally congruent stream would achieve cross-sensory phase tracking enhancement, by regularizing low-frequency phase to the optimal phase angle more robustly in each response trial.
We explored the relationship between the cross-trial phase coherence and the corresponding phase angles and observed an increasingly clustered phase angle distribution (around 0 and ±) for higher phase coherence in both auditory and visual areas (Figure 7a, upper and lower panel). As shown in Figure 7b, we further quantified the deviation of phase distribution from uniform distribution as a function of cross-trial phase coherence values, and the results confirm that higher phase coherence corresponds to larger deviation from uniform distribution (2-way ANOVA, F(19, 95) = 67.99, p<0.001), thus suggesting a trend of non-uniform phase clustering for the robust phase tracking pattern. (Note that the drop in the deviation values for the highest phase coherence (∼1) may be due to the artifacts produced by small samples and large variance across subjects during such a high coherence regime.) The findings demonstrate that it is mainly the stimulus-induced delta-theta phase resetting to the preferred phase angle (0 or ±) that regularizes the low-frequency phase pattern in each response trial to improve the phase tracking reliability. In addition, as shown in Figure 7c, the matched movies showed a larger fraction of optimal phase angle (0 or ±) than mixed movies for higher phase coherence (>0.7) in both auditory and visual areas, as hypothesized; statistical testing confirms that phase angle at ± was more relevant to preferred or optimal phase (2-way ANOVA, main effect of condition, F(1, 5) = 5.794, p = 0.06) than phase angle at 0 (2-way ANOVA, main effect of condition, F(1, 5) = 2.856, p = 0.152), commensurate with optimal phase findings in neurophysiological studies [20],[22],[45]. The results support the view that the visual (auditory) stream in a matched movie modulates the auditory (visual) cortical activity by aligning the phase to the optimal phase angle so that the expected auditory (visual) input arrives during a high excitability state, to be amplified and achieve the cross-sensory enhancement. In contrast, mixed, incongruent audiovisual streams cannot benefit from the cross-sensory phase regularization and thus are driven to the preferred phase angle with a significantly smaller fraction than matched movie stimuli.
We examined multi-sensory interaction in early sensory areas in MEG responses recorded from human subjects viewing and listening to natural audio-visual movies. We show that the low-frequency, delta and theta phase pattern in early visual and auditory cortices tracks (and can discriminate among) naturalistic visual and auditory stimuli, respectively, in single MEG response trials. In addition, the low-frequency phase pattern in one sensory domain can, to some extent, represent and track the stimulus structure of the other modality. Importantly, temporally aligned audio-visual streams (“matched”) elicit stronger low-frequency trial-by-trial phase response reliability than non-aligned streams (“mixed”), supporting an active cross-modal phase modulation versus a “passive stimulus following response” interpretation. Finally, the delta-theta phase clusters for stronger phase tracking, indicating that it is phase resetting to the preferred or “optimal phase” that tracks the “within-modality” and “across-modality” stimulus structure. Congruent multisensory stimuli lead to mutual driving towards “optimal phase” more reliably, perhaps to achieve temporally optimized cross-sensory enhancement. We conjecture that the ongoing phase pattern of slow oscillatory activity in sensory cortices provides a unified temporal frame of reference in which continuous multi-sensory streams are seamlessly represented and integrated into a coherent percept.
Unlike pairings of transient artificial stimuli used in most previous audiovisual studies, we examined the cross-modal integration effects in presumptively unimodal areas by employing naturalistic audiovisual movies that are ethologically natural and extended in time (30-s film clips). Naturalistic stimuli contain complex structure and rich dynamics in the time domain, and it has been suggested that the relevant neural mechanisms are in part shaped by the statistical structure of natural environments [47],[48]. Our previous MEG studies revealed that the phase pattern of theta-band responses reliably tracks and discriminates natural spoken sentences [46]. Here we build on and extend the previous findings by showing that delta-theta phase tracking exists for multi-sensory streams and that the low-frequency phase response in auditory and visual cortices reliably tracks audio-visual movies concurrently. There is emerging consensus that the signals quantified in neuroimaging (e.g., MEG signals) reflect synchronized large-scale neuronal ensemble activity and have been found to mainly derive from LFP rather than spiking activity [49]. A recent neurophysiological study in monkeys quantified the information different codes carry about natural sounds in auditory cortex and found that spiking responses interpreted with regard to the relative phase of the accompanying slow ongoing LFP are more informative about the properties of the dynamic sound than spiking responses alone [50]. The same encoding scheme has also been observed in visual cortex in response to natural movies [51]. Our results from human neuroimaging converge with these neurophysiological studies on low-frequency phase tracking for naturalistic streams and are commensurate with the observed essential role of brain oscillations in sensory processing, feature integration, and response selection within the various sensory modalities [30],[34]–[36],[52]. It has been argued that intrinsic rhythms undergo significant phase resetting in response to stimulus presentation [35],[53],[54], and crucially, some studies demonstrate that neuronal oscillations enhance the response robustness to natural stimulation by modulating the excitability state (phase resetting) for spiking activity [55].
Could one argue that the observed delta-theta phase tracking is due to different levels of attention to a given modality, given the important role of attention in multisensory integration [25],[56],[57]? Such a view cannot be a sufficient explanation because the low-frequency phase pattern distinguishes the audio-visual streams belonging to the matched or mixed conditions, both of which elicit similar attentional states. (The three matched (or mixed) movies should elicit similar attentional states, and therefore the delta-theta phase pattern should not be able to discriminate them only based on attentional state.) Interestingly, previous studies show that such cross-sensory interactions occur in anaesthetized animals [19],[21]. These observations suggest that the general attentional level is not the main source underlying the observed delta-theta phase tracking. Recent studies [56],[57] revealed that the phase of low-frequency oscillations in auditory and visual cortex entrains to the rhythm of the attended sensory stream amidst multi-sensory inputs and thus could track either a visual or auditory stimulus. They suggest the phase modulation mechanism to underlie temporally based attention. Their results further challenge an attentional-load explanation for the present data, given the observed modality-specific characteristics (the double dissociation results), and support that the observed delta-theta phase tracking is not due to global modality-independent attentional modulation.
Uncontrolled eye movements also constitute a possible confounding factor, given previous findings reporting the effect of eye position on the auditory cortical responses [17]. We believe that the eye-movement-related activity may contribute to phase modulation in early sensory activity, but not in a dominant way, given that the cross-modal phase modulation exists under both anesthetized conditions [19],[21] and controlled eye fixation conditions [22]. Note that eye movements by themselves cannot account for the observed stronger modulation for matched over mixed audiovisual stimuli; both carry the same visual stream; which should result in a comparable pattern of eye movements. More generally, during the free viewing of movies, eye movements are argued to be tightly correlated with stimulus dynamics, which in turn induces phase tracking in brain signals, and therefore the phase modulation mechanism may also be integral to the temporally based attention. Fries [35] recently proposed a rhythmic input gain model to link attention to brain oscillations and suggested that the strength of gamma-band synchronization (binding by synchronization) is modulated with the theta rhythm, the phase of which makes or breaks selections of input segments, thus constituting a strong link to the “biased competition” modal in visual attention [33].
We found that low-frequency phase patterns were sufficiently reliable to continuously track the naturalistic audiovisual streams. The crucial relevance of low-frequency oscillations to perceptual analysis has been observed in several studies [20],[22],[46],[50],[51]. The acoustic structure of both natural sounds and movies contain rich dynamics on multiple time scales, but with power dominance in the low-frequency range [48],[58]–[60]. Accumulating evidence demonstrates that a coarse representation suffices for the comprehension of natural streams [61]. For example, from the perspective of speech processing, a temporal window of ∼200 ms corresponds to mean syllable length across languages, and such a temporal window has been suggested as a fundamental unit for speech perception [62],[63]. The observed tracking ability of slow quasi-rhythmic (and aperiodic) activity may be simply driven by the input temporal pattern, but we conjecture that it reflects an internal stable processing rhythm [64] that is ideally suited to match the gross statistical temporal structure of natural streams. Recent data [65] demonstrate robust temporal correspondence in the delta-theta range (2∼7 Hz) between visual and auditory streams in multisensory speech signals, supporting this interpretation.
In addition to the essential role of long-duration time scales in natural stimuli, the dynamic structure at other biologically relevant scales, especially the short windows (e.g., ∼25 ms) corresponding to gamma band oscillation, also carries important information [62],[64]. Several previous studies show the relevance of gamma oscillations to multisensory integration, but in contexts of transient or evoked responses [40],[42], which is a very different approach from ours. In the current work, we examine the sustained response pattern to natural complex audiovisual scenes and the relevance to multisensory integration. A possible factor accounting for the absence of evidence for fast, gamma rhythms in tracking might lie in the task demands; subjects were only asked to passively view and listen to the audiovisual streams, without requiring their focused, selective attention to fast transitions, phonemes, any aspect of sublexical information, etc. Crucially, both unimodal and multimodal naturalistic streams contain various temporal scales that are nested within each other. For example, in human speech, high-frequency events (e.g., formant transitions) are temporally nested within low-frequency structures (e.g., syllables, phrases). Correspondingly, human cortical oscillations at different frequencies also manifest similar temporally nested relationships and tend to be phase-amplitude coupled [66]. Such cross-scale coupling in both naturalistic extended stimuli and brain oscillations are consistent with the “sampling window hypothesis” for speech perception [62], and further indicate a general cross-scale modulation mechanism underlying multi-sensory interaction [56].
The central finding concerns the hypothesis of active cross-modality phase modulation of endogenous oscillations in a multi-sensory context. Specifically, we observed that the auditory and visual modalities can mutually and actively modulate the phase of the internal low-frequency rhythms in early sensory cortical regions and that such cross-sensory driving efficiency depends on the relative audiovisual timing. A study recording A1 in awake macaques [20] revealed phase modulation in multi-sensory interaction: somatosensory inputs enhanced auditory processing by resetting the phase of ongoing neuronal oscillations in A1 so that the accompanying auditory input arrived during a high-excitability phase. A further neurophysiological experiment exploring the impact of visual stimulation on auditory responses demonstrated that visual stimuli modulated auditory cortex activity, at the level of both LFP and single-unit responses [22]. Importantly, they too found that the observed cross-sensory enhancement correlated well with the resetting of slow oscillations to an optimal phase angle, and the multi-sensory interactions were sensitive to the audiovisual timing. Moreover, they discovered that matched audiovisual stimuli enhanced the trial-to-trial response reliability in auditory cortex of alert monkeys [45], precisely like one of our central findings of a tight link between cross-sensory modulation efficacy and relative audiovisual timing congruency. Our results in humans are thus in good agreement with these animal data and also implicate neural mechanisms accounting for previous behavioral results showing temporally matched visual amplification of auditory processing, in both monkeys [67] and human subjects [4],[68].
Given the simple binary design here (matched versus mixed), further studies need to be executed by continuously jittering the temporal relationship between auditory and visual stimuli and investigating the influences in both behavior and cross-modal low-frequency phase modulation in a more systematic way. Recently, Schroeder et al. [44] proposed a phase-resetting-based mechanism to solve the “cocktail party” problem using such a mechanism and hypothesized that the visual amplification of speech perception is operating through efficient modulation or “shaping” of ongoing neuronal oscillations. Our results support such a model and indicate that multi-sensory integration is at least in part based on a cross-modal phase resetting mechanism in early cortical sensory regions. The phase patterns of the ongoing rhythmic activity in early sensory areas help construct a temporal framework that reflects both unimodal information and multimodal context from which the unified multisensory perception is actively constructed. However, we do not exclude the existence of multiple multisensory integration pathways, as shown in a recent study [29] demonstrating the convergence of lateral and feedback in multisensory integration, given the complex characteristics of integration. In a more general sense, we surmise that the dynamic interplay of neural populations [28] constitutes a unified temporal framework where the segmented senses unfold and merge, resulting in the seamless multisensory-integrated dynamic world we perceive. Further human studies with better spatial resolution (e.g., intracranial EEG in humans and fMRI+EEG recording) may help to address the issue in a more granular way. The results from this human MEG experiment suggest that neuroimaging data can make a fruitful contribution to our understanding of neural coding, building on concepts of neural timing that can be exploited productively at the levels of analysis of large neuronal populations.
Six right-handed subjects provided informed consent before participating in the experiment. All subjects had normal vision and hearing. We have acquired data from additional four subjects (10 subjects in total then) to specifically investigate matched versus mixed cross-trial low-frequency phase coherence difference (as shown in Figure 5). Neuromagnetic signals were recorded continuously with a 157 channel whole-head MEG system (5 cm baseline axial gradiometer SQUID-based sensors; KIT, Kanazawa, Japan) in a magnetically shielded room, using a sampling rate of 1,000 Hz and an online 100 Hz analog low-pass filter, with no high-pass filtering.
Three audio-visual movie clips (V1+A1, V2+A2, V3+A3) were selected from the movie “Dumb and Dumber” (1994, New Line Platinum Series) to form the three “Matched” movie stimuli (see Figure S1). We constructed another three “Mixed” movie clips, by shuffling the auditory and visual combinations (V1+A3, V2+A1, V3+A2). All six movie clips contained natural conversation in an audiovisual setting and were 30 s in duration. Prior to the movie experiment, the subjects participated in one auditory localizer pretest in which they were presented with 1 kHz tone pips (duration 50 ms) and one visual localizer pretest in which they were presented with alternating checkerboard stimuli. Both pretests were performed to collect functional localization data for auditory and visual cortices (to identify the most responsive channels, Figure S2). Subjects were told to passively view and listen to the six audio-visual stimulus streams (no explicit task) presented on a rear projection screen in the shielded room screen (the clips subtended ∼18 deg horizontal and 11 deg vertical visual angles, presented at typical photopic luminance values) without restriction on eye movements. Each of the six movie clips was presented 15 times, in two separate blocks (Matched block and Mixed block), with the audio track presented at a comfortable loudness level (∼70 dB).
In the auditory localizer pretest, the large electrophysiological response peak with latency around 100 ms after tone-pip onset was determined (M100 or N1m) and the 20 channels with largest response amplitude were defined as the auditory channels. These channels, unsurprisingly, largely lie over the temporal lobe. In the visual localizer pretest, the 20 channels with largest response amplitude at the response peak with latency around 150 ms were selected as visual channels (typically occipital). The channel selection procedure was performed for each subject separately, and all subsequent analysis was done on those independently selected channels to represent auditory and visual cortical activity, respectively. There was no overlap among the channel groups.
For each of the six audio-visual stimuli (15 trials of each), 12 out of 15 response trials were chosen and termed “within-group” signals (six within-group signals corresponding to six movie stimuli). Note that selecting 12 trials out of 15 trials here was simply due to this specific discrimination analysis that required trial number to be an integer number of 6 (the stimulus condition number); the following other analyses were performed on all the 15 response trials. Two response trials (one-sixth of the 12 trials for each stimulus condition) were chosen from each of the six groups and combined to construct a 12-trial “across-group” signal. Six across-group signals were constructed by repeating the combination procedure six times. For each of the twelve 12-trial signal groups (six within-group and six across-group signals), the spectrogram of the entire 30 s of each single trial response was calculated using a 500 ms time window in steps of 100 ms, for each of the 20 auditory channels and 20 visual channels defined for each subject. The phase and power were calculated as a function of frequency and time and were stored for further analysis. The “cross-trial phase coherence” () and “cross-trial power coherence” () were calculated aswhere and are the phase and absolute amplitude at the frequency bin i and temporal bin j in trial n, respectively. These calculated cross-trial coherence parameters ( and ) are dimensionless quantity and were compared between each of six within-group signals and each of six across-group signals separately. The discrimination function (also dimensionless quantity) for each frequency bin i was defined as
The resulting six discrimination functions for each of the six subjects were then averaged. A value significantly above 0 indicates larger cross-trial coherence of within-group signals than across-group signals. The average values within delta and delta-theta ranges (∼2–7 Hz) from and were then selected for further analysis, given the above-zero discrimination score in this frequency range in function (upper panel of Figure 1). Importantly, note the different formulas from which phase coherence and power difference are derived, due to their different characteristics. We calculated power coherence in terms of the cross-trial standard deviation of power pattern normalized by the power in each frequency band, similar to the Fano factor calculation in neurophysiology, but the value is in reversed direction (smaller Fano factor corresponds to larger reliability, and Fano factor can be below or above 1). Therefore, correspondingly, the power coherence values, as a result of the current computation, would not necessarily be smaller than 1, which is different from the phase coherence range (0–1), and therefore cannot be directly compared as quantities.
For the cross-movie coherence analysis (Figure 3, Figure 4), for each of the three matched movie clips (V1A1, V2A2, V3A3), we first selected the corresponding SameVis (V1A3, V2A1, V3A2), SameAud (V2A1, V3A2, V1A3), and NoSame (V3A2, V1A3, V2A1) movie stimulus in the mixed group, and then calculated the cross-movie delta-theta phase coherence () and power coherence () (both of them are dimensionless quantities) for each of the 20 auditory and 20 visual channels defined in localizer pretest in each subject, by
Note that the cross-movie coherence values derived from the above equation actually quantify the similarity extent of the response from two movies, in either phase or in power pattern (see Text S1 for the difference between the cross-movie analysis employed here and traditional cross-channel coherence analysis). For example, , , and indicate how similar the delta-theta phase responses elicited by two movies sharing the same visual stream but different auditory input are (, as shown in Figure 3). We calculated it in auditory channels and visual channels separately.
The across-movie delta-theta phase coherence distribution maps (Figure 4) for and conditions were constructed, respectively, in terms of the corresponding values of all 157 MEG channels for each subject.
To evaluate the low-frequency inter-trial phase and power coherence (Figure 5ab) for matched (, ) and mixed (, ) conditions, we first calculated the low-frequency inter-trial phase coherence for each of the six movie stimuli (Movie1∼Movie6: V1A1, V2A2, V3A3, V1A3, V2A1, V3A2) and then averaged the inter-trial delta-theta phase coherence and power coherence for the three matched movies and the three mixed movies separately, by
The cross-area analysis is similar to the cross-movie analysis but calculates the pattern similarity between auditory channels and visual channels, instead of that between movie 1 and movie 2 in auditory and visual channels separately in cross-movie analysis.
In the classification analysis (Figure 6), for each of the six movies, the delta-theta phase pattern as a function of time for one single trial under one stimulus condition was arbitrarily chosen as a template response for that movie. The delta-theta phase pattern of the remaining trials of all stimulus conditions was calculated, and their similarity to each of the six templates was defined as the distance to the templates [46]. Responses were then classified to the closest movie template. The classification was computed 100 times for each of the 20 auditory and 20 visual channels in each subject, by randomly choosing template combinations.
In the optimal phase analysis (Figure 7), for each of the 20 auditory and 20 visual channels in each subject, the calculated cross-trial phase coherence (i denotes time index and j denotes frequency index in range between 2∼7 Hz) was divided into 20 bins ranging from 0 to 1. The phase angle (n denotes the trial index) histograms in the range of in each of the 20 value ranges was then constructed, and the resulting matrix was averaged across six stimulus conditions and 20 selected channels for each subject (Figure 6a shows the grand average of the matrices). The deviation of the phase histogram ( indicates the and indicates the ) from uniform distribution was quantified by deviation function as a function of by , as shown in Figure 6b.
We then selected all the phase angles with corresponding above 0.7 for all the selected channels in each subject and quantified the number of phase angles around 0 and around for the matched and mixed movie stimuli, respectively.
We also performed a control analysis to rule out “leaking” induced cross-modal modulation (see Text S2 for details).
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10.1371/journal.pbio.1001427 | Human-Specific Histone Methylation Signatures at Transcription Start Sites in Prefrontal Neurons | Cognitive abilities and disorders unique to humans are thought to result from adaptively driven changes in brain transcriptomes, but little is known about the role of cis-regulatory changes affecting transcription start sites (TSS). Here, we mapped in human, chimpanzee, and macaque prefrontal cortex the genome-wide distribution of histone H3 trimethylated at lysine 4 (H3K4me3), an epigenetic mark sharply regulated at TSS, and identified 471 sequences with human-specific enrichment or depletion. Among these were 33 loci selectively methylated in neuronal but not non-neuronal chromatin from children and adults, including TSS at DPP10 (2q14.1), CNTN4 and CHL1 (3p26.3), and other neuropsychiatric susceptibility genes. Regulatory sequences at DPP10 and additional loci carried a strong footprint of hominid adaptation, including elevated nucleotide substitution rates and regulatory motifs absent in other primates (including archaic hominins), with evidence for selective pressures during more recent evolution and adaptive fixations in modern populations. Chromosome conformation capture at two neurodevelopmental disease loci, 2q14.1 and 16p11.2, revealed higher order chromatin structures resulting in physical contact of multiple human-specific H3K4me3 peaks spaced 0.5–1 Mb apart, in conjunction with a novel cis-bound antisense RNA linked to Polycomb repressor proteins and downregulated DPP10 expression. Therefore, coordinated epigenetic regulation via newly derived TSS chromatin could play an important role in the emergence of human-specific gene expression networks in brain that contribute to cognitive functions and neurological disease susceptibility in modern day humans.
| Primate and human genomes comprise billions of base pairs, but we are unlikely to gain a deeper understanding of brain functions unique to human (including cognitive abilities and psychiatric diseases) merely by comparing linear DNA sequences. Such determinants of species-specific function might instead be found in the so-called “epigenetic” characteristics of genomic regions; differences in the protein-packaged chromatin state in which genomic DNA exists in the cell. Here, we examine neurons from the prefrontal cortex, a brain region closely associated with the evolution of the primate brain, and identify hundreds of short DNA sequences defined by human-specific changes in chromatin structure and function when compared to non-human primates. These changes included species-specific regulation of methylation marks on the histone proteins around which genomic DNA is wrapped. Sequences subject to human-specific epigenetic regulation showed significant spatial clustering, and despite being separated by hundreds of thousands of base pairs on the linear genome, were in direct physical contact with each other through chromosomal looping and other higher order chromatin features. This observation raises the intriguing possibility that coordinated epigenetic regulation via newly derived chromatin features at gene transcription start sites could play an important role in the emergence of human-specific gene expression networks in the brain. Finally, we identified a strong genetic footprint of hominid evolution in a small subset of transcription start sites defined by human-specific gains in histone methylation, with particularly strong enrichment in prefrontal cortex neurons. For example, the base pair sequence of DPP10 (a gene critically important for normal human brain development) not only showed distinct human-specific changes, but also evidence for more recent selective pressures within the human population.
| Cognitive abilities and psychiatric diseases unique to modern humans could be based on genomic features distinguishing our brain cells, including neurons, from those of other primates. Because protein coding sequences for synaptic and other neuron-specific genes are highly conserved across the primate tree [1],[2], a significant portion of hominid evolution could be due to DNA sequence changes involving regulatory and non-coding regions at the 5′ end of genes [3],[4]. Quantifying these differences, however, is ultimately a daunting task, considering that, for example, the chimpanzee–human genome comparison alone reveals close to 35×106 single bp and 5×106 multi-bp substitutions and insertion/deletion events [3]. While a large majority of these are likely to reflect genetic drift and are deemed “non-consequential” with respect to fitness, the challenge is to identify the small subset of regulatory sequence alterations impacting brain function and behavior.
Here, we combine comparative genomics and population genetics with genome-scale comparisons for histone H3-trimethyl-lysine 4 (H3K4me3), an epigenetic mark sharply regulated at transcription start sites (TSS) and the 5′ end of transcriptional units in brain and other tissues [5]–[8] that is stably maintained in brain specimens collected postmortem [7],[9]. Our rationale to focus on TSS chromatin was also guided by the observation that the human brain, and in particular the cerebral cortex, shows distinct changes in gene expression, in comparison to other primates [10]. While there is emerging evidence for an important role of small RNAs shaping human-specific brain transcriptomes via posttranscriptional mechanisms [11] and increased recruitment of recently evolved genes during early brain development [12], the role of TSS and other cis-regulatory mechanisms remains unclear. Here, we report that cell type-specific epigenome mapping in prefrontal cortex (PFC, a type of higher order cortex closely associated with the evolution of the primate brain) revealed hundreds of sequences with human-specific H3K4me3 enrichment in neuronal chromatin, as compared to two other anthropoid primates, the chimpanzee and the macaque. These included multiple sites carrying a strong footprint of hominid evolution, including accelerated nucleotide substitution rates specifically in the human branch of the primate tree, regulatory motifs absent in non-human primates and archaic hominins including Homo neanderthalensis and H. denisova, and evidence for adaptive fixations in modern day humans. The findings presented here provide the first insights into human-specific modifications of the neuronal epigenome, including evidence for coordinated epigenetic regulation of sites separated by megabases of interspersed sequence, which points to a significant intersect between evolutionary changes in TSS function, species-specific chromatin landscapes, and epigenetic inheritance.
The present study focused on the rostral dorsolateral PFC, including cytoarchitectonic Brodmann Area BA10 and the immediately surrounding areas. These brain regions represent a higher association cortex subject to disproportionate morphological expansion during primate evolution [13], and are involved in cognitive operations important for informed choice and creativity [14],[15], among other executive functions. Given that histone methylation in neuronal and non-neuronal chromatin is differentially regulated at thousands of sites genome-wide [7], we avoided chromatin studies in tissue homogenates because glia-to-neuron ratios are 1.4- to 2-fold higher in mature human PFC as compared to chimpanzee and macaque [16]. Instead, we performed cell type-specific epigenome profiling for each of the three primate species, based on NeuN (“neuron nucleus”) antigen-based immunotagging and fluorescence-activated sorting, followed by deep sequencing of H3K4me3-tagged neuronal nucleosomes.
Prefrontal H3K4me3 epigenomes from NeuN+ nuclei of 11 humans, including seven children and four adults [7], were compared to four chimpanzees and three macaques of mature age (Table S1). Sample-to-sample comparison, based on a subset of highly conserved Refseq TSS with one mismatch maximum/36bp, consistently revealed the highest correlations between neuronal epigenomes from the same species (Table S2). Strikingly, however, the H3K4me3 landscape in human neurons was much more similar to chimpanzee and macaque neurons, when compared to non-neuronal (NeuN−) cells [7] from the same specimen/donor or to blood (Figure 1A). Therefore, PFC neuronal epigenomes, including their histone methylation landscapes at TSS, carry a species-specific signature, but show an even larger difference when compared to their surrounding glial and other NeuN− cells.
To identify loci with human-specific H3K4me3 enrichment in PFC neurons, we screened 34,639 H3K4me3 peaks that were at least 500 bp long and showed a consistent >2-fold H3K4me3 increase for the 11 humans as compared to the average of the seven chimps and macaques and (ii) minimum length of 500 bp. We identified 410 peaks in the human genome (HG19) with significant enrichment compared to the two non-human primate species (with reads also mapped to HG19) after correcting for false discovery (FDR), and we call these peaks “HP” hereafter for “human-specific peaks” (Figure 1D; Table S3). We had previously reported that infant and child PFC neurons tend to have stronger peaks at numerous loci, compared to the adult [7]. To better age-match the human and non-human primate cohorts, we therefore repeated the analysis with our entire, recently published cohort of nine adult humans without known neurological or psychiatric disease [7],[8]. Using the same set of filter criteria (>2-fold increase in humans compared to chimpanzees and macaques), we identified 425 peaks and 296 of them overlapped with the original 410 HP (Table S3). Furthermore, 345 of the 410 peaks overlapped with the overlapped with the peaks with >1.5-fold increase for nine adult humans (compared to non-human primates; with correction for FDR) (Table S4), indicating that HPs can be detected reliably.
To obtain human depleted peaks we used a reciprocal approach where initial peaks were detected in chimpanzee and macaque. For the original cohort of 11 children and adult humans, this resulted in 61 peaks with a significant, at least 2-fold depletion in human PFC neurons (Table S5). 50 peaks defined by human-specific depletion in the mixed cohort of 11 children and adults were part of the total of 177 peaks with >1.5-fold decrease in the cohort of nine adults (compared to each of the two non-human primate species; Table S6). From this, we conclude that at least 471 loci in the genome of PFC neurons show robust human-specific changes (gain, 410; loss, 61) in histone methylation across a very wide postnatal age range.
We further explored chimpanzee-specific changes in the H3K4me3 landscape of PFC neurons by comparing human and chimpanzee peaks within the chimpanzee genome. To this end, we constructed a mono-nucleosomal DNA library from chimpanzee PFC to control for input, and mapped the neuronal H3K4me3 datasets from four chimpanzee PFC specimens, and their 11 human counterparts, to the chimpanzee genome (PT2). We identified 551 peaks in the PT2 genome that were subject to >2-fold gain and 337 peaks subject to >2-fold depletion, compared to human regardless of the H3K4me3 level in macaque (Tables S7 and S8). A substantial portion of these PT2-annotated peaks (133 and 40 peaks, respectively) with gain or loss in chimpanzee PFC neurons matched loci with the corresponding, reciprocal changes specific to human PFC neurons in HG19 (410 and 61 peaks as described above). Genetic differences among these genomes and additional, locus-specific differences in nucleosomal organization (leading to differences in background signal in the input libraries) are potential factors that would lead to only partial matching of peaks when species-specific H3K4me3 signals are mapped within the human, or chimpanzee genome, respectively. These findings, taken together, confirm that genome sequence differences in cis are one important factor for the species-specific histone methylation landscapes in PFC neurons.
Both catalytic and non-catalytic subunits of H3K4 methyltransferase complex are associated with transgenerational epigenetic inheritance in the worm, Caenorhabditis elegans, and other simple model organisms [17], and furthermore, H3K4me3 and other epigenetic markings such as DNA cytosine methylation are readily detectable in non-somatic (“germline”-related) cells such as sperm, potentially passing on heritable information to human offspring [18]. Therefore, we wanted to explore whether a subset of the 410 loci with at least 2-fold H3K4me3 enrichment in human neurons are subject to species-specific epigenetic regulation in germ tissue. To this end, we screened a human and chimpanzee sperm database on DNA methylation [19], in order to find out which, if any of the 410 sequences with human-specific H3K4me3 gain in brain overlap with a set of >70,000 sequences defined by very low, or non-detectable DNA methylation in human and chimpanzee sperm (termed (DNA) “hypomethylated regions” in [19]). Of note, the genome-wide distribution of H3K4me3 and DNA cytosine methylation is mutually exclusive in germ and embryonic stem cells, and gains in DNA methylation generally are associated with loss of H3K4me3 in differentiated tissues [20],[21]. Unsurprisingly therefore, 300/410 HP peaks in brain matched a DNA hypomethylated sequence in sperm of both species. Strikingly, however, 90/410, or approximately 22% of HP were selectively (DNA) hypomethylated in human but not in chimpanzee sperm (Table S3), a ratio that is approximately 4-fold higher than the expected 5.7% based on 10,000 simulations (p<0.00001; see also Text S1) (Figure 1B). Conversely, the portion of HP lacking DNA hypomethylation in male germ cells of either species altogether (18/410 or 4%), or with selective hypomethylation in chimpanzee sperm (2/410 or 0.5%), showed a significant, 5-fold underrepresentation in our dataset (Figure 1B). Thus, approximately one-quarter of the 410 loci with human-specific gain in histone methylation in PFC neurons also carry species-specific DNA methylation signatures in sperm, with extremely strong bias towards human (DNA) hypomethylated regions (22%) compared to chimpanzee-specific (DNA) hypomethylated regions (0.5%). In striking contrast, fewer than ten of the 61 loci with human-specific H3K4me3 depletion in PFC neurons showed species-specific differences in sperm DNA methylation between species (six human- and three chimpanzee-specific DNA hypomethylated regions; Table S5).
We noticed that, at numerous chromosomal loci, HP tended to group in pairs or clusters (Table S3). There were more than 245 (163) from the total of 410 HP spaced less than 1 (or 0.5) Mb apart, which is a highly significant, 2- (or 3-) fold enrichment compared to random distribution within the total pool of 34,639 peaks (Figure 1C; Text S1). Therefore, sequences with human-specific gain in H3K4me3 in PFC neurons appear to be co-regulated with neighboring sequences on the same chromosome that are decorated with the same type of histone modification. Likewise, the actual number of human-depleted peaks within one 1 Mb (n = 6) was higher than what is expected from random distribution (n = 2.6), (p = 0.051), albeit no firm conclusions can be drawn due to the smaller sample size (n = 61).
This type of non-random distribution due to pairing or clustering of the majority of human-enriched sequences broadly resonates with the recently introduced concept of Mb-sized topological domains as a pervasive feature of genome organization, including increased physical interactions of sequences carrying the same set of epigenetic decorations within a domain [22]. Of note, H3K4 trimethylation of nucleosomes is linked to the RNA polymerase II transcriptional initiation complex, and sharply increased around TSS and broadly correlated with “open chromatin” and gene expression activity [5],[6]. Therefore, we reasoned that a subset of human-enriched “paired” H3K4me3 peaks could engage in chromatin loopings associated with transcriptional regulation. This is a very plausible hypothesis given that promoters and other regulatory sequences involved in transcriptional regulation are often tethered together in loopings and other higher order chromatin [23],[24].
To explore this, we screened a database obtained on chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) for RNA polymerase II, a technique designed to detect chromosomal loopings bound by the Pol II complex [25]. Indeed, we identified at least three interactions that matched to our H3K4me3 peaks with human-specific gain in PFC neurons (Table S9), including a loop interspersed by approximately 2.5 Mb of sequence in chromosome 16p11.2–12.2. This is a risk locus for microdeletions that are linked to a wide spectrum of neurodevelopmental disease including autism spectrum disorder (ASD), intellectual disability (ID), attention deficit hyperactivity disorder (ADHD), seizures, and schizophrenia [26]–[31]. We were able to validate this interaction by chromosome conformation capture (3C), a technique for mapping long range physical interactions between chromatin segments [32], in 2/2 human PFC specimens and also in a human embryonic kidney (HEK) cell line (Figure 2). We conclude that human-specific H3K4me3 peaks spaced as far apart as 1 Mb are potentially co-regulated and physically interact via chromatin loopings and other higher order chromatin structures.
Next, we wanted to explore whether sequences with human-specific gain in histone methylation, including those that show evidence for pairing and physical interactions, could affect the regulation of gene expression specifically in PFC neurons. To this end, we first identified which portion from the total of 410 human-specific peaks showed much higher H3K4me3 levels selectively in PFC neurons, when compared to their surrounding non-neuronal cells in the PFC. Thus, in addition to the aforementioned filter criteria (2-fold increase in human PFC neurons compared to non-human primate PFC neurons), we searched for peaks with differential regulation among PFC neurons and non-neurons (see Text S1). We found 33 HP with selective enrichment in neuronal PFC chromatin (termed neuHP in the following) (Figure S1; Table S10). Among these were two HP spaced less than 0.5 Mb apart within the same gene, DPP10 (chr2q14.1), encoding a dipeptidyl peptidase-related protein regulating potassium channels and neuronal excitability (Figure 3A–3B) [33]. Interestingly, rare structural variants of DPP10 confer strong genetic susceptibility to autism, while some of the gene's more common variants contribute to a significant risk for bipolar disorder, schizophrenia, and asthma [34]–[36]. Histone methylation at DPP10 was highly regulated in species- and cell type-specific manner, with both DPP10-1 and DPP10-2 peaks defined by a very strong H3K4me3 signal in human PFC neurons (Figure 3A), but only weak or non-detectable peaks in their surrounding NeuN− (non-neuronal) nuclei (Figure S1; Table S10) or blood-derived epigenomes [7].
We then employed 3C assays across 1.5 Mb of the DPP10 (chr2q14.1) in PFC of four humans. To increase the specificity in each 3C PCR assay, we positioned both the forward and reverse primer in the same orientation on the sense strand, and samples processed for 3C while omitting the critical DNA ligation step from the protocol served as negative control (Figure 3A–3B). Indeed, 3C assays on four of four human PFC specimens demonstrated direct contacts between the DPP10-1 and -2 peaks (Figure 3A). As expected for neighboring fragments [32], DPP10-1 also interacted with portions of the interspersed sequence (CR2 in Figure 3A). These interactions were specific, because several other chromatin segments within the same portion of chr2q14.1 did not show longer range interactions with DPP10-1 (CR1, CR3 in Figure 3A). We further verified one of the DPP10-1/2 physical interactions (the sequences captured by primers 6 and 17 in Figure 3A) in four of five brains using 3C-qPCR with a TaqMan probe positioned in fragment 6. Furthermore, DPP10-2 interacted with a region (“CR3” in Figure 3A) 400 kb further downstream positioned in close proximity to a blood-specific H3K4me3 peak. No interactions at the DPP10 locus were observed in cultured cells derived from the H9 embryonic stem cell line (H9ESC in Figure 3A), suggesting that these chromatin architectures are specific for differentiated brain tissue. Of note, similar types of DPP10 physical interactions were found in 3C assays conducted on PFC tissue of three of three macaques (Figure 3B). Because macaque PFC, in comparison to human, shows much weaker H3K4 methylation at these DPP10 sequences, we conclude that the corresponding chromatin tetherings are not critically dependent on human-specific H3K4me3 dosage.
Next, we wanted to explore whether human-specific H3K4 methylation at the DPP10 locus is associated with a corresponding change in gene expression at that locus. Notably, H3K4me3 is on a genome-wide scale broadly correlated with transcriptional activity, including negative regulation of RNA expression by generating very short (∼50–200 nt) promoter-associated RNAs. These short transcripts originate at sites of H4K4me3-tagged nucleosomes and act as cis-repressors in conjunction with polycomb and other chromatin remodeling complexes [37],[38]. Therefore, transcriptional activities due to the emergence of novel H3K4me3 markings in human PFC is likely to be complex, with unique functional implications specific to each genomic locus. To explore the transcriptome at the DPP10 locus in an unbiased manner, we performed RNA-seq on a separate cohort of three adult human PFC (not part of the aforementioned ChIP-seq studies) and compared their transcriptional landscapes to similar datasets from chimpanzee and macaque [39],[40]. Indeed, we found an antisense RNA, LOC389023, emerging from the second DPP10 peak, DPP10-2 (chr2q14.1) (Figures 3A and 4A). In an additional independent analyses (using a set of human postmortem brains different from the ones used for RNAseq) quantitative reverse transcriptase (RT)-PCR assays further validated the much higher expression of DPP10 antisense transcript in human (Figure 4B), which occurred in conjunction with decreased expression of DPP10 exons downstream of the DPP10-2 promoter (compared to chimp/macaque) (Figure 4A).
Consistent with the H3K4me3 enrichment specifically in neuronal chromatin, the cellular expression of LOC389023 in adult PFC was confined to a subset of the neuronal layers (II–IV), but absent in neuron-poor compartments such as layer I and subcortical white matter (Figure 5A and unpublished data). Furthermore, the transcript was expressed in fetal and adult PFC but not in cerebellar cortex (Figure 5B). We noticed that LOC389023 harbored a GC-rich stem loop motif that is known to associate with cis-regulatory mechanisms involved in transcriptional repression, including binding to TSS chromatin and components of Polycomb 2 (PRC2) complex (Figure 5C) [37],[41]. Consistent with a possible function inside the nucleus, LOC389023 was highly enriched in nuclear RNA fractions from extracted prenatal and normal (non-degenerative) adult human PFC, but not cerebellar cortex (Figure 5B). Indeed, in transiently transfected (human) SK-N-MC neuroblastoma cells, LOC389023 showed a specific association with H3K4-trimethylated nucleosomes and SUZ12 (Figure 5D), a zinc finger protein and core component of PRC2 previously shown to interact with stem loop motifs similar to the one shown in Figure 5C [37]. In contrast, EZH2, a (H3K27) methyltransferase and catalytic component of PRC-2, did not interact with LOC389023 (Figure 5D), consistent with previous reports on other RNA species carrying a similar stem loop motif [37]. These observations, taken together, are entirely consistent with the aforementioned findings that levels of DPP10 transcript, including exons positioned downstream of the DPP10-2 peak from which LOC389023 originates, are significantly decreased in human PFC as compared to macaque and chimpanzee. Conversely, these two primates show non-detectable (RNAseq) or much lower quantitative RT-PCR (qRT-PCR) LOC389023 levels in the PFC, as compared to human (Figure 4A–4B). Taken together then, these findings strongly suggest that LOC389023 emerged de novo in human PFC neurons and interacts with localized chromatin templates to mediate transcriptional repression at the DPP10 locus (Figure 6).
The aforementioned human-specific gains in histone methylation at DPP10 and the emergence of human RNA de novo at this locus could reflect a phylogenetically driven reorganization of neuronal functions that may have contributed not only to the emergence of human-specific executive and social-emotional functions, but also for increased susceptibility for developmental brain disease [42]. In this context, we noticed that the 33 neuHP (which are defined by two criteria which are (i) human-specific gain compared to non-human primates and (ii) high H3K4me3 in PFC neurons but not their surrounding non-neuronal cells) included multiple genes conferring susceptibility to neurological disease. Three loci, including DPP10 on chromosome 2q14.1 and two genes in close proximity on chromosome 3p26.3, CNTN4 and CHL1, both encoding cell adhesion molecules [34],[43]–[45], confer very strong susceptibility to autism, schizophrenia, and related disease. Other disease-associated loci with human-specific gain selectively in PFC neurons include ADCYAP1, a schizophrenia [46],[47] and movement disorder gene [48] that is part of a cAMP-activating pathway also implicated in posttraumatic stress [49]. PDE4DIP (MYOMEGALIN) (Figure 1D) encodes a centrosomal regulator of brain size and neurogenesis [50] that in some studies was 9-fold higher expressed in human as compared to chimpanzee cortex [51],[52]. SORCS1 is implicated in beta amyloid processing and Alzheimer disease [53],[54] and attention deficit hyperactivity disorder [55], which again are considered human-specific neurological conditions [10]. Because four of 33, or 12% of neuHP overlapped with neurodevelopmental susceptibility genes (CNTN4, CHL1, DPP10, SORCS1), we then checked whether the entire set of 410 human-specific peaks is enriched for genes and loci conferring genetic risk for autism, intellectual disability, and related neurological disease with onset in early childhood. However, there was only minimal overlap with the Simons Foundation Autism Research Initiative database (SFARI) [56], and Human unidentified Gene Encoded protein database (HuGE) for pervasive developmental disorder (including autism) associated polymorphism [57], and recent reference lists for mental retardation and/or autism-related genes (each of these databases five or fewer of the human-enriched peaks) [58]. Likewise, there was minimal, and non-significant overlap with the set of 61 human- and 337 chimpanzee-depleted peaks, or the 551 chimpanzee-enriched in PFC neurons (five or fewer of peaks/database). None of the lists of peaks with human- or chimpanzee-specific gain or loss of H3K4me3 revealed statistical significance for any associations with the Gene Ontology (GO) database. We conclude that DNA sequences subject to differential histone methylation in human or chimpanzee PFC neurons are, as a group, not clustered together into specific cellular signaling pathways or functions. Table 1 presents examples of disease-associated genes associated with human-specific gain, or loss of H3K4-trimethylation.
We then asked whether the subset of DNA sequences with species- and cell type-specific epigenetic regulation, including the neuHP peaks mentioned above carry a strong footprint of hominid evolution. Indeed, nucleotide substitution analysis revealed that both DPP10 peaks DPP10 -1/2, as well as ADCYAP1, CHL1, CNTN4, NRSN2, and SIRPA show a significantly elevated rate, with 2- to 5-fold increase specifically in the human branch of the primate tree, when compared to four other anthropoid primate species (Pan troglodytes, Gorilla gorilla, Pongo abelii, Macaca mulatta) (Table S11). The finding that both DPP10 peaks, DPP10-1 and -2 showed a significant, >4-fold increase in nucleotide substitution rates in the human branch of the primate tree—indicating “co-evolution” (or coordinated loss of constraint)—is very plausible given that chromatin structures surrounding these DNA sequences are in direct physical contact (discussed above), reflecting a potential functional interaction and shared regulatory mechanisms between peaks.
To further confirm the role of phylogenetic factors in the emergence of human-specific H3K4me3 peaks, we focused on the set of 33 neuHP and calculated the total number of human-specific sequence alterations (HSAs), in a comparative genome analyses across five primates (H. sapiens, P. troglodytes, G. gorilla, P. abelii, M. mulatta). We recorded altogether 1,519 HSAs, with >90% as single nucleotide substitutions, five >100 bp INDELs, one (Alu) retrotransposon-like element at TRIB3 pseudokinase consistent with a role of mobile elements in primate evolution [3], and gain or loss of hundreds of regulatory motifs (Table S12). When compared to a group of (neuronal) H3K4me3 peaks showing minimal changes between the three primate species (Table S13), the neuHP, as a group, showed a significant, 2.5-fold increase in the number of HSA (20.08±5.52 HSAs versus 8.36±2.44 HSAs per 1-kb sequence, p = 2.4e−06, Wilcoxon rank sum test; Figure S3). The findings further confirm that genetic differences related to speciation indeed could play a major role for changes in the brain's histone methylation landscape, particularly for H3K4me3 peaks that are highly specific for human neurons (neuHP). Interestingly, none of the above loci showed evidence for accelerated evolution of neighboring protein coding sequences (Table S11), reaffirming the view that protein coding sequences for synaptic and other neuron-specific genes are extremely conserved across the primate tree [1],[2].
These DNA sequence alterations at sites of neuron-restricted H3K4me3 peaks (with human-specific gain) point, at least for this subset of loci, to a strong evolutionary footprint before the split of human–chimpanzee lineage several million years ago [3]. Next, we wanted to find out whether there is also evidence for more recent selective pressures at these loci. Indeed, a subset of neuHP contain H. sapiens-specific sequences not only absent in rodents, anthropoid primates, but even in extinct members of the genus homo, including H. neanderthalensis and H. denisova [59]. Some of the ancestral alleles (including MIAT, SIRPA, NRSN) shared with archaic hominins exhibit very low frequencies at 0%–3% in all modern populations, and therefore it remains possible that positive selection for newly derived alleles contributed to their high population frequencies in modern humans (Table S14). However, for the entire set of neuHP that are defined by high H3K4me3 levels in PFC neurons (but not non-neurons), the number of HSAs that emerged after the human lineage was split from H. denisova or H. neanderthalensis were 3.31% and 1.75%, respectively, which is approximately 2-fold lower as compared to 32 control H3K4me3 peaks with minimal differences among the three primate species (5.03% and 3.77%). The 2-fold difference in the number of H. sapiens-specific alleles (neuHP compared to control peaks) showed a strong trend toward significant (p = 0.067) for the Denisova, and reached the level of significance (p = 0.034) for the Neanderthal genome (based on permutation test with 10,000 simulations [60]). Taken together, these results suggest that at least a subset of the TSS regions with H3K4me3 enrichment in human (compared to non-human primates) were exposed to evolutionary driven DNA sequence changes on a lineage of the common ancestor of H. sapiens and the archaic hominins, but subsequently were stabilized in more recent human evolution, after splitting from other hominins.
To further test whether or not there were recent, perhaps even ongoing selective pressures at loci defined by human-specific gain in H3K4me3 peaks of PFC neurons, we searched for overlap among the peaks in our study with hundreds of candidate regions in the human genome showing evidence of selection during the past 10–100,000 years from other studies. These loci typically extend over several kb, and were identified in several recent studies on the basis of criteria associated with a “selective sweep,” which describes the elimination of genetic variation in sequences surrounding an advantageous mutation while it becomes fixed [61]–[64]. However, screening of the entire set of 410 human gain and 61 human depleted H3K4me3 sequences against nine datasets for putative selection in humans [65] revealed only five loci with evidence for recent sweeps (Table S15). One of these matched to the neuHP on chromosome 2q14.1, corresponding to the second DPP10 (DPP10-2) peak (see above). In independent analyses, using the 1,000 genome database, we further confirmed recent adaptive fixations around DPP10-2 (Table S16), as well as two other loci, POLL and TSPAN4. While it is presently extremely difficult to determine how much of the genome has been affected by positive selection (of note, a recent metanalysis of 21 recent studies using total genomic scans for positive selection using human polymorphism data revealed unexpectedly minimal overlap between studies [65]), we conclude that the overwhelming majority of loci associated with human-specific H3K4me3 gain or loss in PFC neurons (compared to non-human primates) indeed does not show evidence for more recent selective pressures.
To provide an example on altered chromatin function due to an alteration in a regulatory DNA sequence that occurred after the human lineage split from the common ancestor with non-human primates, we focused on a change in a GATA-1 motif (A/TGATTAG) within a portion of DPP10-2 found in human, within an otherwise deeply conserved sequence across many mammalian lineages (Table S17). Gel shift assays demonstrate that the human-specific sequence harboring the novel GATA-1 site showed much higher affinity to HeLa nuclear protein extracts, compared to the chimpanzee/other mammal sequence (Figure 4C). The emergence of a novel GATA-1 motif at DPP10 is unlikely to reflect a systemic trend because the motif overall was lost, rather than gained in neuHP (10/355 versus 4/375, χ2 p = 0.053). Therefore, evolutionary and highly specific changes in a small subset of regulatory motifs at DPP10 and other loci could potentially result in profound changes in nuclear protein binding at TSS and other regulatory sequences, thereby affecting histone methylation and epigenetic control of gene expression in humans, compared to other mammals including monkeys and great apes. Of note, potentially important changes in chromatin structure and function due to human-specific sequence alterations at a single nucleotide within an otherwise highly conserved mammalian sequence will be difficult to “capture” by comparative genome analyses alone. For example, when the total set of 410 HP was crosschecked against a database of 202 sequences with evidence for human-specific accelerated evolution in loci that are highly conserved between rodent and primate lineages [66], only one of 410 HP matched (Table S15).
H3K4me3 is a transcriptional mark that on a genome-wide scale is broadly associated with RNA polymerase II occupancies and RNA expression [67]. However, it is also associated with repressive chromatin remodeling complexes and at some loci the mark is linked to short antisense RNAs originating from bidirectional promoters, in conjunction with negative regulation of the (sense) gene transcript [37],[38]. Indeed, this is what we observed for the DPP10 locus (Figure 6). Therefore, a comprehensive assessment of all transcriptional changes associated with the evolutionary alterations in H3K4me3 landscape of PFC neurons would require deep sequencing of intra- and extranuclear RNA, to ensure full capture of short RNAs and all other transcripts that lack polyadenylation and/or export into cytoplasm. While this is beyond the scope of the present study, we found several additional examples for altered RNA expression at the site of human-specific H3K4me3 change. There were four of 33 neuHP loci associated with novel RNA expression specific for human PFC, including the aforementioned DPP10 locus. The remaining three human-specific transcripts included two additional putative non-coding RNAs, LOC421321(chr7p14.3) and AX746692 (chr17p11.2). There was also a novel transcript for ASPARATE DEHYDROGENASE ISOFORM 2 (ASPDH)(chr19q13.33) (Figure S2). Furthermore, a fifth neuHP, positioned within an intronic portion of the tetraspanin gene TSPAN4 (chr11p15.5), was associated with a dramatic, human-specific decrease of local transcript, including the surrounding exons (Figure S2). Comparative analyses of prefrontal RNA-seq signals for the entire set of the 410 HP included at least 18 loci showing a highly consistent, at least 2-fold increase or decrease in RNA levels of human PFC, compared to the other two primate species (Table S18).
In the present study, we report that on a genome-wide scale, 471 loci show a robust, human-specific change in H3K4me3 levels at TSS and related regulatory sequences in neuronal chromatin from PFC, in comparison to the chimpanzee and macaque. Among the 410 sequences with human-specific gain in histone methylation, there was a 4-fold overrepresentation of loci subject to species-specific DNA methylation in sperm [19]. This would suggest that there is already considerable “epigenetic distance” between the germline of H. sapiens and non-human primates (including the great apes), which during embryonic development and tissue differentiation is then “carried over” into the brain's epigenome. The fact that many loci show species-specific epigenetic signatures both in sperm [19] and PFC neurons (Figure 1B) raises questions about the role of epigenetic inheritance [68] during hominid evolution. However, to further clarify this issue, additional comparative analysis of epigenetic markings in brain and germline will be necessary, including histone methylation maps from oocytes, which currently do not exist. However, the majority of species-specific epigenetic decorations, including those that could be vertically transmitted through the germline, could ultimately be driven by genetic differences. On the basis of DNA methylation analyses in three-generation pedigrees, more than 92% of the differences in methylcytosine load between alleles are explained by haplotype, suggesting a dominant role of genetic variation in the establishment of epigenetic markings, as opposed to environmental influences [69]. A broad overall correlation between genetic and epigenetic differences was also reported in a recent human–chimpanzee sperm DNA methylation study [19], and there is general consensus that the inherent mutability of methylated cytosine residues due to their spontaneous deamination to thymine is one factor contributing to sequence divergence at CpG rich promoters with differential DNA methylation between species [19],[70]. Furthermore, human-specific sequences in the DNA binding domains of PRDM9, which encodes a rapidly evolving methyltransferase regulating H3K4me3 in germ cells, were recently identified as a major driver for human–chimpanzee differences in meiotic recombination and genome organization [71]. It will be interesting to explore whether PRDM9-dependent histone methyltransferase activity was involved in the epigenetic regulation of the human-enriched H3K4me3 peaks that were identified in the present study.
Another interesting finding that arose from the present study concerns the non-random distribution of histone methylation peaks with human-specific gain, due to a significant, 2- to 3-fold overrepresentation of peak-pairing or -clustering on a 500 kb to 1 Mb scale. This result fits well with the emerging insights into the spatial organization of interphase chromosomes, including the “loopings,” “‘tetherings” and “globules” that bring DNA sequences that are spatially separated on the linear genome into close physical contact with each other [72]. Specifically, many chromosomal areas are partitioned into Mb-scale “topological domains”, which are defined by robust physical interaction of intra-domain sequences carrying the same set of epigenetic decorations [22]. These mechanisms could indeed have set the stage for coordinated genetic and epigenetic changes during the course of hominid brain evolution. The DPP10 (2q14.1) neurodevelopmental susceptibility locus provides a particularly illustrative example: here, two H3K4me3 peak sequences with strong human-specific gain were separated by hundreds of kilobases of interspersed sequence, yet showed a strikingly similar, 4-fold acceleration of nucleotide substitution rates specifically in the human branch of the primate tree. Importantly, the two H3K4me3 peaks, DPP10-1 and -2, as shown here, are bundled together in a loop or other types of higher order chromatin. Therefore, our findings lead to a complex picture of the human-specific shapings of the neuronal epigenome, including a mutual interrelation of DNA sequence alterations and epigenetic adaptations involving histone methylation and higher order chromatin structures. The confluence of these factors could then, in a subset of PFC neurons (Figure 5A), result in the expression of a novel antisense RNA, which associates with transcriptional repressors to regulate the target transcript in cis, DPP10 (Figures 5D and 6).
While the present study identified a few loci, including the aforementioned DPP10 (chromosome 2q14.1), in which DNA sequences associated with a human-specific gain in neuronal histone methylation showed signs for positive selection in the human population, it must be emphasized that the overwhelming majority of sites with human-specific H3K4me3 changes did not show evidence for recent adaptive fixations in the surrounding DNA. Therefore, and perhaps not unsurprisingly, neuronal histone methylation mapping in human, chimpanzee, and macaque primarily reveals information about changes in epigenetic decoration of regulatory sequences in the hominid genome after our lineage split from the common ancestor shared with present-day non-human primates.
Moreover, according to the present study, the subset of 33 sequences with human-specific H3K4me3 gain and selective enrichment in neuronal (as opposed to non-neuronal) PFC chromatin show a significant, 3-fold increase in human-specific (DNA sequence) alterations in comparison to non-human primate genomes. This finding speaks to the importance of evolutionary changes in regulatory sequences important for neuronal functions. Strikingly, however, the same set of sequences show a significant, approximately 1.5- to 2-fold decrease in sequence alterations when compared to the two archaic hominin (H. denisova, H. neanderthalensis) genomes. This finding further reaffirms that sequences defined by differential epigenetic regulation in human and non-human primate brain, as a group, are unlikely to be of major importance for more recent evolution, including any (yet elusive) genetic alterations that may underlie the suspected differences in human and neanderthal brain development [73]. However, these general conclusions by no means rule out a critical role for a subset of human-specific sequence alterations on the single nucleotide level within any of the HPs described here, including the DPP10 locus.
Such types of single nucleotide alterations and polymorphisms may be of particular importance at the small number of loci with human-specific H3K4me3 gain that contribute to susceptibility of neurological and psychiatric disorders that are unique to human (though it should be noticed that as a group, the entire set of sequences subject to human-specific gain, or loss, of H3K4me3 are not significantly enriched for neurodevelopmental disease genes). The list would not only include the already discussed ADYCAP1, CHL1, CNTN4, and DPP10, which were among the narrow list of 33 human-specific peaks highly enriched in neuronal but not non-neuronal PFC chromatin), but also DGCR6, an autism and schizophrenia susceptibility gene [74],[75] within the DiGeorge/Velocardiofacial syndrome/22q11 risk locus, NOTCH4 and CACNA1C encoding transmembrane signaling proteins linked to schizophrenia and bipolar disorder in multiple genome-wide association studies [76],[77], SLC2A3 encoding a neuronal glucose transporter linked to dyslexia and attention-deficit hyperactivity disorder [78],[79] and the neuronal migration gene TUBB2B that has been linked to polymicrogria and defective neurodevelopment [80]. Furthermore, among the 61 peaks with human-specific loss of H3K4me3 is a 700-bp sequence upstream of the TSS of FOXP2, encoding a forkhead transcription factor essential for proper human speech and language capabilities [81] and that has been subject to accelerated evolution with amino acid changes leading to partially different molecular functions in human compared to great apes [82],[83]. The homebox gene LMX1B is another interesting disease-associated gene that is subject to human-specific H3K4me3 depletion (Table 1). While expression of many of these disease-associated genes is readily detectable even in mouse cerebral cortex [84], the neuropsychiatric conditions associated with them lack a correlate in anthropoid primates and other animals. This could speak to the functional significance of H3K4 methylation as an additional layer for transcriptional regulation, with adaptive H3K4me3 changes at select loci and TSS potentially resulting in improved cognition while at the same time in the context of genetic or environmental risk factors contribute to neuropsychiatric disease. More generally, our findings are in line with a potential role for epigenetic (dys)regulation in the pathophysiology of a wide range of neurological and psychiatric disorders [85]–[88].
Our study also faces important limitations. While we used child and adult brains for cross-species comparisons, human-specific signatures in the cortical transcriptome are thought to be even more pronounced during pre- and perinatal development [89]. Therefore, younger brains could show changes at additional loci, or more pronounced alterations at the TSS of some genes identified in the present study, including the above mentioned susceptibility genes CNTN4 and myelomegalin/PDE4DIP, which are expressed at very high levels in the human frontal lobe at midgestation [90]. In this context, our finding that a large majority, or 345 of 410 H3K4me3 peaks showed a human-specific gain both in children and adults, resonates with Somel and colleagues [11] who suggested that some of the age-sensitive differences in cortical gene expression among primate species are due to trans-acting factors such as microRNA,s while cis-regulatory changes (which were the focus of the present study) primarily affect genes that are subject to a lesser regulation by developmental processes. More broadly, our studies supports the general view that transcriptional regulation of both of coding and non-coding (including antisense) RNAs could play a role in the evolution of the primate brain [91].
Furthermore, the cell type-specific, neuronal versus non-neuronal chromatin studies as presented here provide a significant advancement over conventional approaches utilizing tissue homogenate. However, pending further technological advances, it will be interesting to explore genome organization in select subsets of nerve cells that bear particularly strong footprints of adaptation, such as the Von Economo neurons, a type of cortical projection neuron highly specific for the hominid lineage of the primate tree and other mammals with complex social and cognitive-emotional skill sets [92]. Furthermore, our focus on PFC does not exclude the possibility that other cortical regions [93], or specialized sublayers such as within the fourth layer of visual cortex that shows a complex transcriptional architecture [94], show human-specific histone methylation gains at additional TSS that were missed by the present study.
More broadly, the approach provided here, which is region- and cell type-specific epigenome mapping in multiple primate species, highlights the potential of epigenetic markings to identify regulatory non-coding sequences with a potential role in the context of hominid brain evolution and the shaping of human-specific brain functions. Remarkably, a small subset of loci, including the aforementioned DPP10 (chromosome 2q14.1), shows evidence for ongoing selective pressures in humans, resulting in DNA sequence alterations and the remodeling of local histone methylation landscapes, after the last common ancestor of human and non-human primates.
Text S1 contains detailed description for sample preparation for ChIP-seq and RNA-seq, qRT-PCR, gel shift, and 3C assays including primer sequences, RNA immunoprecipitation and in situ hybridization, bioinformatics and analyses of deep sequencing data, exploration of regulatory motifs, calculation of nucleotide substitution rates in the primate tree, and sweep analyses for polymorphic regions.
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10.1371/journal.ppat.1002068 | Acquisition of Human-Type Receptor Binding Specificity by New H5N1 Influenza Virus Sublineages during Their Emergence in Birds in Egypt | Highly pathogenic avian influenza A virus subtype H5N1 is currently widespread in Asia, Europe, and Africa, with 60% mortality in humans. In particular, since 2009 Egypt has unexpectedly had the highest number of human cases of H5N1 virus infection, with more than 50% of the cases worldwide, but the basis for this high incidence has not been elucidated. A change in receptor binding affinity of the viral hemagglutinin (HA) from α2,3- to α2,6-linked sialic acid (SA) is thought to be necessary for H5N1 virus to become pandemic. In this study, we conducted a phylogenetic analysis of H5N1 viruses isolated between 2006 and 2009 in Egypt. The phylogenetic results showed that recent human isolates clustered disproportionally into several new H5 sublineages suggesting that their HAs have changed their receptor specificity. Using reverse genetics, we found that these H5 sublineages have acquired an enhanced binding affinity for α2,6 SA in combination with residual affinity for α2,3 SA, and identified the amino acid mutations that produced this new receptor specificity. Recombinant H5N1 viruses with a single mutation at HA residue 192 or a double mutation at HA residues 129 and 151 had increased attachment to and infectivity in the human lower respiratory tract but not in the larynx. These findings correlated with enhanced virulence of the mutant viruses in mice. Interestingly, these H5 viruses, with increased affinity to α2,6 SA, emerged during viral diversification in bird populations and subsequently spread to humans. Our findings suggested that emergence of new H5 sublineages with α2,6 SA specificity caused a subsequent increase in human H5N1 influenza virus infections in Egypt, and provided data for understanding the virus's pandemic potential.
| Even though highly pathogenic avian H5N1 influenza viruses lack an efficient mechanism for human-human transmission, these viruses are endemic in birds in China, Indonesia, Viet Nam and Egypt. Hotspots for bird-human transmission are indicated in areas where human cases are more than 80% of total H5N1 influenza cases. Circulation among hosts may allow H5N1 virus to acquire amino acid changes enabling efficient bird-human transmission and eventually human-human transmission. The receptor specificity of viral hemagglutinin (HA) is considered a main factor affecting efficient transmissibility. Several amino acid substitutions in H5 virus HAs that increase their human-type receptor specificity have been described in virus isolates from patients, but their prevalence has been limited. In contrast, we show here that new H5 sublineages in Egypt have acquired a change in receptor specificity during their diversification in birds. We found that viruses in those sublineages exhibited increased attachment and infectivity in the human lower respiratory tract, but not in the larynx. Our findings may not allow a conclusion on the high pandemic potential of H5N1 virus in Egypt, but helps explain why Egypt has recently had the highest number of human H5 cases worldwide.
| Since the emergence of highly pathogenic avian influenza virus subtype H5N1 (HPAI H5N1) in 1996, outbreaks have continued in a variety of domestic and wild birds as well as sporadic transmission to humans [1]. Over time, H5N1 viruses have diversified and are currently grouped into clades 0 to 9 according to the unified nomenclature system [2]. Since 2006, clade 2.2, which originated from a large outbreak in wild bird populations at Qinghai Lake in western China [3], [4], has spread rapidly over central Asia, Europe, the Middle East, and Africa [5], [6]. Clade 2.2 has further diversified forming the third-order clade 2.2.1 and three phylogenetically distinct sublineages (Ι, ΙΙ and ΙΙΙ) within clade 2.2 [7], [8].
Although the current H1N1 pandemic [9] may have diverted attention from the continuing worldwide circulation of H5N1 virus, the pandemic threat of H5N1 is still alarming. The cumulative number of confirmed human cases of H5N1 infection reported to the World Health Organization (WHO) to date is 504 with a 60% mortality [10]. According to the World Organization for Animal Health, HPAI H5N1 has become endemic in some areas where human cases constitute more than 80% of the total [10], indicating bird-human H5N1 virus transmission; e.g., China, Indonesia, Viet Nam and Egypt [11].
Since 2006, H5N1 viruses have spread across countries in western, eastern, and northern Africa, where viruses belonging to clade 2.2.1 and three sublineages (Ι, ΙΙ and ΙΙΙ) of clade 2.2 have been detected [7], [8]. As of October 2010, WHO has reported 114 laboratory-confirmed human cases on the African continent [10]. Egypt has experienced a relatively large number of human infections with 112 confirmed cases reported since 2006, when H5N1 was first identified in Egypt. In particular, the cumulative number since 2009 is notable: 61 confirmed cases in Egypt. The worldwide number since is also 112 cases. This indicates that the recent human H5N1 cases in Egypt are more than 50% of the total worldwide. The other 2 human cases of H5N1 virus infection in Africa were reported from Nigeria and Djibouti. The reason(s) for such a high number of human H5N1 cases in Egypt has not been elucidated.
Influenza viruses target glycosylated oligosaccharides that terminate in a sialic acid (SA) residue [12]–[14]. These residues are bound to glycans through an α2,3, α2,6, α2,8 or α2,9 linkage by sialyltransferases that are expressed in a tissue- and species-specific manner [15]–[17]. For example, human upper airway epithelia express mostly α2,6-linked SA (α2,6 SA) [18], whereas duck intestinal epithelia express mainly α2,3-linked SA (α2,3 SA) [19]. Efficient human-human transmission is necessary for influenza A virus to become pandemic. Although the determinants of efficient human-human transmission are not fully understood, it is believed that a change of receptor specificity from α2,3 SA, to which avian influenza A viruses preferentially bind, to α2,6 SA, to which human influenza viruses preferentially bind, is essential [12], [20], [21]. Although H5N1 viruses still lack the ability for efficient human-human transmission, the current prevalence of H5N1 might allow the virus to acquire mutations enabling α2,6 SA recognition. Thus, it is important to monitor the receptor binding affinity of H5N1 viruses in endemic areas and evaluate molecular mechanisms that might promote their pandemic potential.
In this study, we carried out a phylogenetic analysis of avian and human H5N1 viruses circulating in Egypt. The resulting virus phylogenetic tree indicated emergence of new H5 sublineages with each sublineage containing only or mostly human isolates, leading us to hypothesize that the HAs of these viruses might have acquired amino acid change(s) enabling α2,6 SA binding and resulting in the large number of human H5N1 cases in Egypt. Therefore, in this study we examined the receptor binding affinity of H5N1 viruses isolated in Egypt using sialylglycopolymers and human respiratory tract tissues, and assessed the effect of the amino acid changes in the HAs on viral replication in human airway epithelia in vitro and virulence in mice in vivo. We show here that these H5N1 viruses, during their spread in local bird populations, acquired mutations in their HAs that produced α2,6 SA binding affinity, providing a model for influenza virus phylogeny.
We studied the evolution of H5N1 influenza viruses in Egypt by analyzing the sequences of 106 viruses isolated there from birds and humans between 2006 and 2009: 85 sequences were obtained from the National Center for Biotechnology Information (NCBI) database, and 21 sequences were newly obtained in this study. At the time of this investigation, these 106 sequences represented 40% of the complete and partial H5N1 virus sequences from Egypt in public databases. HPAI H5N1 emerged in Egypt first in poultry in 2006, swiftly spread to many species of birds in different geographic regions [11], [22], and was declared endemic in 2008 [11]. Human infections started shortly thereafter and reached 112 cases by October 2010 [1], [10].
Phylogenetic analysis of the 106 H5N1 virus HA genes showed that all of these HA genes clustered in clade 2.2.1, with some of these viruses forming several new H5 sublineages (Figure 1). H5N1 isolates from 2006–2007 were interspersed throughout the phylogenetic tree, indicating rapid spread of the ancestral HA gene. In contrast, most human and avian isolates from 2008–2009 were clustered separately in distinct sublineages, denoted here as sublineages A, B (Ι, ΙΙ), C and D. These phylogenetic relationships indicated that during 2007–2008 the genetic diversity of H5 HA in Egypt increased dramatically and resulted in the establishment of distinct human and avian sublineages. Conversely, phylogenetic analysis of viral neuraminidase (NA) genes revealed that these genes were less divergent (Figure S1), with branches and tree topology different than the HA tree. The NA sequences formed a single monophyletic cluster which included the virus with the ancestral HA gene. These findings suggested that H5N1 viruses circulating in Egypt have diversified without significant genetic linkage, at least between the HA and NA genes.
The phylogenetic distribution of human and avian isolates in Egypt prompted us to investigate whether recent Egyptian isolates had an altered receptor binding specificity. To determine the α2,3 SA- and α2,6 SA-binding affinity of these isolates, we performed direct binding assays with SAα2,3Gal and SAα2,6Gal sialylglycopolymers [23], [24]. Six H5N1 isolates from outbreaks in Egypt during 2007–2009 were tested: A/duck/Egypt/D1Br12/2007 (EG/D1), A/chicken/Egypt/C1Tr13/2007 (EG/C1), A/chicken/Egypt/RIMD11-1/2008 (EG/11), A/chicken/Egypt/RIMD12-3/2008 (EG/12), A/chicken/Egypt/RIMD28-1/2009 (EG/28), and A/chicken/Egypt/RIMD29-3/2008 (EG/29). EG/D1 and EG/C1 were isolated from 2007 outbreaks, shared >99% homology with H5N1 viruses isolated in 2006, and in our phylogenetic tree did not form a sublineage or group with other H5N1 viruses isolated in Egypt, implying that they emerged before the establishment of new sublineages in Egypt and indicating that they were phylogentically close to the original H5N1 genotype in Egypt. The other four isolates belonged to the new H5 phylogenetic sublineages (Figure 1), indicating that they emerged during more recent H5N1 outbreaks.
Preliminary experiments to determine optimal binding assay conditions showed the importance of using appropriate virus titers (i.e., hemagglutination titers), because high virus titers produced exaggerated signals for the weakly binding glycopolymer (α2,6 SA) and low titers only detected binding to the high-affinity glycopolymer (α2,3 SA) (Figure S2). For example, EG/D1, which was expected to have a classical avian influenza virus α2,3 SA specificity, showed strong binding to α2,3 SA as expected, but also measurable binding to α2,6 SA when the virus titer was increased to 512 HAU. Conversely, EG/12, which was assumed to have increased α2,6 SA specificity because it clustered with human sublineage A strains, showed a complete loss of α2,6 SA binding with increasing dilution of the HA titer to 8 HAU. From these results, HA titers from 32 to 128 HAU appeared to be optimal for comparison of receptor binding specificity with our experimental conditions. Therefore, HA titers of all virus samples were adjusted to an HA titer of 64 HAU, relative to a reference EG/D1 sample, and used for the following binding assays.
EG/D1, EG/C1, EG/11, EG/28 and EG/29 viruses had binding specificity for α2,3 SA (Figure 2C–2G). The association constants are shown in Table S1. The binding patterns closely resembled the strong α2,3 SA binding specificity observed with an avian influenza H5N3 virus, A/Duck/Hong Kong/820/80 (Figure 2B). In contrast, EG/12 virus had appreciably increased binding to α2,6 SA, with binding to both α2,3 SA and α2,6 SA (Figure 2H). However, the EG/12 binding affinity for α2,6 SA was less than that of the seasonal human influenza virus A/Japan/434/2003 (Figure 2A). This was confirmed by direct binding assays using recombinant viruses generated by reverse-genetics: each recombinant virus contained one of the HA genes in a background of all of the other EG/D1 virus genes (denoted here as rEG/D1) (Figure 2I–2M). To investigate other sublineage A and B viruses, we synthesized the HAs of three H5N1 viruses isolated in Egypt: a bird isolate; A/goose/Egpt/0929-NLQP/2009 (EG/0929); and two human isolates; A/Egypt/N04822/2009 (EG/4822) and A/Egypt/N02039/2009 (EG/2039). The receptor specificities of these viruses were determined and showed that the H5 HAs of these recent isolates also had increased α2,6 SA binding (Figure 2N–2P). These results indicated differences in HA affinity to α2,6 SA among recent H5 isolates, together with an affinity to α2,3 SA.
To identify mutations enabling α2,6 SA binding, we focused on viruses in sublineages A and B, to which most human isolates belonged. Comparison of 6 HA sequences of sublineage A viruses with 100 HA sequences of other H5 viruses isolated in Egypt identified two amino acid changes in the sublineage A virus HAs (Table 1): Q192H and S235P (H5 HA numbering). Introduction of the Q192H mutation into EG/D1 HA (denoted rEG/D1Q192H) markedly increased viral binding to α2,6 SA (Figure 3A). However, introduction of the S235P mutation into EG/D1 HA (denoted rEG/D1S235P) only slightly increased α2,6 SA binding. There was no synergistic effect with both mutations: the double mutant had similar α2,6 SA binding to that of the single Q192H mutant. In contrast, the H192Q mutation, but not the P235S mutation, in HAs of EG/12 (denoted rEG/D1-EG/12 HAH192Q) and EG/4822 decreased α2,6 SA binding (Figures 3B and S3A). These findings suggested that the Q192H mutation in H5N1 avian viruses increased the binding affinity of HA for the human receptor.
The HA sequences of the 19 H5N1 viruses in sublineage BI (denoted sublineage A1 in a previous report [25]) differed from the 87 HA sequences of other H5N1 viruses isolated in Egypt at three HA amino acid residues: S120N, 129 deletion (Δ) and I151T (Table 1). When introduced as a single mutation into EG/D1 HA, none of these amino acid changes increased binding to α2,6 SA (Figure 4A). However, the 129Δ/I151T double mutation increased α2,6 SA binding. In contrast, both the 129S insertion and the T151I mutation in the HAs of EG/0929 and EG/2039 decreased α2,6 SA binding (Figures 4B and S3B). These results suggested that the 129Δ and I151T mutations acted synergistically to enable α2,6 SA binding by sublineage BI viruses. Sublineage BII viruses have four mutations: the three mutations found in sublineage BI viruses plus an additional V210I mutation (Table 1). When introduced as a single mutation in the HA of EG/D1, V210I partially increased α2,6 SA binding, but there was not an appreciable increase in binding in the V210I/129Δ/I151T triple mutant (Figure 5). These results suggested that the V210I mutation did not increase α2,6 SA binding in sublineage BII viruses above that of the 129Δ/I151T double mutation.
The phylogenetic trees of sublineages A, BI and BII suggested human viruses in these sublineages emerged from avian viruses in these sublineages or closely related avian viruses (Figure 1). The amino acid changes in HA enabling α2,6 SA binding in sublineage A (Q192H) and sublineage BI viruses (129Δ/I151T) were not found in human H5N1 influenza viruses phylogenetically unrelated to sublineage A and B strains (data not shown), indicating that these mutations were associated with the phylogeny of avian H5N1 sublineage A and B viruses in Egypt. A database search of virus gene sequences posted since 2006 also revealed that the prevalence of these amino acid changes increased in human influenza virus HAs in Egypt concurrently with an increase in avian influenza viruses in Egypt (Table 2), although most of the recent avian influenza virus isolates were in sublineages C and D (Figure 1). In contrast, an increased prevalence has not been detected in either birds or humans in Asia. These findings suggested that H5N1 avian viruses in Egypt acquired binding affinity for α2,6 SA during viral diversification in local bird populations, which may have contributed to subsequent virus transmission to humans with higher efficiency.
To investigate whether mutations in avian virus HAs enabling α2,6 SA binding function with similar specificity in the human respiratory tract, the attachment pattern of selected viruses to fixed tissues of the human upper and lower respiratory tract (i.e., larynx, trachea and alveoli) was determined by histochemistry. Histochemical analysis can provide clinically relevant data on virus attachment in human airway epithelia [26], [27] and on the glycan topologies that influenza viruses target for cell-specific infections in airway epithelia [28], [29]. Human H3N2 virus, which was used as a control, attached extensively to ciliated epithelial cells in the larynx and trachea and, to lesser degree, to alveolar cells (type Ι pneumocytes; Figure 6). In contrast, rEG/D1, rEG/D1-EG/11 HA and rEG/D1-EG/29 HA attached predominantly to alveolar cells (type ΙΙ pneumocytes), with little attachment in larynx and trachea, as found for avian H5N3 virus. The attachment pattern of rEG/D1-EG/12 HA was different from the classical avian pattern found for H5N3: little attachment to larynx, moderate attachment to trachea, and significant attachment to alveoli (both type Ι and ΙΙ pneumocytes). The attachment patterns of the rEG/D1Q192H, rEG/D1129Δ,I151T and rEGD1129Δ,I151T,V210I mutants were similar to that of rEG/D1-EG/12 HA. However, all three mutant viruses attached less abundantly to trachea than the H3N2 virus. Also, rEG/D1-EG/12 HAH192Q showed an attachment pattern similar to that of rEG/D1, with rare attachment to trachea. We also performed virus histochemistry on sialidase-treated sections, which abrogated all staining confirming that the viruses in this study did not bind to non-sialic acid residues (Figure S4). Although not quantitative, these results indicated that mutations enabling α2,6 SA binding are clinically significant in affecting the affinity of HA for receptors in the human respiratory tract.
To examine whether the HA mutations enabling α2,6 SA binding also affected virus replication in human airway cells, we studied virus growth in primary human small airway epithelial cells (SAEC) by infecting these cells with selected recombinant viruses and human H3N2 virus, which was used as a control, at a multiplicity of infection (MOI) of 1 or 0.1 and monitoring viral growth kinetics and cytopathicity for 72 h post-infection. For comparison, we studied viral growth kinetics in chicken embryo fibroblast (CEF) cells infected at an MOI of 0.1 or 0.01. All viruses replicated well in CEF cells and produced >107 focus-forming units (FFU)/ml at 24 and 48 h post-infection. The difference in titers of these viruses was <1 log FFU/ml at each time point, indicating that all of the viruses replicated equally well in avian-derived cells (Figure 7A). These results confirmed that there was no incompatibility between EG/12 HA and EG/D1 NA or between mutated EG/D1 HA and EG/D1 NA in the recombinant viruses generated for this study (compare the kinetics of parental EG/D1 and rEG/D1 viruses in Figure 7A). In contrast, in SAEC cells (Figure 7B), rEG/D1Q192H, rEGD1129Δ,I151T and rEG/D1-EG/12 HA replicated more efficiently than rEG/D1 and rEG/D1-EG/12 HAH192Q, with slight differences in their growth, and a final virus titer of rEG/D1Q192H > rEG/D1129Δ,I151T > rEG/D1-EG/12 HA. These viruses replicated in SAEC cells and reached titers more similar to those of human H3N2 virus than of parental EG/D1, especially at a higher inoculum. The difference in virus growth kinetics correlated with cytopathicity in SAEC cells: rEG/D1Q192H, rEG/D1129Δ,I151T and rEG/D1-EG/12 HA produced more severe cytopathic effects and resulted in more detachment of infected cells at 24, 48 and 72 h post-infection than rEG/D1 and rEG/D1-EG/12 HAH192Q (Figure 7C). These results indicated that the Q192H mutant and the 129Δ/I151T double mutant produced a substantial viral growth advantage in human airway epithelial cells.
To assess the effect of enhanced α2,6 SA binding on pathogenicity of H5N1 isolates from Egypt, BALB/c mice were inoculated intranasally with different dilutions of selected recombinant viruses. Mice inoculated with 3×104 FFU rEG/D1Q192H, rEG/D1129Δ,I151T or rEG/D1-EG/12 HA showed considerable weight loss (Figure 8A). In contrast, mice inoculated with 3×104 FFU rEG/D1 or rEG/D1-EG/12 HAH192Q showed no clinical effects during the 14 d observation period, and most mice infected with 3×105 FFU of these viruses survived. The lethality of rEG/D1Q192H, rEG/D1129Δ,I151T and rEG/D1-EG/12 HA was substantially higher: the MLD50 was 8.8×102 FFU for rEG/D1Q192H, 1.5×103 FFU for rEG/D1129Δ,I151T and 1.3×104 FFU for rEG/D1-EG/12 HA (Figure 8B), >50 times less than the MLD50 of 5.9×105 FFU for both rEG/D1 and rEG/D1-EG/12 HAH192Q. Consistent with this result, the virus yield in lungs of mice infected with 3×104 FFU of the three viruses was >10-fold higher 4 d post-infection and >110-fold higher 7 d post-infection, and at a dose of 3×105 FFU was >70-fold higher 4 d post-infection than with parental rEG/D1 virus (Figure 8C).
Lungs of mice infected with 3×104 FFU viruses were examined by histopathology at 7 d post-infection. Mice infected with rEG/D1Q192H, rEG/D1129Δ,I151T or rEG/D1-EG/12 HA had much more dramatic pathological changes in their pulmonary airways and parenchymal tissues. The lungs had moderate to severe bronchiolar necrosis and alveolitis with associated hyperplasia, pulmonary edema and inflammatory cell infiltrates (Figure 9C–9E). In contrast, lung pathology of rEG/D1 and rEG/D1-EG/12 HAH192Q infected mice showed signs of limited lymphohistiocytic cell extravasations (Figure 9B and 9F). Mock-infected mice did not have lesions in their lungs (Figure 9A). H5 antigen was more extensively detected by immunohistochemistry in the alveolar area of lungs infected with rEG/D1Q192H, rEG/D1129Δ,I151T or rEG/D1-EG/12 HA than in lungs infected with rEG/D1 and rEG/D1-EG/12 HAH192Q (Figure 9G–9L). Weak antigen staining was only rarely detected in the bronchiolar area in lungs of mice infected with rEG/D1 and rEG/D1-EG/12 HAH192Q (see insert in Figure 9H and 9L). Therefore, the difference in lethality in mice infected with these viruses was grossly correlated with the growth kinetics and cytopathicity of the viruses in human airway epithelial cells. Collectively, these results indicated that enhanced receptor specificity in vivo enabled rEG/D1Q192H, rEG/D1129Δ,I151T and rEG/D1-EG/12 HA to infect mice at lower titers than rEG/D1 and rEG/D1-EG/12 HAH192Q.
To investigate the structural basis for the changes in human receptor-binding specificity in viruses in the new sublineages, we generated models of the HA structures of EG/D1, EG/D1Q192H and EG/D1129Δ,I151T from the crystal structure of the HA of A/Vietnam/1194/04 (H5N1) (Protein Data Bank ID (PDBID) code 2IBX) [24], and performed a docking study with these models and two types of ligands, SAα2,3Gal (PDIBID code 1MQM) and SAα2,6Gal (PDIBID code 1MQN). In our modeling, HA residues 120, 210 and 235 were distant from the receptor binding sites in the EG/D1 HA structure, whereas residues 129, 151 and 192 were located around them (Figure 10A and 10B). A Gln192 to histidine mutation (and a Gln192 to arginine mutation) generated a positively-charged side chain in the HA carbon backbone at this position, which has been reported [24] to stabilize contact of SAα2,6 Gal-terminated polysaccharides with H5 HA by forming a hydrogen bond with human receptor moieties (also see Discussion below). In addition, deletion of Ser129 led to a hydrogen bond between side chains of the HA carbon backbone at Glu127 and Thr151, affecting orientation of the 130-Loop (Figure 10C). Therefore, the double 129Δ/I151T mutation might affect the contact angle between human-type receptor ligands and viral HA.
In our simulation, the Udock scores of the complexes between the SAα2,6 human-type receptor ligand and EG/D1, EG/D1Q192H and EG/D1129Δ,I151T HA were −13.51, −18.05 and −16.48 kcal/mol, respectively (Figure 10C). Therefore, the Udock scores of the complexes bound to EG/D1Q192H and EG/D1129Δ,I151T HA were more negative than with parental EG/D1 HA, indicating more energetically stable interactions of the mutant HAs with the human receptor analog. In contrast, the Udock scores of the complexes between the SAα2,3 avian-type receptor ligand and EG/D1, EG/D1Q192H and EG/D1129Δ,I151T HA were relatively similar (−14.19, −15.27 and −12.16 kcal/mol, respectively), with the Udock scores of the complexes bound to EG/D1Q192H and EG/D1129Δ,I151T HA not appreciably more negative than with EG/D1 HA. These results indicated that HAs of the viruses in the new sublineages have structurally and energetically more stable conformations for binding human receptors.
In this study of H5N1 avian and human influenza viruses isolated in Egypt, we found that these viruses clustered in several new H5 sublineages, with a higher than expected binding affinity for α2,6 SA, and identified the amino acid mutations responsible for this expanded receptor specificity. Our phylogenetic analyses also indicated that these viruses emerged during 2007–2008 outbreaks in Egypt. This time overlaps with or slightly precedes an increase in the number of human cases of H5N1 virus infection in Egypt [1], [10].
HA plays an important role in the attachment of influenza viruses to host cells and, therefore, influences viral host range and pathogenicity [12], [30]–[32]. In this study of H5N1 virus (clade 2.2.1), we found that an HA Q192H single mutation or a 129Δ/I151T double mutation increased viral binding to α2,6 SA and increased infection in human airway epithelia. Previous assays [24] of A/Vietnam/3028ΙΙ/04 virus (clade 1) and A/chicken/Indonesia/N1/05 (clade 2.1) binding to sialylglycopolymers found that an HA Q192R mutation enhanced binding to α2,6 SA. The Q192H mutation identified in this study was at the same residue as the Q192R mutation in the A/Vietnam/3028ΙΙ/04 and A/chicken/Indonesia/N1/05 viruses, suggesting that these two mutations produced a similar conformational change in HA. These structural changes agreed fairly well with simulation data that the mutation at this position in an H5 HA model electrostatically enhanced HA binding affinity to human-like glycan [33]. The Q192H mutation was not present in H5 HAs in 375 avian influenza viruses and 120 human influenza viruses isolated in Asia, including clade 1, 2.1, 2.2 and 2.3 viruses (Table 2). We examined codon usage in HA of 495 H5 isolates from Asia and 254 isolates from Egypt with Q at residue 192 and found that all of these viruses encode 192Q using codon CAA. This result indicates that an amino acid change from Q to H or R at residue 192 required a one nucleotide change (CAA to CAT/CAC (H) or CGA(R)). The higher frequency of a transversion to encode H may have enabled such a mutation to occur more frequently in HAs of H5 viruses in Egypt.
Since the HA Q192R substitution might be selected during viral growth in a human patient and enhance α2,6 SA binding in the human respiratory tract [24], we constructed rEG/D1 with this HA substitution and found its α2,6 SA binding affinity similar to or slightly greater than rEG/D1Q192H and rEG/D1129Δ,I151T (data not shown). In addition, deletion of HA residue 129 was not found in any of the H5 HAs of the 495 Asian isolates examined, and the I151T substitution was only detected in H5 HAs isolated from 6 birds and 2 human patients in Asia (1.6% and 1.7% prevalence, respectively). H5 HA residues 129 and 151 make atomic contact with sialoglycosides [34]. We showed here that the 129Δ mutation generates a new hydrogen bond between Glu127 and Thr151, resulting in conformational changes around the binding pocket (Figure 10C). This effect around the glycosidic bond in H5 HAs seems to be unique to the viruses isolated in Egypt. We also searched for similar mutations (Q192H and the double 129Δ/I151T mutation) in 4507 avian influenza viruses with HAs H1–13 and H16 and found that only 3 bird isolates had these mutations (Table S2): Quail/Nanchang/12-340/2000 (H1N1), Turkey/Minnesota/40550/1987 (H5N2), and Ruddy turnstone/Delaware/2762/1987 (H11N2). Such mutations were not present in any of the H1, H2 or H3 HAs of the human isolates in the early years of the Spanish flu (1918), Asian flu (1957), Hong Kong flu (1968) and Russian flu (1977) pandemics, in which these avian subtypes crossed the species barrier to humans. However, it is noteworthy that most of the viruses in this study that clustered in sublineage B were reported to have evolved towards an H1N1-like receptor usage, to efficiently replicate in the upper respiratory tract, and that structural properties of the receptor binding sites of Spanish flu viruses and sublineage B viruses are much closer to each other than to other H1N1 and H5N1 viruses [35]. In contrast, a conformational change in HA due to S235P and S120N mutations was not observed in our structural model: these were also shown not to increase HA affinity for α2,6 SA by direct binding assays (data not shown).
Our data suggested that H5N1 viruses from Egypt had acquired amino acid mutations enabling α2,6 SA binding during their transmission among birds, not during viral growth in human patients. First, avian isolates were at the base and within branches of the phylogenetic tree of new sublineages A and B, and clustered closely with human isolates (Figure 1). Moreover, all of the avian isolates already had identical mutations that contributed to binding affinity for the human-type receptor (Tables S3, S4, S5). Second, critical amino acid mutations involved in α2,6 SA recognition (Q192H and the double 129Δ/I151T mutation) were not found in any of the H5 HAs from human isolates phylogenetically unrelated to sublineages A and B. Therefore, it is unlikely that viruses with these mutations were newly selected during viral growth in humans. Third, all viruses examined here exhibited a classical avian α2,3 SA binding affinity and replicated efficiently in CEF cells, suggesting that these viruses had retained HAs for efficient transmission among birds (Figure 7A).
Several amino acid mutations that increase α2,6 SA binding affinity of H5 virus HAs have recently been described in human isolates [23], [24], [36]. It is possible that such mutations were selected in humans and played an important role in viral recognition of human-type receptors. However, there have been only limited reports of those mutations in H5 HAs in infections in human patients [23], [24], [36]. Considering that the mutations in H5 HAs in birds identified here have been found in some population of birds in the vicinity of humans, such viral mutations emerging in birds may be as important risk factors for human H5N1 infections as those mutations emerging in viruses infecting humans. Thus far, there have been few reports of HPAIV in bird populations with increased affinity for α2,6 SA [37].
At present, the determinants of efficient human-human transmission by avian influenza viruses are not completely understood [21], [38]. It is generally thought that both a change in receptor specificity from α2,3 SA to α2,6 SA and the resultant shift in infection to the upper respiratory tract are essential [39]. However, most amino acid mutations in H5 HAs that have been reported to increase α2,6 SA binding have not conferred a complete change in receptor specificity in the original virus genetic background [23], [24], [40], [41]. But, Chutinimitkul et al. have recently reported that some mutations can cause a complete change in the A/Indonesia/5/05 background [42]. Increased α2,6 SA binding affinity and reduced α2,3 SA binding affinity was also observed among North American lineage H7 viruses isolated in 2002–2004 [37]. In contrast, we found that all of the H5N1 viruses in this study retained the classical avian α2,3 SA binding affinity (Figures 2–5). Histochemistry using human tissues also found that viruses in this study with avian H5 HA mutations had little attachment to the larynx, but moderate attachment to trachea and abundant attachment in alveoli (Figure 6), whereas human H3N2 virus extensively bound to both the larynx and trachea. These results suggested that H3 and H5 viruses recognized more complex glycan topologies, which have not yet been fully elucidated in human airway epithelia [28], [29]. This conclusion is in agreement with the suggestion that H5N1 viruses attach to receptors in the human upper respiratory tract that are not detected by lectin histochemistry and with data that H5N1 viruses can productively replicate in ex vivo cultures of human nasopharyngeal tissues [43].
These findings suggest that currently circulating H5N1 viruses in Egypt lack gene products for efficient human-human transmission, even though they have caused a relatively large number of human cases in Egypt. Indeed, most human infections resulted from direct exposure to H5N1 virus-infected poultry or poultry products and no sustained human-human transmission has been documented to date in Egypt [1], [44]. It should be noted that our findings do not allow determination of the potential for an H5N1-derived pandemic virus in Egypt. However, the emergence of sublineage A and B H5N1 viruses is a possible contributing factor to Egypt recently having the highest number of human H5N1 influenza virus cases in the world, with repeated avian infections increasing the probability of avian-human transmission. To our knowledge, this is the first report identifying amino acid changes in H5 HA responsible for an increase in human H5N1 infections in an endemic area.
Mice have been an animal model for studying influenza [45]–[47]. In this study, we found that the HA mutations enabling α2,6 SA binding enhanced viral virulence in BALB/c mice (Figure 8). These results are consistent with a previous report on different influenza viruses and different HA amino acid residues in a ferret model [48]. Reports on lectin histochemistry showed that BALB/c mice express both α2,3 and α2,6 SA in airway epithelia, with α2,3 SA specifically expressed in the upper respiratory tract and α2,6 SA expressed in pulmonary parenchyma [49]. Previous histochemistry report on seasonal human influenza viruses H3N2 and H1N1 showed rare attachment to mouse type I pneumocytes, indicating the presence of glycan topologies in alveoli to which influenza viruses with α2,6 SA binding affinity attach [27]. In this study, recombinant avian H5 viruses with single and double point mutations that should affect receptor binding were found to have acquired α2,6 SA binding affinity, and the resultant expansion of receptor specificity in vivo contributed to enhanced virulence in mice. Indeed, virus titers in the lungs of mice infected with the mutant viruses were more than one log higher than in mice infected with the parental virus (Figure 8C), corresponding to severe histopathological changes (Figure 9). These results were consistent with histochemistry showing that the mutants acquired enhanced attachment affinity to human type I pneumocytes (Figure 6). Type I pneumocytes comprise 96% of the alveolar surface area, which is extremely thin, thereby minimizing the diffusion distance between the alveolar air space and pulmonary capillary blood [50]. Therefore, viral binding specificity for this cell type has implications for the development of pneumonia. However, other factors also need to be considered, such as the low similarity of the SA expression pattern in mice relative to that in humans [26], [27]. Thus, it would be of interest to determine the effect of the substitutions in HA described here on virus virulence in the ferret model, which is a more suitable animal model for human H5N1 viral pneumonia [26], [27]. Our studies also found that EG/D1, an ancestral strain of currently circulating H5N1 viruses in Egypt, was not highly pathogenic in mice, as indicated by an MLD50 >105 FFU (Figure 8B). Avian and human H5N1 viruses in Egypt, including EG/D1, encode PB2-627Lys, which reportedly enhances the host range and virulence of influenza viruses [30], [47], [51]. The results of this study indicate that this amino acid residue alone does not provide sufficient replicative advantage in mammals for the influenza viruses (clade 2.2.1) in Egypt, although it may be a prerequisite for H5N1 virus virulence in mammalian hosts.
The mechanism underlying the emergence of H5N1 viruses in Egypt with both α2,3 SA and α2,6 SA binding affinities is unclear. Some H7 viruses isolated in North America from 2002–2004 showed a marked decrease in α2,3 SA binding together with increased binding to glycans with α2,6 SA [37], and several H5N1 field isolates (clade 2.3.4) in the Lao People's Democratic Republic in 2007–2008 had reduced binding to α2,3 SA receptors [52]. In contrast, H5 viruses isolated in Egypt have retained the classical avian α2,3 SA binding affinity (Figures 2–5). Previous studies have shown passage of H5N1 viruses through land-based poultry as a possible mechanism for emergence of dual receptor specificity [17], [53]. However, most bird isolates in Egypt, found to be clustered in sublineages A and B in this study, were recently reported to be derived from domestic waterfowl, not from land-based poultry [54]. In addition, these H5N1 viruses showed an appreciably different attachment pattern in the human respiratory tract than that of typical avian viruses (Figure 6). Therefore, the binding properties of H5 viruses in Egypt may be the result of geographic and cultural factors that have yet to be identified.
Egypt has a relatively large number of human cases of H5N1 virus infection, and the highest number of cases worldwide since 2009 [1], [10]. The influenza virus phylogenetic tree suggests that sublineages A and B, the focus of this study, emerged during virus diversification in birds. At present, viruses grouped in sublineages C and D are widely disseminated across Egypt. Therefore, it remains possible that repeated circulation in birds would allow sublineage C and D viruses to acquire amino acid change(s) other than those identified here that could enable increased α2,6 SA binding affinity, although the amino acid mutations identified here may be useful markers in assessing H5N1 field isolates for their potential to infect humans. Since clade 2.2 appeared in Egypt in 2006, Egypt has had a single known introduction of a clade 2.2.1 H5N1 virus. Neither introduction of other phylogenetically distinct sublineages of clade 2.2 (I, II and III) nor reassortment events between the sublineages, as detected in neighboring Nigeria [55]–[57], have been documented in Egypt [7], [58]. Such events also were not observed in our phylogenetic analyses of HA and NA genes (Figures 1 and S1). However, introduction of these sublineages into Egypt could accelerate the evolutionary dynamics of H5N1 virus. Moreover, all Egyptian viruses (clade 2.2.1), which emerged during the 2005 Qinghai Lake outbreak in China [3], [4], have mammalian-type PB2-627Lys [30], [47], [51], implying the potential for evolution to a pandemic virus. Therefore, there is a critical need for continued surveillance of birds to monitor receptor specificities of H5N1 field isolates in Egypt as well as the pandemic potential of these strains.
All animal studies were conducted under the applicable laws and guidelines for the care and use of laboratory animals in the Research Institute for Microbial Diseases, Osaka University, approved by the Animal Experiment Committee of the Research Institute for Microbial Disease, Osaka University, as specified in the Fundamental Guidelines for Proper Conduct of Animal Experiment and Related Activities in Academic Research Institutions under the jurisdiction of the Ministry of Education, Culture, Sports, Science and Technology, Japan, 2006.
During outbreaks of highly pathogenic avian influenza in Egypt from January 2007 to February 2009, 27 nasopharyngeal swab and tissue samples (lung and trachea) were collected from sick or dead chickens and ducks from commercial farms and backyard farms. Of these samples, 21 were identified as H5-positive by reverse transcription-polymerase chain reaction (RT-PCR) and selected for virus isolation. Twenty viruses were eventually isolated by single passage in the allantoic cavity of 11-day-old embryonated chicken eggs. The allantoic fluids were then harvested and stored as seed viruses at −80°C. Laboratory strains A/Duck/Hong Kong/820/80 (H5N3) and human influenza A virus A/Japan/434/2003 (H3N2) were kindly provided by Yoshinobu Okuno, Kanonji Institute, The Research Foundation for Microbial Diseases of Osaka University, Kagawa, Japan. For subsequent studies, allantoic fluids were pre-cleared by centrifugation at 3,000 rpm for 20 min and filtration through 0.45 µm filters, and viruses were then purified by centrifugation at 25,000 rpm for 2 h through 20% and 60% sucrose. After collection of the virus-containing fractions, viruses were suspended in PBS and pelleted by centrifugation at 25,000 rpm for 2 h. Virus pellets were resuspended in PBS and aliquots were stored as working stocks at −80°C. Virus titers were assayed as FFU by focus-forming assays [59] on CEF cells for avian influenza viruses and on MDCK cells for human H3N2 virus. All experiments with live H5N1 viruses were performed in Biosafety Level 3+ (BSL 3+) conditions at Osaka University, as approved for work with these viruses by the Ministry of Agriculture, Forestry and Fisheries, Japan.
CEF cells were prepared from 11-day-old embryonated eggs. MDCK cells were purchased from the Riken BioResource Center Cell Bank (http://www.brc.riken.jp/lab/cell/english/). These cell lines were maintained in Dulbecco's Modified Eagle's Medium supplemented with 10% heat-inactivated fetal calf serum at 37°C in a humidified atmosphere of 95% air and 5% CO2 as described previously [60]. Human primary SAEC cells were purchased from the Lonza Corporation (http://www.lonza.com/) and maintained according to the manufacturer's recommendations.
Viral RNA was extracted from viruses using Trizol Reagent (Invitrogen, http://www.invitrogen.com/) according to the manufacturer's protocol. RT-PCR was done using an oligonucleotide (Uni12) complementary to the conserved 3′ end of viral RNA [61]. Gene cloning and sequencing were done on at least 3 independent clones per segment as described previously [62].
The nucleotide sequence data analyzed for viruses in this study are available in the DDBJ/EMBL/GenBank databases under the accession numbers AB601121 to AB601156.
Recombinant viruses were generated with a plasmid-based reverse genetics system [63]. The viral complementary DNAs were cloned into pUC18-based plasmids, between the human RNA polymerase I promoter and the hepatitis delta virus ribozyme (pPOLI). All viruses generated by reverse genetics carried the HA gene of one of the viruses being studied, with the other genes coming from EG/D1. The HA genes of EG/0929, EG/4822 and EG/2039 were synthesized using the sequences registered in the NCBI database Influenza Virus Resource (IVR, http://www.ncbi.nlm.nih.gov/genomes/FLU/FLU.html) and site-directed mutagensis PCR (GeneTailor Site-Directed Mutagenesis System; Invitrogen). Mutant HA genes were generated by PCR-based site-directed mutagenesis in the EG/D1, EG/12, EG/0929, EG/4822 or Eg/2039 HA background. All constructs were sequenced completely to ensure the absence of unwanted mutations. Recombinant viruses were generated by plasmid transfection of co-cultured 293T and CEF cells, and were propagated in eggs. The HA genes of the virus stocks were sequenced to detect the possible emergence of revertants during amplification.
For phylogenetic analysis of HA genes, published HA sequences of 85 representative H5N1 influenza A viruses isolated in Egypt from 2006 to 2009 were obtained from the NCBI database (http://www.ncbi.nlm.nih.gov/nucleotide). Phylogenetic analysis was performed on those 85 HA sequences and on the HA sequences of the 21 viruses isolated in this study using MEGA4 software [64] for the neighbor-joining method, with the nucleotide sequences covering most of HA gene. Estimates of the phylogenies were calculated by performing 1,000 bootstrap replicates. For phylogenetic analysis of NA genes, published NA sequences from the NCBI database of 65 representative H5N1 viruses isolated in Egypt from 2006 to 2009 together with the NA sequences of 19 viruses isolated in this study were analyzed. For a database search, published sequences of 260 HA genes from influenza A viruses isolated in Egypt from 2006 to 2009 from NCBI IVR were analyzed. For comparison, published HA sequences of 495 H5N1 influenza A viruses recently identified in Asia were also obtained from NCBI IVR. These sequences were aligned by the MAFFT program [65] and the HA1 regions were compared with the sequences of the viruses isolated in this study.
Stocks of avian and human influenza viruses were serially diluted with PBS and mixed with 0.5% chicken red blood cells and 0.75% guinea pig red blood cells, respectively. Hemagglutination by avian and human influenza viruses was observed after incubation at room temperature for 30 min or 1 h, respectively to determine their HAU. To correct for differences in HAU values due to different blood lots, a reference virus sample was used and HAU values of all virus samples were adjusted relative to the reference HAU titer of EG/D1, which was used in the optimization analysis of the following receptor specificity assay.
Receptor binding specificity was analyzed by a solid-phase direct binding assay as described previously [23], [24], [52], with a sialylglycopolymer containing N-acetylneuraminic acid linked to galactose through either an α2,3 or α2,6 bond (Neu5Acα2,3LacNAcb-pAP, and Neu5Acα2,6LacNAcb-pAP). Serial dilutions of each sialylglycopolymer were prepared in PBS, and 100 µl was added to each well of 96-well microtiter plates (Polystyrene Universal-Bind Microplates, Corning, http://www.corning.com/). The plates were then irradiated with 254 nm ultraviolet light for 10 min and each well was washed three times with 250 µl PBS. Each well was blocked with 100 µl PBS containing 0.1% Tween 20 (PBST) and 2% bovine serum albumin at room temperature for 1 h. After washing with ice-cold PBST, a solution containing influenza viruses (64 HAU in PBST) was added to each well and the plates were incubated at 4°C for 12 h. After washing five times with ice-cold PBST, mouse anti-NP antibody (against influenza virus NP protein) was added to each well and the plates were incubated at 4°C for 2 h. The wells were then washed five times with ice-cold PBST and incubated with peroxidase-conjugated goat anti-immunoglobulin (Histofine Simple Stain MAX-PO, Nichirei, http://www.nichirei.co.jp/bio/english/) at 4°C for 2 h. After washing five times with ice-cold PBST, 100 µl premixed tetrametylbenzidine-H2O2 substrate was added to each well. After incubation at room temperature for 10 min, the reactions were stopped with 50 µl 1 M H2SO4, and absorbance at 450/630 nm was measured.
Binding data were plotted against the concentration of sialic acid residues in the reaction solution and were analyzed using GraphPad Prism version 5.0 (GraphPad Software, http://www.graphpad.com/). To determine the apparent association constant (Ka) values, nonlinear regression was used to fit the data based on the one-site model. Each data point is the mean ± SD of three to six experiments, which were each performed in triplicate.
SAEC cells were infected in triplicate with the indicated viruses at an MOI of 1 or 0.1. The virus inoculum was removed after 1 h and the cells were washed and airway epithelial growth medium (SAGM; Lonza) containing bovine pituitary extract (BPE; 30 µg/ml), hydrocortisone (0.5 µg/ml), human epidermal growth factor (hEGF; 0.5 ng/ml), epinephrine (0.5 µg/ml), transferrin (10 µg/ml), insulin (5 µg/ml), triiodothyronine (6.5 ng/ml), bovine serum albumin-fatty-acid free (BSA-FAF; 50 µg/ml), retinoic acid (RA; 0.1 ng/ml), gentamycin (30 µg/ml) and amphotericin B (15 ng/ml) was added. Acetylated trypsin (2 µg/ml, Sigma-Aldrich, http://www.sigmaaldrich.com/) was also added to SAEC cultures for propagation of human H3N2 virus. At the indicated times post-infection, virus titers in the cell culture supernatants were determined in triplicate by FFU assays in CEF. For determination of viral growth in CEF cells, the cells were infected in triplicate at an MOI of 0.1 or 0.01. At the indicated times post-infection, virus titers were determined in triplicate by FFU assays. Preliminary lectin-based flow cytochemistry studies indicated a difference in SA expression on the surface of SAEC and CEF cells without growth under air-liquid interface conditions, with predominant expression of α2,6 SA in SAEC cells and of α2,3 SA in CEF cells. Therefore, all cell cultures in this study were established without air-liquid interface conditions as described previously [47], [66].
To produce fluorescein isothiocyanate (FITC)-labeled viruses for histochemistry, influenza viruses, purified and concentrated as described above, were inactivated with formalin in PBS (0.025% final concentration) for 24 h at 37°C. The virus mixture was then dialyzed against PBS for 18 h at 4°C and complete inactivation was confirmed by assay on MDCK cells. A 1 ml sample of inactivated virus was then mixed with 0.1 ml 1.1 M carbonate-bicarbonate buffer (pH 9.5) containing 0.55 mg FITC isomer Ι (Invitrogen)/ml for 1 h at room temperature with constant stirring, followed by dialysis of the mixture against PBS for 42 h at 4°C. To check for hemagglutination activity by the inactivated virus, the viral hemagglutination titer was assayed after formalin inactivation and FITC labeling.
Formalin-fixed paraffin-embedded human respiratory tract tissue sections were obtained from US Biomax, Inc. (http://www.biomax.us/). The paraffin-embedded tissues were deparaffinized with xylene and hydrated using graded alcohols. After blocking with Carbo-Free Blocking Solution (Vector Laboratories, http://www.vectorlabs.com/), the tissues were then blocked with Blocking Reagent (Perkin Elmer, http://www.perkinelmer.com/). FITC-labeled influenza viruses were incubated with tissue sections at 4°C for 12 h at a titer of 128 HAU per section. The FITC label was detected with peroxidase-conjugated rabbit anti-FITC antibody (Dako, http://www.dako.com/). The signal was amplified with a tyramide signal amplification system (Perkin Elmer) according to the manufacturer's instructions. Peroxidase was visualized with 3-amino-9-ethyl-carbozole (AEC+ Substrate Chromogen, Dako), resulting in a bright red precipitate. Tissues were counterstained with hematoxylin and embedded in Aquatex (Merck Chemicals, http://www.merck-chemicals.com/). Omission of FITC-labeled virus was used as a negative control. The specificity of the virus histochemistry was verified as follows. Tissue sections, deparaffinized and hydrated as described above, were treated with Arthrobacter ureafaciens sialidase (100 mU/ml, Nacalai Tesque, http://www.nacalai.co.jp/) in sodium acetate buffer (100 mM, pH 5.8) for 1 h at 37°C or mock-treated before performing virus histochemistry. Micrographs were taken using a Nikon Eclipse TE2000-U Inverted Microscope (Nikon, http://www.nikon.com/).
To determine MLD50 values, groups of 6-week-old female BALB/c mice (Japan SLC, Inc., http://www.jslc.co.jp/), under isoflurane anesthesia, were inoculated intranasally with serial 10-fold dilutions of virus in 75 µl PBS, and MLD50 values were calculated by the Reed-Muench method and expressed as FFU required for 1 MLD50. Mice were observed daily for 14 d for weight loss and mortality. Mice that lost >30% of their original weight were euthanized. At 4 and 7 d after inoculation with 3×104 FFU and at 4 d after inoculation with 3×105 FFU (because of mouse deaths before day 7 at this dose), virus titers in the lungs were assayed as FFU in CEF cells. Virus titers in lungs were expressed as log10 FFU. The lower limit of virus detection was 2 log10 FFU/lung. For histopathology analysis, mouse lungs collected at 7 d after inoculation with 3×104 FFU were fixed in 4% buffered paraformaldehyde, embedded in paraffin, cut into 5 µm sections, stained with hematoxylin and eosin, and examined by light microscopy. Immunohistochemical staining for the H5 antigen was performed on deparaffinized sections using a monoclonal antibody (C43) specific for the nucleoprotein of influenza A virus by a two-step peroxidase method (Hisfine Mouse Stain Kit, Nichirei) with diaminobenzidine as the chromogen and hematoxylin as the counterstain. For controls, unrelated antibodies were used in place of the primary antibody.
The crystal structure of the HA of influenza virus A/Vietnam/1194/04 (H5N1) (Protein Data Bank ID code 2IBX) [24] was used as a template for homology modeling of EG/D1, EG/D1Q192H, and EG/D129Δ,I151T by the Molecular Operating Environment (MOE, http://www.chemcom.com). SA α2,3- and SA α2,6-linked analogs (PDBID code 1MQM and 1MQN) were used as the input for a docking study with the model HA structure using MOE ASEDock [67]. The MMFF94x force field and the generalized Born (GB) solvation model were used for the minimization step. The complexes were evaluated by Udock scores which show the affinity between ligand and receptor. Because SA α2,3- and SA α2,6-linked analogs are a disaccharide and a trisaccharide respectively, the absolute value of their Udock scores cannot be compared between the complex bound to the α2,3-linked analog and that bound to the α2,6-linked analog. However, Udock scores enable the binding mode of the same analog to different HAs to be compared.
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10.1371/journal.ppat.1004627 | Elucidation of the RamA Regulon in Klebsiella pneumoniae Reveals a Role in LPS Regulation | Klebsiella pneumoniae is a significant human pathogen, in part due to high rates of multidrug resistance. RamA is an intrinsic regulator in K. pneumoniae established to be important for the bacterial response to antimicrobial challenge; however, little is known about its possible wider regulatory role in this organism during infection. In this work, we demonstrate that RamA is a global transcriptional regulator that significantly perturbs the transcriptional landscape of K. pneumoniae, resulting in altered microbe-drug or microbe-host response. This is largely due to the direct regulation of 68 genes associated with a myriad of cellular functions. Importantly, RamA directly binds and activates the lpxC, lpxL-2 and lpxO genes associated with lipid A biosynthesis, thus resulting in modifications within the lipid A moiety of the lipopolysaccharide. RamA-mediated alterations decrease susceptibility to colistin E, polymyxin B and human cationic antimicrobial peptide LL-37. Increased RamA levels reduce K. pneumoniae adhesion and uptake into macrophages, which is supported by in vivo infection studies, that demonstrate increased systemic dissemination of ramA overexpressing K. pneumoniae. These data establish that RamA-mediated regulation directly perturbs microbial surface properties, including lipid A biosynthesis, which facilitate evasion from the innate host response. This highlights RamA as a global regulator that confers pathoadaptive phenotypes with implications for our understanding of the pathogenesis of Enterobacter, Salmonella and Citrobacter spp. that express orthologous RamA proteins.
| Bacteria can rapidly evolve under antibiotic pressure to develop resistance, which occurs when target genes mutate, or when resistance-encoding genes are transferred. Alternatively, microbes can simply alter the levels of intrinsic proteins that allow the organism to “buy” time to resist antibiotic pressure. Klebsiella pneumoniae is a pathogen that causes significant blood stream or respiratory infections, but more importantly is a bacterium that is increasingly being reported as multidrug resistant. Our data demonstrate that RamA can trigger changes on the bacterial surface that allow Klebsiella to survive both antibiotic challenge, degradation by host immune peptides and resist phagocytosis. We demonstrate that the molecular basis of increased survival of ramA overexpressing K. pneumoniae, against host-derived factors is associated with RamA-driven alterations of the lipid A moiety of Klebsiella LPS. This modification is likely to be linked to Klebsiella’s ability to resist the host response so that it remains undetected by the immune system. The relevance of our work extends beyond RamA in Klebsiella as other pathogens such as Enterobacter spp and Salmonella spp. also produce this protein. Thus our overarching conclusion is that the intrinsic regulator, RamA perturbs host-microbe and microbe-drug interactions.
| The microbial response to antimicrobial challenge is multifactorial and can be conferred by a combination of extrinsic or intrinsic mechanisms. Those intrinsic mechanisms that confer pleiotropic phenotypes can provide a “stepping stone” to surmounting both the host or drug response. Intrinsic proteins such as the AraC-transcriptional proteins e.g. MarA [1], SoxS [2], Rob [3], RamA [4] and RarA [5], directly regulate genes linked to microbial permeability barriers which results in reduced susceptibility [6] to multiple antibiotic classes. The perturbation of the permeability barrier is identified as a critical step in the development and emergence of higher levels of resistance [7].
The regulatory proteins, typified by the MarA protein, are unique, as unlike other members of the AraC family, these proteins bind DNA as monomers [8], interact with RNA polymerase via a process of pre-recruitment [9] and generally confer reduced antimicrobial susceptibility [10]. Microarray analyses has highlighted the wider effects of increased MarA [1], SoxS [2], RamA [4, 11] and RarA [5] levels in modulating gene expression particularly of those genes linked to virulence. This is further supported by studies reporting that either the inhibition or deletion of these regulators [12] can impair the ability of E. coli to colonise and cause infection in vivo [13]. Taken together, it is evident that these AraC proteins can confer bifunctional phenotypes of reduced drug susceptibility and increased virulence, which facilitate pathogen survival. These findings firstly, underscore the relative importance of these factors in microbial survival and secondly, provide a rationale for the development of “Anti-virulence-type” inhibitors against these transcription proteins.
The ramA gene which encodes the RamA protein is found in Klebsiella, Enterobacter [14], Salmonella [15] and Citrobacter spp [16] where the genetic organisation of the ram locus is conserved in most organisms, with the exception of Salmonella enterica serovar Typhimurium (Fig. 1) which lacks romA, a putative metallo-beta-lactamase gene. The levels of both the romA-ramA genes are repressed at the transcriptional level by the TetR-type family regulator RamR, encoded by the ramR gene, which is divergently transcribed from the romA-ramA operon. In both Klebsiella and Salmonella, an increase in ramA expression can be mediated by inactivating mutations [16–18] or ligand mediated interactions [19] with the cognate repressor, RamR which binds to a highly conserved inverted repeat (atgagtgn6cactcat) [20] overlapping the promoter region of the romAramA operon (Fig. 1). Mutations within the ramR gene in K. pneumoniae resulting in ramA overexpression were initially reported as a result of tigecycline exposure [17, 21]. However, previous work evaluating clinical isolates that pre-date the use of tigecycline demonstrate that ramA overexpressing strains were already present within the nosocomial population of K. pneumoniae, suggesting a broader role for RamA mediated overexpression in antibiotic resistance [16]. Interestingly, studies evaluating the prevalence of ramA-mediated overexpression in clinical isolates of K. pneumoniae and Salmonella spp. indicate that these bacteria are more likely to overexpress ramA than marA or soxS, suggesting that elevated ramA levels may be more relevant to the development of antibiotic resistance in these organisms.
Several studies [4, 11] have addressed the scope of the RamA regulon in Salmonella enterica serovar Typhimurium using microarray profiling. These studies demonstrate that ramA overexpression results in reduced antimicrobial susceptibility due to the differential regulation of acrAB and micF genes, which consequently decrease OmpF levels. One study [4] suggests that genes linked to the Salmonella Pathogenicity Island (SPI-2) are also differentially expressed, leading to the initial observation that RamA may impact on Salmonella-specific virulence attributes. However this link was not corroborated in subsequent in vivo experiments. In K. pneumoniae, the wider impact of RamA-mediated regulation is not known. Despite the apparent similarities in genome structure, the microbial lifestyles of both K. pneumoniae [22] and Salmonella spp. differ. Importantly, the increasing multidrug resistance in Klebsiella spp. demands a thorough understanding of factors within this genus that contribute to the intrinsic microbial ‘resistome’ and survival under selective (host or drug) pressure. Therefore to define the broad effects of RamA-mediated expression on microbe-host and microbe-drug phenotypes we carried out transcriptome profiling using directional RNAseq with the wild type strain K. pneumoniae Ecl8 [23] and its isogenic derivatives Ecl8ΔramA and Ecl8ΔramR. Our key findings show the scope of RamA-mediated regulation significantly alters the transcriptional landscape of K. pneumoniae. This occurs by directly modulating the expression of different genes notably those associated with antimicrobial resistance and host-microbe interactions thereby resulting in the emergence of a less antibiotic susceptible and more virulent K. pneumoniae.
The ram locus encodes a sRNA to maintain basal levels of ramA expression. RamR functions as the primary repressor of both romA-ramA expression in K. pneumoniae by binding the palindromic repeats of the IR element which flanks the TSS for romA at position -64T. ramR, itself, has two transcriptional start sites, located at the -83T and -167A positions where expression analyses using GFP fusions suggest that the primary promoter region for ramR transcription is located at the -83T start site (S1 and S2 Figs.). This site is also repressed 5-fold more than the vector only control by ramR in trans indicating that like other TetR-type regulators, RamR expression is autoregulated (S2 Fig.).
Previous work in Salmonella has shown that the regulatory RNA, StyR3, can control expression at the ram locus [24]. Given the expansive role of ramA in gene regulation, we sought to determine whether the K. pneumoniae ortholog of StyR3, denoted as sRamA5, would function as co-regulator of ramA expression in K. pneumoniae to promote basal ramA levels. The lack of similarity within the intergenic regions located between the ramR and romA-ramA genes or ramR and ramA genes in K. pneumoniae and Salmonella spp. respectively, excluded the possibility of using sequence analyses to identify the StyR3 ortholog. Direct northern blot analyses of RNA derived from K. pneumoniae strain Ecl8 and its derivatives did not produce a detectable signal for the putative regulatory RNA, sRamA5. Thus in order to demonstrate the presence of sRamA5, we cloned the entire intergenic region flanked by the ramR and romA genes and the partial romA open reading frame into the TA cloning vector pGEMTeasy to generate pGEMsRamA5. Northern blot analyses derived from the expression of sRamA5 encoded on pGEMsRamA5, using gene specific probes for sRamA5 and romA ORF, demonstrate the presence of sRamA5 (~ 60nt) (shown in Fig. 2A). Notably, the sRamA5 specific probe also detected a further two RNA molecules (Fig. 2A, arrowed bands 1 and 2). These fragments, detected by both the sRamA5 and romA specific probe, possibly represent primary transcripts initiated from the common start site as determined by 5’ RACE analyses for sRamA5 and romA (S1 Fig.). As expected the romA specific probe did not detect the 60nt sRamA5 molecule (Fig. 2A). Thus we surmise that sRamA5 and romA are co-transcribed into a primary RNA molecule, which undergoes further processing prior to excision proximal to the start of the romA gene, thereby producing sRamA5.
As a classical TetR-family protein, RamR-mediated repression of the romA-ramA locus is likely to be perturbed through ligand-mediated interactions; therefore we hypothesized that to function as a co-regulator of romA-ramA expression RamR would interact with sRamA5. RNA-EMSA (S1 Text) analyses demonstrate that RamR and sRamA5 form a complex, suggesting direct interaction of the RNA (sRamA5) with RamR (Fig. 2B). In order to ascertain whether the interaction of sRamA5 and RamR is attributable to the presence of the highly conserved IR sequence in the ramR-romA inetrgenic region (ATGAGTGcgtactCACTCAT) and thus, act as a competitor for RamR-pI binding, we performed EMSA analyses using the pI promoter, sRamA5 and RamR. Our results show a reduction in affinity of RamR to sRamA5 in the presence of excess pI promoter (Fig. 2B). In contrast, competition experiments with excess sRamA5 show no perturbation of the pI+RamR interaction, suggesting that RamR has a higher affinity for the pI promoter compared to sRamA5 (Fig. 2B).
Simultaneous qPCR measurements utilizing an LNA probe to assess sRamA5 levels demonstrate firstly, that the transcription levels of sRamA5 and romA are not linked as sRamA5 levels are decreased in contrast to elevated romA levels (Fig. 2C). This suggests that despite being transcribed from the same TSS, sRamA5 and romA are likely subject to different rates of degradation. Secondly, the stability of sRamA5 may be dependent on the presence of a functional RamR. In order to investigate the requirement for a functional RamR in sRamA5 stability, we determined both the romA and sRamA5 levels in Ecl8ΔramR before and after complementation with ramR expressed in trans. As expected, our results show that the level of romA transcription was reduced (∼ 30-fold) in Ecl8ΔramR/pACramR compared to the plasmid only control (Ecl8ΔramR/pACYC177) (Fig. 2D). In contrast, the levels of sRamA5 were found upregulated by ∼ 2.8 fold in Ecl8ΔramR/pACramR relative to the plasmid only control (Ecl8ΔramR/pACYC177). Thus the increase in sRamA5 levels in the presence of a functional ramR supports our hypothesis that sRamA5 is stabilized by RamR. Our data also shows that sRamA5 does compete with pI for RamR binding, although this effect may be abrogated by the higher relative affinity of RamR to the pI promoter (Fig. 2B(ii)). Therefore, we surmise that the physiological relevance of RamR-sRamA5 interaction supports the basal level of ramA transcription detected in the wild type K. pneumoniae Ecl8.
To determine the effect of altered RamA levels on the whole transcriptome of K. pneumoniae strain Ecl8, we quantitatively compared the transcriptomes of the three strains (Ecl8, Ecl8ΔramA, Ecl8ΔramR) using the Kolmogorov-Smirnov (K-S) 2-sample test (S3 Fig.) as described in the supplementary data [25]. As expected, the distribution curve of Ecl8 and Ecl8ΔramA were more similar to each other compared to that observed for Ecl8ΔramR, suggesting that under normal growth conditions the deletion of ramA is less likely to perturb the transcriptional landscape as opposed to when it is overexpressed. This supports the notion that ramA functions as a pleiotropic regulator of gene expression in K. pneumoniae.
In all three strains, the 16S and 23S rRNA genes showed the highest number of mapped reads consistent with the lack of depletion for ribosomal RNA. However, pairwise comparisons of the normalized basemean values associated with these ribosomal regions were not differentially expressed between Ecl8 and Ecl8ΔramR or Ecl8 and Ecl8ΔramA. The lack of differential ribosomal gene expression is contrary to previous observations in Salmonella enterica serovar Typhimurium [4]. Other non-ribosomal genes (e.g. fusA_1 (encoding translation elongation factor G), atpA (producing ATP synthase F1, α subunit) and aceE (encoding a pyruvate dehydrogenase)) were also found to have significantly high basemean values relative to most other genes within the genome. The increased expression of these genes is perhaps not surprising as atpA is associated with aerobic growth and aceE catalyses the production of precursors to the TCA cycle.
Potential regions of antisense transcription were also detected. However, in most cases, these regions appeared as antisense because of in silico errors in annotation or due to transcriptional noise from flanking genes within the chromosome. We did, however, identify antisense transcription, such as with BN373_16241 (producing an oxidoreductase) and BN373_02611, which were differentially expressed due to either elevated RamA levels or loss of the ramA gene (S4 Fig.). Coverage plots analyses indicate that the transcription associated with BN373_02611 may be associated with 3’UTR runoff transcription from the divergently transcribed treBC operon, in contrast to BN373_16241, which is upregulated when ramA was overexpressed and may be a “true” antisense RNA (S4 Fig.).
Genome analyses of K. pneumoniae strain Ecl8 [23] identified 11 unique predicted prophage genes encoding phage structural components (BN373_03311, BN373_09871, BN373_10091, BN373_14801, BN373_14811, BN373_14821, BN373_14841, BN373_14921, BN373_21511, BN373_37361, BN373_37371) which were not found to be differentially transcribed in the pairwise comparisons tested (Ecl8 vs Ecl8∆ramA, Ecl8 vs Ecl8∆ramR (S1 Table). However, pairwise comparisons of Ecl8ΔramA and Ecl8ΔramR detected the differential expression of Ecl8-genome specific genes, BN373_33401, BN373_33411, which were repressed (∼2–3 fold) in the ramA overexpressing strain Ecl8ΔramR (S2 Table). Of note, no differential gene expression was noted in the 233 plasmid-coding genes in the ramA null mutant or in the ramA overexpressor (Ecl8ΔramR) with respect to the wild type (Ecl8).
Transcriptome analyses underscores that perturbations in RamA levels can result in the differential expression of open reading frames, antisense transcripts and Ecl8-specific genes. As the majority of reads were mapped to open reading frames, the main focus of our analyses relates to the differential regulation of genes within K. pneumoniae. The RamA regulon in K. pneumoniae was identified by pairwise comparisons of Ecl8∆ramR versus Ecl8 (C) or Ecl8∆ramA (B). The pairwise comparisons of Ecl8 versus Ecl8∆ramA (A)(Fig. 3) indicate the cohort of genes (13) responsive to basal levels of RamA expression; the contrast between Ecl8 versus Ecl8∆ramR (35) specifies genes that are either affected by RamR or RamA, whereas the comparison between Ecl8∆ramR versus Ecl8∆ramA (77) identifies genes that largely react to altered RamA levels. As fewer genes are affected due to perturbations in ramR expression as opposed to RamA levels, we surmise that the majority of genes differentially expressed in our pairwise comparison (B) are associated with RamA-mediated regulation. Based on this assessment, the probable RamA regulon, Fig. 3, constitutes a total of 103 genes (as in genes in categories A, B, AB, BC, CA, ABC) (S2 Table). Of these, 68 genes were found to be activated and 35 were repressed (S2 Table) when levels of RamA is relatively higher.
Genes associated with RamA-mediated regulation were initially mapped to the COG (clusters of orthologous groups) database to explore their biological function. COG functional classifications of the significantly differentially expressed genes reveal that RamA controls a myriad of cellular and metabolic processes (COG data presented in S2 Table). Generally, altered levels of RamA significantly modulate the expression of genes belonging to the COG functional group C (energy production and conversion). Specifically, when ramA is deleted, genes within the COG (G) (carbohydrate metabolism and transport) were also found to be differentially regulated. Pairwise comparison between Ecl8ΔramR versus Ecl8 indicates that COG families associated with transcription (K) and inorganic ion transport and metabolism (P) were also affected. Additionally, when ramA levels are elevated genes associated with cell wall membrane and envelope biogenesis (M), transcription (K) and Function UnknowN (FUN) categories were most differentially affected. Thus the resulting COG analyses also supports the observation where altered levels of RamA triggers a shift in gene functionality consistent with significant modulations in transcription patterns as predicted by the K-S test (S3 Fig.).
A closer analyses of the genes associated with pairwise comparisons of Ecl8ΔramA versus Ecl8ΔramR reveals that firstly, the highest number of genes (77) are differentially expressed and secondly genes (yhbW, nfnB, acrAB, ybhT, yrbB-F) associated with the previously characterized networks for MarA [1], SoxS [26] or Rob [3] in E. coli or RamA in Salmonella enterica serovar Typhimurium [4, 11] are also affected. This is consistent with previous observations that demonstrate that these proteins exhibit considerable gene overlap within the regulons [1, 4, 11]. Importantly, RamA overexpression results in the modulation of efflux pump genes such as acrAB, oqxAB and yrbB-F, which is consistent with phenotypes linked to multidrug resistance [27] and susceptibility to toxic small molecules, which is associated with alterations in the lipid symmetry of the cell wall [28]. However, the pairwise comparisons for Ecl8 and Ecl8ΔramA also suggest that basal levels of RamA are sufficient to trigger the upregulation of genes such as the trehalose transporter operon treBC and the ribose ABC transporter, rbsACB. Uniquely, genes associated with biofilm formation (hha-ybaJ encodes a toxin-antitoxin system) and lipid A biosynthesis BN373_10601 (encodes lipid A biosynthesis lauroyl acyltransferase, lpxL_2) and the related dioxygenase protein encoding gene lpxO (BN373_36331) were also found to be upregulated by RamA.
A total of 51 genes were found to be downregulated. As expected, ompF was significantly repressed in the ramA overexpresser (Ecl8∆ramR) (Fig. 3) in addition to genes encoding the nitrate reductases (narGHJI operon and nirD), BN373_05601 encoding the LysR-type transcriptional regulator, elongation factor EF2 and the riboflavin synthase encoding gene ribH were also found to be significantly downregulated in the ramA overexpresser (Ecl8∆ramR).
Only a subset of those differentially regulated genes was chosen for validation using qPCR. As expected, both the romA and ramA genes were found to show 5.25-log2 fold and 14.5- log2 fold increase in Ecl8∆ramR respectively compared to Ecl8∆ramA (S5A Fig.). When the activated genes (with the exception of romA, ramA) were assessed, increased expression of the following genes was noted (Fig. 4A): tolC (4.8- log2 fold), acrA (4.6- log2 fold), yhbW (1.8- log2 fold), yrbC (2.8- log2 fold), nfnB (3.3- log2 fold), ybjP (3.95- log2 fold), adhP (3.2- log2 fold), BN373_36191 (encodes putative membrane protein, 2.95- log2 fold), BN373_39031 (encodes oxidoreductase, aldo/keto reductase family, 1.5- log2 fold), lpxO (2.8- log2 fold) and lpxL-2 (3.6- log2 fold). As expected, the levels of the ompF (4.2- log2 fold down) and BN373_03291 (encodes conserved hypothetical protein, 1.1- log2 fold down) were also downregulated (Fig. 4A).
In order to determine if some of these differentially expressed genes were under the direct or indirect control of RamA, we performed both EMSA and in vitro transcription (IVT) using purified recombinant RamA protein. The EMSA results show that RamA directly binds the yrbF, ybhT, yhbW, acrA, nfnB, adhP, lpxO and lpxL-2 promoters (Fig. 4B). Of note, our controls, showed no shift in the presence of the test promoters (Fig. 4B).
We then determined whether RamA would directly regulate the different promoters identified. By performing IVT experiments, we initially tested the effects of the RamA protein against the acrAB promoter to ascertain if RamA would function correctly as a transcriptional activator. As expected, the purified recombinant RamA activated the acrAB promoter directly (Fig. 4C) thereby confirming the biological activity of the purified RamA protein. Subsequently, we assessed the test promoters identified by the EMSA in our IVT assays. The results show that RamA upregulates yrbF (4-fold), ybhT (3-fold), yhbW (6.9-fold), acrA (4-fold), nfnB (10-fold), lpxO (8-fold) and lpxL-2 (∼3-fold) (Fig. 4C). Thus purified recombinant RamA alone can directly activate the expression of these promoters in vitro.
RamA regulates genes involved in lipid A biosynthesis. Having established that purified RamA directly binds and activates the expression of lpxL-2 and lpxO gene promoters (Fig. 4B and 4C), we sought to determine whether RamA could regulate other genes associated with the lipid A biosynthetic pathway. The lipid A biosynthetic pathway is governed by nine enzymes encoded by lpxA, lpxC, lpxD, lpxB, lpxK, lpxl, lpxM and lpxO genes [29]. Gene expression analyses using qPCR showed that with the exception of lpxC, none of the other lpx genes showed significant differential expression in Ecl8ΔramR in comparison to Ecl8 or Ecl8ΔramA (Fig. 5A). We then chose to assess whether RamA would directly interact with the lpxC and lpxK promoter regions. Subsequent EMSA analyses demonstrate that RamA directly interacts with the lpxC but not the lpxK promoter (Fig. 5Bi) and increased lpxC transcription (9-fold) in the presence of purified RamA and RNA polymerase (Fig. 5Bii). Previous work has shown that the control of lipid A biosynthetic genes is mediated by the PhoPQ or PmrAB systems [30]. Further interrogation of the transcriptome data and subsequent qPCR analyses shows that the levels for phoP and pmrA levels remained unchanged in K. pnuemoniae Ecl8, Ecl8ΔramA and Ecl8ΔramR. Thus the differential modulation of the lpxO, lpxC and lpxL-2 genes is directly linked to increased RamA levels.
To ascertain whether RamA-mediated transcriptional activation of lpxC, lpxL-2 and lpxO would actually result in modifications within the lipid A moiety, we performed MALDI TOF mass spectrometry (S1 Text for details). The mass spectrometry analyses confirm alterations in lipid A structure of the ramA overexpresser, Ecl8ΔramR compared to the wild type (Ecl8), the null mutant (Ecl8ΔramA) or the double mutant (Ecl8ΔramRA) (Fig. 5C) where peaks (m/z 1840, 1866 and 2079) were found to be elevated. Previous studies in K. pneumoniae [31, 32] indicate that those peaks correspond to LpxO hydroxylated lipid A species containing a hydroxymyristate group at position 2’ as secondary acyl substitution. Therefore, we surmise that RamA mediated activation of the different lipid A biosynthetic genes leads to alterations within the lipid A moiety in K. pneumoniae.
Phenotype microarray analyses. In order to assign phenotypes linked to the differentially regulated genes, Biolog phenotype assays were undertaken for K. pneumoniae Ecl8 and its isogenic derivatives Ecl8ΔramA and Ecl8ΔramR. A comparison of Biolog phenotypic profiles of both Salmonella [11] and K. pneumoniae generally indicates a significant overlap in the susceptibilities to antimicrobial and toxic compounds (S3 Table). As expected, the overexpression of ramA resulted in increased tolerance of Ecl8ΔramR in the presence of antimicrobials such as tetracyclines (doxycycline, chlortetracycline, minocycline), macrolides (erythromycin, spiramycin, troleandomycin), beta-lactams (1st, 2nd, 3rd generation cephalosporins, penams) and (fluoro)quinolones (ciprofloxacin, ofloxacin, nalidixic acid, novobiocin), fungicides (such as chloroxylenol, dodine, domiphen bromide) and toxic anions (potassium tellurite, sodium metasilicate) (S3 Table, S6 Fig.). Notably, comparisons of the Biolog data also indicate that ramA overexpression results in altered polymyxin B susceptibility levels in both K. pneumoniae and Salmonella.
Lipid A synthesis in Gram-negative bacteria is controlled at both the transcriptional and translational levels, where alterations in the lipid A profile can result in perturbations in host-microbe interactions as well as reductions in susceptibility to both the polymyxins and the cationic antimicrobial peptides (cAMPs) [33]. Accordingly, we tested the strain Ecl8 and its isogenic derivatives Ecl8ΔramA, Ecl8ΔramR against colistin, polymyxin B and the cAMP LL-37. The relative survival assays for colistin, polymyxin B and LL-37 demonstrated that the ramA overexpressing strain, Ecl8ΔramR strain was significantly (P < 0.05) less susceptible to polymyxin B, colistin and LL-37 (Fig. 6 A, B, C) compared to the wild type Ecl8 and the null mutant Ecl8ΔramA. The reduction in polymyxin susceptibility, as noted in the survival assays, is also supported by the Biolog data (S3 Table). Taken together these results suggest that RamA-dependent regulation provides an alternative pathway for reduced susceptibility to polymyxins and cAMPs.
Macrophage-Klebsiella interaction. To ascertain whether RamA-mediated alterations can have an impact on microbe-macrophage interactions, we examined if Ecl8 and its isogenic derivatives, Ecl8ΔramR, Ecl8ΔramA and Ecl8ΔramRA would exhibit differential interactions in adherence and intracellularization into murine RAW macrophages. In the adhesion and intracellularization assays, the ramA overexpresser, Ecl8ΔramR, was significantly attenuated in its ability (approximately 50% decrease) to attach to and internalise into the RAW murine macrophage cells compared to wild type K. pneumoniae Ecl8, the mutants Ecl8ΔramA and Ecl8ΔramRA (Figs. 7A, B and C). Two possible explanations exist for the reduction in adherence and intracellularization of Ecl8ΔramR; the first, where altered RamA levels confers resistance to phagocytosis and the second, is due to accelerated killing by the macrophage. In order to ascertain whether the reduced intracellularization of Ecl8ΔramR was linked to accelerated killing by macrophages, we determined the levels of extracellular non-phagocytosed bacteria in our experiments and found significantly higher numbers of recovered bacteria for Ecl8ΔramR compared to the wild type Ecl8, Ecl8ΔramA and Ecl8ΔramRA (Fig. 7D). In previous work [34], resistance to phagocytosis by K. pneumoniae has been linked to bacterial surface structures which include the capsular polysaccharide (cps). However, ugd gene transcription, representative of the cps cluster [35], was not found to be altered in Ecl8, Ecl8ΔramA, Ecl8ΔramR and Ecl8ΔramRA (S5B Fig.), consistent with the RNAseq data. Thus our results underscore that reduced phagocyte adhesion and uptake is linked to RamA-mediated alterations, particularly those associated with lipid A.
In order to assign a broader relevance to altered Klebsiella-host interaction, we performed experiments to assess bacterial recovery using the intranasal inoculation method [36] as described previously. Following a 24-hour infection of 5–7 week old C57BL mice, organ homogenates (spleen and lung) were plated to determine bacterial counts. At 24 h post infection, bacterial recovery rates for the ramA overexpressor, Ecl8ΔramR were found to be significantly higher compared to the wild type Ecl8 or null mutant Ecl8ΔramA from the lung and spleen (Fig. 8(A) and 8(B)). The intranasal route of infection is expected to result in the primary infection of the lung prior to dissemination to other organs. Our results demonstrate that significantly higher levels of Ecl8ΔramR is recovered from both the lung and spleen highlighting that RamR-dependent RamA overexpression, confers reduced microbial clearance and increased systemic dissemination of K. pneumoniae in an intranasal infection model.
The relevance of the MarA, SoxS, Rob, RamA and RarA regulators in microbial survival is attributed to their control of the antimicrobial resistance phenotype in a wide variety of Gram-negative bacteria [10, 37, 38]. Whilst the role of RamA in reduced antibiotic susceptibility is evident from multiple studies [16, 17, 37], its broader role in gene regulation is not known in Klebsiella pneumoniae. Using transcriptome profiling, we demonstrate that RamA-overexpression results in altered K. pneumoniae transcription patterns (S3 Fig.) compared to the null mutant or wild type strain thus highlighting its wider role in gene regulation in K. pneumoniae.
Our data suggests that RamA functions largely as a transcriptional activator of gene expression, where DNA-binding (Fig. 4B) and IVT assays (Fig. 4C) demonstrate that this regulation is direct and likely mediated via a mar/ram-box like element [39] located within the promoter region. Whilst our work is the first to demonstrate direct RamA-mediated activation of gene expression, other studies have shown that related proteins such as MarA, SoxS [40] and RarA [5] also exert explicit control of regulon genes. Comparative transcriptome data analyses suggests that RamA-mediated activation is dependent on regulator concentration (basal versus overexpressed, Fig. 2) in addition to the observation that identical RamA levels induce differential levels of promoter activation as supported by our in vitro data (Fig. 4C). The maintenance of basal ramA levels may be necessary for the K. pneumoniae stress response to a variety of agents as has been previously shown when selecting for fluoroquinolone resistant Salmonella [41] or Klebsiella in a ramA-deleted strain. In K. pneumoniae, basal levels of ramA expression is maintained due to titration of the absolute repressory effects of RamR by the RamR-sRamA5 interaction (S2 Fig.). Uniquely for tetracycline family regulators, RamR, directly interacts with the regulatory RNA, sRamA5, (Fig. 2B) which is produced as a cleaved by-product of the primary romA transcript (Fig. 2B). Whilst the sRamA5-RamR interaction, provides basal levels of ramA expression, ramA transcription as observed in the overexpressor, Ecl8ΔramR or clinical strains [16] are linked to loss of function mutations within RamR. Consequently, our data show that the maximal changes in gene expression profiles are observed when ramA is overexpressed as in Ecl8ΔramR (S3 Fig.). In this gene cohort, we demonstrate that RamA impacts on gene transcription linked to operons associated with efflux pumps, biofilm formation and lipid A biosynthesis (Fig. 3, S2 Table). Whilst it is possible that the differential regulation of these genes is not all directly linked to RamA, we demonstrate that purified RamA directly binds and activates the expression of multiple associated promoters (Fig. 4C & 4D).
A comparison of RamA-mediated regulation in Salmonella enterica serovar Typhimurium [4] and K. pneumoniae establishes key similarities in the genes associated with the respective RamA regulons; particularly in the control of genes associated with antimicrobial resistance acrAB and ompF [4, 11]. Additionally, RamA-dependent direct activation of acrAB is also consistent with phenotypic studies [10, 16–18] which consistently demonstrate that ramA overexpression is linked to increased elevated efflux via acrAB and decreased outer membrane protein levels (OmpF). Given its role in conferring reduced antimicrobial susceptibility, it is perhaps not surprising that we demonstrate that RamA directly regulates other efflux related operons specifically; the AcrAB linked inner periplasmic protein, YbhT [42] associated with detergent sensitivity, the Yrb operon which encodes an ABC transporter linked to the export of quinolones [27] and also lipid asymmetry [30]. The combined effect of the efflux or influx levels and membrane alterations associated with transport and structural variations likely contributes to the substrate range of compounds impacted by ramA overexpression (S3 Table). However, in the absence of a functional acrAB efflux pump, RamA-overexpression does not confer reduced susceptibility to most antibiotics in K. pneumoniae. This observation is consistent with previous studies for the MarA and RarA [38] proteins.
Therefore, it is likely that a functional AcrAB pump is crucial in mediating decreased antimicrobial susceptibility. However, a recent study [43] also suggests that acrAB may play a role in decreased antimicrobial peptide susceptibility and increased virulence in K. pneumoniae. Our findings support this observation and further demonstrate that increased RamA levels can also mediate LPS alterations, which likely contribute towards increased survival to both polymyxins and cationic AMPs (Fig. 5, 6).
Structurally, LPS is composed of three domains, the serovar dependent O-antigen chain, core oligosaccharide consisting of sugars and lipid A which is a phosphorylated disaccharide decorated with multiple fatty acids which anchor the LPS into the bacterial membrane [29]. The endotoxic lipid A component of LPS constitutes the outermost layer of the outer membrane of Gram-negative bacteria thereby playing a critical role in host-microbe interactions in addition to promoting reduced susceptibility to cAMPs [44] such as polymyxins [30] and host derived factors LL-37, HBD-1 [30]. Studies have shown that lipid A modifications can result in multiple outcomes such as reduced polymyxin susceptibility [45] in addition to directly facilitating microbial evasion by reduced immune recognition [46]. Our work suggests that the molecular basis for the modified lipid A structure is linked to the differential regulation of the biosynthesis genes e.g. lpxO, lpxL-2 and lpxC identified in this screen. Despite being constitutively produced the regulation of lpxC, lpxL-2 and lpxO, is still subject to either transcriptional or translational control [44, 46]; generally in response to stress, where, lpxC and lpxL-2 are regulated by the two-component systems, PhoPQ and PmrAB [44]. In contrast, lpxO is not subject to PhoPQ regulation in Salmonella [44, 46].
In Salmonella Typhimurium, the modulation of LpxO levels results in the remodeling of the outer membrane which reduces the net negative charge whilst simultaneously increasing membrane integrity resulting in increased virulence [47]. A similar phenotype is exhibited by the K. pneumoniae Ecl8ΔramR strain, which has altered LpxO levels (Fig. 8). Thus we surmise that the altered host-microbe and polymyxin-microbe interactions are in part attributable to the lipid A modifications.
Macrophages represent a key innate host defence strategy against microbial infections as phagocytosis of incoming pathogens is a trigger for the inflammatory response. Our data show that ramA overexpression protects against macrophage uptake and internalization (Fig. 7) thus providing a basis for the greater dissemination of the ramA overexpressing strain, Ecl8ΔramR in an in vivo infection model. Taken together, these RamA-linked phenotypes underscore its’ role in Klebsiella virulence and survival in vivo.
The molecular basis for phenotypes associated with reduced antimicrobial peptide susceptibility and increased virulence can be attributed to several key loci such as the acrAB pump and lipid A biosynthesis genes, lpxC, lpxL-2 and lpxO. This is supported by studies that demonstrate the involvement of acrAB [48] and lipid A modifications [30, 44] in host-microbe interactions. However to definitively pinpoint the exact contribution of the lipid A biosynthetic genes or acrAB to phenotypes associated with host-pathogen interactions would require the deletion of genes encoding lpxC [49], lpxL-2 and lpxO [50], acrAB individually or in combination with ramA overexpression. We note that previous studies [32, 50] have shown that strains deleted for these genes, result in avirulent microbes and as such, this phenotype would obscure any RamA-associated effects. Nevertheless, our work is first to demonstrate that firstly, RamA functions as an alternate regulator of certain lipid A biosynthesis genes and secondly, these alterations perturb microbe-host interaction.
The significance of our findings lies in the broader implications of RamA-mediated regulation in Enterobacteriaceae. In this work, we describe roles for RamA in both protection against antibiotic challenge but also against the innate host immune response thus resulting in Klebsiellae that are less susceptible to antibiotics and simultaneously more virulent. Notably, our findings highlight that RamA mediated overexpression via both increased acrAB expression and lipid A alterations can result in reduced susceptibility to the last line drugs e.g. tigecycline and polymyxins. This highlights the broader consequences in selecting for ramA overexpression in K. pneumoniae or other members of Enterobacteriaceae. Finally, our study underscores and highlights the importance of intrinsic proteins such as RamA, which regulate survival strategies in K. pneumoniae and likely other Enterobacteriaceae, specifically in priming the microbial population in surviving drug and host immune pressure. This proposes the notion where microbial immune evasive strategies contribute to the development and persistence of antimicrobial resistance.
Bacteria (Table 1) were cultured in Luria-Bertani (LB) medium (10 g/L tryptone, 5 g/L yeast extract, 10 g/L NaCl). Typically, a strain was first grown on an LB plate at 37°C from frozen -80°C stocks. A single colony was picked and inoculated into 5 ml of LB and incubated in a 37°C shaker overnight. A 1 in 100 dilution was made in LB and incubated in a 37°C shaker until the OD600 reached 0.6 unless otherwise stated. Antibiotics such as ampicillin (100 µg/ml) and chloramphenicol (20 µg/ml) were added as required.
The assay was as described previously by Moranta et al [51]. Briefly, bacteria were grown at 37°C in 5 ml LB medium, harvested (5,000 × g, 15 min, 5°C) and washed thrice with phosphate-buffered saline (PBS). A suspension containing approximately 105 CFU/ml was prepared in 10 mM PBS (pH 6.5), 1% tryptone soy broth (TSB; Oxoid), and 100 mM NaCl. Aliquots (5 μl) of this suspension were mixed in tubes with various concentrations of polymyxin B, colistin (0.064 µg/ml to 0.256 µg/ml) and LL-37 (32 µg/ml to 85.3 µg/ml) to a final volume of 30 µl. Following incubation for an hour at 37°C with polymyxin B (Sigma, UK), colistin (Sigma, UK) and LL-37 (Sigma, UK) the samples were diluted 1:10 with PBS prior to plating (100 μl) on LB agar. Colony counts were determined after overnight incubation, where results are expressed as percentages of the colony count of bacteria that were not exposed to the antibiotics or the antimicrobial peptide. Sensitivity profiles of the different mutants using the phenotypic microarray analyses were determined described in S1 Text.
Overnight cultures of strains Ecl8, Ecl8ΔramA, Ecl8ΔramR were inoculated (1/100 dilution) into LB media and incubated at 37 ºC with vigorous shaking. Cell pellets were harvested at OD600 = 0.6 and RNA was extracted using the RNAeasy Extraction Kit (Qiagen, Hilden, Germany), which enriches for RNA molecules larger than 200 nucleotides. No depletion of ribosomal RNA was carried out prior to the synthesis of single-stranded cDNA (sscDNA) as previously reported [52]. RNAseq DNA libraries were constructed as previously described [53]. For RNAseq, independent biological samples in triplicate were assessed for each strain. The resulting sscDNA libraries were sequenced in an Illumina HISeq 2000 sequencer. An average of 0.715 Gb of sequence data was obtained per sample, in 75 bp paired reads (Details of RNAseq analyses are outlined in S1 Text). The RNAseq read data has been deposited under the ENA data repository and ArrayExpress with the accession numbers ERP001994 and E-ERAD-122, respectively.
RNA for quantitative Real-Time PCR experiments was extracted from K. pneumoniae strains (Table 1) using the TRIzol extraction method [16]. Briefly, cells were grown to mid-log phase (OD600 = 0.6) at 37 ºC with shaking and then harvested by centrifugation at 3000g (PK121R, ALC) at 4 ºC. The cell pellet was then resuspended in TRIzol reagent (Invitrogen, Paisley, UK) and chloroform prior to centrifugation to separate the phases. The upper phase was then precipitated using 3 M sodium acetate, glycogen (5 mg/ml), and 100% ethanol.
Both RNA preparations were washed and resuspended in 50 µl DEPC treated water. RNA was treated with TurboDNase to remove DNA contamination (Ambion, New York, USA). All samples were assessed for RNA integrity and quantification using both the Bioanalyzer 2100 RNA nanochip (Agilent, UK) and the ND-1000 (Nanodrop Technologies) [4]. Only those samples with integrity level 9 were taken forward for library construction or qPCR analyses.
In order to validate the RNAseq data, quantitative Real-Time PCR experiments were undertaken. After the removal of contaminating DNA, cDNA synthesis was generated using the AffinityScript cDNA synthesis kit (Agilent, UK). Gene specific primers (S4 Table) were designed using the Primer3 (http://frodo.wi.mit.edu/) software and were tested to produce standard curves with amplification efficiencies ranging from 95–110%. qPCR analyses using the locked nucleic acid probe is detailed in S1 Text. Quantitative Real Time RT-PCR (RT-PCR) was performed using the synthesized cDNA with gene specific primers using the Brilliant III Ultra-fast SYBR Green Kit (Agilent, UK) in the Agilent Mx3005P. All data were analyzed using Agilent MxPro software, which is based on the efficiency corrected method (Pfaffl) of comparative quantification that utilizes the ΔΔCt approach, also taking into account primer efficiency. The relative fold increases in expression levels were determined by firstly normalizing gene expression levels to 16S rDNA and using either Ecl8 or Ecl8∆ramA as calibrators. All comparative analyses were done using the MxPro software (Agilent, UK).
DNA fragments that represent the promoter regions of the genes that were differentially regulated in the presence or absence of RamA or RamR were subjected to the electrophoretic gel shift mobility assay (EMSA) as described previously [54]. Briefly, DNA templates ranging from 250–150bp upstream of the start site were produced by PCR, and purified by StrataPrep PCR Purification kits (Agilent UK). The purified templates were end-labelled with [γ32P]-ATP by T4 Kinase (New England Biolabs, USA). The unincorporated, labelled ATP was removed using Biospin P6 spin columns (Biorad, UK) as per manufacturer’s instructions. Purified RamA was extracted from the recombinant pETramA construct using metal chelation chromatography on superflow nickel / nitrilotriacetate agarose (Qiagen, Germany) (James Hastie, Dundee University). His-tagged RamA (200 nM) and labelled DNA (2 nM) were mixed in binding buffer (125 mM Tris-HCl, 250 mM KCl, 5 mM DTT 5% glycerol) and incubated on ice for 15 min prior to electrophoresis at 75 V on a prechilled 7.5% native polyacrylamide gel in 1 × TBE buffer.
Transcription (IVT) experiments were performed as described previously [55]. Briefly 5 × IVT Buffer, 2 nM PCR product of the test and control (E. coli gnd [56]) promoters, RNA polymerase, RNAseOUT (Invitrogen, UK) was incubated for 15 minutes at 37°C prior to the addition of the transcription mix containing × 5 IVT buffer (50 mM Tris-HCl, 0.1 mM EDTA, 3 mM magnesium acetate, 0.1 mM dithiothreitol, 20 mM sodium chloride, and 250 μg/ml bovine serum albumin at pH 7.8), heparin (1.2 µg/ml), NTPS, and α32P-UTP (Perkin Elmer, UK). The reaction was stopped 5 minutes later followed by the addition of Gel loading buffer II (Ambion, UK). The resulting products were electrophoresed on a 7% polyacrylamide / 8 M urea gel. Quantification was determined by densitometric analysis using Fujifilm Multigauge Software where an increase or decrease in transcription levels is after normalization to the endogenous gnd levels and calibration to the no protein control.
Murine RAW 264.7 macrophage cells (obtained from ATCC TIB-71) were cultured in Dulbecco’s Modified Eagle Medium (PAA, UK) supplemented with 10% endotoxin-free foetal bovine serum (PAA, UK) and penicillin and streptomycin (Invitrogen, UK) in 75-cm2 culture flasks in 5% CO2 for 24 h until subconfluent. Twelve well tissue culture plates were seeded with 5 × 105 cells per well and viability determined using trypan blue exclusion.
Bacterial adhesion and internalization experiments were performed as described previously [57, 58]. For the adhesion assays, RAW cells were washed with PBS and incubated for 2 h at 37°C in 5% CO2 with a suspension of 5 × 107 bacterial cells in DMEM medium alone. After incubation, wells were washed five times with PBS and adherent bacteria were released by addition of 0.5% Triton X-100 (Sigma, UK). Bacterial colonies were quantified by plating appropriate dilutions on LB agar plates. In the internalization assays, after the incubation of the RAW cells with the bacterial suspension, wells were washed twice with PBS and then incubated for 2 h with fresh DMEM medium containing gentamicin (100 µg/ml) to eliminate extracellular bacteria. After the incubation, an aliquot of the medium was plated to confirm killing of extracellular bacteria and the gentamicin-containing medium was washed again. RAW cells were lysed and intracellular bacteria were quantified as described above. To estimate levels of extracellular bacteria, the infection of the RAW cells was carried out as described previously for the adhesion assay. After incubation, the media with the non-phagocytosed extracellular bacteria was collected and quantified by plating appropriate dilutions on LB agar plates. All microscopy images were generated as outlined in S1 Text.
All mouse experiments were performed under the control of the UK Home Office legislation in accordance with the terms of the Project license (PPL2700) granted for this work under the Animals (Scientific Procedures) Act 1986 in addition to receiving formal approval of the document through Queen’s University Belfast Animal Welfare and Ethical Review Body.
Overnight bacterial cultures were washed three times in sterile endotoxin free PBS. The bacteria was resuspended to an optical density of 0.2 and 20 μl (∼ 5 × 107 CFU/animal) and administered to anaesthetised 5–7 week old weight watched Harlan C57BL6 mice (n = 5 per group) using the intranasal inoculation method [36]. In order to ensure maximal delivery of the bacterial inoculation into the lungs the animals were held in a perpendicular position until cessation of laboured breathing. 24 h post inoculation the mice were sacrificed by lethal pentabarbitol injection. Perfused lungs and spleen were harvested and resuspended in 1 ml of sterile PBS. Following mechanical homogenisation dilutions were plated on LB agar plates and incubated at 37°C to establish the CFU/ml.
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10.1371/journal.pcbi.1001068 | Protrusive Push versus Enveloping Embrace: Computational Model of Phagocytosis Predicts Key Regulatory Role of Cytoskeletal Membrane Anchors | Encounters between human neutrophils and zymosan elicit an initially protrusive cell response that is distinct from the thin lamella embracing antibody-coated targets. Recent experiments have led us to hypothesize that this behavior has its mechanistic roots in the modulation of interactions between membrane and cytoskeleton. To test and refine this hypothesis, we confront our experimental results with predictions of a computer model of leukocyte mechanical behavior, and establish the minimum set of mechanistic variations of this computational framework that reproduces the differences between zymosan and antibody phagocytosis. We confirm that the structural linkages between the cytoskeleton and the membrane patch adherent to a target form the “switchboard” that controls the target specificity of a neutrophil's mechanical response. These linkages are presumably actin-binding protein complexes associating with the cytoplasmic domains of cell-surface receptors that are engaged in adhesion to zymosan and Fc-domains.
| Recent micropipette experiments have provided a unique live view of “one-on-one” interactions between human neutrophils and their phagocytic targets. Our results revealed surprising differences between two prominent immunological pathways: the response to fungal targets (mimicked using zymosan particles), and antibody-mediated phagocytosis. Whereas antibody-coated targets were “pulled” into the cell in a straightforward manner, zymosan particles were internalized only after an initial outward “push”. We hypothesized that structural interactions between the cytoskeleton and the membrane patch adherent to a target play a pivotal role in the control of this target specificity. To verify and refine this hypothesis, we here compare our experimental results with predictions of suitable adaptations of a previously validated computational model of neutrophil mechanical behavior. By optimizing the model to best match our experiments, we corroborate that the primary mechanistic origin of the target-specific cell behavior indeed lies in the strength of cytoskeletal membrane anchors.
| Our recent quantitative comparison of the physical responses of human neutrophils to zymosan (an insoluble, particulate fraction from yeast cell walls and prominent model system in the study of fungal infection [1], [2]) and to antibody-coated targets has exposed differences between these two forms of phagocytosis [3]. Zymosan phagocytosis typically commences as a chemotactic-like, pseudopodial protrusion that collides with, and eventually overflows, its target. In contrast, antibody-mediated phagocytosis represents more of an enveloping process in which a thin cellular lamella advances along all sides of a firmly held target to achieve engulfment [3]. The quantitative measure that most clearly illustrates this difference is the distance over which a neutrophil initially pushes an adherent target outwards (Fig. 1A). This push-out distance is 1.03±0.3(SD) µm in the case of zymosan, versus 0.12±0.14(SD) µm for antibody-coated targets. The “detour” in cell deformation at the onset of zymosan phagocytosis proves time-consuming: zymosan engulfment takes ∼2.5 times longer (167±73(SD) s) than the uptake of antibody-coated particles of similar size (66±19(SD) s). The experiments also provided the maximum cortical tension of neutrophils and the fastest speed of target inward motion during the two forms of phagocytosis. A ∼two-fold difference in cortical tension (0.3 mN/m versus 0.14 mN/m, with the higher value observed during zymosan engulfment) contrasts with an indistinguishable target speed (∼33 nm/s) in the two cases. Since the cortical tension is the dominant driver of target inward motion, a conserved target-internalization speed implies that the cytoplasmic viscosity rises concurrently with the cortical tension during phagocytosis, in agreement with a previously reported tight balance between the cortical tension and cytoplasmic viscosity of leukocytes [4].
Such large variations in phagocytic behavior call for a mechanistic explanation. An important clue was provided by our drug-inhibition experiments [3], which exposed a dichotomous mechanical role of actin: in addition to driving the formation of the protrusive pseudopod that pushes zymosan outward at first, actin also participates in the suppression of protrusion during the efficient uptake of antibody-coated targets. We therefore hypothesized that structural interactions between the cytoskeleton and the membrane patch adherent to a target play a pivotal role in the target-specific mechanoregulation of phagocytosis. Although this is a plausible hypothesis, there is no guarantee that it is physically realistic or will explain the observed behavior in detail, be it in terms of cell morphology, target trajectory, or other measurable parameters. It is to verify, adapt, and refine this hypothesis that we present a quantitative analysis of its mechanical implications through the use of a previously validated computational model of neutrophil phagocytosis.
Mechanistic scenarios of target engulfment are “hard-wired” into adaptations of this computational model by translating a set of “cause-effect rules” (or physical/biochemical mechanisms deemed biologically plausible) into mathematical equations, as explained in the Methods section. Our modeling strategy is to implement these equations in a self-consistent manner in a physically realistic computational framework, and solve the resulting problem numerically to predict the cellular behavior. A given model then is iteratively optimized by improving initial guesses of adjustable parameters until the predictions of this framework satisfactorily match experimental observations.
In earlier work, we have developed and extensively validated a finite-element model of the human neutrophil, and applied it successfully to chemotactic and phagocytic mechanics [5], [6]. It is based on the “reactive interpenetrating flow” formalism [5], [7]. Briefly, it conceptualizes the cell interior as a mixture of two materials, the cytoskeleton and the cytosol, enclosed by an envelope, the cell membrane. The cytoskeleton plays the crucial role in controlling the dynamics of cell deformation, whereas the cytosol is a “filler” material whose relocation is driven by pressure gradients. The two phases can convert into each other, reflecting, for instance, the polymerization/depolymerization of G- and F-actin. A movable and deformable boundary endowed with surface tension represents the cortical membrane. Mass and momentum conservation equations determine the evolution of this continuum mechanical model in a self-consistent manner.
To model phagocytic behavior, we have identified a number of necessary prescriptions to account for adhesion, basic signaling, and the generation of mechanical forces [6]. First, we implement an adhesive interaction between the cell surface and the target. Once cell contact with a patch of target is established, detachment is proscribed (as observed). The leading edge of the adherent membrane region is assumed to stimulate transient production of a generic signaling “messenger” that locally triggers conversion of the low-viscosity (cytosolic) phase into the high-viscosity (cytoskeletal) phase, akin to cytoskeletal polymerization (Fig. 1B). The degree of polymerization in turn determines the magnitude of a repulsive (or “disjoining”) force between the membrane and the cytoskeleton that then leads to local protrusion. (This continuum model of protrusion encompasses, but is not limited to, the Brownian ratchet mechanism [8].) Finally, our previous work on neutrophil phagocytosis of antibody-coated beads exposed the necessity to invoke an attractive force between the membrane adherent to the bead and the cytoskeleton that essentially acts to “flatten” the neutrophil onto the bead [6].
Mathematical equations representing the physics of the above mechanistic concepts have been published previously [6]. We solve these model equations numerically through a Galerkin finite-element method using a mesh of quadrilaterals as described in Dembo [9] and Herant et al. [5]. Briefly, the calculation is advanced over a time step Δt (determined by the Courant condition or other fast time scale of the dynamics) by means of five sequential operations:
This computational cycle is repeated until the simulation is complete. Cylindrical symmetry allows the use of a two-dimensional mesh to solve the axisymmetric version of the model equations. Numerical convergence is confirmed by checking that the results are not sensitive to variations of the tolerance of the different iterations performed by the code as well as to variations of the spatial resolution. Calculations are conducted on PC workstations and typically take a few hours per run.
Two distinct yet closely related questions arise from the experimental comparison of the phagocytosis of zymosan and antibody-coated beads [3]: What are the differences in cell-signaling cascades that are triggered by the different receptors recognizing these targets? What distinguishes the mechanical processes that govern the different forms of particle internalization? Biological phagocytosis research primarily addresses the former (e.g., [12], [13]); in contrast, this paper focuses on the latter, i.e., on physical mechanisms that are fundamental to our understanding of not only phagocytosis but all processes involving eukaryotic cell motility.
We have previously established an optimal computational model describing the phagocytic uptake of antibody-coated beads by human neutrophils [6]. Our strategy here is to take this successful computational model of Fcγ-mediated phagocytosis as a baseline, and determine what changes must be made to recover the behavior observed in the phagocytosis of zymosan. The “virtual” phagocytic target in all simulations is a rigid spherical particle with a diameter of 3.2 µm. We prescribe the time course of the neutrophil cortical tension as measured, i.e., increasing from a resting value of 0.025 mN/m to 0.3 mN/m during the engulfment of zymosan, and to 0.15 mN/m in the case of antibody-coated beads. We previously presented a more elaborate model of the behavior of the cortical tension [14]; however, using observed rather than modeled cortical tension values reduces the complexity of the comparative analysis of phagocytic mechanics that is the primary focus of this paper.
Fig. 2 shows the results of our adapted model of zymosan phagocytosis, as well as simulations of Fcγ-mediated phagocytosis using the previously established model [6] (see also Supporting Videos S1 and S2). Differences between the two models are summarized in Table 1. Both simulations are in excellent agreement with corresponding observations. To achieve such agreement in the case of zymosan phagocytosis, the following changes had to be incorporated into our original model of Fcγ-mediated phagocytosis:
We discuss each of these aspects in turn.
We have shown previously that a combination of attractive force and lack of protrusion at the membrane-bead interface accounts for two distinctive features of Fcγ-mediated phagocytosis: the absence of significant outward motion of the bead at the onset of engulfment, and the thin lamellar pseudopod embracing the bead [6]. In contrast, zymosan particles do exhibit significant outward motion before engulfment, and the pseudopods surrounding zymosan are thick (Fig. 2; [3]). Hence we find that the attractive force is superfluous when modeling this case. Yet even after its removal, the modeled outward motion of the target remains much less than typically observed in zymosan phagocytosis (Fig. 3, blue line). To reproduce the measured initial push-out distances of zymosan particles, it is necessary to postulate that in this case, the protrusive force driving out free (non-adherent) membrane also pushes to some extent against the membrane in contact with the target – much as if the contact patch acted as a chemoattractant rather than a locus of contraction-inducing adhesion. On the other hand, when implementing this polymerization-driven protrusion force at the cell-target interface and ascribing to it the same “full” strength as at the free membrane, the resulting outward motion of the target far overshoots the observed distances (Fig. 3, red line). Only by choosing a middle ground—i.e., setting the strength of the protrusive force at the cell-target interface to 50% of the value acting at the free membrane—are we able to recover the correct behavior (Fig. 2).
How does one interpret these differences in local protrusion? Consider a cell-surface patch in adhesive contact with an external rigid object. If the engaged adhesion receptors are strongly anchored in the cytoskeleton, they tightly couple the latter to the external object. Then, any disjoining force (such as due to de novo actin polymerization) must act against the tensile stress in the molecular structures linking the object to the cytoskeleton. A protrusive deformation will ensue only if the disjoining force exceeds the strength of this link. On the other hand, if an external object adheres only via membrane binding without further internal structural linkage, local disjoining forces will drive protrusion as if the contact patch was a region of free membrane. Based on this reasoning, we conclude that the adhesion of human neutrophils to antibody-coated targets exhibits strong cytoskeletal coupling, whereas in phagocytic adhesion to zymosan the cytoskeletal linkage is weak (but not non-existent).
We also see that the completion of engulfment takes longer for zymosan particles than for antibody-coated beads of the same size. This difference in engulfment duration can be accounted for in the simulations of zymosan phagocytosis by a 25% weaker stimulus of polymerization and protrusive force. Finally, the combination of high cortical tension and large initial target-push-out distance should lead to a much faster inward motion of zymosan particles than actually observed. The only remaining parameter that can be adjusted in the simulations to slow down the inward motion of zymosan is the interior viscosity of the cell body. Whereas this viscosity remains constant throughout the simulation of Fcγ-mediated phagocytosis, our model of zymosan phagocytosis implements a five-fold increase of the viscosity that occurs concurrently with the rise of the cortical tension. This change in cytoplasmic viscosity is assumed to take place throughout the cell interior because otherwise, the intracellular region with the lowest viscosity would determine the rate of cell rounding. To match the measurements, this lowest viscosity would have to have the value currently used for the whole cell interior, and the viscosity in the remainder of the cell would be even higher. Physically, the viscosity increase corresponds to a high degree of polymerization and/or cross-linking of cytoskeletal components throughout the cell.
This computational study examines the mechanistic underpinnings of distinct physical responses of human neutrophils to zymosan and antibody-coated targets. A direct quantitative comparison of a finite-element model of the neutrophil with recent single-cell/single-target experiments [3] allows us to corroborate or discard mechanistic hypotheses about the mechanoregulation of phagocytosis. Key to the success of this comparison has been a suitable experimental design, i.e., an essentially axisymmetric configuration that isolated the cell-target interactions of interest from potential interference by other cellular processes (such as cell-substrate interactions of adherently kept immune cells).
Our computational framework integrates the reactive interpenetrating flow formalism [7] with cell adhesion, basic signaling, and the autonomous generation of forces [6]. We use this framework to establish the variations in the modeled interplay of mechanical forces that most closely reproduce the observed differences in cell behavior. This enquiry complements previous studies of zymosan- and antibody-mediated phagocytosis that have focused on differences in receptor-mediated recognition and biochemical signaling [12], [13], [15], [16]. By considering generic mechanistic principles, our computational approach is able to cover mechanical outcomes from underlying processes involving a range of cellular receptors and associated signaling reactions.
Our overall strategy has been to vet mechanistic scenarios of phagocytic target uptake by postulating and testing biologically plausible cause-effect relationships. Additional assumptions implemented in our simulations include the irreversibility of cell-target adhesion and the time dependence of the cortical tension (as measured). We do not impose any particular aspects of the cell morphology; instead, the time courses of both the shape (including surface area) of our “virtual immune cell” as well as the cytoskeletal density distribution (as seen in Figs. 2, B and E, and in the Supporting Videos S1 and S2) are outcomes of the simulation.
Summarizing our findings, a simplified “mechanistic timeline” of zymosan phagocytosis encompasses the following stages:
The primary difference between this mechanistic sequence and Fcγ-mediated phagocytosis is the following. Neutrophil contact with an antibody-coated target suppresses cell protrusion directly underneath the cell-target contact region, presumably due to a stronger structural association of the adherent membrane with the cytoskeleton. On a molecular scale, we speculate that these linkages are actin-binding protein complexes that also associate with the cytoplasmic domains of Fcγ-receptors engaged in adhesion. (The postulated difference in cytoskeletal coupling may be due either to distinct strengths of individual linkages between receptors and actin, or to a difference in densities of the engaged receptors.) As a result, protrusion is limited to the cell surface not in contact with the target, leading to a pseudopodial lamella that envelops the target.
Worth highlighting in this physical perspective of phagocytosis is the common role of the cortical tension as primary driver of inward target motion. Note that this contrasts with the notion that an actual inward pulling force, presumably generated by molecular motors, should mainly be responsible for drawing the target into a cell. Neither our experiments (e.g., in the presence of myosin-II inhibitors) [3] nor our modeling work found evidence for a significant participation of such a contractile force in inward target movement. Instead, the cortical-tension-driven tendency of a cell to round up, in conjunction with strong adhesion between the cell membrane and target, appears to be the dominant mechanical cause of target motion into the cell, as also supported by the synchronous onsets of cortical tension rise and target inward movement seen in Figs. 2, C and D.
In closing, this study not only illuminates fundamental mechanisms driving the target-specific physical immune responses of human neutrophils, it also reinforces that the present computational framework represents a biologically plausible and physically realistic model of “virtual immune cells”. In addition to correctly reproducing distinct cell morphologies observed in a range of experiments, this model also matches the dynamics of cell deformation, such as the overall engulfment times, or the time-dependent target trajectories measured in phagocytosis experiments. As an early model for calculation of a spectrum of autonomous cellular motions at this level of complexity, it is a step toward one of the key goals of computational biology, i.e., achieving true predictive power.
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10.1371/journal.pgen.1005474 | Genome-Wide Analysis of PAPS1-Dependent Polyadenylation Identifies Novel Roles for Functionally Specialized Poly(A) Polymerases in Arabidopsis thaliana | The poly(A) tail at 3’ ends of eukaryotic mRNAs promotes their nuclear export, stability and translational efficiency, and changes in its length can strongly impact gene expression. The Arabidopsis thaliana genome encodes three canonical nuclear poly(A) polymerases, PAPS1, PAPS2 and PAPS4. As shown by their different mutant phenotypes, these three isoforms are functionally specialized, with PAPS1 modifying organ growth and suppressing a constitutive immune response. However, the molecular basis of this specialization is largely unknown. Here, we have estimated poly(A)-tail lengths on a transcriptome-wide scale in wild-type and paps1 mutants. This identified categories of genes as particularly strongly affected in paps1 mutants, including genes encoding ribosomal proteins, cell-division factors and major carbohydrate-metabolic proteins. We experimentally verified two novel functions of PAPS1 in ribosome biogenesis and redox homoeostasis that were predicted based on the analysis of poly(A)-tail length changes in paps1 mutants. When overlaying the PAPS1-dependent effects observed here with coexpression analysis based on independent microarray data, the two clusters of transcripts that are most closely coexpressed with PAPS1 show the strongest change in poly(A)-tail length and transcript abundance in paps1 mutants in our analysis. This suggests that their coexpression reflects at least partly the preferential polyadenylation of these transcripts by PAPS1 versus the other two poly(A)-polymerase isoforms. Thus, transcriptome-wide analysis of poly(A)-tail lengths identifies novel biological functions and likely target transcripts for polyadenylation by PAPS1. Data integration with large-scale co-expression data suggests that changes in the relative activities of the isoforms are used as an endogenous mechanism to co-ordinately modulate plant gene expression.
| The poly(A) tail of eukaryotic mRNAs promotes export from the nucleus, translation in the cytoplasm and stability of the mRNA, and changes in poly(A)-tail length can strongly impact on gene expression. The Arabidopsis thaliana genome encodes three nuclear canonical poly(A) polymerases (PAPS1, PAPS2, PAPS4) that fulfill different functions, presumably by preferentially polyadenylating certain subpopulations of pre-mRNAs. Here, we use a fractionation-based technique to assess the transcriptome-wide impact of reduced PAPS1 activity and identify functional classes of transcripts that are particularly sensitive to reduced PAPS1 activity. Analysis of these transcripts identifies two novel biological functions for PAPS1 in ribosome biogenesis and in redox homeostasis that we confirm experimentally. By overlaying our results with information about genome-wide co-expression, we demonstrate that genes co-expressed with PAPS1 are the most strongly affected in terms of poly(A)-tail length and total-abundance changes in the paps1 mutants. This provides strong evidence that the co-expression of these genes with PAPS1 that is seen across thousands of microarrays is at least partly caused by altered activity of the PAPS1 isoform, suggesting that the plant indeed uses modulation of the balance of isoform activities to coordinately regulate the expression of groups of genes.
| The poly(A) tail is an essential modification found at the 3’ ends of virtually all eukaryotic mRNAs [1,2,3]. After transcription of the pre-mRNA, sequences in the 3’ UTR recruit two protein complexes, Cleavage and Polyadenylation Specificity Factor (CPSF) and Cleavage Stimulation Factor (CStF) that effect endonucleolytic cleavage of the pre-mRNA, exposing a 3’-OH end, and recruit poly(A) polymerase to synthesize the poly(A) tail [1,2,3]. The poly(A) tail serves three major functions: promoting nuclear export of the mRNA, stimulating efficient translation, and stabilizing the mRNA in the cytosol. The poly(A) tail has been reported to channel transcripts into the dedicated mRNA export pathway [4,5], and in yeast this effect appears to be mediated by the poly(A)-binding protein Pab1 [6]. The poly(A) tail stimulates translation of the mRNA by promoting a close contact between the 3’ and 5’ ends of the mRNA and thus promotes efficient translational initiation [7,8]. This is mediated by interactions between the cytoplasmic poly(A)-binding protein PABPC bound to the poly(A) tail and translation initiation factors, in particular eIF4G, bound to the 5’ cap. A correlation has been observed between poly(A)-tail length and translational stimulation, i.e. long poly(A) tails promote translation more strongly than shorter ones, suggesting that modulation of poly(A)-tail length could be used to regulate the efficiency of translation [9,10]. The third major function of the poly(A) tail relates to mRNA stability [1]. For the bulk of cellular mRNAs, deadenylation, i.e. shortening of the poly(A) tail from the 3’ end, down to an oligo(A) tail is a pre-requisite for degradation, triggering decapping at the 5’ end and subsequent 5’-to-3’ exonucleolytic degradation or, less commonly, 3′-to-5′ degradation by the exosome [11,12,13,14]. Most mRNAs in yeast and mammals are believed to start out with a rather uniform length of the poly(A) tail of around 70–80 and 250 As, respectively, and deadenylation occurs with transcript-specific rates [12,15]. This rate of deadenylation is therefore a major determinant of transcript half-life. An additional factor is the stability of the oligoadenylated form of the transcript before decapping and degradation, which appears to differ between mRNAs [12]. The combination of the kinetics of deadenylation and the stability of the oligoadenylated form determines not only the stability of the transcript, but also the average poly(A) tail length of an mRNA in steady-state conditions. Thus, large-scale studies of poly(A)-tail length in mammalian cells have identified a number of very stable transcripts with only very short poly(A) tails [16,17], likely reflecting a high stability of the oligoadenylated state.
Control of gene expression via modulation of poly(A)-tail length plays a prominent role during germ-cell and early embryo development in Drosophila melanogaster, Caenorhabditis elegans and vertebrates [8,18,19,20,21,22,23,24,25]. In vertebrates, maternally deposited mRNAs in the egg cytoplasm are only translated after fertilization when their poly(A) tails are extended by the action of cytoplasmic poly(A) polymerase [8]. The importance of translational regulation by modulating poly(A)-tail length appears to change during animal embryonic development. In particular, a positive correlation between poly(A)-tail lengths and translational efficiency is seen in early embryos, but not in later stages of development [17]. In contrast to cytoplasmic modulation of poly(A)-tail length, initial polyadenylation of pre-mRNAs in the nucleus is thought to not be used for regulating gene expression on a larger scale [1]. Although a non-canonical poly(A) polymerase in mammals, Star-PAP, is required specifically for polyadenylation of certain pre-mRNAs encoding proteins required for the response to oxidative stress [26], the bulk of pre-mRNAs appears to be appended with a standard length of poly(A) tail by canonical nuclear poly(A) polymerases [1]. In mammals, where this initial length control is particularly well understood, the length of 250 As results from an interaction between CPSF and the nuclear poly(A) binding protein PAPBN tethering poly(A) polymerase to the 3’ end, which can only be maintained for poly(A) tails of less than about 250 As; a longer tail cannot be accommodated, causing poly(A) polymerase to revert to its distributive mode of action and to ultimately dissociate from the 3’ end of the transcript [1,27,28].
The Arabidopsis thaliana genome encodes four isoforms of canonical poly(A) polymerase, of which PAPS1, PAPS2 and PAPS4 are located in the nucleus and are expressed widely in the plant in a largely overlapping pattern, while PAPS3 is a cytoplasmic protein expressed mainly in pollen [29,30]. Recent mutant analysis of the nuclear isoforms has uncovered functional specificity amongst them. PAPS1 activity is essential for male gametophyte function, and reduced activity in the sporophyte causes impaired leaf growth, a constitutive immune response, and overgrowth of floral organs; by contrast, even combined loss of PAPS2 and PAPS4 function does not interfere with gametophyte or sporophyte viability, and double mutants do not show any obvious morphological defects [31,32]. Notably, this is not due to PAPS1 simply being responsible for the bulk of pre-mRNA polyadenylation, as even a severe reduction in PAPS1 activity has virtually no effect on bulk poly(A)-tail lengths [32]. Rather, it suggests that there are some pre-mRNAs that are preferentially polyadenylated by PAPS1 and whose misexpression in paps1 mutants causes their specific mutant phenotypes. These specific PAPS1 targets include mRNAs of the SMALL AUXIN UP RNA (SAUR) family, whose poly(A) tail is strongly shortened in paps1 mutants and whose reduced activity appears to contribute to the growth defect of the mutant leaves.
To obtain a transcriptome-wide view of the changes in poly(A)-tail length that result from altered PAPS1 activity, we employed an mRNA fractionation method followed by RNA-seq [16]. Transcripts affected in poly(A)-tail length in paps1 mutants are enriched for ribosomal protein-encoding mRNAs and ones linked to plastid redox status, and paps1 mutants indeed show defective ribosome content, altered plastid redox status and a higher resistance to reactive-oxygen species, which is not seen in paps2 paps4 mutants. Molecular and double-mutant analysis indicates that the activities of PAPS1 and the CPSF subunit OXT6 interact in the oxidative-stress response. Overlaying the changes in poly(A)-tail lengths with information on coexpression of genes provides evidence that changes in the activity level of PAPS1 contribute to altered gene expression in response to environmental or internal cues.
To obtain a genome-wide view of the transcripts whose polyadenylation depends on PAPS1 activity, we estimated poly(A)-tail lengths by using an established fractionation method [16] combined with RNA-seq. Total RNA is hybridized to an excess of biotinylated (dT)17 oligonucleotide and captured on streptavidin beads. After washing, a low-stringency elution recovers those transcripts with only a short poly(A) tail (in our case ones with less than approximately 50 As), before a second elution step recovers the remaining transcripts with longer poly(A) tails (Figs 1A and S1A). The paired fractions obtained from the starting samples of total RNA were then subjected to RNA-seq to quantify transcript abundances, using 50-bp single-end reads (S1 Table). To abolish PAPS1 activity as far as possible, while still allowing for the generation of sufficient material for analysis, we used the paps1-1 mutant that encodes a PAPS1 protein that is inactivated at high temperatures [32]. After growth at 21°C, Ler wild-type and paps1-1 mutant seedlings were shifted to 28°C for two hours before harvesting to abolish the activity of the mutant protein. In the absence of information about the kinetics of deadenylation of PAPS1-target transcripts, and given a maximum poly(A)-tail length of around 150–200 As in A. thaliana (Fig 1A), we chose a cut-off of around 50 As for the fractionation rather than a lower value, in order to increase the chances of detecting tail-length changes between wild-type and mutant also for more slowly deadenylated PAPS1-target transcripts. Four biological replicates per genotype were used. As internal controls for the fractionation, we generated three synthetic RNAs with defined poly(A)-tail lengths of 30 A, 75 A and 134 A by in vitro transcription and added equal amounts of these to each of the eight starting total-RNA samples. After aligning the sequence reads to the Arabidopsis reference genome, fpkm (fragments per kilobase of exon per million fragments mapped) values were calculated for each transcript as a measure of its relative abundance in the fraction. The ratio of transcript abundance in the fraction with short poly(A) tails and that with long poly(A) tails (termed the short and long fractions from here on) was then determined as a proxy for the distribution of poly(A)-tail lengths on each transcript using edgeR (see Methods). The relatively high percentage of reads corresponding to rRNA (S1 Table) likely reflects the presence of oligoadenylated pre-rRNA and rRNA molecules as intermediates in pre-rRNA surveillance and rRNA degradation [33,34]; contamination with these reads results from the fractionation protocol requiring only relatively mild washes so as not to lose a large fraction of the mRNAs with short poly(A) tails. However, as the proportion of these reads is comparable across the different ‘long’ and ‘short’ fractions, it is unlikely to distort the analysis of the mRNA reads.
We technically validated the fractionation and the RNA-seq based estimates of transcript abundances in five ways. First, we performed qRT-PCR against the three spike-in control RNAs on identical-volume aliquots of the 16 fractions (S1B and S1C Fig). We used individual PCR efficiency to the power of-ct (PCReff-ct) as an estimate of abundance in the absence of any reference. As expected, the 30A control was about 2.4 (wild type) and 1.3 (paps1) times more abundant in the short than in the long fraction. By contrast, the 75A and 134A fractions were about 4.9 (wild type), 6.2 (paps1) and 4.4 (wild type), 7.9 (paps1) times more abundant in the long fractions, respectively (S1C Fig).
Second, an analogous estimate was obtained for the three spike-in controls from the RNA-seq data. Relative transcript abundances of the three RNAs as expressed by fpkm values were multiplied with the overall RNA concentrations in the sixteen samples and corrected for the proportion of non-rRNA reads that could be mapped to obtain a proxy for their absolute abundance in each of the sixteen fractions. This estimate should then be comparable to the qRT-PCR measurements above. The 30A control RNA was about 6.1 (wild type) and 3.1 (paps1) times more abundant in the short fraction, while the 75A and 134A controls about 1.7 (wild type), 3.4 (paps1) and 1.9 (wild type), 3.5 (paps1) times more abundant in the long fractions (Fig 1B). We note that both estimates for the spike-in controls indicated that for the paps1-1 samples relatively more of the 30A control RNA was seen in the long fraction, while relatively less of the 75A and 134A controls was found in the short fractions compared to wild-type. This suggested that despite processing wild-type and mutant samples in parallel, there was variation in the fractionation for unknown reasons; as this variation globally affects the absolute values of the long/short ratios for all transcripts, our analysis of PAPS1-specific effects (see below) accounts for this variation by considering the behaviour of groups of genes relative to the respective transcriptome-wide background (i.e. all other transcripts not belonging to the focal group). This approach is thus independent of the absolute values of the long/short-ratios. In addition this group-wise analysis mitigates the effect of technical variation in individual transcript measurements.
Third, we determined the abundances of 15 endogenous transcripts chosen based on preliminary analyses in the 16 fractions by qRT-PCR (as PCReff-ct on identical-volume aliquots) and correlated these with their fpkm values from RNA-seq (S2A Fig). For 13 out of the 16 fractions these values were significantly correlated with Pearson correlation coefficients between 0.61 and 0.85; in the remaining three fractions, there was one strong outlier each from the genes with very low RNA-seq expression estimates. Fourth, a PCR-based assay, the so-called ePAT test [35], was used to independently determine changes in poly(A)-tail length between mutant and wild-type samples (S3 Fig). Out of 40 transcripts with a predicted change in poly(A)-tail length that were selected for validation, we were able to design primers allowing robust and specific amplification for 15 transcripts. Of these, 11 showed evidence for a shorter poly(A)-tail in paps1 mutants, in accordance with the prediction based on RNA-seq, while for three transcripts there was no change and one transcript appeared to have a longer poly(A) tail in the mutant (S3 Fig); the latter four transcripts tended to have lower predicted fold-changes of poly(A)-tail length than the 11 transcripts with successful validation. Fifth, we compared our estimates of poly(A)-tail length with those from a recent transcriptome-wide study that used the novel PAL-seq method on mature A. thaliana leaves [17]. Our estimates are in good agreement with the published values (Fig 1C; Pearson correlation coefficient 0.43).
Thus, we conclude that our fractionation successfully resolved different populations of transcripts based on their poly(A)-tail lengths; that the fpkm values determined by RNA-seq can be used as estimates for the relative abundances of the transcripts in the two fractions; and that the edgeR ratios can be used as proxies for the poly(A)-tail length distributions of the transcripts. We excluded transcripts with low abundance (five or fewer reads in one or more samples) from our analyses, as the samples did not cluster by genotype when including all transcripts. When applying the filter, the samples clustered as expected based on their long/short-ratios, i.e. their estimated poly(A)-tail lengths (S1D Fig).
We next asked whether correlations between poly(A)-tail length and other transcript features observed in other systems are also seen in Arabidopsis. To this end, we related our estimates of poly(A)-tail length in wild type to total transcript abundance, features of the 3’ UTR and transcript stability. Total transcript abundance in seedlings was estimated from independent publicly available transcriptomic experiments using Illumina sequencing (see Methods). The length of the annotated 3’ UTR and the number of annotated 3’ UTRs, i.e. the number of different 3’ cleavage sites detected for a given transcript were based on combined information from TAIR10 annotation (www.arabidopsis.org) and recent large-scale sequencing efforts of RNA 3’ ends [36,37]. Transcript stability has been determined on a genome-wide basis in Arabidopsis thaliana protoplasts [38].
All four correlations of the estimated poly(A)-tail length with transcript abundance, length of the 3’ UTR (expressed as maximum distance between stop-codon and cleavage site, including potential introns), number of annotated or detected 3’ UTRs, and transcript stability were statistically significant (Figs 2A, S4A, S4B and S4C). However, the strength of the correlations, as determined by Pearson correlation coefficient, was very weak for transcript abundance and length and number of 3’ UTRs (S4A, S4B and S4C Fig), questioning their biological significance. By contrast, the measured half-life values showed a moderate negative correlation with our estimates of poly(A)-tail length (Pearson correlation coefficient -0.21; Fig 2A), i.e. mRNAs with a long half-life tended to have a shorter poly(A) tail.
We next asked whether gene families or functional categories (as implemented in MapMan) differ in the distribution of their poly(A)-tail lengths in wild type. To this end, we compared the poly(A)-tail length distributions of genes associated with a given functional-category term with the transcriptome-wide background using Wilcoxon rank-sum tests. This identified ribosomal-protein genes, cell-wall proteins and seed storage/lipid-transfer proteins as the functional-category terms with the most significant enrichment for short-tail transcripts and protein post-translational modification (in particular kinases), regulation of transcription, and DNA repair as those associated with the most significant enrichment for long-tail transcripts (S2 Table). We note that transcripts in the ‘short-tail’ class as defined here are presumably still translated efficiently, given that our cut-off for the fractionation of around 50 As is still in the range of poly(A)-tail lengths that support efficient translational initiation via formation of a closed-loop structure [39].
In order to identify changes in poly(A)-tail length resulting from reduced PAPS1 function, the long/short-ratios for individual genes were compared between the four wild-type and the four mutant samples using R/Bioconductor package edgeR. After correcting for multiple testing, only 97 transcripts showed a significant change in their long/short ratios between the two genotypes (S3 Table and Fig 2B). This suggests that the technical variation in the measurements (see above) renders the analysis of individual transcripts less meaningful. To circumvent this issue, we focussed on gene families and functional MapMan categories and compared the long/short-ratios of members of the group with the transcriptome-wide background using Wilcoxon rank-sum tests to detect significant differences at the family or category levels (S4 Table). Validating our approach, the family of SAUR transcripts showed a significant shortening of poly(A) tails in paps1-1 mutants compared to the transcriptome-wide background (p<0.001), as was predicted from our previous analysis of individual SAUR family members by a PCR-based poly(A)-tail length assay [32]. By far the most significantly changed category with shorter tails in the mutants were genes encoding ribosomal proteins (p = 5.1E-43), followed by genes encoding histones (p = 1.8E-05) and pectin-methylesterase inhibitors (9.1E-05); the most significantly changed category with longer tails in the mutants was major carbohydrate metabolism (p = 3.9E-08), followed by genes encoding proteins involved in amino-acid activation for translation (p = 1.9E-07) and proteins involved in cell division (p = 2.7E-07).
The poly(A) tail is known to stabilize transcripts, and shortening of the poly(A) tail often serves as a first step towards degradation of the mRNA by rendering the mRNA susceptible to decapping and 5’-3’ degradation, or to 3’-5’ degradation by the exosome [11,12,13,14]. Therefore, we predicted that there should be a correlation between the change in the long/short ratios and the change in overall transcript abundance between the wild-type and mutant samples; this prediction assumes that other parameters, such as the stability of the oligoadenylated form of a given transcript, do not differ between mutant and wild type. To estimate changes in overall transcript abundance between the genotypes, we combined the normalized read counts from the long- and short-tail fractions. Independent validation by qRT-PCR on biological replicate RNA samples to those used for the fractionation indicated that the above estimates robustly capture differences in transcript abundance between the paps1-1 and wild-type samples (S2B Fig), as there was a significant correlation between fold-changes estimated by the two methods. The RNA-seq based estimates of the change in transcript abundance showed a highly significant positive correlation with the change in the long/short-ratio (Fig 2B; correlation coefficient 0.32; p-value <1.0E-50), which was even stronger for the 97 genes with significant tail-length change after multiple-testing correction (Fig 2B; correlation coefficient 0.4; p-value = 4.4E−05), suggesting that changing the poly(A)-tail length due to reduced PAPS1 activity alters transcript abundance. As described above, the steady-state poly(A)-tail length is not correlated with transcript abundance when comparing different transcripts in the same genotype (see above; S4A Fig), suggesting that other parameters are more important; by contrast, the correlation shown in Fig 2B supports the notion that a difference in the poly(A) tail of a given mRNA between different genotypes can affect the abundance of this transcript (see Discussion).
What differentiates transcripts whose poly(A)-tail length is sensitive to a reduction in PAPS1 function from the remaining ones? The changes in the long/short ratio showed no statistically significant correlation with the lengths of the annotated 3’ UTRs and only a very weak one with the number of annotated 3’ UTRs (S4D and S4E Fig). As observed for tail length in wild type, the extent of tail-length change between wild type and mutant was negatively correlated to transcript stability (Fig 2C). More stable transcripts also showed more pronounced shortening of the poly(A) tail in the mutant; however, this correlation was weaker than with tail length in wild type (Fig 2A). The base composition in the region 200 bp upstream and 200 bp downstream of the annotated 3’-cleavage sites did not differ between the 1000 genes with the lowest p-values for the change in the long/short ratio and 1000 randomly selected genes (S5A Fig). We used the 1000 genes with the lowest p-values from the test for a poly(A)-tail change between mutant and wild-type, even though most of them were not significant after correcting for multiple testing, as a comparison based only on the 97 significantly changed transcripts would have very limited sensitivity.
To determine whether there are sequence motifs enriched in the 3’ regions of those transcripts that show a high value for the change in the long/short ratio, we tested for all possible hexamer sequences whether genes with the hexamer present in the 100 bp before the polyadenylation site behave differently in the paps1-1 mutant-vs-wild type comparison than genes without the hexamer. This analysis identified several hexamer motifs as significantly associated with an altered tail length in the mutants (S5 Table and S5C Fig); however, the effect sizes of these hexamers on poly(A)-tail change in paps1 are small to minimal and/or the hexamers are rare (e.g. CGCCGA), indicating that they explain very little of the observed effects in the mutant. The hexamer AATAAA represents the canonical poly(A) signal in metazoans, and many variations of this hexamer are found upstream of plant cleavage sites [37]. We tested whether presence or absence of individual variations of this hexamer were associated with differential poly(A)-tail length in mutant and wild-type; while two variants (AAAAAA and AAGAAA) showed a statistically significant association with altered poly(A)-tail length, the effects were extremely weak (S5B and S5C Fig). Thus, a high transcript stability appears to contribute to a higher sensitivity of the transcript to reduced PAPS1 activity. However, neither our unbiased nor more targeted motif analysis identified simple sequence motifs with a strong individual effect on poly(A)-tail length in paps1 mutants.
To test whether the reduced poly(A)-tail lengths of transcripts encoding ribosomal proteins (see above) were functionally relevant, we sought to measure ribosome content in paps1 mutants. As ribosomal RNA (rRNA) is rapidly degraded when not incorporated into functional ribosomes [40,41], the amount of ribosomal RNA can be used as a proxy for ribosome content. Quantifying the proportion of ribosomal RNA in total RNA samples by a BioAnalyzer RNA chip indicated that there was no significant change in the relative amount of ribosomal RNA in paps1-1 mutants (S6A Fig). As the paps1-1 mutant is not a null allele, we sought to determine whether the complete loss of PAPS1 function in the paps1-3 T-DNA insertion mutant would cause an effect on ribosome biogenesis [32]. As no paps1-3 homozygous mutants can be obtained due to a male gametophytic defect, we used a fluorescence-marking system [42] to sort paps1-3 mutant and wild-type pollen from heterozygous plants using fluorescence-activated cell sorting (FACS) [43]. A line carrying a pollen-expressed pLat52::DsRED reporter construct inserted about 290 kb from the wild-type PAPS1 allele was crossed to paps1-3 heterozygous plants, and paps1-3—/ PAPS1 pLat52::DsRED transheterozygous plants were selected. After meiosis, these plants produce roughly 50% paps1-3 mutant pollen lacking DsRED fluorescence and roughly 50% PAPS1 wild-type pollen with DsRED fluorescence (S6B Fig), as well as a negligible number of recombinant pollen grains. Isolation of total RNA from FACS-sorted pollen grains and Bioanalyzer analysis indicated that paps1-3 mutant pollen grains contain a significantly reduced total RNA amount per pollen grain, which is due to a specific reduction in ribosomal RNAs, while non-rRNA was very similar in amount per pollen grain between the two genotypes (Fig 3). Thus, characterization of this null-mutant defect suggests that the shortened tails of transcripts encoding ribosomal proteins cause a reduced accumulation of these proteins and, as a consequence, of ribosomal RNAs in mature ribosomes.
If the set of transcripts with a change in the poly(A)-tail length indeed reflects the biological role of PAPS1, it should be possible to identify additional functions of PAPS1 by analysing this set of transcripts. To this end, we defined the set of 400 transcripts with the lowest p-values when comparing their long/short-ratios in paps1 mutants versus wild type, and compared this list with lists of the 400 most strongly affected genes from each of 600 published microarray experiments using MASTA [44]. Amongst the 18 microarray experiments with the strongest overlap (representing the top 3%), there were three that modulated the redox status (S6 Table). In particular, these involved experiments with a line overexpressing the thylakoid-localized form of ascorbate peroxidase (tAPX), a scavenging enzyme for the reactive oxygen species H2O2 [45].
Genes downregulated in tAPX overexpressing plants can be assumed to be induced by H2O2, while H2O2-repressed genes are expected to be upregulated in tAPX overexpressors. Of the 40 genes in the overlap between the most similar microarray and our experiment, 38 are more strongly expressed in tAPX overexpressors than in wild-type, while two show a lower abundance, and all of the 40 genes show evidence of a shorter poly(A) tail in paps1 mutants (S7 Table). Assuming that at least some of these presumed H2O2-related genes with an altered poly(A)-tail length feed back on H2O2-levels and/or more generally the cellular redox status, we predicted a change in the cellular redox status in paps1 mutants. To address this, we introgressed two reporter lines expressing a redox-sensitive form of GFP, localized either in the cytoplasm or in the chloroplasts, into the paps1-1 mutant (Fig 4A) [46]. The emission maxima of this reporter depend on the excitation wavelength and the local redox potential. The reduced form emits most light at an excitation wavelength of 488 nm and the oxidized form at 405 nm. This makes it possible to use the ratio of fluorescence emission at 527 nm after excitation with the two wavelengths as a measure of the local redox potential. There was no difference in this ratio between mutant and wild-type leaves for the cytoplasmic reporter (Fig 4A and 4B). By contrast, the spectral ratio for the chloroplast-localized reporter was significantly altered in paps1-1 mutant leaves, indicative of a more oxidizing environment in the mutant chloroplasts than in wild type (Fig 4A and 4B). To determine whether PAPS1 function not only influences the steady-state chloroplast redox potential, but also the physiological response to redox changes such as ROS accumulation, we treated plants with the herbicide paraquat (methyl viologen) that causes the increased production of superoxide and H2O2 in chloroplasts. Surprisingly, paps1-1 mutant seedlings were significantly more resistant to paraquat treatment than wild type, both regarding their root and their shoot growth (Figs 4C, 4D, S7A and S7B). This effect is reminiscent of the protection against the effects of high-level ROS accumulation afforded by previous exposure to lower levels of ROS in tobacco plants [47]. By contrast, paps2 paps4 double mutants were more sensitive to oxidative stress in both assays, suggesting opposite functions of PAPS1 and the other two isoforms in this process.
A link between 3’-end processing and oxidative-stress resistance has been documented before. Mutants lacking the activity of the CPSF30 subunit OXT6 are more resistant to oxidative stress than wild-type plants [48]. OXT6/CPSF30 influences the choice of 3’-cleavage and polyadenylation sites in a large number of A. thaliana genes, with an enrichment for stress-response and defense genes [36]. To determine whether the increased resistance to oxidative stress in oxt6 and in paps1 mutants was due to effects on the same set of genes, we determined the distribution of tail-length changes in paps1 mutants relative to the transcriptome-wide background for transcripts with OXT6-dependent abundance changes or alternative polyadenylation (Fig 4E and 4F). The set of transcripts whose abundance is altered in oxt6 mutants showed a significant shortening of the poly(A) tail in paps1 mutants (Fig 4E), and the same was seen, albeit to a lesser extent, for the transcripts with OXT6-dependent alternative polyadenylation (Fig 4F). To test genetically for an interaction between PAPS1 and OXT6, we sought to analyze the corresponding double mutant. No double homozygous plants could be isolated, and in the progeny of oxt6/oxt6; paps1-1/PAPS1 plants, only a minority was heterozygous for the paps1-1 mutation (Table 1). This suggests both synthetic lethality of the double mutant embryos and a functional defect in double mutant gametophytes, supporting the notion that both proteins act on a shared set of targets. By contrast, oxt6 paps2 paps4 triple mutants were viable and showed an intermediate phenotype under oxidative stress treatment relative to oxt6 single and paps2 paps4 double mutants (Fig 4C and 4D).
Taken together, these observations suggest that as a result of the defective polyadenylation of a subset of transcripts paps1-1 mutants accumulate more ROS in their chloroplasts, specifically more H2O2, and are at the same time more resistant to exogenously induced overaccumulation of ROS. While OXT6 and PAPS1 target overlapping sets of transcripts, PAPS2 and PAPS4 act independently of them, providing yet another example for functional specialization between the PAPS isoforms.
Our findings indicate that the Arabidopsis thaliana genome encodes poly(A)-polymerase isoforms with distinct functions that appear to reflect the preferential polyadenylation of sets of transcripts. This raises the possibility that the plant could exploit this specificity to modulate gene expression by altering the relative activities of the isoforms. If this were the case, a plausible prediction would be that alterations in PAPS1 activity in response to environmental or internal cues would co-ordinately affect PAPS1-targeted transcripts. We tested this prediction by exploiting the availability of a large number of Arabidopsis microarray experiments, under the assumption that altered PAPS1 mRNA expression levels would be translated into corresponding changes at the protein level. Based on 14,115 publicly available microarray experiments, gene modules showing co-regulated expression across these experiments were identified using WGCNA [49] (S8 Table). PAPS1 was part of a module (no. 10) comprising 564 genes; a second module (no. 40) comprising 104 genes was very similar to module 10 based on hierarchical clustering (Fig 5A). For each of the modules, the distribution of estimated poly(A)-tail length changes in paps1 versus wild type was compared to the transcriptome-wide background. This analysis identified the two modules 10 and 40 as the ones with the highest differences in the median tail-length change (S9 Table and Fig 5B and 5C). We extended this analysis to include the estimated changes in overall transcript abundance between paps1 and wild type. This yielded the analogous result: The same two co-expression modules showed the highest differences in the median of the distribution of abundance changes (S9 Table and Fig 5B and 5C). Thus, the genes that are co-expressed with PAPS1 across available microarray experiments are the most strongly affected genes in paps1 mutants regarding poly(A)-tail changes and total abundance.
Here, we have used a fractionation-based RNA-seq method to obtain a transcriptome-wide view of the mRNAs whose polyadenylation status changes in paps1 mutants. This has identified two additional biological functions of PAPS1, shed light on the features that determine a transcript’s sensitivity to altered PAPS1 activity levels, and suggested that modulation of PAPS1 activity is used by the plant to coordinately influence the expression of a subset of transcripts.
Two broad classes of techniques have been described to determine poly(A)-tail length on a transcriptome-wide level. The first class fractionates cellular mRNA based on the length of the poly(A)-tail and then determines transcript abundances in the different fractions [16,50,51]; such techniques have for example been used to demonstrate modulation of poly(A)-tail length during yeast cell-cycle progression and circadian rhythms [10,50]. The second class uses next-generation sequencing to either directly sequence the poly(A) tail (TAIL-seq) [52], or incorporates biotinylated dUMP when complementing the poly(A)-tail of captured sequence tags on the Illumina instrument, then sequences the 3’ UTR next to the poly(A) tail and finally measures the fluorescence of fluorophore-tagged streptavidin bound to the biotinylated dUMP (PAL-seq) [17]. The fractionation-based techniques provide more indirect estimates of poly(A)-tail length than those from the second class, and the fractionation step appears to be rather sensitive to even slight fluctuations in conditions. Nevertheless, we find that our estimates of poly(A)-tail length in wild-type seedlings are in good agreement with measurements obtained by PAL-seq from mature leaves; also the RNA-seq based estimates of tail-length changes for SAUR mRNAs in paps1 mutants are in agreement with our previous experimental results [32], indicating that our fractionation-based method provides reliable and biologically meaningful estimates of poly(A)-tail length. This notion is further supported by our identification of two additional biological functions for PAPS1 solely on the basis of our poly(A)-tail length analysis. Thus, we conclude that our analysis has meaningfully captured the transcriptome-wide effect of PAPS1 activity on mRNA poly(A)-tail lengths. As the paps1 mutation affects the activity of a canonical nuclear poly(A) polymerase, we consider the observed changes in poly(A)-tail lengths in the mutant as a result of altered nuclear polyadenylation of the corresponding pre-mRNAs, rather than their selective deadenylation and degradation in the mutant. While selective changes in mRNA stability and increased deadenylation under heat stress have been described, for example for transcripts encoding ribosomal proteins and ribosome biogenesis factors [53], it appears unlikely that this effect explains the changes we observe, as both wild-type and paps1-1 mutant plants were shifted to 28°C (if anything only a very mild heat stress for Arabidopsis thaliana; [54]) before harvesting, so any effects of this treatment should occur equally in both genotypes. Repeating the experiment with only nuclear RNA will be required to validate our above assumption.
Our analysis of poly(A)-tail length in wild type identifies transcripts encoding ribosomal subunit proteins as one of the classes with the shortest poly(A) tails. While the functional significance of this finding is currently unclear, the same effect has been observed in a previous study in yeast, A. thaliana leaves, Drosophila, zebrafish and human cell lines, suggesting a very broad conservation of this relationship [17]. Our analysis also identifies a negative correlation between a transcript’s half-life time and its steady-state poly(A)-tail length distribution. A similar trend has been reported in some studies of yeast and human cell lines, although the results within these organisms differ depending on the details of the half-life and poly(A)-tail measurements [17,50,52]. The main pathway of mRNA degradation occurs via de-adenylation at a transcript-specific rate followed by rapid exonucleolytic digestion of the resulting oligoadenylated form [1,12,14]. The observed transcriptome-wide negative correlation between half-life and poly(A)-tail length suggests that the stability of the oligoadenylated form is a more important determinant of mRNA half-life than the rate of de-adenylation in plants: When the rate of transcription and de-adenylation are the same, a higher stability of the oligoadenylated form would result in both a longer half-life and a shift in the distribution of poly(A) tails towards shorter lengths, mirroring the correlation we observe. By contrast, keeping the rate of transcription and the stability of the oligoadenylated form constant, but decreasing the rate of de-adenylation would result in a longer half-life, but at the same time a longer poly(A) tail on average.
While the steady-state poly(A)-tail length is negatively correlated with transcript stability (Fig 2B), there is only a weak relation between poly(A)-tail length and transcript abundance across different transcripts in the same genotype, suggesting that other parameters are more important in determining transcript abundance. However, comparing the same transcript between paps1 mutants and wild type shows a positive correlation between the change in poly(A)-tail length and the change in transcript abundance (Fig 2C). This is consistent with the notion that, all else being the same, PAPS1-target transcripts that are appended with a shorter poly(A) tail in the paps1 mutant require less time to be deadenylated, resulting in faster turn-over and reduced transcript abundance compared to wild type.
A puzzling and counterintuitive observation is that some transcripts appear to have a longer poly(A) tail in the paps1 mutant than in wild type. With PAPS1 activity strongly reduced, these longer poly(A) tails should reflect the activity of the other two isoforms PAPS2 and PAPS4. It is thus conceivable that for some transcripts PAPS1 competes with PAPS2/4 for polyadenylation in wild type, and that for unknown reasons polyadenylation by PAPS1 results in a shorter steady-state distribution of poly(A) tails on these transcripts. Comparing the results presented here with those from an analogous analysis of paps2 paps4 double mutants will be required to test this possibility further.
While a transcript’s sensitivity to reduced PAPS1 activity was related to its stability and number of alternative polyadenylation sites, we did not identify any simple sequence feature, i.e. hexamer motif, that would robustly discriminate PAPS1-sensitive and insensitive transcripts. Several explanations are conceivable for this, such as combinatorial action of partly redundant sequence elements or a greater importance of secondary structure than nucleotide sequence in the 3’ UTR, and further experimentation will be required to discriminate between these.
A link between 3’-end processing and oxidative-stress resistance has been documented before by studying the function of the CPSF30 subunit OXT6 [36,48]. Both the sets of transcripts whose abundance is changed in oxt6 mutants and those with evidence of OXT6-dependent alternative polyadenylation had a significantly shorter poly(A)-tail distribution in paps1 mutants versus wild type. Also, no oxt6 paps1-1 double mutants could be identified due to synthetic lethality and a defect of double mutant gametophytes. While this indicates a functional interaction between the two factors, likely via their action on an overlapping set of transcripts, no binding of PAPS1 to OXT6/CPSF30 could be detected in comprehensive yeast two-hybrid interaction tests [29]. Unraveling the mechanistic basis of the observed overlaps may provide insight into the way that PAPS1 is recruited preferentially to some transcripts over the PAPS2/4 isoforms and vice versa.
Just like the other phenotypes of paps1 mutants [31,32], their higher resistance to ROS and synthetic lethality with oxt6 are specific effects of changes in PAPS1 activity, and are not seen in mutants lacking the activity of the other two canonical nuclear poly(A) polymerases PAPS2 and PAPS4. These phenotypes in paps1 mutants correlate with altered poly(A)-tail length of mRNAs whose abundance changes in plants with an altered redox status in chloroplasts. What is unclear at present is whether the changes in expression of these transcripts in response to redox alterations are caused–at least partly–by dynamic changes in their poly(A)-tail length, mediated by altered activities of the PAPS1 isoform. Answering this question might identify a concrete example where the balance of enzymatic activities of the different nuclear PAPS isoforms is altered in response to environmental factors in order to coordinately modulate the expression levels of distinct subsets of genes. While such an example is still missing, our co-regulation analysis provides strong support that this mechanism is being used by the plant. Searching for modules of co-expressed genes across more than 14,000 published A. thaliana microarrays identified two modules of 564 and 104 genes showing close co-expression with PAPS1. Strikingly, the genes in these modules behave very differently from the rest of the genome in our poly(A)-tail profiling of paps1 mutants relative to wild-type. Genes in the two modules show the strongest shortening of the poly(A)-tail in the paps1 mutants compared to all other identified co-expression modules. An analogous result was seen when considering the estimates of overall abundance changes in paps1 mutants versus wild type. These highly statistically significant results were obtained by comparing independent large-scale data sets generated by independent methods. In our view, this provides strong evidence that the co-expression seen in the microarray experiments is at least partly due to changes in PAPS1 activity in response to environmental or internal cues, presumably via its effects on the polyadenylation of its preferential target mRNAs. Thus, our analysis supports the existence of an additional layer of gene regulation based on functional specialization amongst canonical nuclear PAPS isoforms.
In summary, we demonstrate here that a fractionation approach based on poly(A)-tail length coupled with RNA-seq can be used to identify groups of target transcripts that are preferentially polyadenylated by a particular PAPS isoform. Analysis of these groups uncovered two novel roles for the PAPS1 isoform from A. thaliana in ribosome biogenesis and in redox homeostasis and oxidative-stress resistance. Data integration with publicly available microarray experiments provides strong evidence that the relative activities of the different PAPS isoforms are modulated in plants to co-ordinately alter the expression of groups of transcripts.
The paps1-1 mutant, the paps1-1 mutant introgressed into Col-0, the paps2-1 paps4-1 mutant, the oxt6 mutant and the roGFP-expressing lines have been described before [32,46,48]. The mapped pLat52::XFP insertion lines have been described before [42]. The line used here, 1376, is located at position 6,488,210 on chromosome 1, approximately 290 kb from the PAPS1 locus. Growth conditions were as described before [32]. To ensure comparability of the developmental stage despite the slower development of paps1 mutants, paps1 mutant seeds were germinated two days before the other genotypes.
One microgram of each linearized plasmid was diluted in 13.6 μl of H2O. Subsequently 2 μl 5 mM NTP solution (BioLine), 2 μl 10x T7 transcription buffer (NEB) and 1 μl 2 mg/ml BSA (NEB) were added. After mixing, 1 μl RNase Inhibitor (Promega) and 0.4 μl T7 Polymerase (NEB) were added. After incubating for 2 hours at 37°C, the reactions were stopped by adding 1.5 μl TURBO DNase (Ambion) and incubating for 30 min at 37°C. Phenol:Chloroform extraction on the samples was carried out. RNA concentrations were measured with the Picodrop and a mix of all RNAs with a concentration of 1 ng/μl was made. 1 μl of this mix was added to each RNA sample before fractionation (see below).
The mRNA fractionation was carried out with the Promega PolyATract System 1000 and the protocol modified as follows: The GTC, DIL, ß-mercaptoethanol (BME), biotinylated oligod(T), 0.5x SSC and H2O were allowed to reach room temperature. Forty-one microliter of BME were added per ml of GTC (GTC/BME) and 20.5 μl BME were added per ml of DIL and preheated to 70°C. The SSC buffer was diluted to a concentration of 0.085x. In a 2 ml tube, 80 μg of total RNA (in a maximum of 40 μl) were mixed with 400 μl GTC/BME, 15 μl biotinylated oligo d(T) (Promega) and 816 μl DIL/BME and heated to 70°C for 5 min. Afterwards the samples were spun at 13 000 rpm for 10 minutes at room temperature. In the meantime the paramagnetic beads were washed. Afterwards the beads were resuspended in 600 μl 0.5x SSC. The supernatant of the spun samples was added to the washed beads and the biotinylated oligod(T) was allowed to bind the beads by rotation at room temperature for 15 min. In the following step, the beads were captured and the supernatant transferred to a new tube (unbound fraction). The beads were washed three times with rotation for at least 5 min between each wash step. Afterwards, the beads were resuspended in 400 μl of 0.085 x SSC and rotated for 10 min at room temperature. The beads were captured and the eluate transferred to a new tube (short fraction). This step was repeated once (total of 800 μL). The beads were then washed with 400 μl nuclease free water (rotation for 10 min) twice and the eluates transferred to a new tube (800 μl, long fraction). All collected samples where centrifuged for 10 min at 13000 rpm, 4°C to remove any transferred beads. Then 0.1 vol of Co-precipitant Pink buffer (BioLine) were added and the samples mixed well. Afterwards 30 μg of Co-precipitant Pink (BioLine) were added, mixed well, and 1 vol 100% ethanol was added. The samples were incubated overnight (15–16 hours) at -20°C. In the next step, the samples were centrifuged at 13000 rpm, 4°C for 30 min. The supernatant was removed, the pellet washed with 500 μl 80% ethanol, dried, and dissolved in 15 μl DEPC treated water.
1 μl of each fraction was taken for reverse transcription, the rest was used for Illumina sequencing using the TruSeq protocol with a read length of 50 bp and single-end protocol. The eight long and the eight short fractions were each sequenced on one lane of a HiSeq2000 instrument. Sequencing was performed at LGC Genomics (Berlin, Germany).
Bulk poly(A)-tail lengths of mRNAs in the short and long fractions were determined as described before [32].
1 μl of each sample from the oligo(dT) fractionated mRNA was used for reverse transcription with oligo(dT)17 primers using SuperScript III. Expression levels were analysed using a Roche LightCycler 480.
For estimating changes in total transcript abundance, RNA was extracted from paps1-1 mutant and Ler wild-type seedlings grown and treated in parallel to those used from RNA fractionation. Reverse-transcription and qRT-PCR were performed as above using primers listed in Table 3 and Table 4.
Total RNA was extracted from nine-day old Ler and eleven-day old paps1-1 mutant seedlings after incubation at 28°C for two hours. RNA was extracted using Trizol and DNase-digested using Turbo DNase (Ambion). The extension poly(A)-tail length (ePAT) assay was performed as described [35]. To estimate the length of the transcript without the poly(A) tail, an aliquot of RNA was reverse-transcribed using the oligonucleotide 5’- GCGAGCTCCGCGGCCGCG(T)12VN-3’ to prime the reverse transcriptase; the sequence preceding the twelve T residues corresponds to the universal reverse primer used for the ePAT assay. For single PCR reactions, 34 cycles were used; for nested-PCR assays, 24 cycles were used for the first PCR, of which 1 μl was used as template for the nested PCR (20 μl reaction) with 24 cycles. PCR-products were analyzed on an Agilent Bioanalyzer 2100 using the DNA 1000 chip. Band intensities were extracted and analyzed as described [32]. To calculate the difference in the relative abundance between the ePAT and the (dT)12VN control products as a proxy for the poly(A)-tail length distribution, individual samples were normalized by dividing each value by the mean of all sample points within the region of interest. These relative abundances were averaged by sample type to finally subtract values for control products from ePAT products. We note that this is likely to underestimate the true poly(A)-tail length, as any PCR-products resulting from mispriming of the (dT)12VN control primer inside the poly(A) tail during reverse transcription will substract from the signal ascribed to the poly(A) tail.
The selection of candidate genes for ePAT was based on an initial analysis of the RNA-seq data. 40 candidates were chosen manually within the 250 genes with the lowest p-value for a tail-length change, requiring above-average expression as determined by a logCPM value of close to 4 or above. Of these 40 candidates, we were able to successfully establish primers for specific amplification of the 3’ UTR and poly(A) tail for 15 genes. Their analysis is presented in S3 Fig. The relatively low rate of success reflects the low sequence complexity and high AT-content of plant 3’ UTRs, in which primers have to be designed for ePAT analysis, in order to keep amplicon lengths short. Oligonucleotides used to assay the 15 transcripts are listed in Table 5.
All the following analyses were done using R (R Core Team, 2015). Illustrations were done using the R package Lattice [55].
To estimate the fraction of rRNA from total RNA, total RNA was extracted from 13 (paps1 mutants) and 11 (other genotypes) days-old seedlings of the different genotypes grown at 28°C to obtain a severe paps1-1 mutant phenotype [32]. Total RNA was separated on an Agilent Bioanalyzer using RNA chips according to the manufacturer’s protocol.
To determine ribosome content in paps1-3 mutant pollen, pollen was collected from paps1-3—/ PAPS1 pLat52::DsRED plants. Plant growth and pollen isolation were performed as previously described [43]. Subsequently the pollen pellet obtained was resuspended in 2 ml of sperm extraction buffer and subjected to FACS.
Fluorescence-Activated Cell Sorting was carried out on a MoFlo high speed cell sorter (Beckman Coulter, Fort Collins, USA) equipped with a 488 nm laser (200 mW air-cooled Sapphire, Coherent) at 140 mW used for scatter and autofluorescence measurements. A 561 nm laser (50 mW DPSS, CrystaLaser) at 30 mW was used for DsRED excitation. Autofluorescence and DsRED were detected using 530/40 nm and 630/75 nm bandpass filters, respectively. The instrument was run at a constant pressure of 207 kPa (30 psi) with a 100 μm nozzle and frequency of drop formation of approximately 40 kHz. Cells were collected into RNA extraction buffer maintained at 4°C.
Total RNA was isolated using a RNeasy Mini kit (Qiagen) and RNA integrity and amount of ribosomal RNA was assessed using an Agilent 2100 Bioanalyser with RNA 6000 Nano Assay (Agilent Technologies).
RNA-seq data sets have been deposited in NCBI GEO under accession number GSE57690 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE57690).
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10.1371/journal.pntd.0007176 | Anopheles mosquito surveillance in Madagascar reveals multiple blood feeding behavior and Plasmodium infection | The Madagascar National Strategic Plan for Malaria Control 2018 (NSP) outlines malaria control pre-elimination strategies that include detailed goals for mosquito control. Primary surveillance protocols and mosquito control interventions focus on indoor vectors of malaria, while many potential vectors feed and rest outdoors. Here we describe the application of tools that advance our understanding of diversity, host choice, and Plasmodium infection in the Anopheline mosquitoes of the Western Highland Fringe of Madagascar.
We employed a modified barrier screen trap, the QUadrant Enabled Screen Trap (QUEST), in conjunction with the recently developed multiplex BLOOdmeal Detection Assay for Regional Transmission (BLOODART). We captured a total of 1252 female Anopheles mosquitoes (10 species), all of which were subjected to BLOODART analysis. QUEST collection captured a heterogenous distribution of mosquito density, diversity, host choice, and Plasmodium infection. Concordance between Anopheles morphology and BLOODART species identifications ranged from 93–99%. Mosquito feeding behavior in this collection frequently exhibited multiple blood meal hosts (single host = 53.6%, two hosts = 42.1%, three hosts = 4.3%). The overall percentage of human positive bloodmeals increased between the December 2017 and the April 2018 timepoints (27% to 44%). Plasmodium positivity was frequently observed in the abdomens of vectors considered to be of secondary importance, with an overall prevalence of 6%.
The QUEST was an efficient tool for sampling exophilic Anopheline mosquitoes. Vectors considered to be of secondary importance were commonly found with Plasmodium DNA in their abdomens, indicating a need to account for these species in routine surveillance efforts. Mosquitoes exhibited multiple blood feeding behavior within a gonotrophic cycle, with predominantly non-human hosts in the bloodmeal. Taken together, this complex feeding behavior could enhance the role of multiple Anopheline species in malaria transmission, possibly tempered by zoophilic feeding tendencies.
| Malaria continues to be a significant threat to public health in Madagascar. Elimination of this disease is impeded by numerous factors, such as vector surveillance that does little to account for the potential role of secondary malaria vectors, which tend to rest and feed outdoors. In this study, we designed a low cost, modified barrier screen trap, called the QUadrant Enabled Screen Trap (QUEST). We used this in conjunction with the recently developed BLOOdmeal Detection Assay for Regional Transmission (BLOODART) to assess mosquito feeding behavior in the Western Highland Fringe of Madagascar. Our analysis revealed significant variability in mosquito density, diversity, host choice, and Plasmodium infection across traps placed within and between two nearby villages at two timepoints; indicating a strong, small-scale spatial component to disease transmission that warrants further investigation. Many of the mosquitoes in this sample (46.4%) fed on two or three host species, indicating complex feeding behaviors that could influence malaria transmission. Further, Plasmodium DNA was detected in the abdomens of numerous vectors of supposed secondary importance, indicating a neglected parasite reservoir and an increased need to account for these species in routine surveillance efforts.
| The Madagascar National Strategic Plan for Malaria Control 2018 (NSP), developed in coordination with the Madagascar National Malaria Control Program (NMCP), outlines pre-elimination strategies and a plan of action for malaria control in Madagascar [1]. The priorities of this plan reflect lessons learned from a century of malaria control efforts in the country. Between World Wars I and II, the antimalaria service of Madagascar implemented 1.) limited prophylaxis, 2.) mosquito larval control with Paris Green insecticide, 3.) the introduction of mosquito larvae-eating fish (Gambusia sp.) to cisterns and irrigation ditches, and 4.) drainage works to limit mosquito breeding sites [2,3]. The first major success followed the 1950s introduction of the insecticide DDT to the Central Highlands for indoor residual spraying [4]. This campaign combined DDT and chemoprophylaxis to achieve interruption of transmission in the region [5]. Much of this success may be attributed to the elimination of the primary vector (Anopheles funestus) from the highlands by 1952 [6]. Believing the intervention complete, spraying ceased in 1975. The following decade was characterized by the rapid deterioration of the health network through the erosion of health facilities, drug stock outages, and medical staff absenteeism [7–10]. A plea for the reintroduction of control measures followed the 1986 discovery of a single Plasmodium positive An. funestus in the highlands [9]. No action was taken, and an epidemic followed that took the lives of ~40,000 people [5,11,12].
Today, an evolved perspective of the entomological side of the 1980s epidemic has emerged. Spraying campaigns are never truly comprehensive, leaving reservoirs that facilitate future recolonization events [12]. Anopheles funestus populations from the highlands are genetically similar to populations on the east coast [13], suggesting that the highlands reinvasion of An. funestus may have come from the east. Furthermore, it is likely that additional Anopheline vector species contributed to the malaria outbreak [9]. Mosquitoes are often classified as primary or secondary vectors; assigned by purported importance for malaria transmission in a particular region [14,15]. Secondary vectors, specifically, were recently defined by the World Health Organization as “species of Anopheles thought to play a lesser role in transmission than the principal vector; capable of maintaining malaria transmission at a reduced level [16].” Loosely-defined designations such as this likely contribute to the current knowledge gap between primary and secondary vectors.
Numerous secondary vectors, present in Madagascar, have been documented with Plasmodium sporozoites by dissection of the salivary glands: An. coustani [14,17], An. mascarensis [18], An. squamosus [17,19], An. pharoensis [17], An. rufipes [14], and An. maculipalpis [14]. Circumsporozoite positive ELISA further implicates An. coustani [10,20,21], An. mascarensis [10,18,22], and An. squamosus [23] as malaria vectors. While common sampling strategies are biased toward endophilic and endophagic mosquitoes [24], most of these potential Malagasy malaria vectors are both exophagic and exophilic [6,22,25]. Detailed goals of the NSP describe objectives for entomological surveillance of sentinel sites in Madagascar, seeking data on vector taxonomy, density, feeding behavior, insecticide susceptibility, parity, age, and sporozoite rate. Here we seek to advance our understanding of the diversity, feeding behavior, and Plasmodium infection status of potential malaria vectors in the Western Highland Fringe of Madagascar to achieve a more comprehensive picture of mosquito behavior in the region. To accomplish this goal, we utilized the recently developed Bloodmeal Detection Assay for Regional Transmission (BLOODART), designed to simultaneously assess Anopheles mosquito species, mammalian hosts, and Plasmodium parasites from an excised mosquito abdomen or abdomen squash [26].
In conjunction with BLOODART, we employed a modified barrier screen trap, the Quadrant Enabled Screen Trap (QUEST). Barrier screens represent a relatively recent collection method, designed to address the paucity of effective and unbiased collection tools for exophilic mosquitoes [27]. Outdoor traps typically attempt to replicate existing outdoor resting spots for mosquitoes, requiring them to compete with potentially more abundant natural options [28,29]. Further, seeking resting mosquitoes by manually searching amongst the vegetation requires significant time and effort for a small return on samples [30,31]. Standard barrier screens offer the advantage of being permeable to visual and olfactory cues, perhaps making them less likely to divert the mosquito [27]. These screens appear to intercept mosquitoes irrespective of species-specific resting site or host preferences [32], reducing the potential for bias. This may be reflected in the equal or greater Anopheline diversity captured on barrier screens compared to human landing catches [27]. Standard barrier screens provide some sense of directionality by assessing which side of the net mosquitoes were captured from. We sought to provide greater directional resolution and capture numbers than a standard barrier screen by designing a cross-shaped barrier screen with built in eaves. As mosquitoes tend to move up and over physical barriers [33], we suspect the addition of eaves might prolong mosquito detainment. The objective of this study, achieved by superimposing our QUEST captured mosquitoes and BLOODART analysis, was to assess mosquito density, diversity, host choice, and Plasmodium positivity with spatial resolution, demonstrating the ability of these tools to contribute to the entomological surveillance goals of the NSP [1].
Wild-caught mosquitoes were collected by Case Western Reserve University and Madagascar NMCP entomologists in December 2017 (six nights) and April 2018 (five nights), corresponding to the beginning and end of the rainy season (December to March [34]), observed to coincide with the malaria transmission in Madagascar [35]. A peak in clinical malaria has been observed in April-May for the Tsiromandidy Health District [35]. Collections were performed in the villages of Amparihy and Ambolodina (Fig 1), located in the fokontany of Kambatsoa (Commune Maroharona, Tsiroanomandidy Health District). Epidemiological surveys have been previously performed in this area in partnership with the Madagascar NMCP [36], and are consistent with protocols approved by the Madagascar Ministry of Health for the present study (N°099-MSANP/CE). Additionally, community and household approvals were obtained following fokontany-based meetings prior to initiating all study activities.
Mosquitoes were collected using QUEST, modified from a previously described barrier screen design [27]. One round of indoor pyrethroid spray catch was conducted in Amparihy (10 dwellings) and Ambolodina (seven dwellings) in December 2017 as previously described [26], with permission from the owner of the residence. QUEST collections began at 18:00 hrs and continued at three-hour increments with a final collection at 06:00 hrs the following day, for a total of five sampling events. Only female Anopheles mosquitoes were collected. Mosquitoes were aspirated on a quadrant-by-quadrant basis and deposited live into pre-labeled paper cups at the beginning of each collection timepoint. Collected mosquitoes were incapacitated by ether and keyed to species using local unpublished keys from Institut Pasteur de Madagascar. Mosquitoes were preserved in individual 2 ml tubes containing 90% ethanol with a label including a unique specimen voucher code containing the prefix “APY.” A subset of mosquitoes in April 2018 were collected on filter paper (n = 115) after ethanol supplies were exhausted.
QUESTs were designed to improve yields and increase our understanding of mosquito mobility within the study site (Fig 2). Poles for erecting the traps were sourced from trees onsite. The net material used for the traps was a flexible white fabric similar to an untreated bed net. The mesh was of a lighter weight material. The net was not secured to the ground. Two “L” structures were made around the central pole by securing the net to the poles with a staple gun, creating a cross-shaped trap (aerial view), with each extension measuring 5 m in length and approximately 1.8 m tall. The top of each extension was affixed with a 0.25 m overhang tied at a 45° angle, creating an “eave” to trap insects attempting to bypass the barrier by flying up and over. A small stick was tied perpendicular to the structural poles approximately 15 cm down from the upper terminal end of the net. This provided the structural support necessary to position the eaves in the net. The design provided four isolated quadrants, with each extension pointing in a cardinal direction. The quadrants were uniformly numbered as follows: 1-Northwest, 2-Northeast, 3-Southwest, 4-Southeast. Three QUEST were set up in a village (Amparihy Trap 1 –Trap 3, Ambolodina Trap 1 –Trap 3), placed in settings where both human dwellings and animal enclosures were in close proximity, while being evenly spread throughout the village. A single standard barrier screen [27] (Ambolodina Trap 4; Fig 2E and 2F) was installed inside a cattle pen in the December 2017 collection. This trap was approximately 1.5 m tall and 15 m in length, built with locally sourced poles and a similar net-like material. We used three QUESTs for 11 nights, therefore a total of 33 trap-nights. The standard barrier screen was used for two nights. GPS coordinates for the traps are located in S1 Table.
High-resolution images of preserved specimens (taken ~60 days post collection) were captured using a Passport Storm system (Visionary DigitalTM, 2012), including: a Stackshot z-stepper, a Canon 5D SLR, a MP-E 65 mm macro lens, three Speedlight 580EX II flash units, and a computer running Canon utility and Adobe Lightroom 3.6 software. The z-stepper was controlled through Zerene Stacker 1.04 and images were stacked using the P-Max protocol. To prepare for photography, the specimen was removed from EtOH, allowed to dry for several minutes, and temporarily affixed to a pin with a small dab of K-Y Personal Lubricant. Images were captured over an 18% gray card background and processed in Adobe Photoshop CC 2018 to adjust lighting and sharpness, and to add scale bars. Minor adjustments were made using the stamp tool to correct background and stacking aberrations.
DNA extraction and PCR amplification of targets for mosquito species, mammalian host, and Plasmodium parasites were carried out as previously described [26].
We utilized the Bloodmeal Detection Assay for Regional Transmission [26] with the addition of several new probes, including An. squamosus, two probes that distinguish An. gambiae sensu stricto from An. arabiensis, ringtail lemur (Lemur catta), Coquerel’s sifaka (Propithecus coquereli), and the house mouse (Mus musculus). A list of probes and fluorescent microspheres for the mammalian and mosquito probes is located in S2 Table. Probes for Plasmodium species are described elsewhere [37].
Contingency tables and plots were generated in R Version 3.4.0 [38] using the compilation package ‘Tidyverse’ [39]. Data analyzed in this study has been added to VectorBase (VBP0000345).
We captured a total of 1252 mosquitoes, 501 in December 2017, and 751 in April 2018. The QUEST captured 1143 mosquitoes over 33 trap-nights (15 Amparihy, 18 Ambolodina) for 34.6 mosquitoes per trap/night. Overall, there was a greater concentration of mosquitoes on the traps at the April 2018 timepoint. The distribution of mosquitoes within each trap differed, with some QUESTs showing a homogenous distribution of mosquitoes across the four quadrants while others had a clear concentration of samples on a subset of the four quadrants (Fig 3, S3 Table). The standard barrier screen captured 101 mosquitoes in two nights, thus a total of 50.5 mosquitoes per trap/night. On the standard barrier screen, 91% of mosquitoes were captured on the same side of the net where the Malagasy zebu rested and slept. The standard barrier screen captured seven of the 10 Anopheline species observed in this study (S4 Table). However, the species missing for this trap had low overall sample numbers. Mosquitoes began to appear on the QUESTs at the 21:00 hrs collection point (29.3%), peaked at 00:00 hrs (32.9%), and tapered off into the early morning collections at 03:00 hrs (25.5%) and 06:00 hrs (12.3%). Peak activity deviated significantly from the general trend for several species (excluding An. flavicosta, An. gambiae, and An. pretoriensis due to insufficient sample size) (χ2 = 102, 18 DOF, p < 0.005), with An. maculipalpis and An. rufipes being more active at the 21:00 hrs timepoint, while An. mascarensis activity peaked at 03:00 hrs. The mosquito activity pattern for individual nights was variable. No mosquitoes were captured at 18:00 hrs. Only eight mosquitoes were captured by pyrethroid spray catch across 17 dwellings, with all mosquitoes identified as An. funestus.
The mosquito probe set used in this study was designed to capture species detected in prior surveys conducted in our study villages [26]. As they cannot be morphologically distinguished, An. gambiae s.s. and An. arabiensis were considered as the An. gambiae sensu lato complex in our comparison of morphological versus BLOODART identifications. Further data analyses here preferentially used the species determination of BLOODART over morphology. Where BLOODART identifications were inconclusive, we used the original morphological identification. Of the 64 mosquitoes designated as “inconclusive” by BLOODART, 16 produced a clean PCR band, 24 produced either a smear or several bands of unexpected size, five produced faint bands, and 19 produced no band at all. Concordance for individual probes ranged from 93–99% (Table 1). The unknown species probe of Tedrow, 2019 [26], hybridized primarily to specimens morphologically identified as An. mascarensis (22/34), and will be considered in this manuscript as the An. mascarensis probe. Probes for An. pretoriensis, An. flavicosta, and An. fuscicolor were not included. We provide several photographs (S1 and S2 Figs) of Anopheline species captured in this study (prior to processing for BLOODART analysis). This serves to provide publicly available images for several of these poorly studied species.
The predominant mosquito species, pooling all trapping methods, were An. coustani (n = 469), An. maculipalpis (n = 263), An. rufipes (n = 147), and An. squamosus (n = 228). Species captured in lower numbers were An. arabiensis (n = 64), An. mascarensis (n = 35), An. funestus (n = 26), An. gambiae s.s. (n = 14), An. pretoriensis (n = 5), and An. flavicosta (n = 1). The abundance of particular species shifted on either side of the rainy season, with an even mix of An. arabiensis and An. gambiae s.s. in December 2017 shifting to exclusively An. arabiensis in April 2018. The proportion (Fig 4, S5 Table) and distribution (Fig 3, S3 Table) of Anopheles species on the QUESTs was complex. There were nearly seven times more mosquitoes captured in Amparihy in April 2018 compared to December 2017. In April 2018, traps in Amparihy collected proportionally more An. arabiensis, An. coustani, and An. funestus than the traps in Ambolodina, while the proportion of An. rufipes declined.
We collected 1035 visibly blood fed, and 217 visibly unfed mosquitoes. We were able to detect a mammalian host in 1195 specimens, including 160/217 visibly unfed mosquitoes. We were unable to detect a host in 57 specimens. Blood feeding rates were calculated based on detection of host DNA rather than visible engorgement. Blood feeding rates were as follows: An. arabiensis = 92.2% (59/64), An. coustani = 94.9% (445/469), An. funestus = 96.2% (25/26), An. flavicosta = 100% (1/1), An. gambiae = 100% (14/14), An. maculipalpis = 93.9% (247/263), An. mascarensis = 97.1% (34/35), An. pretoriensis = 100% (5/5), An. rufipes = 96.6% (142/147), and An. squamosus = 97.8% (223/228).
Mosquitoes in this collection exhibited complex blood feeding behaviors. Six mammalian hosts were detected across our blood meal samples, with the predominant constituents including bovine (90.3%), followed by human (39.5%) and porcine (15.3%) blood (Fig 5). Canine (4.7%), feline (0.3%) and hircine (0.1%) blood were detected infrequently. No mosquito was positive for lemur or murine blood. In addition to evidence of many blood meal hosts, individual mosquitoes frequently harbored blood from two or three different host species: single host = 53.6%, two hosts = 42.1%, three hosts = 4.3%. The incidence of multiple blood feeding was significantly different (χ2 = 42.3, 2 DOF, p < 0.0001) from December 2017 (single host = 41.8%, two hosts = 52.3%, three hosts = 5.8%) to April 2018 (single host = 60.9%, two hosts = 35.7%, three hosts = 3.2%). The most anthropophilic species were An. coustani and An. mascarensis. The propensity for human feeding increased significantly from the December 2017 (27% human positive) to the April 2018 (44% human positive) collection (χ2 = 44.8, 1 DOF, p < 0.0001), with anthropophilic shifts observed for An. coustani (32% to 60%), An. funestus (9% to 50%), An. rufipes (23% to 44%), and An. arabiensis (38% to 53%) (Fig 5, S6 Table).
The distribution of host bloodmeals identified on the QUEST varied significantly (when constraining the analysis to hosts with sufficient sample size: human, cow, and pig positive bloodmeals) between village (AMB vs AMP in 2017: χ2 = 23.5, 2 DOF, p < 0.005, AMB vs AMP in 2018: χ2 = 25, 2 DOF, p < 0.005) and timepoint (Amparihy 2017–2018: χ2 = 33.3, 2 DOF, p < 0.005, Ambolodina 2017–2018: χ2 = 56.3, 2 DOF, p < 0.005) (Fig 6, S7 Table). The presence of human-positive bloodmeals increased in April 2018 for both villages, from 4% to 48% in Amparihy and 32% to 41% in Ambolodina. Amparihy also exhibited shifts in the number of bovine-positive (96% to 73%) and porcine-positive (96% to 7%) bloodmeals from December 2017 to April 2018. Ambolodina showed a decrease in porcine-positive bloodmeals (22% to 1%), while bovine-positivity increased (88% to 99%). The presence of human-only and pig-only bloodmeals was restricted to traps from Amparihy. Furthermore, human-only bloodmeals were observed across all traps and all quadrants in the April 2018 collection.
All four common human Plasmodium species (P. falciparum, P. vivax, P. malariae, and P. ovale) were detected in our assay (Fig 7A) with a total of 75 positive among 1252 collected mosquitoes (6%). The predominant parasites were P. falciparum (n = 50) and P. vivax (n = 19), followed by P. malariae (n = 10) and P. ovale (n = 8). There was a total of 12 mixed Plasmodium species infections, including the combinations P. falciparum/P. vivax (n = 9), P. falciparum/P. malariae (n = 1), P. falciparum/P. ovale (n = 1), and P. malariae/P. ovale (n = 1). This parasite profile is consistent with human malaria surveys in the region [36]. Plasmodium-infected bloodmeals were commonly observed across Anopheles species that the Madagascar NMCP typically consider to be secondary or non-vectors, such as An. coustani (n = 26; 6% of screened An. coustani specimens), An. maculipalpis (n = 20; 8%), An. squamosus (n = 13; 7%), and An. rufipes (n = 8; 6%). These secondary vector infection rates (70/1078) are not significantly different from infection rates in primary vectors (5/99) (χ2 = 0.282, 1 DOF, p > 0.05): An. funestus (n = 1; 4%), An. arabiensis (n = 4; 6%), and An. gambiae (n = 0; 0%). Human positive bloodmeals were not significantly more likely to be associated with Plasmodium parasites (χ2 = 1.31, 1 DOF, p > 0.05). However, Plasmodium prevalence was higher in human positive (33/440, 7.5%) than human negative (42/737, 5.7%) bloodmeals.
The December 2017 collection contained 18 of the 19 overall observed P. vivax infected mosquitoes. The abundance of positive samples, and diversity of Plasmodium spp. within them, shifted in these two villages, with P. falciparum comprising nearly all of the Plasmodium positive bloodmeals in April 2018. The village of Amparihy had no Plasmodium-positive mosquitoes in December 2017, while similar numbers of Plasmodium-positive mosquitoes were observed between the villages in April 2018 (Fig 7B). There was not a significant difference in the number of Plasmodium positive bloodmeals between timepoints (2017, n = 29/501; 2018, n = 46/751; χ2 = 0.0537, 1 DOF, p > 0.05). Apart from this difference in Plasmodium positive mosquito distributions between Amparihy and Ambolodina, no specific pattern of Plasmodium positive mosquitoes was observed across QUESTs (S3 Fig, S8 Table). Overall, 66 positive mosquitoes were collected on QUESTs (F = 39, V = 8, M = 8, O = 2, FV = 8, FM = 1, MO = 1), nine on the standard barrier screen (V = 2, O = 4, FV = 1, FO = 1), and zero from PSC.
This study demonstrates the ability of QUESTs and BLOODART to refine perspectives on mosquito collection and assessment. QUESTs collect a diverse and substantial sample of Anopheles mosquitoes with a simple design. Many of the components can be sourced directly from the environment for the initial setup or repair. By collecting additional data from the surrounding environment (such as a host census including humans and domesticated animals, weather conditions, and nearby breeding sites), we could obtain further insight into the behavior of these medically important mosquitoes. The data produced by a BLOODART analysis of captured mosquitoes augments the utility of QUESTs (or any other mosquito trap strategy), efficiently monitoring additional important vector indicators. By analyzing all of our captured Anopheline mosquitoes, as opposed to focusing only on the predetermined important vectors, we detected a significant parasite reservoir in the abdomens of secondary vectors. In the context of limited time and resources in outbreak or surveillance scenarios, secondary vectors may be passed over for assessment of vector indicators, or never collected at all. This highlights the problematic nomenclature surrounding “primary” or “secondary” vectors, in that they may constrain a thorough investigation of complex disease transmission networks. Further studies should seek to characterize the role of these secondary vectors in malaria transmission.
As a result of this study, and training missions carried out at the Madagascar NMCP, the skillset necessary to use both QUEST and BLOODART is currently in place. This study provides a framework for how these tools can be deployed to enhance Madagascar’s capacity for entomological surveillance and malaria elimination.
QUEST collection was effective at capturing a diverse sample of female blood fed Anopheles mosquitoes. Placing the QUESTs in the same locations in December 2017 and April 2018 allowed for comparison of QUEST data across the two timepoints. The QUESTs AMB1, AMB3, and AMP1 had significantly different mosquito distributions across the four quadrants between the December 2017 and April 2018 timepoints. In December 2017, the traps AMB3 and AMB4 had non-homogenous mosquito distributions across the QUEST quadrants. In April 2018, the traps AMB1 and AMP1 had non-homogenous mosquito distributions across the QUEST quadrants (Fig 3, S3 Table). This study did not perform a formal comparison between QUEST and standard collection methods (such as human landing catch or CDC light traps), or a direct comparison to the standard barrier screen or pyrethroid spray catch. The mosquito diversity of the indoor pyrethroid spray catch was limited to a single species, An. funestus. The standard barrier screen captured a substantial number of mosquitoes per sampling night, likely due to its placement directly inside a Malagasy zebu corral with a high concentration of available bovine hosts. The standard barrier screen captured seven of the 10 Anopheles species sampled in this study. The species that were not captured on the standard barrier screen had low capture numbers overall (S3 Table), suggesting that these species may have surfaced given a greater number of sampling nights. The standard barrier screen and PSC were informally conducted alongside our initial QUEST collections, but ultimately discontinued when the QUESTs procured a sufficient number of mosquitoes, and because the standard barrier screen was compromised by cattle.
Two mosquito species, An. maculipalpis and An. rufipes, were most abundant in the early evening (21:00 hrs), deviating from the general trend of a 00:00 hrs peak activity time. Alternately, An. mascarensis activity peaked later in the evening (03:00 hrs). More frequent collections throughout the night would provide greater resolution for these mosquito activity patterns.
Concordance between mosquito morphological and BLOODART identifications ranged from 93–99%. Unique specimen vouchers enabled us to refer back to discordant specimens, which revealed that morphological misidentification was responsible for many of the discrepancies between morphological and molecular identifications. Field conditions (such as poor lighting through the microscope) likely contributed to less than optimal species identifications. Additionally, vouchered specimens allowed us to associate a novel Anopheles ITS2 sequence probe [26] with the morphologically identified species An. mascarensis, facilitating the first online deposition of genetic data for this common Malagasy species (Genbank accession: MH560267). By vouchering, photographing, and depositing our mosquito data on VectorBase, we have secured the future utility of our mosquito collection for the purpose of further morphological and molecular investigations.
The predominant mosquito species in this collection have been considered vectors of secondary importance. However, their abundance relative to primary vectors necessitates closer examination of their potential impact on human health. Anopheles coustani, comprising 37% of our mosquito collection, was recently implicated as a vector of malaria [10]. Despite being primarily exophagic and exophilic, very high prevalence could result in this species being responsible for the majority of indoor bites in Madagascar despite the presence of endophagic and endophilic species [10]. Similarly, An. maculipalpis, An. rufipes, An. squamosus, and An. mascarensis have all been documented with Plasmodium parasites [16–22]. The relative abundance of these species in conjunction with their malaria transmission potential warrants further investigation.
Anopheles gambiae s.s. is uncommon in the highlands of Madagascar [41], with An. arabiensis being the predominant representative of the An. gambiae s.l. complex in this region. Permanent shifts in species dominance from An. gambiae s.s. to An. arabiensis have been reported in continental Africa [42,43]. Typically, this is viewed as a succession event, a product of the differential effects of indoor residual spraying and insecticide treated nets on these two species. These mosquito control strategies are most effective against the endophilic/endophagic An. gambiae s.s., with negligible impacts on the exophilic/exophagic An. arabiensis [42,43]. The habitat preferences for these two species differ as well, with An. gambiae s.s. preferring a more humid climate, while An. arabiensis is found in both humid and arid environments [5,20,44,45]. Considering that our timepoints occur at the beginning and end of the 2017–2018 rainy season (considered to be the malaria transmission season in Madagascar [35]), and that Madagascar is subject to substantial climatic variation from year to year [46,47], we might naturally expect to see seasonal variation in species occurrence and abundance. Further, longitudinal monitoring of these sites would likely provide more insight on the population dynamics of these closely related species. Insecticide treated nets were observed to be virtually absent from these villages. Consequently, it was not surprising that the mosquitoes collected here did not exhibit the behavioral adaptation of feeding earlier in the evening [48].
There are several phenomena that limit direct comparison of mosquito data between villages. Due to logistical issues ranging from inclement weather to security, the number of nights sampled in each village differed. Further, villages were sampled consecutively as opposed to concurrently, introducing a unique mixture of irretrievable ecological factors into each trapping night across the overall sampling period. Nevertheless, there are common observations, namely a diversity of mosquitoes, host choice, and multiple blood feeding behavior, between the villages.
The distribution of mosquitoes collected from the QUEST is potentially influenced by wind direction, precipitation, temperature, lunar cycle, proximity to breeding sites, and distance to viable hosts [33,49–51]. Specific data on these variables were not collected, limiting our conclusions. Numerous mosquitoes were caught in the eaves of the trap. Mosquitoes tend to move up and over physical barriers [33], suggesting that the eaves may play a role in prolonging mosquito detainment or improved capture. The height at which mosquitoes were captured was not recorded. As such, we cannot conclude whether the eaves improved our catch.
We were able to detect host DNA in 74% of visibly unfed mosquitoes. This suggests that visibly unfed mosquitoes should not be precluded from bloodmeal analyses. The mosquitoes in this survey exhibited complex feeding behaviors. Individual mosquito species frequently fed on human and non-human hosts, even among species considered highly anthropophilic. As demonstrated in a metanalysis of the human blood index in An. gambiae sibling species, sampling site (indoors vs outdoors) is more closely associated with human positive bloodmeals than mosquito species [52]. The higher diversity of host choice in this study may reflect a reduction in sampling bias for endophilic vector species like An. gambiae s.s. and An. funestus. The BLOODART analysis revealed multiple blood feeding behavior (46.4%) in our mosquito sample. PCR has the capacity to detect blood consumed 36–48 hrs after ingestion [26,53,54], lasting through the feeding phase of the gonotrophic cycle [55]. The increased incidence of multiple blood feeding, as compared to most surveys, could be a difference in bloodmeal assessment technique, with many studies choosing to pursue a narrower range of possible hosts [27,31,56,57]. High incidence of multiple blood feeding has important epidemiological implications. Numerous mosquitoes showed evidence of feeding on humans and one or two additional mammal species. This may increase the probability that a mosquito is also feeding on multiple individuals within a species, amplifying the vectorial capacity of the potential malaria vectors in these villages. The zoophilic preferences of the mosquitoes in this sample, however, may temper their impact on malaria transmission by dedicating potentially infective bites to non-human hosts. There was a substantial shift toward human feeding in the April 2018 collection, primarily observed in the species An. coustani, An. funestus, An. rufipes, and An. arabiensis. An increase in human feeding could lead to an increase in the presence of Plasmodium positive mosquitoes, though evidence of such from this study was variable between villages (Fig 7B). The possible reasons for an anthropophilic shift include host availability [40,58,59], insecticide treated net coverage [60,61], a shift in the distribution and density of mosquito species, or perhaps a plastic trait influencing host choice.
At times, there may be approximately as many Malagasy zebu as humans in these villages, characteristic of much of the region [44]. Mosquitoes will target hosts with a greater surface area or weight [62], and considering the relative size of the Malagasy zebu, the 90.3% prevalence of bovine blood may be expected. Frequent feeding between non-human hosts, both within and between species, could increase the risk for outbreaks of mosquito-borne epizootic diseases. There were numerous pigs, semi-domesticated dogs, and cats present in both villages. Goats were spotted several kilometers from the villages, but none were observed within them. Lemurs and rodents were not observed.
The distribution of host choice across mosquitoes on the nets (Fig 6) may be influenced by the proximity of hosts to the net. It is also likely influenced by the composition of mosquito species on individual QUESTs, each of which displayed an individual hierarchy of host choice (Fig 5). Although no empirical census of hosts was conducted, there did anecdotally appear to be more pigs in Amparihy and more cattle in Ambolodina, potentially explaining the higher percentage of pig and cattle DNA, respectively, in the abdomens of mosquitoes from these villages.
The incidence of Plasmodium infection for the mosquitoes collected in this study (6%, n = 75) likely reflects our unbiased approach to mosquito collection and processing, as we did not limit our molecular analysis to predetermined primary vector species. However, we should consider that Plasmodium positivity in this assay is observed in the abdomen of the mosquito, and does not indicate the status of sporozoites in the mosquito’s salivary glands. An infectious mosquito is typically defined as a mosquito with sporozoites in the head/thorax [63], which, due to daily rates of mortality [64], will only be a subset of the mosquitoes with the earlier parasite stages in the gut [65]. We chose not to bisect the mosquito (cut anterior to the rear legs to prevent false positives [66]) for independent extraction and analysis of the head/thorax and the abdomen. By bisecting posterior to the rear legs, we preserved the anterior portion for future taxonomic and/or morphological analyses. It should be reiterated, however, that all of the mosquito species in our sample have been previously documented with sporozoites in Madagascar [10,14,17–23], and the predominant species observed here, An. coustani, was recently implicated as an important vector of Plasmodium parasites [10] in the region. The combined impact of An. coustani and the remaining secondary vectors may be responsible for sustained malaria transmission in these villages, which have low primary vector density. Consequently, we have identified a potential reservoir of Plasmodium parasites outside the realm of many current vector interventions. Therefore, targeted efforts focused on the suppression of An. funestus and An. gambiae s.l., which primarily include indoor interventions, may not be sufficient to effectively reduce malaria transmission.
We assume that the observation of human negative/Plasmodium positive bloodmeals indicates that the mosquito acquired the Plasmodium parasite from a human, digested the human blood, yet still retains the Plasmodium DNA, likely as an oocyst-stage parasite.
We revealed differences in the occurrence of Plasmodium spp. positivity across space and time. This may reflect the seasonality of P. falciparum and P. vivax malaria, which peaks in the study region around the time of the second round of surveys described here [35]. Our mosquito data reflect a pattern of P. vivax positivity restricted to the beginning of the rainy season, and a predominance of P. falciparum toward the end of the rainy season. The distribution of Plasmodium positive mosquitoes on the nets was fairly homogeneous, with the exception of the village of Amparihy in December 2017, which had no positive mosquitoes. The observation of a statistically significant pattern is limited by the total number of positive mosquitoes (n = 75) distributed across 48 possible quadrants.
To achieve the goal of malaria elimination in this country, the role of neglected secondary vector species must be considered. The lack of comprehensive surveillance, and the absence of adequate distribution of artemisinin combination therapy drugs, insecticide treated nets, and rapid diagnostic tests in remote areas of the country provide a safe refuge for Plasmodium parasites. Substantial parasite reservoirs in neglected vector species adds to the series of gaps that expose hard-earned malaria-free districts to the perpetual threat of recrudescence.
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10.1371/journal.ppat.1003522 | Malaria Parasite-Synthesized Heme Is Essential in the Mosquito and Liver Stages and Complements Host Heme in the Blood Stages of Infection | Heme metabolism is central to malaria parasite biology. The parasite acquires heme from host hemoglobin in the intraerythrocytic stages and stores it as hemozoin to prevent free heme toxicity. The parasite can also synthesize heme de novo, and all the enzymes in the pathway are characterized. To study the role of the dual heme sources in malaria parasite growth and development, we knocked out the first enzyme, δ-aminolevulinate synthase (ALAS), and the last enzyme, ferrochelatase (FC), in the heme-biosynthetic pathway of Plasmodium berghei (Pb). The wild-type and knockout (KO) parasites had similar intraerythrocytic growth patterns in mice. We carried out in vitro radiolabeling of heme in Pb-infected mouse reticulocytes and Plasmodium falciparum-infected human RBCs using [4-14C] aminolevulinic acid (ALA). We found that the parasites incorporated both host hemoglobin-heme and parasite-synthesized heme into hemozoin and mitochondrial cytochromes. The similar fates of the two heme sources suggest that they may serve as backup mechanisms to provide heme in the intraerythrocytic stages. Nevertheless, the de novo pathway is absolutely essential for parasite development in the mosquito and liver stages. PbKO parasites formed drastically reduced oocysts and did not form sporozoites in the salivary glands. Oocyst production in PbALASKO parasites recovered when mosquitoes received an ALA supplement. PbALASKO sporozoites could infect mice only when the mice received an ALA supplement. Our results indicate the potential for new therapeutic interventions targeting the heme-biosynthetic pathway in the parasite during the mosquito and liver stages.
| We demonstrated about two decades ago that the malaria parasite could make heme on its own, although it imports heme from red blood cell hemoglobin during the blood stages of infection. We investigated the role of parasite-synthesized heme in all stages of parasite growth by knocking out two genes in the heme-biosynthetic pathway of Plasmodium berghei that infects mice. We found that the parasite-synthesized heme complements the function of hemoglobin-heme during the blood stages. The parasite-synthesized heme appears to be a backup mechanism. The parasite incorporates both sources of heme into hemozoin, a detoxification product, and into mitochondrial cytochromes. The parasite-synthesized heme is, however, absolutely essential for parasite growth during the mosquito and liver stages. We restored the sporozoite formation and liver-stage development of the knockout parasites by providing the missing metabolite. Thus, the heme-biosynthetic pathway could be a target for antimalarial therapies in the mosquito and liver stages of infection. The knockout parasite could also be tested for its potential as a genetically attenuated sporozoite vaccine.
| Plasmodium falciparum (Pf) and Plasmodium vivax account for more than 95% of human malaria. P. falciparum is widely resistant to the antimalarial drugs chloroquine (CQ) and antifolates. Sporadic resistance is also seen in P. vivax [1]. Emerging resistance to the artemisinin-based combination therapies [2] and the absence of an effective vaccine highlight an urgent need to develop new drug targets and vaccine candidates [3], [4]. The de novo heme-biosynthetic pathway of the malaria parasite offers potential drug targets and new vaccine candidates. The malaria parasite is capable of de novo heme biosynthesis despite its ability to acquire heme from red blood cell (RBC) hemoglobin. During the intraerythrocytic stages, the parasite detoxifies hemoglobin-heme by converting it into hemozoin [5], [6]. The source of the heme used in the parasite mitochondrial cytochromes and the parasite heme requirements during the mosquito and liver stages are yet unknown. Hence, the role of the de novo heme-biosynthetic pathway throughout the entire parasite life cycle is a subject of considerable interest [7].
Detailed studies in our laboratory and elsewhere have completely characterized all the enzymes in P. falciparum heme-biosynthetic pathway. The parasite enzymes are unique in terms of their localization and catalytic efficiencies. The first enzyme, δ-aminolevulinate synthase (PfALAS) [8], [9], and the last two enzymes, Protoporphyrinogen IX oxidase (PfPPO) and Ferrochelatase (PfFC) [10], [11] localize to the mitochondrion. The enzymes that catalyze the intermediate steps: ALA dehydratase (PfALAD) [12], [13], Porphobilinogen deaminase (PfPBGD) [9], [14], and Uroporphyrinogen III decarboxylase (PfUROD) [15] localize to the apicoplast (a chloroplast relic), whereas, the next enzyme Coproporphyrinogen III oxidase (PfCPO) is cytosolic [16]. Figure 1 depicts the pathway. The enzymes that localize to the apicoplast have very low catalytic efficiency compared with RBC counterparts [17], [18]. Earlier studies showed that host ALAD and FC are imported into the parasites in the intraerythrocytic stages, suggesting that the host machinery may augment parasite heme synthesis [6], [19].
The apicoplast is involved in the synthesis of heme, fatty acids, iron-sulfur proteins, and isoprenoids [20]. Yeh and Risi [21] showed that a chemical knockout of apicoplast function could be rescued by isopentenyl pyrophosphate supplement to P. falciparum cultures in vitro. This suggests that during the intraerythrocytic stages, the parasite requires apicoplast function for isoprenoid synthesis but not for heme or fatty acid synthesis. However, heme as such is essential for parasite survival in the intraerythrocytic stages, minimally constituting the cytochrome component of the Electron Transport Chain (ETC). The ETC is used as a sink for electrons generated in the pyrimidine pathway [22]. Atovaquone inhibits parasite growth by inhibiting cytochrome bc1 activity of the ETC, most likely by competitively inhibiting the cytochrome b quinone oxidation site [23], [24]. Previously, we showed that PfPPO requires the ETC and is likewise inhibited by atovaquone [10]. Heme can also serve as a source of iron for the iron-sulfur proteins involved in isopentenyl pyrophosphate synthesis [20].
The question arises whether the parasite depends on de novo heme biosynthesis or heme from hemoglobin or a combination of both to make mitochondrial cytochromes. The steps involved in the acquisition of heme from RBC hemoglobin and the storage of heme as hemozoin in the food vacuole of the parasite are reasonably well understood [7], [25]. In addition to the possibility of acquiring heme from hemoglobin to make cytochromes in the blood stages, there is also a suggestion that Plasmodium may be able to scavenge heme in the liver stages as well, as is the case with organisms infecting nucleated cells such as T. cruzi, Leishmania and M. tuberculosis [7]. A direct approach to examine the role of the heme-biosynthetic pathway throughout the Plasmodium life cycle, including the sexual stages in the mosquitoes and liver stages in the animal host, is to knockout genes in the pathway and determine the effect of the knockouts (KOs) using the P. berghei (Pb)-infected mouse model.
We used an in vivo animal model of parasite infection to determine the role of heme biosynthesis during all the stages of parasite development. Figure 2A depicts the double crossover recombination strategy followed to obtain PbALAS and PbFC KOs. Table S1 shows the primers used to amplify the 5′ upstream and 3′ downstream regions of PbALAS and PbFC. Figure 2B–M shows the detailed characterization of the KOs based on RT-PCR, Southern, Northern, and Western analyses. We bypassed the liver stage of the infection cycle by injecting 105 intraerythrocytic-stage parasites intraperitoneally into mice. There was no significant difference in the growth of the PbKO parasites compared with the PbWT parasites (Figure 3). These results indicate that the parasite may be acquiring host heme during the intraerythrocytic stages.
The potential of human RBCs and mouse reticulocytes to synthesize heme was explored in this study. We detected the ALAS and FC proteins by Western analysis in mouse reticulocytes but not in human RBCs (Figure S1A and B). Unlike in human RBCs, it was possible to radiolabel the total heme and hemoglobin-heme in short-term mouse reticulocyte cultures incubated with [4-14C]ALA (Figure S1C–G). Because P. berghei prefers reticulocytes, the experimental system made it feasible to study the availability of hemoglobin-heme not only for hemozoin formation but also for parasite cytochromes. Furthermore, we were able to block heme labeling in the mouse reticulocyte cultures using succinyl acetone (SA), a specific inhibitor of ALAD (Figure S2A–C). Since P. berghei can only grow but poorly infect fresh reticulocytes in vitro, reticulocytes infected in vivo with PbWT and PbKO parasites were used to perform short-term radiolabeling experiments in the presence of [4-14C]ALA. We found [4-14C]ALA incorporation into total heme and hemozoin-heme of PbWT parasites and both of the PbKO parasites. SA inhibited the radiolabeling (Figure 4A and B). Radiolabeled heme appearing in the PbWT and PbALASKO parasites could come from host hemoglobin as well as from parasite heme biosynthesis. But, we would not expect to find [4-14C]ALA incorporated into the heme synthesized by the PbFCKO parasites. The ethyl acetate∶acetic acid mixture used to extract heme did not extract hemozoin. Therefore, we extracted hemozoin using acid-acetone solvent.
We analyzed the labeling of mitochondrial proteins by non-denaturing PAGE and observed a sharp band at the top of the gel after silver staining. The band was radiolabeled in PbWT parasites and in both of the PbKO parasites. The radiolabeling was almost completely inhibited by SA (Figure 4C–E). SDS-PAGE analysis of the band excised and eluted from non-denaturing PAGE showed five separate protein bands and MALDI analysis revealed the presence of two cytochrome oxidase subunits. The sharp silver-stained band in non-denaturing PAGE thus appeared to represent a complex of proteins and needs to be further characterized in detail (Figure S3). For now, it is clear that the PbWT parasites and both of the PbKO parasites incorporated hemoglobin-heme into mitochondrial hemoproteins and into hemozoin.
Next, we examined whether the parasite could use hemozoin-heme to make mitochondrial cytochromes. We tested the effect of CQ, which is known to block hemozoin formation [26], on P. berghei-infected short-term reticulocyte cultures. PbFCKO parasite was used to avoid any contribution from parasite-synthesized heme. CQ was injected into PbFCKO-infected mice as described in the Materials and Methods. After 7 h, the infected reticulocytes were incubated in short-term cultures and the incorporation of [4-14C]ALA into hemozoin and mitochondrial cytochromes over a period of 9 h was measured. We resorted to in vivo treatment of the animals with the drug, since we found that direct addition of the drug to reticulocyte culture failed to inhibit hemozoin formation under the conditions used, even at high concentrations. Figures 4F and G show that the CQ treatment inhibited hemozoin labeling by 70% but did not affect the labeling of mitochondrial cytochromes. These results suggest that host hemoglobin may provide heme to mitochondrial cytochromes and hemozoin through independent pathways.
The radiolabeling of hemoglobin-heme made it impossible to assess the contribution of parasite-synthesized heme using [4-14C]ALA in P. berghei-infected reticulocytes. We could, however, assess the contribution of parasite-synthesized heme in P. falciparum cultures. In those cultures, all of the radiolabeled heme was synthesized by the parasite. The hemoglobin-heme was not radiolabeled in the P. falciparum cultures because the human RBCs used in the in vitro cultures lacked the mitochondrial enzymes required to synthesize heme (Figure S1). Although not radiolabeled, the preformed hemoglobin in the RBCs could act as a heme source for the parasite. We found [4-14C]ALA incorporation into the total heme, hemozoin-heme, and mitochondrial hemoproteins in the P. falciparum cultures. SA (50 µM) inhibited the radiolabeling (Figure 4H–J). Earlier studies used SA at a fixed concentration ranging from 1 to 2 mM to inhibit heme synthesis and parasite growth [5]. The present study showed that while the 50% growth inhibitory concentration was around 1 to 2 mM (Figure S4A), concentrations as low as 50 µM inhibited heme synthesis (Figure 4H–J). We observed similar results in short-term P. berghei cultures (Figure S4B). The P. falciparum mitochondrial cytochromes also formed a complex in non-denaturing PAGE and need to be further characterized in detail.
Thus, we showed that both hemoglobin-heme and parasite-synthesized heme could be incorporated into hemozoin in the food vacuole and into mitochondrial cytochromes. Hemozoin formation from host hemoglobin in P. falciparum is well characterized [25]. Hemozoin formation from heme synthesized in the parasite mitochondrion, however, needs to be studied further. The relative contributions of hemoglobin-heme and parasite-synthesized heme to parasite cytochrome biosynthesis during the intraerythrocytic stages need to be assessed under different environmental conditions.
To examine the role of parasite-synthesized heme in the mosquito stages, we allowed Anopheles mosquitoes to feed on mice infected with PbWT and PbKO parasites. Figure 5 shows that both PbWT and PbKO parasites formed ookinetes. We found no difference between the WT and KO ookinetes in vitro using gametocyte cultures or in vivo using midgut preparations. In contrast, Figure 6 shows a drastic decrease in PbKO oocysts formation in the midgut and absence of PbKO sporozoites in the salivary glands. We examined whether ALA supplement could overcome the block in PbALASKO parasites for which 0.1% ALA was supplemented in feeding solution (PbALASKO(Mq+ALA)). The results obtained indicate that the formation of oocysts and sporozoites were restored (Figure 6). Our results reveal that parasite heme synthesis was required for oocyst and sporozoite development in the mosquitoes. In the case of PbFCKO parasites, we attempted to supplement heme through blood feeding on mice, but we were not able to rescue the defect. This suggests that the parasite could not acquire heme from the mouse hemoglobin in the mosquito blood meal or from any other mosquito source during the sexual stages of its development.
We examined the ability of PbALASKO(Mq+ALA) sporozoites to reinfect mice by measuring the parasitemia in the mice on subsequent days with and without ALA supplement (0.1% in drinking water). We did not detect any parasites in the mice infected with PbALASKO(Mq+ALA) sporozoites that did not receive ALA supplement (PbALASKO(Mq+ALAMi−ALA)). We did, however, detect parasites in the mice infected with PbALASKO(Mq+ALA) sporozoites that received ALA supplement (PbALASKO(Mq+ALAMi+ALA)). The infected animals died after 14–16 days, when the parasitemia levels reached around 60% (Figure 7). Mosquitoes infected with PbALASKO parasites (without ALA supplement) failed to give rise to blood-stage parasites in mice when we allowed them to feed. This is an additional proof to suggest that the PbALASKO parasites did not form sporozoites in the mosquito salivary glands. We reproduced all the mosquito transmission experiments by intravenously injecting the sporozoites obtained from mosquito salivary gland extracts into mice. Thus, our results suggest that parasite heme synthesis is absolutely essential for liver-stage development. Our results discount the suggestion [7] that the parasite may import host-synthesized heme during the liver stage.
In this study, we assessed the role of parasite-synthesized heme in all stages of malaria parasite growth. We generated ALAS and FC KOs in P. berghei. We used the KOs to track parasite-synthesized heme and host hemoglobin-heme during the intraerythrocytic stages of the parasite. The KOs did not affect parasite growth in mice when the parasites were injected intraperitoneally. All infected animals died within 10 to 12 days, when parasitemia reached around 60%. The synthesis of mitochondrial cytochromes is essential for parasite survival, so our results mean that the PbKO parasites used hemoglobin-heme to synthesize cytochromes during the intraerythrocytic stages. We demonstrated this by radiolabeling hemoglobin-heme with [4-14C]ALA in short-term mouse reticulocyte cultures.
In the short-term in vitro P. berghei cultures, we found radiolabeled hemozoin and mitochondrial cytochromes in reticulocytes infected with PbWT, PbALASKO, and PbFCKO parasites. We could not, however, distinguish between the contributions of hemoglobin-heme and parasite-synthesized heme in those cultures, because the use of [4-14C]ALA to radiolabel heme would bypass the potential ALASKO block. At the same time, the PbFCKO parasites would not be able to incorporate [4-14C]ALA into heme. We showed in a prior study that P. berghei imports host ALAD as well as host FC [6]. Therefore, we cannot rule out the possibility that the parasite used FC imported from the host to synthesize heme. We addressed this possibility using P. falciparum in human RBC culture. Western analysis indicated that the human RBCs used to culture P. falciparum did not contain detectable levels of ALAS and FC. Again, the RBCs did not incorporate [4-14C]ALA into heme (Figure S1). Thus, all of the radiolabeled heme in P. falciparum was synthesized de novo by the parasite. We found that 50 µM SA completely inhibited heme synthesis in P. falciparum (Figure 4H–J) but did not affect parasite growth (Figure S4A). This means that P. falciparum can use hemoglobin-heme to sustain growth under these conditions. Earlier studies used a fixed, high concentration of SA (1–2 mM) [5], which inhibited both heme synthesis and parasite growth. In this study, SA was found to inhibit heme synthesis at a much lower concentration than that required to inhibit parasite growth, indicating that de novo heme synthesis is not essential for P. falciparum growth in culture. This is likely to be true of P. berghei as well, because 50 µM SA completely inhibited heme synthesis in P. berghei-infected reticulocytes (Figure 4A–E) but did not affect P. berghei growth in short-term cultures (Figure S4B). The earlier studies correlating the growth of the parasite with inhibition of heme synthesis or host enzyme import [5], [6], [17] have now been re-evaluated with the use of specific gene KOs in the pathway.
Because the parasite can survive in the absence of de novo heme synthesis, it may appear that the parasite heme-biosynthetic pathway has no role in the intraerythrocytic stages. However, our results show for the first time that P. falciparum growing in human RBCs incorporated parasite-synthesized heme radiolabeled with [4-14C]ALA into hemozoin as well as into mitochondrial cytochromes. Hemoglobin-heme in the RBCs was not radiolabeled; so the heme in the parasite hemozoin and mitochondrial cytochromes was synthesized de novo by the parasite. It has long been assumed that only hemoglobin-heme is converted into hemozoin in the parasite food vacuole. We found that parasite-synthesized heme can also give rise to hemozoin in the food vacuole. Since hemoglobin transport into the food vacuole involves cytostomes and other vesicle-mediated transformations [25], it is not clear at this stage how the parasite-synthesized heme made in the mitochondrion finds its way to the food vacuole.
Our results also emphasize the fact that hemozoin is, perhaps, the only mechanism for heme detoxification in the parasite. A recent study showed that the malaria parasite lacks the canonical heme oxygenase pathway for heme degradation and relies on hemozoin formation to detoxify heme [27], although an earlier study suggested the possible presence of heme oxygenase in the apicoplast [7], [28]. It appears that the parasite mitochondrion would need a two-way transporter for heme: one to incorporate hemoglobin-heme into the mitochondrion and another to transport mitochondrial heme into the pathway leading to hemozoin formation in the food vacuole. Free heme was also detected in the erythrocyte at a concentration around 1 µM [29] and the parasite may be able to scavenge this heme directly [7]. It was also suggested that ferriprotoporphyrin could leach from the food vacuole into the parasite cytosol [30]. We found that SA inhibited the radiolabeling of hemozoin and of mitochondrial cytochromes in PbFCKO parasites. But, CQ inhibited the radiolabeling of hemozoin but not of mitochondrial cytochromes (Figure 4F and G). These results suggest that hemoglobin-heme may be incorporated into mitochondrial cytochromes and into hemozoin through independent processes. Figure 8 gives some of the pathways that may be involved.
It needs to be established whether hemoglobin-heme and parasite-synthesized heme are functionally equivalent. The parasite-synthesized heme may be a backup mechanism that could be of significance only if hemoglobin-heme is not available, as may be the case with sickle cell and other hematological disorders. It has been proposed that low levels of free heme in the plasma induce heme oxygenase-1 to generate carbon monoxide that binds with sickle hemoglobin-heme. This could prevent the release of the heme, and thus suppress the heme-mediated pathogenesis of cerebral malaria, without affecting the parasite load [31]. In another scenario, it was suggested that human hemoglobin variants offer protection by interfering with host actin remodeling in P. falciparum-infected erythrocytes. These variant hemoglobins are unstable and undergo oxidation, leading to the denaturation and release of heme and oxidized forms of iron that can affect host actin dynamics and thus affect parasite virulence. However, malaria parasites develop normally in such erythrocytes, both in culture and in vivo [32]. Therefore, parasite-synthesized heme may sustain parasite survival when hemoglobin-heme is unavailable, although pathogenesis is ameliorated. It is also possible that the parasite-synthesized heme has a function that is presently unknown.
The growth pattern of the KO parasites in the mosquito stages was striking. While ookinetes formed, oocysts formation decreased substantially, and no sporozoites appeared in the salivary glands. Furthermore, when these mosquitoes fed on mice, we found no intraerythrocytic-stage parasites in the blood of the mice. ALA supplement to the mosquitoes enabled PbALASKO to form oocysts and sporozoites. This is clear proof that de novo parasite heme synthesis is required for parasite development in mosquitoes. Hence, inhibitors of heme and porphyrin synthesis, such as diphenyl ether herbicides, can be explored to prevent parasite development in mosquitoes [33]. Equally striking was the growth pattern of PbALASKO parasites in the liver stage. The sporozoites formed in the mosquitoes with ALA supplement could infect mice only when the mice received ALA supplement. This again shows that parasite de novo heme synthesis is required for development in the liver stage. The liver stage is a major focus of malaria interventions and the role of parasite heme synthesis in liver-stage development needs to be investigated in more detail.
Inhibitors of parasite heme synthesis offer newer drug candidates for blocking infection and transmission, since the parasite enzymes involved have unique properties [10], [11], [14]–[18]. Irradiated sporozoites serve as a malaria vaccine candidate [4]. There are several current efforts to design and stabilize irradiated sporozoites for large-scale clinical trials [34]–[36]. Our results with PbALASKO(Mq+ALA) sporozoite infections in mice offer some additional options for a genetically attenuated sporozoite vaccine that can be tested in the animal model.
The biology of parasite heme synthesis may change drastically between the intraerythrocytic stages and the mosquito and liver stages. The malaria parasite essentially depends on glycolysis to generate ATP in the intraerythrocytic stages. Hemoglobin is available as a heme source in addition to parasite-synthesized heme. In the mosquito and liver stages, the parasite depends entirely on its own biosynthetic machinery to provide heme. It is possible that the de novo heme-biosynthetic pathway of the parasite is augmented during the mosquito and liver stages. The ATP synthesized by the ETC may be necessary to provide the energy needed for ookinetes in the mosquito midgut to develop into sporozoites in the mosquito salivary glands. The energy provided by the ETC may also be necessary for the sporozoites to explore the mammalian host from the skin to liver and give rise to merozoites in the hepatocytes.
Animal experiments were carried out as per the guidelines of the Committee for the Purpose of Control and Supervision of Experiments on Animals (CPCSEA), Government of India (Registration No: 48/1999/CPCSEA). The guidelines of National Institute of Malaria Research, New Delhi, were followed for all the mosquito infection studies. All the experiments were carried out as approved by the Institutional Animal Ethics Committee of the Indian Institute of Science, Bangalore (CAF/Ethics/102/2007-435 and CAF/Ethics/192/2010).
In vitro cultures for P. falciparum 3D7 isolate were maintained continuously on human O+ red cells of 5% hematocrit supplemented with 10% O+ serum or 0.5% Albumax II in RPMI 1640 medium containing L-glutamine (GIBCO) by the candle jar method [37] or in a CO2 incubator. Synchronization was carried out by sorbitol treatment [38] and parasites at the late trophozoite and schizont stages were freed from infected erythrocytes by treatment with an equal volume of 0.15% (w/v) saponin in PBS [39]. The released parasites were centrifuged at 10,000× g for 10 minutes and the pellet obtained was washed four times with ice cold PBS to remove any detectable hemoglobin. The routine propagation of P. berghei ANKA strain (MRA-311, MR4, ATCC Manassas Virginia) was carried out in 6–8 weeks old Swiss mice. In brief, mice were injected intraperitoneally with 105 P. berghei infected-RBCs/reticulocytes and the parasite growth was routinely monitored by assessing the percentage of parasitemia in Giemsa stained thin smears prepared from tail vein blood. On day 8–10 post-infection, mice were anesthetized with ketamine/xylazine and the infected blood was collected through cardiac puncture. The blood obtained was diluted with PBS to initiate fresh infections in mice [40], [41]. Parasite isolation was carried out as described earlier [39].
To generate the knockout parasites, primers were designed and PCR was carried out with P. berghei genomic DNA to amplify the 670–740 bp fragments that correspond to the 5′- UTR and 3′-UTR regions of PbALAS/FC genes. The resultant fragments were cloned into the appropriate restriction sites flanking the human DHFR selection cassette of pL0006 replacement plasmid (MRA-755, MR4, ATCC Manassas Virginia). The plasmid constructs were then digested with ApaI and NotI, and transfected into P. berghei schizonts that were purified from intraerythrocytic stage infections initiated by sporozoite injections [42]. In brief, P. berghei schizonts were purified and subjected to nucleofection with the appropriate constructs, followed by pyrimethamine selection. Limiting dilution was carried out for pyrimethamine-resistant parasites [43] and the targeted deletion of PbALAS and PbFC genes in the respective knockout parasites were confirmed by PCR, Southern, Northern and Western analyses. The details of the primers and restriction sites are provided in Table S1.
A. stephensi mosquitoes were reared under standard insectary conditions maintained at 27°C and 75–80% humidity with a 12 h light and dark photo-period as described [44], [45]. Larvae were reared on yeast tablets at a fixed density of one larva per ml. Upon maturation, the pupae were segregated for adult emergence. The emerged adult mosquitoes were fed on filter-sterilized 10% glucose solution containing 0.05% paraminobenzoic acid. For egg production, adult female mosquitoes were allowed to take blood feeding on mice anesthezied with ketamine/xylazine.
P. berghei infection studies in A. stephensi mosquitoes were carried out as described elsewhere [46]–[48]. In brief, antibiotic-treated adult female mosquitoes of 5–7 days old, starved for 12 h, were allowed to feed on anesthetized-P. berghei infected mice with 8–12% parasitemia showing 2–4 exflagellation centres per field. The fully engorged mosquitoes were then separated and maintained at 19°C. At 20 h post feeding, the mosquito midguts were dissected to remove the blood bolus and ookinete numbers were quantified as described [49]. On day 10 post feeding, mercurochrome staining was carried out for the dissected midguts to determine the number of oocysts formed [50], followed by the dissection of salivary glands on day 19 to examine and count the number of sporozoites present [51]. To supplement the PbALASKO-infected mosquitoes, routine feeding was carried out with sugar solution containing 0.1% ALA from 20 h post feeding until the dissection of salivary glands on day 19. To supplement PbFCKO-infected mosquitoes, blood feeding was given to the mosquitoes in six days interval from the day of infection till the sporozoite analysis, besides the routine feeding with sugar solution.
The ability of the sporozoites to develop asexual stage infections was studied by allowing the mosquitoes infected with P. berghei wild-type and knockout parasites to feed for 15–20 min on 6–8 weeks old Swiss mice (30 mosquitoes/mouse) anesthetized with ketamine/xylazine. The development of asexual stage parasites was monitored by examining the Giemsa stained blood smears from day 5 post infection. To inject 104 sporozoites intravenously in mice, salivary gland extracts of the infected mosquitoes were prepared and sporozoites were counted as described [51]. ALA supplement in mice was carried out immediately after sporozoite infection and continued for 7 days by including 0.1% ALA in drinking water.
In vitro radiolabeling of heme in mouse reticulocytes was carried out at 37°C in a CO2 incubator, for a period of 9 h in RPMI-1640 medium containing 10% FBS, by adding 1 µCi of [4-14C]ALA to a total volume of 5 ml containing 109 reticulocytes. In brief, reticulocytosis was induced in mice by injecting a single dose of phenylhydrazine (2.5 mg in saline/mouse) intraperitoneally. Two days later, reticulocytes from the mice blood were separated by performing the density-gradient centrifugation on isotonic percoll [52], washed thrice with the medium and used for labeling. Labeling studies in human RBCs were also carried out in a similar fashion with 109 human RBCs in RPMI-medium containing 10% human serum. To perform in vitro labeling for the intraerythrocytic stages of P. berghei wild-type and knockout parasites, the respective blood-stage infections were initiated in phenylhydrazine-treated mice by intraperitoneal injection of 105 infected erythrocytes. The blood was collected when the parasitemia reached around 5–8% with parasites predominantly in early trophozoites. After washing thrice with RPMI-1640 containing FBS, the cells were resuspended in 10 ml of the medium to a final hematocrit of 5% and labeling was carried out for 9 h as described for reticulocytes by adding 3 µCi of [4-14C]ALA. To study the in vitro effect of SA on heme labeling, cultures were treated for 3 h with 50 µM SA prior to the addition of [4-14C]ALA, and the labeling was carried out for 9 h in the presence of SA. For CQ treatment, PbFCKO-infected mice were injected intraperitoneally with two doses of 0.5 mg CQ dissolved in water at 6 h time interval, when the blood stage parasites were predominantly in early rings. The blood was collected 1 h after the second dosage and the cells were washed with medium, followed by in vitro labeling for 9 h with 3 µCi of [4-14C]ALA. In vitro labeling for P. falciparum in the presence and absence of SA was carried out with synchronized cultures harbouring 5–8% early trophozoites maintained in RPMI-1640 containing 10% O+ serum or 0.5% Albumax II .
Mitochondria isolation was carried out as described [53] by homogenizing the parasite pellet in 10 volumes of buffer pH 7.4 containing 5 mM Hepes-KOH, 75 mM sucrose, 225 mM mannitol, 5 mM MgCl2, 5 mM KH2PO4 and 1 mM EGTA with protease inhibitors. The homogenate was then centrifuged at 4500×g for 5 min at 4°C and the supernatant obtained was subjected to 44,700×g for 7 min at 4°C to pellet mitochondria. Labeling of hemoproteins in the parasite mitochondria was examined by solubilizing the pellet in 20 mM Tris buffer pH 7.5 containing 5% Triton X-100 and protease inhibitors, and centrifuging at 20,000×g to remove membrane debris, followed by loading the supernatant on to a 5% Native-PAGE. The radiolabeled sharp band seen at the top of the gel in silver staining was subjected to MALDI analysis. To measure the intensity of radiolabeling, the gel was dried and exposed to phosphorimager screen for 24 h. For food vacuole preparation, 4500×g pellet was processed as described [54], [55]. After lysis in ice cold water pH 4.2 and DNaseI treatment in uptake buffer (25 mM HEPES, 25 mM NaHCO3, 100 mM KCl, 10 mM NaCl, 2 mM MgSO4, and 5 mM sodium phosphate, pH 7.4), food vacuoles were purified by titurating the pellet in 42% percoll containing 0.25 M sucrose and 1.5 mM MgSO4, and centrifuging at 16,000 g for 10 min at 4°C. The food vacuole pellet obtained was washed with 1 ml of uptake buffer to remove percoll.
Extraction of free and protein-bound heme (total heme) was carried out as described earlier [10]. Briefly, the parasite pellet was extracted with 10 volumes of ethyl acetate∶glacial acetic acid (4∶1) for 30 min at 4°Cand centrifuged at 16,000×g for 10 min. The organic phase containing heme and porphyrins was separated and washed thrice with 1.5 N HCl of one-third total volume, and twice with water to remove porphyrins and any ALA present. The extracted organic phase containing heme was dried under a stream of nitrogen and dissolved in methanol, followed by thin-layer chromatography (TLC) on silica gel using the mobile phase 2,6-lutidine and water (5∶3) in ammonia atmosphere [56]. The intensity of radiolabeling was quantified by exposing the TLC sheets to phosphorimager screen for 8 h. To extract heme from hemozoin, food vacuole pellet was resuspended in 10 volumes of cold acetone containing 0.1 N HCl, vortexed for 30 min at 4°C and centrifuged at 16,000×g for 10 min. The supernatant obtained was dried, dissolved in methanol and analyzed by TLC as described for total heme. The complete extraction of heme from hemozoin can be easily visualized by the color change of the pellet from dark brown to pale and if necessary, the extraction was carried out twice.
Parasite genomic DNA was isolated by SDS/proteinase K method [57]. Total RNA from the parasite was prepared using Trizol reagent (Invitrogen) according to the manufacturer's protocol. PCR, Western, Southern and Northern analyses were carried out using standard procedures. Polyclonal antibodies for ALAS and FC, cross-reacting with the proteins of both human and mouse origin, were procured from Santa Cruz Biotechnology, Inc. To detect P. berghei ALAS and FC, polyclonal antibodies raised against P. falciparum ALAS and FC, cross-reacting with P. berghei proteins were used. All these antibodies were used in 1∶1000 dilution for Western blotting. In vitro ookinete formation in P. berghei wild-type and knockout parasites was analyzed by injecting 2×107 parasites in phenylhydrazine-treated mice, followed by sulfadiazine treatment for two days to remove asexual stages. After removing the leukocytes using CF-11 cellulose columns, the gametocyte-infected blood was diluted with nine volumes of ookinete culture medium and incubated at 19°C for 19–21 h [58]. Hemoglobin was purified from mouse reticulocytes and human RBCs by resuspending the cells in hypotonic lysis buffer containing 20 mM Tris pH 7.5 and protease inhibitors. The lysate was incubated in ice for 30 min, followed by centrifugation at 20,000×g for 20 min and the supernatant obtained was loaded on to a UNOsphereQ column (Bio-Rad). After washing the column with 10 mM NaCl, haemoglobin was eluted with lysis buffer containing 50 mM NaCl. To perform MALDI analysis, the protein complex was eluted from 5% Native-PAGE and resolved in 12% SDS-PAGE, followed by in-gel trypsin digestion. Proteins were identified by searching the National Center for Biotechnology Information (NCBI) nr protein database using MASCOT peptide mass fingerprint with cysteine carbamidomethylation and methionine oxidation as fixed and variable modifications, respectively, and taking into account of one missed cleavage and 0.5 Da peptide mass tolerance. MALDI analysis was carried out at Proteomics Facility, Molecular Biophysics Unit, Indian Institute of Science.
Statistical analysis was performed using unpaired t-test of Excel software with two-tailed distribution and unequal sample variance. P values of <0.05 were considered as significant. Graphs were prepared using Sigmaplot 10.0. Error bars given in the figures represent the standard deviations. The band intensities were quantified using Fujifilm Multi guage V3.0 software.
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10.1371/journal.pntd.0005068 | Environmental Nontuberculous Mycobacteria in the Hawaiian Islands | Lung disease caused by nontuberculous mycobacteria (NTM) is an emerging infectious disease of global significance. Epidemiologic studies have shown the Hawaiian Islands have the highest prevalence of NTM lung infections in the United States. However, potential environmental reservoirs and species diversity have not been characterized. In this cross-sectional study, we describe molecular and phylogenetic comparisons of NTM isolated from 172 household plumbing biofilms and soil samples from 62 non-patient households and 15 respiratory specimens. Although non-uniform geographic sampling and availability of patient information were limitations, Mycobacterium chimaera was found to be the dominant species in both environmental and respiratory specimens. In contrast to previous studies from the continental U.S., no Mycobacterium avium was identified. Mycobacterium intracellulare was found only in respiratory specimens and a soil sample. We conclude that Hawai’i’s household water sources contain a unique composition of Mycobacterium avium complex (MAC), increasing our appreciation of NTM organisms of pulmonary importance in tropical environments.
| In the U.S., the Hawaiian Islands have the highest number of nontuberculous mycobacterial (NTM) lung disease cases per capita. The tropical climate, geographical isolation of the islands, and aquifer water sources may have influence such prevalence. Previous studies suggest that NTM thrive in water biofilms and soil. To broaden our understanding of potential environmental reservoirs and species composition of NTM in the Hawaiian Islands, we sampled environmental sites and examined patient isolates. Our recovery and identification of Mycobacterium chimaera and several other clinically relevant NTM species and the absence of Mycobacterium avium in both the indigenous environment and clinical specimens underscore the need for further studies to define the environmental factors that drive NTM lung disease and species composition in high prevalence locations such as the Hawaiian Islands.
| Nontuberculous mycobacteria (NTM) are ubiquitous inhabitants of natural and human-engineered environments. To date, there are over 175 species of NTM with standing in nomenclature [1]. They range in virulence from benign environmental microorganisms to difficult-to-treat human pathogens [2]. Potentially pathogenic NTM have been documented in households, institutions (i.e., hospital premise plumbing), and soil [3]. In the continental United States (U.S.), household plumbing and environmental aerosols are thought to be important point sources of infection [4–8]. The most common NTM species to cause lung disease in the continental U.S. are those of the Mycobacterium avium complex (MAC)–slowly growing mycobacteria (SGM) that include Mycobacterium avium subsp. “hominissuis” and Mycobacterium intracellulare [9]. Clinically relevant environmental rapidly growing mycobacteria (RGM) include Mycobacterium abscessus subsp. abscessus, massiliense, and bolletii as well as the closely related species, Mycobacterium chelonae [10]. The current hypothesis is that NTM lung infections follow exposure to NTM from the home or other environmental source. [6]. Of interest, the predominant NTM species responsible for lung disease varies by geographic region, suggesting that environmental conditions (e.g., pH, oxygen, organic matter, and salinity) and the presence of other microorganisms influence NTM species numbers and diversity [11].
Despite the almost universal exposure to environmental NTM, pulmonary infections are relatively rare in otherwise healthy, non-bronchiectatic individuals and more common in individuals with abnormal lung architecture such as bronchiectasis and emphysema [12]. Nevertheless, it is important to identify the environmental niches that harbor potentially pathogenic NTM in geographical areas with a high prevalence of disease. In the U.S., the Hawaiian Islands were found to have the highest period prevalence of NTM lung disease (396 cases/100,000 persons for a total ten year time period) in a sampling of 2.3 million Medicare Part B beneficiaries enrolled from 1997 to 2007 [13]. In a follow-up study, spatial modeling revealed high-prevalence locations for NTM lung disease in this state [14]. The Hawaiian Islands also showed the highest age-adjusted mortality rates from NTM lung disease in the U.S., particularly in women over 55 years of age [15].
The high prevalence of NTM lung disease in the Hawaiian Islands provided the impetus to explore potential sources of infection and to determine the predominating NTM species in both environmental and clinical specimens. These islands are recognized for their unique island geology, flora, and fauna which are largely impacted by the tropical climate and isolation of the archipelago in the Pacific Ocean. Unlike most areas in the continental U.S. for which surface water serves as the primary public water source, underground aquifers provide water there. The Hawaiian Islands are also home to the highest number of elderly Asian-Pacific Islanders in the U.S.—a population previously recognized to be more susceptible to NTM infection [14]. To better understand NTM lung disease as a neglected tropical disease of emerging importance in this geographic area, the objective of the current work was to employ state-of-the-art molecular techniques to describe the indigenous NTM species composition in indoor and outdoor environments. A secondary objective was to analyze the genetic relatedness between the Hawaiian Island environmental NTM specimens (including 15 patient respiratory specimens) and continental U.S. NTM isolates.
In this cross-sectional study, we use the term “Hawaiian Islands” to designate the eight islands of the State of Hawai’i; the term “Hawai’i” refers to the youngest and largest island among the eight islands. Sample collection was conducted between December 2012 and January 2013. Samples were collected from 62 non-patient households located on the islands of Oahu, Molokai, Kauai, and Hawai’i. Detailed written instructions for collecting household water biofilms and soil samples were provided to local residents who volunteered to collect samples from their home as part of this study. As NTM are most commonly found in premise plumbing biofilms, samples were obtained by swabbing with sterile cotton-tipped applicators the inner surface of showerheads, kitchen and bath faucets, kitchen sink sprayers, refrigerator water dispensers, laundry room sinks, and shower drains [5, 6]. Samples from random sites in outdoor gardens or yards were also collected by clearing away surface leaves and other detritus and then scooping soil from the top five centimeters of ground into sterile 50 ml conical screw cap tubes as described [16].
Respiratory isolates of slowly-growing NTM recovered from 15 de-identified Oahu patients suspected of mycobacterial lung disease whose sputum had been submitted for mycobacterial culture were randomly selected from saved isolates at Diagnostic Laboratory Services, Inc. (Aiea, HI). Mycobacterium tuberculosis was not recovered in any of these sputum samples where NTM were isolated. As these were de-identified patient residual isolates, where only age and gender were noted from routinely ordered laboratory testing, Institutional Review Board (IRB) consent was waived. However, it was impossible to determine whether these patients met current American Thoracic Society/Infectious Disease Society of America (ATS/IDSA) diagnostic criteria for NTM pulmonary disease as private health information were delinked [9].
Genome identification of environmental and patient NTM isolates was conducted through the amplification and sequencing of a 723 bp segment of the RNA polymerase beta subunit (rpoB) gene, also known as region 5 [17]. Sequences were trimmed for quality and compared against rpoB type strain sequences deposited in the National Center for Biotechnology Information (NCBI) GenBank using the BLAST algorithm. Definitions of species by single genes or spacer region were those of the Clinical Laboratory Standards Institute (CLSI) [18]. A sequence similarity cutoff of ≥ 98.3% was used to determine the species identification according to previously described cutoffs validated by studies of rapidly-growing mycobacteria [17]. The sequencing of NTM strains derived from patients was approved by the National Jewish Health Human Subject IRB.
To determine whether NTM isolates from the Hawaiian Islands have shared sequence similarity with isolates obtained elsewhere, NTM type strains were included in genetic analyses. Type strains are denoted by superscript “T” and include M. porcinum CIP 105392 T, M. abscessus subsp. abscessus ATCC 19977T, M. abscessus subsp. bolletii CIP 108541T, M. chelonae ATCC 35752T, and M. chimaera CIP 107892 T. Additionally, 33 clinical respiratory isolates of M. chimaera (one per patient) from seven other states–Maryland, Texas, Louisiana, North Carolina, Oregon, Mississippi, and Arkansas–submitted for molecular identification to the Nocardia/Mycobacteria Research Laboratory, University of Texas Health Science Center, Tyler, Texas were included. Those isolates were identified to species by partial 16S rRNA and region 5 rpoB gene sequencing. This work was approved by the Human Subjects Committee of the University of Texas Health Science Center, Tyler, Texas.
Partial rpoB gene sequences from 166 Hawaiian Island NTM isolates and 33 M. chimaera isolates from the continental U.S. were deposited in the GenBank nucleotide database. The GenBank accession numbers for type strain and representative isolate rpoB gene sequences of M. porcinum, M. abscessus, M. chelonae, and M. chimaera from NCBI are also listed in S1 Table
Partial rpoB sequences of respiratory and environmental NTM isolates (n = 166) were aligned using MUSCLE [19] and sequence alignments were trimmed to remove missing data from the ends of the final alignment. Phylogenetic trees were generated using the neighbor-joining method based on the number of nucleotide differences and uniform rates among sites while omitting any sites in the alignment with gaps or missing data in MEGA version 6 [20].
For rpoB sequence variant analyses, only sequences greater than 600bp and with no ambiguous base calls were included. Sequences were grouped by species and compared to selected type and non-type strain sequences from NCBI. The PopART population genetics software was used to examine intraspecies sequence variation, generate species-specific rpoB sequence variant networks, and label isolates by isolation source: i.e., kitchen, bathroom, soil, patient [21]. For the M. porcinum, M. abscessus, and M. chelonae analyses, the environmental Hawaiian Island isolates and both type and non-type strains were included. For the M. chimaera analysis, environmental and clinical Hawaiian Island isolates, type, and non-type strains, as well as clinical isolates from seven states across the continental U.S. were included.
Statistical analyses were performed using R version 2.13.2 [22]. Fisher’s Exact Tests were used to evaluate differences in proportions of NTM species or species groups between household areas (i.e., bathroom, kitchen, and soil) or sample type (biofilm and soil).
From a total of 62 households across four islands (Fig 1A), a total of 172 biofilm and soil samples were collected. The majority of the samples (n = 134, 78%) were collected from Oahu and included 35 showerheads (26%), 41 kitchen faucets (31%), 6 bathroom sink faucets (4%), 2 refrigerator water taps (1%), 3 other biofilm samples from laundry room faucets (2%), and 47 soil samples (35%). The remaining 38 samples (22%) were collected from 13 households on the neighbor islands.
Among all 172 biofilm and soil samples collected from the 62 households, NTM were isolated from 44% of samples (75/172) (Table 1). NTM were identified in nearly half of the samples on Oahu (65/134, 49%) and in approximately a quarter of samples from the neighbor islands (10/38, 26%). Overall, the NTM culture positivity rate for biofilms was 59% (67/113), which was significantly greater than for soil (14%, 8/59; p = 6.0x10-9).
The majority of the environmental samples collected were from 49 households in seven different towns on Oahu, the most populated island (Fig 1B). NTM were recovered by culture from 82% of the Oahu households (Fig 1A). For the neighboring islands, NTM were also recovered in households on Kauai, Molokai, and Hawai’i (Fig 1A). Among the 62 collective households sampled in this study, only 14 had no NTM isolated (23%). However, the number of households with one, two, and three different NTM species isolated were 26/62 (42%), 18/62 (29%), and 4/62 (6%), respectively (Fig 1C). Overall, the majority of households (43/62, 69%) had at least one clinically relevant species of MAC, M. abscessus subsp., or M. chelonae—(Table 2).
To determine the diversity of NTM in non-household sites, 13 environmental samples (n = 7 biofilm and n = 6 soil) were collected from eight public areas on Oahu and Kauai (Table 3). On Oahu, a total of six biofilms from public sites were collected including gymnasium showerheads and water fountain taps. Four soil samples were also collected from public sites on Oahu. Two water biofilm and two soil samples were collected from public sites on Kauai. One Oahu public site soil sample contained M. chimaera (1/6 = 17%) and one biofilm sample contained M. chelonae (1/7 = 14%), but the majority (5/13 = 38%) yielded other RGM species (i.e., M. barrassiae, M. alvei, and M. septicum). RpoB sequences from four distinct isolates did not have NCBI database matches above 95% sequence identity, suggesting they represent novel species.
Among the 75 environmental samples from the households that were NTM culture-positive, 20 different NTM species were identified (Fig 2A) and 17% (13/75) grew out multiple NTM species. The most common species recovered from households were MAC organisms with M. chimaera being the predominant species (42/75, 56%) (Fig 2B, left). The next most frequently isolated species were M. chelonae (12/75, 12%) and M. porcinum (11/75, 11%). All isolates of M. abscessus were confirmed as M. abscessus subsp. abscessus (10/75, 10%) [23, 24]. Less frequently isolated NTM species (<10%) included M. phocaicum, M. gadium, M. alvei, M. gordonae, M. paraffinicum, M. marseillense, and M. colombiense. No isolates of M. avium or M. intracellulare were recovered from household biofilm samples, though M. intracellulare was isolated from a single soil sample. While M. chimaera and M. chelonae were identified in non-household samples, the majority classified as other NTM included potentially novel species (Fig 2B, right).
To determine whether NTM were present in particular household locations, the frequencies of NTM recovery between bathroom biofilms, kitchen biofilms, and soil were compared (Fig 3). M. chimaera was frequently identified from both bathroom (22/34, 65%) and kitchen (15/30, 50%) biofilms and was also identified in soil (2/7, 29%). M. porcinum was overrepresented in bathroom (8/34, 24%) compared to kitchen biofilms (2/30, 7%; p = 0.09), while M. chelonae was significantly more common in kitchen (9/30, 35%) compared to bathroom biofilms (3/34, 9%; *p = 0.05). M. abscessus was observed in similar proportions between bathroom (5/34, 15%) and kitchen (4/30, 13%) biofilms. M. porcinum, M. chelonae, and M. abscessus were not recovered from soil. M. marseillense was recovered only from soil and not identified in any of the household biofilm samples. NTM species that showed low prevalence in our study (i.e., one isolate per species identified in the entire sample set and labeled “other RGM” and “other SGM”) were primarily isolated from soil samples.
To examine population diversity among RGM isolates from individual households, rpoB sequences of M. porcinum, M. abscessus, and M. chelonae were analyzed (Fig 4). Type and non-type strain rpoB sequences were included for comparison. In the M. porcinum dataset (n = 25 sequences), a total of seven sequence variants were identified (Fig 4A). All isolates from the bathroom, kitchen, and outside faucets were in the same sequence variant group as the M. porcinum type strain, CIP 105392T, except for one kitchen isolate that contained a single SNP difference. Among all M. abscessus sequences (Hawaiian Island and type/reference strains; n = 38), six sequence variants of subsp. abscessus, four variants of subsp. massiliense, and one of subsp.bolletii (Fig 4B) were identified. Environmental M. abscessus isolates grouped with other M. abscessus subsp. abscessus and the majority of M. abscessus isolates (13/16 = 81%) shared an identical rpoB sequence with the type strain, ATCC 19977T. Three additional isolates differed by one SNP each from the ATCC 19977T type strain. Finally, M. chelonae isolates (Fig 4C) showed the greatest rpoB sequence variation with a total of 14 rpoB sequence variants. Hawaiian Island M. chelonae isolates fell into seven rpoB sequence variant groups, but the majority (15/20 = 80%) fell into two main subgroups: one group (6/15 and 40%) sharing the M. chelonae ATCC 19237 rpoB variant and a second group (5/15 and 33%) related to the M. chelonae ATCC 35752T rpoB variant.
As the majority of the Hawaiian Island environmental NTM isolates from this study were M. chimaera, 15 random respiratory SGM isolates from Oahu patients presenting to a pulmonary clinic with suspected mycobacterial lung disease were used as pilot samples to evaluate for the presence of M. chimaera in clinical specimens. As a group, the median age of the 15 patients was 75 years (95% CI, 68; 81 years) and 67% (10/15) were female (Table 4). Ten isolates were identified as M. chimaera (10/15, 67%) four as M. intracellulare (4/15, 27%), and one as M. marseillense (1/15, 6%). Of the ten patients with M. chimaera, 60% (6/10) were female. All four patients with M. intracellulare were female (100%; 5/5) and the patient with M. marseillense was male (Table 4). M. avium was not identified from any of the Oahu clinical isolates.
To measure the genetic similarity among a diverse collection of environmental and clinical M. chimaera, we analyzed rpoB sequence variation between the 57 Hawaiian Island environmental M. chimaera isolates and the 10 Oahu respiratory M. chimaera isolates. However, the rpoB sequence of one clinical M. chimaera isolate was excluded from these analyses due to the presence of ambiguous bases. Also included were NCBI non-type strains (n = 2), type strains (n = 2), and other M. chimaera respiratory isolates (n = 33) from seven states in the continental U.S. In total, 103 M. chimaera sequences were analyzed and only two rpoB sequence variants were observed (Fig 5). The larger variant subgroup comprised over 90% of the isolates including all of the Oahu respiratory and biofilm M. chimaera isolates. This group also contained the majority of continental U.S. clinical isolates and the CIP107892T type strain. The smaller variant subgroup contained continental U.S. clinical isolates, non-type strains from NCBI, and Hawaiian Island soil isolates.
To our knowledge, this is the first assessment of environmental NTM prevalence and species composition in the Hawaiian Islands. This archipelago is approximately halfway between the continental U.S. and Asia; thus, one might speculate that the spectrum of NTM observed mirrors the results from other environmental studies from the continental U.S. or Asia. Due to the prevalence of M. avium subsp. “hominissuis” reported in studies from the continental U.S. and Japan [25–27], we suspected this species would be prevalent in Hawaiian Island household biofilms and patient samples; however, it was seemingly absent, at least in the samples examined in this study. In general, NTM are rare in groundwater [29] whereas M. avium subsp. “hominissuis” has been isolated from surface water sources [28]. Aquifers provide most of the drinking water in the Hawaiian Islands [30] which may be one reason for the lack of M. avium detection in our samples. However, given the widespread prevalence of M. chimaera and the RGM in Hawaiian Island household biofilms, local aquifers may be a potential reservoir for M. chimaera and other NTM. Future studies are needed to examine this hypothesis.
To date, species diversity assessments of environmental NTM in other tropical Pacific Islands remains scant. A recent study described the identification of the M. fortuitum complex in Polynesian residents with suspected tuberculosis [31] and other reports from the area highlight NTM-associated skin disease [32, 33]. On Australia, M. intracellulare was reported as the species responsible for most lung disease cases and yet only M. avium subsp. “hominissuis”, M. kansasii, and M. abscessus isolates had a species that match between patients and their household water system [34, 35].
An unexpected finding of this study was the frequent identification of M. chimaera from both the environmental samples collected from bathroom, kitchen, and soil samples (Fig 3) and patient isolates with suspected mycobacterial lung disease. Although the number of patient isolates was small and their disease status were not known, the correspondence between the high proportion of both environmental and clinical M. chimaera isolates is intriguing and offers direction for future investigations. M. chimaera was first described in 2004 [36] and was recently reported to cause health-care associated infections after open-heart surgery with the use of heater-cooler units [37, 38]. As this is a relatively newly described species, there are no simple methods to differentiate M. chimaera from M. intracellulare. Furthermore, low frequency of presence in lung samples of patients from Germany, Italy, Zambia, and China [39–41] is most likely due to its misidentification as M. intracellulare. A greater adoption of more refined molecular methods to distinguish M. chimaera from M. intracellulare has facilitated the more precise speciation of M. chimaera (33). In a previous U.S. study, water biofilm isolates originally reported as M. intracellulare, proved to be M. chimaera or other MAC-X [4]. Provisionally, it appears that the main environmental source of M. chimaera in the Hawaiian Islands are water biofilms and less from the soil (Fig 3), whereas M. intracellulare was absent in water biofilms and only recovered from soil, consistent with the finding of others 4 (Fig 3, other SGM). Soil should also be regarded as a potential reservoir for M. marseillense.
Among our environmental samples, M. porcinum, M. chelonae, and M. abscessus were the most frequently identified RGM species. The M. fortuitum complex including M. porcinum were found to comprise the majority of clinical isolates examined in French Polynesia (42/87, 48%) using partial rpoB gene sequencing [31]. Of these, M. porcinum was identified in three patients who fulfilled ATS criteria for NTM lung disease. To our knowledge, M. porcinum infections have not yet been reported in the Hawaiian Islands, but the organism has been isolated from water supplies in other U.S. areas (e.g., Texas) [42, 43]. M. abscessus was recently associated with an outbreak in cystic fibrosis patients at a hospital in Hawai’i [44]. M. chelonae infection was reported in a case study of an individual from Hawai’i after laser in situ keratomileusis (LASIK) surgery [45]. It is important to mention that among the environmental samples in this study, these particular RGM were more commonly identified in bathroom and kitchen biofilm samples and absent from soil (Fig 3), suggesting a preferential environmental niche for these particular RGM species.
Phylogenetic analyses were performed to evaluate whether the genetic diversity among environmental NTM species identified from the Hawaiian Island samples differed from those collected from the continental U.S. A relatively high genetic diversity among M. chelonae was observed with four major rpoB subgroups present, while most isolates of M. porcinum and M. abscessus belonged to one major genetic group per species (Fig 4). The presence of only two genetic subtypes of M. chimaera among a geographically diverse population of environmental and suspect respiratory Oahu specimens, as well as clinical isolates from seven other states in the continental U.S. suggests a low level of genetic divergence occurring in this species (Fig 5). Whole genome sequence comparisons will be necessary to improve our understanding of the genetic relationships between environmental and respiratory populations of M. chimaera.
This study has some limitations including the following in methodology: (i) we were unable to consistently collect a large number of samples from the same indoor sites for each participating household, (ii) a sampling bias exists as the majority of samples were collected from Oahu (home to the majority of the state’s population) with only a few household samples collected from the less populated Molokai, Kauai, and Hawai’i and none from Kaho’olawe, Maui, Lanai, or Ni’ihau, and (iii) instead of a single person conducting all environmental sampling, household areas were sampled by local citizens, which added a layer of non-equivalency to the process of sample collection. To reduce non-uniformity in the collection process, we applied a well-accepted citizen science approach to minimize variability introduced by handling of samples by different people [46]. Although we cannot be certain our findings represent the true geographic diversity of NTM in the Hawaiian Islands, this work describes the largest study of environmental NTM in this geographic area with a documented high NTM disease burden. We would advocate for a larger, randomized systematic study of the distribution of environmental NTM in future work. To the best of our knowledge, all environmental samples were from households whose occupants are not known to have NTM lung disease; thus, it will be imperative to sample NTM patient households in a larger future study especially as a more thorough comparison of prevalence and numbers of NTM species in patients and their local environment can be assessed. We were also unable to confirm that the clinical isolates used in this study were etiological agents of respiratory disease or due to benign colonization from environmental exposures. Additionally, this pilot clinical isolate panel did not contain any RGM. Nevertheless, the observation that M. chimaera was the most common species in both environmental and clinical isolates examined suggests the possibility of environmental exposures and clinical NTM lung disease. To determine whether NTM in the household environment contributes to clinical disease, we hope to initiate a large-scale genomic study of matched household and clinical NTM isolates from NTM-infected Hawai’i patients who fulfill ATS/IDSA criteria for lung disease. Undoubtedly, the data collectively presented in this study will be valuable in guiding the design of a more comprehensive study.
In summary, this study describes environmental sampling, microbiological selection, and molecular identification to determine the NTM species diversity in the Hawaiian Island environment. The observation that M. chimaera was the most common NTM species identified in both our Hawai’i environmental samples as well as in a small sampling of respiratory specimens from patients with suspected mycobacterial lung disease suggests that M. chimaera may be an important environmentally acquired respiratory pathogen. Furthermore, M. chimaera may be unique in prevalence in tropical climates such as Hawai’i. Additional studies with systematic collection of matched environmental and respiratory specimens, high-resolution genotyping methods, and correlation with demographic and epidemiological data (i.e. age, gender together with ethnicity and host risk and genetic factors) will be necessary to further characterize this observation and the important clinical implications.
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10.1371/journal.pcbi.0030150 | Chemotaxis Receptor Complexes: From Signaling to Assembly | Complexes of chemoreceptors in the bacterial cytoplasmic membrane allow for the sensing of ligands with remarkable sensitivity. Despite the excellent characterization of the chemotaxis signaling network, very little is known about what controls receptor complex size. Here we use in vitro signaling data to model the distribution of complex sizes. In particular, we model Tar receptors in membranes as an ensemble of different sized oligomer complexes, i.e., receptor dimers, dimers of dimers, and trimers of dimers, where the relative free energies, including receptor modification, ligand binding, and interaction with the kinase CheA determine the size distribution. Our model compares favorably with a variety of signaling data, including dose-response curves of receptor activity and the dependence of activity on receptor density in the membrane. We propose that the kinetics of complex assembly can be measured in vitro from the temporal response to a perturbation of the complex free energies, e.g., by addition of ligand.
| Chemotaxis allows bacteria to sense and swim toward nutrients and away from toxins. The remarkable sensing properties of the chemotaxis network, such as high sensitivity to small changes in the chemical environment, are thought to originate from receptor complexes in the membrane, which act as antennas to magnify weak signals. To adapt to persistent stimulation, receptors are covalently modified. While the individual protein components of the chemotaxis network are well characterized, making the system well suited for quantitative and computational analysis, direct experimental visualization of receptors and receptor complexes is difficult within the current limits of fluorescence and electron microscopy. To address questions such as how large are complexes and why do they assemble, we analyze in vitro signaling data using a previously developed model of signaling by receptor complexes. Based on the data, we propose a statistical physics model for the distribution of complex sizes in the membrane. Within this model, complex size depends on the receptor free energy with contributions from receptor modification level, ligand binding, receptor–receptor coupling, and binding to accessory proteins. Our model results compare favorably with a variety of different signaling data, and suggest new experiments to measure the kinetics of assembly of receptor complexes.
| The chemotaxis network allows bacteria to sense and swim toward attractants (nutrients such as amino acids and sugars) and away from repellents. For this purpose, cells are equipped with ∼10,000 chemoreceptors, forming large arrays at one or both cell poles. The chemotaxis network has remarkable properties, including signal integration by multiple types of chemoreceptors [1], precise adaptation to persistent stimulation [2,3], and high sensitivity to changes in ligand concentration [1] over several orders of magnitude of background concentrations. These signaling properties are thought to originate from strongly coupled receptor complexes [4,5]. Specifically, in vivo fluorescence resonance energy transfer (FRET) measurements of receptor sensitivity [1] and Hill coefficients [6] indicate coupled complexes of up to 10–20 receptor homodimers [6–10]. Despite the importance of complex size to signaling, little is known about what controls receptor complex size (for recent reviews see [11,12]). In vivo observation of complex size and dynamics, e.g., by fluorescence recovery after photobleaching (FRAP), is currently not practical because of limited spatial resolution. However, the close relation between complex size and the sensitivity and cooperativity of signaling means that receptor activity can be used to probe complex size [8]. To demonstrate the potential of this approach, we analyze in vitro receptor-activity data [13–15] and present a simple biophysical model for the energetics of complex assembly.
Here we mainly focus on data from Bornhorst and Falke [13], whose in vitro receptor-activity assay employed a chemotaxis null strain of Escherichia coli overexpressing one of the five receptor types, the high-abundance receptor Tar. The Tar receptor specifically binds aspartate and its nonmetabolizable analogue methyl-aspartate (MeAsp). The cytoplasmic membranes were isolated, and incubated with purified CheW, CheA, and CheY proteins. In vivo, CheW enhances complex formation and mediates binding to the kinase, CheA. Active CheA autophosphorylates using ATP and transfers the phosphate to the response regulator, CheY. Phosphorylated CheY diffuses to the flagellar motor and induces clockwise rotation and cell tumbling. In vitro, CheA kinase activity was measured by assaying the rate of phosphorylation of CheY using radiolabeled ATP. CheA activity is inhibited by an increase of attractant concentration. For the assay, receptors were genetically engineered to have either a glutamate (E) or a glutamine (Q) at each of four specific modification sites in the cytoplasmic domain. In vivo, these four modification sites are used for adaptation, with the enzyme CheR methylating glutamates to increase the kinase activity, and the enzyme CheB demethylating methylated glutamates to decrease the kinase activity. In chemotaxis, a Q is functionally similar to a methylated E. For instance, Tar{QQQQ} is highly active at zero attractant concentration, while Tar{EEEE} is generally inactive.
Figure 1 shows experimental in vitro dose-response curves from Bornhorst and Falke [13], i.e., CheA activity versus stimulation by different amounts of attractant, for Tar receptors in defined modification states. Hill coefficients are smaller (and sensitivities are lower) than typical for in vivo studies of cells overexpressing Tars [6,8], indicating smaller in vitro clusters. The in vitro Hill coefficients (nH ≈ 2–3) are in line with expectations from partial crystal structures [16] and cross-linking experiments [17,18] indicating that receptors oligomerize into mixed trimers of homodimers as the smallest unit of complexes. In vivo, larger complexes possibly form with a hexagonal lattice structure [19,20]. Modeling in vitro data using receptor complexes of a single fixed size (e.g., trimers of dimers) does not describe the data well (inset Figure 1). Here we examine a model in which the receptor modification state determines the amount of trimers of dimers, yielding a significantly better fit to the data (solid lines in Figure 1) and suggesting that receptor modification may vary complex size, possibly along with other parameters [21].
In this paper, we analyze in detail the in vitro activity data from Bornhorst and Falke [13], Shrout et al. [14], and Lai et al. [15]. We model homodimers of Tar receptors in membranes as an ensemble of different species, including single dimers, dimers of dimers, trimers of dimers, and the signaling complex formed by the kinase CheA bound to trimers of dimers, in line with recent experiments [22]. The relative free energies of these species determine their equilibrium distribution, accounting for the different amounts of actively signaling trimers of dimers indicated by the data. We further propose that the kinetics of receptor-cluster assembly can be measured experimentally by perturbing the receptor free energies, e.g., through addition of ligand.
The experimental dose-response curves in Figure 1 for Tar receptors in different modification states were obtained from in vitro reaction mixtures which always contained the same total amounts of receptor, adapter protein CheW, kinase CheA, and response regulator CheY [13]. Addition of MeAsp inhibits the kinase activity, while the number of Qs per receptor increases the kinase activity. Previously, similar dose-response curves from living cells, obtained by in vivo fluorescence resonance energy transfer (FRET), were successfully modeled using the Monod–Wyman–Changeux (MWC) model [23] of strongly coupled two-state receptors [24], and revealed complex sizes of order N = 10 receptors [6–10]. Here we employ the same MWC model to estimate the size of receptor complexes in the in vitro assays of Bornhorst and Falke.
In the MWC model, the receptor complex activity is simply the probability for the complex to be on, which is fully determined by the free-energy difference between on and off states of the complex (Equation 1). For a homogenous complex of Tar receptors, this free-energy difference is the product of the number of receptors, N, in the complex and the free-energy difference between on and off states of a single Tar receptor. The free-energy difference of a single receptor has two contributions. One contribution, Δɛ(m), depends on receptor modification level, m, and ranges from positive for fully demethylated (m = 0) receptors to negative for fully methylated (m = 8) receptors. The other contribution arises from attractant binding and depends on the ligand dissociation constants
and
of the on and off states, respectively. If the activity is low in the absence of ligand (e.g., for demethylated receptors), the inhibition constant (ligand concentration at half maximal activity) is Ki ≈
/N and the Hill coefficient is nH ≈ 1. In contrast, if the activity is high in the absence of ligand (e.g., for highly methylated receptors), the inhibition constant is
and the Hill coefficient is nH ≈ N, where N is the number of receptors in the complex ([8], see Methods). Inspection of the experimental dose-response curves in Figure 1 shows that the inhibition constant of the low-activity QEEE curve is about Ki ≈ 0.01 mM MeAsp and that Hill coefficients of the other curves are nH ≈ 2–3. Hence, based on the MWC model and the previously determined value
= 0.02 mM for Tar receptors binding MeAsp [8], the signaling complexes responsible for the data in Figure 1 are likely to be trimers of dimers.
Indeed, the MWC model using N = 3 for trimers of dimers and a different Δ∈(m) for each receptor modification state m (Equations 1 and 2) fits the shapes of the in vitro curves well, while allowing each curve to have a free amplitude αm (solid curves in Figure 1). However, in the MWC model, Δ∈(m) is also supposed to determine the relative amplitudes of the curves. Although amplitudes still depend systematically on the number of Qs (m), the relative amplitudes from the MWC model are substantially different and do not describe the data well (inset in Figure 1). Hence, each dose-response curve is well described by the MWC model for trimers of dimers, but the MWC model does not describe the relative amplitudes correctly. (Use of a two-state model without cooperativity [21] or use of an alternative MWC model with a methylation-dependent
to fit experimental amplitudes both produce lower than observed Hill coefficients.) The discrepancy in amplitudes raises the following question—given that all experiments use the same total amount of receptor, why should the amplitudes systematically differ from the MWC model predictions for different receptor-modification states?
According to recent in vitro experiments, only receptors in trimers of dimers can signal [22]. Therefore, the presence of some receptors as (inactive) single dimers and dimers of dimers could account naturally for the different amplitudes observed in Figure 1. We therefore suggest that in the in vitro assays not all receptors form trimers of dimers, some also partition into single dimers and dimers of dimers, with the fraction in trimers of dimers depending on the receptor-modification state. In fact, such a partition is required by thermodynamic equilibrium, with entropy favoring single dimers and dimers of dimers over trimers of dimers. In the following, we formulate an equilibrium model to predict the amounts and activities of trimers of dimers as a function of receptor-modification state. For this purpose, we include CheA binding to trimers of dimers only, leading to an equilibrium between free trimers of dimers, without signaling capability, and CheA-bound trimers of dimers, the signaling complex. (For simplicity, we assume that CheW is present at saturation.)
In our model for Tar receptors in membranes, we consider single dimers, dimers of dimers, trimers of dimers, and CheA-bound trimers of dimers. These different species can either be active (on) or inactive (off) as illustrated in Figure 2, but only active CheA-bound trimers of dimers can signal. The relative free energies of the various species determine their equilibrium distribution. To compare the free energies of the different species, we introduce homodimer–homodimer coupling energies, which can be different between active homodimers (Jon) and between inactive homodimers (Joff). We also include a chemical potential, μ, to adjust the receptor density. The resulting free-energy expressions are given in Equations 3–10. To facilitate calculations, we treat the membrane as a lattice where each site can be either empty, or occupied by a single dimers, a dimer of dimers, a trimer of dimers, or a CheA-bound trimer of dimers, yielding the partition function in Equation 11. To model the in vitro experiments, in which the same total amount of receptor was used for each assay, we multiply the probability that a given CheA-bound trimer of dimers is active by the fraction of receptors in CheA-bound trimer of dimers (cf. Equation 14 in Methods).
This equilibrium-assembly model (dashed lines in Figure 1) describes the data as well as the ad hoc model with free amplitudes (solid lines in Figure 1). Specifically, the equilibrium-assembly model accounts for the systematic dependence of the dose-response curve amplitudes on receptor modification state. Since for each curve we assume a fixed fraction of CheA-bound trimers of dimers, set by the incubation conditions, the shape of each curve is still determined by the MWC model with N = 3 (Equation 13 in Methods). While the equilibrium-assembly model requires seven parameters,
,
, Jon, Joff, ∈A, μ, and α, plus an offset energy, Δ∈, for each receptor-modification state, some of these parameters are nearly redundant. For example, Δ∈ and Jon − Joff play nearly equivalent roles, as do μ and (Jon + Joff)/2, differing only in their effects on the ratio of dimers of dimers and trimers of dimers. Therefore, our parameter choices represent only one consistent set of values.
In their data, Bornhorst and Falke [13] observed a strong correlation between the activity in the absence of MeAsp and the inhibition constant Ki. Figure 3A shows this correlated data for all possible modification states except EEEE, for which the measured activity was zero. The observed functional relation between activity and Ki supports our suggestion that not all receptors form CheA-bound trimer of dimers. To illustrate, in Figure 3A we have plotted, as a dotted curve, the expected relation between activity and Ki if all receptors did form CheA-bound trimers of dimers. The curve has a noticeably different shape from the experimental data. In contrast, the equilibrium-assembly model, with the same parameters as in Figure 1, is able to capture the observed relation between activity and Ki (dashed curve). In either case, the one-to-one relation between activity and Ki follows because both quantities depend uniquely on the receptor offset energy Δ∈. For ease of comparison, we used the same amplitude parameter α = 10 for both curves in Figure 3A. This means that the ratio of the two curves gives the fraction of receptors in CheA-bound trimers of dimers in the equilibrium-assembly model, because only those receptors in CheA-bound trimer of dimers contribute to the activity. The actual fraction of receptors in CheA-bound trimers of dimers (and in all trimers) is shown in Figure 3B, both for the equilibrium-assembly model and, by inference, for the in vitro data. Why does the fraction of receptors in CheA-bound trimers of dimers increase with Ki? Within the model, the inhibition constant Ki increases as the offset energy Δ∈ decreases; this behavior follows because decreasing Δ∈ favors the active state of receptors, and therefore more attractant is required to inactivate them. The same shift of receptors toward higher activity causes the fraction of receptors in CheA-bound trimers of dimers to increase, both because Jon < Joff implies a stronger tendency of active receptors to form trimers of dimers, and simply because increasing the total concentration of active receptors increases their equilibrium partition into trimers of dimers.
Our suggestion that not all receptors form trimers of dimers or CheA-bound trimers of dimers is given further experimental support by Shrout et al. [14] and Lai et al. [15] who used a receptor-activity assay similar to that of Bornhorst and Falke but with E. coli Tar receptors. Shrout et al. measured the kinase activity for different modification states of cytoplasmic Tar-receptor fragments at zero attractant concentration. While the measured activities depended strongly on modification state, the same activities normalized by the amount of bound CheA were almost independent of modification state. We find the same behavior in our equilibrium-assembly model. Figure 4 shows the calculated activity and activity per CheA (activity divided by the fraction of receptors in CheA-bound trimers of dimers) for four different receptor-modification states (cf. Figure 1). We observe qualitative agreement with the data in Figure 2A of Shrout et al. [14], although their receptor fragments tend to be more active than complete receptors [25]. In the equilibrium-assembly model, if the CheA-bound trimers of dimers were always fully active (on), the normalized activities would be completely independent of the modification state. However, for receptors with few Qs, the CheA-bound trimers of dimers are not fully active even at zero attractant concentration, resulting in the weak modification-level dependence of the normalized activity seen in Figure 4B.
If an equilibrium exists among single dimers, dimers of dimers, trimers of dimers, and CheA-bound trimers of dimers, one would expect changes in the receptor density to affect the distribution of different sized receptor clusters. Consistent with this expectation, Lai et al. [15] reported the activity per Tar{QEQE} receptor, in the absence of attractant, as a function of the receptor fraction of total membrane protein. As shown in Figure 5, they observed an increase in and saturation of the activity per receptor with increasing receptor fraction. We interpret their data to mean that at low receptor fractions (densities), it is thermodynamically unfavorable for receptors to come together and form trimers of dimers (or even dimers of dimers), and consequently single dimers, which lack signaling capability, predominate. This density-dependent activity per receptor is captured by our equilibrium-assembly model, as shown in Figure 5 (solid lines), using the same parameters as in Figure 1. The calculated activity is scaled by an overall factor to convert to the activity scale of Lai et al. [15], and the calculated receptor density (Equation 15) is also rescaled. Within the equilibrium-assembly model, the kinase activity per receptor increases with receptor density entirely because of the increasing fraction of receptors in CheA-bound trimers of dimers expected from thermodynamics.
The large amount of in vitro data from Bornhorst and Falke [13] can be used to test an additional hypothesis. Specifically, do the offset energies from each of the four modification sites Δ∈i=1,2,3,4 contribute additively to give the total offset energy Δ∈? The total offset energy Δ∈ for each of the 15 modification states can be obtained from the inhibition constants Ki [13] based on our model that only CheA-bound trimers of dimers can signal (see Methods). This value can be compared with the additive model, where the Δ∈i are treated as fitting parameters. Figure 6 shows that the additive model for the total offset energy is indeed a reasonably good approximation. Interestingly, modification sites 1 to 3 make a similar contribution (approximately −0.5 to −0.6 kBT ) while site 4 makes a smaller contribution (approximately −0.3 kBT) to the offset energy (see Methods). This may have to do with the fact that, relative to the CheA binding site, modification sites 1 to 3 are nearby on the N-terminal side of the receptor and modification site 4 is on the C-terminal side of the receptor.
The chemotaxis network of E. coli exhibits remarkable sensing and signaling properties that rely on receptor complexes. Despite recent high resolution electron microscopy [19,20], fluorescence images [26–28], and in vivo fluorescence recovery after photobleaching (FRAP) measurements of protein dynamics (V. Sourjik, personal correspondence), very little is known about what determines receptor-complex size [11,12]. Interestingly, because complex size and signaling sensitivity or cooperativity are closely related [8], receptor kinase activity can be used to probe complex size. Starting from in vitro dose-response data of the activity of Tar receptors in native membranes [13–15], we presented a simple biophysical model for the energetics of complex assembly that can account for these and other data. An essential feature of the model is that not all receptors form signaling complexes, i.e., kinase CheA-bound trimers of dimers. Our model for receptor complexes is based on an MWC model, with constants
and
, in which receptor modification state affects complex size only through the offset energy Δ∈ (which depends additively on contributions from the four modification sites). At this stage, we cannot rule out alternative models, e.g., in which modification state affects other parameters as well [21].
In our model, Tar receptors form an ensemble of different species, including single dimers, dimers of dimers, trimers of dimers, and CheA-bound trimers of dimers, as illustrated in Figure 2. The different species can either be active (on) or inactive (off), but only active CheA-bound trimers of dimers can phosphorylate CheY. This is in line with recent in vitro experiments where trimers of dimers were found to signal, but single dimers and dimers of dimers did not signal [22]. The relative free energies of the various species determine their equilibrium distribution, leading naturally to the observed variation in the signaling activity of receptors in different modification states (cf. Figures 1, 3, 4, 5). We find that the fraction of receptors in trimers of dimers and CheA-bound trimers of dimers increases with the number of Qs at the modification sites (or with Ki, see Figure 3B). Within this picture, the “superactivity” of certain mutant receptors can be attributed to more efficient complex formation rather than enhanced CheA binding or kinase velocity [25].
Our free-energy model assumes that complex assembly/disassembly is slow compared with changes in signaling. For instance, if attractant is added together with ATP to initiate the activity measurement, the ensemble of clusters is assumed to stay frozen, i.e., the ratio of {single dimers}:{dimers of dimers}:{trimers of dimers}:{CheA-bound trimers of dimers} is assumed to be unaffected by the addition of attractant, even though the kinase activity is immediately affected. This separation of time scales is reflected in Equation 14, where the fraction of receptors in CheA-bound trimers of dimers (first factor) is evaluated at the incubation attractant concentration ([L0] = 0), while the activity (second factor) is evaluated in the presence of the added attractant ([L]). To model the case where attractant is added during incubation, one only needs to set [Lo] = [L]. In this case, shown by solid curves in Figure 7, inhibition occurs at lower attractant concentrations, in agreement with the data of Lai et al. [15] for Tar{QEQE} incubated in the presence of MeAsp (solid symbols). In the model, the inhibition at lower attractant concentrations can be traced to the loss of trimers of dimers in favor of single dimers and dimers of dimers in the new equilibrium produced by incubation with attractant (see inset Figure 7). Incubation with attractant is exactly the opposite of adding Qs in terms of receptor free energies, and therefore favors smaller rather than larger complex sizes.
The dose-response curves in Figure 7 for incubation without attractant (dashed curves) and with attractant (solid curves) are easily distinguishable, which suggests a way to measure the kinetics of complex assembly. During the period after the addition of attractant, as the clusters re-equilibrate, the dashed curves must evolve toward the solid curves. The rate of evolution can be quantified by measuring the kinase activity at specific times following the addition of attractant. In this way, information can be obtained about the kinetics of assembly and disassembly of receptor complexes. Our equilibrium-assembly model, augmented by kinetic rate constants, provides an appropriate theoretical framework for planning and interpreting kinetic experiments of this type.
There are previously published models for chemoreceptor complex assembly. These models, however, do not consider the effects of ligand binding, and hence cannot address dose-response data. Furthermore, Lai et al. [15] assume all receptors form trimers of dimers, hence their model cannot explain the activity versus receptor density data in Figure 5. Shrout et al. [14] assume that CheA binding directly depends on the receptor modification state. While this assumption can explain the increase of activity with modification level, it violates the conventional view of precise adaptation based on the two-state receptor model, where receptors are either on (active) or off (inactive). Precise adaptation occurs because receptor modification responds exclusively to receptor activity so as to exactly balance the effects of ligand binding. If CheA binding depended directly on receptor modification level, this would increase kinase activity at higher attractant concentrations and, hence, interfere with precise adaptation. In contrast, in our model, CheA binds to trimers of dimers irrespective of modification level or activity. The recent model by Asinas and Weis [25] considers the competitive assembly of wild-type and activity-mutant receptors. The authors come to a similar conclusion to ours, i.e., that receptor activity determines cluster assembly and, consequently, CheA recruitment and activity (see also Li and Weis [29]).
An approach similar to ours may allow measurement of the kinetics of receptor complexes in living cells. Complex sizes of 10–20 receptors or more have been inferred from in vivo dose-response curves [6–10] and, in E. coli cells lacking an adaptation system, polar clustering appears to depend on receptor-modification level ([28,30,31]; V. Sourjik, personal correspondence). This suggests that dose-response curves can be used to measure the real-time evolution of in vivo cluster sizes in response to perturbations of receptor free energy, e.g., addition of attractant or repellent. It is not clear why in vivo complexes are significantly larger than the trimers of dimers seen in vitro and why receptors localize predominately at the cell poles. It is known that receptors are inserted into the membrane by the Sec translocon machinery [32] in large cell-spanning spirals [33]. Once inserted into the membrane, receptors may localize at the cell poles due to the higher membrane curvature [34] and/or different lipid composition [35–37] at the poles. A means to probe receptor-assembly kinetics may help reveal what determines complex size in vivo.
Compared with previous modeling of in vivo data [8–10], the offset energies, Δ∈, obtained from in vitro data are much larger. This can be traced to the fact that we explicitly include homodimer–homodimer interactions, which lead to an effective offset energy for each receptor in a trimer of dimer of Δ∈ + Jon − Joff, close to estimated in vivo values. However, in a large in vivo complex, if each receptor participates in six homodimer–homodimer interactions, as on a hexagonal lattice, the effective offset energy per receptor would be Δ∈ + 3(Jon − Joff), which is much more negative than the estimated in vivo values. One possible resolution might be that, in an in vivo cluster, homodimers in different trimers of dimers are coupled together more weakly than homodimers within a trimer of dimers. However, the coupling between trimers of dimers must still be strong enough to cause clusters of 10–20 receptors to switch on and off together. An important open question is what mediates the interactions among receptor homodimers in trimers of dimers, or between trimers of dimers? One way to address this question may be to measure in vitro or in vivo dose-response curves of mutant receptors specifically engineered to interrupt or strengthen homodimer–homodimer interfaces. Possible insight can be gained from the observation of large in vitro Tsr clusters [29], pointing toward a difference between Tsr:Tsr and Tar:Tar interfaces [15].
We expect that a better understanding of the assembly of E. coli chemoreceptor complexes may provide insights into the oligomerization of other membrane proteins, including bacterial outer membrane proteins such as porins (e.g., LamB). For other membrane-bound receptors that form complexes, including ryanodine receptors [38,39] and rhodopsin [40], we hope that analysis of complex size and assembly kinetics based on dose-response curves may also prove feasible.
We mainly model the data of Bornhorst and Falke [13], who used an in vitro activity assay to study chemotaxis signaling. Briefly, Tar receptors of Salmonella typhimurium were engineered to be in a particular modification state, e.g., QQQQ, QEQQ, QEQE, or QEEE, where Q is approximately equivalent to a methylated E. Using a chemotaxis null strain of E. coli, the Tar receptor was overexpressed from a plasmid. Cytoplasmic membranes were isolated in which Tar receptors constituted approximately 5%–10% of total membrane protein. Reaction mixtures of the same total amount of Tar and purified CheA, CheW, and CheY were prepared and incubated for 45 min to allow for complex formation in native membranes. Signaling was initiated by adding radiolabeled ATP. The activity of CheA was measured by assaying the rate of phosphorylation of CheY and normalized to QEQE (wild-type). Quantified attractant (MeAsp) was added with the ATP.
In the MWC model [6,8,23], two-state receptors (homodimers) [24,41] form complexes with all receptors in a complex either on or off together. At equilibrium, the probability that an MWC cluster of N Tar receptors will be active is
where N fon (N foff) and fon (foff) are the free energies of the complex as a whole and an individual receptor to be on (off), respectively. The individual receptor free-energy difference is given by
Here, [L] is the ligand (MeAsp) concentration, m is the number of Qs per receptor (m = 0,…,8), and
and
are the ligand dissociation constants in the on and off states, assumed to be independent of m. All energies are expressed in units of the thermal energy kBT.
In our model, Qs or methylated Es favor the on state of a receptor by lowering Δ∈ (m), while attractant binding favors the off state, i.e.,
<
. Importantly, the model exhibits two regimes [8]. In regime I, where Δ∈ > 0 (e.g., for Tar{EEEE}), receptors have a low activity and an inhibition constant (ligand concentration at half maximal activity), Ki ≈
/N, indicating an N times higher sensitivity than for a single receptor. In regime II, where Δ∈ < 0 (e.g., for Tar{QQQQ}), receptors are highly active, and turn off at large attractant concentration Ki ≈
exp(|Δ∈|) with high cooperativity, i.e., a Hill coefficient nH ≈ N. The possible MWC complexes considered here are the single receptor dimer, the dimer of dimers, and the trimer of dimers, corresponding to complex sizes N = 1, 2, and 3, respectively.
We use statistical mechanics to predict the partitioning of receptors into active and inactive single dimers, dimers of dimers, trimers of dimers, and CheA-bound trimers of dimers, as illustrated in Figure 2. Since CheA-bound trimers of dimers are the signaling complex, only trimers of dimers can signal, not single dimers and dimers of dimers, in line with recent experiments [22]. To compare the energies of the different-sized complexes, we generalized the MWC model to include homodimer–homodimer interactions. The interaction energy between active homodimers (Jon) and the interaction energy between inactive homodimers (Joff) can be different. These homodimer–homodimer interactions may originate from interactions of the periplasmic or cytoplasmic domains of the receptors, possibly mediated by the adapter protein CheW. Specifically, single dimers, dimers of dimers, and trimers of dimers (as well as CheA-bound trimers of dimers) have zero, one, and three homodimer–homodimer interactions, respectively. We also introduce a receptor chemical potential, μ, which determines the receptor density, ρ, in the membrane, and a free energy, ∈A, for the binding of the kinase CheA to trimers of dimers (assuming for simplicity an equilibrium between bound CheAs and free CheAs at some invariant concentration). The resulting complex free energies for a single dimer (SD), a dimer of dimers (DD), a trimer of dimers (TD), and a CheA-bound trimer of dimers (A:TD) are given by
To regularize our calculations, we treat the membrane as a lattice where each lattice site can be either empty, or occupied by a single dimer, a dimer of dimers, a trimer of dimers, or a CheA-bound trimer of dimers, determined by their relative free energies. The equilibrium partition function of a single lattice site is given by
The probability that a site is occupied by species s (= SD, DD, TD, or A:TD) is given by
The probability that a particular CheA-bound trimer of dimers is active is given by the MWC model (cf. Equation 1), now also depending on Jon − Joff,
To compare with experiments on a per receptor basis, the probability that each CheA-bound trimer of dimers is active needs to be multiplied by the fraction of receptors in CheA-bound trimers of dimers, i.e.,
where α is an overall amplitude parameter, and can be interpreted as the maximal possible activity, which would be achieved if all receptors were in active CheA-bound trimers of dimers. The ligand concentrations [L0] and [L] indicate that the ensemble of species can equilibrate at one ligand concentration, e.g., [L0] = 0, while signaling can be measured at another concentration, [L].
Assuming a constant density, ρ, of receptors in the membrane, we find the chemical potential, μ, that yields this density. The definition of the density,
can be solved for μ by solving the cubic equation
for x = eμ and choosing the largest real root. The coefficients are given by
where
indicates that the chemical potential is removed from the free energy F, i.e.,
. The resulting chemical potential μ can be used in Equation 14 to calculated the activity.
Given
= 0.5 mM and
= 0.02 mM [8], Equation 14 for the activity depends on amplitude parameter α and five additional parameters: the second factor (
) depends on Δ∈(m) and Jon − Joff, and the first factor (fraction of receptors in CheA-bound trimers of dimers) depends additionally on ρ(μ), Jon or Joff, and ∈A. We chose α = 10, somewhat above the activity 5.5 observed for superactive receptor mutants, to constrain the other energy parameters to be of reasonable size, i.e., on the order of kBT. Fitting the four dose-response curves in Figure 1 provides Δ∈(QEEE) = 3.1, Δ∈(QEQE) = 2.6, Δ∈ (QEQQ) = 2.2, Δ∈(QQQQ) = 1.8, ∈A = −1.32, Jon = 0.01, and Jon − Joff = −3.39 in units of kBT, and ρ = 0.045 receptors per site. For comparison, constraining the values to Jon = Joff leads to a fit as poor as that in the inset to Figure 1.
We test whether modifications of the four receptor sites contribute additively to the total offset energy Δ∈. From the measured inhibition constants, Ki, of 15 different receptor modification states (QEEE, etc., except EEEE) [13], the total Δ∈ can be calculated from Equation 13, assuming only CheA-bound trimers of dimers can signal
with Jon − Joff = −3.39 from the previous paragraph. These values for the total offset energies can be compared with the corresponding values within an additive model
where Δ∈i = 1,…,4 is the contribution to the total offset energy from the presence of a Q at site i. The values Δ∈i = 0,…,4 are treated as fitting parameters obtained from minimizing
With M indexing the 15 modification states. Δ∈0 allows a fully unmodified receptor EEEE to have a nonzero offset energy. The best fit parameters are Δ∈0 = 3.738, Δ∈1 = −0.603, Δ∈2 = −0.504, Δ∈3 = −0.589, Δɛ∈ = −0.289. The resulting total offset energies, Δ∈(QEEE) = 3.135, Δ∈(QEQE) = 2.546, Δ∈(QEQQ) = 2.257, and Δ∈ (QQQQ) = 1.753, compare well with the values from the previous paragraph. All energies are given in units of the thermal energy, kBT.
To further test whether the additivity assumption is valid, we permutated the data, i.e., randomly reassigned all 15 Ki values to the 15 different receptor modification states, and calculated χ2 values after minimization. One thousand such permuted calculations were used to plot the histogram in the inset in Figure 6. Remarkably, the χ2 fit to the original data is smaller than all fits to the permutated datasets, indicating that the linearity assumption is meaningful and that the good fit in Figure 6 has not occurred by chance.
The primary protein accession numbers from the Swiss-Prot databank (http://www.ebi.ac.uk/swissprot/) for the E. coli proteins mentioned in the text are: Tar MCP2 ECOLI (P07017), Tsr MCP1 ECOLI (P02942), CheW O157 CHEW ECO57 (P0A966), CheA CHEA ECOLI (P07363), and CheY O157 CHEY ECOLI (P0AE67). |
10.1371/journal.pcbi.1004557 | High-Specificity Targeted Functional Profiling in Microbial Communities with ShortBRED | Profiling microbial community function from metagenomic sequencing data remains a computationally challenging problem. Mapping millions of DNA reads from such samples to reference protein databases requires long run-times, and short read lengths can result in spurious hits to unrelated proteins (loss of specificity). We developed ShortBRED (Short, Better Representative Extract Dataset) to address these challenges, facilitating fast, accurate functional profiling of metagenomic samples. ShortBRED consists of two components: (i) a method that reduces reference proteins of interest to short, highly representative amino acid sequences (“markers”) and (ii) a search step that maps reads to these markers to quantify the relative abundance of their associated proteins. After evaluating ShortBRED on synthetic data, we applied it to profile antibiotic resistance protein families in the gut microbiomes of individuals from the United States, China, Malawi, and Venezuela. Our results support antibiotic resistance as a core function in the human gut microbiome, with tetracycline-resistant ribosomal protection proteins and Class A beta-lactamases being the most widely distributed resistance mechanisms worldwide. ShortBRED markers are applicable to other homology-based search tasks, which we demonstrate here by identifying phylogenetic signatures of antibiotic resistance across more than 3,000 microbial isolate genomes. ShortBRED can be applied to profile a wide variety of protein families of interest; the software, source code, and documentation are available for download at http://huttenhower.sph.harvard.edu/shortbred
| High throughput DNA sequencing of the human microbiome presents a tremendous resource for researchers interested in studying microbial community functions such as antibiotic resistance. However, assigning DNA reads to protein families remains a challenging problem, as reads derived from a given protein-coding gene may map spuriously to homologous regions from unrelated proteins, which results in false positives. We addressed this problem with our method ShortBRED, which first identifies short peptide sequences (“markers”) that are highly representative for specific protein families, and then searches for these markers in metagenomic sequencing data to accurately detect and quantify protein families. In this work, we applied ShortBRED to profile antibiotic resistance in the healthy human microbiome of individuals worldwide and across bacterial genomes. ShortBRED can be similarly applied to profile many other protein families of interest.
| Quantifying proteins of interest from metagenomic sequencing data in a fast and accurate manner is a central challenge in microbial community analysis. Whole metagenome shotgun (WMS) sequencing provides millions of short nucleotide sequences (often 100–250 bases long) from the DNA of organisms in a sample; we refer to these short DNA sequences as “reads.” A common approach to profiling protein families from these data involves (i) mapping reads to a database of reference protein sequences followed by (ii) interpreting the mapping results to estimate protein family relative abundance. This process is complicated by regions of local similarity in otherwise unrelated protein families: reads drawn from such regions will map non-specifically, which can result in false positive identifications (reduced specificity). Reducing the time spent on mapping reads is also an important task, as typical metagenomic sequencing depths and reference database sizes continue to grow rapidly.
Protein families are typically profiled in metagenomic sequencing data by one of three approaches: (i) mapping DNA reads to a database of nucleotide sequences [1], (ii) mapping translated DNA reads to a database of protein sequences [2], or (iii) assembling full-length genes from DNA reads de novo and then annotating them via comparison with reference databases [3]. Approaches (i) and (ii) rely on homology-based searches, as enabled by programs such as BLAST [4], USEARCH [5], and RAPSearch2 [6]. Methods such as MEGAN [7] and HUMAnN [2] can achieve very high sensitivity by mapping reads to large nucleotide and protein reference databases. However, this approach is vulnerable to false positives, as a read derived from a given protein-coding sequence may spuriously align to other genes as a result of local sequence homology, as mentioned above [8]. Moreover, searching nucleotide sequences against large, full-length protein reference databases comes at great computational cost, as search time is roughly proportional to database size and translated search is more computationally demanding than searching in nucleotide space. Assembly-based methods, while advantageous for identifying new genes, tend to underrepresent known, low-abundance genes, as more reads are required to assemble a gene than to identify it by homology-based search. Like search-based methods, assembly is also challenged by regions of local homology, which may lead to the construction of chimeric contigs.
False positive hits to regions of local homology can be mitigated by identifying and mapping against only unique substrings of protein sequences. This has the added benefit of reducing search time, as the unique portions of each database sequence constitute a smaller search space. One approach to this method involves identifying unique k-mers within bacterial protein sequences relative to a larger reference database [9]. While this approach makes progress toward increasing specificity and decreasing runtime, exact k-mer matching is not always biologically satisfying as it can fail to model common patterns in protein sequence evolution. For example, two peptide k-mers that differ by a single substitution between glutamic and aspartic acid (biochemically similar amino acids) are biologically similar, but would be scored as completely distinct by exact k-mer matching. Moreover, while k-mer approaches focus on matches to unique substrings of specific protein sequences, many metagenomics applications—particularly those involving poorly characterized microbial communities—benefit from alignment to more sequence-diverse protein families.
Here we present ShortBRED (Short, Better Representative Extract Dataset): a method for profiling protein family abundance in metagenomic data by first identifying short peptide markers that (i) are conserved within protein families and (ii) uniquely distinguish families from one another. ShortBRED achieves equivalent sensitivity, enhanced specificity, and enhanced speed relative to profiling strategies that map reads against full-length protein sequences. Unlike k-mer based profiling, ShortBRED relies on standard sequence homology-based methods to map reads to peptide markers, thus making it robust to common patterns in protein sequence evolution. By enabling faster, more accurate profiling of protein families in large metagenomes, ShortBRED allows researchers to better measure the prevalence and abundance of protein families of interest, and can lead to better understanding of biological phenomena. As proof-of-principle, we applied ShortBRED to profile antibiotic resistance (AR) families in both human microbiomes and bacterial isolate genomes, revealing new, population-specific, and phylogenetic trends in the distribution of this important class of proteins.
We developed ShortBRED as a method to quickly and accurately quantify the relative abundance of protein families in WMS sequencing data. ShortBRED profiles protein family abundance in metagenomes by a two-step process: (i) ShortBRED-Identify isolates representative peptide sequences (markers) for the protein families, and (ii) ShortBRED-Quantify maps metagenomic reads against these markers to determine the relative abundance of their corresponding families (Fig 1). To evaluate ShortBRED, we measured its speed and accuracy in profiling synthetic metagenomes, and then tested its specificity by searching for yeast proteins in a synthetic bacterial metagenome. We next applied ShortBRED to profile AR genes in the gut microbiomes of healthy American [10], Chinese [11], and Venezuelan and Malawian [12] populations, as well as ~3,000 microbial isolate genomes.
Current approaches to functional profiling of metagenomic samples often involve mapping reads to full-length protein sequences (e.g. centroid sequences of previously defined protein families). ShortBRED obtains higher speed and specificity relative to these approaches by reducing protein families to short, highly representative peptide sequences (markers), and then mapping reads against only those markers. To create the markers, ShortBRED-Identify uses two inputs: (i) a FASTA file of proteins-of-interest and (ii) a comprehensive reference database of additional protein sequences (provided as a FASTA file or preformatted BLAST database; Fig 1). ShortBRED-Identify first clusters the protein sequences of interest to identify protein families by global sequence homology, with each collapsed to form a single consensus sequence. Regions of a family’s consensus sequence that share strong, local sequence homology (“overlaps”) with proteins outside of the family are then penalized. Based on these overlaps, ShortBRED then isolates short peptide markers from the consensus that best represent the protein family. We classify these markers into three groups: True Markers, which do not overlap with the other protein families, Junction Markers, which overlap partially with the other protein families, and Quasi Markers, which are completely overlapped by another protein family.
The marker creation process is run once for a given set of proteins, resulting in a reusable and distributable marker database. ShortBRED-Quantify then (i) maps WMS sequencing reads against a given protein marker database using a translated search, (ii) counts high-quality hits, (iii) normalizes the counts based on marker length and sequencing depth, and (iv) produces a relative abundance profile of the protein families of interest represented by the marker database (Fig 1). Creating a highly specific marker sequence database has two major advantages: (i) searches against this database are more accurate, as the exclusion of non-specific (overlap) regions reduces false positive hits, and (ii) searches proceed more quickly, as the search space is considerably reduced relative to the full database.
We constructed synthetic datasets to train ShortBRED’s default parameter settings and validate its performance. For one set of AR protein families (ARDB [13]) and one set of virulence factor protein families (VFDB [14]), we created three synthetic bacterial metagenomes spiked with the proteins of interest at 5%, 10%, and 25% relative abundance. We first tuned ShortBRED’s ability to correctly call the presence and absence of protein families in the 5%-spiked metagenomes by varying the initial protein clustering identity (80%, 85%, 90%, 95%, and 100%) and minimum allowed marker length (8, 10, 12, 15, 18, 20, 22, 25, and 30 amino acids; S1–S8 Figs). We first restricted the allowed parameter space to those combinations yielding a specificity of at least 99% in our initial evaluation. From among these combinations, we selected a clustering identity of 85% and minimum marker length of 8 amino acids as ShortBRED’s defaults as they gave the best sensitivity performance on the ARDB-spiked metagenome (there was little variation in performance on the VFDB-spiked metagenome). These parameter settings were used for the remaining analyses in this work; they can be easily tuned with command-line arguments for other applications.
To further validate our parameter settings and ShortBRED’s performance, we generated markers for the ARDB and VFDB protein families based on the optimal settings described above (Table 1). We then used these markers to profile six synthetic metagenomes, including the 10%- and 25%-spiked metagenomes that were not used in the training process. We compared ShortBRED to an alternative profiling strategy in which reads were mapped directly to the centroid sequences of protein families. Centroids were obtained by clustering the proteins of interest at 85% identity; during the quantification stage, any matches to centroids with length ≥30 amino acids and ≥95% identity were considered valid hits.
An ideal search methodology will correctly identify all protein families present in a metagenome (true positive rate, TPR, equal to 1) and will not erroneously identify any protein families absent from the metagenome (false positive rate, FPR, equal to 0). As we intensify the criteria for calling a family as present (e.g. requiring a higher normalized count for the family), TPR and FPR will both decrease: a tradeoff we quantify using a receiver operating characteristic (ROC) curve (Fig 2A and 2B; S1 Table). Notably, even treating a single hit to a ShortBRED marker as evidence of the corresponding protein family’s presence resulted in exceptional sensitivity with very low false positives (<5%). As we increased the number of protein families present and the share they comprised of the metagenome, ShortBRED achieved TPR and FPR values comparable to or exceeding those of the centroids method (S1 Table). Since greater spike-in percentages provided more opportunities for centroids from one cluster to match to reads from another cluster by local homology, the centroids method performed well for the 5%-spiked metagenomes but experienced a substantial drop in specificity at similar levels of sensitivity to ShortBRED in analyses of the 10%- and 25%-spiked metagenomes.
Beyond correctly calling protein family presence and absence, an ideal search strategy will be able to accurately quantify the relative abundances of these families in a metagenome, which may vary over several orders of magnitude. Using Spearman’s correlation to compare known and predicted relative abundances, ShortBRED outperformed the centroid-based method in the six Illumina metagenomes (median r = 0.95 versus 0.82; S1 Table). The weaker performance of the centroid-based method was due in part to a larger fraction of false positive detection events (defined to have 0 expected abundance; Fig 2C and 2D). We additionally performed a more challenging evaluation on sequences with either 3% or 5% amino acid substitutions, retaining specificity >0.95 and >0.98 respectively, and sensitivities >0.88 and >0.79 (S1 Table). This is in contrast to centroid matching on the same datasets, which achieved minimum specificities of only 0.80 and 0.77, respectively. Thus, ShortBRED’s increased specificity not only provides a more accurate qualitative profile of protein family presence and absence, but also contributes to more accurate quantitative profiling. As an additional evaluation of specificity, we applied ShortBRED and the centroid-based profiling method to search for yeast proteins in a synthetic bacterial metagenome. Given that yeast and bacteria are extremely distantly diverged [15], homology between a short, bacterial DNA sequence and a yeast protein is likely to have resulted from chance. ShortBRED did not identify any false positive hits to yeast proteins among the bacterial DNA reads, while the centroid-based method produced fifteen high-identity and long-length hits (S2 Table). The centroid-based profiling method offered some advantages over ShortBRED when working with shallow sequencing data (e.g. as derived from older 454 sequencing experiments), wherein reads are less likely to have been sampled from marker regions (S9 Fig). However, this limitation vanishes when working with typical modern sequencing depths, while the drawbacks of the centroid-based approach will only grow as typical depths continue to increase.
In addition to increasing the accuracy of metagenomic search, ShortBRED’s focus on a reduced sequence database (the markers) results in considerably shorter run-times relative to searching against full-length centroids (Fig 3). Focusing on metagenomes spiked with proteins from ARDB, we were able to process ~10,400 reads/sec (on average) by mapping against full-length ARDB centroid sequences, while ShortBRED processed ~19,600 reads/sec using the previously generated ARDB marker sequences (a 1.9x increase in speed). For the VFDB-spiked metagenomes, we were able to map ~5,400 reads/sec against centroid sequences, while ShortBRED processed ~11,300 reads/sec (a 2.1x increase in speed). All mapping experiments were carried out on the same computer hardware using 5 CPU cores and the same underlying mapping program (USEARCH); hence, ShortBRED’s increased speed can be attributed to the reduced size of the marker database. For a modern metagenomics study producing 100s of millions of reads for 100s of samples, this speedup corresponds to savings of 100s of CPU-hours of compute time.
We leveraged the improved specificity of ShortBRED to accurately quantify antibiotic resistance (AR) worldwide in the human gut microbiomes of 552 individuals from the United States [10, 12], China [11], Malawi, and Venezuela [12] (Fig 4 and S3 Table). We identified centroid sequences (appropriate for the more shallow 454 sequencing in [12]) and ShortBRED markers (for Illumina sequences from 10–12) for 849 AR protein families derived from the ARDB and independent curation [16]. These families were further grouped into broader classes such as such as “Class A beta-lactamase” and “quinolone resistance.” 107 microbiome samples based on older 454 sequencing methods were mapped to centroid sequences to avoid loss of sensitivity from low sequencing depths; all other samples were profiled with ShortBRED (see Methods).
Our results support AR as a core function in the human gut microbiome, with every individual gut microbiome containing at least one AR determinant (Fig 4). As previously observed [17, 18], resistance to the tetracyclines was the most widespread AR function in the human gut microbiome, with at least one tetracycline resistance mechanism being identified in 99% of individuals across all three studies' global populations (97% ribosomal protection; 87% efflux; 3% inactivation). In addition, Class A beta-lactamases were identified in 90% of individuals and were widespread throughout all populations, with CfxA and CblA the most common variants (as represented by families P30898 at 68.5% prevalence and P30899 at 60.1% prevalence; see S4 Table). Based on the diversity of participant ages present particularly in the Yatsunenko et al study, these prevalent AR families appear early in life and appear cross-sectionally across a wide range of subject demographics.
Consistent with previous findings [19], this global distribution of AR determinants in the human gut microbiome appears to be driven by the underlying bacterial phylogenetic profile. For example, while Class A beta-lactamases are known to be the most diverse and widely disseminated class of beta-lactamase genes [20], the most abundant variants (CfxA and CblA) have been previously shown to be specific to Bacteroides species [21, 22]. Hence, enrichment for these families may be a direct marker for the presence of specific bacterial clades in the gut microbiota rather than a response to selective pressures of individual-specific antibiotic use. The relationship between microbiome-specific AR and phylogenetic profiles are addressed in greater detail in subsequent sections.
In addition to the universal AR trends described above, ShortBRED revealed several consistent differences in AR profiles between global populations. For example, Chinese individuals were particularly enriched for a number of AR factors: quinolone resistance, aminoglycoside acetyltransferases, and genes modulating antibiotic efflux. Among these individuals, the two most prevalent quinolone resistance families (variants of fluoroquinolone-resistant DNA topoisomerases) were found in 78% and 29% of individuals, the most prevalent aminoglycoside acetyltransferase (YP_002559372) was found in 99.7% of individuals, and the next-most-prevalent (P13246) followed at 19.3% of individuals. Four gene-modulating antibiotic efflux families (phoQ_1, soxR_5, marA_1, and baeR_2) had individual prevalence values exceeding 58% (S4 Table). In addition, while many AR genes were discretely strongly present or absent within the Chinese cohort (Fig 4), their gut resistomes were differentiated into four clear clusters based largely on the abundance of antibiotic efflux pumps, including major facilitator superfamily (MFS) antibiotic efflux, resistance/nodulation/cell division (RND) antibiotic efflux, and small multidrug resistance (SMR) antibiotic efflux pumps. Many multidrug antibiotic efflux pumps are chromosomally encoded and highly conserved across all members of a given bacterial species [23], further suggesting that observed AR distribution patterns are driven by underlying community membership and phylogeny.
In comparison with the Chinese cohort, gut microbiome samples from the American (HMP) cohort were much more homogeneous. This difference was likely influenced by the greater diversity in membership among the Chinese cohort, which contained individuals with and without type II diabetes and a wide range of ages (13–86). In comparison, the HMP cohort consisted solely of young (ages 18–40), healthy individuals. Differences between the cohorts may also reflect variation in the sampling and sequencing protocols used by their corresponding studies (in addition to real biological variation). American individuals were characterized by increased abundance of four protein families within the Class A beta-lactamases (CfxA_11, AAA22905, P30898, and P30899; S4 Table). Conversely, these individuals were depleted for aminoglycosides and acetyltranferases. These observed differences between the American and Chinese cohorts stress that, while AR is (at a high level) core to the global human gut microbiome, variation emerges in specific resistances present in individual populations.
In order to understand and control the spread of AR, it is necessary to characterize the connections between AR determinants, source genomes and their phylogeny, and the relative propensity of horizontal gene transfer (HGT). In addition to their usefulness in metagenomics profiling, ShortBRED markers can aid in this goal by providing highly specific signatures of AR protein families for microbial genome annotation. We used ShortBRED to profile the 849 AR protein families introduced above across 3,305 phylogenetically diverse microbial isolate genomes [24] (see Methods).
Over 40% of microbial isolate genomes surveyed encoded at least one AR determinant, with significant enrichments among particular genera (Fig 5). For example, Escherichia and Salmonella are closely related bacterial genera that contain many human pathogens [25, 26]; both were highly enriched for AR determinants. Specifically, all Escherichia and Salmonella encoded at least one AR class, with an average of 20.3 AR genes for Escherichia and 11.2 AR genes for Salmonella (S5 Table). In addition, while these two genera shared many similar AR determinants, they appear to resist beta-lactam antibiotics using largely orthogonal mechanisms: 94.6% of Escherichia genomes were enriched for Class C beta-lactamases and were completely depleted of Class B beta-lactamases, while Salmonella showed the opposite trend (6.5% of genomes encoded Class C resistance, while all genomes encoded Class B resistance, S10 Fig, S5 Table). While these examples illustrate cases of strong coupling between AR determinants and particular genera, this was not always the case. For example, glycopeptide resistance was highly variable within the genus Enterococcus, with ~1/3 of isolate genomes possessing the function while the remaining 2/3 lacked it.
Our observations further suggested that AR functions could be subdivided into two categories of phylogenetic distribution: (i) functions that are clade-specific, i.e. highly conserved across all members of a bacterial clade, and (ii) functions that are broadly distributed across the phylogenetic tree. Both distribution patterns were observed among abundant AR classes in the human gut microbiome (Fig 5 and S5 Table). For example, multi-drug antibiotic efflux pumps and rRNA methyltransferases showed strong signatures of clade-specific enrichment among the Staphylococcus, Escherichia, Salmonella, and Yersinia genera. Functions that are tightly linked to particular clades are notable in that their presence and abundance can be inferred from profiles of community composition alone, including profiles based on lower-resolution amplicon sequencing [28]. Conversely, tetracycline ribosomal protection determinants were widely dispersed across the phylogenetic tree—a pattern more consistent with recent spread by mechanisms such as HGT [1]. The presence and abundance of functions in this category would be difficult to infer from community profiling and are best quantified directly from a metagenome—a process facilitated by ShortBRED.
The previous section stressed that, while some AR functions can be accurately quantified based on microbial community composition, broadly distributed functions pose a greater challenge. To further explore this idea, we compared observed and predicted AR profiles for 82 gut metagenomes from HMP individuals. We predicted the AR profile for a given gut metagenome by first quantifying the sample’s microbial community composition with MetaPhlAn [29]. This step resulted in a vector of relative abundance measurements for species present in the sample (in RPKM units). Then, using the ShortBRED based-annotations of AR functions in bacterial genomes described above, we computed the abundance of each AR function in the sample by multiplication. For example, if species A had a relative abundance of 5 RPKM and contained 1 copy of AR protein X, while species B had a relative abundance of 10 RPKM and contained 2 copies of AR protein X, then the total abundance of AR protein X in the metagenome was predicted to be:
1(5RPKM)+2(10RPKM)=25RPKM.
This procedure was repeated for all samples and AR functions.
At the level of individual AR gene families, ShortBRED and the predicted profiles co-detected 63 families, 57 were detected by ShortBRED but never observed in the predicted profiles, and 14 were predicted to be present but never confirmed by ShortBRED. Among the co-detected families, the average quantitative agreement between the ShortBRED and predicted profiles (as measured by Spearman’s correlation) was 0.43. When gene families were grouped into broader AR classes, 17 were co-detected, 5 were found only by ShortBRED, and 1 was predicted to occur but not confirmed by ShortBRED. Average quantitative agreement for the 17 co-detected classes was 0.33 (Spearman’s correlation). The AR classes most under-represented by the community composition-based predictions were tetracycline ribosomal protection, Class A beta-lactamase, rRNA methyltransferase, MFS antibiotic efflux, and RND antibiotic efflux (S6 Table). Notably, tetracycline resistance was also among the most broadly-distributed AR classes.
In addition, individual-specific ShortBRED-based versus predicted AR profiles showed poor quantitative agreement (average Spearman correlation = 0.53). There are a number of reasons why the two profiles would agree poorly on an individual basis or for particular AR families. While ShortBRED is able to profile AR gene abundance in cases where the genes are present in uncharacterized genomes, the taxonomic profile method is limited to species with known isolate genomes. Hence, predicting AR content from taxonomic composition will tend to underestimate AR content, and explains why ShortBRED detects several families that the predictive method does not. In instances where multiple isolate genomes were available for a species detected in a sample, the species’ contributions were based on the median gene copy number for each AR family across its isolate genomes. If the sample isolate contained fewer copies of an AR gene than the median estimate, then we would tend to overestimate its abundance; conversely, if the sample isolate contained more copies of an AR gene than the median estimate, then we would tend to underestimate its abundance (both serving to weaken signal-to-noise ratio among the predictions). For these reasons, directly profiling AR content in a metagenome is preferable to predicting functional content from community composition. ShortBRED offers a means to profile AR content and other protein families in a fast, highly specific manner.
In this work, we have presented and validated ShortBRED: a tool to build short peptide markers for protein families and then apply them to profile protein family content in a metagenomic sequencing sample. We demonstrated that ShortBRED is both faster and more accurate than the common approach of mapping reads to full-length protein family centroid sequences. ShortBRED is extensible to a diverse collection of functional profiling tasks. The most straightforward of these was demonstrated in our profiling of antibiotic resistance in human gut metagenomes, which we discuss further below. Although this example was based on DNA sequence data, ShortBRED’s markers are also applicable for profiling microbial community RNA-Seq data (metatranscriptomics), which reveals the relative functional activity of protein families in a community. In addition to profiling meta’omic sequencing data, ShortBRED’s markers have proven useful for identifying protein families of interest in microbial isolate genomes, as the markers’ small sizes and highly representative sequences facilitate rapid, unambiguous gene annotation. The functional profiles produced in these applications are amenable to a variety of downstream analysis methods, including comparing functional composition in case versus control samples or monitoring temporal variation in functional composition or activity from longitudinal samples.
Mapping metagenomic reads to protein families of interest is an example of a search problem in which new queries (samples) arise more frequently than changes to the database (proteins of interest). In such cases, it is desirable to pre-process the database in order to accelerate downstream search. ShortBRED accomplishes this by reducing large numbers of protein sequences first to clusters of related proteins (families) and then to representative peptide markers. Searching a new metagenomic sample against these marker sequences represents a considerable savings in computation relative to searching against the full database. ShortBRED’s pre-processing steps, while not computationally trivial, can reduce a collection of ~1,000 protein families to identifying markers in a matter of hours on typical desktop or server hardware (i.e. taking advantage of multiple CPU cores for parallelization, but not requiring special high-memory or accelerated file I/O configurations). The bottleneck in this process is the BLAST-based search of the proteins of interest against the universal protein reference database. In the future, it may be possible to further accelerate pre-processing steps by incorporating an alternative program for protein homology search, provided that it meets or exceeds BLAST’s sensitivity. In the same vein, downstream performance mapping reads to markers depends largely on the speed of ShortBRED’s chosen translated search tool (currently USEARCH), which could also be replaced or supplemented by future alternatives.
In our evaluations, the vast majority of protein families could be identified by one or more unique amino acid subsequences (True Markers). Although these sequences are used here for protein family identification and quantification, they are themselves interesting targets for investigation. For example, the conservation of these sequences within a family may indicate the presence of a functionally relevant domain, peptide recognition motif, or enzyme active site. The small minority of protein families that lacked unique identifying subsequences are also worthy of consideration (Tables 1 and 2). In such cases, ShortBRED constructs a Quasi Marker to represent the family: i.e. the amino acid sub-sequence which, while not unique to the family, is found in a minimal number of other families. Users may wish to exclude Quasi Markers (and their associated families) in their analyses to increase specificity. That said, Quasi Markers were always included in the analyses reported here and were found to compromise specificity only slightly (far less than the centroid-based approach; Fig 2 and Table 2). In the future, an expectation maximization (EM) step could be incorporated in ShortBRED-Quantify to improve the accuracy of protein family quantification when mapping reads to ambiguous Quasi-Markers
We demonstrated ShortBRED’s utility by generating and applying AR gene markers to profile AR gene content in 552 human gut metagenomes and 3,305 bacterial isolate genomes. AR determinants in pathogens are increasingly compromising infectious disease treatment due to their acquisition from commensal or environmental bacteria [30, 31]. The human gut microbiome serves as a transferable reservoir of AR readily available to human pathogens [32], leading to an increased focus on characterization and quantification of AR genes in large metagenomic studies [17, 18, 33]. However, accurate quantification of AR genes using short reads is challenging: AR determinants are often originally genes with diverse native functions repurposed through mutation or expression modulation to provide AR [34], therefore sharing large sequence similarity to genes with no AR function. For example, when particular RND efflux pumps (such as CmeABC, AcrB, and Mex) highly expressed, they are capable of exporting multiple antibiotics [35]. However, members of the RND efflux pump superfamily also serve important functions as transporters of proteins required for nodulation and cell division and, while they do not always demonstrate inherent AR activity, they share high sequence similarity with proteins shown to serve resistance functions. As a result, previous attempts to profile antibiotic resistance in human gut metagenomes by mapping short reads to full-length protein sequences may have been compromised by spurious mapping events.
Our ShortBRED-based profiles avoided this complication by using only the most information-rich portions of AR genes for identification and quantification of AR in microbial communities and isolate genomes. Notably, our results agree with several of the major findings from previous profiling attempts, specifically (i) high relative abundance of AR genes in the gut microbiota of Chinese individuals compared to individuals from other countries as well as (ii) ubiquity of tetracycline resistance worldwide [17, 18]. Hence, we can be confident that these results are not the result of spurious mapping to full-length protein sequences. ShortBRED demonstrated increased sensitivity for identification of additional classes of AR genes, including resistance to the quinolone class of antibiotics. The application of ShortBRED to the identification and quantification of AR genes in microbial communities addresses a significant challenge in the computational investigation of AR using high-throughput sequencing technology. In addition, just as ShortBRED markers enabled confident differentiation between closely-related AR and non-AR proteins in metagenomes, the same advantage applied to annotating full-length protein sequences in bacterial isolate genomes. Indeed, we used this method to dissect phylogenetic properties of the AR families under study, revealing distinct patterns of clade-specific versus broad distribution. In the future, the same technique could be applied to quickly and accurately determine AR gene content in a newly-sequenced bacterial strain—an application with relevance to infectious disease management.
To facilitate such applications, the antibiotic resistance markers produced here are available for download, along with the ShortBRED software and documentation, at the ShortBRED website: http://huttenhower.sph.harvard.edu/shortbred. Although the preceding examples have focused on applying ShortBRED to profile antibiotic resistance in genomes and metagenomes, this is only one possible application. Indeed, the same analyses described above can be applied to a wide range of protein families of interest, limited largely by the imagination of the user. To that end, users who produce marker sets with ShortBRED and who would also like to share them with the scientific community are encouraged to submit the markers (along with a relevant citation) for posting on the ShortBRED website.
ShortBRED-Identify takes two inputs: (i) a FASTA file of proteins of interest and (ii) a comprehensive catalog of reference protein sequences (as a FASTA file or preformatted BLAST database). The reference database used here was based on version 3.5 of the Integrated Microbial Genomes database [24]. The full version of this database contained 12,607,998 protein coding sequences, which we previously reduced to 4,981,629 representative protein coding sequences proteins by clustering at 80% nucleotide identity [36]. As of this writing, IMG is no longer available for download, and we recommend using UniRef100 or UniRef90 as alternative comprehensive protein reference datasets [37].
ShortBRED-Identify first clusters the proteins of interest at 85% identity using CD-HIT [38, 39] to group them into highly conserved protein families. For each clustered protein family, ShortBRED-Identify first calls MUSCLE [40] to generate a multiple sequence alignment (MSA) for the family, then uses Biopython [41] to generate a consensus sequence for the MSA. If the most common amino acid for a given MSA column occurred in less than 95% of sequences, the corresponding position in the consensus sequence is marked as ambiguous (“X”).
ShortBRED-Identify then uses BLAST [4] to query consensus sequences (i) against one another and (ii) against the reference protein database. The results of these searches are used to identify short segments of each consensus sequence that align with high sequence identity (≥90%) to unrelated proteins in the reference database, or share high identity with a length greater than 80% of minimum marker length with other consensus sequences. (A short sequence is defined as having a length between 80% of the minimum marker length and 15% of a target sequence in the reference database.) Metagenomic reads derived from such segments will be prone to false positive matches across protein families. ShortBRED-Identify thus interprets the BLAST results to find segments of a consensus sequence that participate in a minimal number of such alignments (markers) and then uses these sequences as a basis for more accurate functional profiling. Consensus sequences from different families can share long regions of similarity even after initial clustering at high sequence identity. Because of this, ShortBRED-Identify penalizes high-identity alignments of any length greater than 80% of marker length between pairs of consensus sequences in order to minimize inter-family false positives. ShortBRED does not penalize high-coverage, high-identity alignments between a consensus sequence and a protein from the reference database, as such proteins are likely members of the protein family represented by the consensus.
ShortBRED counts the number of times each amino acid of each consensus sequence appeared in a valid alignment with another protein. These “overlap counts” are then used to identify the most representative segments (markers) for the consensus. For a given consensus sequence, ShortBRED will first try to build as many “True Markers” as possible. A True Marker is a contiguous sequence of amino acids with zero overlap count; i.e. the corresponding peptide was unique among the consensus sequences and non-member reference sequences. If no True Markers are found above a minimum length (with a default of 8 amino acids), ShortBRED next tries to make up to three “Junction Markers” for the consensus sequence. A Junction Marker is a sequence of amino acids that partially overlaps with other consensus sequences or reference sequences, but is not completely overlapped by any single consensus or reference sequence. Note that when mapping reads to marker sequences, ShortBRED-Quantify requires high-identity (≥95%) and high-length (minimum of the marker’s length or 95% of a read length), and hence these partial overlaps will not lead to false positive matches. If it is not possible to build a True Marker or a Junction Marker for a consensus sequence, ShortBRED-Identify will create a single “Quasi Marker” for the consensus, which is a sequence of amino acids above a given minimum length (with a default of 33 amino acids) that has the lowest total adjusted overlap count. The adjusted overlap count is the fourth root of the raw overlap count, and helps to down-weight very short outlier regions with extremely high overlap counts. Protein families with similar Quasi Markers and Junction Markers (≥95% identity) are merged, and then all marker sequences are output as a FASTA file for use by ShortBRED-Quantify.
For Junction Markers and Quasi Markers, ShortBRED also lists the percentage of each marker that overlaps with each other consensus sequence. Each sequence is given a weight, which is defined as its total length in amino acids divided by the sum of that value, and all overlapping amino acids from other reference or consensus sequences. The weight is printed in the FASTA header, along with other highly overlapping consensus sequences from the input database. An additional text file lists the overlapping regions from the consensus sequences and the reference database.
This ShortBRED-Identify process requires ~100 CPU-hours to complete given a set of proteins of interest which cluster to ~1,000 protein families. The bottleneck in this process is the BLAST-based search of the protein family consensus sequences against the comprehensive reference database. Notably, this process is highly parallelizable, as each consensus sequence can be searched independently of the others. By allowing ShortBRED-Identify to use multiple cores during the search process, the actual run-time can be reduced considerably. Once the initial BLAST results have been generated, new markers can be generated in a few minutes provided that the initial clustering identity and consensus thresholds are not changed. Precomputed markers for the antibiotic resistance proteins (ARDB) [13] and virulence factors (VFDB) [14] are available for download at http://huttenhower.sph.harvard.edu/shortbred. Notably, the ShortBRED-Identify process needs to be applied only once to produce a set of markers, which can then be used repeatedly to profile metagenomic datasets using ShortBRED-Quantify.
After markers have been created for each protein family, the user can call ShortBRED-Quantify to profile the relative abundance of these families in a whole metagenomic shotgun (WMS) sequencing sample. ShortBRED-Quantify calls USEARCH [5] to find the best matching marker for each nucleotide read. USEARCH specializes in fast search for high-identity matches, which fits with ShortBRED’s objective of profiling metagenomic samples quickly with high specificity.
By default, ShortBRED-Quantify will record a hit to a marker if the resulting alignment has at least 95% identity, and is at least as long as the minimum of (i) the marker length or (ii) 95% of the read length. For each marker, ShortBRED-Quantify computes an adjusted marker length, which takes into account how much of the marker is available to participate in a hit meeting our length and percent identity requirements. When a marker of length L is longer than the average read length (R), a read from the corresponding gene anywhere in the region from 5% downstream of the marker to 5% upstream of the marker is allowed to align to the marker. Therefore, the adjusted marker length (L’) is:
L′=L−0.9R+1
When the marker is shorter than the expected read length (L<R), the we require the entire marker to align to the read. Thus, the adjusted marker length is:
L′=R−L−1
ShortBRED then normalizes the number of raw USEARCH hits to a marker (H) to produce a normalized count (C), adjusting for average read length, marker length, and sequencing depth (number of reads in the sample, N):
C=H(L′103)(N106)=HL′N×109
The normalized count is in units of RPKMs (reads per kilobase of reference sequence per million sample reads). For protein families characterized by multiple markers, a normalized count is first computed for each marker separately and then the median of these values is taken to represent the protein family; this procedure adds robustness to variation in sequencing depth across the markers. Finally, ShortBRED-Quantify outputs these normalized counts as a relative abundance table for the protein families of interest.
We used GemSim [42] to create synthetic metagenomes containing five million 100 nucleotide-long reads, designed to mimic a typical WMS-sequencing run on an Illumina HiSeq instrument (Illumina, San Diego, CA). Reads were drawn from twenty bacterial genomes obtained from the KEGG database [43, 44]. We used USEARCH [5] to identify and exclude from these genomes any naturally-occurring antibiotics resistance genes and virulence factors (defined as a sequence matching a gene from the ARDB or VFDB with >90% identity). This ensured that the only ARDB and VFDB sequences in our synthetic metagenomes were those that had been artificially spiked in for the purposes of evaluating ShortBRED. Each bacterial genome was assigned an abundance value drawn from a log-normal distribution with unit mean and standard deviation.
We created six Illumina-like synthetic metagenomes with material spiked in from the ARDB and VFDB sequence datasets. Three metagenomes were made for each dataset, with 150, 500, and 1,000 genes from the corresponding protein dataset spiked among the genomic reads at 5%, 10%, and 25% relative abundance. Two additional sets of Illumina-like synthetic metagenomes were created with 3% and 5% of the amino acid content of the sequences mutated based on relative amino acid mutability and transition probabilities from the BLOSUM62 table. An additional set of six metagenomes were created using the same procedure but based on a simulated 454 sequencing instrument (454 Life Sciences, Branford, CT); these samples contained only 155,890 reads each, consistent with the lower sequencing depth of the 454 platform. We used 164 nucleotide sequences corresponding to ARDB protein sequences as a base for the ARDB metagenomes and 2,296 VFDB nucleotide sequences as a base for VFDB metagenomes. Nucleotide sequences were not always provided for ARDB proteins; in these cases, we used the EMBOSS program backtranseq [45] to create nucleotide sequences that were compatible with the available amino acid sequences.
Code for creating the synthetic metagenomes can be found at http://bitbucket.org/biobakery/shortbred_doit.
We applied ShortBRED to profile antibiotic resistance (AR) in the human gut microbiome. We first produced a set of new AR marker sequences by applying ShortBRED-Identify to a combination of (i) a curated version of the ARDB which we obtained by deleting sequences no longer stored at NCBI and (ii) a set of known antibiotic resistance genes obtained from resistant bacterial libraries. We then used ShortBRED-Quantify to profile the relative abundance of corresponding AR protein families across 552 gut metagenomes: 82 from U.S. adults sampled during the Human Microbiome Project (HMP) [10], 363 from Chinese adults with and without diabetes [11], and 107 individuals from Malawi, Venezuela, and the U.S. [12]. We used the first-visit samples from multi-visit HMP subjects to avoid redundancy. For 454-based samples characterized by sub-optimal sequencing depth, we mapped reads to full-length centroid sequences to avoid compromising sensitivity.
ShortBRED can be applied to identify protein families in a bacterial isolate genome given a corresponding set of ShortBRED markers for those families. To do so, ShortBRED first creates a USEARCH database for the genome and then searches the markers against that database (allowing for multiple hits per marker query). For protein families characterized by more than one marker sequence, ShortBRED requires that a critical fraction of the markers map to a gene in the genome before assigning it to that protein family. The default value for this cutoff is 10% [i.e. 1 in 10 markers], but it can be tuned to be more conservative.
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10.1371/journal.pcbi.1004271 | SeqGL Identifies Context-Dependent Binding Signals in Genome-Wide Regulatory Element Maps | Genome-wide maps of transcription factor (TF) occupancy and regions of open chromatin implicitly contain DNA sequence signals for multiple factors. We present SeqGL, a novel de novo motif discovery algorithm to identify multiple TF sequence signals from ChIP-, DNase-, and ATAC-seq profiles. SeqGL trains a discriminative model using a k-mer feature representation together with group lasso regularization to extract a collection of sequence signals that distinguish peak sequences from flanking regions. Benchmarked on over 100 ChIP-seq experiments, SeqGL outperformed traditional motif discovery tools in discriminative accuracy. Furthermore, SeqGL can be naturally used with multitask learning to identify genomic and cell-type context determinants of TF binding. SeqGL successfully scales to the large multiplicity of sequence signals in DNase- or ATAC-seq maps. In particular, SeqGL was able to identify a number of ChIP-seq validated sequence signals that were not found by traditional motif discovery algorithms. Thus compared to widely used motif discovery algorithms, SeqGL demonstrates both greater discriminative accuracy and higher sensitivity for detecting the DNA sequence signals underlying regulatory element maps. SeqGL is available at http://cbio.mskcc.org/public/Leslie/SeqGL/.
| Transcriptional regulation is the cell’s primary mode of controlling gene expression. Transcription factors (TFs) are proteins that recognize and bind specific DNA sequence signals to regulate the expression of target genes. Recent years have seen the rapid development of genome-wide assays to profile the binding locations of a single TF or, more generally, regions of open chromatin that are occupied by a complex repertoire of DNA binding factors. New methods are therefore needed to detect and represent DNA sequence signals in these genome-wide regulatory element maps. Here we present a novel tool called SeqGL to extract multiple TF binding signals from genome-wide maps. SeqGL employs a machine learning framework to identify features that best discriminate the peaks, where we expect DNA sequence signals to occur, from the flank regions that should not contain these signals. Our tool performed significantly better than widely used motif discovery methods in discriminative accuracy and achieved higher sensitivity in detecting the numerous sequence signals underlying regulatory element maps.
| Transcription factor (TF) ChIP-seq profiles and genome-wide regulatory element maps based on DNase I hypersensitive site sequencing (DNase-seq) or transposase-accessible chromatin sequencing (ATAC-seq) implicitly contain rich information about the cell-type specific and genomic-context dependent binding of multiple factors. Traditional analysis of ChIP-seq profiles involves searching for motifs that are significantly enriched in peaks relative to a background model, either using a library of known motifs [1–3] or through de novo motif discovery algorithms [4–9]. However, we hypothesize that motif discovery approaches may miss more subtle cofactor signals that explain a subset of the ChIP peaks and may fail to adequately generalize to the high multiplicity of TF binding signals in DNase profiles. Meanwhile, several methods use DNase-seq profiles to scan for instances of known motifs [10, 11], and one recently proposed approach exploits the read-level properties of high-depth digital genomic footprinting (DGF) to improve localization of known motifs [12]. However, these methods do not enable de novo discovery of binding signals that are not represented in TF motif databases, and methods that rely on the depth and read-level properties of DNase I cleavage in DGF may not readily generalize to newer assays like ATAC-seq, which can be used in low cell number settings where DNase-seq is not feasible.
Here we present a new and flexible discriminative learning tool called SeqGL (Fig 1) that uses group lasso regularization [13] to identify multiple context-dependent TF binding signals from a single ChIP-, DNase-, or ATAC-seq profile. SeqGL does not search for instances of known TF motifs but rather learns binding signals de novo from the profile. These binding signals are based on weighted k-mer scoring and can be summarized as motifs and compared to known TF motif databases; however, SeqGL has the potential to discover novel motifs or distinct variants of known motifs. In extensive benchmarking experiments on ENCODE TF ChIP-seq data, we show that SeqGL outperforms widely used motif discovery methods both for the discriminative task of distinguishing TF ChIP peaks from flanking sequences and for cofactor signal detection. Further, SeqGL successfully scales to the complexity of regulatory signals in DNase-seq or ATAC-seq profiles, identifying numerous TF binding signals in DNase- or ATAC-mapped regulatory regions that are confirmed by ChIP-seq. Finally, we show how SeqGL can be trained in a multi-task setting, where we jointly train on experiments from multiple cell types in order to identify shared and cell-type specific binding signals or encode information about genomic context, such as gene proximity or chromatin state, into the task structure to reveal more detailed regulatory sequence information.
SeqGL identifies TF sequence signals underlying ChIP-seq or DNase-seq/ATAC-seq peaks by training a discriminative model based on k-mer features on peaks (positive examples) versus their flanks (negative examples), building on our previous efforts in learning discriminative models of TF binding preferences [14, 15] (Fig 1). We use a k-mer-based feature representation related to the wildcard kernel [16] for the learning framework (Materials and Methods). Hierarchical clustering of these features across peak and flank sequences reveals a block structure, identifying subsets of k-mers that co-occur in subsets of examples. Thus we encode these k-mer clusters or groups using a sparse group lasso constraint [13] in a logistic regression model, which assigns non-zero weights to k-mer groups that significantly discriminate between peaks and flanks while setting other groups uniformly to zero (Materials and Methods). We view each non-zero k-mer group as the potential binding signal of a particular TF. In order to associate each group signal with the TF motif for visualization and identification, we first determine examples that are significantly discriminated by the k-mer group using an empirical null distribution and extract sequence windows containing the significant hits (Materials and Methods). We then use an existing motif algorithm (HOMER, [5, 17]) to generate a motif from these windows. Note that the motifs identified by HOMER are used for visualization and comparison to existing motif databases and not for prediction of binding sites. Thus SeqGL predicts multiple TF binding profiles for a DNase-seq/ATAC-seq or ChIP-seq experiment corresponding to k-mer group signals, along with associated motifs and significant hits. TFs are organized as structural families that often share a motif or have very similar motifs in existing databases. Therefore, SeqGL typically associates each non-zero k-mer group with the motif of a TF family rather than a specific factor. In analyses and validations presented below, we used existing ChIP-seq data or mRNA expression when available to resolve the specific factor of the family.
We compared the performance of our method for the task of discriminating peaks from flanks to a number of widely used motif finding tools: HOMER (a PSSM-based approach designed for TF ChIP-seq data) [5], DREME (a k-mer-based discriminative motif tool in the MEME suite) [4], and MEME-ChIP (an EM-based motif tool) [6]. Our benchmark dataset consisted of 105 different ENCODE [18] ChIP-seq experiments across two cell lines: GM127878, a lymphoblastoid cell line, and H1-hESC, an embryonic stem cell line. We used the multiple motifs identified by each tool in different settings to compare the performance. “Best motif” uses PSSM scores from the best motif identified by the tool for each example (mean auROC for HOMER:. 775, DREME:. 747 and MEME-ChIP:. 777). “Max motif” uses the maximum log odds score of any motif for each example (mean auROC for HOMER:. 739, DREME:. 781 and MEME-ChIP:. 738). We found that SeqGL performs significantly better than all the tools in both these settings (Wilcoxon rank sum p-values < 7e-3) (S1 Fig; mean auROC for SeqGL:. 921). We note that all methods find the “known” motif in almost the same number of experiments (S2 Table); therefore, the performance advantage of SeqGL derives in part from combining multiple signals. For this reason, we also trained a “Motif elastic” model for each motif discovery tool, using elastic net logistic regression [19] with the PSSM scores for all motifs as features. (Materials and Methods). Motif elastic is the method most comparable to SeqGL since we expect each k-mer group in SeqGL to represent binding preferences of a particular transcription factor. We note that the “Motif elastic” is the best performance setting for each motif tool, and yet SeqGL significantly outperforms “Motif elastic” for all tools (Fig 2, mean auROC for HOMER:. 858, DREME:. 846 and MEME-ChIP:. 876). This result demonstrates the advantage of representing binding signals as weighted k-mer scoring models and learning these signals at the same time as the peaks-vs-flanks classifier.
Several other k-mer based discriminative models have recently been proposed to learn TF binding preferences from ChIP-seq data, including two SVM methods: the di-mismatch kernel, based on k-mer features in the dinucleotide alphabet counted with mismatches [14]; and the gkm-SVM method [20], which is very similar to the wildcard kernel introduced some time ago [16]. Importantly, both these kernel methods represent the binding model as a “bag of k-mers”, which does not allow obvious extraction of multiple distinct binding signals. Nevertheless, both methods were able to outperform single motif methods for the statistical task of discriminating peaks from non-peaks in held-out examples from the training ChIP-seq experiment, and the gkm-SVM (similar to the wildcard kernel) computes the kernel over all k-mers with a fixed length and number of wildcards and trains in the dual space. Therefore, it is worth comparing to these approaches to confirm that our group lasso regularization in the primal space of a reduced set of k-mers still retains the advantage of previous kernel methods.
For our method comparison, we used SeqGL both with the default 5K features (8-mers with up to two consecutive wildcards) and with 30K features. We compared to the di-mismatch kernel using 5K features and published parameters and to gkm-SVM using 10-mers with up to 4 wildcards; we also performed a simple elastic net regularization with logistic regression on the set of 10-mer features with up to 4 wildcards (Fig 2). When we evaluated performance differences between SeqGL (5K features) and other k-mer methods with a Wilcoxon rank sum test, no method significantly outperformed SeqGL, while SegGL did have a significant win over the di-mismatch kernel (median auROC of. 921 versus. 884, p < 2e-10, Wilcoxon rank sum test); when we used all di-mismatch features, its performance improved (median auROC of. 906) to a statistical tie with SeqGL. The gkm-SVM method obtained a slightly higher median auROC of. 931, but the performance difference compared to SeqGL was not statistically significant (p = 0.06, Wilcoxon rank sum test). When we increased SeqGL to retain 30K k-mer features, performance improved (mean auROC of. 927), giving a statistically tie with gkm-SVM and elastic net on 10-mer features with up to 4 wildcards. All the k-mer based methods outperformed all the motif elastic methods (p < 3e-4, Wilcoxon rank sum test, for all pairwise comparisons). We therefore concluded that SeqGL, even with shorter k-mers and only 5K features, achieved statistically equivalent discriminative performance to more computationally expensive kernel methods that use a much larger implicit feature space.
PAX5 is an important B cell lineage factor expressed at early stages of B cell differentiation [21]. Therefore we used PAX5 ChIP-seq data in GM12878 to examine the co-factor binding profiles identified by SeqGL (Fig 3). SeqGL was run with 20 groups and identified 7 groups to be significantly predictive of peaks compared to flanks. The top panel of Fig 3A shows the group scores for three highest scoring groups ranked by their predictive power of peaks compared to flanks, and the bottom panel shows the ChIP-seq read counts for the corresponding TFs. While SeqGL identified 7 groups as predictive of PAX5 peaks, we are highlighting the three highest ranked groups for simplicity. As expected, the top-scoring group in the PAX5 ChIP-seq experiment identifies sites that are strongly associated with the canonical PAX5 motif. The other groups are associated with AP family and PU.1 motifs, which have prominent roles in B cell function [21]. We next used existing ChIP-seq data to validate these predictions (S2 Fig, Materials and Methods) and found that PAX5 peaks predicted by these two groups are indeed bound by AP family factors and PU.1 respectively. Furthermore we also identified BATF as the specific AP factors since peaks associated with this group are most enriched for BATF ChIP-seq peaks. Interestingly, even though the PAX5 ChIP-seq read densities are uniform across all peaks, the group scores show significant differences, and a significant number of PAX5 peaks do not have a sequence signal for PAX5. We propose that this observation is due to different modes of binding. Fig 3B shows specific examples of these modes of binding. The left panel shows the direct binding mode: a TF recognizing its canonical motif. The middle panel shows that even though there is a strong PAX5 peak, the sequence signal is actually derived from a different factor, BATF, indicating either indirect PAX5 binding via a protein-protein interaction or potentially a distal looping interaction. This illustrates that for a region with ChIP-seq peaks for multiple factors; the binding signals need only come from a subset of those factors. Finally the right panel demonstrates co-binding of PAX5 and PU.1 with each factor recognizing its respective motif. These observations are consistent with the different modes of interaction between TFs identified by Wang et al. [8]. The fraction of peaks with non-canonical signal is dependent on the TF; we observed a continuous spectrum across ChIP-seq experiments, with some TFs showing exclusively canonical signals and others showing a mix of canonical and non-canonical signals (S3 Fig and S3 Table). We note that gkm-SVM has a procedure for producing PSSMs from the top ranked k-mers in the model and reports three motifs per TF ChIP-seq experiments. However, when we compared results with SeqGL and HOMER, we saw that gkm-SVM missed many of the co-factor signals identified by SeqGL and indeed often returned three variants of the same motif (S4 Table).
We next used SeqGL to examine the connection between binding context and sequence signals in TF occupancy profiles. To this end, we used a multitask technique [22] to identify gene proximal and distal binding profiles of POU2F2, a B cell maturation factor [23]. Briefly, proximal and distal peaks are considered two different classification tasks for multitask learning. This formulation combines the peaks of the two tasks to create a third task also called the “common” task. All the three tasks are solved simultaneously to identify factors that are not only common to both tasks but also specific to each task i.e., context independent as well as context-specific factors (Materials and Methods, S4 Fig). Fig 4A illustrates that as expected the octamer motif representing the OCT TF family is strongly associated with both proximal and distal peaks, as is the motif for ETS, which binds at both proximal and distal sites. Interestingly, the proximal sites show a strong association with CG-rich motifs whereas the distal sites are associated with factors like BATF and TCF, which are necessary for B cell function [21]. This is consistent with the observation that cell type information is encoded at distal enhancers rather than proximal promoters [24]. Note that the motifs for YY1, ETS, ZNF and TCF families were detected specifically by SeqGL and not by HOMER. On a similar note, we used the same multitask technique to encode the cell type context of TCF12 by joint training on ChIP-seq data for this factor in both GM12878 and H1-hESC (Fig 4B). As expected, the TCF motif is associated with peaks common to both cell types whereas the candidate binding partners are completely different and are key regulators of the particular cell type (BATF and RUNX for GM12878; TEAD and PRDM for H1-hESC).
The expression levels of binding partners may play an important role in determining the context for binding of certain transcription factors. In both the POU2F2 and TCF12 analysis, the enhancer and cell-type specific profiles respectively had cell-type specific transcription factors as candidate binding partners. To explore this further, we applied SeqGL to the binding profiles of the enhancer binding factor p300 in different cell types. p300 peaks across different cell types are largely cell-type specific (Fig 4C) and the binding profiles are enriched for factors that are expressed specifically and thus functionally relevant in the respective cell type (Fig 4D). Please note that the specific factors were identified using mRNA expression for groups associated with TF families. BATF, IRF4 and IRF8 show GM12878-specific expression; TEAD4 and NANOG, genes that play a central role in embryonic stem cells [25], are specifically expressed in H1-hESC along with ELK1; HNF4A, a gene necessary for liver development [26], is specifically expressed in HepG2, a hepatocellular carcinoma cell line; and finally GATA1, which is involved in myeloid development [27], and MYB are specifically expressed in K562, a myelogenous leukemia cell line. These results demonstrate that beyond the DNA sequence signal, the cell type and genomic context of binding for a particular TF may define its binding partners, and furthermore the expression of potential binding partners can lead to altered binding profiles.
SeqGL is particularly effective for determining TF binding profiles in DNase-seq data since DNase peaks contain signals for a large multiplicity of transcription factors. Our group lasso approach is well suited to capture this diversity of sequence signals in DNase peaks. We used DNase-seq data from GM12878 because of the availability of an immense collection of ChIP-seq experiments in this cell type. As a first step, we used MACS [28] to identify broad DNase peaks, followed by PeakSplitter [28] to identify subpeaks within the broader peaks (S5 Fig). We then used IDR [29] to identify a robust set of reproducible subpeaks across replicates (Materials and Methods).
Fig 5A shows the predicted TF binding profiles of broad DNase peaks in GM12878 after summarizing the group scores over subpeaks. A total of 16,891 peaks are shown with group scores for top 30 groups. 68/200 groups are associated with DNase peaks and contain motifs for 38 TFs (S5 Table). A number of groups are associated with motif variants of the same TF. HOMER and MEME-ChIP were unable to identify 14/38 motifs (S5 Table). While HOMER is able to find motifs that are significantly enriched in the peaks, SeqGL specifically identified motifs for TFs such as EBF1, E2A, and SOX4, which are important for B cell function but present in a smaller fraction of DNase peaks. Furthermore we validated 37/46 groups with ENCODE ChIP-seq data (indicated by “*” in Fig 5A). There is a strong enrichment for transcription factors with known function in B cell identity and activity (S6 Table). Closer inspection of the group scores revealed that a subset of peaks (9262 out of 34303) have a signal for a single transcription factor (Fig 5B, left panel) whereas a larger subset (20048 out of 34303) have sequence signals for multiple factors (FDR-corrected p < 0.01) (Fig 5B, middle panel). Note that the BATF-RUNX pattern is one of the many strongly appearing co-binding patterns (S7 Table). This observation is similar to the results from PAX5 ChIP-seq (Fig 3B), where a PAX5 peak is not necessarily accompanied by an underlying PAX5 motif. Furthermore subpeaks identified from a single broad peak can have sequence signals for different groups/factors (Fig 5B, right panel) highlighting the value of splitting broad peaks into their constituent components. Thus SeqGL learns extensive regulatory sequence information from DNase-seq by predicting binding profiles for multiple TFs and identifying their combinations. Furthermore, a number of groups are associated with motifs that only partially match to known motifs indicating that these are either variants of existing motifs or potentially novel motifs that have not been characterized (S6 Fig).
We further assessed the ability of SeqGL to identify binding profiles for multiple TFs using the recently developed ATAC-seq assay in GM12878, an alternative approach for mapping regions of open chromatin that can be performed on 500–50,000 cells [30]. Using the same settings as for DNase-seq peaks, SeqGL identified 30 group signals associated with ATAC-seq peaks. Corresponding TF ChIP-seq data is available for 23 group signals, and we were able to validate the predictions for 18 of these 23 groups (S8 Table). The TFs identified in ATAC-seq peaks are primarily a subset of TFs identified using GM12878 peaks with the more frequent TFs identified in both datasets. Fig 6 shows the distribution of maximum scoring TFs for DNase-seq (Fig 6A) and ATAC-seq peaks (Fig 6B). Cell type factors like BATF, IRF and RUNX are strongly represented in both datasets whereas the promoter binding TF NRF and insulator protein CTCF have strong enrichment in DNase-seq and ATAC-seq peaks, respectively. Interestingly, the fraction of intergenic peaks is significantly higher in ATAC-seq compared to DNase-seq (42% in ATAC-seq compared to 33% in DNase-seq). This difference in TF signal distribution is also present in peaks common to DNase-seq and ATAC-seq (S7 Fig), suggesting that the assay-dependent training set alters the relative strengths of the group signals for different TFs.
Cell type and chromatin state both provide the context determinants for the TF binding profiles underlying mapped regulatory elements. For example, while a number of DNase peaks are cell-type specific, a significant fraction show comparable accessibility in multiple cell types (S8 Fig). We built binding profiles for DNase peaks common to both GM12878 and H1-hESC and peaks specific to the two cell types (Materials and Methods). As expected, the cell-type specific profiles are associated with cell-type specification transcription factors: BATF, IRF, PU.1 and RUNX in GM12878 and OCT4, SPI1 and NANOG in H1-hESC (Fig 7A). Intriguingly, peaks that are common to both cell types contain not only promoter-associated factors like NFY but also the insulator protein CTCF. This appearance of insulator/structural proteins was consistently observed in many comparisons and thus may indicate that the broader domains of regulation remain consistent across different cell types [31]. Furthermore, we also explored the chromatin context in the H1-hESC cell line. Using ENCODE ChromHMM segments [32], we predicted TF binding profiles for DNase peaks in active promoters and enhancers. Active promoters as expected contained CG-rich and motifs for TFs such as NFY and SP1 that are known to bind promoter regions whereas, interestingly, enhancers are associated not only with cell-type specification factors but also with CTCF (Fig 7B). This result suggests that both cell-type specification factors and structural proteins are needed to build the enhancer landscape of cells.
The use of k-mers for the representation and discovery of regulatory motifs has a long history. Several early papers used over-represented k-mers to identify TF binding sites and other sequence signals (e.g. [33, 34]), and methods like Weeder and MITRA organized efficient searches for enriched k-mers and composite k-mer patterns, respectively, using traversal of suffix tree or retrieval tree data structures to count occurrences of k-mer occurrences with inexact matches [35, 36]. The first k-mer based string kernels were introduced shortly afterwards [37, 38] for the problem of SVM classification of protein domains and used the same retrieval tree data structure for efficient kernel computation of k-mer features with mismatches, wildcards, or gaps [16]. However, it was also recognized in those early years that k-mer based SVMs could be used to model regulatory sequences in DNA and RNA. The original application in this domain was for extracting intronic splicing silencers and enhancers [39], and subsequently k-mer kernel methods were introduced for recognition of alternatively spliced exons, gene structure prediction, and nucleosome positioning [40–42], where in each case, the discriminative k-mer model captured subtle regulatory sequence signals.
With the advent of large-scale in vitro and in vivo TF binding assays, discriminative learning of TF binding preferences using discriminative k-mer methods became feasible. The first study of this kind introduced the di-mismatch kernel with SVR and SVM models to learn TF binding preferences from protein binding microarray (PBM) and TF ChIP-seq data [15], and this model was later used to systematically examine the cell-type specificity of TF sequence preferences using large-scale ChIP-seq data from two ENCODE cell lines [14]. Other studies adapted previous k-mer kernels to train discriminative models on TF ChIP-seq data [20, 43] or used k-mer features with lasso regularization to investigate sequence signals associated with histone marks [44]; many k-mer methods were also benchmarked in a DREAM competition for learning TF binding models from PBM data, though the focus of this study was clearly to compare methods generating PSSMs [45]. Indeed, despite the 10-year history of k-mer based discriminative learning methods and several previous reports that these methods outperform traditional motif discovery for the statistical problem of discriminating TF ChIP-seq peak from non-peak sequences [14, 20], it is inarguable that traditional PSSM methods are far more widely used than k-mer based methods in the larger genomics and biology communities. It is therefore worth asking what is the essential limitation of k-mer methods, as they exist in the literature, that prevents their more widespread adoption.
Here we propose that the key limitation of existing k-mer based discriminative methods is the difficulty of determining what information the model is using to achieve its improved performance, extracting these sequence signals from the model, and using them to dissect the regulatory code. We are indeed aware of only one previous method that tries to interpret the sequence information from a k-mer based discriminative model, the POIM (positional oligomer importance matrix) framework for weighted degree kernels [46], and this method requires that examples are aligned to each other and assumes at most a single “motif” per position. In our setting, a discriminatively trained k-mer model for TF ChIP-seq data may be capturing subtle preferences of the ChIP-ed TF, co-factor signals, and general compositional biases associated with regions of open chromatin—all of which are biologically interesting sequence signals but are difficult to deconvolve from a standard k-mer kernel SVM. These sequence signals also depend on cellular context, suggesting that we should be cautious about touting performance on the purely statistical problem of predicting occupancy on held-out peaks/non-peaks from the training experiment. From a practical standpoint, this is an artificial problem, as genome-wide occupancy in the training experiment is already known; the more meaningful question is how well the discriminative model can explain occupancy in a distinct cell type, so that a new ChIP-seq experiment need not be done. This problem necessitates an investigation into what the TF binding model is capturing, and how well we might expect it to generalize to a new cellular context. In our previous di-mismatch work [14], we showed that for some TFs, discriminative k-mer models indeed capture cell-type specific TF binding preferences, which we were able to interpret as variants in the binding model of the ChIP-ed TF. What were unable to do what do cleanly extract co-factor signals that may also account for cell-type specificity of TF occupancy. A more recent study using the gkm-SVM method [20] (similar to the wildcard kernel [16]) presented a heuristic for extracting PSSMs from the SVM but also largely missed co-factor signals. This gap in the literature motivated the current work.
SeqGL represents a new methodology for deciphering multiple binding sequence signals in epigenomic data sets. It combines discriminative learning on a wildcard k-mer representation with group lasso regularization to retain the better accuracy of k-mer based methods compared to traditional motif discovery algorithms while achieving greater sensitivity for identifying multiple sequence signals. Furthermore, the framework scales to handle the large multiplicity of TF binding signals in DNase-seq data. Through multitask training; SeqGL can identify TF binding signals that are common or specific to different genomic contexts or cell types. The use of structured constraints in the primal space (here based on group lasso) together with multitask learning provides the necessary framework to disentangle multiple constituent signals associated with context-specific regulatory information. As such, we believe that SeqGL represents an important advance for using discriminative k-mer methods to address biologically meaningful questions in regulatory genomics. SeqGL is available as an open source R package at http://cbio.mskcc.org/public/Leslie/SeqGL/.
ChIP-seq and DNase-seq data was downloaded from ENCODE [18]. We used a total of 105 TF ChIP-seq experiments for the GM12878 and H1-hESC cell lines (S1 Table). Peaks called by ENCODE were used for ChIP-seq analysis and histone context was identified using the ENCODE-defined ChromHMM segments. ENCODE data accession numbers: GSE32465, GSE31477, GSE29692.
We first pooled DNase-seq data for all replicates of a given cell type and identified peaks using MACS [28] with a low threshold of FDR-corrected p < 1e-3 using the Benjamini-Hochberg procedure for multiple hypotheses correction. The broader peaks identified by MACS were then split into smaller peaks, to localize binding of a single TF, using PeakSplitter [28]. Reproducible peaks were then identified using IDR [29] at a threshold of 0.01 in every pairwise replicate comparison (S9 Fig). We identified a total of 43,105 subpeaks spanning 34,303 peaks in GM12878 and 102,349 subpeaks spanning 78,180 peaks in H1-hESC.
ATAC-seq data was processed using the procedure described for DNase-seq data. We used fragments of length < = 100 for peak calling and downstream analysis.
We downloaded the RNA-seq bam files for all cell types from ENCODE. We used the summarizeOverlaps function from the GenomicRanges Bioconductor package [47] to count the reads mapping per gene. We used these counts to determine mean RPKM values for each gene across replicates for a cell type.
We compared the performance of SeqGL to a number of motif finding tools: HOMER, DREME and MEME-ChIP. We analyzed 105 different ChIP-seq experiments from the GM12878 and H1-hESC cell lines (S1 Table) and used area under the Receiver Operating Curve (auROC) on the test set as the performance measure. We determined the top 2000 peaks in each ChIP-seq experiment and split them evenly into training and test sets. auROCs were determined for the test sets after using the training set for learning the model or motifs. The same training and test sets were used for SeqGL, HOMER and DREME. A first order Markov model was estimated from the negative sequences in the same training set for MEME-ChIP. Note that increasing the number of peaks used for training and test does not significantly alter performance (S11 Fig). We also tested SeqGL using dinucleotide shuffled sequences as negatives instead of sequences in the flanks (S12 Fig). This leads to significantly better performance (p < 2e-7, Wilcoxon rank sum test), demonstrating that shuffled sequences are relatively “easy” negatives and therefore not a strong adversary. Dinucleotide shuffled sequences also lead to better performance compared to HOMER and MEME-ChIP in the “Motif elastic” setting (S11 Fig; p < 2e-10, Wilcoxon rank sum test). Elastic net was performed using the glmnet R package [19].
We used regularized multitask learning as proposed by Evgeniou & Pontil [22] (S3 Fig) to learn context-specific candidate binding partners for POU2F2 and TCF12 ChIP-seq (Figs 4A and 4B). In addition to the two tasks (POU2F2: (a) Proximal peaks vs flanks & (b) Distal peaks vs flanks and TCF12: (a) GM12878 peaks & (b) H1-hESC peaks respectively), we defined a “common” task that solves both the tasks as a single classification problem. The common task is expected to capture the information common to both the tasks (POU2F2 and TCF12 binding preferences respectively) whereas the task specific model will capture the context-dependent binding partners. We ran clustering for each task separately to identify task specific groups. We then learned models for all these tasks using the multitask learning formulation below. Let
L(xi,yi,ti)=log(1+exp(−yi(wc+wti)⋅xi))
represent the logistic loss, and the objective function can be defined as
Minwc,wT1,wT2∑iL(xi,yi,ti)+λ1(α∑tϵ{t1,t2}∑g‖wt,g‖2+β∑g‖wc,g‖2)+λ2(α∑tϵ{t1,t2}∑m|wtm|+β∑m|wcm|)
where wc, wT1 and wT2 are the common, first task, and second task models respectively. L(xi, yi, ti) is the logistic loss defined for each example belonging to task ti. The second component of the equation encodes the group lasso constraints for all group models, and third component encodes the sparsity constraint with w t,g representing a vector of k-mer weights which belong to group g in task t and wtm representing the m-th k-mer weight in task t. α and β trade off between the task specific and common task components. We used α = 1.5 and β = 1 which gave us the best test performance.
We validated the binding partner prediction using existing ChIP-seq data from ENCODE. We again used the auROC measure for validation (S1 Fig). We first identified the overlaps of the training set with the peaks of a particular TF. These overlapping peaks are considered positive examples and the non-overlapping training peaks as the negative set. We then used the group scores for each sample as predictions to determine the auROC. We consider a motif prediction validated if the predicted TF or a member of the TF family is in top five auROC predictions. Many of the cases that are not validated show only marginally matching motifs to the TF under consideration. In these cases, the derived motif and known TF motif are dissimilar to each other, and therefore the associated TF might not necessarily be correct. We resolve the ambiguity among TF family members by using the expression levels of different family members in the cell type or the ChIP-seq experiment with the best auROC in this validation. As an example, BATF is identified as co-factor of PAX5 since Group 11 is best predicted by BATF and not other members of the AP.1 family, and RUNX3 is identified in GM12878 DNase-seq since RUNX3 is expressed at significantly higher levels compared to other RUNX family members.
We learned all the models separately for identifying context-dependent binding signals using DNase-seq data. We used DNase peaks falling completely within specific ChromHMM states for learning the results in Fig 7B. The ChromHMM state “Active Promoters” was used for promoter peaks and “Weak Enhancers” and “Strong Enhancers” states for the enhancer peaks.
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10.1371/journal.pgen.1006332 | PARAQUAT TOLERANCE3 Is an E3 Ligase That Switches off Activated Oxidative Response by Targeting Histone-Modifying PROTEIN METHYLTRANSFERASE4b | Oxidative stress is unavoidable for aerobic organisms. When abiotic and biotic stresses are encountered, oxidative damage could occur in cells. To avoid this damage, defense mechanisms must be timely and efficiently modulated. While the response to oxidative stress has been extensively studied in plants, little is known about how the activated response is switched off when oxidative stress is diminished. By studying Arabidopsis mutant paraquat tolerance3, we identified the genetic locus PARAQUAT TOLERANCE3 (PQT3) as a major negative regulator of oxidative stress tolerance. PQT3, encoding an E3 ubiquitin ligase, is rapidly down-regulated by oxidative stress. PQT3 has E3 ubiquitin ligase activity in ubiquitination assay. Subsequently, we identified PRMT4b as a PQT3-interacting protein. By histone methylation, PRMT4b upregulates the expression of APX1 and GPX1, encoding two key enzymes against oxidative stress. On the other hand, PRMT4b is recognized by PQT3 for targeted degradation via 26S proteasome. Therefore, we have identified PQT3 as an E3 ligase that acts as a negative regulator of activated response to oxidative stress and found that histone modification by PRMT4b at APX1 and GPX1 loci plays an important role in oxidative stress tolerance.
| Oxidative stress is a major stress in plant cells when biotic and abiotic stresses are imposed. While the response to oxidative stress has been extensively studied, little is known about how the activated response is switched off when oxidative stress is diminished. By studying Arabidopsis mutant paraquat tolerance3, we identified the genetic locus PARAQUAT TOLERANCE3 (PQT3) as a major negative regulator of oxidative tolerance. PQT3 encodes an E3 ubiquitin ligase and is rapidly down-regulated by oxidative stress. Subsequently, we identified PRMT4b as a PQT3-interacting protein. PQT3 was demonstrated to recognize PRMT4b for targeted degradation via 26S proteasome. By histone methylation, PRMT4b may regulate the expression of APX1 and GPX1, encoding two key enzymes against oxidative stress. Therefore, we have identified PQT3 as an E3 ubiquitin ligase that turns off the activated response to oxidative stress. Our study provides new insights into the post-translational regulation of plant oxidative stress response and ROS signaling.
| Sessile plants cannot avoid harsh living conditions such as drought, salinity, cold and hot temperature. These stresses alter the normal cell homeostasis and increase the generation of reactive oxygen species (ROS) [1]. ROS could also be generated by paraquat, one widely used herbicide [2]. Accumulation of ROS directly destroys biological membranes and macromolecules, accelerates cell senescence, induces irreversible damages to cells and even leads to cell death [3]. The level of ROS is increased sharply under stress conditions [1, 4–9]. Two protection systems, enzymatic and non-enzymatic, have evolved to scavenge ROS and protect plant cells from oxidative damage. Enzymatic system mainly includes the ascorbate peroxidase (APX), glutathione peroxidase (GPX), catalase (CAT), superoxide dismutase (SOD), and peroxiredoxin Q (PRXQ) [8, 10, 11]. The transcript level and activity of antioxidant enzymes correlate with paraquat tolerance [12].
The activation of oxidative response involves many layers of regulations [8, 13]. Little is known about the regulation by histone methylation of the genes involved in oxidative stress response. Histone methylation plays important roles in the plant development and growth as well as in some stress responses [14–18]. The methylation marks are written on lysines or arginines respectively by protein arginine methyltransferases (PRMTs) and histone lysine methyltransferases (HKMTs). In Arabidopsis, nine PRMTs are found in the genome [14]. It has been reported that PRMTs are involved in salt stress responses, flowering time as well as circadian cycle [19, 20]. Two different types of PRMTs catalyze asymmetric di-methylation (ADMA) and symmetric di-methylation (SDMA) on the Arg residues, respectively [17, 21]. The prmt5 mutant has decreased level of histone H4 Arg3-SDMA, leading to enhanced drought tolerance [22]. A pair of PROTEIN ARGININE METHYLTRANSFERASE4 (PRMT4) homologs, AtPRMT4a and AtPRMT4b, is required for the asymmetrical di-methylation of Arg-2, Arg-17, and Arg-26 in histone H3 [14]. The prmt4aprmt4b double mutants are sensitive to salt stress [20]. Protein arginine methylation plays essential roles in diverse biological processes, such as RNA processing and transcriptional regulation [21, 23].
Oxidative stress could be perceived by multiple mechanisms, including sensor or cellular receptor. The perception by receptors results in the activation of Ca2+–calmodulin and mitogen-activated protein kinase (MAPK) cascade signaling transduction pathway. The activation or suppression of different transcription factors regulates a variety of defense pathway subsequently, such as ROS-scavenging, heat-shock proteins (HSPs), and photosynthesis [8, 10, 13]. Several paraquat tolerance mutants have been analyzed in Arabidopsis [24]. While much attention has been paid to how plants respond to oxidative stress, we know little about how plants switch off the activated responses when stress is diminished. A common regulatory mechanism is to control the protein level of the stress responsive factors. The most studied mechanism of protein degradation is the ubiquitin/26S proteasome system [25]. The ubiquitin/26S proteasome pathway is involved in different regulatory processes of eukaryotic cells, as it can rapidly eliminate the specific proteins in the cell [25–27]. Ubiquitin, containing 76 amino acids, could be attached to the target protein under the action of three different enzymes [27]. The ubiquitin system can identify and modify many intracellular proteins, such as proteins involved in signal transduction, transcription factors, and receptors on cell surface, to participate in the regulation of physiological processes [28]. The target proteins with ubiquitin have different fates. For monoubiquitination, one lysine residue of substrate is modified by a single ubiquitin. If several individual lysine residues of target protein are attached with single ubiquitin respectively, the protein modification is named multiubiquitination. Both mono- and multi-ubiquitination could affect protein activity and intracellular localization [29–31]. For polyubiquitination, one ubiquitin is attached to lysine residue of substrate firstly. The C-terminal glycine of next ubiquitin is linked with lysine residue of the preceding ubiquitin to form polyubiquitin chain subsequently [32]. As ubiquitin contains seven lysines, polyubiquitin chains are divided into different types according to different linkages between two adjacent ubiquitins [33]. Proteins with Lys48-Linkage polyubiquitination could be recognized and degraded by the 26S proteasome [34]. It has also been reported that Lys29-Linkage polyubiquitination involved in proteasome-dependent degradation [35]. In addition, Lys63-Linkage polyubiquitination plays roles in endocytosis, repair of DNA damage, protein synthesis and signal transduction [36, 37].
In the ubiquitin degradation process, E3 played a crucial role. E3 is responsible for specific recognition of substrate protein and accurate positioning of the binding site between substrate protein and ubiquitin [38]. In Arabidopsis thaliana, the genes encoded the subunits of E3 ubiquitin ligases make up approximately 90 percent of about 1400 genes encoded the components of ubiquitin/26S proteasome pathway. [26, 39]. The large and diverse family of plant E3 ubiquitin ligases can be divided into HECT domain- and RING/U-box domain-containing E3 ubiquitin ligases [25]. The HECT family is relatively small compared with the RING domain-containing family that contains several hundreds of proteins and can be further divided into single subunit RING/U-box E3 ligases and multi subunit RING E3 ligases [26, 40]. The large number of E3 ubiquitin ligases in higher plants indicates their important regulatory roles in diverse biological processes [41].
Several RING E3 ligases play positive roles in ABA-mediated drought tolerance [42–45]. In addition, overexpression of RING E3 ligases Rma1 inhibited the trafficking of aquaporin PIP2;1 and promote protein degradation of PIP2;1 to enhance drought tolerance [46]. A few E3 ligases have also been identified to turn off the activated stress responses [47]. HOS1, as a RING E3 ligase, acts as a negative regulator in cold tolerance. HOS1 inhibits the expression of CBFs and downstream cold-responsive genes through the degradation of ICE1 in ubiquitin-proteasome pathway [48–50]. Both PUB22 and PUB23, as E3 ligases contained U-box, play roles as negative regulators in drought tolerance. They could recognize and degrade RPN12a, a subunit of 19S regulatory particle belonged to 26S proteasome, to affect Arabidopsis drought tolerance [51]. The negative function of PUB18 and PUB19 in ABA-mediated drought tolerance has also been reported [52]. The drought-induced AtERF53 could be degraded by RING E3 Ligase RGLG1 and RGLG2 that acts as negative regulators in drought tolerance [53]. Overexpression of salt-induced RING E3 ligase OsSIRP1 reduced salt tolerance in Arabidopsis [54]. In addition, several substrate receptors of CUL4 E3 ligases, DWAs, regulate ABA responses negatively [55, 56]. The function of an enormous number of E3 ligases still remains to be identified. Here we report a novel negative regulator of oxidative stress response by PARAQUAT TOLERANCE3 (PQT3) in Arabidopsis. We identified a paraquat tolerant mutant, paraquat tolerance3 (pqt3), and cloned the gene PQT3 that encodes an E3 ligase containing RING/U-box domain. The function of PQT3 was revealed, although PQT3 (At4g17410) had been identified in previous microarray data on salt stress response and cold acclimation [57, 58]. The expression of APX1 and GPX1 was up-regulated in pqt3, while PQT3 was down regulated by oxidative stress. PQT3 was able to interact with PRMT4b. PRMT4b may catalyze histone methylation on APX1 and GPX1 chromatin and up-regulate their expressions, therefore protect plants from oxidative stress. When oxidative stress is diminished, PQT3 level increases and acts as E3 ubiquitin ligase to specifically target PRMT4b for degradation. Based on our results, PQT3 is a negative regulator that turns off the activated response of oxidative stress.
The mutant pqt3 was obtained after screening an activation-tagging library [59]. This library consisting of approximately 55, 000 independent lines was screened for mutants with enhanced tolerance to different stresses [60, 61]. To isolate tolerant mutant to oxidative stress, we germinated seeds on MS medium with 2 μM paraquat. Growing green seedlings were selected as putative mutants and were named paraquat tolerance (pqt) because of their enhanced tolerance to paraquat. The pqt3, one of such mutants, was further characterized and marked as pqt3-1. The enhanced oxidative tolerance of pqt3-1 mutant was confirmed by germinating seeds on MS medium containing 0 or 2 μM paraquat. Based on the observation of green cotyledons, survival ratio were counted. In presence of paraquat, more than 60% pqt3-1 seeds germinated with green cotyledons but only 2% wild type seeds did, while all seeds of both wild type and pqt3-1 survived on MS medium without paraquat (Fig 1A and 1B). Genetic analysis showed that the mutation was recessive. All F1 backcross offsprings (pqt3-1 x wild type) were paraquat sensitive and F2 selfing population showed typical 3:1 segregation ratio (sensitive: resistant; 85:27, χ2 = 0.0476). The result suggested that pqt3-1 mutant may have a more efficient mechanism of ROS scavenge, which was caused by loss-of-function mutation in a single nuclear gene PQT3 (At4g17410).
In pqt3-1 mutant, a single T-DNA insertion was located in the fourth intron of At4g17410(S1A Fig). The exact integration site of the T-DNA right border was 803bp downstream of the ATG initiation codon of At4g17410. As a result, the expression of At4g17410 was completely disrupted as confirmed by RT-PCR analysis (S1B and S1D Fig). The expressions of its neighboring genes, At4g17390 and At4g17420, were not affected (S1B Fig). The At4g17410 locus includes 13 exons and 12 introns. By prediction, the open reading frame encodes a 91 kD polypeptide composed of 827 amino acids. Based on the conserved RING/U-box domain, this protein is predicted as an E3 ubiquitin ligase.
To further determine whether the loss of At4g17410 resulted in the enhanced oxidative tolerance of pqt3-1 mutant, we used another allele of pqt3, the T-DNA insertion mutant Salk_065409, which was ordered from Arabidopsis Biological Resource Center (ABRC) and its T-DNA insertion was confirmed by RT-PCR (S1A, S1C and S1D Fig). As the first identified pqt3 mutant was named as pqt3-1, the Salk_065409 was marked as pqt3-2. The pqt3-2 mutant showed similar enhanced oxidative tolerance to paraquat and had high survival ratio under different concentrations of paraquat treatment as pqt3-1 did (Fig 1C). The survival ratio of pqt3-1 and pqt3-2 were 50% and 20%, respectively, under 2 μM paraquat treatment, while none of the wild type seedlings survived under the same condition. In addition, both pqt3-1 and pqt3-2 showed a late-flowering phenotype (Fig 1D).
To confirm further, we generated functional complementation (FC) lines and 35Spro:PQT3 overexpression lines (S1E and S1F Fig). FC lines and 35Spro:PQT3 lines showed similar if not higher paraquat sensitivity to wild type under 2 μM paraquat treatment while the pqt3-1 and the pqt3-2 mutants displayed enhanced paraquat tolerance (Fig 1E and 1F). These results indicate that PQT3 is a negative regulator of oxidative stress tolerance and is responsible for the phenotype of pqt3 mutants.
In addition, we assayed H2O2 level in the leaf with DAB staining, in which the chemical reaction between hydrogen peroxide and DAB lead to the formation of brown precipitate that indicates hydrogen peroxide distribution and oxidative damage. After 6 μM paraquat treatment for 12 or 24 hours, the result of 3,3′- diaminobenzidine (DAB) staining showed that the brown precipitate in the leaves of the wild type was more than that of pqt3 mutants (Fig 1G–1R). As several stresses could cause oxidative damage to plants, the sensitivity of pqt3 mutants to other environmental stresses was analyzed subsequently. The result indicated that pqt3 mutants have enhanced tolerance to CdCl2, mannitol, NaCl, and drought stress (S2 Fig).
To investigate the spatiotemporal pattern of PQT3 expression, we generated PQT3pro:GUS reporter lines. GUS staining results showed that PQT3 was expressed in both shoot and root tissues under normal condition (Fig 2A–2G). GUS expression was detected in the root tissues at all developmental stages we analyzed (Fig 2A–2C). For the 1-week-old seedlings, strong GUS staining was observed in the cotyledons, hypocotyls and root tissues (Fig 2A). For the 3-week-old seedlings, strong GUS staining was also detected in cotyledons, young leaves, and root tissues, but weakly stained in older leaves (Fig 2B). In 7-week-old adult plants, GUS expression was detected in rosette leaves, cauline leaves, the tip and basal junction of siliques, and was significantly higher in the flower petals, stamens and stigma of pistil (Fig 2D–2G).
PQT3 has two predicted nuclear localization signals (NLSs) in the carboxyl terminus (S3 Fig), implicating its nuclear localization. To confirm this, the 35Spro:PQT3-GFP construct was made and transiently expressed in onion epidermal cells. The 35Spro:GFP construct was used as control (Fig 2H–2J). PQT3-GFP signal was indeed detected in the nucleus (Fig 2K–2M).
For further confirmation, we transformed the PQT3pro: PQT3-GFP fusion construct into the Arabidopsis and obtained transgenic plants. Fluorescent microscopy results showed that GFP signal was accumulated in the nucleus of root cells (Fig 2N–2P), which is in agreement with the presence of two NLSs of the PQT3.
The expression of PQT3 was rapidly down-regulated by paraquat treatment and maintained at a low level as long as the paraquat treatment was applied (Fig 3A). This result is supportive for our previous opinion that PQT3 is a negative regulator of plant oxidative tolerance. The suppressed transcript level of PQT3 was restored to the previous level when PQ stress diminished (S4 Fig). By extrapolation, the expression of PQT3 could be down-regulated by other stress conditions. Indeed, our results showed that the expression of PQT3 was down-regulated by H2O2 (Fig 3B), mannitol (Fig 3C), drought (Fig 3D) and CdCl2 (Fig 3E) at the indicated time points. Among these stresses, CdCl2 treatment led to the most significant reduction of the expression of PQT3 (Fig 3E). The expression of PQT3 could also be down-regulated by NaCl stress at 3h. However, unlike the above results, the expression of PQT3 was activated by salt treatment at other time points (Fig 3F).
The PQT3pro:GUS pattern was observed under different stresses subsequently (Fig 3G–3DD). Compared with transgenic seedlings under normal conditions, GUS staining was weaker in the seedlings under paraquat, H2O2, mannitol, CdCl2, and NaCl treatment. The changed PQT3pro:GUS pattern was consistent with the altered expression of PQT3 detected by quantitative RT-PCR under different stresses.
Enzymatic protection systems are very important for ROS elimination. The transcript levels of ascorbate peroxidase (APX), glutathione peroxidase (GPX), catalase (CAT), cytosolic Cu/Zn SOD (CSD1), plastidic Cu/Zn SOD (CSD2), FeSOD (FSD), atypical Cys-His rich thioredoxin (ACHT), glutaredoxin C (GRXC), 2-Cys peroxiredoxin B (2CPB), peroxiredoxin Q (PRXQ) and mitochondrial MnSOD (MSD) were analyzed by quantitative RT-PCR in pqt3 and wild type. The results show that transcript levels of APX1 and GPX1 were up-regulated in pqt3 under normal conditions compared with that in the wild type (Fig 4A–4J). The elevated transcript levels of APX1 and GPX1 may contribute to the improved oxidative tolerance.
The enzyme activity of APX and GPX in wild type and pqt3 mutant was also detected. The pqt3 mutant had higher enzyme activity of APX and GPX compared with wild type (Fig 4K and 4L).
To study the molecular mechanisms that underlie the enhanced stress tolerance of pqt3, we screened cDNA library for potential candidate target proteins of PQT3 using yeast-two-hybrid (Y2H). Several proteins were isolated from the screen. Among these candidate interactors, PRMT4b, a member of arginine methyl transferase family, was frequently presented. To reveal the domain of PQT3 responsible for the interaction with PRMT4b, the PQT3 protein was divided into four parts: N-terminal DWNN, zfCCHC, U-box (RING finger), and C-terminal section containing the NLS1 and NLS2 domains, based on the predicted domains of PQT3 protein (S3A Fig). Full-length PQT3 and four protein sections were used for Y2H assay as baits. Full-length protein and the C-terminus of PQT3 were able to interact with PRMT4b in Y2H assays (Fig 5A). The interaction between PQT3 and PRMT4b was confirmed by colonies that grew on the SD-Leu-Trp-His plate with 50 mM 3-amino-1, 2, 4-triazole (3-AT) and displayed the blue color in X-gal assay (Fig 5A). However, PRMT4a was never isolated in the screening. The Y2H assay was also performed to further study the potential interaction between PQT3 and PRMT4a, since PRMT4a is a close related gene of PRMT4b. The result showed that the PQT3 did not interact with PRMT4a (S5 Fig).
The interaction between PQT3 and PRMT4b was further confirmed by protein pull-down assay in vitro. MBP-PQT3-C66 protein containing the NLS domain (S3A Fig) and His-PRMT4b protein were expressed in E. coli and purified subsequently. His-PRMT4b was incubated with amylose resin bound with recombinant MBP-PQT3-C66 protein. Pulled-down protein complex was detected by SDS-PAGE (Fig 5B) and western blotting using anti-His antibody (Fig 5C). The pull-down result clearly shows that PQT3 interacts with PRMT4b in vitro.
To determine whether the interaction also occur in vivo, we used the bimolecular fluorescence complementation (BiFC) system. The N-terminus of yellow fluorescent protein (YFP) was fused to full-length PQT3 cDNA, while full-length PRMT4b cDNA was fused to the C-terminal region of YFP. The empty plasmids were used as negative controls. Different plasmid combinations were co-infiltrated into epidermal cell of N. benthamiana leaves. The yellow fluorescence was observed in epidermal cell contained both NE-PQT3 (the N-terminus of YFP fused with PQT3) and CE-PRMT4b (the C-terminus of YFP fused with PRMT4b) (Fig 5D). No fluorescence was observed from the negative controls (NE-PQT3/CE, NE/CE-PRMT4b and NE/CE) (Fig 5D). The nuclei were stained by Hoechst and detected by confocal. These results indicate that PQT3 can interact with PRMT4b in the nucleus of plant cell.
Not all the proteins with the predicted RING domain function as an ubiquitin ligase [62]. The E3 activity of PQT3 was determined via self-ubiquitination system. Both full-length (GST-PQT3) and C-terminal deletion (GST-PQT3-N40) proteins showed the E3 ubiquitin ligase activity (Fig 6A and S3B Fig). The ubiquitinated bands of PQT3 were detected by western blotting in the presence of E1 (from wheat), E2 (UBCh5b, from human), and 6×His-tagged ubiquitin (UBQ14, from Arabidopsis). When any of essential reaction components was missing, self-ubiquitination of PQT3 was not detected (Fig 6A).
To determine whether PRMT4b is a substrate recognized by PQT3, we resorted to the in planta ubiquitination assay [63]. Leaf infiltration was conducted via Agrobacterium tumefaciens strains containing different combination of constructs. The infiltrated parts of N. benthamiana leaves were harvested. Total protein was extracted and detected via western blotting with anti-HA antibody. A smear of bands, which were larger than the size of HA-PRMT4b and showed the features of ubiquitinated form of the PRMT4b proteins, could be detected by anti-HA antibody in the samples co-infiltrated with PQT3 and HA-PRMT4b (Fig 6B). The cell lysates were immunoprecipitated with anti-HA antibody subsequently. Immunoprecipitated samples were detected via western blotting with anti-ubiquitin antibody. In the PQT3-PRMT4b co-infiltration sample, these high molecular size bands could also be detected by anti-ubiquitin antibody (Fig 6B). These results indicated that these high molecular size bands were ubiquitinated forms of PRMT4b. PQT3 protein could ubiquitinate the PRMT4b protein in tobacco. The decline of PRMT4b protein was also found in the samples co-infiltrated with PQT3 and HA-PRMT4b (Fig 6B).
In addition, PRMT4b protein levels in different PQT3 genetic background under MG132 treatments were also supportive of PRMT4b as a substrate of PQT3. As shown in the Fig 6C and 6D, the protein level of PRMT4b in pqt3-1 was higher than that of the wild type under normal conditions. Under paraquat treatment, it remained lower in wild type than that of pqt3-1 at the same time point, although protein level of PRMT4b increased gradually in wild type and pqt3-1 with the prolonged paraquat treatment (Fig 6C and 6D). The transcript level of PRMT4b was unlikely to cause the observed difference of protein levels between the mutant and wild type (S6 Fig). Under MG132 treatment, the protein level of PRMT4b was increased in both wild type and pqt3-1 mutant. When the seedlings were co-treated with paraquat and MG132 for 12 h, PRMT4b was accumulated in both wild type and pqt3-1 mutant, and no significant difference of PRMT4b protein level was found between the wild type and pqt3-1 mutant (Fig 6C and 6D) because the degradation of the PRMT4b protein through ubiquitination-26S proteasome pathway was inhibited by MG132.
In order to confirm further the PQT3-dependent ubiquitination of PRMT4b, the phenotype of wild type and pqt3-1 mutant treated without or with paraquat in presence or absence of proteasome inhibitor MG132 was studied. Survival ratio of pqt3-1 seedlings was higher than that of wild type under paraquat treatment. No significant difference of survival ratio could be observed when the wild type and pqt3-1 seedlings were co-treated with paraquat and MG132 (Fig 6E). The wild type gained enhanced paraquat tolerance under MG132 treatment to the level of pqt3-1 mutant.
To reveal whether PRMT4b could play any roles in oxidative tolerance of plants, we obtained the prmt4b mutant and 35Spro:PRMT4b lines (S1G, S1H and S1L Fig) and observed the phenotypes of prmt4b and 35Spro:PRMT4b under different concentrations of paraquat treatment. Survival ratio of wild type, pqt3-1, pqt3-2, prmt4b and 35Spro:PRMT4b were counted (Fig 7A). 35Spro:PRMT4b had similar phenotype as pqt3-1 and pqt3-2, while the prmt4b mutant was more sensitive to paraquat treatment than wild type (Fig 7A). Under CdCl2 treatment, primary root elongation of prmt4b mutant was also slower than that of wild type (Fig 7B). Furthermore, the overexpression lines of PRMT4b were analyzed under other stress conditions. 35Spro:PRMT4b increased tolerance to CdCl2 and NaCl stresses compared with wild type. The 35Spro:PRMT4b showed the opposite phenotype of the 35Spro:PQT3 which was more sensitive to CdCl2 and NaCl stresses as compared with wild type (S7 Fig). These results show that PRMT4b is a positive regulator for plant oxidative tolerance, which is also consistent with the function of PQT3. The prmt4a mutant and prmt4aprmt4b double mutants were subsequently examined (S1I–S1K Fig). The prmt4aprmt4b double mutants had the similar phenotype to prmt4b under paraquat treatment, and the knockout of PRMT4a did not affect the oxidative tolerance (Fig 7C–7E). In addition, the transcript levels of APX1, GPX1 and other antioxidant enzyme genes were down-regulated in prmt4b mutant and up-regulated in 35Spro:PRMT4b as compared with that in the wild type (Fig 7F and S8 Fig).
The transcript level of APX1 and GPX1 was higher in pqt3 than that in wild type under normal conditions (Fig 4A and 4B), which could be regulated by PRMT4b. The modification status of H3R17me2a in the chromatin of APX1 and GPX1 was compared between wild type and pqt3 mutant via chromatin immunoprecipitation (ChIP) assays. ChIP assays were performed with wild type and pqt3 plants using antibody against H3R17me2a. As shown in the Fig 8A and 8B, the APX1 and GPX1 chromatin was divided into different regions and the enriched chromosome fragments were detected by quantitative RT-PCR. The results showed that histone H3R17me2a modification of APX1 and GPX1 chromatin was increased in pqt3 mutant (Fig 8C and 8D). The enriched chromosome fragments of APX1 (C, D and E fragments) and GPX1 (C, D and I fragments) were further analyzed in prmt4b mutant and pqt3prmt4b double mutants without or with paraquat treatment (Fig 8E–8J). Histone H3R17me2a modification of APX1 (C, D and E fragments) and GPX1 (C, D and I fragments) chromatin was decreased in prmt4b mutant and pqt3prmt4b double mutants without paraquat treatment as compared with wild type. It remained lower in wild type than that of pqt3 mutant, although H3R17me2a modification of APX1 (C, D and E fragments) and GPX1 chromatin (C, D and I fragments) in both wild type and pqt3 mutant were enhanced under paraquat treatment. H3R17me2a modification of APX1 (C, D and E fragments) and GPX1 chromatin (C, D and I fragments) was kept at a low level, although the modification in prmt4b mutant and pqt3prmt4b double mutants could also be affected by paraquat treatment. These results suggest that PRMT4b may target APX1 and GPX1 to enhance the oxidative tolerance by increasing asymmetric dimethylation of H3 at R-17 in APX1 and GPX1 chromatin.
To confirm further that PRMT4b is the target of PQT3, the pqt3prmt4b double mutants were obtained (S1M and S1N Fig). The survival ratio of pqt3prmt4b under paraquat treatment was intermediate between pqt3 and prmt4b, which demonstrates that the PRMT4b protein is one of the targets of PQT3 and suggests that PQT3 may also target other proteins that contribute to the tolerance to oxidative stress (Fig 9A–9C).
We also obtained the pqt3prmt4a double mutants (S1O and S1P Fig) and found that the survival ratio of pqt3prmt4a under paraquat treatment had no significant difference from that of pqt3 mutant (Fig 9D–9F), again indicating that PRMT4a is not involved in oxidative stress response.
PQT3 is a member of Arabidopsis RING-finger/U-box E3 ligase family. Secondary structure prediction using InterProScan protein sequence analysis software revealed four conserved domains including DWNN (domain with no name), zinc finger domain, RING-finger domain, and U-box domain in N-terminus of PQT3 protein (S9A and S9B Fig). DWNN is a novel ubiquitin-like domain, which is a highly-conserved domain in eukaryotic plants and animals [64]. The DWNN domain of PQT3 contains 76 amine acids. DWNN domain is only found in the N-terminus of the members in the splicing-associated RBBP6 (Retinoblastoma Binding Protein 6) protein family [64]. The RING-finger domain was also found in RBBP6 protein family. The existence of the RING-finger domain suggests that DWNN domain may play its role as ubiquitin-like regulatory factors [64]. CCHC-type Zinc finger domain is also known as zinc knuckle, which can be found in a large number of RNA binding proteins [65, 66]. As mentioned above, RING-finger domain can combine with the E2 in the cascade reaction of ubiquitination system,while U-box is a modified RING-finger [67]. In addition, two predicted NLS sequences (471–477 and 696–711 amino acids) were also found in the C-terminus of PQT3, which is consistent with nuclear localization of the protein (Fig 2H–2P and S9A and S9B Fig). Phylogenetic tree analysis further revealed the high homology proteins of PQT3 in other species (S9C Fig). DWNN domain and RING-finger/U-box domain in N-terminus of PQT3 were highly conserved in homologous proteins in different plant species (S10 Fig). The PQT3 may play its role as an ubiquitin ligase in different species and its function may be conserved throughout the plant kingdom.
In vitro ubiquitination assay shows that PQT3 has the E3 ligase activity (Fig 6A). We noticed that the in vitro activity was not as high as expected, which could be contributed by suboptimal reaction conditions such as E2 source. This E3 activity was confirmed by in planta ubiquitination assay, in which PRMT4b protein could be ubiquitinated by expressed PQT3 protein in N. benthamiana (Fig 6B). Consistent with this result, pqt3-1 mutant had higher protein level of PRMT4b than the wild type, as the PRMT4b protein was degraded by PQT3 in wild type under normal conditions (Fig 6C and 6D). The transcript level of PRMT4b could be induced in wild type under paraquat treatment (S6 Fig), while the transcript level of PQT3 was decreased by paraquat treatment (Fig 3A). In this case, PQT3-mediated PRMT4b degradation could be weakened in wild type. Consequently, the level of PRMT4b protein in wild type was elevated, but lower than that of pqt3 (Fig 6C and 6D). The proteasome inhibitor MG132 could block the degradation of the PRMT4b protein in wild type and enhance paraquat tolerance of wild type (Fig 6C–6E). Taken together, PQT3, as an E3 ubiquitin ligase, play its role in oxidative tolerance through the ubiquitination-degradation of PRMT4b.
A series of environmental stress could lead to oxidative damage to plants [10, 13, 68]. Both biotic stress and abiotic stress result in the production of ROS which in excess cause oxidative stress. Besides paraquat tolerance, pqt3 mutants have enhanced tolerance to various environment stresses (Fig 1 and S2 Fig). Further studies may reveal new mechanisms of PQT3 in multiple stress tolerance. Analysis of PQT3-interacting proteins may be a good start point to further understand the function of PQT3. In the screen for PQT3-interacting proteins with Y2H, we isolated 66 positive colonies based on the expression of reporter genes included His3 and LacZ. Several proteins were identified from these positive colonies repeatedly. The most frequent interacting partner is 20S core protease subunit of 26S proteasome. The result suggested that PQT3 act as an E3 ubiquitin ligase. Among these proteins, PRMT4b, as previously mentioned, may be responsible for the increased degree of Arg-17 methylation which may further regulate APX1 and GPX1 genes to enhance oxidative tolerance of plants (Figs 4A, 4B and 8). It has been reported that arginine methylation was involved in signal transduction, transcriptional control, DNA repair, RNA processing, and nuclear transport [17, 19, 23, 69–72]. The functions of PRMTs have been extensively analyzed [15, 19, 20]. Here, the prmt4b mutant was found to be more sensitive to paraquat and CdCl2 treatment than wild type (Fig 7A–7E). Thus, PRMT4b plays a role in oxidative stress response of plants. It is known that PRMT4-mediated methylation at Arg-17 of histone H3 is linked to transcription activation [73]. The Arg-17 of histone H3 is the major site of PRMT4-mediated methylation, although it was reported that other protein site could also be methylated by PRMT4 [74–76]. We suggest that the increased Arg methylation degree of specific regions in APX1 and GPX1 chromatin was caused by PRMT4b (Fig 8).
The increased transcript level of APX1 and GPX1 may be caused by PRMT4b-mediated histone Arg methylation (Figs 4A, 4B and 8). In presence of ascorbate as electron donor, the cytosolic enzyme, APX1, catalyze the degradation of H2O2 [77]. The response of APX1 to oxidative stress has been studies in Arabidopsis. The crucial role of APX1 in multiple stress response was reported [78]. It could be activated by multiple stresses to protect plants against oxidative stress [79–81]. The tobacco overexpressing APX1 could be more tolerant against UV-C-caused oxidative damage [82]. GPXs also have important functions in oxidative signaling, which can protect plants from harmful effects of excessive oxidation [83]. It has also been reported that enhanced peroxide scavenging and decreased oxidative damage was found in transgenic tobacco seedlings overexpressing tobacco glutathione S-transferase that showed glutathione peroxidase (GPX) activity [84].
The pqt3 mutants also have late-flowering phenotype (Fig 1D). It has been reported that prmt4aprmt4b double mutants display late-flowering phenotype [15]. The Y2H result showed that the PQT3 can not interact with PRMT4a (S5 Fig). As compared with wild type, the prmt4a mutant also has no significant difference in the oxidative tolerance (Fig 7C–7E). The phenotype of pqt3prmt4a double mutants demonstrate that PRMT4a protein was not involved in the response regulation of oxidative stress by PQT3 (Fig 9D–9F). The late-flowering phenotype of pqt3 may be regulated by other mechanisms, rather than PRMT4a and PRMT4b. The flowering-related transcription factor AGAMOUS (AG) was found to be a potential target interacted with PQT3 in Y2H library screening. AG is involved in carpel development, leaf development, identification of floral organs, and stamen development [85]. Targeted removal of AG by PQT3 may be related to the late-flowering phenotype of pqt3. In addition, one member of a large G protein family and the proteins with unknown function were also found to be potential targets interacted with PQT3 in Y2H library screening. By interacting with different partners, PQT3 may mediate multiple functions in diverse biological processes.
The pqt3 has enhanced tolerance to multiple stresses. PQT3 is also down-regulated by various oxidative stresses. PQT3 act as negative regulator in multiple stress responses. The interacting partner will be further analyzed to reveal the molecular function of PQT3. As research continues, other combinations, also targeted by PQT3, may be revealed, where PQT3 act as a positive regulator. We could improve crop tolerance to multiple stresses through PQT3 mutation. More candidate genes could be further determined from the potential targets interacted with PQT3. These genes may also have high application value after the revelation of molecular mechanisms.
In conclusion, paraquat may enter the cell through plasma membrane-localized paraquat transporters PDR11 and RMV1 [59, 86]. The intracellular paraquat may further activate different downstream signaling pathways to regulate the expression of PQT3 and PRMT4b. In addition, paraquat stress increases the generation of ROS. ROS is also an important signal molecule that mediate the responses to environmental stress [87]. ROS-activated signaling pathways may also be responsible for the regulation of PQT3 and PRMT4b. The oxidative stress activates the expression of PRMT4b, represses the expression of PQT3, and weakens PQT3-mediated the ubiquitinated degradation of PRMT4b, synergistically resulting in increased accumulation of PRMT4b. Consequently, the increased level of PRMT4b protein may lead to higher degree of histone methylation on the APX1 and GPX1 chromatin. As a result, the transcription of APX1 and GPX1 is activated, leading to more APX1 and GPX1 enhancing oxidative tolerance of plants. When the stress disappears, transcription repression of PQT3 by oxidative stress is removed. The function of PQT3, as a negative regulator of oxidative stress response, is restored. The PRMT4b is then degraded by PQT3 in ubiquitination pathway. The activated response to oxidative stress is switched off. We propose a working model for PQT3 as a negative regulator of oxidative stress response (Fig 10).
Approximately 55,000 individual lines were screened for paraquat tolerant mutant, which came from the activation-tagging library constructed with Arabidopsis Columbia ecotype and pSKI015 vector [59].
All Arabidopsis transgenic lines and mutants were based on the genetic background of Columbia ecotype used as wild type in the study. Salk_065409 (pqt3-2), Salk_097442C (prmt4b) and Salk_033423 (prmt4a) were obtained from ABRC (Arabidopsis Biological Resource Center). The seeds were sterilized in 10% bleach for 10 min. Then the seeds were washed for 5 times at least with sterile water. For vernalization, the seeds were kept in the dark with water at 4°Cfor 3 days to ensure the synchronous germination. Sterile seeds were germinated on MS medium. The seedlings were grown at 22°C under 16-h-light /8-h-dark cycle with light intensity 100 μE m-2 s-1.
The constructs were electroporated into competent cell of Agrobacterium tumefaciens C58C1. The floral-dip method was used to transfer these constructs into Arabidopsis as described [88, 89].
The different tissues of plants were used for RNA extraction with Trizol method. Then RNA reverse reaction was carried out by TransScript Kit (TransGen Biotech). Specific primers were designed for RT-PCR analysis and the PCR products were detected by agarose gel electrophoresis. Applied Biosystem Step One real-time PCR system was used for the quantitative RT-PCR detection with specific primers listed in the S1 Table and Premix Ex Taq II SYBR (TaKaRa). UBQ5 was used as the internal control.
The identification of homozygous mutants was performed by genomic PCR for T-DNA insertion lines: Salk_065409, Salk_097442C and Salk_033423 [90]. RT-PCR was carried out as previously described to confirm the results of genomic PCR. The 35Spro:PQT3 and 35Spro:PRMT4b plasmids were transformed into Col-0 to obtain the overexpression lines of PQT3 and PRMT4b. For the FC line, the 35Spro: PQT3 was transformed into pqt3 mutant, and the line with the same expression level of PQT3 as wild type was chosen and used as FC line. 35Spro: PQT3, 35Spro: PRMT4b and FC line were identified by glufosinate screening and quantified by RT-PCR or quantitative RT-PCR.
The seeds were germinated on MS medium containing different concentrations of paraquat, mannitol, CdCl2 and NaCl, respectively. The phenotype was observed and survival ratio was scored at the indicated time points.
For drought tolerance assay, the wild type, pqt3-1 mutant and pqt3-2 mutant seeds were germinated in one pot at same density. The 15-day-old seedlings were used for drought tolerance assay. Watering was withheld for another 15 days before re-watering. The photos were taken before re-watering and after re-watering for 1 day and 7 days. The survival ratio was scored after re-watering for 1 day and 7 days.
DAB staining was performed as described [91]. DAB staining solution (pH 6.0) was prepared by adding 0.05% (v/v) Tween-20 and 10 mM Na2HPO4 to the DAB solution (1 mg/ml DAB, pH3.0). For each treatment condition, at least 3 leaves per plant were obtained from 3 independent plants for each line (Col-0, pqt3-1 and pqt3-2). Arabidopsis leaves from different lines were treated using MS liquid medium without or with 6 μM paraquat for 12 hr or 24 hr. These leaves were stained in 6-well culture plates with DAB staining solution subsequently and 10 mM Na2HPO4 (pH 6.0) was used as the negative control. The 6-well plates were covered with aluminum foil and placed on a shaker for 4–5 h. Follow the incubation, the bleaching solution (glycerol: acetic acid: ethanol = 1:1:3) was used in the discoloration after DAB staining solution was removed. The 6-well plates were placed into boiling water bath at 95°C for 15–20 min. Discoloration process was repeated using fresh bleaching solution. The brown precipitate caused by the reaction between DAB and H2O2 could be observed on the leaves. Photos were taken using a camera. Special attention should be paid to light avoidance through the whole operation. The experiment was repeated for three times.
The promoter of PQT3 was cloned into pCB308R [92, 93]. The transgenic lines containing PQT3pro: GUS were isolated by glufosinate screening. The T2 population was used for GUS staining. Histochemical staining for GUS activity in Arabidopsis was carried out as described previously [94] and GUS staining solution was prepared as described before [59]. The experimental materials were incubated in staining solution at 37°C. Then Arabidopsis tissues were destained and stored in 70% ethanol. The GUS activities of individual parts were observed via a light microscope Axio skop2 plus (ZEISS, Germany) with a video camera.
The full length CDS of PQT3 was cloned into the binary vector pCB2008E to construct the PQT3-GFP fusion vector [93]. After the detection of gene sequencing for inserted sequence, the recombinant plasmid was transducted into the epidermal cells of onion along with particle gun bombardment for transient expression assay. PQT3-GFP was also transferred into Col-0 via floral-dip method to create transgenic plant for the analysis of PQT3-GFP fusion protein localization. ZEISS fluorescence microscope (Axio skop2 plus) with video camera was used to observe the green fluorescence in both the epidermal cells of onion and the root cells of Arabidopsis transgenic lines.
For APX and GPX enzyme activity assay, Arabidopsis seedlings were ground in liquid nitrogen and resuspended in precooling Enzyme extraction buffer (50 mM phosphate buffer (Na2HPO4-NaH2PO4), pH = 7.0; 2% (w/v) polyvinylpolypyrrolidone (PVPP); 0.05% (v/v) TritonX-100; 1 mM EDTA and 1 mM ascorbic acid) on ice. Extraction solution was centrifuged for 20 min (16,000 g/min, 4°C). The protein concentration of supernatant was detected by One-drop micro-ultraviolet spectrophotometer and SDS-PAGE before the supernatant was used for enzyme activity analysis. The detection of APX activity was performed as described with modifications [95]. For APX activity, 50 μl enzyme and 2950 μl reaction mixture (50 mM Tris-HCl, pH7.0; 0.1 mM EDTA; 0.1 mM H2O2 and 0.5 mM ascorbic acid) was mixed. The decreased OD290 was recorded per 10 s. The enzyme amount oxidized 1 mM AsA in one minute set as one activity unit (U) of APX. The APX activity was defined as U • g-1 protein. GPX activity was measured indirectly through the detection of glutathione reductase (GR) activity using GPX Activity Measurement Kit (Beyotime Biotech, China). GR activity was detected as described with modifications [96]. The OD340 was recorded per 30 s. The enzyme amount consumed 1 mM NADPH in one minute set as one activity unit (U) of GPX. The GPX activity was also defined as U • g-1 protein.
For western blot analysis, proteins were electroblotted from 12% acrylamide gel to nitrocellulose membrane (Immobilon-P, MILLIPORE Corporation, USA) after the separation of SDS-PAGE. Antibodies used in western blot were as follows: anti-HA antibody (M20003, Mouse mAb, Abmart, Shanghai, China), 1:1,000 for western blot; anti-PRMT4b antibody, 1:500 for western blot; anti-Ubiquitin antibody (ab7254, abcam, USA), 1:1,000 for western blot; anti-His antibody (M30111, Mouse mAb, Abmart, Shanghai, China), 1:1,000 for western blot and goat anti-mouse lgG-HRP (Santa Cruz Biotechnology, USA), 1:5,000 for western blot. Image Quant LAS 4000 (GE, USA), as the CCD camera system, was used for the result examination with Super Signal West Femto Trial Kit (Thermo, USA). Band densities were quantified via Quantity One software (Bio-Rad, USA).
The in vitro E3 ubiquitin ligase activity assay was carried out as described previously [43]. GST-PQT3 fusion protein was obtained from E. coil and purified subsequently. His-tagged ubiquitin of Arabidopsis (UBQ14) was also expressed using bacterial expression system and purified. In addition, the wheat (Triticum aestivum) E1 (GI: 136632) and human E2 (UBCh5b) were also used in the reaction. Reactions were performed for 1.5 h at 30°C. For the immunoblot, His-tagged ubiquitin of Arabidopsis was detected using Nickel-HRP (Kirkegaard & Perry Laboratories, nickel–nitrilotriacetic acid agarose conjugated to horseradish peroxidase).
The bait plasmid was transformed into Mav203 strain of yeast, which was constructed with pDEST32 vector and the full-length PQT3 cDNA. Yeast two-hybrid screening was performed using two-hybrid cDNA library of Arabidopsis. The cDNA library containing fragments of fusion proteins composed of prey proteins and GAL4—AD was used to transform Mav203 cells harboring bait plasmid in Y2H assay. Positive clones were screened using SD/-Leu-Trp-His and X-gal assay. Then it was identified by nucleotide sequencing with corresponding primers. Two-hybrid screening was carried out based on the protocol described in Two-Hybrid System Manual (Invitrogen, USA). The result of screening was further confirmed by the two-hybrid assay. The full-length CDS of PQT3 and the four segments of PQT3 were inserted into pDEST32 vector to construct the bait plasmid, while the prey plasmid was constructed with pDEST22 vector and the full-length CDS of PRMT4b. For two-hybrid assay, the primers could be found in S1 Table.
MBP-PQT3-C66 and His-AtPRMT4b fusion protein were expressed using prokaryotic expression system and purified. MBP-PQT3-C66 fusion protein was incubated with MBP beads (amylose resin) at 4°C for 2 h, and the MBP tag was used as a negative control. The beads were cleaned with washing buffer for 4 times. Then the beads were incubated with His-AtPRMT4b at 4°C for 2 h respectively. The beads were cleaned with washing buffer for 4 times. Western blot was used to detect the SDS-PAGE separation results of pulled-down mixtures in nitrocellulose membrane with anti-His antibody.
Agrobacterium tumefaciens strain C58C1 was used in the experiments. Agroinfiltration procedure was carried out as described previously [63]. At first, these strains were grown on LB medium with Gentamicin and Kanamycin. Single colony was transferred into 5 ml LB liquid medium containing same resistance and grown for 48 h in a 28°C shaker. The bacteria solution was inoculated into new LB liquid medium containing 40 μM acetosyringone (1:100 ratio, v/v) and 10 mM 2-(N-morpholine)-ethanesulfonic acid (MES; pH 5.6). Bacteria were developed in a 28°C shaker until OD600 reached 3.0 approximately. The bacteria were collected gently by means of 10 min centrifugation (3,200 g/min), and the resuspension of pellets was performed with 10 mM MgCl2 until OD600 reached 1.5 approximately. The bacteria solution was kept at room temperature with a final concentration of 200 μM acetosyringone for at least 3 h without shaking. The different plastid combinations were transformed into epidermal cells of N. benthamiana leaves by disposable syringe.
NE-PQT3 (the N-terminus of YFP fused with PQT3) and CE-PRMT4b (the C-terminus of YFP fused with PRMT4b) were constructed. These constructs were transferred to Agrobacterium strains C58C1 respectively. As mentioned above, the different plastid combinations were transformed into epidermal cells of N. benthamiana leaves by agroinfiltration. YFP was observed 1–2 days after leaf infiltration using confocal. The nuclei were stained by Hoechst subsequently and fluorescence detection by confocal was performed. For BiFC assay, the primers could be found in S1 Table.
Native extraction buffer 1 [10 mM EDTA; 50 mM TRIS-MES, pH 8.0; 1 mM MgCl2; 5 mM DTT; 0.5 M sucrose; protease inhibitor cocktail for plant cell and tissue extracts (Sigma, USA)] was chosen for protein extraction buffer. Other steps of protein extraction were carried out as described previously [63].
N. benthamiana leaves was infiltrated using the mixture of Agrobacterium strains containing different constructs, from which total proteins were extracted 1 day after Agroinfiltration. The p19 was used as gene-silencing suppressor. The anti-HA antibody was used for western blot detection of the total proteins after the separation of SDS-PAGE. HA-PRMT4b is a fusion protein composed of HA-tag and PRMT4b.The protein level of PRMT4b could be analyzed via the HA-tag detection using anti-HA antibody. Ponceau S staining of the Rubisco protein was used as loading control. In the presence of MG132, total proteins were immunoprecipitated with protein A agarose beads (Millipore, USA) and anti-HA antibody (Abmart) subsequently. Detailed steps of immunoprecipitation were carried out as described previously [63]. Immunoprecipitated samples were analyzed using western blot with anti-ubiquitin antibody (Abcam). Image Quant LAS 4000 (GE, USA), as the CCD camera system, was used for the result examination.
The wild type, pqt3 mutant, prmt4b mutant and pqt3prmt4b double mutants were used for ChIP assay without or with 6 μM paraquat treatment for 24h. UBQ5 was chose for internal control. ChIP was performed as previously described [97, 98]. The regions with Arg-17 methylation were precipitated via anti-H3R17me2a antibodies (anti-Histone H3 asymmetric dimethyl R17 antibody-ChIP grade, ab8284, Abcam, USA) from input DNA. The corresponding primers were designed for quantitative RT-PCR to detect the enrichments of different DNA fragments in APX1 and GPX1 chromatins [99, 100]. The primers used in ChIP assay were showed in S1 Table.
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10.1371/journal.ppat.1004798 | Wolbachia Utilize Host Actin for Efficient Maternal Transmission in Drosophila melanogaster | Wolbachia pipientis is a ubiquitous, maternally transmitted bacterium that infects the germline of insect hosts. Estimates are that Wolbachia infect nearly 40% of insect species on the planet, making it the most prevalent infection on Earth. The bacterium, infamous for the reproductive phenotypes it induces in arthropod hosts, has risen to recent prominence due to its use in vector control. Wolbachia infection prevents the colonization of vectors by RNA viruses, including Drosophila C virus and important human pathogens such as Dengue and Chikungunya. Here we present data indicating that Wolbachia utilize the host actin cytoskeleton during oogenesis for persistence within and transmission between Drosophila melanogaster generations. We show that phenotypically wild type flies heterozygous for cytoskeletal mutations in Drosophila profilin (chic221/+ and chic1320/+) or villin (qua6-396/+) either clear a Wolbachia infection, or result in significantly reduced infection levels. This reduction of Wolbachia is supported by PCR evidence, Western blot results and cytological examination. This phenotype is unlikely to be the result of maternal loading defects, defects in oocyte polarization, or germline stem cell proliferation, as the flies are phenotypically wild type in egg size, shape, and number. Importantly, however, heterozygous mutant flies exhibit decreased total G-actin in the ovary, compared to control flies and chic221 heterozygous mutants exhibit decreased expression of profilin. Additionally, RNAi knockdown of profilin during development decreases Wolbachia titers. We analyze evidence in support of alternative theories to explain this Wolbachia phenotype and conclude that our results support the hypothesis that Wolbachia utilize the actin skeleton for efficient transmission and maintenance within Drosophila.
| The world’s most common intracellular infection, Wolbachia pipientis, infects 40% of insect species and is currently used to prevent transmission of Dengue by mosquitoes. The bacterium targets the germline of insects, where it is faithfully transmitted to the developing oocyte and the next generation. Here we identify host cytoskeletal proteins required by Wolbachia in order to be efficiently transmitted between Drosophila melanogaster generations. We show that after only two generations in a phenotypically wild type, heterozygous mutant fly, Wolbachia infections are cleared or reduced in titer. Characterization of the mutants suggests that Wolbachia is sensitive to the regulation of actin in the ovary and that actin may be used by Wolbachia to both target and proliferate within host tissues and to be faithfully, maternally transmitted.
| Wolbachia pipientis is an intracellular α-proteobacterium that forms symbioses with an extremely broad array of hosts, including isopods, nematodes, and insects [1]. Wolbachia were first noted in the tissues of the mosquito, Culex pipiens, by Hertig and Wolbach in 1924, but subsequently, many more insects were found to harbor Wolbachia. Current estimates suggest that upwards of 40% of insect species may be infected by the parasite, making Wolbachia one of the most common intracellular bacteria on the planet [2]. Wolbachia are well known for the reproductive effects induced in the host, which range from the exotic (male killing) to the most common of reproductive effects, cytoplasmic incompatibility (CI) [1]. This recalcitrant, obligate symbiont has received much attention recently due to medical relevance. Wolbachia are heavily studied as potential drug targets for filarial nematode infection [3,4] and are currently being implemented to prevent transmission of Dengue fever from mosquitoes to humans [5,6]. Wolbachia may be one answer to controlling some vector borne human diseases—indeed mosquitoes harboring a virus-blocking strain of Wolbachia are presently being released in underdeveloped parts of the world with this hope in mind [6–8]. Given the ubiquity of Wolbachia in the insect world, and its relevance to human health, it is essential to understand the biological basis of transmission of the symbiont between host generations.
Wolbachia are maternally transmitted bacteria that infect the germline of their hosts such that their transmission fidelity in wild populations is extraordinarily high. Although physiologically stressful conditions are known to induce the loss of superinfections [9], perfect transmission has been measured in control laboratory Drosophila populations as well as in insects harboring transferred Wolbachia infections [10–12]. Localization in the germline, and in the developing oocyte, is critical to Wolbachia’s maternal transmission and in addition, densities in the embryo, and posterior localization, are correlated with reproductive phenotype (e.g. CI) [13,14].
Previous studies have provided some support for Wolbachia interactions with host cytoskeletal elements. Specifically, in Drosophila, Wolbachia require host microtubules and the motors Dynein and Dynactin for anterior localization early in development and Kinesin-1 for posterior localization in mid oogenesis, positioning them for inclusion in the germline [15,16]. This localization is thought to be crucial to the bacterium’s faithful transmission to subsequent generations at the appropriate densities. Additionally, Wolbachia use astral microtubules during asymmetric divisions in the developing embryo, leading to the widespread, but uneven, pattern of localization of the bacteria in adult tissues [17]. In both worms and flies, Wolbachia undergo somatic cell to germline transmission, suggesting an ability for the bacterium to alter the host actin cytoskeleton to facilitate uptake by germ cells [18,19]. More recently, work has suggested interactions between Wolbachia proteins from the Brugia malayi symbiont and host actin [20], although Wolbachia ultrastructure in Brugia does not reveal any obvious mechanism (such as actin comet tails produced during infection in other Rickettsiales) [21]. These previous studies have relied on microscopy and in vitro biochemistry and until now, no genetic evidence of interaction between Wolbachia and actin has been reported.
Here we present data showing that Wolbachia persistence and transmission within Drosophila melanogaster is sensitive to mutations affecting the actin cytoskeleton. The importance of actin during Wolbachia infection was investigated by acquiring Drosophila mutants in actin binding proteins, both involved in the regulation of F-actin filaments: the homologs of profilin (chickadee), which regulates the formation of filamentous actin, and villin (quail), which bundles actin filaments. We show that flies heterozygous for mutations in profilin (chic221/+ and chic1320/+) or villin (qua6-396/+) lose Wolbachia infection after only a few generations. Importantly, the effect is due to both an inability of Wolbachia to efficiently colonize germaria in heterozygous mutant hosts and by a reduction in titer when the host is infected. Importantly, both the less severe chic allele (chic1320), known to decrease an oocyte specific isoform of Drosophila profilin chickadee [22], as well as the null chic allele (chic221) produced a Wolbachia titer phenotype. We identified two different actin binding proteins (profilin and villin) that affect Wolbachia transmission and maintenance, supporting the conclusion that Wolbachia persistence within the host is sensitive to actin.
Standard methods were used for all crosses and culturing. The following stocks were obtained from the Bloomington Drosophila Stock Center (BDSC) at Indiana University (http://flystocks.bio.indiana.edu/): stock number 145, which carries w1 was used as the Wolbachia infected control line. Two chickadee mutant fly stocks were used in this study. The chic221 cn1/CyO; ry506 flies carry a null recessive allele resulting from the deletion of 5’ non-coding and some chic-coding sequences [22]. The P{PZ}chic01320 cn1/CyO; ry506 flies carry a strong homozygous infertile loss-of-function allele in chickadee, generated by P-element insertion [23]. The quail mutant flies, qua6-396/SM1, carry a female sterile, recessive mutation induced by ethyl methanesulfonate [24]. We also utilized two chromosomal deficiency stocks: #9507, w1118; Df(2L)BSC148/CyO, is a chromosomal deletion of segments 36C8-36E3, covering the region containing the quail locus. The second of these stocks #24377, w1118; Df(2L)BSC353/CyO, covers segments 26A3-26B3, the region containing the chic locus. Both of these chromosomal deletions are part of the aberration stock collection and were created by FLP-mediated recombination between FRT-bearing transposon insertions [25]. Wolbachia were introduced into the heterozygous mutant backgrounds through crosses between w1 infected females (stock 145) and uninfected heterozygous males (mutant/CyO). In order to control for genetic background, we also created isogenized lines by backcrossing stock 145 and each mutant line to an uninfected w; Sco/Cyo stock for three generations (as per [26], S1 Fig). We used sibling controls to identify Wolbachia titer differences related to genotype.
In addition to these isogenized lines, and to examine the effect on Wolbachia titer of profilin knockdown during development, we utilized a fly stock carrying a UAS inducible profilin-specific short hairpin silencing trigger (RNAi; stock #34523, genotype y1 sc* v1; P{TRiP.HMS00550}attP2) [27]. In order to test the effect of induction on fly development (to recapitulate the developmental lethality of the profilin null) we crossed homozygous females from this line to w; P{w+, Act GAL4} /TM3 males. In order to knock down profilin, we then crossed homozygous females from this line to a homozygous Hsp70:Gal4 driver (a generous gift from Brian Calvi). An additional control for expression from the Hsp70:Gal4 driver included a UAS:GFP stock (also a gift from Brian Calvi). Flies were shocked at 37C for 10 minutes to induce the short hairpin. Wobachia infection status for stocks acquired from the BDSC was determined via PCR and Western blot targeting the gene wsp or its product (see methods below). All flies examined for Wolbachia infection in the experiments below were age matched in order to avoid confounding correlations between fly age and Wolbachia titer.
Flies were ground in 1.5ml centrifuge tubes using an electric hand drill and disposable pestle in lysis buffer: 150mM NaCl, 1% Triton X-100, 50mM TrisHCl (pH8) containing HALT protease inhibitor cocktail (Thermo Scientific) and 5 mM EDTA. The lysates were centrifuged for 1 minute at 8000 X g to pellet debris. Samples were heated for 5 minutes at 95°C in Laemmli sample buffer containing 5% β-mercaptoethanol (Bio-Rad) prior to SDS-PAGE electrophoresis. Proteins were separated on 4–20% Tris-Glycine NB precast gels (NuSep) in 1X Tris/Glycine/SDS running buffer (Bio-Rad) and transferred to PVDF membrane in Tris-Glycine transfer buffer with 15% methanol at 40v on ice for 3–4 hours. The membrane was blocked for 5 minutes in Starting Block T20 (TBS) Blocking Buffer (Thermo Scientific), followed by incubation in primary antibody (for 1 hour at RT or O/N at 4°C) according to standard protocols. SuperSignal West Pico Chemiluminescent Substrate (Thermo Scientific) was used according to the manufacturer’s instructions to detect HRP (after incubation with secondary antibodies) on the immunoblots. Blots were re-probed after stripping in 100mM Glycine, 0.15 ND-40, 1% SDS, pH 2 for 1 hour at RT, then overnight at 4°C. PageRuler prestained protein ladder (Thermo Scientific) was used as a molecular mass marker. The following antibody was obtained through BEI Resources, NIAID, NIH: Monoclonal Anti-Wolbachia Surface Protein (WSP), NR-31029, and used at a dilution of 1:1000. Additionally, we used anti-actin monoclonal at 1:10,000 (Seven Hills Bioreagents) as a loading control as well as secondary antibodies: HRP enzyme conjugates (Invitrogen) at 1:5000. Densitometry measures were made in ImageJ using scanned film with same exposure times across multiple experiments. Control and experimental flies were included on the same blot in order to ensure consistencies in measured ratios.
Immunohistochemistry was performed as follows: ovaries for immunolocalization were dissected in Ringer’s solution 3–5 days after fly eclosion, then fixed as previously described [28] with following modification: 6% formaldehyde devitellinizing buffer was replaced with 5.3% paraformaldehyde in same (Electron Microscopy Sciences). After a series of washes in PBS buffer, ovaries were blocked with 0.5% BSA in PBST for 10 min. The monoclonal anti-Heat Shock Protein 60 (HSP60), clone LK2, H 3524 (Sigma) was diluted 1:150 in PBST with 1% BSA or a custom antibody created against full length Wolbachia FtsZ was diluted 1:150 in PBST with 1% BSA. Cy3 conjugated to goat anti-mouse secondary antibody (Jackson Immunoresearch) or rabbit secondary antibody (Jackson Immunoresearch) diluted 1:250 in PBST + BSA was used to detect the primary antibody. For F-actin detection we used Acti-stain 488 Fluorescent Phalloidin (Cytoskeleton, Inc). Tissues were mounted in Slow Fade “Gold” antifade reagent (Invitrogen) and stored at 4°C.
To confirm staining by immunohistochemistry, we also used fluorescent in situ hybridization, following published protocols [18] with the following modifications: post-fixation in 4% paraformaldehyde in DEPC treated PBS, ovaries were dehydrated in methanol and stored overnight at -20°C. In the morning, washes in DEPC-PBST preceded a 5 minute proteinase K treatment (0.05 mg/mL) at 37C before prehybridization in hyb buffer (50% formamide, 5X SSC, 250 mg/L SS DNA, 0.5x Denhardts, 20 mM Tris-HCl and 0.1% SDS). Universal bacterial probe EUB338 conjugated to Alexa488 (Molecular Probes) was used to detect Wolbachia in the ovarioles. Hybridized ovaries were mounted in Slow Fade “Gold” antifade reagent (Invitrogen).
Images were taken as Z-series stacks at 1.5 um intervals using a Nikon E800 fluorescent microscope with 40x oil objective and processed using Metamorph imaging software (Molecular Devices). Care was taken such that exposure times were normalized across all experiments. For quantification of Wolbachia and F-actin within the germarium z-sections maximum projections were used and regions of the germarium demarcated using masks (S2 Fig). We were careful to exclude the peritoneal sheath for F-actin quantification and for Z-stacks where the sheath was difficult to exclude (due to placement of the sections), the images were not included in the F-actin quantification. Germaria showing aggregates of Wolbachia were scored based on a striking pixel intensity in the presumed somatic stem cell niche.
DNA was extracted from flies utilizing the Qiagen DNeasy Blood and Tissue Kit (Qiagen) according to directions with the following modification. Flies were ground in a 1.5ml centrifuge tube using a disposable pestle and an electric hand drill in 180 ul PBS, 200 ul ALT buffer, and 20 ul Proteinase K solution. The samples were incubated at 56°C for 10 minutes with vigorous shaking and then centrifuged briefly to pellet debris before continuing with the ethanol precipitation in the kit protocol. DNAs were quantified by measuring absorbance at 260nm using an Epoch spectrophotometer (Biotek). Semi-quantitative PCR was performed by standardizing the amount of DNA in each reaction. We utilized Phusion High Fidelity PCR Master Mix with HF buffer (New England Biolabs). The protocol for amplification was: 98°C for 3 minutes, followed by 25 cycles of 98°C for 10 seconds, 56°C for 45 seconds, 72°C for 1 minute 30 seconds with a final 10 minute extension at 72°C. Primers were as follows: wsp F1 5’-GTC CAA TAR STG ATG ARG AAA C—3’ and wsp R1 5’- CYG CAC CAA YAG YRC TRT AAA -3’ [29]. RNA and DNA were extracted from individual flies or pupae using a modified Trizol extraction protocol. Briefly, 500 uL of Trizol was added to flies and samples homogenized using a pestle. After a 5 minute incubation at room temperature, a 12,000 rcf centrifugation (at 4C for 10 min) was followed by a chloroform extraction. Aqueous phase containing RNA was extracted a second time with phenol:chloroform before isopropanol precipitation of RNA. This RNA pellet was washed and resuspended in The RNA Storage Solution (Ambion). DNA extraction from the same flies or pupae was performed using ethanol precipitation of the organic phase during the first chloroform extraction. Quantitative PCR was performed on the DNA to detect the Wolbachia titer (with reference to the host) using an Applied Biosystems StepOne Real-time PCR system and SybrGreen chemistry (Applied Biosystems). We used wsp primers for Wolbachia (Forward: CATTGGTGTTGGTGTTGGTG; Reverse: ACCGAAATAACGAGCTCCAG) and Rpl32 primers for the host (Forward: CCGCTTCAAGGGACAGTATC; Reverse: CAATCTCCTTGCGCTTCTTG) at the following temperatures: 95°C for 10 min, then 40 cycles of 95°C for 15 seconds and 60°C for 1 minute. To detect number of profilin transcripts we utilized the RNA extracted from these flies and the SensiFAST SYBER Hi-ROX One-step RT mix (Bioline) and the following primer set: chicF: TGCACTGCATGAAGACAACA, chicR: GTTTCTCTACCACGGAAGCG (FlyPrimerBank, DRSC). Reactions were performed in a 96-well plate and calibration standards were used in every run to calculate primer efficiencies. These efficiencies, along with the CT values generated by the machine, were used to calculate the relative amounts of Wolbachia using the ΔΔ Ct (Livak) and Pfaffl methods [30].
In order to identify the ratio of filamentous to globular actin in ovaries from age matched flies, we used ultracentrifugation coupled to SDS-PAGE and Western blots using an in vivo F/G actin assay kit (Cytoskeleton, Inc). Age-matched, virgin female flies from chic221/Cyo or control (stock #145) were dissected in LAS2 buffer at 37°C and incubated for 10 minutes at 37°C. A brief 300 g centrifugation step (5 minutes) was followed by a 1 hour ultracentrifugation at 100,000 g at 37°C. Supernatants containing globular actin were removed and pellets resuspended in actin depolymerization buffer on ice, by pipetting up and down every 15 minutes for 1 hour. Pellets containing F-actin fractions and supernatants containing G-actin fractions were run on an SDS-PAGE gel and Western blots performed (as above) using a primary mouse monoclonal anti-actin antibody. Bands were quantified using densitometric analysis in ImageJ (as above).
All three actin binding protein mutant fly stocks used in this study were uninfected with Wolbachia upon receipt from the Bloomington Drosophila Stock Center. In order to establish an infection in the flies, infected control females were crossed with mutant uninfected males to generate F1 progeny, half of which carried the mutation, and half of which carried the Cyo balancer (a second chromosome containing inversion breakpoints and a dominant visible mutation of curly wings). F1 heterozygous mutants for the actin binding protein alleles were then back-crossed to the paternal mutant line (mutant/Cyo) and F2 progeny from that cross, carrying the mutation and harboring straight wings, were collected. We screened both the F1 and F2 progeny for Wolbachia infection using PCR against the Wolbachia surface protein gene (wsp) (Fig 1A). We observed a trend where Wolbachia transmission was not complete in these crosses. For example, the bacterium could be introduced into some heterozygous mutant backgrounds; F1 progeny were infected if they resulted from crosses between control females and chic1320/CyO as well as qua6-396/CyO fathers, but the bacterium failed to colonize chic221/+ F1 progeny efficiently. We were unable to detect Wolbachia in many of the F2 progeny (Fig 1A). In order to quantify this reduction in titer, we performed qPCR on DNA extracts from F1 progeny from each of five individuals from the heterozygous mutants and compared these results to the quantified Wolbachia loads found in control flies (Fig 1B). Progeny from each F1 cross have a statistically significant reduction in Wolbachia titer (as quantified through qPCR) compared to the control lines (p < 0.01 for all pairwise comparisons, using a Bonferroni correction for df = 8). As additional support for the importance of the chic and qua loci in the Wolbachia titer defects we observed, we also quantified the amount of Wolbachia within two chromosomal deficiency stocks (deletions in the same region as either chic or qua in isogenic backgrounds)[25]. These deficiencies showed the same phenotype as our chic and qua mutants, supporting our observation that these genomic loci are responsible for the Wolbachia titer defect (Fig 1B). In addition to reductions in the F1 progeny, we also quantified a reduction in F2 progeny for the three actin mutant lines. For flies in which we can detect Wolbachia, the F2 progeny are further reduced in titer compared to the F1 lines (ratio of expression F1 versus F2: min = 0.56, max = 0.78).
In order to control for effects of host genetic background on Wolbachia titer, we created isogenized lines from the control stock (145) and each of the mutant stocks by backcrossing to an uninfected w; Sco/Cyo line for three generations. We then crossed these Wolbachia infected F3 females (w; Sco/Cyo) to Wolbachia uninfected w; mutant/Cyo males (S1 Fig). In the F5 generation, we observed a significant effect of genotype on Wolbachia titer. Specifically, and regardless of mutant allele, mutant/Cyo progeny were reduced in Wolbachia titer by 1/3 compared to their w; Sco/Cyo siblings (mean relative ratio wsp/rpl32; t = -4.514; df = 9; p = 0.001). This result suggested to us that the reduction in titer was at least partially due to a result of a developmental defect in Wolbachia maintenance and persistence within the heterozygous mutant hosts.
As an additional control for host genetic background and to explore direct effects on profilin knockdown during development, we took advantage of an infected fly stock carrying a UAS inducible profilin-specific short hairpin silencing trigger (RNAi; stock #34523, genotype y1 sc* v1; P{TRiP.HMS00550}attP2) [27]. In order to test the effect of induction on fly development (to recapitulate the developmental lethality of the profilin null) we crossed homozygous females from this line to w; P{w+, Act GAL4} /TM3 males. From this cross we only recovered stubble progeny, suggesting that this particular RNAi line, which hadn’t previously been utilized in a publication to knock down profilin expression, is effective. In order to test the effect of induction on fly development we crossed homozygous females (y1 sc* v1; P{TRiP.HMS00550}attP2) to a homozygous Hsp70:Gal4 driver [2–5]. Third instar larvae were shocked at 37C for 10 minutes to induce the short hairpin and late pupae collected for RNA and DNA extraction (N = 8 for each treatment and genotype; y1 sc* v1; P{TRiP.HMS00550}attP2 with or without Hsp70:Gal4 and with or without heat shock). In the maternal y1 sc* v1; P{TRiP.HMS00550}attP2 background, heat shock did not affect either Wolbachia titers (t = 1.207, df = 2, p = 0.351) nor profilin expression (t = -1.144, df = 2, p = 0.371). In contrast, profilin expression was statistically significantly reduced in flies expressing the RNAi construct compared to non-heat shocked siblings (the mean expression ratio chic/rpl32 = 0.57; t = -6.240; df = 2; p = 0.025). In addition, knockdown of profilin did have a significant and measurable effect on Wolbachia titers in these same flies; the fly Wolbachia titers were reduced by 1/3 compared to their non-heat shocked siblings (mean relative ratio wsp/rpl32 = 0.66, t = -8.593; df = 2; p = 0.013).
To provide additional support for the reduction in titer observed via PCR, we probed Western blots of pooled or individual fly lysates produced from the F1 and F2 progeny and their parents for Wsp (Fig 1C, 1D and 1E). Results corroborated our previous finding that Wolbachia transmission was imperfect in the mutant flies (Fig 1A and 1B). Specifically, infected F1 progeny, especially in the chic mutant backgrounds, appeared to carry a reduced titer of Wolbachia when compared to the maternal, infected line (Fig 1). Indeed, flies from control crosses are consistently higher titer in Wolbachia, as based on densitometric quantitation of Western blot bands (Average +/- STERR over 5 experiments for Control = 13,106 +/- 3,294; chic1320/+ = 6,418 +/- 4,890; chic221/+ = 6,545 +/- 1,576; qua6-396/+ = 6,179 +/- 645; t-test; p = 0.036, 0.001, 0.002 for each heterozygous mutant compared to control). Additionally, we could detect a statistically significant reduction between the F1 and F2 heterozygous mutant flies (p = 0.012). As observed in our results based on PCR, Wolbachia titer (based on quantity of protein on a Western blot) is also reduced, with some variability, in the F2 progeny (Fig 1D). We hypothesized that the loss of Wolbachia in some F2 progeny was a result of a reduction in Wolbachia titer in F1 females during oogenesis. We therefore visualized the Wolbachia infection in the germarium in F1 females (mutant/+; below).
To colonize the oocyte, and therefore complete maternal transmission, Wolbachia occupy the germline and somatic stem cell niches (SSCN) in their hosts [18,26,31]. Wolbachia can achieve this localization after injection into the fly abdomen, suggesting that the stem cell niche targets are essential for Wolbachia infection [31]. The Drosophila ovariole provides an opportunity to view oocyte development and Wolbachia localization within each progressive stage. Wolbachia concentrate preferentially in the somatic stem cell niche, which is thought to serve as a source of infection for the germline. As germline development progresses from regions 2a to 2b, Wolbachia are thought to infect via the somatic stem cell niche, increasing the numbers of bacteria found within the germline after association with the SSCN [31]. We utilized immunohistochemistry to detect Wolbachia in the germarium of our flies, producing localizations expected based on previous publications [15,18,26,31](S1 Table and S2 and S3 Figs). Wolbachia infection within the entire germarium is significantly reduced in heterozygous mutant flies (when comparing the amount of fluorescence observed in control flies to that found in either chic221/+, chic1320/+ or qua6-396/+, respectively; Mann-Whitney U = 171.5, Z = -3.995, p < 0.001; Mann-Whitney U = 98, Z = -5.295, p < 0.001; Mann-Whitney U = 55.5, Z = -5.496, p < 0.001; Figs 2 and 3). When either chic1320/+ or qua6-396/+ heterozygous mutant flies are infected, the Wolbachia titer in region 2 (as quantified by anti-Hsp60 staining) is also significantly reduced, compared to the control maternal line (Mann-Whitney U = 194, Z = -3.78, p < 0.001; Mann-Whitney U = 134; Z = -4.097, p < 0.001, pairwise comparison between control and chic1320/+ or qua6-396/+ heterozygous mutants, respectively; Fig 3). Additionally, Wolbachia infection within early egg chambers (stage 1) is significantly reduced in all heterozygous mutant flies (when comparing the amount of fluorescence observed in control flies to that found in either chic221/+, chic1320/+ or qua6-396/+, respectively; Mann-Whitney U = 74, Z = -5.74, p < 0.001; Mann-Whitney U = 58, Z = -5.872, p < 0.001; Mann-Whitney U = 39, Z = -5.767, p < 0.001; Figs 2 and 3). In order to quantify this reduction, for each germarium, we calculated the ratio of fluorescence intensity in the earliest egg chamber over that found in region 2 (as quantified by anti-Hsp60 staining). Each of the three mutant lines showed a statistically significant reduction in this ratio when compared to control germaria (average ratios for control flies: 1.72; chic221/+: 0.52; chic1320/+: 0.64; qua6-396/+: 0.80, t-test; p < 0.0001). The reductions in infection in the germaria suggest two things: (1) that Wolbachia has difficulties in transiting or maintenance in a population within the germarium during development in the heterozygous mutant flies and (2) even when region two, the location of the SSCN, is occupied by Wolbachia, the bacteria are deficient in colonization of the early egg chamber in the heterozygous mutant flies (Fig 3). We did not quantify differences in staining of the presumed germline stem cell niche due to variability in staining in this region within the control flies.
Within heterozygous mutant flies, we observed that the Wolbachia that successfully manage to colonize the germarium do so with a distinctive localization; these Wolbachia appear as aggregates, in sharp contrast to the more even distribution of Wolbachia within control germaria (Table 1 and Fig 2). Under high magnification (100x), the Wolbachia aggregates within the heterozygous mutant flies appear to be multiple Wolbachia forming micro-colonies within the tissue, based on shape and size and consistent localization within the genotypes.
Both Drosophila profilin (chickadee) and villin (quail) are important in the regulation of F-actin during oogenesis. Because profilin promotes the polymerization of F-actin filaments and villin stabilizes these filaments through bundling, we were curious to know whether or not the heterozygous mutant flies differed in the quantity of F-actin found in the germarium, when compared to control flies. In addition, visualization of the F-actin cytoskeleton allowed us to examine the actin ring canals in the heterozygous mutant flies at all stages of oocyte development. At no point were ring canals occluded by nuclei, supporting our finding that cytoplasmic streaming and maternal dumping are unaffected in heterozygous mutant flies (N = 300, scored by eye). Using quantified fluorescence of F-actin in the images we were unable to detect a statistically significant difference between median levels of phalloidin staining in control flies compared to the heterozygous mutants (Kruskal Wallis test: χ2 = 4.005, df = 3, p = 0.261; S4A Fig). Because F-actin levels in the germarium (observed through phalloidin staining) did not correlate with Wolbachia intensity (as quantified by anti-Hsp60 staining), the Wolbachia titer phenotype observed in these flies may not be directly related to the F-actin network in the germarium. We therefore examined the in vivo amounts of filamentous and globular actin in ovaries from control flies and compared this to that seen in heterozygous mutant chic221 flies. Using ultracentrifugation coupled to Western blot, we found that we could consistently detect globular actin in the ovaries of control flies. In contrast, we found a statistically significant decrease in total amount of globular actin detected in the heterozygous mutant lines (Kruskal Wallis: χ2 = 4.192, df = 1, p = 0.041; S4B Fig).
The difference in G actin between control and heterozygous chic221 flies prompted us to investigate expression of profilin in the chic221/+ F1 mutants and control flies. The rationale was that although these flies are phenotypically wild type, the dosage effect of a single, wild type chromosome in the chic221/+ F1 mutants might be significant and correlate with Wolbachia absence. We extracted both RNA and DNA from individual F1 chic221/+ female flies as well as age-matched control flies and used quantitative RT-PCR to detect profilin transcript levels (in total RNA) and Wolbachia surface protein (in total DNA) relative to Rpl32. Control flies express, on average, 2x as much profilin as heterozygous mutant F1 progeny (means control μ = 4.03; mutant μ = 2.28; t = 2.590, df = 11.31, p = 0.025). Additionally, although we could detect Wolbachia in each of the wild type flies included (N = 10), we were only able to detect a Wolbachia infection in three of the heterozygous mutant F1 flies (S4C Fig). Wolbachia may have been present in these flies, but at titers below the limit of detection for this method.
Because Wolbachia target the germline, and within the germarium, the stem cell niche [18,31], the number of egg chambers produced by the host may affect Wolbachia’s ability to be transmitted between generations. Flies that are homozygous mutants in chickadee show defects in germline stem cell proliferation as well as enclosure by somatic cyst cells [32,33] so it was therefore important to confirm that the heterozygous mutant flies do not display similar defects. We counted the number of viable progeny resulting from individual crosses within mutant fly lines and compared the number of resulting offspring to those from control crosses. Heterozygous villin or profilin mutant flies do not show a defect in fertility when compared to control flies (S5 Fig). Additionally, we observed over 300 eggs for each of the fly mutant stocks and did not see any morphological abnormalities when compared to the control stock (N = 300, scored by eye).
Wolbachia maternal transmission in Drosophila melanogaster is normally extremely effective, with perfect transmission observed in laboratory populations and near perfect transmission in the wild [10–12]. Wolbachia are thought to localize in the germarium, and ultimately in the oocyte, in order to accomplish this maternal transmission. Previous work has shown that Wolbachia use host microtubules to localize preferentially to the oocyte during development [15–17]. The striking anterior localization of Wolbachia during oogenesis can be perturbed by feeding Drosophila microtubule inhibitors such as colchicine, or by mutations that perturb the microtubule cytoskeleton [15]. In contrast, direct treatment of dissected ovaries with actin disrupting drugs (such as cytochalasin-D) does not alter this localization [15]. However, other pieces of evidence suggest that Wolbachia manipulate the host actin cytoskeleton. For example, Wolbachia injected into the abdominal cavity of Drosophila migrate to the germline stem cell niche, a feat that requires traversing several host tissues and cell types [31]. Also, Wolbachia in the terrestrial isopod Armadillidium vulgare are not found in all primary oocytes and instead, enrichment of Wolbachia is seen during the course of development [34]. Finally, Wolbachia are associated with areas of weak cortical actin staining in filarial nematodes, suggestive of a mechanism for entry into the germline from somatic cells [17,19]. Therefore, it is likely that Wolbachia use both microtubules and actin for persistence in the host and maintenance across host generations.
Oogenesis in Drosophila relies on rearrangements of both the actin and microtubule networks [35]. We were therefore careful in our analysis to separate direct effects of actin modulation from indirect effects resulting from perturbations of the reproductive biology of the fly. Products of both the quail and chickadee loci are necessary for fly reproduction [22,36–38]; homozygous or hemizygous mutants in either gene result in fertility defects or are lethal. Importantly, in this study we followed Wolbachia infections in phenotypically wild type flies harboring a functional copy of the actin binding protein in question. These heterozygous mutant flies produce the same number of offspring as the control flies and produce eggs with the same morphology as controls, however the flies do not faithfully maintain a Wolbachia infection. Several hypotheses partially explain our data, and below we delineate our hypothesis and alternative hypotheses and summarize our evidence to support or refute them.
The developing oocyte is loaded with maternal determinants (e.g. mRNA and protein), a process which begins early (stage 1), and continues until about stage 10 when maternal nurse cells dump their remaining cytoplasmic contents into the oocyte [35]. The actin cytoskeleton is critical to this process, as mutations in actin binding proteins have been known to cause severe defects. Specifically, cytoplasmic actin bundles are required to restrain the nurse cell nuclei during transport; mutations in quail, which regulates bundling of cytoplasmic actin, cause a dumpless phenotype [39,40]. In quail mutant flies, nurse cell nuclei can be observed extending through the actin ring canals [39]. We reasoned that although heterozygous mutant flies (chic221/+, chic1320/+ and qua6-396/+) produce viable progeny, and we found no occluded ring canals in any of these backgrounds, a subtle defect in maternal cytoplasmic dumping could alter the ability of Wolbachia to be transmitted faithfully to the oocyte. Wolbachia has been suggested to utilize cytoplasmic dumping to increase titer in the oocyte (as compared to the nurse cells) [15]. In addition to regulating the bundling of microtubules and therefore cytoplasmic streaming, profilin is also required for posterior patterning in the oocyte as chic mutants fail to localize STAUFEN and oskar mRNA [41]. Wolbachia utilizes these posterior determinants to localize in the oocyte, as disruption of osk and stau results in mislocalization of Wolbachia in D. melanogaster [16]. If heterozygous mutant flies are defective in cytoplasmic dumping or polarization, we should observe both egg size and morphology defects. Over 300 eggs were scored for each of the mutant lines, as well as control flies, without any phenotypic differences detected. Importantly, however, the primary loss of Wolbachia in these heterozygous mutants occurs in the germarium, before defects would begin to affect Wolbachia titers. Therefore, although our fly mutants could conceivably exhibit subtle polarization defects, these defects alone would not entirely explain the observed phenotype.
In addition to serving important roles during maternal loading in the late stage oocyte, profilin functions in germline stem cell (GSC) maintenance and germ cell enclosure by somatic cyst cells [32,33]—homozygous chickadee mutants fail to maintain germline stem cell number. However, chic221/+ flies are equivalent to wild type [32]; that is to say, heterozygous mutant flies do not have a GSC deficiency. Importantly, although Wolbachia are known to alter germline stem cell proliferation [26] and some Wolbachia colonize the germline stem cell niche [18], wMel colonizes the somatic stem cell niche in Drosophila melanogaster (Fig 2). Regardless, a defect in fertility, resulting from defects in GSC maintenance might affect Wolbachia proliferation in these mutant flies. We therefore counted the number of viable progeny (a measure of fertility) for each of the mutant lines. No statistically significant difference was observed for any of the heterozygous, mutant flies, when compared to the control (S5 Fig). We therefore did not find support for this hypothesis to explain the Wolbachia clearing phenotype of profilin and villin heterozygous mutants.
There is significant evidence that Wolbachia colonize the primordial germ cells and the posterior pole of developing embryos in numerous insect hosts. In D. melanogaster, for example, strain wMel concentrates at the posterior pole in a poleplasm dependent fashion [16,42,43]. However, this posterior concentration of Wolbachia is not universal in insects nor in Drosophila. Wolbachia strain wRi infects the entire embryo uniformly while B group Wolbachia actually show exhibit anterior localization [14]. Similarly, in other Drosophila species there are different patterns of Wolbachia colonization: although wWil infects primordial germ cells, wAu infects the entire embryo [44]. This posterior localization is clearly important—the extent of CI is correlated with the number of Wolbachia in the posterior of the embryo [14]. However, this posterior localization is not necessarily correlated with maternal transmission, which is near 100% for some Drosophila species and quite low for others [45–47]. This result suggests that high titer localization to primordial germ cells and the posterior pole does not guarantee maternal transmission. However, if our heterozygous mutant flies induce defects in these early localization patterns (to the posterior pole or to the developing germ line), we might expect the inefficient transmission phenotype observed.
What other ways might Wolbachia use to eventually colonize the germline? Wolbachia colonization of somatic tissues has been known for some time [48] but recently, it has been suggested that Wolbachia infection of the soma may serve as a reservoir for germline infection. In the terrestrial isopod, Armadillidium vulgare, Wolbachia is absent from many early oocytes and infects the older oocytes late in development, an enrichment that is thought to come from a somatic reservoir (the follicle cells) [34]. In nematodes, Wolbachia initially are concentrated in the posterior of the P2 blastomere, the precursor of the adult germ line. However, Wolbachia are subsequently excluded from the germ line in the next cell division and instead, invade the germ cells later, from the surrounding somatic gonadal cells [19]. This soma to germ cell invasion in Brugia is correlated with a disruption in polymerized actin at those foci [19]. Because we observed a reduction in anti-Hsp60 staining in stage 1 egg chambers of heterozygous mutant flies as well as transmission defects, one interpretation of our data is that Wolbachia require actin for soma to germline transmission. Importantly, however, we did not observe actin disruptions (similar to those seen in Brugia) within Drosophila germaria.
Our data suggest that Wolbachia rely on the actin cytoskeleton to achieve adequate titer in the Drosophila host during development. First, we observe reductions in titer of Wolbachia in heterozygous mutants compared to both their non-mutant sibling controls as well as parental controls (Fig 1). Second, knockdown of profilin in third instar larvae reduces Wolbachia titer in pupae, suggesting that the regulation of actin is important to the maintenance of a Wolbachia infection during development. Additionally, passage of Wolbachia through heterozygous mutant lines for multiple generations results in the enrichment for mutant Wolbachia; the heterozygous mutant flies bottleneck the Wolbachia infection, increasing the stochastic segregation of variants [49]. This decrease in titer may explain the inefficient transmission of Wolbachia observed in the mutant flies. Actin may be used by Wolbachia to properly localize during development, or may support the infection via other unknown mechanisms.
Both of the proteins investigated here (profilin and villin), are known to increase the amount and stability of F-actin in the Drosophila egg chamber. Profilin promotes F- actin in the follicular epithelium while villin bundles and binds to filamentous actin [37,50]. One potential cause of the Wolbachia phenotype in these backgrounds is a mis-regulation in F-actin content. Interestingly, chic mutants have been previously observed to exhibit decreased F-actin levels in the follicle cells [50]. Both the somatic stem cell niche and the follicular epithelium have been suggested to be a source of Wolbachia during oogenesis [18,34]. Because Wolbachia densely colonize the follicular epithelium tissue, and because it surrounds the oocyte throughout development, this tissue may be a candidate for the source of the infection. We detected a significant reduction in the amount of actin in heterozygous mutant chic221 flies compared to controls, which corresponded to a decrease in profilin transcripts and a decrease in detected Wolbachia (S4 Fig). These data are suggestive of a role for actin in Wolbachia maintenance and transmission but do not elucidate an exact mechanism.
We have shown that the host actin cytoskeleton is clearly important for the maintenance of a Wolbachia infection. Perhaps this reproductive parasite secretes proteins that interact directly with eukaryotic actin or host actin binding proteins. Indeed, other members of the Rickettsiales are known for their striking coopting of host actin in the production of comet tails [51]. However, when intracellular, Wolbachia persist within membrane-bound compartments and no such comet-like structures have been observed to be associated with the vacuole [21]. That said, our results here and the work of others strongly suggest that Wolbachia is able to enter and exit eukaryotic cells; Wolbachia transit to the germline from the fly abdomen and are loaded into the germ cells from surrounding somatic cells [18,26,31]. Wolbachia’s success likely depends upon an ability to secrete proteins that modify host actin to promote internalization by non-phagocytic cells. Recently, in vitro biochemical associations between the filarial nematode Wolbachia (wBm) PAL-like protein wBm0152 and actin have been observed, although results do not conclusively implicate this particular protein in interactions with host actin during infection [20]. Regardless, as is clear from our work, a Wolbachia infection depends on the actin cytoskeleton. Therefore, future work to identify and characterize Wolbachia proteins that bind to or alter host actin dynamics will be important for understanding the molecular basis of the interaction between the host and the symbiont.
In order for intracellular, maternally transmitted symbionts to successfully infect the next generation, the bacteria must target the oocyte. Wolbachia achieves this through a specific infection of the somatic stem cell niche in the germarium of Drosophila melanogaster [18]. Here we show that Wolbachia is extraordinarily sensitive to the regulation of actin, such that phenotypically wild type heterozygous mutant flies cannot faithfully transmit the bacterium to their progeny. Our results, particularly that titer is significantly reduced in the germaria of chic221/+, chic1320/+, and qua6-396/+ flies, suggest that Wolbachia utilize host actin to enter and persist within host tissues during Drosophila development. Additionally, our finding that these heterozygous mutant flies cannot transmit the infection suggests that Wolbachia titers within a host are reduced when actin regulation is disrupted, impacting transmission efficiency.
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10.1371/journal.pgen.1005112 | Selection against Heteroplasmy Explains the Evolution of Uniparental Inheritance of Mitochondria | Why are mitochondria almost always inherited from one parent during sexual reproduction? Current explanations for this evolutionary mystery include conflict avoidance between the nuclear and mitochondrial genomes, clearing of deleterious mutations, and optimization of mitochondrial-nuclear coadaptation. Mathematical models, however, fail to show that uniparental inheritance can replace biparental inheritance under any existing hypothesis. Recent empirical evidence indicates that mixing two different but normal mitochondrial haplotypes within a cell (heteroplasmy) can cause cell and organism dysfunction. Using a mathematical model, we test if selection against heteroplasmy can lead to the evolution of uniparental inheritance. When we assume selection against heteroplasmy and mutations are neither advantageous nor deleterious (neutral mutations), uniparental inheritance replaces biparental inheritance for all tested parameter values. When heteroplasmy involves mutations that are advantageous or deleterious (non-neutral mutations), uniparental inheritance can still replace biparental inheritance. We show that uniparental inheritance can evolve with or without pre-existing mating types. Finally, we show that selection against heteroplasmy can explain why some organisms deviate from strict uniparental inheritance. Thus, we suggest that selection against heteroplasmy explains the evolution of uniparental inheritance.
| Mitochondria contain genes that encode the machinery needed to power cells. Unlike the nuclear genome, the mitochondrial genome is typically inherited from one parent only (uniparental inheritance). The most common explanation for uniparental inheritance is the genomic conflict theory, which states that uniparental inheritance evolved to prevent the spread of ‘selfish’ mitochondria that replicate quickly but produce energy inefficiently. Current explanations have a major problem: when using realistic parameters, mathematical models cannot show that uniparental inheritance can replace biparental inheritance. Clearly, we need a new explanation that fits with standard population-genetic theory. Recent evidence suggests cells may incur a cost when they carry multiple types of mitochondria. Here we show mathematically that uniparental inheritance could have evolved to avoid the costs of maintaining multiple mitochondrial lineages within a cell. Our results explain the long-standing evolutionary mystery of uniparental inheritance and provide insight into the evolution of mating types and binary sexes. Selection against heteroplasmy also has implications for the evolution of the mitochondrial genome because new mitochondrial haplotypes always lead to heteroplasmy before becoming fixed in the population. Thus, selection against heteroplasmy may explain why mtDNA coding-genes have slower substitution rates than analogous genes within the nucleus.
| During sexual reproduction, offspring receive two genomes: nuclear genomes from both parents and haploid cytoplasmic genomes, contained in mitochondria and chloroplasts (in plants and algae), usually from one parent. Although uniparental inheritance is nearly ubiquitous, the reasons behind its evolution remain unresolved [1, 2]. Cells contain multiple mitochondria, and the mitochondrial genome (mtDNA) encodes polypeptide subunits of the electron transport chain, which the cell uses to generate ATP via oxidative phosphorylation [2]. If mutations increase mtDNA replication rate but simultaneously decrease respiration, then increased mtDNA fitness comes at the expense of cell and organism fitness [3–5]. Nuclear and mitochondrial genomes are thus potentially in conflict. The genomic (or selfish) conflict theory argues that uniparental inheritance evolved because biparental inheritance facilitates the spread of such selfish mitochondria [1, 3–6]. Although the conflict theory has been the predominant explanation for uniparental inheritance for over three decades [3, 4], other explanations exist. A second theory suggests that uniparental inheritance facilitates the removal of deleterious mutations. Uniparental inheritance decreases variation of mtDNA within cells, but increases variation between cells, allowing purifying selection against cells with increased mutation load [1, 7]. A third hypothesis argues that because the oxidative phosphorylation pathway is composed of interacting nuclear- and mitochondrial-encoded polypeptides, uniparental inheritance optimizes mitochondrial-nuclear coadaptation by maintaining coevolved mitochondrial-nuclear combinations [1, 8]. While uniparental inheritance spreads in mathematical models of the above hypotheses [1, 5, 6], it cannot replace biparental inheritance under realistic assumptions and parameter values [1, 5]. Thus, despite decades of theoretical work, we still lack a convincing explanation for why uniparental inheritance is widespread amongst extant organisms [1, 2].
Although uniparental inheritance is the general rule in eukaryotes, there are a few exceptions. Probably the best-known exception is baker’s yeast (Saccharomyces cerevisiae) in which both parents contribute mitochondria to offspring [9, 10]. However, the repeated division of cells that contain two mitochondrial lineages (heteroplasmy) leads to cells that contain a single type of mitochondria (homoplasmy) [9, 10]. Another example is the male bivalve (Mytilus), which also inherits mitochondria from both parents. But in this case maternal and paternal mitochondria do not mix within single cells, as maternal mitochondria segregate to the soma while paternal mitochondria segregate to the gonads [11]. Thus, even when mitochondria are inherited from both parents, heteroplasmy is avoided. Recent experimental evidence suggests that this is because heteroplasmy imposes a cost on the organism. A study on mice found that the mere mixing of different, but phenotypically normal, mitochondria within a cell leads to physiological and behavioral abnormalities [12]. Could uniparental inheritance have evolved simply because carrying multiple mitochondrial types imposes a cost on the organism? Here we use a mathematical model to explore whether selection against heteroplasmy could have led to the evolution of uniparental inheritance.
Our model is based on an idealized life cycle of a single-cell diploid eukaryotic organism, such as the algae Chlamydomonas reinhardtii. Diploid cells contain n mitochondria and haploid cells have n/2 mitochondria. All mitochondria are initially wild type but mitochondria can mutate from wild type to mutant (and vice versa). The starting population contains haploid gametes with a nuclear allele regulating biparental inheritance (B). Gametes are evenly split between two nuclear self-incompatible mating types (B1 and B2). In the basic model, we assume no recombination between the mitochondrial inheritance and mating type loci because these are tightly linked in many isogamous organisms [9] (later we explore recombination and no mating types). Cell types are characterized by the proportion of wild type and mutant mitochondria that they carry and their nuclear allele (haploid) or genotype (diploid).
Our life cycle has four discrete stages and is similar to the life cycles used in previous models [1, 5, 8]. Since we begin with a population of gametes, the first stage is random mating. Here, gametes randomly mate with the opposite mating type to produce diploid cells. Matings are controlled by the nuclear allele in gametes. In biparental inheritance (between B1 and B2 gametes), both gametes contribute mitochondria to the B1B2 diploid cells (see later for uniparental inheritance). The second stage is mutation. Each mitochondrion can mutate to the other haplotype with probability μ. The third stage is selection. Here, diploid cells have a relative fitness based on the proportion of each haplotype in the cell. We assume that fitness decreases as the level of heteroplasmy increases. The fourth stage is meiosis, where diploid cells produce gametes that contain a single nuclear allele and n/2 mitochondria. As mitochondria are stochastically partitioned into gametes [9], diploid heteroplasmic cells produce gametes with varying degrees of heteroplasmy.
First, we let the population of B1 and B2 gametes reach mutation-selection equilibrium. We then simulate a mutation leading to uniparental inheritance of mitochondria by converting a small proportion (10−2) of B1 gametes to U1 gametes. We assume no further mutations between B and U alleles. Matings between U1 and B2 gametes result in uniparental inheritance, in which the U1B2 cell inherits mitochondria from U1 alone. (Matings between U1 and B1 are not possible as they are the same mating type.) The population now consists of three alleles (U1, B1 and B2) and two genotypes (U1B2 and B1B2). The model tracks the proportion of each cell type at each stage of the life cycle. U1 spreads at the expense of B1 when uniparental inheritance is more advantageous than biparental inheritance (the frequency of B2 always remains at 0.5), and the simulation ends when the alleles reach equilibrium (see Model and S1–S6 Model for details of the model).
To explore whether a cost to heteroplasmy could have led to the evolution of uniparental inheritance, we study several scenarios. We first examine the simplest case, where mutations in mitochondria are neither advantageous nor disadvantageous (neutral mutations), but heteroplasmic cells incur a fitness cost proportional to the degree of heteroplasmy. Because no empirical data relate fitness to the degree of heteroplasmy, we consider three forms of fitness function to describe selection against heteroplasmy: concave, linear and convex (Fig 1A). For each fitness function, we vary the cost of heteroplasmy (ch), given by ch = 1 − h where h is the fitness of the most heteroplasmic cell in the population, to see how this affects the spread of U1. We generate the concave fitness function by
w(i)={1−ch(in/2)2for0≤i<n/2,1−ch(n−in/2)2forn/2≤i≤n,
the linear function by
w(i)={1−ch(in/2)for0≤i<n/2,1−ch(n−in/2)forn/2≤i≤n,
and the convex function by
w(i)={1−chin/2for0≤i<n/2,1−chn−in/2forn/2≤i≤n.
We also vary μ (mutation rate) and n (number of mitochondria) to ensure that our findings are robust. Second, we explore the effect of advantageous or deleterious mutations (non-neutral mutations) on the spread of U1. Third, we relax the assumption of tight linkage between mating type and inheritance loci by exploring two cases: recombination between mating types and the absence of mating types altogether. Finally, we examine whether selection against heteroplasmy can explain the rare, but nevertheless important, exceptions to uniparental inheritance. To ensure that our results generalize to more than two mitochondrial types, we developed a second model that considers three mitochondrial types (S6 Model).
We find that U1 always replaces B1, resulting in complete uniparental inheritance in the population (Fig 1B). These findings are independent of the number of mitochondria per cell (Fig 1C), mutation rate (Fig 1D), fitness function (Fig 1E), and cost of heteroplasmy (Fig 1F) (see S1–S10 Tables for more parameter combinations). We find the same results when we generalize the model to three mitochondrial haplotypes (S1 Fig).
In our model, heteroplasmic cells are generated by mutation. During meiosis, heteroplasmic cells produce gametes with varying levels of heteroplasmy, including homoplasmic gametes. Uniparental inheritance maintains this variation created by meiosis, which leads to homoplasmic U1B2 cells (Fig 2A–2B and S2A–S2B Fig). Mutants that arise in U1B2 cells quickly segregate into U1 gametes that carry mutant haplotypes only (Fig 3A–3B and S3A–S3B Fig), which leads to U1B2 cells that are homoplasmic for mutant mitochondria (Fig 2B and S2B Fig). Since we assume that mutations are neutral, cells homoplasmic for mutant mitochondria suffer no fitness costs.
U1B2 cells carrying mutant mitochondria produce B2 gametes that also carry mutant mitochondria (Fig 3D and S3D Fig). When these B2 gametes mate with B1 gametes carrying wild type mitochondria, the resulting B1B2 cells are highly heteroplasmic (Fig 2C–2E and S2C Fig). As U1 spreads, matings between U1 and B2 become more likely, increasing the level of heteroplasmy in both B1B2 cells and in B1 and B2 gametes (Figs. 2C–2E and 3C–3F and S2C and S3C–S3D Figs.). Increased levels of heteroplasmy reduce the fitness of both B1 and B2 gametes (w¯B1, w¯B2 in Fig 3A and S3A Fig) and B1B2 cells (w¯B1B2 in Fig 2A and S2A Fig). The difference in fitness between B1 and B2 becomes stronger (Fig 3A and S3A Fig) as more B2 gametes that carry mutant mitochondria are produced (Fig 3D and S3D Fig). As a result U1 spreads at the expense of B1.
In the above description (Figs. 2 and 3), the mutation from B1 to U1 occurred in gametes homoplasmic for wild type mitochondria. When U1 is introduced into heteroplasmic gametes, it takes fewer generations to reach equilibrium because B2 gametes homoplasmic for mutant mitochondria are produced more quickly (S4 Fig). Our results are robust to changes in the frequency at which U1 gametes are introduced (S5 Fig). For more detailed model dynamics, see S1 Text and S1–S2 Videos.
U1 spreads more slowly when mutation rate (μ) is lower (Fig 1D) and number of mitochondria (n) is higher (Fig 1C). Reducing μ slows the spread of U1 because mutant mitochondria are produced more slowly, slowing the generation of B2 gametes that only carry the mutant haplotype. Increasing n has the same effect.
While varying the cost of heteroplasmy does not change the qualitative behavior of the model, it does affect the number of generations required for U1 to replace B1 (Fig 1F). In general, U1 spreads more quickly when the cost of heteroplasmy is low for all three fitness functions (Fig 1F). Strong selection against heteroplasmy (e.g. ch = 1) slows the production of B2 gametes homoplasmic for the mutant haplotype because a transition via heteroplasmy is needed to lead to U1B2 cells homoplasmic for mutant mitochondria. Heteroplasmy levels thus remain low in B1B2 cells, and U1 takes longer to replace B1 (S6A and S6D Fig). At lower costs of heteroplasmy (e.g. ch = 0.2), more B2 gametes that are homoplasmic for the mutant haplotype are produced and levels of heteroplasmy in B1B2 cells increase, leading to a faster spread of U1 (S6B and S6E Fig). Although levels of heteroplasmy in B1B2 cells increase even further as the cost of heteroplasmy approaches 0 (e.g. ch = 0.01), selection against heteroplasmy is now very weak, which slows the spread of U1 compared with ch = 0.2 (S6C and S6F Fig). When the number of mitochondria is higher, U1 spreads more quickly when the cost of heteroplasmy is low. This is because B2 gametes homoplasmic for mutant mitochondria are produced more slowly at higher values of n and strong selection against heteroplasmy compounds this problem (S7 Fig). A similar logic can be applied to understand the differences between the three fitness functions. Since heteroplasmic cells are under weaker selection when fitness is concave (followed by linear and convex respectively) (Fig 1A), the level of heteroplasmy is highest using a concave function (S8 Fig). Thus, U1 spreads more quickly using a concave function (followed by linear and convex respectively) when the cost of heteroplasmy is high because it is easier to generate heteroplasmic cells, and thus easier to generate B2 gametes homoplasmic for mutant mitochondria, when selection against heteroplasmic cells is weaker (Fig 1F and S8 Fig). As the cost of heteroplasmy decreases, the number of generations for U1 to spread under the three fitness functions converges because it becomes easier to generate B2 gametes homoplasmic for mutant mitochondria (Fig 1F).
We next investigate how the U1 allele spreads when mutations are non-neutral, as is the case for most mtDNA mutations [13]. We start by assuming that mutations are deleterious so that cells carrying mutant mitochondria are more strongly selected against than cells that carry wild type mitochondria. We assume that a mutation from wild type to mutant haplotype is more common than the reverse [14]. We let the probability of a mutation from mutant to wild type haplotype be μb = μ/100. We vary the selection coefficient of the mutant haplotype to see how this affects the spread of the U1 allele (the fitness of a cell homoplasmic for the mutant haplotype is 1 − sd, where sd is the selection coefficient of the mutant haplotype). Essentially there are now two fitness functions: one governing the effect of mitochondria on cell fitness (where the selection coefficient determines the magnitude of the effect) and one governing the cost of heteroplasmy. For deleterious mutations, we assume that fitness decreases as a concave function of the number of mutants, as this relationship is experimentally established [15]. We examine both concave and convex fitness functions for selection against heteroplasmy (yielding two combinations).
Again, U1 replaces B1 unless the fitness of heteroplasmic cells and the fitness of deleterious mutants are governed by a concave function and the selection coefficient is sufficiently large (S9 Fig and S11–S12 Tables). U1 generally spreads more slowly as sd increases and it always spreads more slowly compared to when mutations are neutral (S11–S12 Tables). Stronger selection against mutant haplotypes leads to fewer B2 gametes homoplasmic for mutant mitochondria, which slows the spread of U1 (S10 Fig).
Next we explore the effect of advantageous mutations on the spread of U1. In this case, cells that carry mutant haplotypes have an advantage over those carrying wild type haplotypes (the fitness of a cell homoplasmic for the wild type haplotype is 1 − sa, where sa is the selection coefficient of the mutant haplotype). We account for the rarity of advantageous mutations by setting μb = 100μ. Because it is unknown how fitness relates to the accumulation of advantageous mtDNA mutations, we model this relationship with both a concave and convex function. As in the deleterious case, we model selection against heteroplasmy by testing both concave and convex fitness functions (giving four combinations).
U1 always replaces B1 unless mutations are highly advantageous (sa = 0.1) and both the fitness of heteroplasmic cells and the fitness of advantageous mutants are governed by a concave function (S9 Fig and S13–S14 Tables). U1 spreads more quickly when sa = 0.001 and sa = 0.01 because B2 gametes homoplasmic for mutant haplotypes now have a fitness advantage and are produced more quickly (S10 Fig). In contrast, U1 spreads more slowly when sa = 0.1 because the mutant haplotype quickly replaces the wild type as the dominant haplotype before U1 has replaced B1. Once B1 gametes carry mostly mutant haplotypes, B1 × B2 matings are less costly because they predominantly involve mutant haplotypes. We find the same patterns for non-neutral mutations when we generalize our model to three mitochondrial types (S15 Table).
Previously, U × U matings were not possible because we assumed tight linkage between mating type and inheritance loci. But if we allow recombination to occur between these loci, U1 × U2 matings become possible. In this scenario, the number of gametes increases to four (B1, B2, U1 and U2), as does the number of genotypes (B1B2, U1B2, U1U2 and U2B1). There are three main ways in which mitochondrial inheritance could be regulated in U1 × U2 matings. (1) One U allele is dominant to the other, leading to uniparental inheritance; (2) each U allele ensures inheritance of its mitochondria, resulting in biparental inheritance; or (3) inheritance is more or less random so that some matings result in uniparental inheritance and some in biparental inheritance. We model all three cases.
When U1 × U2 matings lead to uniparental inheritance, the U1U2 genotype always spreads until it is fixed in the population, leading to complete uniparental inheritance (Fig 4A and S16–S18 Tables). When U1 × U2 matings lead to biparental inheritance, however, uniparental inheritance does not become fixed and the population reaches a polymorphic equilibrium (Fig 4B–4C). Under these conditions, the frequency of uniparental inheritance at equilibrium is ≤ 0.5 (S19–S21 Tables). Uniparental inheritance cannot exceed 0.5 because increasing the frequency of U1 or U2 simply increases the proportion of biparental U1 × U2 matings. The frequency of uniparental inheritance remains very low when we assume a concave fitness function (Fig 4B), but reaches its maximum (0.5) when we assume a linear or convex fitness function (Fig 4C) (see S12–S13 Figs. for an explanation).
When the probability of recombination (Pr) is sufficiently high (10−4 ≤ Pr ≤ 0.5 in S11 Fig), the U1B2 and U2B1 genotypes have the same frequency at equilibrium (S11B–S11D Fig). Now uniparental inheritance is no longer associated with a single mating type but is evenly split between the two mating types (S19–S21 Tables). When Pr is sufficiently small (Pr = 10−5 in S11 Fig), the recombination rate is so low that the mating type and inheritance loci are essentially linked and the U1B2 genotype becomes fixed (as in the general model) (S11A Fig).
When we assume a mixture of uniparental inheritance and biparental inheritance, we let U1 × U2 matings lead to biparental inheritance with probability Pb and to uniparental inheritance with probability 1 − Pb. Lowering Pb increases the frequency of uniparental inheritance, and uniparental inheritance becomes fixed when Pb = 0 (Fig 4A and 4E). Under linear and convex fitness functions, the equilibrium always maximizes the level of uniparental inheritance (Tables S22–S23). Under concave fitness, however, uniparental inheritance is only maximized for particular values of Pb (roughly Pb ≤ 0.2 for the parameter values we considered) (S22 Table; rows 2–3). (See S5 Model for how we determine when uniparental inheritance is maximized.)
We also find that uniparental inheritance can evolve in the complete absence of mating types. The no mating types scenario differs from the recombination case in that UB equals the sum of U1B2 and U2B1 at equilibrium (Fig 4A and 4F) (see S2 Text for more details).
In this section, we explore whether relaxing some of the assumptions in our general model can lead to mitochondrial inheritance patterns that resemble some of the known exceptions to uniparental inheritance. Exceptions to uniparental inheritance fall in three main categories: organisms that (1) regularly inherit mitochondria from both parents; (2) normally inherit mitochondria from one of the two parents but on occasion inherit mitochondria from both; and (3) inherit mitochondria from either or both parents.
Baker’s yeast, Saccharomyces cerevisiae, regularly inherits mitochondria from both parents (though uniparental inheritance also occurs), but heteroplasmy is transient because the diploid cell has only a few mitochondria [16] and divides repeatedly, which separates heteroplasmic cells into cells homoplasmic for either mitochondrial type (vegetative segregation) [9, 10]. Vegetative segregation is usually completed within twenty generations, but up to 50% of zygotes may be homoplasmic after the first division ([10] and references therein). Thus, Saccharomyces may restore homoplasmy as quickly as organisms that actively destroy one mitochondrial lineage [17]. Similarly, the geranium Pelargonium zonale often inherits cytoplasmic organelles from both parents (chloroplasts in this case). As with Saccharomyces, heteroplasmy is transient in Pelargonium because of rapid vegetative segregation of heteroplasmic cells shortly after syngamy [9]. We added mitotic divisions to our model to test whether vegetative segregation could maintain biparental inheritance under selection against heteroplasmy. When we include mitosis before selection (which assumes that vegetative segregation occurs swiftly, before selection has time to act), uniparental inheritance does not spread, provided that the number of mitochondria is low (n = 4) and the number of divisions is high (S24 Table; rows 7–8). Under these conditions, biparental inheritance is stable because heteroplasmic cells resulting from biparental inheritance segregate into homoplasmic cells before selection acts. If there are insufficient mitotic divisions, or if selection acts before vegetative segregation is complete, then uniparental inheritance replaces biparental inheritance, although it spreads much more slowly than when there are no mitotic divisions (S24 (rows 3–6) and S25 Tables). When there are more mitochondria per cell (e.g. n = 8), biparental inheritance is only stable if the number of cell divisions increases to compensate (S24 Tables; rows 9–10). Thus, biparental inheritance can be stable under selection against heteroplasmy but only under a narrow set of conditions, explaining why this form of inheritance is rare.
In other isogamous organisms, including the acellular slime molds Physarum polycephalum and Didymium iridis and the algae Chlamydomonas reinhardtii, mitochondria from both gametes mix before one mitochondrial lineage is destroyed post-fertilization, often by nucleases [18–20]. This mechanism is not perfect and these organisms sometimes deviate from strict uniparental inheritance [9, 18–20]. While uniparental inheritance is the norm in the slime mold P. polycephalum, sometimes both mitochondrial lineages survive, leading to varying degrees of biparental inheritance [18]. Could uniparental inheritance still spread under such conditions? Since mating types and inheritance loci are tightly linked in Physarum [18], we explore this question using our general model that assumes linkage. Now, U1 × B2 matings lead to biparental inheritance with probability Pb and to uniparental inheritance with probability 1 − Pb. For the parameter values that we examined, the U1B2 genotype always goes to fixation when Pb < 1 and the fitness function is linear or convex (S26 Table). (When fitness is concave, Pb must be roughly <0.05 for the U1B2 genotype to become fixed.) Under these conditions, the frequency of biparental inheritance at equilibrium is equal to Pb (S26 Table). In this scenario, the level of biparental inheritance in the population simply reflects the likelihood that an individual mating results in biparental inheritance.
Chlamydomonas reinhardtii and Didymium iridis can inherit mitochondria from either or both parents [19, 20]. Chlamydomonas normally inherits mitochondria from the mt − parent and chloroplasts from the mt + parent, but under some circumstances it can inherit mitochondria from mt + and chloroplasts from mt − or mitochondria and chloroplasts from both [20]. Didymium iridis has random, biased, or dominant patterns of uniparental inheritance. Under random uniparental inheritance, either parental strain is equally likely to be the mitochondrial donor while, under biased inheritance, one strain is more likely to be the mitochondrial donor [19]. Under dominant inheritance, one strain is always the donor. Didymium also has low levels of biparental inheritance [19]. In this scenario, we test whether selection against heteroplasmy could lead to the evolution of a system with a mixture of uniparental inheritance (from either parent) and biparental inheritance. We assume that mating types can recombine and that U1 × U2 matings can lead to mitochondria being inherited from U1, U2 or both. Mitochondria are inherited from U1 with probability PU1, from U2 with probability PU2 and from both parents with probability Pb (where PU1+PU2+Pb=1). Now, uniparental inheritance comes from U1 × B2 matings, U2 × B1 matings and those U1×U2 matings with uniparental inheritance. Irrespective of the values of PU1 and PU2, we find the same results as with our earlier model in which U1×U2 matings led to a mixture of uniparental and biparental inheritance (S22–S23 Tables). This is because equilibrium depends only on the value of Pb. (Since uniparental inheritance quickly eliminates most heteroplasmic cells, U1U2 cells are almost entirely homoplasmic regardless of which gamete donates mitochondria.) Consequently, different probabilities of inheriting mitochondria biparentally (Pb), from mating type 1 (PU1) or from mating type 2 (PU2) lead to a range of inheritance patterns that include uniparental inheritance (from both parents) and biparental inheritance (see S27 Table for some examples).
Lastly, selection against heteroplasmy provides an explanation for the cases in which mitochondria are inherited from one parent while chloroplasts are inherited from the other (e.g. in Chlamydomonas and pines [20, 21]). If uniparental inheritance simply evolved to maintain homoplasmy in cells, it should not matter which parent donates mitochondria or chloroplasts.
Our model shows that selection against heteroplasmy can lead to the fixation of uniparental inheritance in an ancestrally biparental population. We find that uniparental inheritance replaces biparental inheritance under almost all tested scenarios and parameter values. Our model also explains many of the known exceptions to strict uniparental inheritance. We show that uniparental inheritance can replace biparental inheritance whether mutations lead to neutral or non-neutral haplotypes. Relaxing our initial assumptions of pre-existing mating types and lack of recombination does not prevent uniparental inheritance from evolving. As we make no attempt to resolve the evolution of mating types within the context of mitochondrial inheritance, as others have previously attempted [1, 22], our findings thus leave open the possibility that mating types preceded uniparental inheritance, evolved as a consequence of uniparental inheritance, or evolved after uniparental inheritance.
In contrast to previous models, we show that uniparental inheritance can spread under realistic mutation rates and number of mitochondria per cell. The lowest value of μ that we tested (10−10) is eight orders of magnitude lower than required by the genomic conflict theory [1] and compares favorably with empirical mutation rates (10−7 to 10−8 per site per generation [23–25]). Both the genomic conflict and mutation clearance hypotheses require unrealistic mutation rates and number of mitochondria per cell for uniparental inheritance to replace biparental inheritance, while uniparental inheritance cannot replace biparental inheritance under any parameter values in the mitochondrial-nuclear coadaptation model [1]. The genomic conflict model requires a mutation rate of 1% per generation before uniparental inheritance can replace biparental inheritance [1]. The only known example that satisfies this assumption is the petite mutant in Saccharomyces cerevisiae, which is a hyper-mutable selfish mitochondrion that can spontaneously arise at a rate of 1% per generation [26]. Under this mutation rate, however, the genomic conflict model requires that cells contain at least 50 mitochondria [1], whereas most extant isogamous species, including Saccharomyces, contain fewer than 20 mitochondria at syngamy [16, 18]. As mutant mitochondria lack a transmission advantage over wild type mitochondria in the mutation clearance hypothesis, the mutation clearance model requires even higher mutation rates [1]. To the best of our knowledge, no extant organism satisfies the assumptions of the genomic conflict or mutation clearance hypotheses.
Why do our results differ from the findings of previous models? In the genomic conflict and mutation clearance models, wild type mitochondria mutate to selfish or deleterious mitochondria. Biparental inheritance results in cells that are heteroplasmic for wild type and mutant mtDNA, while U1 gametes mostly contain wild type mitochondria [1]. Because U1 purges B2 gametes of mutant mitochondria, B1×B2 matings involve increasingly fewer mutant mitochondria as the frequency of U1 increases [1, 5]. U1 is thus subject to negative frequency-dependent selection, and the population reaches equilibrium well before uniparental inheritance replaces biparental inheritance at realistic mutation rates [1]. The mitochondrial-nuclear coadaptation model assumes that mitochondria are well matched or poorly matched to nuclear alleles [1, 8]. Because mutation can lead to matched nuclear-mitochondrial states becoming unmatched, the effective mitochondrial mutation rate is lower in the mitochondrial-nuclear coadaptation model, which prevents uniparental inheritance from displacing biparental inheritance under any parameter values [1].
Evidence for a cost of heteroplasmy comes from a recent study that compared the effect of two mtDNA haplotypes (NZB and 129S6) in a cogenic nuclear background on the functioning of mice [12]. Mice homoplasmic for NZB or 129S6 were phenotypically normal, but NZB-129S6 heteroplasmic mice suffered from reduced activity, lowered food intake, compromised respiration, heightened stress response, and impaired cognition [12]. While the mechanism(s) behind the cost of heteroplasmy is unknown, there are a few possibilities. Heteroplasmy may disrupt cell signaling by altering production of reactive oxygen species (ROS) [27] and there are indications that heteroplasmy can increase mitochondrial ROS levels [28, 29], leading to phenotypes that differ from cells that are homoplasmic for either haplotype [29, 30]. Alternatively heteroplasmy may lead to deleterious interactions between polypeptides from different mitochondria within the same electron transport chain [12, 31]. Because chloroplasts also contain independent genomes, are involved in cellular bioenergetics, and generally show uniparental inheritance [9], our findings likely apply to both mitochondria and chloroplasts.
Although the evidence in mice is compelling [12], it is unknown whether selection against heteroplasmy is a general phenomenon in eukaryotes. While Sharpley and colleagues [12] used different mitochondrial lineages to construct heteroplasmic individuals, our model assumes that mutations accumulated within a single generation can cause mitochondrial types to become sufficiently distinct to lead to negative effects for the cell. At this stage we do not know how different mitochondrial genomes have to be for selection against heteroplasmy to apply. It could also be that there are regions of the genome in which heteroplasmic mutations have a stronger effect on fitness than others. To support or refute our model, we now need solid empirical data on a range of organisms showing the cost, if any, of heteroplasmy on organism fitness.
While we have referred to n as the number of mitochondria in the cell, n actually refers to the number of segregating units of mtDNA at syngamy. Mitochondria pack DNA into DNA-protein complexes called nucleoids, which themselves may contain multiple copies of mtDNA [32, 33]. It is currently unknown whether the segregating unit is the mtDNA molecule itself, the nucleoid, the mitochondrion or another level of mtDNA organization [33]. But as nucleoids are predominantly homoplasmic, even in heteroplasmic tissues [32, 33], the number of mitochondria may be a reasonable approximation of the number of segregating units in the cell. If the segregating unit is at a lower level of organization (e.g. the mtDNA molecule), then n, as used in our model, will apply to the number of segregating units not the number of mitochondria per cell (e.g. n = 200 would then apply to a cell with 200 segregating units, which may be a cell with far fewer than 200 mitochondria).
By assuming an infinite population size, a common assumption in studies of this kind [1, 5, 6, 8] we have ignored genetic drift, which can be a powerful force in population genetics. While it is beyond the scope of this study to formally model the effects of genetic drift on the evolution of uniparental inheritance, we can anticipate some of its effects. As the mutation leading to uniparental inheritance has a small advantage when its frequency is low, genetic drift will lead to the frequent loss of those mutations. Thus, the initial invasion of a mutation for uniparental inheritance may be largely determined by genetic drift rather than by positive selection. As the frequency of uniparental inheritance increases, however, so too does its advantage, reducing the probability that the mutation is lost to drift. The potential for rare mutations to be lost to drift is not unique to our model. The genomic conflict hypothesis requires stringent conditions for uniparental inheritance mutations to invade [6, 34]. Under this hypothesis, a mutation for uniparental inheritance must arise within a population that contains selfish mutants but in which the selfish mutant is not fixed. Otherwise, uniparental inheritance cannot become associated with non-selfish mitochondria. Any mutations leading to uniparental inheritance that arise outside of this window will have no selective advantage and will be more likely to be lost by genetic drift [6, 34].
Selection against heteroplasmy has implications for the evolution of the mitochondrial genome. Because of a smaller effective population size, which is more strongly affected by genetic drift, and higher mutation rates, mtDNA should be less conserved than the nuclear genome [35, 36]. Indeed, mitochondrial transfer RNAs and synonymous sites mutate 5–50 times more frequently than comparable elements in the nuclear genome [35, 37]. Because the mitochondrial genome is effectively asexual, any deleterious mutations in the fittest haplotype cannot be rescued (except by unlikely back mutations). This effect, known as Muller's Ratchet, should eventually lead to irreparable genome meltdown [38, 39]. In stark contrast to theoretical predictions, however, mitochondrial coding genes are more conserved than analogous nuclear oxidative phosphorylation genes [36]. When mtDNA mutates, only one of the many mtDNA molecules in the cell is affected, leading to a heteroplasmic cell. Selection against heteroplasmy should reduce the probability that mtDNA mutations spread throughout the cell, which, in turn, should oppose changes to mtDNA. Thus, selection against heteroplasmy may not only explain the evolution of uniparental inheritance but also why mitochondrial coding genes have thus far managed to resist the effects of Muller's Ratchet.
Our model tracks the distribution of cell types through each stage of the life cycle across multiple generations. The redistribution of cell types is based on probability theory, but the model itself is deterministic. We assume that the population is effectively infinite and unaffected by genetic drift, as is regularly assumed in models such as ours [1, 5, 6, 8]. Consequently, the probability that a cell takes a particular state equates to the proportion of that cell type in the population. We take a similar approach to previous models [1, 5], but our model differs slightly in our treatment of mutation. Hastings does not include mutation [5], while Hadjivasiliou and colleagues treat mutation as a one-way process from wild-type to mutant mitochondria in the conflict and mutation clearance models [1]. When examining the mitochondrial-nuclear coadaptation model, however, Hadjivasiliou and colleagues allow mutation to proceed both ways as we have done here [1]. In our model, mutation is designed to capture the ability of a mitochondrial type to mutate from its current state to other haplotypes (one type in our main model and two types in our supplementary model, but an extremely large number of haplotypes in reality).
Diploid cell types are described by the vector Mt,τα=(i,G), where i corresponds to the number of mutant mitochondria and takes values in {0,1…n}, t indicates the generation, and τα indicates the stage of the life cycle. If we know the number of mutant mitochondria (i), the number of wild type mitochondria (which we denote j) is fixed as j = n – i. G indicates the nuclear genotype and takes values in {U1B2,B1B2}. Gametes are described by the vector Mt,τα=(p,g), where p is the number of mutant mitochondria and takes values in {0,1…n / 2} and g represents the nuclear allele and takes values in {U1,B1,B2}. The probability of obtaining a particular diploid cell type is written as P(Mt,τα=(i,G)) and the probability of obtaining a particular gamete is written as P(Mt,τα=(p,g)).
These probabilities can also be thought of as the proportion of the population with that particular cell or gamete type.
There are n+1 total mitochondrial states for diploid cells and n / 2+1 possible mitochondrial states for haploid cells. For the case in which mating type and inheritance loci are linked, the total number of diploid cell types is 2(n+1) while the total number of haploid cell types is 3(n / 2+1). We obtained numerical solutions to our model via scripts that we developed in MATLAB (version 2013b).
The starting population is evenly split between B1 and B2 gametes, and all gametes contain type wild type mitochondria (i.e. P(M0,τ1=(0,B1))=0.5,P(M0,τ1=(0,B2))=0.5 and P(M0,τ1=(p,g))=0,∀p>0andg=U1). We first allow this population to reach equilibrium, which we define as the point at which the proportion of cell types change by less than 10−12 (except when the probability that a mitochondrion mutates into another mitochondrion is 10−10 (μ = 10−10), in which case we define equilibrium to be a change of less than 10−13). We then introduce U1 gametes that are homoplasmic for wild type mitochondria by setting P(Mge1,τ1=(0,U1))=0.01, where ge1 is the number of generations taken to reach the first equilibrium. To maintain the total proportion of the population at 1, we remove the corresponding proportion of cells from the B1 population (i.e.P(Mge1,τ1=(0,U1))=P(Mge1,τ1=(0,U1))−0.01). In two instances, we alter the way in which U1 is introduced. In S4 Fig, we introduce U1 into the most heteroplasmic gamete with a frequency greater than 0.01, and in S5 Fig we vary the introductory frequency of U1. Our life cycle is very similar to the life cycle used by Hadjivasiliou and colleagues [1], which examined the genomic conflict, mutational clearance, and mitochondrial-nuclear coadaptation hypotheses.
Gametes with n / 2 mitochondria randomly mate with the opposite mating type to produce diploid cells containing n mitochondria. In effect, this is random mating in which all matings between the same mating type (i.e. U1U1, B1B1, B2B2 and U1B1) are lethal, and the only viable genotypes are U1B2 and B1B2.
Consider a biparental mating involving a gamete in state Mt,τ1=(p,B1), where τ1 is the gamete stage of the life cycle. For this gamete to produce a diploid cell with type Mt,τ2=(i,B1B2), where τ2 is the diploid cell stage of the life cycle that precedes mutation, it must mate with a gamete of type Mt,τ1=(i−p,B2). The probability of this mating is 2P(Mt,τ1=(p,B1))P(Mt,τ1=(i−p,B2)), where the factor of 2 accounts for the two ways in which we can choose B1 and B2 (B1 then B2 or B2 then B1). We restrict the values of p and i – p to biologically valid combinations. First, 0 ≤ p ≤ n / 2, as the B1 gamete cannot carry negative numbers of mutant mitochondria nor can it contain more mutant mitochondria than the total number of mitochondria in the gamete. Likewise, 0 ≤ i – p ≤ n / 2 for the B2 gamete, which, when rearranged, gives i – (n / 2) ≤ p ≤ i. Valid values for p lie in the range of intersection of these two inequalities, giving max(0,i – (n / 2)) ≤ p ≤ min(n / 2,i).
We can thus obtain the probability of forming any given diploid cell type after random mating with the sum,
P(Mt,τ2=(i,B1B2))=2(∑p=max(0,i−n/2)min(n/2,i)P(Mt,τ1=(p,B1))P(Mt,τ1=(i−p,B2))).
Because uniparental matings between U1 and B2 gametes contain mitochondria from U1 alone, U1B2 cells initially have n / 2 mitochondria. To restore the total complement of n mitochondria, we sample n / 2 mitochondria with replacement from the n / 2 mitochondria in the U1B2 cell and add the n / 2 sampled mitochondria to the original set of mitochondria to form a cell with n mitochondria.
For a gamete with identity Mt,τ1=(p,U1) to produce a diploid cell with identity Mt,τ2=(i,U1B2), it must sample n / 2 mitochondria containing i – p mutant mitochondria and n / 2 – (i – p) wild type mitochondria. The mitochondrial state of the B2 gamete is irrelevant because its mitochondria are discarded and we will refer to this cell as Mt,τ1=(r,B2).
Sampling of the n / 2 mitochondria follows a binomial distribution, which we denote T(i – p;n / 2,(2p) / n), where i – p refers to the number of mutant mitochondria that need to be sampled, n / 2 refers to the number of mitochondria being sampled, and (2p) / n is the probability of drawing a single mutant mitochondrion from a U1B2 cell with p (out of n / 2) mutant mitochondria (where (2p) / n is obtained by rearranging p / (n / 2)).
The probability of sampling i – p mutant mitochondria (and (n / 2) – (i – p) wild type mitochondria) is given by
T(i−p;n2,2pn)=(n/2i−p)(2pn)i−p(1−2pn)n2−i−p.
The restrictions on p and i – p are the same as those in biparental mating. Since U1 will form the same initial U1B2 cell regardless of the B2 gamete with which it mates, the probability of producing each type of U1 gamete is multiplied by the probability of selecting any B2 gamete. The probability of forming a given U1B2 cell after random mating is determined by
P(Mt,τ2=(i,U1B2))=∑p=max(0,i−n2)min(n2,i)(2P(Mt,τ1=(p,U1))T(i−p;n2,2pn)∑r=0n2P(Mt,τ1=(r,B2))).
We denote the post-mutation states of cells as Mt,τ3=(i,G), (where τ3 indicates the post-mutation life cycle stage). If we define the number of wild type mitochondria that mutate to mutant mitochondria to be a and the number of mutant mitochondria that mutate to wild type mitochondria as b, a post-mutation cell in state Mt,τ3=(i,G) must be derived from a pre-mutation cell in state Mt,τ2=(i−a+b,G) (because the pre-mutation cell gains a mutant mitochondria and loses b mutant mitochondria to form the post-mutation cell). Similarly, if the post-mutation cell has j wild type mitochondria, then the pre-mutation cell must have j + a – b wild type mitochondria, where j = n – i.
First, we must work out the probability that a cell mutates a of its wild type mitochondria to mutant mitochondria. We define Y(a;n – i + a – b,μ) as the probability that a pre-mutation cell has a mutations in its n – i + a – b wild type mitochondria given that each mitochondrion mutates with probability μ. The accumulation of mutations is binomially distributed such that
Y(a;n−i+a−b,μ)=(n−i+a−ba)μa(1−μ)n−i−b.
Similarly, we define Y(b;i – a + b,μb) to be the probability that a pre-mutation cell acquires b mutations in its i – a + b mutant mitochondria given that each mitochondrion mutates with probability μb. This probability is given by
Y(b;i−a+b,μb)=(i−a+bb)μbb(1−μb)i−a.
For any combination of values for a, b and i, multiplying Y(a;n – i + a – b,μ) by Y(b;i – a + b,μb) gives the probability of a particular transition from a pre-mutation cell with identity Mt,τ3=(i−a+b,G) to a post-mutation cell with identity Mt,τ3=(i,G). To get the overall probability that such a transition occurs, we multiply the probability of the transition by the proportion of pre-mutation cells in the population. To produce the post-mutation population, we sum all possible transitions between pre-mutation and post-mutation cells. All valid transitions must satisfy 0 ≤ a ≤ i (because the post-mutation cell cannot receive more than i mutant mitochondria) and 0 ≤ b ≤ n – i (because the post-mutation cell cannot receive more than n – i wild type mitochondria). Thus, we can determine the post-mutation population by
P(Mt,τ3=(i,G))=∑a=0i∑b=0n−iY(a;i−a+b,μ)Y(b;n−i+a−b,μb)P(Mt,τ2=(i−a+b,G)).
In the neutral scenario, μ = μb (i.e. the rate of mutation from wild type to mutant is equal to the rate of mutation from mutant to wild type).
The relative fitness of a cell, w(i), is a measure of how likely a cell type is to survive and reproduce, and we assume that cells carrying multiple mitochondrial types have lower fitness. For the first fitness function, the relative fitness of a cell with i mutant mitochondria is determined according to the following piecewise concave function:
w(i)={1−ch(in/2)2for0≤i<n/2,1−ch(n−in/2)2forn/2≤i≤n,
(1)
for even values of n and 0 ≤ ch ≤ 1, where ch is the cost of heteroplasmy. In this function, a cell containing n / 2 mutant and n / 2 wild type mitochondria has minimum relative fitness.
The post-selection population of each cell type is then given by:
P(Mt,τ4=(i,G))=w(i)P(Mt,τ3=(i,G)).
We also make use of two alternative fitness functions. The first of these is the piecewise linear function:
w(i)={1−ch(in/2)for0≤i<n/2,1−ch(n−in/2)forn/2≤i≤n.
(2)
The third fitness function is the piecewise convex function:
w(i)={1−chin/2for0≤i<n/2,1−chn−in/2forn/2≤i≤n.
(3)
We normalize the post-selection population by
P(Mt,τ5=(i,G))=P(Mt,τ4=(i,G))σ,
where
σ=∑i=0nP(Mt,τ4=(i,U1B2))+P(Mt,τ4=(i,B1B2)),
so that the sum of the proportions of the population equals 1.
The cell must first duplicate its chromosomes and double its mitochondrial complement (from n to 2n). This cell with 2n mitochondria then produces gametes with n / 2 mitochondria. Meiosis occurs in two steps. First, we sample n mitochondria with replacement from a cell containing n mitochondria and add the set of sampled mitochondria to the original set of mitochondria to form a cell containing 2n mitochondria (this is the same process that occurs in uniparental mating only with n mitochondria rather than n / 2 mitochondria). We let Mt,τ6=(l,2G) represent the cell with doubled mitochondria and nuclear genotype, where l takes values in {0,1…2n} and 2G takes values in {U1U1B2B2,B1B1B2B2}.
For a cell to contain l mutant mitochondria after duplication of its mitochondria, it must sample l – i mutant mitochondria. We denote the probability of sampling l – i mutant mitochondria from Mt,τ5=(i,G) as F(l – i;n,i / n). Sampling follows a binomial distribution such that
F(l−i;n,in)=(nl−i)(in)l−i(1−in)n−l+i.
We obtain Mt,τ6=(l,2G) by
P(Mt,τ6=(l,2G))=∑i=max(0,l−n)min(l,n)F(l−i;n,in)P(Mt,τ5=(i,G)).
During the second step of meiosis, the cells with 2n mitochondria produce gametes with n / 2 mitochondria. Biologically, this occurs in two steps. In meiosis 1, the homologous chromosomes are pulled apart to produce two haploid cells that contain two identical nuclear alleles (sister chromatids) and n mitochondria. In meiosis 2, the two cells divide to produce four gametes, each with a single nuclear allele and n / 2 mitochondria. Since mitochondria segregate independently of nuclear alleles during cell partitioning, we model this as a single step.
We define S(p;2n,l,n / 2) to be the probability of obtaining p mutant mitochondria in n / 2 draws from a cell in state Mt,τ6=(l,m,2G). Here, sampling is without replacement and follows a hypergeometric distribution, giving
S(p;2n,l,n2)=(lp)(2n−ln2−p)(2nn2).
Gametes produced by meiosis are represented by Mt+1,τ1=(p,g). We determine the probability of obtaining a particular gamete using
P(Mt+1,τ1=(p,U1)
)=12(∑l=02nS(p;2n,l,n2)P(Mt,τ6=(
l,U1U1B2B2
))),P(Mt+1,τ1=(p,B1))=12(∑l=02nS(p;2n,l,n2)P(Mt,τ6=(l,B1B1B2B2))),
and
P(Mt+1,τ1=(p,B2))=12(∑l=02nS(p;2n,l,n2)P(Mt,τ6=(l,U1U1B2B2)))+12(∑l=02nS(p;2n,l,n2)P(Mt,τ6=(l,B1B1B2B2))).
Factors of 1 / 2 in the above three equations take into account that half of the gametes produced from parent cells with nuclear genotype U1B2 will carry the U1 allele and the other half will carry the B2 allele (with similar logic applied for gametes produced from parent cells with nuclear genotype B1B2). Meiosis completes a single generation of the life cycle.
The relative fitness of U1B2 cells is given by
w¯U1B2=∑i=0nP(Mt,τ3=(i,U1B2))w(i)∑i=0nP(Mt,τ3=(i,U1B2)),
while the relative fitness of B1B2 cells is
w¯B1B2=∑i=0nP(Mt,τ3=(i,B1B2))w(i)∑i=0nP(Mt,τ3=(i,B1B2)).
Although gametes are not subject to selection in our model, and thus do not technically have fitness values, it is informative to track gamete relative fitness throughout the simulation. We define a gamete’s relative fitness as the fitness that a diploid cell would have if it had the same mitochondrial composition as the gamete. Since gametes contain n / 2 mitochondria, they will have minimum fitness when they carry n / 4 wild type and n / 4 mutant mitochondria. To rescale the fitness function, we substitute n / 2 for n in the diploid cell fitness functions. For example, Equation (1) becomes
wg(i)={1−ch(in/4)2for0≤i<n/4,1−ch((n/2)−in/4)2forn/4≤i≤n/2.
Once the fitness function is scaled to gametes, we can determine the relative fitness of the three gametes by
w
¯
U
1
=
∑
i=0
n/2
P(
M
t,
τ
1
=(
i,
U
1
)
)
w
g
(
i
)
∑
i=0
n/2
P(
M
t,
τ
1
=(
i,
U
1
)
)
,
w
¯
B
1
=
∑
i=0
n/2
P(
M
t,
τ
1
=(
i,
B
1
)
)
w
g
(
i
)
∑
i=0
n/2
P(
M
t,
τ
1
=(
i,
B
1
)
)
,
and
w¯B2=∑i=0n/2P(Mt,τ1=(i,B2))wg(i)∑i=0n/2P(Mt,τ1=(i,B2)).
See S1–S6 Model for details of the other models.
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10.1371/journal.pgen.1007701 | The tumor suppressor BRCA1-BARD1 complex localizes to the synaptonemal complex and regulates recombination under meiotic dysfunction in Caenorhabditis elegans | Breast cancer susceptibility gene 1 (BRCA1) and binding partner BRCA1-associated RING domain protein 1 (BARD1) form an essential E3 ubiquitin ligase important for DNA damage repair and homologous recombination. The Caenorhabditis elegans orthologs, BRC-1 and BRD-1, also function in DNA damage repair, homologous recombination, as well as in meiosis. Using functional GFP fusions we show that in mitotically-dividing germ cells BRC-1 and BRD-1 are nucleoplasmic with enrichment at foci that partially overlap with the recombinase RAD-51. Co-localization with RAD-51 is enhanced under replication stress. As cells enter meiosis, BRC-1-BRD-1 remains nucleoplasmic and in foci, and beginning in mid-pachytene the complex co-localizes with the synaptonemal complex. Following establishment of the single asymmetrically positioned crossover on each chromosome pair, BRC-1-BRD-1 concentrates to the short arm of the bivalent. Localization dependencies reveal that BRC-1 and BRD-1 are interdependent and the complex fails to properly localize in both meiotic recombination and chromosome synapsis mutants. Consistent with a role for BRC-1-BRD-1 in meiotic recombination in the context of the synaptonemal complex, inactivation of BRC-1 or BRD-1 enhances the embryonic lethality of mutants defective in chromosome synapsis. Our data suggest that under meiotic dysfunction, BRC-1-BRD-1 stabilizes the RAD-51 filament and alters the recombination landscape; these two functions can be genetically separated from BRC-1-BRD-1’s role in the DNA damage response. Together, we propose that BRC-1-BRD-1 serves a checkpoint function at the synaptonemal complex where it monitors and modulates meiotic recombination.
| Our genomes are passed down from one generation to the next through the specialized cell division program of meiosis. Meiosis is highly regulated to coordinate both the large scale chromosomal and fine scale DNA events to ensure fidelity. While the tumor suppressor BRCA1-BARD1 is essential for genome integrity, its specific role in meiosis has been difficult to uncover. Taking advantage of attributes of the Caenorhabditis elegans system, we have analyzed the function of the BRCA1-BARD1 complex in meiosis in this simple metazoan. We find that BRCA1 and BARD1 localize dynamically to the proteinaceous structure that aligns maternal and paternal chromosomes, where it regulates homologous recombination. Although BRCA1 and BARD1 mutants have only subtle meiotic defects, we show that this complex plays critical roles in meiotic recombination when meiosis is perturbed, and this is separable from BRCA1-BARD1’s function in response to DNA damage in somatic cells. These results highlight the complexity of ensuring accurate transmission of the genome and uncover the requirement for this conserved complex in meiosis.
| BRCA1 was identified twenty-eight years ago as the causative agent of early-onset familial breast cancer [1]. Subsequently, BRCA1 was shown to interact with BARD1 through their RING domains [2], to form an E3 ubiquitin ligase, which adds the small polypeptide ubiquitin to protein substrates [3] (hereafter referred to as BRCA1-BARD1). While BRCA1-BARD1 has been extensively studied with respect to its crucial tumor suppressor activities, we still do not fully understand how this protein complex mediates the diverse functions that have been ascribed to it (e.g., DNA metabolism, checkpoint signaling, chromatin dynamics, centrosome amplification, and transcriptional and translational regulation [4, 5]). This is due in part to the diversity of protein-protein interactions involved in generating numerous distinct BRCA1-BARD1 multi-protein complexes [6]. An additional impediment to understanding BRCA1-BARD1 function is that the corresponding mouse knockouts are embryonic lethal [7, 8].
The simple metazoan Caenorhabditis elegans offers several advantages to the study of this key complex. First, unlike in mammals, C. elegans BRCA1 and BARD1 orthologs, BRC-1 and BRD-1, are not essential yet play critical roles in DNA replication and the DNA damage response, as well as in homologous recombination, which is critical for repairing programmed double strand breaks (DSBs) during meiosis [9–14]. Additionally, attributes of the C. elegans system, including sophisticated genetics, ease of genome editing, and the spatio-temporal organization of the germ line allow us to overcome some challenges inherent in studying this complex in mammalian meiosis.
Meiosis is essential for sexual reproduction and results in the precise halving of the genome for packaging into gametes. During meiosis, homologous chromosomes are connected by crossover recombination to facilitate their alignment and segregation on the meiotic spindle. Recombination is integrated and reinforced with chromosome pairing and synapsis, although the extent of dependencies of these critical meiotic processes are distinct in different organisms (reviewed in [15, 16]). While it is well established that BRCA1-BARD1 plays an important role in DNA repair and recombination [5], the specific function of BRCA1-BARD1 in meiotic recombination is not known. In mice, partial deletions of BRCA1 result in early apoptosis of male germ cells due to failures in meiotic sex chromosome inactivation [17, 18]. BRCA1 has been shown to co-localize with RAD51 on asynapsed chromosomes in mouse spermatocytes, suggesting it functions in meiotic recombination [19]. In C. elegans, brc-1 and brd-1 mutants have mild meiotic phenotypes consistent with a role in some aspect of meiotic recombination [9, 10]. However, the relationship between BRC-1-BRD-1 function in synapsis and recombination has not been explored.
Here, we assessed BRC-1 and BRD-1 dynamics in the C. elegans germ line. Surprisingly, BRC-1-BRD-1 localizes to the synaptonemal complex (SC), becomes concentrated onto chromosome regions upon crossover designation, and at late meiotic prophase is restricted to the short arm of each bivalent as defined by the single crossover site on C. elegans chromosomes. BRC-1 and BRD-1 are interdependent for localization to the SC and proper localization is dependent on meiotic recombination and chromosome synapsis. Further, our data suggest that the BRC-1-BRD-1 complex promotes homologous recombination under meiotic dysfunction by stabilizing the RAD-51 filament and altering the patterning of crossovers. Similar findings are reported by Janisiw et al. in the accompanying paper.
To examine BRC-1 and BRD-1 expression and localization in C. elegans, we engineered GFP::BRC-1 and BRD-1::GFP fusions at the endogenous loci using CRISPR-Cas9 [20]. brc-1 and brd-1 mutants have low levels of embryonic lethality, produce slightly elevated levels of male progeny (X0), a readout of X chromosome nondisjunction, and display sensitivity to γ-irradiation (IR) [10]. Worms expressing these fusions as the only source of BRC-1 or BRD-1 produced wild-type levels of viable progeny and males, and were not sensitive to IR (S1A–S1C Fig), suggesting that the fusions are fully functional.
We monitored the localization of GFP::BRC-1 and BRD-1::GFP by live cell imaging. In whole worms, GFP fluorescence was observed in embryos and in the germ line, with very little signal in the soma (note auto-fluorescence of gut granules also observed in wild type; Fig 1A). Immunoblots of whole worm extracts of gfp::brc-1; fog-2, which are true females [21] and therefore do not contain embryos, compared to self-fertilizing gfp::brc-1 hermaphrodites containing embryos, revealed that <10% of the GFP::BRC-1 signal is due to expression in embryos (S1E Fig). Thus, BRC-1 and BRD-1 are expressed predominantly in the germ line.
The C. elegans germ line is arranged in a spatio-temporal gradient, with proliferating germ cells (premeiotic) and all stages of meiosis arrayed from the distal to proximal end [22] (Fig 1B). We first focused on the premeiotic zone, where germ cells are mitotically proliferating. GFP::BRC-1 and BRD-1::GFP were observed diffusely throughout the nucleus, with occasional foci that partially co-localized with the recombinase RAD-51 (Fig 1C and 1D). In mammalian cells RAD51 marks stalled/collapsed replication forks [23], and BRCA1/BRC-1 has been implicated in repair of damaged forks in both mammals and C. elegans [14, 24]. To determine whether BRC-1-BRD-1 responds to stalled/collapsed replication forks, we treated worms with the ribonucleotide reductase inhibitor, hydroxyurea (HU). HU slows replication causing fork stalling and collapse, and cell cycle arrest leading to enlarged nuclei [23, 25]. GFP::BRC-1 and BRD-1::GFP fluorescence became enriched in many foci following exposure to HU, and these exhibited substantial co-localization with RAD-51 (Fig 1C and 1D). Consistent with a role in resolving collapsed replication forks, both brc-1 and brd-1 mutants were sensitive to HU as measured by embryonic lethality (S1D Fig). These results suggest that BRC-1-BRD-1 responds to replication stress and concentrates in foci where it co-localizes with RAD-51, presumably to resolve stalled/collapsed replication forks.
In early meiotic prophase (transition zone/early pachytene), GFP::BRC-1 and BRD-1::GFP direct fluorescence were observed diffusely on chromatin and in foci (Fig 2A). These foci partially overlapped with RAD-51, which marks meiotic DSBs [26]. We noticed that the relative intensity of the foci was weaker in fixed versus live imaging (see Figs 3 and 4), suggesting that these foci were sensitive to fixation conditions. Beginning at mid-pachytene, GFP::BRC-1 and BRD-1::GFP were observed in tracks along the entire chromosome length, and then concentrated to a portion of each chromosome at late pachytene (Fig 2A). In diplotene and diakinesis, GFP::BRC-1 and BRD-1::GFP were further restricted to six short stretches on the six pairs of homologous chromosomes (Fig 2A). As oocytes continued to mature, GFP::BRC-1 and BRD-1::GFP were disassembled from chromosomes in an asynchronous manner, with some chromosomes losing signal before others. Thus, in diakinesis nuclei we did not always observe six stretches of fluorescence, and the fluorescence intensity varied between chromosomes.
The concentration of BRC-1-BRD-1 into tracks at mid-pachytene suggested that the complex localized to the SC. To investigate this, we co-stained with antibodies against GFP and the SC central region component, SYP-1 [27]. Homologous chromosomes begin synapsing early in meiotic prophase (29); however, GFP::BRC-1 was not observed on tracks until after the SC appeared to be fully formed (Fig 2B). Interestingly, the concentration of GFP::BRC-1 to a portion of each chromosome preceded the relocalization of SYP-1 to the short arm of the bivalent (arrows in late pachytene images of GFP::BRC-1; Fig 2B). As the SC reorganizes as a consequence of crossover maturation [28], we examined worms co-expressing TagRFP-T::BRC-1 (TagRFP-T is a RFP variant with improved photostability [20, 29]) and GFP::COSA-1, a cyclin related protein that marks presumptive crossover sites [30]. TagRFP-T::BRC-1 also appeared to be fully functional (S1A–S1C Fig), although the fluorescent signal was weaker than GFP, and could only be detected in mid-late pachytene through diakinesis. GFP::COSA-1 was observed at one end of each TagRFP-T::BRC-1 stretch (Fig 2C). Thus, BRC-1 and BRD-1 localize to the SC and are redistributed concomitant with crossover designation, suggesting that BRC-1-BRD-1 functions in one or more aspects of meiotic recombination within the context of the SC.
In both mammalian cells and C. elegans, BRCA1/BRC-1 and BARD1/BRD-1 form a stable complex [2, 31]. To probe the relationship between C. elegans BRC-1 and BRD-1 in vivo, we imaged live worms heterozygous for both TagRFP-T::BRC-1 and BRD-1::GFP (brc-1 and brd-1 are linked). In the heterozygous state the TagRFP-T signal could only be detected at late pachytene through early diakinesis when BRC-1 and BRD-1 are concentrated on short tracks. The TagRFP-T and GFP signals overlapped, suggesting that BRC-1 and BRD-1 are localized together on the SC (Fig 3A).
To examine localization dependencies between BRC-1 and BRD-1 in C. elegans germ cells, we monitored GFP::BRC-1 and BRD-1::GFP in the corresponding brd-1(ok1623) and brc-1(xoe4) null mutant backgrounds by live cell imaging. In the absence of BRD-1 we observed diffuse GFP::BRC-1 fluorescence within the nucleoplasm from proliferative zone to mid-pachytene, with no evidence of tracks (Fig 3B). In late pachytene, weak GFP::BRC-1 foci were observed; however, in diplotene and diakinesis only a diffuse nucleoplasmic signal was detected, with no concentrated regions of GFP::BRC-1. This result suggests that BRD-1 is required for the correct localization of BRC-1 in meiotic cells. In worms harboring a null allele of brc-1, BRD-1::GFP was largely cytosolic, except at diakinesis where it was observed in the nucleoplasm. Analysis of steady state protein levels by immunoblot revealed that BRC-1 and BRD-1 were present, albeit at reduced levels, in the absence of the other partner (in brc-1(xoe4), BRD-1::GFP = 60% of wild-type levels; in brd-1(ok1623), GFP::BRC-1 = 50% of wild-type levels; Fig 3C). Thus, BRC-1 and BRD-1 are mutually dependent for localization to meiotic chromosomes.
To provide insight into the relationship between BRC-1-BRD-1 and the progression of meiotic recombination, we monitored the localization of GFP::BRC-1 in mutants that impair different steps of meiotic recombination: spo-11 mutants are unable to form meiotic DSBs [32, 33], rad-51 mutants are blocked prior to strand invasion [34–36], and msh-5 mutants fail to form crossovers [37, 38]. In live spo-11 mutants, we observed many fewer GFP::BRC-1 foci in transition zone and early pachytene compared to WT (TZ: 1.29±0.12 vs. 0.18±0.05; EP: 4.61±0.36 vs. 0.91±0.22 foci/nucleus in WT and spo-11, respectively; p <0.0001; S2A Fig). At mid-pachytene GFP::BRC-1 was observed in tracks in the spo-11 mutant similar to wild type, as synapsis occurs in the absence of meiotic DSB formation in C. elegans [32] (Fig 4). In late pachytene, GFP::BRC-1 fluorescence did not concentrate on a portion of each chromosome pair nor retract to the short arm of the bivalent as in wild type, consistent with these events being dependent on meiotic recombination. However, in 20.23±1.78% of nuclei (n = 4 germ lines) there was enrichment of GFP::BRC-1 on one or sometimes two tracks, in addition to weak staining on other tracks. This is similar to what has been previously reported for synapsis markers, including the phosphorylated form of SYP-4 [39–41], and likely represents spo-11-independent lesions capable of recruiting meiotic DNA repair components and altering SC properties. Consistent with this, we observed GFP::BRC-1 enrichment on the phospho-SYP-4-marked chromosome in spo-11 mutants (S2B Fig). However, GFP::BRC-1 did not retract to chromosome subdomains as in wild type in diplotene and diakinesis, suggesting that the relocalization of BRC-1-BRD-1 is dependent on formation of meiotic DSBs. As expected, BRD-1::GFP was observed in a similar pattern to GFP::BRC-1 in spo-11 mutants throughout meiotic prophase (S2C Fig).
Following DSB formation and processing, RAD-51 is loaded onto resected single-stranded DNA and facilitates strand exchange [36]. GFP::BRC-1 localization was altered in the rad-51 mutant (Fig 4). Significantly increased levels of GFP::BRC-1 foci were observed throughout the germ line. In the proliferative zone, wild type had 0.55±0.04, while rad-51 had 0.96±0.10 foci per nucleus (S2A Fig). These most likely represent concentration of GFP::BRC-1 at stalled/collapsed replication forks. In transition zone, wild type had 1.29±0.12, while rad-51 had 3.98±0.31 foci/nucleus, and this was further increased in early pachytene (WT: 4.6±0.4 vs. rad-51: 13.3±0.7; S2A Fig). These foci presumably represent resected meiotic DSBs that fail to undergo strand invasion in the absence of RAD-51, as they cannot be solely accounted for by the elevated foci observed in proliferating cells. Track-like structures were not observed until late pachytene in the absence of RAD-51. The punctate nature of GFP::BRC-1 was particularly pronounced in diplotene and diakinesis, with no clear concentration to six regions. This is consistent with the disorganized chromatin masses observed in rad-51 diakinesis nuclei [35], and suggests that RAD-51 is required for the proper organization and retraction of GFP::BRC-1.
In msh-5 mutants, GFP::BRC-1 appeared similar to wild type from the proliferative zone to mid pachytene, localizing in the nucleoplasm and concentrating in foci before converging on tracks (Fig 4; S2A Fig). Similar to spo-11, 26.27±2.25% of msh-5 late pachytene nuclei (n = 4 germ lines) contained enrichment of GFP::BRC-1 on one or occasionally two chromosomes. In diplotene, GFP::BRC-1 was observed in long tracks, with no evidence of retraction. The presence of more than six stretches of GFP::BRC-1 in diakinesis suggests that BRC-1 remains associated with the univalents in msh-5 mutants. Taken together, our data suggest that GFP::BRC-1 localizes to the SC and its retraction to the short arm of the bivalent is dependent on processing of meiotic DSBs into crossovers.
We also examined localization of GFP::BRC-1 when synapsis is blocked by mutation of a component of the central region of the SC, syp-1 [27]. GFP::BRC-1 in syp-1 looked similar to wild type in proliferating germ cells (Fig 4). However, as cells entered meiosis GFP::BRC-1 was observed in many foci (in TZ, WT: 1.29±0.12 vs. syp-1: 7.29±0.36 foci/nucleus; S2A Fig). The number of foci increased through early and mid pachytene but GFP::BRC-1 never attained nuclear track staining, supporting a dependency on the SC for track localization. Similarly, the GFP::BRC-1 signal did not localize to sub-regions of condensed (DP and DK) chromosomes, but rather was found in a small number of nuclear foci. Thus, GFP::BRC-1 localization to tracks is dependent on SC formation.
To examine localization under conditions where a subset of chromosomes fail to synapse and recombine, we monitored GFP::BRC-1 localization in the zim-1 mutant, in which chromosomes II and III cannot synapse [42]. In transition zone and early pachytene, GFP::BRC-1 was observed in many foci in the zim-1 mutant, similar to the syp-1 mutant (TZ: WT: 1.29±0.12 vs. zim-1: 4.5±0.36 foci/nucleus; Fig 4; S2A Fig). However, as meiosis progressed GFP::BRC-1 was observed on tracks that condensed to the short arm of the bivalent on multiple chromosomes. Many times we observed more than four stretches of GFP::BRC-1 fluorescence at diplotene/diakinesis (Fig 4), suggesting that there are more than four chiasmata in the zim-1 mutant. We address the role of BRC-1 in chiasmata formation in the zim-1 mutant below.
Given the association of GFP::BRC-1 and BRD-1::GFP with the SC (Fig 4), we next examined the functional consequence of removing BRC-1-BRD-1 when synapsis is perturbed. For these studies we focused on the zim-1 mutant, as the appearance of more than four short tracks of GFP::BRC-1 at diplotene/diakinesis (Fig 4) suggested that these BRC-1-BRD-1-associated regions were altered in the absence of zim-1. Additionally, unlike mutants such as syp-1 that result in a complete failure in synapsis and therefore 95% embryonic lethality [27], loss of ZIM-1 results in 73.9% inviable progeny [42], allowing us to determine whether removal of BRC-1-BRD-1 enhances embryonic lethality.
In the course of our experiments we discovered that strain DW102 [31] harbors mutations in both brc-1 and brd-1; sequence analysis revealed that brc-1(tm1145) is an in-frame deletion, removing 71 amino acids (116–186) C-terminal to the predicted RING domain, which in the mammalian ortholog is responsible for E3 ubiquitin ligase activity and dimerization with BARD1 [3, 43, 44] (Fig 5A). The brd-1 mutation in DW102 is identical to brd-1(dw1) [31]; cDNA analysis revealed that the mutation results in the use of an alternative splice site to generate a protein missing 327 amino acids, leaving the RING domain intact (Fig 5A and S3A Fig). To discern the contributions of BRC-1 and BRD-1 we used CRISPR-Cas9 to generate a complete deletion of BRC-1, brc-1(xoe4) (Fig 5A and S3A Fig). We also examined the brc-1(tm1145) and brd-1(dw1) single mutants, the brc-1(tm1145) brd-1(dw1) double mutant and brd-1(ok1623), which results in the removal of 359 amino acids C terminal of the RING domain (Fig 5A and S3A Fig). As expected, brc-1(xoe4), brd-1(dw1), brc-1(tm1145) brd-1(dw1), and brd-1(ok1623) displayed slightly elevated embryonic lethality (Fig 5B), male progeny (Fig 5C), and IR sensitivity (Fig 5D). On the other hand, brc-1(tm1145) was not statistically different from wild type for embryonic lethality, production of male progeny or IR sensitivity, suggesting that this allele is not a null mutation (Fig 5B–5D). Consistent with this, BRD-1::GFP was stable (Fig 3C) and localized similarly to wild type in the brc-1(tm1145) mutant background (S3B Fig).
In contrast to the differential impact of the alleles on embryonic lethality, male progeny, and IR sensitivity, loss of zim-1 in any of the brc-1 or brd-1 mutants resulted in enhanced embryonic lethality compared to the single zim-1 mutant (p<0.0001; Fig 5E). These results suggest that the region C-terminal to the BRC-1 RING domain, which is deleted in brc-1(tm1145), is important for promoting embryonic viability when chromosome pairing and synapsis are perturbed.
To determine the nature of the enhanced embryonic lethality of zim-1 mutants when BRC-1-BRD-1 is impaired, we first monitored germline apoptosis. Apoptosis is an output of checkpoint signaling and is important for culling defective germ cells [45–47]. Previous studies had established that both brc-1 [9] and zim-1 [48] have elevated checkpoint-dependent germline apoptosis. We found that all brc-1 and brd-1 alleles, including brc-1(tm1145), had elevated apoptosis (Fig 5F). Loss of zim-1 resulted in higher levels of apoptosis than brc-1 and brd-1 mutants; however, the levels of apoptosis in the double brc-1; zim-1 and brd-1; zim-1 mutants were not significantly different than zim-1 alone. We also analyzed SUN-1 phosphorylated on Serine12 (Sun-1 S12P), which is dephosphorylated following establishment of the obligate crossover, and serves as a readout of meiotic progression [49]. Loss of ZIM-1 resulted in persistent SUN-1 S12P, which was unaltered in the absence of BRC-1 (S3C Fig). These results suggest that BRC-1-BRD-1 does not function in known signaling pathways responsible for monitoring unrepaired DSBs or crossovers leading to apoptosis or cell cycle delay.
We next monitored RAD-51 assembly/disassembly in the spatiotemporal organization of the germ line. Previous analyses revealed that brc-1 and brd-1 mutant hermaphrodites have elevated RAD-51 foci in late pachytene, suggesting that repair of a subset of meiotic DSBs is delayed in the absence of BRC-1-BRD-1 [9]; this was also observed in the brc-1(tm1145) brd-1(dw1) and brd-1(ok1623) mutants (S4A Fig). Further, blocking synapsis on some or all chromosomes results in elevated RAD-51 levels genome wide [26, 50], as observed in the zim-1 mutant (Fig 6A and 6B). Surprisingly, brc-1; zim-1 and brd-1; zim-1 double mutants resulted in fewer RAD-51 at mid-late pachytene: RAD-51 foci appeared at similar levels compared to the zim-1 single mutant early in meiotic prophase, but in the latter half of pachytene many fewer RAD-51 were detected on chromosomes (Fig 6A and 6B and S4B Fig). High levels of RAD-51 were observed again at the gonad bend, as nuclei exited pachytene and entered diplotene (Fig 6A and 6B and S4B Fig). Similar patterns were observed when BRC-1-BRD-1 was removed in other mutants that perturb synapsis (i.e., syp-1; S4B Fig). These results suggest that when synapsis and therefore crossover formation is impaired, BRC-1-BRD-1 plays a role in DSB formation, DNA end resection, RAD-51 loading, and/or stabilization of the RAD-51 filament in mid-late pachytene.
To differentiate between these possible meiotic functions of BRC-1-BRD-1, we analyzed the pattern of the single-stranded binding protein RPA-1 (GFP::RPA-1; [51]). RPA-1 binds resected ends prior to RAD-51 loading [52, 53] and is also associated with recombination events at a post-strand-exchange step, which can be observed in chromosome spreads [54]. In the brc-1(tm1145); zim-1 germ line we observed an inverse pattern between RAD-51 and RPA-1 at mid-late pachytene: GFP::RPA-1 foci were prevalent in the region where RAD-51 foci were reduced (Fig 6A). In the zim-1 single mutant, fewer GFP::RPA-1 foci were observed at this stage, while RAD-51 remained prevalent. We also observed very few RPA-1 foci at mid-late pachytene in wild type or brc-1(tm1145) brd-1(dw1) double mutant whole mount gonads (S4C Fig). These results suggest that BRC-1-BRD-1 is not required for DSB formation per se in this region of the germ line, as we observed an increase in GFP::RPA-1 foci, not a decrease as would be expected if BRC-1-BRD-1 mediates DSB formation. Additionally, this result argues against a role for BRC-1-BRD-1 in promoting resection as RPA-1 loads on exposed single stranded DNA [52]. Thus, at mid to late pachytene BRC-1-BRD-1 either facilitates the assembly of RAD-51 on new breaks, and/or stabilizes the RAD-51 filament.
The lack of RAD-51 in mid to late pachytene in brc-1; zim-1 and brd-1; zim-1 mutants is reminiscent of the RAD-51 “dark zone” observed in the rad-50 mutant following exposure to IR, which likely reflects a requirement for RAD-50 in loading RAD-51 at resected DSBs on meiotic chromosomes [55]. However, the distal boundary of the dark zone in the brc-1; zim-1 double mutant is distinct from the rad-50 mutant: the dark zone in rad-50 extends from meiotic entry to late pachytene [55], while in the brc-1; zim-1 and brd-1; zim-1 mutants reduction in RAD-51 was limited to mid-late pachytene (Fig 6A and 6B and S4B Fig), suggesting that the nature of the dark zone is different in these mutant situations. If BRC-1-BRD-1 is required for loading RAD-51 on breaks in mid-late pachytene, then a time course analysis would reveal a diminution of the dark zone by twelve hours following IR exposure, as was observed for rad-50 mutants (Fig 7A, loading defect on left) [55]. On the other hand, if BRC-1-BRD-1 was important for protecting RAD-51 from disassembly, then the dark zone should be maintained throughout the time course as RAD-51 would be disassembled as nuclei with pre-installed RAD-51 move through the mid-late pachytene region of the germ line (Fig 7A, stabilization defect on right). SPO-11 remains active under conditions where crossovers have not formed on all chromosomes [56, 57], making it difficult to distinguish a RAD-51 loading defect onto new breaks in this region of the germ line versus a defect in RAD-51 stability. Therefore, we performed these experiments in the spo-11 mutant background [32], as IR will induce breaks uniformly in the germ line at a single point in time and as nuclei move through the germ line, no new breaks will be formed. spo-11 is tightly linked to zim-1; consequently, we used RNAi against SYP-2, which in our hands is more efficient than zim-1(RNAi), to block synapsis and crossover formation. To that end, we exposed spo-11 and brc-1(tm1145) brd-1(dw1); spo-11 mutants depleted for SYP-2 to 10 Gy of IR and examined RAD-51 over time. At one, four, eight, and twelve hours following IR, the dark zone was maintained in the absence of BRC-1-BRD-1 (Fig 7B). This result is consistent with the hypothesis that BRC-1-BRD-1 stabilizes the RAD-51 filament rather than facilitates loading of RAD-51 on new DSBs at mid-late pachytene.
A subset of RAD-51 strand invasions are processed into crossovers, which are marked by CNTD1/COSA-1 [30, 58]. Given the reduction in RAD-51 in mid-late pachytene in brc-1; zim-1 and brd-1; zim-1 mutant hermaphrodites, we next analyzed crossover precursor formation in the various mutants. In C. elegans, each of the six chromosome pairs forms a single crossover; consequently, there are six COSA-1 foci in hermaphrodite germ cells at late pachytene [30] (Fig 8A). We also observed six COSA-1 foci in late pachytene nuclei in the brc-1 and brd-1 mutants (Fig 8A), indicating that breaks are efficiently processed into crossovers in the absence of BRC-1-BRD-1 in an otherwise wild-type worm. This is consistent with the presence of six bivalents at diakinesis and the low embryonic lethality of brc-1 and brd-1 [9, 10] (Fig 5B). In zim-1 mutants we expected to observe four COSA-1 foci per nucleus, one on each of the four paired chromosomes, but not on the unpaired chromosomes II and III. Contrary to our expectations, zim-1 had an average of 6.12±0.12 COSA-1 foci (χ2: p<0.005), with a very broad distribution ranging from 2 to 9 foci; such a wide distribution is never observed in wild type [30] (Fig 8A; S5 Fig). Inactivation of BRC-1 and/or BRD-1 in zim-1 reduced the number of GFP::COSA-1 foci to a range of 4.3–4.8 in the various mutants, closer to expectations although still significantly different than expected (χ2: p<0.005), and the distribution remained broad (p <0.0001; Fig 8A). These results suggest that when crossovers are unable to form between some homologs, additional COSA-1-marked crossover precursors are generated, and some of these are dependent on BRC-1-BRD-1.
The higher than expected numbers of COSA-1 foci observed in zim-1 mutants could reflect recombination intermediates that do not go on to form chiasmata (i.e., non-crossovers or inter-sister crossovers). Alternatively, COSA-1 could mark bona fide inter-homolog crossovers, such that some chromosomes have more than one chiasma, as has been observed in mutants where the X chromosomes fail to pair and synapse [50]. As these two possibilities are not mutually exclusive, the extra COSA-1 foci could be due to a combination of both recombination outcomes. To provide insight into the nature of the extra COSA-1 foci, we analyzed COSA-1 in syp-1 mutants, where no chiasmata can form as all chromosomes fail to synapse, and found that there were on average 4.85±0.07 COSA-1 foci at late pachytene (Fig 8A; S5 Fig). These results suggest that under conditions of meiotic dysfunction when chromosomes are unable to pair/synapse, COSA-1 is recruited to recombination intermediates that are processed into non-crossovers and/or inter-sister crossovers. Similar numbers of COSA-1 foci, associated with MSH-5, were observed in syp-3 mutants; high resolution cytological analyses indicated that these recombination sites are non-randomly distributed but with some abnormalities, consistent with the formation of nonproductive intermediates or inter-sister crossovers [59]. As with zim-1 mutants, inactivation of BRC-1-BRD-1 in the syp-1 mutant background led to fewer COSA-1 foci (Fig 8A; S5 Fig), suggesting that BRC-1-BRD-1 promotes COSA-1-associated recombination processing when chiasma formation is impaired.
To determine whether the extra COSA-1 foci on synapsed chromosomes could form chiasmata, we examined zim-1 and brc-1(tm1145); zim-1 diplotene/diakinesis nuclei, where chromosomes are individualized and cross-shaped structures indicative of crossovers between homologs can be observed. Consistent with the formation of extra chiasmata in the zim-1 mutant background, we observed 52% of diplotene/diakinesis nuclei (n = 52) containing at least one ring-shaped structure, and six had two ring-shaped structures. The simplest interpretation is that there was a chiasma on each end of the chromosome pair (arrow; Fig 8B). This was reduced to 21% of diplotene/diakinesis nuclei (n = 43) containing ring-shaped chromosomes in the brc-1(tm1145); zim-1 double mutant (zim-1 vs. brc-1(tm1145); zim-1, p = 0.0028 Mann-Whitney). These results suggest that BRC-1-BRD-1 promotes chiasma formation when some chromosomes are unable to interact with their partner.
To examine genetic crossovers, we monitored linkage between SNP markers on chromosomes V and X in Bristol/Hawaiian hybrid strains to assess both crossover numbers and distribution. While inactivation of brc-1 had no effect on crossover numbers on chromosome V (WT = 48.1cM; brc-1 = 50.8cM), we observed an altered distribution compared to wild type (Fig 8D and 8E; S1 Table). In C. elegans, crossovers are enriched on the arms [28, 60–62]; in the brc-1(tm1145) mutant we observed a more even distribution, with more crossovers in the center and fewer on the right arm (Fig 8E; S1 Table). On the other hand, in brc-1(tm1145), neither crossover frequency nor distribution were significantly different on the X chromosome (Fig 8D and 8E), which has an altered crossover landscape compared to the autosomes [63, 64].
We next monitored linkage between SNP markers in the zim-1 and brc-1(tm1145); zim-1 mutants. We observed a significant increase in the recombination map on chromosome V in zim-1 (70.8cM), and multiple double crossovers were observed (Fig 8D; S1 Table). Extra crossovers were also observed on autosomes in worms unable to pair and synapse X chromosomes [50]. Inactivation of BRC-1 in the zim-1 background resulted in significantly fewer double crossovers (DCOs) on chromosome V (p = 0.0242; Fig 8C, S1 Table), although the overall genetic map length was not significantly different compared to the zim-1 single mutant (68.2cM; Fig 8D). This is most likely a consequence of an increase in the single crossover class (SCO; zim-1 vs. brc-1(tm1145); zim-1, p = 0.0007; Fig 8C, S1 Table). On the X chromosome crossover frequency and distribution were altered in the center region in both zim-1 and brc-1(tm1145); zim-1 and in the left interval in zim-1; however, the overall map lengths were not statistically different between any of the strains.
C. elegans exhibits strong interference, which is the phenomenon that a crossover at one position on a chromosome decreases the probability of formation of a crossover nearby, resulting in a single crossover per chromosome [62]. Given the detection of DCOs on chromosome V in the zim-1 and brc-1(tm1145); zim-1 mutants, we calculated the interference ratio. While wild type and brc-1 had absolute intereference of 1, as no double crossovers were observed, the zim-1 mutant displayed reduced interference in the left-center and left-right intervals and negative interference in the center-right interval (Table 1). Inactivation of BRC-1 in the zim-1 mutant restored positive interference in the center-right interval; however, this fell short of statistical significant (p = 0.064). In addition to the non-randomness in the number and position of crossovers, interference also operates on the level of chromatids such that a crossover between any two non-sister chromatids can affect the probability of those chromatids being involved in other crossovers [65]. Chromatid interference has been shown to occur in fungi, Drosophila, maize and humans [65–70]. Since we assayed single products of meiosis, the SCO class includes single crossovers as well as recombinants that are the result of three- or four-strand double crossovers, while only two strand-events can be detected as DCOs. The elevated numbers of SCOs and reduction in two-strand DCOs on chromosome V in the brc-1(tm1145); zim-1 mutant compared to the zim-1 single mutant (Fig 8C), suggest that there may be more three- and/or four-strand double crossovers when BRC-1 is inactivated. Thus, BRC-1 may counteract chromatid interference under meiotic dysfunction, such that more two-strand double crossovers occur. Taken together, the reduced number of COSA-1 foci and alteration in the genetic map in the brc-1(tm1145); zim-1 mutant suggest that BRC-1-BRD-1 modifies recombination patterning under meiotic dysfunction.
Here we show that C. elegans BRC-1 and BRD-1 orthologs localize to the SC and regulate recombination when meiosis is perturbed. Our results suggest that BRC-1-BRD-1 plays an important role in monitoring and modulating processing of meiotic DSBs into crossovers in the context of the specialized meiotic chromosome structure.
In mouse spermatocytes BRCA1 is associated with RAD51 and enriched on asynapsed regions of meiotic chromosomes, including the X-Y sex body [18, 19]. Here we show that C. elegans BRC-1 and BRD-1 partially co-localize with RAD-51 in early meiotic prophase, but become enriched on synapsed chromosomes as meiosis progresses, co-localizing with SYP-1, a SC central region component (Fig 2B). The enrichment of mammalian BRCA1 on asynapsed chromosomes versus BRC-1 on synapsed chromosomes in C. elegans most likely reflects alteration in the relationship between meiotic recombination and SC formation in these organisms. Meiotic chromosomes can pair and synapse in the absence of meiotic recombination in C. elegans [32], while these events are interdependent in mammals [15, 16]. The HORMAD axial components also show differences in chromosome association in mice and worms: in mice, HORMAD1 and HORMAD2 are enriched on asynapsed chromosomes [71, 72], while C. elegans HORMADS, HIM-3, HTP-1/2, and HTP-3, remain associated with synapsed chromosomes [73–76]. However, the function of HORMADs in preventing inter-sister recombination and in checkpoint signaling appears to be similar in these different organisms [77–82]. Thus, the association of BRC-1-BRD-1 to the SC in C. elegans is likely a consequence of the inter-relationship between SC formation and meiotic recombination in this organism and not due to different functions for this complex in worm versus mammalian meiosis.
Another difference between C. elegans and mammals is the nature of the kinetochore. C. elegans chromosomes are holocentric while in many organisms, including yeast and mice, chromosomes are monocentric. Holocentricity dictates that a single off-centered crossover is formed on each homolog pair to define the long and short arms necessary to ensure regulated sister chromatid cohesion release at meiosis I and II [60–62, 83]. Interestingly, BRC-1-BRD-1 becomes restricted to the short arm of the bivalent, as defined by the crossover site, and this precedes SC reorganization. While the absence of BRC-1-BRD-1 alone does not affect crossover formation on chromosome V and the X chromosome, it does have a subtle effect on the distribution of crossovers along chromosome V such that more occur in the middle of the chromosome (Fig 8D and 8E). The change in crossover distribution in brc-1 mutants may contribute to the slightly increased nondisjunction observed in the absence of the BRC-1-BRD-1 complex.
We show that the concentration of BRC-1-BRD-1 to a portion of each chromosome track in late pachytene is dependent on meiotic DSB formation and processing into crossovers (spo-11, rad-51 and msh-5; Fig 4). Interestingly, in both spo-11 and msh-5 mutants there are occasional chromosomal tracks in late pachytene, which are highly enriched for BRC-1. While synapsis markers also show occasional enrichment to single tracks in the absence of spo-11, and these partially overlap with BRC-1 (S2B Fig), no enrichment of synapsis markers is observed when crossover factors (i.e., msh-5, cosa-1 or zhp-3) are removed [39–41]. While it has been proposed that spo-11-independent lesions can recruit meiotic DNA repair components [39–41], the enrichment of BRC-1 in the absence of such crossover factors suggests that BRC-1-BRD-1 can respond to other repair intermediates in addition to those leading to inter-homolog crossovers. One possibility is that when inter-homolog crossover formation is blocked, DSBs are repaired through site-specific nucleases [84–86], a subset of which leads to the concentration of BRC-1-BRD-1 on chromosomes in late pachytene. This is also consistent with the observation that BRC-1 is maintained on chromosomes in spo-11, rad-51 and msh-5 mutants in diakinesis nuclei. Perhaps the failure to form interhomolog crossovers in these mutants leads to continued association of BRC-1-BRD-1 on chromosomes.
BRCA1 forms a potent E3 ubiquitin ligase only in complex with its partner BARD1 [2, 3]. Biochemical and structural studies have defined the RING domains and associated helices of these proteins as critical for catalytic activity and BRCA1-BARD1 interaction [43]. However, while the BRCA1-BARD1 heterodimer exhibits substantially greater E3 ligase activity in vitro than BRCA1 alone, only the BRCA1 RING domain interacts with the E2 for ubiquitin transfer, suggesting that BRCA1 is the critical subunit for E3 ubiquitin ligase activity [3, 87]. Structure-function analysis of the BARD1 RING domain suggests that BARD1 may serve to attenuate BRCA1 E3 ligase activity [88]. Interestingly, while the localization of BRC-1 and BRD-1 were interdependent (Fig 3B), BRD-1 appeared to be excluded from the nucleus in the absence of BRC-1, while BRC-1 was nucleoplasmic and formed foci in late pachytene in the absence of BRD-1 (Fig 3B). These differences may reflect the nature of the alleles examined: brc-1(xoe4) produces no protein, while the two brd-1 alleles are predicted to produce truncated proteins, where the RING domain and associated helices remain intact (Fig 5A). In humans, BRCA1 nuclear localization signals in the middle of the protein can directly mediate nuclear import, or import can occur indirectly through interaction with BARD1 [89]. Thus, the truncated BRD-1 protein produced from the brd-1(ok1623) allele could associate with BRC-1 and facilitate nuclear localization of the albeit nonfunctional complex. Alternatively, C. elegans BRC-1 may be uniquely required for nuclear localization or retention, and in its absence BRD-1 cannot enter or be retained in the nucleus. The weak nucleoplasmic BRD-1 signal observed at the end of meiotic prophase in the absence of BRC-1 most likely reflects differences in the nuclear membrane as oogenesis proceeds [90, 91].
In addition to the N-terminal RING domains, both BRC-1 and BRD-1 contain long linker and phosphoprotein binding BRCA1 C-terminal (BRCT) domains. BRCT domains are phosphorylation-dependent interacting modules that have been implicated in tumor suppressor activity [92]. Interestingly, only BRD-1 contains Ankyrin (ANK) repeat interaction domains. Recent structural and functional analyses of the ANK domain in TONSL-MMS22L, a complex involved in homologous recombination, revealed that the ANK domain interacts with histone H4 tails [93]. The BARD1 ANK domains have a very similar fold [93], suggesting that BARD1 ANK domains may be important for association with chromatin. The predicted truncated proteins produced in the brd-1 mutants, which behave as nulls (Fig 5 and S4 Fig), lack at least part of the BRCT domains and all of the ANK domains, suggesting that some combination of these domains are critical for BRD-1 function with respect to both DNA damage signaling and meiosis.
It has long been appreciated that BRCA1-BARD1 mediates its tumor suppressor activity at least in part through regulating homologous recombination [6]. Given the importance of homologous recombination in repairing DSBs during meiosis, it is not surprising that removing BRC-1-BRD-1 impinges on meiotic recombination. Unexpectedly, we identified a small region C-terminal to the BRC-1 RING and associated helices as being important specifically for meiosis, suggesting that the function of BRC-1-BRD-1 in DNA damage response and meiosis are distinct. While containing no specific fold or homology, this region has several potential phosphorylation sites based on prediction algorithms that may mediate its interaction with key meiotic proteins.
BRCA1-BARD1 associates with the recombinase RAD51 in both mammals and C. elegans [19, 31, 94]. BRCA1 has also been shown to be required for the assembly of DNA damage induced RAD51 foci on chromatin [95], and this has been interpreted as a requirement for BRCA1 in RAD51 filament assembly. However, recent biochemical analyses using purified proteins found that BRCA1 is not required for RAD51 assembly on RPA coated single stranded DNA and instead promotes DNA strand invasion [94]. Further, a BARD1 mutant that cannot interact with RAD51 does not promote DNA strand invasion, and also does not form foci in vivo. Thus, it is likely that BRCA1-BARD1 is not required for RAD51 filament assembly per se. Our IR time course analysis of C. elegans brc-1 brd-1 mutants is consistent with a function for this complex in stabilizing the RAD-51 filament. It is possible that similar to the mammalian complex, BRC-1-BRD-1 promotes RAD-51 strand invasion; however, in vivo the RAD-51 filament may be subject to disassembly by other proteins in the absence of BRC-1-BRD-1, which would not be recapitulated in vitro. One such protein is the FANCJ/DOG-1 helicase, which interacts with BRCA1 [96], and can disassemble RAD51 on ssDNA in vitro [97]. It is also likely that BRCA1-BARD1 plays multiple roles during homologous recombination and interacts with, and coordinates the activity, of many proteins, including RAD51, and these interactions are modulated under different conditions, including DNA damage, meiosis, meiotic dysfunction, as well as at different stages of the cell cycle. Consistent with this, Janisiw et al. found that BRC-1 associates with the pro-crossover factor MSH-5.
brc-1 and brd-1 mutants have very subtle defects in meiosis. These include low levels of X chromosome nondisjunction [10] (Fig 5C), a delay in repair of a subset of DSBs through the inter-sister pathway [9], and elevated heterologous recombination [12]. However, removing BRC-1-BRD-1 when meiosis is perturbed in mutants that impair chromosome pairing, synapsis and crossover recombination leads to enhanced meiotic dysfunction, including elevated embryonic lethality (Fig 5E), impaired RAD-51 stability (Fig 7), and alteration of COSA-1 numbers and the crossover landscape (Fig 8). These results suggest that BRC-1-BRD-1 plays a critical role in meiotic recombination when meiosis is impaired.
In both C. elegans and Drosophila melanogaster, preventing crossover formation on a subset of chromosomes leads to additional events on other chromosomes, and is referred to as the interchromosomal effect [50, 98–101]. There is also evidence in humans that Robertsonian translocations elicit the interchromosomal effect [102]. Our analyses of the zim-1 mutant, where chromosomes II and III fail to recombine, revealed elevated COSA-1 foci genome wide and an increase in genetic crossovers on chromosome V (but not the X chromosome, Fig 8), consistent with the interchromosomal effect. Further, our data suggest that when meiosis is impaired as in syp-1, and perhaps zim-1 mutants, COSA-1 can mark events that do not ultimately become interhomolog crossovers (see also [59]). Interestingly, removal of BRC-1-BRD-1 in zim-1 and syp-1 mutants decreased the number of COSA-1 foci. On the other hand, in the brc-1(tm1145); zim-1 mutant we detected elevated levels of single crossovers but reduced levels of two-strand double crossovers on chromosome V compared to the zim-1 single mutant, with no change in the overall map length (Fig 8D). One possibility to explain the observed COSA-1 and crossover pattern is that COSA-1 does not become enriched on a subset of crossovers in brc-1; zim-1 mutants even though these events are dependent on the canonical meiotic crossover pathway, as observed in the rtel-1 and dyp-28 mutants [30, 103–105]. Alternatively, the extra crossovers that are not marked by COSA-1 in the absence of BRC-1-BRD-1 may be the result of activation of alternative crossover formation pathways. In either scenario, BRC-1, and presumably BRD-1, appear to dictate the patterning of crossovers among non-sister chromatids. As interference is mediated by meiotic chromosome structure [106], perhaps SC-associated BRC-1-BRD-1 counteracts chromatid interference in the context of meiotic dysfunction.
In conclusion, our results suggest that BRC-BRD-1 serves a critical role in monitoring the progression of meiotic recombination in the context of the SC when meiosis cannot proceed normally, suggesting that BRC-1-BRD-1 serves a checkpoint function. When crossover formation is blocked, BRC-1-BRD-1 stabilizes the RAD-51 filament and promotes processing of recombination intermediates marked by COSA-1. In this context, BRC-1-BRD-1 joins a growing list of proteins that monitor meiotic recombination to promote accurate chromosome segregation, including protein kinases and ubiquitin/SUMO E3 ligases [39, 56, 57, 107–110]. Future work will examine the relationship between BRC-1-BRD-1 and other meiotic checkpoint pathways and identify substrates of BRC-1-BRD-1-ubiquitination to understand how this complex modulates recombination under conditions when meiosis is perturbed.
Fluorescent protein knock-ins were generated using CRISPR-mediated homology dependent repair with self-excising cassette containing hygromycin resistant as selection [20]. The brc-1(xoe4) deletion allele was generated using Cas9-snRNPs and an single strand oligonucleotide repair template [111]. Cas9 protein was purchased from Innovative Genomics Institute, UC Berkeley. For a list of sgRNAs and repair templates refer to S2 Table. All CRISPR-generated lines were back crossed a minimum of three times, with the exception of JEL744 brc-1(xoe4) brd-1::gfp, which was only back crossed once.
C. elegans var. Bristol (N2), was used as the wild-type strain. Other strains used in this study are listed in S3 Table. Some nematode strains were provided by the Caenorhabditis Genetics Center, which is funded by the National Institutes of Health National Center for Research Resources. Strains were maintained at 20°C.
Embryonic lethality in the absence or presence of 5mM hydroxyurea (HU) (16 hrs), or 75 Grays (Gy) of γ-irradiation (IR) from a 137Cs source, was determined over 3 days by counting eggs and hatched larvae 24 hr after removing the hermaphrodite and calculating percent as eggs/ (eggs + larvae); male progeny was assessed 48 hr after removing the hermaphrodite. A minimum of 10 worms were scored for each condition.
Acridine orange (AO) staining of apoptotic germ cells in WT (N2), brc-1 and brd-1 alleles as well as zim-1 and corresponding double and triple mutants were performed as in [48]. Briefly, 0.5 ml of 50 mg/ml AO (Molecular Probes, Invitrogen; Carlsbad, CA) in M9 was added to 60-mm plates containing 48 hr post L4 worms and incubated at room temperature for 1 hr. Worms were transferred to new 60-mm plates, allowed to recover 15 min, and then mounted under coverslips in M9 on 3% agarose pads containing 1 mM tetramisole (Sigma-Aldrich; St. Louis). Apoptotic bodies were scored by fluorescence microscopy and DIC.
Gonads were dissected and fixed with 1% paraformaldehyde in egg buffer plus 0.01% Tween20 for 5 min, freeze-cracked and post-fixed in either ice-cold 100% methanol for indirect immunofluorescence, or ice-cold 100% ethanol for direct fluorescence (GFP::BRC-1, TagRFP-T::BRC-1, BRD-1::GFP, GFP::RPA-1, GFP::COSA-1) for 1 min [112]. For staining with antibodies against phospho-SYP-4, gonads were dissected, freeze-cracked, incubated in 100% methanol for 1 min and post-fixed in 4% paraformaldehyde in PBS, 80mM HEPES(pH7.4), 0.8mM EDTA, 1.6mM MgS04 for 30 min [40]. The following primary antibodies were used at the indicated dilutions: rabbit anti-RAD-51 (1:10,000; Catalog #29480002; Novus Biologicals, Littleton, CO), rabbit anti-GFP (1:500; NB600-308; Novus Biologicals, Littleton, CO), goat anti-SYP-1 (1:200; generously provided by Anne Villeneuve); rabbit anti-phospho-SYP-4 (1:100; [40]), and guinea pig anti-SUN-1 S12P (1:1,000; generously provided by Verena Jantsch). Secondary antibodies Alexa Fluor 594 donkey anti-rabbit IgG, Alexa Fluor 555 donkey anti-goat IgG, Alexa Fluor 488 donkey anti-rabbit IgG, Alexa Fluor 488 goat anti-guinea pig IgG from Life Technologies were used at 1:500 dilutions. DAPI (2μg/ml; Sigma-Aldrich) was used to counterstain DNA.
Collection of fixed images was performed using an API Delta Vision deconvolution microscope, a Nikon TiE inverted microscope stand equipped with an 60x, NA 1.49 objective lens and Andor Clara interline camera, or were captured on a spinning-disk module of an inverted objective fluorescence microscope [Marianas spinning-disk confocal (SDC) real-time 3D Confocal-TIRF (total internal reflection) microscope; Intelligent Imaging Innovations] equipped with an 63x, NA 1.46 objective lens using a Photometrics QuantiEM electron multiplying charge-coupled device (EMCCD) camera. Z stacks (0.2 μm) were collected from the entire gonad. A minimum of three germ lines was examined for each condition. Images were deconvolved using Applied Precision SoftWoRx or Nikon NIS Elements Offline batch deconvolution software employing either “Automatic3D” or “Richardson-Lucy” deconvolution modes and subsequently processed and analyzed using Fiji (ImageJ) (Wayne Rasband, NIH).
RAD-51 foci were quantified in a minimum of three germ lines of age-matched hermaphrodites (18–24 hr post-L4). As zim-1 mutants have an extended transition zone [42], we divided germ lines into four equal zones from the beginning of the transition zone (leptotene/zygotene), as counted from the first row with three or more crescent-shaped nuclei, through diplotene (Fig 6B). The number of foci per nucleus was scored for each region.
To assess formation of RAD-51 foci following IR treatment, 18–24 hrs post-L4 worms were exposed to 10 Gy of IR; gonads were dissected 1, 4, 8, and 12 hr following IR treatment and fixed for immunofluorescence as above.
GFP::COSA-1 foci were quantified from deconvolved 3D data stacks; nuclei were scored individually through z-stacks to ensure that all foci within each individual nucleus were counted. Nuclei with features indicative of apoptosis (compact and DAPI-bright) were excluded. Foci were counted in the last five rows of pachytene nuclei as in [30].
For live cell imaging, 18–24 hr post L4 hermaphrodites were anesthetized in 1mM tetramisole and immobilized between a coverslip and an 2% agarose pad on a glass slide. Z-stacks (0.33 μm) were captured on a spinning-disk module of an inverted objective fluorescence microscope (NIH 1S10RR024543) with a 100×, NA 1.46 objective, and EMCCD camera. Z-projections of stacks were generated, cropped, and adjusted for brightness in Fiji.
Pearson’s Correlation Coefficient (PCC) was determined by drawing a Region of Interest (ROI) around a nucleus and using the co-localization function in Fiji.
Whole worm lysates were generated from indicated worms; unmated fog-2(q71) worms were used to eliminate embryos. ~100 worms were collected, washed in M9 buffer and resuspended in equal volume of 2X Laemmli sample buffer (Bio-RAD). Lysates were resolved on 4–15% SDS-PAGE gradient gels (Bio-RAD) and transferred to Millipore Immobilon-P PVDF membranes. Membranes were blocked with 5% nonfat milk and probed with rabbit anti-GFP (1:1000; NB600-308; Novus Biologicals, Littleton, CO) and mouse anti-α-tubulin (1:1000; Sigma-Aldrich; T9026) as loading control, followed by IRDye680LT- and IRDye800-conjugated anti-rabbit and anti-mouse IgG secondary antibodies (1:20000; LI-COR Bioscience Lincoln, NE). Immunoblots were imaged on a LI-COR Odyssey Infrared Imager, signal was quantified using Fiji and normalized with the α-tubulin signal.
RNA-mediated interference (RNAi) was performed at 20°C, using the feeding method [113]. Cultures were plated onto NGM plates containing 25 μg/ml carbenicillin and 1 mM IPTG and were used within 2 weeks. L4 worms were transferred to RNAi plates, and resulting progeny were exposed to IR as described above. The efficiency of RNAi was tested in parallel by examining embryonic lethality.
Meiotic crossover frequencies and distribution were assayed utilizing single-nucleotide polymorphism (SNP) markers as in [114]. The SNP markers located at the boundaries of the chromosome domains were chosen based on data from WormBase (WS231) and [64], and are indicated in Fig 8D. The SNP markers and primers used are listed in [86]. PCR and restriction digests of single embryo lysates were performed and confirmed with additional SNPs as described in [115, 116] (Fig 8D). Statistical analyses were performed using the two-tailed Fisher's Exact test, 95% C.I., as in [117, 118]. For statistical analyses of interference we conducted χ2 tests on 2-by-2 contingency tables of observed and expected DCOs [119].
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10.1371/journal.pgen.1003813 | The Molecular Mechanism of a Cis-Regulatory Adaptation in Yeast | Despite recent advances in our ability to detect adaptive evolution involving the cis-regulation of gene expression, our knowledge of the molecular mechanisms underlying these adaptations has lagged far behind. Across all model organisms, the causal mutations have been discovered for only a handful of gene expression adaptations, and even for these, mechanistic details (e.g. the trans-regulatory factors involved) have not been determined. We previously reported a polygenic gene expression adaptation involving down-regulation of the ergosterol biosynthesis pathway in the budding yeast Saccharomyces cerevisiae. Here we investigate the molecular mechanism of a cis-acting mutation affecting a member of this pathway, ERG28. We show that the causal mutation is a two-base deletion in the promoter of ERG28 that strongly reduces the binding of two transcription factors, Sok2 and Mot3, thus abolishing their regulation of ERG28. This down-regulation increases resistance to a widely used antifungal drug targeting ergosterol, similar to mutations disrupting this pathway in clinical yeast isolates. The identification of the causal genetic variant revealed that the selection likely occurred after the deletion was already present at high frequency in the population, rather than when it was a new mutation. These results provide a detailed view of the molecular mechanism of a cis-regulatory adaptation, and underscore the importance of this view to our understanding of evolution at the molecular level.
| Evolutionary adaptation is the process that has given rise to the ubiquitous, yet remarkable, fit between all living organisms and their environments. The molecular mechanisms of these adaptations have been a subject of great interest, but we still know very little about their mechanisms, particularly in the case of regulatory adaptations. In this work, we investigate the molecular mechanism of a regulatory adaptation that we previously identified in ERG28, a component of the ergosterol biosynthesis pathway in budding yeast. Ergosterol is an abundant lipid component of the fungal plasma membrane, and is of major biomedical importance, being targeted by numerous antifungal drugs. We identified the causal mutation underlying the ERG28 adaptation, a two-base deletion in its promoter which leads to lower abundance of its mRNA. This deletion acts via disrupting the binding of at least two transcription factors, Mot3 and Sok2, to the promoter. The deletion increases resistance to a widely used antifungal drug, Amphotericin B, which targets ergosterol. This effect is reminiscent of misregulation of the ergosterol pathway observed in clinical yeast isolates that have evolved resistance to Amphotericin B. Our results may therefore have medical implications, while also advancing our basic understanding of evolutionary mechanisms.
| Evolutionary adaptation is the process that has given rise to the ubiquitous, yet remarkable, fit between all living organisms and their environments [1]. The origins of these adaptations at the molecular level have been a subject of great interest, with active debate surrounding the relative roles of two major classes of molecular mechanism: changes in protein sequences vs. changes in the expression levels/patterns of those proteins [2]–[5]. Until recently, the evidence cited in favor of both mechanisms was either anecdotal (involving studies of single genes) or theoretical in nature [2]–[4]. However, the advent of methods for characterizing gene expression adaptation genome-wide [6]–[9] (as well as methods for measuring cis-regulatory changes that may or may not be adaptive [10]–[11]) has paved the way for this question to be addressed in an unbiased, systematic fashion [5].
Although the distinction between protein sequence vs. gene expression regulation is important, it is only one of many levels at which molecular mechanisms can be distinguished. For example among cis-regulatory adaptations, mutations might act via alterations in transcription factor (TF) binding, nucleosome positioning, mRNA processing, binding of RNA-binding proteins, etc. As the field matures, it is likely that the distinctions between these more detailed mechanistic levels will be of increasingly greater interest, since only by investigating these mechanisms will we fully understand the nature of adaptation at the molecular level.
In order to investigate the molecular mechanism of an adaptation, it is generally necessary to first identify the causal mutation(s) (though see [12]). This prerequisite has been a significant bottleneck in studies of cis-regulatory adaptation. Because we cannot computationally predict the effects of most non-coding mutations, and such mutations can act at long distances from their target genes in many species (resulting in a large search space), only a handful of causal mutations underlying cis-regulatory adaptations have been reported. For example, large deletions of an enhancer driving the pelvic expression of the Pitx1 gene in sticklebacks have been found to result in adaptive pelvic reduction in freshwater populations [13]. In another case, five non-coding mutations at the ebony locus contributed to dark abdominal pigmentation found in high-altitude populations of Drosophila melanogaster [14] (although other examples exist where causal cis-regulatory mutations have been identified [15]–[17], these have not been shown to be adaptive). However even for these intensively studied cis-regulatory adaptations, and others where important factors such as fitness effects have been estimated [18], the molecular mechanisms by which the causal mutations act—e.g. which TFs and/or epigenetic states are affected by the mutations—remain unknown.
We previously reported a genome-wide scan for gene expression adaptation between two strains of the budding yeast Saccharomyces cerevisiae: a laboratory strain (BY4716, hereafter “BY”) and a vineyard strain (RM11-1a, hereafter “RM”) [8]. We found that over 200 genes had likely been subject to recent positive selection in these strains via reinforcing cis and trans-acting regulatory adaptations. Among these genes, there was a particularly strong enrichment of down-regulating mutations in one metabolic pathway: ergosterol biosynthesis. Ergosterol is an abundant lipid component of the fungal plasma membrane, and is of major biomedical importance, being targeted by numerous antifungal drugs [19]. Indeed, a common mechanism of resistance to ergosterol-targeting drugs (such as amphotericin B) is reducing ergosterol levels via disruption of this pathway [19]–[21]. We previously found that six genes within the pathway (underlined and red in Figure 1A) showed the strongest signs of selection, based on patterns of reinforcing cis/trans-regulatory mutations, as well as a population-genetic signature of selective sweeps in the genomes of multiple strains [8]. This represents the first known example of a polygenic gene expression adaptation, from any species. Here, we sought to gain a deeper understanding of this adaptation.
Because our initial identification of the polygenic gene expression adaptation within the ergosterol (ERG) biosynthesis pathway was based on expression data from genome-wide microarrays [22], we first sought to more precisely measure the cis-regulatory divergence at these loci. This divergence can be measured for any gene as the ratio of mRNA abundances of the two alleles present in a hybrid diploid: in the absence of cis-acting differences, the mRNA from the two alleles will be present in equal amounts (as they are in the genomic DNA), whereas they will be unequal in the presence of cis-regulatory divergence. To measure this ratio we employed pyrosequencing, a method that accurately quantifies allelic ratios at individual heterozygous sites [23].
Of the six genes we previously implicated, five were amenable to this approach (the sixth, ERG26, lacked any BY/RM sequence differences in its mRNA, so the alleles could not be distinguished). All five showed reproducible allelic imbalance in the expected direction (lower expression from the BY allele), with magnitudes ranging from 1.13-fold to 1.94-fold (Figure 1B). This result confirms that the “local eQTL” (genetic markers showing a statistical association with a nearby gene's expression level) previously mapped for these genes [22] likely represent cis-acting genetic variants.
To investigate if the polygenic adaptation extends beyond the six genes we originally identified, we also performed pyrosequencing on three additional ERG genes adjacent in the pathway to those already implicated: ERG25, ERG27, and ERG2 (allelic bias of ERG1, the other adjacent pathway member, could not be measured because it has no sequence differences between BY and RM). We found reproducible allelic bias in favor of RM for both ERG25 and ERG27, but not for ERG2 (Figure 1B). This suggests that the adaptive down-regulation extends to a total of at least eight genes, forming a contiguous block within the ERG pathway (Figure 1A, in red) that has been specifically targeted by natural selection.
Interestingly, in addition to the clear clustering of the down-regulated genes within the pathway, the genes with the strongest cis-regulatory differences correspond precisely to the core proteins in a stable complex organized by Erg28. Erg28 is the only known member of the ERG pathway lacking enzymatic activity; it is an endoplasmic reticulum transmembrane protein, highly conserved across eukaryotes (including humans), that acts as a scaffold promoting co-localization of ERG enzymes [24]–[26]. Erg28 physically interacts most strongly with Erg27 (and is thus shown next to Erg27 in Figure 1), but has also been found to interact strongly with itself and three other proteins: Erg25, Erg6, and Erg11; its other interactions are significantly weaker [24]. These five interacting proteins are not only all components of the polygenic adaptation (Figure 1), but are specifically those components with the strongest cis-acting down-regulation: all five have at least 1.25-fold differences between RM and BY alleles, while no other genes quite reach this threshold (Figure 1B). This pattern suggests that the precise magnitude of down-regulation may be influenced both by pathway position and by membership in the protein complex organized by Erg28.
We decided to focus on ERG28 for further investigation. Not only is Erg28 the central member of the protein complex apparently targeted by natural selection, but sequence divergence in its promoter region was also minimal: there are only two sequence differences between BY and RM in the 590 bp upstream of the ERG28 transcription start site (TSS). These are one two-bp deletion (located in an 11 bp poly-A tract 112 bp upstream of the TSS, termed the AA112Δ allele), and one T/C SNP (229 bp upstream of the TSS, the T229C allele) (Figure 2A). Because promoters in S. cerevisiae are compact (generally <400 bp [27]), we decided to focus on these two candidate variants.
To definitively identify the mutation(s) underlying a cis-regulatory adaptation, the mutations must be individually tested for their effects on expression of the associated gene. Therefore we constructed allelic replacement strains in which individual BY variants were introduced into the RM genome. Using a method of in vivo site-directed mutagenesis known as delitto perfetto [28], we engineered strains that differed only by the desired mutation. We refer to the two resulting strains as RM AA112Δ and RM T229C (Figure 2b).
If a mutation can fully account for the 1.30-fold cis-acting difference between the RM/BY alleles of ERG28 (Figure 1B), and no additional mutations have any effect, then this mutation can be deemed causal. To test if this was the case for either of our candidate mutations, we measured the expression level of ERG28 in each strain, as well as in wild-type RM, by quantitative PCR (qPCR). While we found no effect of the T229C mutation (1.05-fold difference), we observed that the AA112Δ mutation led to a 1.26-fold decrease in mRNA level (Figure 2c), indistinguishable from the 1.30-fold change expected for the causal mutation(s).
To further test if the AA112Δ mutation could fully account for the RM/BY difference, we mated the RM AA112Δ strain with BY, and measured the allelic ratio of ERG28 mRNA in the resulting diploid strain. The causal mutation would be expected to reduce the 1.30-fold allelic difference to ∼1, while any non-causal mutation would have the same the 1.30-fold allelic imbalance found in the BY/RM hybrid. Consistent with the qPCR results, the RM AA112Δ/BY hybrid strain showed a 1.03-fold difference between alleles, while the RM T229C/BY hybrid showed a 1.27-fold difference (Figure 2d). Together, these results suggested that the AA112Δ mutation likely accounted for all, or nearly all, of the cis-acting divergence at ERG28 between RM and BY.
We considered two potential mechanisms for how the AA112Δ mutation may be down-regulating transcription: nucleosome positioning and TF binding. Both processes are known to play important roles in determining rates of transcription initiation, and could potentially be affected by a 2-bp deletion.
Nucleosome positioning was an especially plausible mechanism because the 11-bp poly-A sequence in which the 2-bp deletion occurred is a strong nucleosome-disfavoring sequence [29]. Therefore we took advantage of published data on genome-wide nucleosome positions from BY and RM [30] to determine whether the nucleosome overlapping the deletion was affected. There was no significant difference between BY and RM in the nucleosomal occupancy or positioning at this location (nor was it differentially acetylated on histone H3 lysine 14 [30]), suggesting that nucleosome occupancy was not greatly affected by this deletion.
We therefore turned to TF binding as a second possible mechanism. Utilizing a published map of putative TF binding sites [31] we identified two highly conserved (across Saccharomyces sensu stricto) binding sites for the TFs Mot3 and Sok2, flanking the deletion (Figure 3a). Mot3 is a well-known repressor of ERG pathway genes, exerting its greatest effect in hypoxic or hyper-osmotic conditions [32]–[33], whereas Sok2 has not been previously linked to the ERG pathway to our knowledge. Neither binding site motif is directly affected by the 2-bp deletion; rather the only effect is on their spacing, reducing the distance between motif centers from 16 bp to 14.
To test if the AA112Δ deletion may affect the regulation of ERG28 by either of these two TFs, we created knockout strains for each TF in both the wild-type RM and RM AA112Δ backgrounds. Several outcomes are possible (Figure 3b). First, if the TF does not regulate ERG28, then deleting it should have no effect in either genetic background. Second, if the TF does regulate ERG28 but is not affected by the AA112Δ deletion, then the effect of TF deletion should be equal in the two backgrounds. Finally, if the AA112Δ deletion is affecting the TF's regulation of ERG28, then the effect of TF deletion will depend on the background—for example, having an effect on ERG28 expression in wild-type RM but not in RM AA112Δ.
Consistent with Mot3's known role as a repressor of ERG pathway genes, we found that ERG28 was induced 1.85-fold in an RM mot3Δ strain compared with wild-type RM (Figure 3c). Likewise, Sok2 was found to be an activator of ERG28, with 1.21-fold lower expression in RM sok2Δ compared to wild-type RM. However neither TF had any measurable effect on ERG28 expression when deleted from the RM AA112Δ strain (Figure 3c). This suggests that although both TFs regulate ERG28 in RM, this regulation was abolished by the 2-bp deletion.
The effect of AA112Δ on regulation of ERG28 by Mot3 and Sok2 suggested that their binding to the promoter may be affected by the deletion. To investigate this, we performed chromatin immunoprecipitation (ChIP). Specifically, we HA-tagged both TFs in both wild-type RM and RM AA112Δ backgrounds, and quantified their binding to specific regions by quantitative PCR (qPCR). We found that for both factors, binding at the ERG28 promoter was reduced in RM AA112Δ, compared to wild-type RM: Sok2 showed ∼19-fold lower binding, while Mot3 had ∼31-fold lower binding (Figure 3d). This suggests that the loss of ERG28 regulation by these TFs in the AA112Δ background (Figure 3c) is likely due to their severely reduced binding.
In order to investigate the phenotypic effects of the AA112Δ allele, we measured the growth rates of our engineered strains and RM in several environments (see Materials and Methods). While we did not observe any fitness advantage of the RM AA112Δ strain in most conditions (e.g. rich synthetic defined [SD] media; paired t-test p = 0.46 for RM AA112Δ vs. RM and p = 0.83 for RM T229C vs. RM; Figure 4a), we did find a growth advantage of this strain in the presence of the antifungal drug amphotericin B (Figure 4b). Specifically, RM AA112Δ had a 1.3% higher growth rate than RM when grown in the presence of the drug (p = 0.014), whereas RM T229C had no measurable difference from RM (p = 0.86). This suggests that the fitness benefit conferred by the AA112Δ allele is condition-specific.
Our identification of the AA112Δ allele as causal allows us to examine the distribution of this adaptive mutation across other yeast strains, in order to study its history. In particular, we wished to address the question of whether the selection occurred when the deletion was a new mutation that just recently arose (e.g. in the laboratory), or whether it was present as “standing variation” in S. cerevisiae for some time before the selection occurred. Population geneticists have theorized about the consequences of selection acting on pre-existing variation, as opposed to waiting for rare advantageous mutations to occur, but few clear examples exist [34]–[36].
To distinguish between these alternatives, we first examined the distribution of the AA112Δ allele across a set of 36 sequenced strains of S. cerevisiae [37]. The deletion is present in 12/36 sequenced strains (in addition to BY; Figure S1). These 12 strains are diverse in terms of both geography (from the Americas, Asia, Africa, and Europe) and lifestyle (lab strains, wild strains, sake strains, palm wine strains, and other fermentation strains). Furthermore they are genetically diverse, as evidenced by their lack of clustering within the S. cerevisiae phylogeny (Figure S1). This broad distribution across the species suggests that the AA112Δ allele is present at appreciable frequency in many populations of S. cerevisiae.
To further investigate this, we sequenced the ERG28 promoter in EM93, the wild strain that accounts for ∼88% of the BY genome [38]–[39]. Since EM93 is a diploid, we sequenced the promoter in the four spores from a single EM93 tetrad, in order to capture both alleles with no ambiguity. We found that the AA112Δ mutation was heterozygous within EM93, supporting our inference that it is commonly found in the wild. Together, these results suggest that the selection on ERG28 in the BY lineage [8] was likely acting on standing variation, as opposed to a new mutation. Because EM93 is heterozygous, we can infer the selective sweep most likely occurred in the descendants of EM93, after its introduction to the laboratory.
To attempt a similar analysis for the seven other ERG genes involved in this adaptation (Figure 1A), we sequenced their promoters in the same four EM93 spores. Because we do not know the causal variants, we performed this analysis at the level of promoter haplotypes (sets of co-occurring alleles). We found that for all seven genes, the complete BY promoter haplotype was either homozygous (for two genes, ERG25 and ERG26) or heterozygous (for five genes) in EM93, indicating that their cis-acting down-regulations were likely not due to new mutations occurring in the lab. Each of these BY haplotypes was also observed in between zero and six additional sequenced strains, indicating that some of the haplotypes are segregating at an appreciable frequency in S. cerevisiae. However the absence of a complete BY haplotype does not imply the absence of the causal BY variant, since most ERG promoter variants are not in perfect linkage disequilibrium with their neighboring variants. For example, although the AA112Δ variant was found in 12 strains (Figure S1), only five of these also had the T229C variant (and thus the complete BY promoter haplotype). This highlights the importance of identifying causal variants in order to study the evolutionary histories of specific adaptations.
We have identified the causal mutation underlying a cis-regulatory adaptation that affects the ergosterol biosynthesis pathway in yeast, and characterized its molecular mechanism of action. The mutation, a 2 bp promoter deletion, reduces the expression of ERG28 by ∼1.3-fold. This effect is mediated by two TFs, Mot3 and Sok2, which bind immediately adjacent to the deletion; these TFs bind and regulate the wild-type RM ERG28 promoter, but not the ERG28 AA112Δ promoter.
Although it may seem surprising that a 2 bp deletion outside of TF binding sites can have such a strong effect on TF binding, it is consistent with previous work. First, most between-strain variation in the binding of the Ste12 TF in yeast cannot be linked to variation in any known TF motif, even when only considering those binding sites where occupancy was associated with nearby genetic markers [40]. Second, it was recently shown that changes in the positions of TF binding sites as small as 1–2 bp can result in substantial (>1.5-fold) effects on transcription [41]. Finally, minor changes in the copy number of very short tandem repeats in yeast promoters can also impact transcription [42].
It is also at first counterintuitive that decreased binding of a repressor (Mot3) could contribute to the down-regulation of ERG28 by AA112Δ, in particular since the repressive effects of Mot3 appear to be stronger than the activation by Sok2 (Figure 3C). We hypothesize that the AA112Δ mutation may have altered the TF binding landscape upstream of ERG28, not only for Mot3 and Sok2, but possibly for other TFs or their cofactors as well. The deletion's effect on transcription would then be determined by this altered landscape.
In addition to the focus on ERG28, our results also further characterize the polygenic ERG pathway adaptation as a whole. We found that two genes not implicated in our previous analysis of microarray data [8], ERG25 and ERG27, also show reduced expression from the BY allele (compared to RM). Moreover, our precise measurements of the cis-acting effect size for each ERG gene led us to an intriguing discovery: the five proteins that form the core of a complex at the ER membrane are also the five with the strongest cis-regulatory change. This pattern suggests an exquisite specificity of selection, in which the precise level of down-regulation is determined not only by position within the pathway, but also by membership in a particular protein complex.
While a handful of causal mutations underlying cis-regulatory adaptations in other model organisms have been previously reported [13]–[14], their molecular mechanisms are unknown. Compared to these, our knowledge of the ERG28 AA112Δ mutation is now relatively detailed, though still incomplete; for example, how the deletion disrupts binding has not been established. A plausible explanation is that Sok2 and Mot3 may bind cooperatively to the ERG28 promoter in wildtype RM; if this cooperativity is disrupted by the 2-bp deletion (which brings the binding sites ∼6.8 Å closer together and changes their relative angles by ∼70°), then neither factor would bind well to the AA112Δ promoter.
At the phenotypic level, we found that AA112Δ confers a condition-specific growth advantage in the presence of the antifungal drug amphotericin B. Because the AA112Δ mutation may also lead to a fitness advantage in other environments that were not tested, we cannot conclude whether amphotericin B is related to the specific selection pressure that gave rise to the ERG pathway adaptation in BY. However our results are quite consistent with previous observations that the down-regulation or inactivation of ERG pathway genes confers resistance to amphotericin B in diverse clinical yeast isolates [19]–[21]. Thus in addition to aiding our understanding of the molecular mechanisms of cis-regulatory adaptation, our results may shed light on potential mechanisms by which antifungal drug resistance can evolve.
We carried out all strain engineering in RM, as opposed to BY, because BY contains a very recent loss-of-function transposon insertion in the transcription factor HAP1, which alters the regulation of many ERG genes, including ERG28. Because this mutation was so recent (not even present in the very closely related lab strain W303 [8]), it must have happened after the ERG28 cis-regulatory adaptation, so the functional HAP1 in RM should more accurately reflect the original effects of any cis-regulatory mutations.
In vivo site-directed mutagenesis, known as delitto perfetto, was performed as described [27]. Briefly, the pCORE-UH cassette, containing K. lactis URA3 and hyg, was amplified using primers containing ∼70 bp of homology to the RM ERG28 promoter (Table S1). This PCR product was transformed into RM, and correct incorporation into the ERG28 promoter was verified by PCR. The site of incorporation was chosen in between the two candidate genetic variants, so that the same CORE cassette transformant could be used for engineering both mutations. The CORE cassette was then removed by separately transforming two PCR products from the BY ERG28 promoter, containing the desired mutation (either AA112Δ or T229C) as well as enough flanking DNA sequence (identical between RM and BY) to allow specific targeting of the PCR product. Because the efficiency of delitto perfetto is maximized when transforming longer DNA molecules, as well as double-stranded DNA [20], transforming long PCR products from BY (as opposed to shorter, single-stranded synthetic oligonucleotides) is a useful modification. Counter-selection of the resulting transformants on 5-FOA allowed isolation of successfully engineered strains that had replaced the CORE cassette with the desired mutation, which were then sequence-verified.
The complete coding regions of MOT3 and SOK2 were replaced with the hphMX6 antibiotic resistance gene via PCR-mediated gene disruption [43] in both RM and RM AA112Δ. Transformants were grown on hygromycin B, and verified by PCR. These two TFs were also HA-tagged at their C-termini via transformation of a PCR product including the HA tag, hphMX6, and flanking regions with 40 bp of homology to the targeted regions [43]. Transformants were grown on hygromycin B, and then verified by PCR and sequencing.
Table S2 lists all strains used in this work.
With the exception of growth rate experiments (Figure 4), all strains were grown in standard YPD media at 30°C, and harvested in log-phase (OD600 ∼1) for either RNA extraction or chromatin immunoprecipitation.
We extracted total RNA with the Epicentre Biotechnologies RNA Purification kit, which includes a DNase treatment to remove contaminating genomic DNA. RNA concentration was quantified with a NanoDrop2000 spectrophotometer. For cDNA synthesis, total RNA samples were diluted to a concentration of 500 ng/µL. RNA was reverse transcribed into cDNA with SuperScript III RT (Invitrogen), following manufacturer protocols.
Pyrosequencing was performed on a PyroMark Q24 (Qiagen), following manufacturer's protocols. Primers (Table S1) were designed to target individual SNPs in transcribed regions using the PyroMark Assay Design Software (Qiagen). Negative controls using no primers, or no cDNA template, were performed for each assay.
cDNA was diluted 1∶100 prior to qPCR. qPCR was performed on an Eco Real-Time PCR machine (Illumina) following manufacturer's protocols. To quantify changes in ERG28 mRNA abundance, six control genes previously noted for their stability across conditions [44] were measured in each experiment: ACT1, TDH3, ALG9, TAF10, TFC1, and UBC6. All experiments were done in at least biological triplicate and technical duplicate. Experiments in Figure 3c were done in biological sextuplicate and technical quadruplicate. Data were analyzed using qBase Plus software (Biogazelle) [45].
Chromatin immunoprecipitation (ChIP) was performed essentially as described [46]. Briefly, wildtype cells and cells expressing either Mot3-HA or Sok2-HA were grown to mid-log phase in 100 mL YPD. Cross-linking was performed by treating yeast with 1% formaldehyde for 15 minutes at 25°C. Chromatin was isolated from whole-cell extracts generated by spheroplasting and sheared by sonication. Immunoprecipitation was performed from 5 µg chromatin using mouse monoclonal anti-HA (Invitrogen, clone 5B1D10) and immune complexes were captured with Ultralink Immobilized Protein A/G resin (Pierce). Protein-DNA complexes were eluted with 1% SDS/0.1 M NaHCO3. Eluates were incubated at 65°C overnight to reverse cross-links and treated with proteinase K (Invitrogen) and RNAse A. DNA was phenol-chloroform extracted, ethanol-precipitated, and resuspended in water prior to qPCR.
ChIP DNA was amplified on an Eco Real-Time PCR machine (Illumina) following manufacturer's protocols. We quantified the abundance of the ERG28 promoter region containing the Mot3 and Sok2 binding sites, as well as part of the ACT1 coding region as a control to quantify the amount of DNA in each reaction. The concentration of ERG28 promoter DNA was normalized against this control before comparing across genetic backgrounds (RM vs. RM AA112Δ) for each TF.
To perform quantitative growth rate measurements (Figure 4), we grew strains in 96-well plates and measured OD600 at 15-minute intervals using an automated plate reader (Tecan) until cultures reached saturation. Data shown in Figure 4 are the mean log2 ratios of the maximum log-phase growth rates (estimated by Magellan software, Tecan) for 48 replicate growth curves of each strain. Growth conditions were SD media alone or 0.8 ug/ml amphotericin B in SD media, both at room temperature (22°C). P-values were calculated using a paired t-test, pairing wells in the same row on each plate. Other conditions tested in an initial screening phase were hyperosmotic stress (NaCl or menadione) and temperature stress (heat/freezing).
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10.1371/journal.pgen.1007356 | An interplay between multiple sirtuins promotes completion of DNA replication in cells with short telomeres | The evolutionarily-conserved sirtuin family of histone deacetylases regulates a multitude of DNA-associated processes. A recent genome-wide screen conducted in the yeast Saccharomyces cerevisiae identified Yku70/80, which regulate nonhomologous end-joining (NHEJ) and telomere structure, as being essential for cell proliferation in the presence of the pan-sirtuin inhibitor nicotinamide (NAM). Here, we show that sirtuin-dependent deacetylation of both histone H3 lysine 56 and H4 lysine 16 promotes growth of yku70Δ and yku80Δ cells, and that the NAM sensitivity of these mutants is not caused by defects in DNA double-strand break repair by NHEJ, but rather by their inability to maintain normal telomere length. Indeed, our results indicate that in the absence of sirtuin activity, cells with abnormally short telomeres, e.g., yku70/80Δ or est1/2Δ mutants, present striking defects in S phase progression. Our data further suggest that early firing of replication origins at short telomeres compromises the cellular response to NAM- and genotoxin-induced replicative stress. Finally, we show that reducing H4K16ac in yku70Δ cells limits activation of the DNA damage checkpoint kinase Rad53 in response to replicative stress, which promotes usage of translesion synthesis and S phase progression. Our results reveal a novel interplay between sirtuin-mediated regulation of chromatin structure and telomere-regulating factors in promoting timely completion of S phase upon replicative stress.
| Proliferating cells duplicate their genetic material via a highly-ordered process called DNA replication. Genetic lesions caused by a variety of environmental chemicals can inhibit DNA replication progression, thereby causing genetic abnormalities and cell death, as well as promoting the development of diseases such as cancer. To fit within the confines of the cell’s nucleus, DNA is wrapped around proteins called histones, which play critical roles in promoting accurate DNA replication. In this study, we reveal unexpected functional links between histones and cellular factors that regulate telomeres, which are structures protecting the extremities of DNA molecules in cells. We demonstrate that a functional interplay between histone- and telomere-regulating factors allow cells to duplicate their genetic material in a timely manner in the presence of replication-blocking chemicals. Overall, our study highlights new mechanisms through which proliferating cells avoid the deleterious consequences associated with compromised DNA replication.
| Histone post-translational modifications influence chromatin structure and serve as recruitment platforms for diverse protein complexes [1]. Acetylation of histones on lysine residues is catalysed by histone acetyltransferases (HAT) and reversed by histone deacetylases (HDAC). Four HDAC classes are defined based on sequence identity and catalytic mechanism [2]. Class III HDACs are referred to as sirtuins because of their sequence homology to yeast Sir2. These enzymes deacetylate lysine residues in histone and non-histone proteins in a reaction that requires nicotinamide adenine dinucleotide (NAD+) and releases nicotinamide and O-acetyl ADP ribose [3,4]. Sirtuins are evolutionarily conserved, and regulate several DNA-associated processes including gene silencing, DNA replication, and DNA repair [5].
The genome of the budding yeast Saccharomyces cerevisiae encodes 5 sirtuins: Sir2 and Homolog of Sir Two (Hst) 1–4 [6,7]. Sir2-dependent deacetylation of histone H4 lysine 16 (H4K16ac) controls gene silencing at the yeast mating and ribosomal DNA (rDNA) loci [7,8] as well as at telomeres [9], and modulates replicative lifespan [10,11]. Hst1 regulates sporulation gene expression [12,13], and also controls thiamine biosynthesis and intracellular NAD+ levels at the transcriptional level [14,15]. Hst2 displays partial functional redundancy with Sir2 as its overexpression can rescue silencing defects in sir2Δ mutants [16,17]. Hst3 and Hst4 reverse histone H3 lysine 56 acetylation (H3K56ac) [18], a modification catalyzed by the HAT Rtt109 on virtually all newly synthesized histones in yeast [19,20]. H3K56ac-harboring nucleosomes are assembled behind DNA replication forks to maintain appropriate nucleosomal density on daughter chromatids following parental histone segregation, and are deacetylated genome-wide by Hst3/4 during the G2/M phase. Cells lacking both Hst3 and Hst4 present constitutive H3K56ac throughout the cell cycle, which causes severe phenotypes including spontaneous DNA damage, chromosomal instability, elevated replicative stress and DNA damage-induced signaling, as well as extreme sensitivity to high temperature and drugs that impede DNA replication [18,21,22]. However, the precise molecular mechanisms by which Hst3/4-mediated H3K56ac deacetylation promotes resistance to replicative stress remain unclear.
The kinases Mec1 and Rad53 are activated during replicative stress to phosphorylate multiple substrates which cooperate to inhibit DNA replication origin activation, stabilize stalled replication forks, and increase dNTP pools [23]. Rad53 activation depends on the mediator protein Rad9, which is recruited to chromatin through interaction with phosphorylated serine 128 of histone H2A (γ-H2AX, a DNA damage-induced modification) and methylated histone H3 lysine 79 (H3K79me) [24–27], the latter being catalyzed by the methyltransferase Dot1 [28,29]. Recent data demonstrate that cells have evolved mechanisms that limit Rad53 activation upon replicative stress, as well as others that permit its progressive inactivation upon DNA lesion resolution [30–32]. While the precise consequences of Rad53 “hyperactivation” are incompletely characterized, its biological relevance is highlighted by the fact that it causes sensitivity to replicative stress-inducing drugs [31]. Interestingly, we and others have shown that limiting Rad53 activation via DOT1 deletion or histone gene mutations that inhibit H3K79me promotes resistance to DNA replication-blocking drugs in several yeast mutants, including hst3Δ hst4Δ cells, by elevating usage of error-prone translesion synthesis (TLS) [22,33–35]. These observations emphasize the importance of the interplay between chromatin and DNA damage checkpoint signalling in regulating the cellular response to replicative stress.
In eukaryotes, DNA replication is initiated in a temporally ordered manner at genomic regions called “origins” that are activated in early, mid, or late S phase [36]. Genomic context influences the timing of origin activation (or “firing”); for example, in the yeast S. cerevisiae origins located near telomeres and within rDNA repeats are activated during late S, while those close to centromeres fire earlier [37–40]. Interestingly, the silent information regulator (SIR) HDAC complex, which comprises the Sir2-Sir3-Sir4 subunits and deacetylates telomeric/subtelomeric chromatin, has been shown to prevent early firing of telomeric origins [41]. Telomere length also influences origin activity; indeed, cells with short telomeres, such as those lacking the Yku70/80 complex, initiate DNA replication at telomeric and subtelomeric regions abnormally early during S phase [42–44]. While several telomere-associated factors have been shown to influence the timing of telomeric DNA replication origin [43–46], the functional significance of such regulation is poorly understood.
The Yku70/80 complex is present at chromosomal ends where it protects telomeres from nucleolytic degradation and promotes recruitment of telomerase. Cells lacking Yku70/80 heterodimers present short but stable telomeres harboring abnormally long stretches of ssDNA [47–51]. Yku70/80 is also involved in DNA double-strand break (DSB) repair by non-homologous end joining (NHEJ). This complex binds DSB ends where it recruits the DNA ligase machinery composed of Lif1-Dnl4 and Nej1, thereby promoting end ligation [47,52,53]. Interestingly, cells lacking Yku70/80 are sensitive to genotoxins that generate DNA replication-blocking lesions without directly causing DSBs, suggesting an NHEJ-independent role for this complex during replicative stress [54,55]. However, the extent to which the other cellular functions of Yku70/80, e.g., at telomeres, might influence the cellular response to DNA replication stress is unclear.
A genetic screen conducted by our group in S. cerevisiae identified yku70Δ and yku80Δ mutants as sensitive to pharmacological inhibition of sirtuin HDACs by nicotinamide (NAM) [35]. Since NAM causes replicative stress [35], we originally postulated that the Yku70/80 complex might influence DNA replication progression in the absence of sirtuin activity. Here, we reveal that a novel interplay between multiple sirtuins promotes completion of DNA replication in yku70Δ and yku80Δ cells, and that telomere shortening is the root cause of the sensitivity of these mutants to NAM-induced sirtuin inhibition. Our data further indicate that misregulation of replication origin firing at short telomeres, as well as modulation of DNA damage checkpoint kinase activity by chromatin structure, influence the resistance to NAM- and genotoxin-induced replicative stress in cells with short telomeres.
S. cerevisiae yku70Δ and yku80Δ mutants are sensitive to NAM [35], a pan-sirtuin inhibitor [3,56]. To identify which among the five yeast sirtuins (Sir2, Hst1-4) are responsible for this phenomenon, single deletions of each sirtuin gene were combined with yku70Δ by mating, and double mutants isolated via tetrad dissection (S1A Fig). None of the double mutants displayed noticeable growth defects, suggesting that NAM-induced growth inhibition in yku70Δ mutants is likely due to concurrent inhibition of multiple sirtuins. We previously showed that deletion of the H3K56ac acetyltransferase RTT109 rescues the sensitivity of yku70Δ and yku80Δ mutants to NAM [35]. We therefore tested whether this reflects NAM-induced inhibition of the H3K56ac-deacetylases Hst3 and Hst4 [18]. Consistently, we found that yku70Δ hst3Δ hst4Δ cells displayed moderate but significant decrease in growth rate and doubling time compared to hst3Δ hst4Δ cells (Fig 1A and 1B), and that the H3K56A mutation, which prevents H3K56ac, allows proliferation of yku70Δ cells in NAM (S1B and S1C Fig). Nevertheless, growth of the yku70Δ hst3Δ hst4Δ mutant could still be significantly exacerbated by exposure to NAM (S2A Fig). Thus, while H3K56 hyperacetylation is an essential component of the NAM sensitivity of yku70Δ mutants, inhibition of sirtuins other than Hst3/4 also probably contribute to this phenomenon.
Mutations inhibiting H4K16 acetylation (H4K16ac), the levels of which are regulated by Sir2 and Hst1 in vivo [57,58], partially rescue certain phenotypes of hst3Δ hst4Δ cells [22]. Interestingly, deletion of SAS2, a gene encoding the catalytic subunit of the H4K16 acetyltransferase complex SAS-I [57,59,60], or mutation of H4K16 to alanine (H4K16A), rescued growth of yku70Δ cells in NAM (Fig 1C and 1D). We could not directly test whether reduced Sir2 activity exacerbates the growth defects of yku70Δ hst3Δ hst4Δ mutants since sir2Δ causes synthetic lethality when combined with hst3Δ hst4Δ [22,61]. Sir2 is recruited to rDNA repeats and HMR/HML/telomeres as part of either the RENT (Sir2/Cdc14/Net1) or SIR (Sir2/Sir3/Sir4) complexes, respectively [62–64]. We could not evaluate the impact of RENT subunit-encoding CDC14 or NET1 genes on hst3Δ hst4Δ cells since their deletion causes lethality. rDNA silencing defects arising from lack of Sir2 activity in the context of the RENT complex can be rescued by deletion of FOB1, which encodes a component of the rDNA replication fork barrier [10,65,66]. We found that deletion of FOB1 did not rescue the sensitivity of yku70Δ cells to NAM (S1D Fig); moreover, combining hst3Δ hst4Δ yku70Δ with either sir3Δ or sir4Δ did not cause synthetic growth defects (S1E Fig). Overall these results suggest that growth of yku70Δ cells depends on Hst3/4 and either i) on sirtuins other than Sir2, or ii) on Sir2-dependent processes that are not associated with the RENT or SIR complexes.
The Hst1-Sum1-Rfm1 complex promotes H4K16ac deacetylation in vivo [58]. Interestingly, deletion of either HST1 or SUM1 provoked synthetic growth defects when combined with hst3Δ hst4Δ yku70Δ (Fig 1E). Since constitutive hyperacetylation of H3K56 in hst3Δ hst4Δ mutants causes spontaneous DNA damage [18], we reasoned that elevated sensitivity to replicative stress in cells lacking both Hst1-Sum1-Rfm1 and Yku70/80 might explain the observed synthetic lethality. Consistently, deletion of SUM1 sensitized yku70Δ mutants to the DNA alkylating agent methylmethane sulfonate (MMS; Fig 1F). This was not the case for hst1Δ, implying that Hst1-independent Sum1 functions might influence growth of yku70Δ cells in MMS (S2B Fig). Published data indicate that Sir2 interacts with Sum1 to promote transcriptional silencing in the absence of Hst1 [67]. While deletion of SIR2 did not confer increased MMS sensitivity in yku70Δ mutants, the sir2Δ hst1Δ yku70Δ triple mutant was strongly sensitized to MMS compared to control double mutants (Fig 1F, S2B Fig). Moreover, the MMS sensitivity of sir2Δ hst1Δ yku70Δ was similar to that of sum1Δ yku70Δ, and was not further increased in sir2Δ hst1Δ sum1Δ yku70Δ cells (Fig 1F). We also note that sir2Δ hst1Δ yku70Δ mutants remain sensitive to NAM (S2C Fig), consistent with the notion that concurrent inhibition of multiple sirtuins, i.e., Sir2, Hst1, Hst3 and Hst4, causes the sensitivity of yku70Δ cells to this agent.
We next tested whether preventing H4K16 acetylation alleviates other phenotypes of cells lacking Yku70/80. In addition to their MMS sensitivity, yku70Δ mutants exhibit growth and DNA replication defects at elevated temperatures [68]. We found that the H4K16A and sas2Δ mutations rescued the MMS sensitivity of yku70Δ and yku70Δ sum1Δ mutants (Fig 1G and 1H, S2D Fig). The temperature sensitivity of yku70Δ cells could also be suppressed by mutations that reduce H4K16ac levels (Fig 1G and 1H, S2D Fig), and was exacerbated by sum1Δ in a H4K16ac-dependent manner (S2D Fig). Overall, our data indicate that the inability of yku70Δ mutants to grow in the presence of NAM results in part from lack of Sum1-Hst1/Sir2 activity, which leads to misregulation of H4K16ac levels and consequent sensitization to replicative stress caused by constitutive H3K56ac.
The Yku70/80 complex is required for both DNA repair by non-homologous end joining (NHEJ) and telomere maintenance [47,48]. NHEJ-abolishing mutations (lif1Δ, nej1Δ and dnl4Δ) did not cause notable growth defects in NAM (Fig 2A), indicating that the sensitivity of yku70Δ/80Δ mutants to this chemical is unlikely to result from defective DSB repair by NHEJ. Cells lacking Yku70/80 present very short, but stable, telomeres [48,49]. Pre-senescent haploid cells lacking the telomerase subunit-encoding genes EST1 and EST2 also present very short telomeres [47,69], and are as sensitive to NAM as yku70Δ cells (Fig 2B), suggesting that reduced telomere length might cause NAM sensitivity. On the other hand, the Yku70/80 complex promotes telomerase intracellular trafficking and recruitment to telomeres by binding to the TLC1 telomerase RNA [51,70–73], raising the possibility that defective recruitment of telomerase to telomeres, and not telomere length per se, might influence NAM sensitivity. Contrary to this notion, the yku80-135i mutation, which eliminates the TLC1-binding functions of Yku80, while only slightly reducing telomere length, did not sensitize cells to NAM (S3A and S3B Fig).
To further investigate the impact of telomere length on NAM sensitivity, we performed a time course experiment using a strain expressing an auxin-inducible degron (AID)-tagged YKU70 allele [74,75]. Auxin addition to the growth medium provoked rapid (within one hour) degradation of Yku70 and progressive telomere shortening over several days of cell growth, while auxin removal allowed rapid Yku70 re-expression and progressive telomere length recovery (Fig 2C and 2D, S4 Fig). We reasoned that if Yku70 activity/presence within the cell is important for NAM resistance, sensitivity to this agent should increase within hours of auxin treatment. In contrast, if telomere length homeostasis promotes NAM resistance, progressive decrease in telomere length caused by Yku70 depletion should lead to a concomitant increase in NAM sensitivity over several days of growth. At every time point analyzed, we tested the capacity of cells to grow in NAM with or without auxin, i.e., with or without Yku70 re-expression during NAM exposure (Fig 2E). Strikingly, NAM sensitivity correlated well with overall telomere length of the cell population, and re-expression of Yku70 during the growth assay (by omitting auxin in NAM-containing medium) did not reverse this trend. Further supporting the notion that telomere length, and not presence of Yku70/80 per se, is important to promote survival in NAM, expression of a Cdc13-Est1 chimera that bypasses Yku70/80 in telomerase-mediated telomere elongation [51,76] rescued growth of yku80Δ mutants in NAM (Fig 2F, S5 Fig). Finally, mutation of ELG1, which was shown to extend telomeres et re-establish telomeric origin repression in yku70Δ cells [44], also rescued the growth of yku70Δ mutants in NAM (S6A Fig). We note that NAM did not significantly affect telomere length in conditions used for our experiments (S6B Fig). Together, our results indicate that telomere length is an important determinant of NAM sensitivity in cells lacking Yku70/80.
Intriguingly, deletion of TEL1, despite rendering telomeres as short as those of yku70Δ or est1/2Δ mutants [77,78], did not provoke growth defects in NAM, and even rescued the NAM sensitivity of yku70Δ cells (Fig 3A). Telomeres of yku70Δ mutants, but not tel1Δ, are resected by the nuclease Exo1 and therefore accumulate ssDNA [49,79]. However, deleting EXO1 did not eliminate the growth defects of yku70Δ mutants in NAM (S3C Fig), indicating that elevated ssDNA at telomeres cannot explain the differential sensitivity of yku70Δ vs tel1Δ cells to NAM. In contrast to yku70Δ and yku80Δ mutants, cells lacking Tel1 activate their telomeric replication origins in late S; moreover, deletion of TEL1 restores late firing of telomeric origins in cells devoid of Yku70/80 [42–44,46]. To explore the possibility that Tel1-dependent activation of telomeric origins in early S might influence the response to NAM-induced replicative stress in yku70Δ mutants, we first evaluated the impact of Tel1 on S phase progression in yku70Δ cells. Strikingly, NAM-treated yku70Δ mutants accumulated in early-mid S in a Tel1-dependent manner, indicating that Tel1 influences global dynamics of DNA replication in yku70Δ cells experiencing replicative stress (Fig 3B). Pre-senescent telomerase mutants (est1Δ and est2Δ) also presented NAM-induced S phase progression defects which became worse over cell generations (Fig 3C), strongly suggesting a link between progressive shortening of telomeres and compromised DNA replication. Interestingly, we did not detect significant increase in the frequency of Rad52-YFP and Rfa1-YFP foci in yku70Δ cells exposed to NAM compared to WT, indicating that replication defects in cells with short telomeres are unlikely to result from elevated induction of DNA lesions in these conditions (Fig 3D and 3E). We note that replication proceeds slowly in telomeric regions even in the absence of exogenous DNA damage, presumably because non-histone protein complexes impede replication fork (RF) progression at these loci [80]. Indeed, cells devoid of the Rrm3 helicase, which promotes DNA replication across genomic regions harboring chromatin-bound protein complexes, display a 10-fold increases in the number of stalled RFs at telomeres [80]. We found that deletion of RRM3 caused synthetic MMS sensitivity when combined with yku70Δ (Fig 3F), suggesting that an abnormal abundance of stalled RFs at telomeres might contribute to the phenotypes of cells lacking Yku70/80.
Our data indicate genome-wide reduction in replication progression in NAM-treated yku70Δ cells vs WT (Fig 3C), which cannot only reflect abnormal RF progression at telomeric/subtelomeric regions since they represent a minor proportion of a cell’s total DNA. Instead, the Tel1-dependent NAM sensitivity of yku70Δ mutants suggests that abnormal activation of telomeric origins in early S influence DNA replication dynamics genome-wide. Elevated origin activation at repetitive loci harboring replication origins, e.g., rDNA repeats in sir2Δ mutants, diminishes origin activity at unlinked loci by titrating replication initiation factors that are in limiting abundance [81,82]. Lack of YKU70 causes misregulation of a significant number of origins: yeast cells possess at least one origin per telomere (32 per haploid cell), and origin activation repression extends up to 40 kb inward from chromosome ends [44]. We hypothesized that origin activation in early S at telomeric regions, combined with that occurring at canonical early replicating loci, might i) generate an overwhelming number of stalled RFs in early S in response to NAM-induced replicative stress, leading to ii) sequestration of replication factors. This might compromise activation of origins throughout chromosomes and overall replication progression in mid/late S, and prevent rescue of stalled RF by converging forks. We first tested whether increasing the number of replication origins competing for limiting pools of factors sensitizes otherwise WT cells to replicative stress. Consistently, cells harboring 200–400 copies of a YEPFAT7.5 2μ plasmid [83], which depends on endogenous DNA replication factors for its propagation, were noticeably more sensitive to MMS and NAM than WT cells (S7A and S7B Fig). Conversely, we reasoned that increasing the availability of limiting DNA replication initiation factors might rescue replicative stress-induced growth defects in cells lacking Yku70/80. Cdc45, Sld3 and Sld7 (45/3/7) are required to trigger activation of licensed replication origins; moreover, these factors are limiting in abundance and their overexpression rescues genome-wide anomalies in DNA replication dynamics caused by early S activation of rDNA origins in sir2Δ mutants [81,84]. We found that 45/3/7 overexpression partially rescued growth of yku70Δ mutants in response to MMS or NAM (Fig 3G and 3H), as well as the strong synthetic sensitivity of rad52Δ yku70Δ mutants to MMS (Fig 3I). These results suggest that increasing the availability of limiting replication factors can mitigate DNA replication defects caused by short telomeres in yku70Δ cells.
As was the case for tel1Δ, the H4K16A mutation rescued NAM-induced S-phase progression defects in yku70Δ cells (Fig 4A and 4B). The effect of sas2Δ was partial, which may reflect the fact that this mutation does not completely abolish H4K16ac, in contrast to H4K16A [22]. We first explored the possibility that mutations abolishing H4K16ac might suppress replication-associated phenotypes of yku70Δ mutants by reversing early activation of telomeric and subtelomeric origins. We synchronized cells in G1 using alpha factor and released them in early S in the presence of hydroxyurea (HU) and BrdU for 90 minutes, followed by BrdU immunoprecipitation and quantitative PCR to monitor dNTP incorporation at origins. We note that the amplitude of signals obtained by this technique has been shown to correlate well with origin activation/efficiency in early S phase [85–88]. We examined BrdU incorporation at two telomeric origins, ARS102 and ARS610, and one sub-telomeric origin, ARS522. As expected, these origins were more active when telomeres are shortened in the yku70Δ mutant, but not in tel1Δ cells (Fig 4C and 4D). We found that sas2Δ did not significantly influence the activity of telomeric/subtelomeric replication origins, alone or when combined with YKU70 deletion (Fig 4C and 4D). Importantly, neither sas2Δ or H4K16A significantly modulated telomere length (Fig 4E), indicating that H4K16ac impedes S phase progression of cells with short telomeres via other mechanisms.
We previously showed that mutations which prevent H4K16ac cause a reduction in tri-methylated H3K79 (H3K79me3) levels [22], an effect that we also observed in yku70Δ mutants (Fig 5A). Interestingly, we found that abolishing H3K79 methylation via deletion of the histone methyltransferase-encoding gene DOT1 (Fig 5A) significantly rescued growth and S phase progression defects in yku70Δ cells exposed to NAM (Fig 4B, Fig 5B). This suggests that H4K16ac might influence the NAM sensitivity of yku70Δ cells at least in part by modulating H3K79 methylation levels. While the biological consequences and molecular mechanisms of co-reduction in H4K16ac and H3K79me3 are uncharacterized, we originally speculated that modulation of H4K16ac might influence the DNA damage response (DDR) by influencing the recruitment of the H3K79me3-binding DDR protein Rad9 to damaged chromatin and subsequent Rad53 activation [24–27]. We found that upon exposure to NAM, yku70Δ cells displayed increased Rad53 activation, which was strongly reduced by mutating either SAS2 or DOT1 (Fig 5C). Directly limiting the amplitude of DDR signaling, via RAD9 mutation or expression of a hypomorphic rad53-HA allele [33], rescued the growth of yku70Δ mutants in NAM (Fig 5D and 5E). rad9Δ also rescued DNA replication progression in yku70Δ cells upon NAM exposure (Fig 5F). Conversely, deletion of either PPH3 or SLX4, which cripples cellular pathways that act to limit Rad53 activation upon replicative stress [30,31], caused synthetic sensitivity to MMS when combined with yku70Δ, an effect which was found to depend on the H4K16ac acetyltransferase Sas2 (Fig 5G and 5H). Taken together, our results indicate that i) co-dependent H4K16ac and H3K79me3 promote DDR signalling in yku70Δ mutants, and ii) elevated Rad53 activity contributes significantly to the sensitivity of cells lacking Yku70 to MMS and NAM-induced replicative stress.
Rad53 activation is known to inhibit DNA replication origin firing, thereby delaying S phase progression upon replicative stress [89,90]. This effect can be bypassed by preventing Rad53-dependent phosphorylation of Dbf4 and Sld3, which are two key proteins of the origin activation cascade [90]. We found that yku70Δ yeast strains expressing non-phosphorylable alleles of Dbf4 and Sld3 are as sensitive to NAM as control strains (S8 Fig), suggesting that other consequences of Rad53 hyperactivation influence the ability of cells lacking Yku70/80 to proliferate in response to replicative stress. Limiting the activation of Rad53, by deleting DOT1 or expressing a hypomorphic rad53-HA allele, increases resistance to MMS-induced replicative stress by elevating lesion bypass via the translesion synthesis (TLS) pathway [33,34]. Our data indicate that the sensitivity to MMS of cells lacking Yku70 complex is exacerbated by mutating REV3, which encodes the catalytic subunit of TLS polymerase zeta required for the bypass of MMS-induced lesions (Fig 6A and 6B). In addition, we found that the rescue of the MMS sensitivity of yku70Δ by deletion of SAS2 or DOT1 depends on Rev3 (Fig 6A and 6B). TLS is intrinsically error-prone, and elevated usage on this pathway increases mutagenesis [91]. Concordantly, deletion of SAS2 or DOT1 in either WT or yku70Δ cells led to a statistically significant increase in MMS-induced CAN1 mutation frequency, an effect which was reverted by rev3Δ (Fig 6C, p-value < 0.05). Together, these results suggest that H4K16ac and H3K79me influence TLS-dependent bypass of replication blocking DNA lesions in cells lacking Yku70/80 by modulating DDR signalling.
In the current study, we investigated the molecular basis of the sensitivity of cells lacking Yku70/80 to the pan-sirtuin inhibitor NAM. Our results reveal a novel interplay between telomere length and sirtuin-mediated regulation of chromatin structure in protecting cells against DNA replication stress. Indeed, we provide compelling evidence that in the absence of Yku70/80, telomere shortening, but not NHEJ deficiency nor other telomere-related defects, diminishes the ability of yeast cells to respond to replicative stress. Notably, we found that telomerase (est1/2Δ) mutants phenocopied yku70Δ both in terms of NAM-induced growth defects and S phase accumulation, and that manipulating telomere length via inducible Yku70 degradation or expression of a Cdc13-Est1 fusion modulates the sensitivity of cells to NAM. We note that sudden telomere shortening occurs naturally in vivo in WT yeast and is thought to result from replication fork collapse in telomeric tracts [92]. However, since such telomere shortening events are relatively infrequent and are expected to involve only one or a few telomeres in any given cell, the impact on DNA replication stress responses as described here is expected to be minor. This is in contrast with the situation for cells exhibiting uniform reduction in the length of all telomeres, such as telomerase and yku70Δ/yku80Δ mutants. Overall, our results are consistent with the fact that senescence caused by lack of telomerase activity is associated with induction of classical markers of the DNA damage response [93,94], and raise the interesting possibility that compromised responses to replicative stress might be a general phenomenon arising in mutants with short telomeres.
An important exception to the above involves the tel1ΔΔ mutation which, despite rendering telomeres extremely short, does not sensitize cells to NAM. Rather, we found that deletion of TEL1 rescued NAM-induced growth inhibition and DNA replication defects in yku70Δ cells. Previous reports established that telomere shortening caused by deletion of YKU70/80 provokes Tel1-dependent firing of telomeric origins in early S phase, as opposed to late S in WT cells [43–46]. Since RFs progress slowly within telomeric and subtelomeric regions [70,80,95], it is plausible that activation of telomeric origins in early S in yku70Δ cells, but not in yku70Δ tel1Δ mutants, generates an overwhelming number of stalled RFs over a short period in the presence of replicative stress-inducing drugs. In addition, we provide evidence suggesting that RF stalling at telomeric regions in early S may compromise genome-wide replication dynamics by causing sequestration, and eventual exhaustion, of limiting replication factors, which in turn is expected to negatively impact further activation of origins later in S. We propose that such reduction in RF initiation events might prevent rescue of stalled RFs by converging forks in mid/late S, thereby contributing to the sensitivity of yku70Δ cells to replicative stress. We note however that while the above-described models rationalize our observations regarding the impact of Tel1 on the sensitivity of yku70Δ cells to NAM, we cannot exclude that consequences of telomere shortening other than misregulation of telomeric origins might also be implicated.
Consistent with the notion that cells with short telomeres exhibit an elevated number of stalled RFs during replicative stress, we found that the DDR kinase Rad53 is strongly activated upon NAM exposure in yku70Δ cells. Our data further indicate that such hyperactive DDR signalling contributes to the phenotypes of these mutants; indeed, we found that i) yku70Δ causes synthetic sensitivity to MMS when combined with either slx4Δ or pph3Δ, which are both known to limit Rad53 activity in response to DNA replication impediments [30–32], and ii) mutations that cripple Rad53 activation rescue the NAM sensitivity of yku70Δ cells. While the consequences of Rad53 hyperactivation during replicative stress are incompletely characterized, our published data and those of others [33,34] suggest that restricting DDR signalling improves cell survival and replication progression in response to genotoxins at least in part by promoting DNA damage tolerance via TLS. Overall, the results presented here are consistent with the above, and highlight the importance of mechanisms that dampen DDR signalling in promoting the survival of cells with short telomeres upon DNA replication stress.
In contrast to most other mutations causing NAM sensitivity that we analysed so far [35], deletion of YKU70 does not cause synthetic lethality when combined with hst3Δ hst4Δ, implying that other sirtuins are essential for survival of cells presenting short telomeres. Indeed, our genetic data support the notion that the redundant ability of Sir2 and Hst1 to deacetylate H4K16ac promotes resistance to replicative stress in cells lacking Yku70/80, whereas Hst3/4-dependent removal of H3K56ac mainly acts to limit the generation of endogenous DNA damage in this context. We note that even though the identity of the DNA lesions generated by constitutive H3K56ac is unknown, our results showing that the ability of Yku70/80 to promote NHEJ is not necessary for NAM resistance suggest that the predominant lesions caused by hyperacetylated H3K56 are unlikely to be DSBs. This is also consistent with the sensitivity of yku70Δ mutant to MMS, which produces few, if any, DSBs in yeast [55]. While other Sir2/Hst1 targets might also contribute to this phenomenon, abolishing H4K16ac rescued the NAM, MMS, and temperature sensitivity of yku70Δ cells, indicating that this histone modification is a critical determinant of the phenotypes of these mutants. We note that the SIR complex was previously shown to suppress origin firing at telomeres [41] which, combined with our results, raised the possibility that this effect might depend on H4K16ac levels. However contrary to this idea, our data clearly indicate that reducing H4K16ac by deletion of the SAS2 acetyltransferase does not impact origin activity at short telomeres (Fig 4), consistent with a prior report indicating that telomeric origins in yku70Δ mutants are activated in early S independently of histone tail acetylation [44].
Our data highlight a novel role for H4K16ac regulation in limiting the activation of Rad53 upon replicative stress in yku70Δ mutants, presumably by modulating H3K79 trimethylation levels and Rad9 activity/recruitment to chromatin. This is noteworthy since H4K16ac is very abundant and is removed by sirtuins only at specific transcriptionally silent genomic loci, e.g., mating loci (HMR and HML), as well as at telomeric regions [29,57,96]. These regions present intrinsic impediments to DNA replication fork progression, often in the form of chromatin-bound protein complexes [80,97–99]. In view of this, our results raise the intriguing possibility that cells may have evolved mechanisms to limit H4K16ac levels in these genomic regions in part to mitigate the deleterious consequences of unchecked DDR signalling arising from frequent RF stalling. We also note that senescent yeast cells have been shown to manifest reduced levels of Sir2, and consequently exhibit elevated H4K16ac at telomeric regions [100]. Moreover, SAS2 deletion was demonstrated to extend replicative life span [100]. It is tempting to speculate that misregulation of H4K16ac might contribute to certain phenotypes of senescent cells, e.g., elevated DNA damage, by promoting intense DDR signaling in response to spontaneous replicative stress arising at loci that are intrinsically difficult to replicate, including telomeric regions [101–103].
Experiments were performed using standard yeast growth conditions. Yeast strains used in this study are listed in Table 1. To avoid frequent emergence of spontaneous suppressor mutations in cells with constitutive H3K56 hyperacetylation, hst3Δ hst4Δ strains used in this study were propagated with a URA3-harboring centromeric plasmid encoding Hst3. To evaluate the phenotypes caused by hst3Δ hst4Δ, cells were plated on 5-Fluoroorotic Acid (5-FOA)-containing medium immediately before experiments to select cells that spontaneously lost the plasmid, or during the experiment (spot assays on 5-FOA-containing plates). For experiments involving telomerase mutants (est1Δ or est2Δ), fresh haploid clones were obtained from tetrad dissection of heterozygous diploids to ensure that cells were not undergoing senescence during experiments. For spot assays, cells were grown to saturation in YEP with 2% glucose or 2% raffinose in a 96-well plate. Five-fold serial dilutions of these cultures with identical OD were then plated on indicated media and allowed to grow for 2 to 5 days. Growth assays in NAM were done as previously described [35]. Cells were diluted to OD600 0.0005 in 100 μL of YPD with increasing NAM concentrations in a 96-well plate. OD630 were acquired using a BioTek EL800 plate reader, and growth of each strain was normalized relative to an untreated control well. For doubling time assessments, cells were diluted to OD600 0.01 in 100 μL of YPD in a 96-well plate and incubated at 30°C in a BioTek EL808 plate reader for 48h. Every 30 minutes, plates were shaken for 30 seconds and OD630 readings were acquired. Doubling times were derived from exponential regression of the resulting growth curve.
Monitoring of telomere length by southern blotting was performed as described [107]. Briefly, genomic DNA was digested with XhoI (New England Biolabs) and run on a 1.2% agarose gel for 17 hrs in 1x TBE buffer. Telomeric repeats were detected with a TG1-3 probe kindly provided by Dr Raymund Wellinger (Université de Sherbrooke).
Proteins were extracted from samples by alkaline cell lysis [108] and run on 10% or 15% acrylamidegels to resolve Yku70and histones respectively. Flag epitope was detected using an anti-Flag-M2 antibody (Sigma), histones modifications were detected using anti-H3K79me3 (Abcam, AB2621) and anti-H4K16ac (EMD Millipore, 07–329) antibodies. Antibodies against histone H3 (AV100) and histone H4 (AV95) were kindly provided by Dr Alain Verreault (Université de Montréal).
Cells were fixed in 70% ethanol, sonicated, treated with 0.4 ug/mL RNAse A in 50mM Tris-HCl pH 7.5 for 3 hours at 42°C followed by treatment with 1mg/mL Proteinase K in 50mM Tris-HCl pH 7.5 for 30 minutes at 50°C. DNA content was assessed by Sytox Green (Invitrogen) staining as previously described [109]. DNA content analysis was performed on a FACS Calibur flow cytometer equipped with Cell Quest software. Graphs were produced using FlowJo 7.6.5 (FlowJo, LLC).
Cells expressing Rad52-YFP or Rfa1-YFP were fixed with formaldehyde as previously described [106,110] and stained with DAPI. Fluorescence was examined with a DeltaVision microscope equipped with SoftWorx version 6.2.0 software (GE Healthcare). Images were examined using a custom MATLAB script (version R2017a; MathWorks) to extract the number of cells with Rfa1-YFP or Rad52-YFP foci. Briefly, a mask was created based on DAPI signals to identify cell nuclei and count the number of cells within an image. A second mask was created with the YFP channel to mark foci by finding spots with elevated YFP fluorescence compared to surrounding regions. Nuclei with at least one focus were listed as cells with Rfa1-YFP foci.
Protein samples were prepared by trichloroacetic acid/glass beads lysis, separated on 10% acrylamide gels and transferred to a PVDF membrane. Autophosphorylation assays were carried out as previously described [111].
Cells were maintained in logarithmic phase for the indicated number of days (see Fig 2) by dilution in fresh YPD ± 2 mM Auxin (3-indoleacetic acid, Sigma). For each time point, a growth assay was performed in YPD with increasing concentrations of NAM ± 2 mM Auxin. Growth was normalized to the untreated control.
Cultures were synchronized in G1 with α-factor at 30°C. 30 minutes prior to release, 400 ug/mL BrdU was added to cultures except for the control condition. Release was carried out by addition of 50 μg/mL pronase and 200 mM hydroxyurea and cells were incubated for 90 minutes at 30°C. 0.1% sodium azide was added and cultures were incubated for 10 minutes on ice. Cells were centrifugated and pellets were washed once with TBS, transferred to screwcap tubes and frozen on dry ice. Immunoprecipitation (IP) was performed as previously described [112]. Quantitative PCR was done using 2x SYBR Green qPCR Master Mix (Bimake) per the manufacturer’s guidelines. qPCR plates were analyzed on a ABI7500 real-time PCR system. Since replication at origins doubles the amount of DNA, normalizing on the input signal from probed origins might reduce BrdU signals from replicated regions, and interfere with quantification of changes in origin activity. To remove the contribution of replicated DNA from qPCRs quantifications, we instead normalized IP signals to a region that is expected to remain unreplicated throughout our experiments. We chose the ACT1 locus that is located 15 kb and 54kb away from the closest origin of replication (ARS603) and telomere, respectively. Replication forks are expected to travel ~3–7.5kb away from origins in our experiments [113], and are therefore not expected to reach the ACT1 locus. Data from replicate experiments were further normalized to a highly efficient replication origin, ARS305, to account for differences in BrdU incorporation between strains. Data are represented as values relative to the WT strain. Primer pairs are listed in S1 Table.
6 colonies of relevant genotype were grown to saturation for 48 hours in YPD containing 0.001% MMS. Cells were plated on YPD assess the number of colony forming units and on synthetic media containing 60 μg/mL canavanine to determine the number of CAN1 mutation events. The frequency of canavanine resistance was calculated as the ratio between colonies growing on canavanine plates and the initial number of plated viable cells (assessed by plating appropriate dilutions on YPD plates). The median CAN1 mutation frequency of the 6 clones was then determined and data is represented as the average of the median from several experiments. Statistical significance was determined using two-tailed student’s T-tests.
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10.1371/journal.ppat.1007276 | Cellular sheddases are induced by Merkel cell polyomavirus small tumour antigen to mediate cell dissociation and invasiveness | Merkel cell carcinoma (MCC) is an aggressive skin cancer with a high propensity for recurrence and metastasis. Merkel cell polyomavirus (MCPyV) is recognised as the causative factor in the majority of MCC cases. The MCPyV small tumour antigen (ST) is considered to be the main viral transforming factor, however potential mechanisms linking ST expression to the highly metastatic nature of MCC are yet to be fully elucidated. Metastasis is a complex process, with several discrete steps required for the formation of secondary tumour sites. One essential trait that underpins the ability of cancer cells to metastasise is how they interact with adjoining tumour cells and the surrounding extracellular matrix. Here we demonstrate that MCPyV ST expression disrupts the integrity of cell-cell junctions, thereby enhancing cell dissociation and implicate the cellular sheddases, A disintegrin and metalloproteinase (ADAM) 10 and 17 proteins in this process. Inhibition of ADAM 10 and 17 activity reduced MCPyV ST-induced cell dissociation and motility, attributing their function as critical to the MCPyV-induced metastatic processes. Consistent with these data, we confirm that ADAM 10 and 17 are upregulated in MCPyV-positive primary MCC tumours. These novel findings implicate cellular sheddases as key host cell factors contributing to virus-mediated cellular transformation and metastasis. Notably, ADAM protein expression may be a novel biomarker of MCC prognosis and given the current interest in cellular sheddase inhibitors for cancer therapeutics, it highlights ADAM 10 and 17 activity as a novel opportunity for targeted interventions for disseminated MCC.
| The majority of cancer-related deaths occur due to metastatic disease. Therefore, understanding the molecular and cellular mechanisms underlying the process of metastasis is essential to developing new therapeutic interventions to improve cancer patient survival. Merkel cell carcinoma (MCC) is an aggressive and highly metastatic cancer. Merkel cell polyomavirus (MCPyV) has been implicated as the causative agent in the majority of MCC cases. The MCPyV small tumour antigen (ST) is believed to function as the major oncoprotein. However, little is known about the mechanisms through which MCPyV ST may be implicated in causing the high rates of metastatic spread observed in MCC tumours. Here we show that specific cellular sheddases, namely A disintegrin and metalloproteinase (ADAM) 10 and 17 protein levels are increased upon MCPyV ST expression. Moreover, we show that MCPyV ST-induced ADAM 10 and 17 are required to breakdown cell-cell junctions resulting in increased cell dissociation, migration and invasion. As such, ADAM protein expression may provide a novel biomarker of MCC prognosis. In addition, linking cellular sheddases to MCPyV-positive MCC metastasis may provide novel therapeutic interventions.
| Merkel cell carcinoma (MCC) is a highly aggressive neuroendocrine cancer of the skin [1]. Although rare, the incidence of MCC has increased over the past twenty years in both Europe and the United States of America [2], attributed to advances in reporting, diagnostic improvements and known risk factors. UV light appears to be an important factor in MCC, with a positive correlation between geographic UVB radiation indices and age-adjusted MCC amongst Caucasians [1, 3]. The predominance of MCC in elderly persons also highlights immunosuppression as an important risk factor, supported by disproportionally higher rates of MCC in patients on long-term iatrogenic immunosuppression, in addition to patients with lymphoproliferative disorders and HIV/AIDs [2]. Due to its aggressive nature MCC carries a high risk of local, regional and distant recurrence [4]. As such, the 5-year survival rates range from 60–87% for local disease to 11–20% for metastatic disease [5–7].
The majority of MCC cases, ~80%, are associated with Merkel cell polyomavirus (MCPyV) [8], whilst the remaining cases contain a high degree of single nucleotide polymorphisms consistent with UV-mediated mutations [9, 10]. MCPyV is a common skin commensal causing an asymptomatic infection usually acquired in childhood. Like other polyomaviruses, MCPyV expresses a variety of early spliced variant regulatory proteins required for viral replication and pathogenesis, including the small and large tumour antigens (ST and LT, respectively) [11]. Upon loss of immunosurveillance, the MCPyV genome integrates into the host genome prior to clonal expansion of tumour cells [12, 13]. A further prerequisite for MCPyV-mediated tumourigenesis is the truncation of the LT antigen rendering the virus replication defective [13]. These truncations lead to the loss of functional LT domains associated with virus replication, although all preserve the LXCXE Retinoblastoma (Rb) protein-binding domain, which alters cell cycle progression contributing to increased cell proliferation [14, 15].
Both MCPyV ST and truncated LT antigens are essential for MCC cell survival and proliferation, exemplified by siRNA-mediated depletion of either protein leading to cell cycle arrest and apoptosis [16]. Moreover, genetically engineered mice expressing MCPyV T antigens in the stratified epithelium display signs of neoplastic progression [17]. However, in contrast to the prototype polyomavirus, simian virus 40 (SV40), MCPyV truncated LT forms cannot initiate cellular transformation alone and function in an accessory role by binding host factors which regulate cellular proliferation, such as Rb and Hsc70 [18, 19]. Conversely, MCPyV ST expression is sufficient to transform rodent cells to anchorage- and contact-independent growth and induce serum-free proliferation of human cells [18]. In addition, preterm transgenic mice co-expressing epidermis-tagged MCPyV ST and the cell fate determinant atonal bHLH transcription factor 1 developed widespread cellular aggregates representative of human intraepidermal MCC [20]. Together these observations show that MCPyV ST is the major oncogenic driver of MCC. Several MCPyV ST-mediated mechanisms contribute to MCC development and proliferation. ST expression leads to the hyperphosphorylation of the translation regulatory protein, 4E-BP1, resulting in dysregulation of cap-dependent translation [18] and prevents SCFFwb7-mediated degradation of MCPyV LT and several cellular oncoproteins [21]. It induces centrosome overduplication, aneuploidy, chromosome breakage and the formation of micronuclei by targeting cellular E3 ubiquitin ligases [22]. MCPyV ST also functions as an inhibitor of NF-κB-mediated transcription [23, 24]. Moreover, ST activates gene expression by associating with MYCL and the EP400 histone and chromatin remodelling complex [25], inducing transcriptional changes effecting for example glycolytic metabolic pathways [26].
The poor survival rates of MCC strongly correlate to the high dissemination rates and metastatic nature of MCC [5]. Whether MCPyV T antigens contribute to MCC metastasis is yet to be fully elucidated. Metastasis is a complex process, with several discrete steps required for the formation of secondary tumour sites [27]. These metastatic hallmarks include loss of cell adhesion, gain of cell motility, dissemination via the vasculature, and colonisation of distant sites [28, 29]. Recent quantitative proteomic studies suggest MCPyV ST expression can promote cell motility and migration [30–32] by inducing differential expression of cellular proteins involved in microtubule [30] and actin-associated cytoskeletal organization and dynamics [31], leading to microtubule destabilization and filopodium formation. These results suggest that MCPyV may be associated with the highly metastatic nature of MCC, and is supported by studies showing that engraftment of MCC cell lines into SCID mice results in circulating tumour cells and metastasis formation [33].
One key trait that underpins the ability of cancer cells to become invasive and metastasise is how they interact with the surrounding extracellular matrix (ECM) and adjoining tumour and stromal cells [34, 35]. Cell–cell junctions are sites of intercellular adhesion that maintain the integrity of epithelial tissue and regulate signalling between cells [36]. The expression of cell adhesion molecules is tightly regulated, as dysregulation of cell adhesion between tumour cells and turnover of the surrounding ECM plays a critical role in malignant transformation and the initiation of the metastatic cascade [37]. A key mediator of cell adhesion in epithelial tissues is E-cadherin and its loss can promote invasive and metastatic behaviour in many epithelial tumours [38]. The cytoplasmic domain of E-cadherin binds to members of the catenin family, linking this multiple protein complex to the actin cytoskeleton through alpha-E-catenin. The clustering of cadherin-catenin complexes on adjacent cells leads to localised actin remodelling required for the formation of adheren junctions [39]. Notably, the loss of E-cadherin and associated cell adhesion molecules, results in the suppression or weakening of cell–cell adhesion which is regarded as a crucial step in the epithelial–mesenchymal transition (EMT) [40, 41], a process enabling a cell to acquire a more migratory and invasive mesenchymal phenotype. Loss of E-cadherin and associated cell adhesion molecules in human tumours is caused by multiple factors, including germline mutations, promoter methylation, downregulation of EMT-associated transcriptional repressor proteins and the upregulation of cellular proteinases causing proteolytic cleavage of cell adhesion molecules [42–44].
ADAMs (a disintegrin and metalloproteinases), are a family of zinc-dependent transmembrane proteins implicated in the ectodomain shedding of various membrane-bound proteins [45]. Of the 21 human largely cell-membrane associated ADAMs, 13 have proteolytic sheddase capacities modulating the activity of membrane cytokines and growth factors, their receptors and cell adhesion molecules, including cadherins, selectins and integrins [46]. ADAM sheddase activities have been implicated in several physiological and pathological processes including inflammation, tumour growth and metastatic progression [47], reinforced by upregulation of proteolytic ADAMs in both tumour tissues and cancer cell lines [48–50]. Correlations exist between levels of specific ADAMs and parameters of tumour progression, implying that these sheddases are implicated in the process of cancer development and the dissemination of metastatic tumour cells [51]. ADAMs are now emerging as potential cancer biomarkers for aiding cancer diagnoses and predicting patient outcome [52]. In addition, selective ADAM inhibitors have promising anti-tumourigenic effects in in vitro and in vivo studies and are progressing into clinical trials [53].
Here we demonstrate that the cellular sheddases, ADAM 10 and 17, are upregulated in a MCPyV ST-dependent manner. Work highlights the essential role of ADAM sheddases in MCPyV ST-mediated disruption of cell adhesion leading to enhanced cell dissociation and motility. This suggests that ADAM protein expression may be a novel biomarker of MCC prognosis and inhibiting ADAM activity may provide a novel opportunity for targeted interventions for disseminated MCC.
Cell-cell adhesion and cell interaction to the extracellular matrix is required for tissue integrity [54]. Disrupting cell-cell adhesion enhances cell scattering, which is essential to initiate cell migration and metastatic spread [55]. To determine whether MCPyV ST expression affects the integrity of cell junctions, EGFP and EGFP-ST transfected HEK 293 cells were stained with an Alpha-E-catenin-specific antibody. Alpha-E-catenin, which is predominantly expressed at the plasma membrane mediating cell adhesion and its breakdown impliess a loss of structural integrity at cell junctions [56]. Results demonstrate that Alpha-E-catenin in control EGFP-expressing cells primarily localised to the plasma membrane, in contrast a reduced and incomplete plasma membrane localisation is observed in EGFP-ST-expressing cells, indicative of diminished cell-cell adhesion (Fig 1A). A similar result was also observed upon inducible MCPyV ST expression in a HEK 293 FlpIn-derived cell line (i293-ST) [30] (S1 Fig). In addition, immunoblotting these cell lysates showed a decrease in Alpha-E-catenin protein levels (S1 Fig). Quantification of Alpha-E-catenin levels at the plasma membrane in EGFP and EGFP-ST-expressing cells was then performed using flow cytometry. Results validated the immunofluorescence data demonstrating a reduction in Alpha-E-catenin levels upon MCPyV ST expression (Fig 1B and 1C). To confirm the disruption of cell junctions, the levels of a second cell adhesion-associated protein, Zona occludin 1 (ZO-1) [57], was compared in EGFP versus EGFP-ST-expressing cells. Consistent with the reduction in Alpha-E-catenin levels, immunoblot analysis showed a significant decrease in ZO-1 expression upon MCPyV ST expression (Fig 1D and 1E). Together, these results provide the first indication that MCPyV ST dysregulates cell-cell adhesion.
Loss of cell junction integrity enhances the ability of a cell to migrate and dissociate from its primary site. To assess whether MCPyV ST induces cell dissociation and scatter, a cell scatter assay was performed as previously described [58]. Here EGFP and EGFP-ST transfected HEK 293 cells were incubated in low serum to induce aggregation, upon reintroduction of serum cells were fixed and stained with DAPI at 6 hourly intervals and clusters of cells were analysed to quantify the distance between each cell nucleus (Fig 1F). Results show that EGFP control cells scarcely dissociate, instead remaining in cell clusters. In contrast, MCPyV ST-expressing cells dissociated significantly from their initial cell clusters. Similar results were also observed in the MCPyV negative cell line MCC13, transfected with either EGFP or EGFP-ST expression constructs (S1 Fig), although results in MCC13 cells were less pronounced than in HEK 293 cells. These results suggest that MCPyV ST expression can lead to the breakdown of cell junctions enhancing cell dissociation.
Cellular sheddases function predominantly in the ectodomain cleavage of various membrane-bound proteins, including cell adhesion molecules. Therefore, to identify potential cellular sheddases induced upon MCPyV ST expression, we re-analysed a previously published SILAC-based quantitative proteomic dataset which determined alterations in the host cell proteome upon inducible MCPyV ST expression in a HEK 293 FlpIn-derived cell line (i293-ST) [30]. MCPyV ST expression led to an increase in the levels of two specific cellular sheddases, namely ADAM 10 and 17 proteins by 7.6 and 4.3 fold, respectively (S1 Fig). To confirm an increase in ADAM protein levels upon MCPyV ST expression, cell lysates of uninduced and induced i293-ST cells were analysed by immunoblotting. Results demonstrated a significant increase in ADAM 10 and 17 mature protein levels, compared to ADAM TS1 (Fig 2A). Densitometry-based quantification of the immunoblot analysis showed an increase in the mature forms of ADAM 10 and 17 expression of 6 and 4 fold, respectively (Fig 2B). A similar fold increase was also observed in MCC13 cells, transfected with either EGFP or EGFP-ST expression constructs (Fig 2C and 2D). The increase observed in ADAM protein levels occurs at the transcriptional level, as RT-qPCR showed significant changes in the mRNA levels of both ADAM proteins upon MCPyV ST expression in both HEK 293 and MCC13 cells (Fig 2E), correlating with recent results showing MCPyV ST can dynamically alter the transcriptome of human cells [26].
To further investigate the differential expression of ADAM 10 and 17 proteins in the context of MCC, multicolour immunochemistry analysis was performed on formalin-fixed, paraffin-embedded (FFPE) sections of primary MCC tumours. Sections were stained with ADAM 10 and 17, cytokeratin 20 (CK20) (a marker widely used to distinguish MCC) and MCPyV LT specific antibodies. An isotyped-matched control was also used as a negative control. CK20 staining confirmed MCC status of the sections and results show increased levels of ADAM 10 and 17 expression coincident with LT staining in regions of both MCPyV-positive MCC tumours (Fig 3A). Moreover, immunoblot analysis was performed on cell lysates of two unrelated MCPyV-positive MCC tumour samples comparing protein levels against a negative control non-tumour cadaveric skin sample. Results again demonstrated a similar increase in both ADAM 10 and ADAM 17 protein levels in MCC tumour samples compared to control, which was MCPyV negative as indicated by the lack of ST and LT expression (Fig 3B and 3C). Moreover, we compared the MCPyV-negative MCC13 cell line versus two MCPyV-positive cells lines, WAGA and PeTa. Similar results were observed showing that the presence of MCPyV ST increases ADAM 10 and 17 protein levels (S1 Fig). Immunoblot analysis was also performed on cellular lysates of the MCPyV-positive MCC cell line, WAGA, transduced with lentiviruses containing a shRNA scrambled control or shRNA targeting ST, as previously described [31]. Results demonstrated that MCPyV ST depletion did not affect MCPyV LT levels but led to a reduction in ADAM 10 and ADAM 17 protein levels. Conversely, ST depletion leads to increased Alpha-E-catenin levels (Fig 3D). To confirm these observations and determine if ADAM 10 transcripts are significantly increased in MCPyV-positive MCC compared with MCPyV-negative MCC, gene expression profiles for a total of ninety-four patients were obtained from a publicly available dataset (accession number GSE39612 [9]). Bioinformatic analysis identified a significant increase (2.5 fold, p = 0.03) in ADAM 10 expression in MCPyV-positive MCC compared with MCPyV-negative MCC control samples. Moreover, a similar analysis was performed to analyse ADAM protein expression in control GFP versus MCPyV ST expressing cell datasets (accession number GSE79968) [26]. A significant increase in both ADAM 10 (p = <0.0001) and ADAM 17 (p = <0.0001) was observed upon 48 hours MCPyV ST expression. Together these data suggest that ADAM 10 and 17 protein levels are increased upon MCPyV ST expression and in MCPyV-positive MCC tumour samples.
For active ADAM proteins to cleave their chosen substrate, they are required to be present at the same subcellular location [59]. As adhesion molecule receptors are localised at the plasma membrane, we next determined whether MCPyV ST enhancement of ADAM 10 and 17 protein levels led to their accumulation at the plasma membrane [60]. HEK 293 cells transfected with EGFP or EGFP-ST were fixed and stained for endogenous ADAM 10 and ADAM 17 in non-permeabilised cells. MCPyV ST-expressing cells showed increased levels of both ADAM 10 and 17 proteins at the plasma membrane, in comparison to the EGFP control cells (Fig 4A). To confirm these results, cell surface accumulation of ADAM proteins was measured by surface biotinylation assays in EGFP versus EGFP-ST expressing HEK 293 cells. Immunoblotting of surface biotinylated proteins confirmed that MCPyV ST expression specifically increased the plasma membrane levels of ADAM 10 and 17 proteins, in contrast the control cell surface protein, CD71, showed no such increase (Fig 4B). Densitometry-based quantification of the immunoblot analysis showed a significant increase in both ADAM 10 and 17 accumulation at the plasma membrane by 5 fold and 2.5 fold, respectively (Fig 4C). Further validation was performed using flow cytometry with ADAM 10- and ADAM 17-specific antibodies (Fig 4D and 4E). Notably however, both assays showed a greater accumulation of ADAM 10 compared to ADAM 17 at the cell surface. Together, these results suggest that MCPyV ST expression results in the accumulation of cellular sheddases, primarily ADAM 10, at the plasma membrane.
To determine whether ADAM protein accumulation at the plasma membrane is implicated in the observed disruption of cell junctions upon MCPyV ST expression, EGFP and EGFP-ST HEK 293-expressing cells were incubated in the absence or presence of two distinct ADAM protease inhibitors. MTS assays identified non-cytotoxic concentrations of an ADAM 10-specific inhibitor (GI254023X) and dual ADAM 10/17 inhibitor (TAPI-2) (S2 Fig), no specific ADAM 17 inhibitor is commercially available. Following a 24 hour incubation period, cells were fixed and non-permeabilised cells stained with an Alpha-E-catenin-specific antibody. As previously shown in Fig 1, incomplete staining of the cell junctions was observed in MCPyV ST-expressing cells, compared to control EGFP cells. However, retention of the cell junctions was observed in the presence of both the ADAM 10-specific and dual ADAM 10/17 inhibitors, implying that inhibition of ADAM sheddase activity, and specifically ADAM 10, is sufficient to prevent MCPyV ST-induced breakdown of cell-cell junctions (Fig 5A). Importantly, there was no observed change in the cell junction staining in EGFP control cells after incubation with either inhibitor. The inhibition of MCPyV ST-induced cell junction breakdown was also confirmed by quantifying the cell surface levels of Alpha-E-catenin using flow cytometry in EGFP versus EGFP-ST-expressing cells. Results demonstrated increased levels of Alpha-E-catenin expression at the cell surface upon addition of the inhibitors (Fig 5B). Notably, taking into consideration the greater accumulation of ADAM 10 over ADAM 17 at the plasma membrane in MCPyV ST-expressing cells and no enhancement of Alpha-E-catenin expression at cell junctions in the presence of the dual ADAM10/17 inhibitor over the ADAM 10 inhibitor alone, these results suggest that ADAM 10 may be the main cellular sheddase required for MCPyV ST-induced cell junction disruption.
To confirm that ADAM 10 was required for the enhanced cell dissociation observed in MCPyV ST-expressing cells, the cell scatter assay was repeated in EGFP control and MCPyV ST-expressing cells, in the absence and presence of the ADAM 10 specific inhibitor, GI254023X, at non-cytotoxic concentrations. Addition of GI254023X resulted in little change in the EGFP-expressing control cells. However, a significant decrease in cell dissociation, over the course of 48 hours, was observed in the presence of GI254023X compared to DMSO-treated MCPyV ST-expressing cells (Fig 6A). A similar level of cell dissociation inhibition was also observed using the ADAM10/17 dual inhibitor, TAPI-2 (S3 Fig), showing that no enhancement of inhibition is seen by targeting both ADAM 10 and 17. To confirm the specific role of ADAM 10 in MCPyV ST-induced cell dissociation, siRNA-mediated depletion of ADAM 10 was performed in EGFP and EGFP-ST-expressing HEK 293 cells (Fig 6B). Immunoblotting confirmed that MCPyV ST depletion led to Alpha-E-catenin protein levels comparable to EGFP control cells (Fig 6B and 6C). Cell scatter assays were then repeated in EGFP control or MCPyV ST-expressing cells, in the presence of either scrambled or ADAM 10-specific siRNAs. Depletion of ADAM 10 resulted in a similar reduction in cell dissociation levels observed with the specific ADAM 10 inhibitor (Fig 6D). These data therefore suggest that ADAM 10 is required for the increased ability of cells to dissociate upon MCPyV ST expression.
ADAM-mediated shedding of cell adhesion molecules may also stimulate cell signalling pathways to induce cell motility [30, 31]. Therefore, we next examined if ADAM proteins have any downstream impact on the motility and migratory potential of MCPyV ST-expressing cells. Here, the migrating potential of EGFP control and EGFP-ST HEK 293 and MCC13-expressing cells were assessed using Incucyte kinetic live cell imaging, in the absence or presence of non-cytotoxic concentrations of the ADAM 10-specific (GI254023X) and dual ADAM 10/17 (TAPI-2) inhibitors. Incubation of the ADAM 10 (GI254023X) inhibitor showed a slight but insignificant decrease in the motility of EGFP control cells, implying that any changes observed in migratory rates of MCPyV ST expression cells is not due to changes in cell viability or cytotoxicity. In contrast, ADAM 10 inhibition resulted in a significant decrease in the distance travelled of MCPyV ST-expressing cells, reminiscent of control cell migration (Fig 7A). A similar trend was also observed with the dual ADAM 10/17 (TAPI-2) inhibitor (Fig 7B), suggesting that inhibition of ADAM 10 alone was sufficient to repress the MCPyV ST-induced cell migratory phenotype. To validate the use of ADAM-specific inhibitors, similar live cell imaging motility assays were also performed in ADAM 10-depleted EGFP and MCPyV ST-expressing HEK 293 cells, which resulted in a reduction in the motility of MCPyV ST-expressing cells, to levels similar to control EGFP-expressing cells (Fig 7C).
To demonstrate that ADAM 10 is required for cell motility and migration of MCPyV-positive MCC cell lines, haptotaxis migration assays were performed. This assay investigates the three-dimensional migration of cells towards a chemoattractant across a permeable chamber. Two MCPyV-positive MCC cell lines, WAGA and PeTa, were incubated in the absence or presence of the ADAM 10 inhibitor (GI254023X) at non-toxic concentrations assessed by MTS assay (S4 Fig) or upon siRNA-mediated scramble or ADAM 10-specific depletion. After treatment, cells were allowed to migrate for 24 h before migration was assessed by immunofluorescent staining of cells that had migrated into the chambers. Results showed that migration of MCPyV positive MCC cell lines were significantly reduced compared to control, upon treatment with GI254023X (Fig 8A) or upon ADAM 10 depletion (Fig 8B), suggesting that MCPyV positive MCC cell line migration is ADAM 10 dependent. Together, these results suggest that ADAM 10 is required for MCPyV ST-mediated enhanced cell motility and migration.
MCPyV ST has emerged as the major transforming factor in MCPyV-positive MCC. Recently we reported a potential role for MCPyV ST in MCC metastasis, whereby ST cultivates a pro-migratory cell phenotype by destabilising microtubules [30], inducing filopodia formation [31] and modulating cellular chloride channels [32]. Cancer metastasis occurs via a series of complex events that are collectively known as the invasion-metastasis cascade [61]. The apex event in the metastatic cascade is broadly accepted to be mediated by an EMT, providing tumour cells increased motility allowing invasion of the ECM. Most oncoviruses have been shown to manipulate the EMT axis, for example, human papillomavirus 16, Epstein-Barr virus (EBV), hepatitis B virus and the polyomavirus simian virus 40 have all been shown to induce metastasis, through a variety of mechanisms including; cellular adhesion complexes, cytoskeletal reorganisation and gene expression modulation [62–65]. EBV latent membrane protein-1, for example orchestrates EMT via several different routes, including the transcriptional repression of E-cadherin via activation of DNA methyltransferases [66] and increased expression of the pleiotropic EMT transcription factors, Twist and Snail [67, 68].
Here we expand on recent observations suggesting that MCPyV ST can trigger elements of the EMT and initiate the invasion-metastasis cascade, by demonstrating that MCPyV ST induces cell-surface expression of cellular sheddases, specifically ADAM 10 and 17. Moreover, we show that MCPyV ST-mediated induction of ADAM 10 is required for MCPyV ST-induced cell-cell junction disruption which in turn enhances cell dissociation, migration and invasion. Although we focus herein on the link between MCPyV ST induction of ADAM proteins in metastatic spread, it must be noted that activation of ADAM10 may also serve in MCPyV fitness. Fibroblasts are a target of MCPyV infection [69] and is it known that MCPyV is shed from the surface of the skin, it is plausible therefore ADAM10 expression be a way for infected fibroblasts to migrate into the epidermis or hair follicle so the virus can be shed into the environment. How MCPyV ST regulates ADAM 10 expression is not yet clear, although results suggest this is likely to be at the transcriptional level. The ADAM 10 promoter contains functional binding sites for Sp1 and USF [70] and has been reported to be activated by numerous transcriptional activators including, XBP1, JUN, ACAD8, PPARG, SCAND1 and ITGB3BP [71, 72]. Interestingly, ACAD8, PPARG and ITGB3BP all appear in a recent RNA-seq data set of MCPyV ST-induced genes [26], raising the possibility that these transcription factors may be responsible for MCPyV ST-mediated induction of ADAM 10 expression.
There is a growing appreciation for the role played by ADAM proteins in numerous human diseases [73], including Alzheimer’s disease, cardiovascular disease, rheumatoid arthritis and cancer [52]. The best characterised sheddase in terms of cancer aetiology is ADAM 17, which is implicated in the development and progression of numerous neoplasms [74]. ADAM 17 came to prominence due to its ability to shed the soluble form of the inflammatory cytokine, TNFα from it precursor product [75, 76], however, despite TNFα being widely implicated in tumour development and progression, it is the ability of ADAM 17 to hydrolyse and promote the release of epidermal growth factor receptor (EGFR)/human EGFR (HER) precursor ligands that features most frequently in published studies. For example, ADAM 17-mediated shedding of TGFβ is implicated in breast [77, 78] and renal [79] cancer progression. Moreover, release of the transmembrane protein with EGF and two follistatin motifs (TMEFF2) increases prostate cancer cell motility [80]. We observed significant upregulation of ADAM 17 in response to MCPyV ST expression and in MCC tumours, however, comparison of ADAM 10 and ADAM 10/17 inhibitor experiments suggest that ADAM 17 is not required for the EMT-associated phenotypes observed following expression of MCPyV ST. This supposition is supported by bioinformatic analysis of MCPyV-positive MCC compared with MCPyV-negative MCC tumours, which identified significantly increased expression of ADAM 10, but not ADAM 17 in 94 patient samples. The role of ADAM 10 in cancer metastasis is less clear, however emerging evidence suggests that ADAM 10 maybe cell-type specific, driving motility and invasion in breast [81], pancreatic [82], melanoma [83] and bladder [84] metastasis compared with primary tumours, but having alternative effects on proliferation in other tissue types. Interestingly, while HER ligand release is generally ADAM-specific, overexpression of individual ADAM proteins drives promiscuity in terms of ligand cleavage [85]. This raises the possibility that MCPyV ST-induced overexpression may enable ADAM 10 to cleave proteins ordinarily regulated by other sheddases, a scenario that needs to be considered when investigating downstream targets of ADAM 10 in MCC.
Generally, metastasised MCC is treated with various regimens of broad-spectrum chemotherapy agents. However, metastatic MCC responses are not robust and often associated with high toxicity in elderly patients [86]. Response rates range from 52% to 61% in the distant metastatic setting, with progression-free survival (PFS) and overall survival typically measured in months [87–89]. One of the strongest predictors for survival is a high level of intratumoural CD8+ T cells most frequently observed in MCPyV-positive MCC [90, 91]. MCPyV-specific CD8+ T cells express high levels of PD-1 and TIM-3 (the T cell immunoglobulin and mucin domain-3), which prompted immunotherapy-based clinical trials in MCC patients with the anti-PD-1 antibodies, pembrolizumab [92] and avelumab [93]. Both phase 2 trials reported encouraging and positive response rates with improved PFS, leading to pembrolizumab being listed as a treatment option for late-stage MCC in the National Comprehensive Cancer Network 2017 guidelines and avelumab being granted accelerated FDA approval as a first-line treatment for metastatic MCC. Whilst promising, around half of the patients involved in these clinical trials derived limited benefit from either drug [94], indicating the importance of identifying additional agents to use in combination with anti-PD-1 antibodies. This approach may have exciting possibilities for ADAM 10/17 inhibitors, as TIM-3 is shed by both ADAM 10 and 17 and ADAM 10 cleaves MHC-I [95]. Notably, monoclonal antibody blocking of TIM-3 reduced PD-1 expression and increased cytokine production [96], indicating that TIM-3 functions to dampen the immune system [97]. Therefore, ADAM 10 and 17 inhibitors may stimulate the immune system by reducing TIM-3 cleavage.
One of the most widely characterised ADAM inhibitory compounds is INCB3619 (Incyte), a dual ADAM 10 and 17 inhibitor which inhibits the catalytic activity of ADAM proteins by chelating zinc at the active site [53]. In vitro studies using breast and small cell lung cancer cell lines, have shown that INCB3619 reduced the cleavage of HER2 and amphiregulin, thereby sensitising cells to the EGFR tyrosine kinase inhibitor, gefintinib or a dual EGFR/HER2 inhibitor, GW2974 [98–100]. These observations have also been extended in animal models where INCB3619 shows anti-cancer activity against malignancies of the lung (non-small cell), breast, head and neck [98, 99]. Notably, a structurally similar compound with enhanced pharmokinetic properties, IMCB7839 (Aderbasib), has undergone phase I/II clinical trials in patients with HER2-positive breast cancer, in combination with Herceptin (trastuzumab). Results showed improved clinical responses in a subset of HER2-positive metastatic breast cancer patients, expressing the p95 form of HER2 [52, 98]. At present, additional phase I/II clinical trials are ongoing, for example in patients with diffuse large B cell non-Hodgkin lymphoma using INCB7839 in combination with the monoclonal antibody rituximab [52]. Therefore, given our data showing a significant upregulation of ADAM 10/17 in MCC cell lines and tumours and the integral role played by ADAM 10 in MCPyV ST-mediated enhanced cell dissociation and invasion, selective inhibitors of ADAM 10 and 17 may prove to be potent novel therapeutics when given in combination with immune checkpoint inhibitors for the treatment of advanced MCC.
The expression vectors for EGFP-ST has been previously described [23, 30, 31]. MCPyV ST-tagging shRNA plasmids were kindly provided by Dr Masa Shuda, Pittsburgh. ADAM 10 and 17-specific siRNAs were purchased from Dharmacon. Antibodies against ADAM 10, ADAM 17, ADAM TS1, and GAPDH were purchased form Abcam and used at a dilution range of 1:100–1:500, the ZO-1, CD71 and Alpha-E-catenin antibodies were purchased from Cell signalling and used at 1:100 dilution. The 2T2 hybridoma was provided by Dr Buck, National Cancer Institute, Bethesda, MD. All antibodies used for immunofluorescence were diluted 1:200. ADAM 10 specific inhibitor, GI254023X and ADAM 10/17 dual inhibitor, TAPI-2 where purchased from TOCRIS and Merck Millipore, respectively. Cell toxicity was measured using a MTS-based CellTiter 96 AqueousOne Solution Proliferation assay (Promega), as previously described [101].
HEK-293 Flip-In cell line was purchased from Invitrogen. i293-ST, i293-GFP, and i293-GFP-ST cell lines were derived from HEK-293 Flip-Ins using manufacturer’s protocol as previously described [23]. HEK-293 cells were obtained from ECACC and were maintained in Dulbecco’s modified Eagle’s medium (DMEM) containing 10% foetal bovine serum (FBS) and 1% penicillin/streptomycin as previously described [102]. The MCPyV negative cell line MCC13 (ECACC) and positive MCC cell lines, WAGA and PeTa (ATCC), were grown in RPMI 1640 (Sigma) supplemented with 10% FBS. ST-FLAG, EGFP and EGFP-ST expression was induced from i293-ST, i293-GFP, and i293-GFP-ST cells respectively with 2 μg/ml Doxycycline hyclate for up to 48 hours. Cells were plated into 6-well plates and transfections routinely used 1 μg plasmid DNA and Lipofectamine 2000 (Life Technologies) or 5 μg plasmid DNA and nucleofection (Lonza) following the manufacturer’s instructions.
Immunofluorescence was carried out as previously described [103]. If appropriate, cells were treated with inhibitors for24 hours prior to fixation. Cells were viewed on a Zeiss LSM880 confocal laser scanning microscope under an oil-immersion 63x objective lens. Images were analysed using the LSM imaging software as previously described [104].
EGFP and EGFP-ST-transfected cells were detached using Versene (Sigma-Aldrich). The harvested cells were washed with ice-cold PBS and resuspended at 2x106 cells/ml in freshly made staining buffer (PBS, 10% FCS, 3% BSA). Cells were then incubated with appropriate dilutions of primary antibody or staining buffer for 1 hour at room temperature in the dark, washed with staining buffer and then incubated with Alexa-Fluor-tagged secondary antibodies or staining buffer for 1 hour at room temperature. Cells were washed twice in PBS with centrifugation (350x g, 5 min) and then analyzed by flow cytometry on a FACSCalibur, (BD Bioscience, Wokingham, UK) and the data analyzed using FlowJo software (Tree Star, Ashland, OR, USA).
Skin and MCC tumour biopsy samples were crushed using a pestle and mortar on dry ice, and homogenised by sonication prior to lysis in RIPA buffer (50 mM Tris-HCl pH 7.6, 150 mM NaCl, 1% NP40), supplemented with protease inhibitor cocktail (Roche) as previously described [105]. Proteins were separated by SDS-PAGE, transferred to nitrocellulose membranes and probed with the appropriate primary and HRP-conjugated secondary antibodies. Proteins were detected using EZ-ECL enhancer solution (Geneflow) as previously described [106]. Densitometry was performed using ImageJ software.
RNA was extracted using TRIzol (Invitrogen) and DNase treated using the Ambion DNase-free kit, as per the manufacturer’s instructions, before RNA (1μg) from each fraction was reverse transcribed with SuperScript II (Invitrogen), as per the manufacturer’s instructions, using oligo(dT) primers (Promega). 10ng of cDNA was used as template in SensiMixPlus SYBR qPCR reactions (Quantace), as per manufacturer’s instructions, using a Rotor-Gene Q 5plex HRM Platform (Qiagen), with a standard 3-step melt program (95 °C for 15 seconds, 60 °C for 30 seconds, 72 °C for 20 seconds) as previously described [107]. With GAPDH as internal control mRNA, quantitative analysis was performed using the comparative ΔΔCt method as previously described [108].
EGFP and EGFP-ST-transfected HEK 293 cells were seeded in DMEM containing 10% FBS at a density of 2 × 104 per 35 mm culture dish. 18 hours later, cells were serum starved for 24 hours to induce aggregate formation. Upon reintroduction of serum, cells were fixed and stained with DAPI at 6 hourly intervals and clusters of cells were imaged using a Zeiss LSM880 confocal laser scanning microscope using a 10x objective lens. Images were analysed using the LSM imaging software to quantify the distance between each cell nucleus.
Formalin-fixed, paraffin-embedded (FFPE) sections from primary MCC tumours were purchased from Origene and analysed as previously described [32]. Primary antibodies were: FITC-conjugated anti-CK20 (Dako, dilution 1:50), MCPyV LT CM2B4 (Santa Cruz Biotechnology, dilution 1:125) and ADAM 10 and 17 (Abcam, dilution 1:250). An isotype-matched irrelevant antibody was used as a negative control on sections of tissues in parallel, a rabbit polyclonal isotype control antibody (Abcam) was used to match the ADAM 10 primary antibody. Sections were incubated with appropriate secondary antibodies labelled with different fluorochromes (Alexa Fluor 546 IgG2B, 643 IgG2A, Invitrogen, and IgG (H+L)-TRITC, Jackson ImmunoResearch). All slides were mounted with Immuno-Mount and images were captured with a Zeiss LSM880 confocal laser scanning microscope.
Metadata and pre-processed data (FPKM) were downloaded from Gene Expression Omnibus (GSE79968) [26] and GSE39612 [9]. Data were normalised by the trimmed mean of M-values methods using edgeR package to account for batch effects and differences in sequencing depth among the samples using R/Bioconductor [109]. The differential expression analysis was performed using the R Bioconductor packages, voom and limma.
Cell surface biotinylation was performed using the Pierce Cell Surface Protein Isolation kit (Thermo Scientific) according to the manufacturer’s protocol. Cells were incubated a cell-impermeable, cleavable biotinylation reagent, EZ-LINK Sulfo-NHS-SS-Biotin, to label exposed primary amines of proteins on the cell surface. After cell lysis, biotinylated cell surface proteins were affinity-purified using NeutrAvidin Agarose Resin (Thermo Scientific). Precipitated proteins were then analysed using immunoblotting with ADAM 10- and ADAM 17- specific antibodies. A CD71-specific antibody was used as a suitable loading control.
Cell motility was analysed using an Incucyte kinetic live cell imaging system as directed by the manufacturer. HEK293 cells or i293-GFP/i293-GFP-ST cells were seeded at a density of 25,000 cells per well of a 6 well plate, MCC13 cells were seeded at a density of 100,000 cells per well of a 6 well plate. After 12 hours, the cells were transfected with 1 μg of DNA per well and/or induced using doxycycline hyclate. For transfected cells, media was changed after 6 hours (HEK-293 or derivatives) or 12 hours (MCC13). If appropriate, cells were treated with inhibitors for 24h pre-imaging. Imaging was performed for a 24 hour period, with images taken every 30 minutes. Cell motility was then tracked and analysed using ImageJ software.
Migration assays were performed using a CytoSelect 24-well Haptotaxis Assay Collagen coated plates (Cell Biolabs, Inc), as directed by the manufacturer. All conditions were performed in triplicate.
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10.1371/journal.pntd.0004121 | A Receptor-Based Explanation for Tsetse Fly Catch Distribution between Coloured Cloth Panels and Flanking Nets | Tsetse flies transmit trypanosomes that cause nagana in cattle, and sleeping sickness in humans. Therefore, optimising visual baits to control tsetse is an important priority. Tsetse are intercepted at visual baits due to their initial attraction to the bait, and their subsequent contact with it due to landing or accidental collision. Attraction is proposed to be driven in part by a chromatic mechanism to which a UV-blue photoreceptor contributes positively, and a UV and a green photoreceptor contribute negatively. Landing responses are elicited by stimuli with low luminance, but many studies also find apparently strong landing responses when stimuli have high UV reflectivity, which would imply that UV wavelengths contribute negatively to attraction at a distance, but positively to landing responses at close range. The strength of landing responses is often judged using the number of tsetse sampled at a cloth panel expressed as a proportion of the combined catch of the cloth panel and a flanking net that samples circling flies. I modelled these data from two previously published field studies, using calculated fly photoreceptor excitations as predictors. I found that the proportion of tsetse caught on the cloth panel increased with an index representing the chromatic mechanism driving attraction, as would be expected if the same mechanism underlay both long- and close-range attraction. However, the proportion of tsetse caught on the cloth panel also increased with excitation of the UV-sensitive R7p photoreceptor, in an apparently separate but interacting behavioural mechanism. This R7p-driven effect resembles the fly open-space response which is believed to underlie their dispersal towards areas of open sky. As such, the proportion of tsetse that contact a cloth panel likely reflects a combination of deliberate landings by potentially host-seeking tsetse, and accidental collisions by those seeking to disperse, with a separate visual mechanism underlying each behaviour.
| Tsetse flies transmit trypanosomes that cause sleeping sickness. Visual baits to attract and kill tsetse are an important method of vector control, and the rational improvement of these baits depends on a mechanistic understanding of tsetse behaviour. Visual baits are often panels of insecticide-treated cloth which tsetse must contact to become dosed with insecticide. However, most of the tsetse that are attracted to approach visual baits circle them rather than landing. Colour is one factor that might be important in eliciting landing responses, and thus bait optimisation. Visually-driven tsetse behaviour can be understood by investigating how a fly’s five types of photoreceptor respond to differently coloured baits, and determining how each of these photoreceptors contributes to behaviour. I applied this approach to data recorded in two previous field studies. I found that tsetse contacted visual baits due to two behavioural mechanisms: a comparison between the responses of several photoreceptors that underlies attraction and landing, and a UV photoreceptor-driven mechanism that likely drives dispersal towards open sky and causes tsetse to collide with visual baits accidentally. If the mechanistic basis of tsetse behaviour is understood, it may be possible to design baits that exploit these mechanisms and optimise tsetse control.
| Tsetse flies (Glossina spp.) occur in sub-Saharan Africa and transmit the trypanosomes that cause nagana in cattle, and sleeping sickness (human African trypanosomiasis, HAT) in humans [1]. Riverine tsetse (Palpalis species group) are responsible for most cases of HAT [2]. In contrast to savannah tsetse (Morsitans species group) which respond strongly to odour cues, riverine flies characteristically respond weakly [3]. Effective odour cues for attracting riverine tsetse may yet be identified [4], but at present odourless, insecticide-treated cloth panels are advocated for the cost-effective control of these flies [2,5,6]. Understanding the visually-guided behaviours that draw tsetse to such baits can contribute to current efforts to optimise the cost and efficiency of control operations, and one factor that has received much attention is the role of colour [7,8,9,10].
Studies to understand tsetse attraction to baits have often employed grids of electrocuting wires which can enclose simple panels of coloured cloth bait material (e-cloths), or of fine net (e-nets). E-cloths sample tsetse that land on the cloth bait, whilst e-nets are difficult for tsetse to detect and sample those flies that accidentally collide with them [11,12,13]. This allows tsetse to be sampled not only when they contact a particular bait but also when circling nearby, allowing sophisticated investigation of their behaviour (e.g. [14]). As a result, it is recognised that tsetse are intercepted at baits as a function both of their initial attraction to approach the bait from a distance, and their propensity to land on the bait (or enter a trap) once close (e.g. [15]). A variety of interacting olfactory and visual cues can contribute to these behavioural processes (for reviews, [16,17]), but among them reflected light wavelength cues are both important, and relevant to the optimisation of the visual baits currently advocated for riverine tsetse control.
The role of colour cues in enticing tsetse to approach a stationary visual bait is relatively well understood, and the phthalogen blue dye for cotton fabrics produces a particularly attractive colour (e.g. [9]). Field studies monitoring combined tsetse catches at coloured e-cloths and flanking e-nets (sampling tsetse landing on the coloured cloth, and those circling it), have found positive contributions of blue wavelengths, and negative contributions of green/yellow/red and UV wavelengths, to the tsetse catch [8,9]. The same trends were also found in studies of tsetse catches in three-dimensional traps of various designs, although these catches would have resulted both from attraction into the vicinity of the traps, and trap entry responses [7,8]. The above insights were gained by direct analysis of visual bait reflectance spectra, but it is the responses of photoreceptors to these spectra that guide a fly’s behaviour. Across the majority of ommatidia in the fly compound eye, excluding the male fovea and the polarisation-sensitive dorsal marginal area, there are five classes of photoreceptor with varying spectral sensitivities (Fig 1) [18,19,20]. Recently, the datasets produced during the above tsetse field studies have been reanalysed using the calculated excitations of fly photoreceptors as predictors of attraction. The result of this reanalysis was that fly photoreceptors R7y (UV-blue) and R8p (blue) contribute positively, whilst R7p (shorter wavelength UV) and R8y (green) contribute negatively [10]. Perhaps because photoreceptors R7y and R8p provide somewhat redundant information, the attraction of tsetse to approach a visual bait, and the special attractiveness of phthalogen blue cotton, could be parsimoniously explained by a simple opponent mechanism involving the calculated excitations (E) of three of these photoreceptors as follows: +ER7y –ER8y –ER7p [10].
Videographic observations demonstrate that when tsetse alight on a black cloth target, they very rarely do so after having made a direct approach to it. Instead, the initial approach is followed by local circling or alighting on the ground before the fly eventually lands on the target [13]. This accords with data gained using combinations of e-cloth and flanking e-net, where the e-net sample of circling flies often exceeds the e-cloth sample of those that land directly [5,6,9]. Furthermore, intricate studies using e-nets reveal that only some of the flies attracted to a bait ultimately land at all, the others departing after having circled it [14,15]. Since the insecticide-treated cloth panels used for tsetse control can only be effective if tsetse make contact with them, insecticide-treated flanking nets are advocated to intercept and kill circling flies by inducing accidental collisions [5,6,21]. However, the cues that induce tsetse to alight remain an interesting and little understood area of investigation. Where field studies have employed combinations of e-cloth and flanking e-net, the catch of the e-cloth expressed as a proportion of the combined catch of the e-cloth and e-net (henceforth, Pcloth) is used to provide a measurement of tsetse preference for direct landing over circling (see Fig 2). As such, this measurement is commonly referred to as the ‘landing score’. Pcloth is positively influenced by a bait’s reflectance of UV wavelengths, or low overall luminance, and the former observation has lead to the assertion that UV wavelengths are important cues for eliciting landing [8,15,22,23] (but see also [9]). Hence, a number of studies have investigated dual-colour baits, incorporating panels of colour that strongly stimulate tsetse to approach, and others that provide the putative landing cues (e.g. [22,24]).
The idea that landing responses are positively influenced by UV wavelengths appears to be at odds with the negative contribution of these wavelengths to the chromatic mechanism of attraction to the vicinity of the bait [7,8,9,10]. This would imply that a visual cue that is unattractive at long-range is attractive at close-range, and that entirely different behavioural mechanisms underlie visual attraction, broadly defined, at these different ranges. In this study I aim to shed light on this apparently paradoxical aspect of tsetse behaviour by providing a mechanistic explanation for Pcloth measurements based upon calculated excitation values for fly photoreceptors (c.f. [25,26,27,28]).
The distribution of tsetse catches between e-cloth and flanking e-net was analysed for two field datasets [8,9], out of the four recently analysed to determine a photoreceptor-based model of tsetse attraction [10]. These datasets were selected because they were obtained using simple, two-dimensional e-cloths of various colours, with adjacent two-dimensional e-nets, both oriented vertically (for a simplified schematic representation, see Fig 2).
Data for catches of G. fuscipes fuscipes at small e-cloths (0.25 m x 0.25 m) with equal-sized flanking e-nets were obtained from [9]. In total 37 cotton or polyester e-cloths of different colours were tested in 15 separate experiments. Each experiment investigated tsetse catches at five differently coloured e-cloths, one of which was always a phthalogen blue-dyed cotton standard. Phthalogen blue is often reported to be extremely attractive to tsetse, but the dye can only be applied to cotton fabrics [9]. The original study reported the proportion of the combined catch taken from the e-cloth (there termed the landing score), and absolute numbers of flies in the combined catch, for each e-cloth in each experiment. The number of landing flies was calculated from these data, rounding to the nearest whole number.
Data for catches of G. palpalis palpalis at large e-cloths (1.0 m x 1.0 m) with flanking e-nets (0.5 m x 1.0 m) were obtained from [8]. In total, 27 e-cloths of different colours were tested in 10 separate experiments. Where the type of fabric comprising the e-cloths was stated, it was reported to be cotton [8]. Each experiment investigated tsetse catches at four differently coloured e-cloths, one of which was always a phthalogen blue standard. The original study reported the percentage of the combined catch taken from the cloth panel (there termed the landing score) for each e-cloth in each experiment, although the absolute numbers of tsetse in the combined catch was stated only for the phthalogen blue standards.
Fly photoreceptor excitation values elicited by each coloured e-cloth in the above tsetse field studies were calculated during a previous study [10]. That study made freely available in its supplementary materials the calculated excitation values and the materials required to calculate them, and completely described the calculation procedure (dx.doi.org/10.1371/journal.pntd.0003360) [10]. A brief recap of those methods is provided here for convenience.
Methods with which to calculate photoreceptor excitation from spectra of illumination, stimulus reflectance, background reflectance, and photoreceptor sensitivity are now well established and widely employed (e.g. [25,29]). For each fly photoreceptor type the effective quantum catch (P) of reflected light from a given e-cloth was calculated according to:
P=R∫310600IS(λ)S(λ)D(λ)dλ
Where IS(λ) is the spectral reflectance function for the e-cloth; S(λ) is the spectral sensitivity function of the photoreceptor in question; and D(λ) is the illuminant function. R is the range sensitivity factor which adjusts photoreceptor sensitivity such that background stimulation would elicit a half maximal response in each receptor class, and was calculated by:
R=1/∫310600IB(λ)S(λ)D(λ)dλ
Where IB(λ) is the spectral reflectance function of the assumed background.
Quantum catches were non-linearised to represent the transduction process in each photoreceptor, providing excitation (E) by:
E=P/(P+1)
Calculated photoreceptor excitations have values between 0.0 and 1.0, and through the above procedures the adapting background elicits a half-maximal response of 0.5 units in each photoreceptor [10,29].
The reflectance spectrum of a typical green leaf was used as the background reflectance spectrum, and the illuminant used was the D65 standard expressed as relative quanta (these are provided in S5 table). Both functions were obtained from [29], and were linearly interpolated to achieve 2 nm wavelength resolution. E-cloth reflectance spectra were obtained from the supplementary materials of [9], and linearly interpolated for 2 nm wavelength resolution, or extracted from figures in [8] using Datathief software [30] (the latter are provided in S5 table, whilst the former are freely available online at dx.doi.org/10.1371/journal.pntd.0001661). Photoreceptor sensitivity functions were those typical of Musca and Calliphora extracted from [18] using Datathief (see Fig 1). Although sensitivity functions have been recorded for G. morsitans morsitans, the flies used lacked carotenoid screening pigments due to dietary deficiency [19]. Carotenoid pigments were, however, extracted from the retinae of G. p. palpalis raised on a different diet [19]. The extent of visual screening in wild tsetse is thus unknown and would presumably vary with diet, but the underlying organisation of photoreceptors in tsetse aligns with that for Musca and Calliphora [18,19,31].
The approach taken in this study was to seek statistical explanations for landing scores based upon individual photoreceptor excitation values, and/or indices representing the combined responses of two or more photoreceptor types (c.f. [25,26,27,28]). One such combination was an opponent index representing the chromatic mechanism proposed to underlie attraction, calculated as follows: + ER7y –ER8y –ER7p [10]. This index was previously shown to predict combined e-cloth plus e-net catches in the G. f. fuscipes and G. p. palpalis datasets analysed here [10]. In order to aid in data interpretation, the opponent index was also calculated for leaves in the adapting background (+ 0.5–0.5–0.5 = -0.5).
The two tsetse catch datasets each included a number of separate experiments in which sub-sets of e-cloths were compared, and these were often clustered around similar values of the opponent index. Therefore, I used Generalized Estimating Equations (GEEs) to try to model the clustering of data within experiments [32,33], without including ‘experiment’ as a factor in the analysis because this might have masked the overall relationship with opponent index or other predictors. The original experiments used latin squares designs to block out variation due to bait location and day, but the experiments themselves were separated in time. Thus, it was reasonable to expect that tsetse catches within each experiment would be related, but no particular structure was expected to the relatedness within experiment. As such, an exchangeable working correlation matrix was appropriate.
Because Pcloth is calculated from a known total number of flies in each combined catch, it is appropriate to analyse these measurements using a binary logistic model which correctly models the variance of such proportions [34]. This was possible for the G. f. fuscipes dataset where the total numbers of flies in each combined catch were directly reported. For these data a binomial distribution—logit link GEE model was employed. However, in the G. p. palpalis dataset absolute combined catches were reported only for the phthalogen blue cloth, with percentage catches for each of the other cloths within an experiment. The stated percentage catches were often not achievable by dividing any absolute catch integer value by that stated for the standard, presumably because the percentage catches were calculated from detransformed means as in other previous studies [9]. Hence, the numbers of flies in each combined catch could not be determined with certainty, and I decided instead to analyse Pcloth values directly, after logit transformation [34], using a normal distribution—identity link GEE model. Such approaches incorrectly assume equal variances across measured proportions, which can reduce their statistical power to detect differences [34]. Nevertheless, the distribution of the residuals from the normal—identity GEE analyses reported in the main text did not differ markedly from a normal distribution (as determined by Kolmogorov-Smirnov tests and visualisation of Q-Q plots), or demonstrate a strongly marked pattern when plotted against values for the linear predictor.
The goodness of fit of GEE models was assessed using the quasi-likelihood under independence model criterion (QIC), and a version of this statistic that corrects for model complexity and small sample size (QICC) [35,36]. QIC is a modification to Akaike’s information criterion (AIC) for use with GEE models [35], and lower values for such criteria indicate improved fit to the data. With respect to AIC, models within 2 units of the best model are sometimes considered to be competitive [37]. All analyses were conducted using SPSS version 22.0 (IBM Corp., Armonk NY, USA).
The chromatic mechanism proposed to underlie tsetse attraction can be approximated by a simple opponent index, and the combined catch of an e-cloth and flanking e-net was previously shown to have a positive relationship with this index (see Fig 7 of [10]). Fig 3 shows the relationship between this same opponent index and Pcloth (the proportion of the combined catch that was caught on the e-cloth), which represents the propensity of tsetse to directly contact the cloth panel in preference to first, or only, circling around it (see Fig 2). In contrast to combined catches, Pcloth did not have a simple, positive relationship with opponent index. GEE models containing a quadratic term had lower QIC and QICC versus simpler linear models for all datasets (Table 1; Fig 3). However, whilst the fit of the quadratic model was substantially better than that of the linear model for G. f. fuscipes, the two models were competitive for G. p. palpalis (for which the linear model described a negative relationship between Pcloth and opponent index).
The green vertical line in each panel of Fig 3 shows the opponent index value calculated for leaves in the adapting background, to which each photoreceptor responds with a half-maximal response of 0.5 units of excitation [10,29]. The fitted quadratic relationships suggest that Pcloth tended to increase with opponent index for visual baits that were more attractive than their background, although this trend was much more marked for G.f. fuscipes than for G. p. palpalis. Such a trend might be expected if the mechanism implicated in initial attraction also underlay landing responses (Fig 3, to the right of the green lines). However, inconsistent with this explanation, the fitted quadratic relationships also tended to increase as visual baits became increasingly less attractive than their background, although in this respect the trend was more marked for G.p. palpalis than G. f. fuscipes (Fig 3, to the left of the green lines). These quadratic relationships between Pcloth and opponent index were not considered to be biologically meaningful in themselves, but were hypothesised to be evidence that a second behavioural mechanism interacts with opponent index in determining tsetse catch distribution.
I next conducted GEE analyses to model Pcloth based upon the opponent index describing visual attraction, excitation values of photoreceptors that may drive a second behavioural mechanism, and the interaction between these two mechanisms (Tables 2 and 3). With the exception of the model containing photoreceptor R8p excitation for the female G. f. fuscipes dataset, all of these models resulted in reductions in QIC and QICC over the linear relationships with opponent index alone presented in table 1. Of these models, that which used the shorter wavelength UV photoreceptor R7p’s response consistently fitted each dataset better than models using excitation values for any other photoreceptor type, and in the R7p models the effects of all predictors were significant (Tables 2 and 3; Fig 4). Judged by differences in QIC >2, no other model was deemed competitive with the R7p model, although for the G. p. palpalis dataset QICC differences <2 provided some support for the alternative models other than that using R8p excitation. Removing the interaction term from any R7p model reduced its fit to the data. Elaborating any R7p model with an additional photoreceptor excitation value and its interaction term also reduced its fit to the data (S1 and S2 tables).
To further support the adequacy of the opponent index/R7p model, I also computed sums of, and differences between, the excitation values of every possible combination of photoreceptor pairs and used these in GEE models that also contained opponent index and an interaction term (S3 table). Alongside opponent index, summed excitation values of photoreceptor pairs (representing an additional achromatic mechanism) generally fitted the data better than computed differences between the excitation values of photoreceptor pairs (representing an additional chromatic mechanism). In the G. f. fuscipes datasets, the model including summed R7p and R7y excitations alongside opponent index was the only one for which there was a substantial improvement in QIC or QICC over the opponent index/R7p model, but this was evident only for males and not for females (S3 table). In the G. p. palpalis datasets, many summed photoreceptor models provided largely equivalent QIC or QICC values (i.e. within 2 units) to the opponent index/R7p model, but among these reductions were only evident in QICC, and only for models in which R7p excitation was part of the photoreceptor sum (S3 table).
In order to rule out the potentially simpler possibility that Pcloth might result entirely from a single achromatic or chromatic mechanism, I examined GEE models containing every possible combination of between one and five photoreceptor types to predict Pcloth (S4 table). These models fitted the data substantially less well than the above models with two interacting mechanisms, indicating that they did not provide a better explanation for tsetse behaviour. Thus, overall, these analyses support the assertion that Pcloth can be predicted by the colour opponent model that was proposed to underlie initial attraction, and an additional, interacting achromatic mechanism reliant on excitation from photoreceptor R7p (Fig 4). However, the additional contribution of other photoreceptors to that achromatic mechanism should not be ruled out.
In this study I reanalysed tsetse catch distribution across coloured e-cloths and flanking e-nets based upon a chromatic mechanism recently proposed to explain tsetse attraction to approach visual baits. I found that Pcloth increased as cloth panels became more attractive by an index describing this mechanism, as expected if the same chromatic mechanism of attraction underlay both the approach to a bait, and subsequent landing upon it. However, I also found that Pcloth increased as excitation of the UV-sensitive photoreceptor R7p increased, indicating that tsetse are also driven to contact cloth panels as a result of a separate but interacting achromatic mechanism.
It seems intuitive that tsetse should more readily alight upon cloth panels that are more attractive by the chromatic mechanism implicated in their initial attraction to approach them. However, the involvement of a second, achromatic mechanism in causing tsetse to directly contact such cloth panels is less easy to explain. Flies are well-known to display an innate attraction to UV light and in Drosophila the R7 photoreceptors are important in driving this response [38,39]. This behaviour is often called the ‘open space response’, and is presumed to guide flies towards areas of open sky. This is because the sky is strongly radiant in UV wavelengths, whilst many features of the terrestrial environment are characterised by strong UV absorption (e.g. see [40]). Earlier tsetse work has already suggested that UV wavelengths may functionally represent skylight, causing highly UV-reflective cloth panels to elicit high Pcloth values not by eliciting landing responses, but as a result of accidental collisions by tsetse attempting to disperse [15,23]. The R7p-driven achromatic mechanism suggested by my analysis appears well aligned with these explanations, which would suggest that tsetse catch distributions are affected by two distinct behavioural motivations. In further support of this idea, G. tachinoides caught on e-cloths tended to have lower fat content than those caught on flanking e-nets, which was interpreted as an indication that the relatively more starved flies were more prone to land directly in preference to circling, due to their requirement to be less discriminating in host seeking [24]. In the same study, female flies caught over the UV-reflective white portion of a half-blue, half-white e-cloth had higher fat content than those caught over the blue portion, and their fat content was equivalent to that of flies caught at flanking e-nets of other target designs in the same experiment [24]. This trend was, however, not evident for males. Nevertheless, since highly UV-reflective baits are unattractive to host-seeking tsetse [7,8,9,10], the fact that better-nourished and potentially more discriminating female flies tended to make contact with them [24], would be consistent with the explanation that these flies were attempting to disperse rather than land on a perceived host. However, detailed observations of tsetse behaviour prior to interception on UV- and non-UV-reflective cloth panels, as have been made of tsetse behaviour prior to alighting on black panels [13], will be required to directly test this hypothesis and provide persuasive evidence for the above explanation.
A UV effect on Pcloth was not evident in the authors’ original analysis of the G. f. fuscipes dataset [9], and in this reanalysis the analogous R7p effect was notably weaker than that seen for G. p. palpalis. A plausible explanation for this difference between datasets is the different size of the e-cloths in the two studies: those in the G. f. fuscipes study were 1/16th the size of those in the G. p. palpalis study. Alighting responses of savannah tsetse increase with the size of blue or black targets [41,42], whilst the alighting responses of riverine species are relatively little affected by changes in the size of such a target [5]. A potential explanation for this is the effect of habitat geometry on tsetse movement and expression of host-seeking behaviour [43]. However, if some of the tsetse intercepted by UV-reflecting baits are in fact attempting to orient towards perceived open spaces rather than alighting on perceived hosts, it is plausible that the larger area of those open spaces enhanced this separate behavioural response, resulting in the difference between the datasets. However, a number of other explanatory factors cannot be ruled out. The UV effect was clearly evident in a study of the riverine tsetse G. p. palpalis [8], but only for a sub-set of UV-reflective baits which also allowed some light to pass through them in a study of the savannah tsetse G. pallidipes [23]. It is certainly possible that species differences in behaviour explain such discrepancies, but it must also be borne in mind that the highly UV-reflective baits that elicit high Pcloth values also tend to attract the lowest combined catches, resulting in greater error around Pcloth measurements for such baits. This factor has special relevance to the current analysis, since the binomial—logit GEE model applied to G. f. fuscipes data correctly modelled the variance of Pcloth measurements, whilst this was not true of the normal—linear GEE model that was applied to logit-transformed G. p. palpalis Pcloth values for reasons of data availability. For this reason, some caution should be exercised in evaluating the trends for G. p. palpalis, although trends in that dataset were strongly evident, and substituting binary logistic models for linear ones might be expected to reduce statistical power [34].
An additional factor that might cause variation in the UV effect on Pcloth is the specific positioning of a visual bait. This might lead to variability in the effect of colour cues on attraction and landing as a result of variation in the background they are viewed against, or their spectrum of illumination. In possible support of this general notion, a study of G. tachinoides in Cote d’Ivoire found significant differences in attraction to blue, violet, red, and black e-cloths between replicates conducted in gallery forest, and those conducted on more open riverbank habitat [24]. Furthermore, Pcloth was significantly higher for males in the riverbank replicate. However, whilst this supports the general notion that bait positioning may be an important factor affecting visual cues and behavioural responses to them, the same study provided no evidence that such factors might affect the UV effect on Pcloth specifically. This was because there were no apparent differences between replicates of an experiment incorporating high- and low-UV reflectance white baits in the same two habitats, and the UV effect on Pcloth was only evident for females in the combined data from both replicates [24]. An additional way in which bait positioning may affect Pcloth is via active avoidance of e-nets, which has been shown to be greater in shade than full sun [13]. Active avoidance of the e-net would cause an increase in Pcloth, as a result of a reduction in combined catch. Finally, other cues which were not quantified in the original field studies may also influence landing responses. For example, polarotaxis has been implicated in attraction and landing of tabanid flies on potential hosts and artificial baits [44,45,46], but the visual baits analysed in this study were not quantified with respect to reflected polarised light.
Alongside the R7p-driven achromatic mechanism, this study also provides evidence that the chromatic mechanism guiding tsetse attraction towards a visual bait might also encourage them to land upon it. This suggests that the same mechanism underlies attraction at both long- and close-range. By comparison with findings for plant-seeking insects (e.g. [28]) it was argued that blue-green (R7y-R8y) opponency provides a means to distinguish vegetation from other objects, such as potential vertebrate hosts [10], which aligns with previous explanations for the blue preference of tsetse [16]. The additional, negative input of photoreceptor R7p improved the fit to the data, and was thus implicated in the opponent mechanism underlying attraction [10]. Given the above interpretation of the functional role of R7p and UV wavelengths, this input may function to distinguish patches of open sky from vegetation and potential hosts. However, in light of the analyses presented in this study, it might be debated whether R7p’s effect on attraction comes about as a result of its input to the proposed chromatic mechanism, or solely via the interaction of the achromatic mechanism suggested here.
Low luminance black fabrics are also well known to elicit strong tsetse landing responses (e.g. [15,42]). Such fabrics are characterised by low reflectance at all wavelengths, including the UV, so these landing responses cannot be explained by the R7p-driven achromatic mechanism suggested here. The opponent index used to describe attraction in this analysis simply subtracts the excitation of photoreceptors R7p and R8y from that of R7y, and as a result the value is negative for all stimuli in this analysis with those closest to zero the most attractive. Because black fabrics have uniformly low luminance, they elicit low excitation values in all photoreceptors, and as a result of that also have opponent indices that are relatively close to zero and, therefore, are predicted to be attractive [10]. With the important caveat that neural computations in a fly’s brain will differ to a greater or lesser extent from their simplified representation here, the ability of black fabrics to elicit tsetse landing responses is compatible with the scheme described in this analysis. However, other explanations must not be ruled out, such as a separate role for low luminance in attraction, or the involvement of polarotaxis for which dark surfaces are particularly effective in providing polarised light cues [20,46]. Studies of a range of Glossina species have reported decreased catches using blue/black combination e-cloths, when the cloth panels inside the electrocuting grids were covered by an adhesive sheet that absorbed UV wavelengths [47,48,49]. This resulted from decreased tsetse catch over the black portion of the cloth panel only. In these studies the UV reflectance of the black cloth was low, meaning that this result is unlikely to be explained by an effect of the UV manipulation on the R7p mechanism described in the current analysis. Since the adhesive film absorbed wavelengths below 400 nm [49], it would have affected not only the repellent R7p response (shorter wavelength UV), but also the attractive R7y response (UV-blue), and may thus have had complex effects on the mechanism of attraction. It is also possible that the adhesive sheet affected other visual cues, such as the polarisation of reflected light [44,45,46].
Intercepting circling tsetse has great potential to augment catches since the majority of the tsetse attracted into the vicinity of a bait circle around it rather than landing [6,9,14,15]. This has motivated the use of insecticide-treated flanking nets to intercept circling flies, and these are important additions to the small cloth panels currently advocated for riverine tsetse control, where their small size and the use of modern netting materials make them robust [5,6]. By contrast, larger visual baits are employed for savannah tsetse, and large flanking nets to accompany these have sometimes been suggested to be damage prone [5,6]. However, although flanking net damage did reduce a bait’s efficacy in field trials, replacement rates were higher for net than cloth portions but low in both cases (0.2 versus 0.1% monthly replacement rate, respectively) [50]. Furthermore, savannah tsetse landing responses increase with bait size [41,42,51], such that large cloths can function just as efficiently as cloth and flanking net combinations of the same size [42]. Therefore, although some riverine tsetse may mistake highly UV-reflective cloths for patches of open sky, even if this finding were transferable to savannah tsetse it is unlikely to mean that UV-reflective cloths can provide a useful substitute for the flanking net. Nevertheless, the suggestion that UV-reflecting cloths likely catch tsetse attempting to disperse rather than host-seek does have implications for visual bait optimisation. Short wavelength excitation of photoreceptor R7p was previously shown to contribute negatively to the chromatic mechanism of attraction [10], and in the current analysis strong excitation of R7p was implicated as interacting with that mechanism. As such, the attractiveness of visual baits is likely best enhanced by reducing UV reflectance. The currently preferred phthalogen blue dye has these properties, but can only be applied to cotton material (e.g. [9]). Modern polyester fabrics offer a number of advantages in terms of cost and robustness, but the blues currently produced for tsetse control have broader reflectance peaks than phthalogen blue that extend into the UV (e.g. see reflectance spectra for blues 7 and 8 in [9]). Curtailing reflectance at low wavelengths and enhancing it in the attractive region using fluorescent dyes, as has been suggested previously [8], may be the key to optimising these fabrics. In addition, the use of stand-alone insecticide-treated, UV-reflective cloth panels without flanking nets might potentially provide a useful complement to the standard baits, if they do indeed attract a different sub-set of the tsetse population.
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10.1371/journal.pgen.1006789 | PCNA ubiquitylation ensures timely completion of unperturbed DNA replication in fission yeast | PCNA ubiquitylation on lysine 164 is required for DNA damage tolerance. In many organisms PCNA is also ubiquitylated in unchallenged S phase but the significance of this has not been established. Using Schizosaccharomyces pombe, we demonstrate that lysine 164 ubiquitylation of PCNA contributes to efficient DNA replication in the absence of DNA damage. Loss of PCNA ubiquitylation manifests most strongly at late replicating regions and increases the frequency of replication gaps. We show that PCNA ubiquitylation increases the proportion of chromatin associated PCNA and the co-immunoprecipitation of Polymerase δ with PCNA during unperturbed replication and propose that ubiquitylation acts to prolong the chromatin association of these replication proteins to allow the efficient completion of Okazaki fragment synthesis by mediating gap filling.
| PCNA is a homotrimeric complex that clamps around the DNA to provide a sliding platform for DNA polymerases and other replication and repair enzymes. The covalent modification of PCNA by ubiquitin on lysine reside 164 has been extensively studied in the context of DNA repair: it is required to mediate the bypass of damaged template bases during DNA replication. Previous work has shown that PCNA is modified by ubiquitin during normal S phase in the absence of DNA damage, but the significance of this modification has not been explored. Here we show that, in addition to regulating bypass of damaged bases, lysine 164 ubiquitylation plays a role in ensuring the completion of unperturbed DNA replication.
| It is well established that the replication machinery encounters a variety of obstacles and is thus designed with a degree of flexibility. This plasticity of DNA replication depends on both alternative components and regulation by post-translational modification. For example, while genetic and physical studies indicate that the leading and lagging strands are primarily replicated by DNA polymerase ε (Polε) and DNA polymerase δ (Polδ), respectively [1–4], this assignment is flexible: Polδ synthesises the leading strands on rare occasions [5–7], synthesises both strands during viral replication [8] and can sustain cell viability in the absence of Polε [9].
Key to orchestrating enzymes for DNA replication is PCNA, which serves as a scaffold for recruiting many of the numerous enzymes involved, including the replicative DNA polymerases. In addition, PCNA ubiquitylation on lysine164 regulates DNA damage tolerance (DTT). When replication is blocked by damaged DNA bases the Rad6-Rad18 E2-E3 ligase complex binds to single stranded DNA coated with RPA and mono-ubiquitylates PCNA to promote translesion DNA synthesises by non-canonical polymerases [10, 11]. Subsequent to mono-ubiquitylation, PCNA can be poly-ubiquitylated by the Ubc13-Mms2-Rad5 complex [12, 13] to initiate damage bypass by HR-dependent template switching [14]. The level and duration of PCNA ubiquitylation is additionally regulated by constitutive deubiquitylation [15–17].
The prevailing view is that PCNA ubiquitylation is a DNA damage-induced phenomena. This is consistent with the budding yeast situation, where PCNA ubiquitylation is barely detectable in unperturbed S phase but robustly induced in response to replication-blocking DNA lesions [10, 12]. However, PCNA is robustly ubiquitylated during unperturbed replication in fission yeast [18] and significant levels of PCNA ubiquitylation are evident during unperturbed replication in frog extracts and metazoan cells [19, 20]. Several observations suggest that PCNA ubiquitylation is linked to DNA replication: PCNA ubiquitylation is upregulated in response to an increase in canonical replication intermediates [21–23] and a recent synthetic genetic array analysis in budding yeast showed that the PCNA ubiquitylation pathway is genetically correlated with the mechanism of lagging strand DNA synthesis [24]. Moreover, in vitro reconstitution of PCNA ubiquitylation demonstrates that efficient mono-ubiquitylation is coupled to DNA synthesis by Polδ [25].
Despite the accumulating evidence that PCNA ubiquitylation is linked to the processes of DNA replication, there have been no reports that examine if the process of unperturbed DNA replication is influenced by the ubiquitylation of PCNA and the role of this modification during unperturbed S phase remain unclear. To address this question experimentally, we investigated how replication dynamics are influenced by PCNA ubiquitylation in fission yeast. We find that, in the absence of PCNA ubiquitylation DNA replication is slower and that there is an increase in single stranded DNA gaps in S phase cells. We also observe that PCNA ubiquitylation increases the amount of chromatin associated PCNA and influences the recruitment of Polymerase δ. We propose that PCNA ubiquitylation facilitates the completion of Okazaki fragment synthesis.
In fission yeast PCNA is ubiquitylated during unperturbed S phase [18] and is not significantly further induced by UV-induced DNA damage during S-phase (Fig 1A and S1A and S1B Fig). Cells arrested in early S phase by hydroxyurea maintained high levels of PCNA ubiquitylation (S1C Fig) and after irradiation in S phase PCNA ubiquitylation persisted for longer (Fig 1A), likely due to the slowed S phase progression. Thus, the PCNA ubiquitylation promoted by UV-irradiation of asynchronous fission yeast cultures (S1D Fig) is primarily a consequence of cells accumulating in S phase. To explore what replication defects result in PCNA ubiquitylation we examined PCNA-Ub in selected temperature-sensitive replication mutants. We observed that inactivation of enzymes required for lagging strand synthesis (DNA ligase 1, Polδ), but not enzymes associated with replisome progression (the MCM complex, Polε), resulted in elevated ubiquitylation levels at lysine 164 of PCNA (S1E and S1F Fig). Collectively, these results indicate that the accumulation of lagging strand intermediates [21–23], but not fork stalling per se, are a major cause of PCNA ubiquitylation.
If incomplete lagging strand synthesis activates PCNA ubiquitylation, it is possible that PCNA-Ub participates in the completion of Okazaki fragment synthesis. To examine this possibility, we first determined the contribution of PCNA ubiquitylation to the progression of unperturbed S phase by assessing replication dynamics in synchronised populations (Fig 1B–1F). Since S. pombe Pcn1 can be modified on lysine 164 by either ubiquitin or SUMO, we first examined cells defective for the Rhp18 E3-ligase (Rhp18 is the S. pombe homolog of S. cerevisiae Rad18. For clarity, we refer to this E3 ligase as Rad18 through the text). While S phase entry was slightly delayed in rad18Δ cells (Fig 1C), bulk replication progression proceeded with similar kinetics when assessed by total bromodeoxyuridine (BrdU) accumulation (Fig 1C). In contrast, while cells carrying the mutation of the ubiquitylated PCNA residue, pcn1-K164R, also slightly delayed S phase entry, their progress through S phase was also defective (S2A and S2B Fig). Importantly, rad18Δ was epistatic with pcn1-K164R for the slight delay to S phase entry (S2A Fig), confirming that the delay seen in rad18Δ cells is PCNA ubiquitylation dependent. It is unclear why the pcn1-K164R mutation also conferred a ubiquitylation-independent defect in S phase progression (S2A Fig). We observed that replication timing was also perturbed and that Polε DNA association during S phase was reduced (see below) in a manner that was independent of the Pli1 SUMO ligase. As pcn1-K164R is thus clearly acting as a hypomorphic allele, we concentrated our analysis on the rad18 deletion mutant cells.
To establish if PCNA ubiquitylation affected the DNA replication kinetics of specific loci we examined enrichment of BrdU across the genome during mid to late S phase by BrdU-IP in rad18+ and rad18Δ cells (Fig 1D). This showed changes to the replication dynamics, with advanced replication close to origins and delayed replication for the inter-origin regions. Because relative BrdU enrichment between two samples does not directly reflect relative replication kinetics (the two samples will not be at exactly the same point in S phase), we performed independent replication time courses for rad18+ and rad18Δ cells and normalised for replication progression in order to directly compare DNA replication timing across the genome (Fig 1E, see Materials and methods for details). Replication progression was calculated at each local region of the genome when the global genome replication level was either 25, 50 or 75%. I.e. we used the global extent of replication to standardise comparisons between rad18+ and rad18Δ strains such that the extent of local replication was compared between strains with equivalent global levels. rad18Δ cells showed delayed replication at regions distal to replication origins which are, relative to origins, late replicating (light blue, Fig 1E). This was compensated for by higher local replication at many origin-associated regions that are relatively early replicating (light red, Fig 1E). Some additional peaks were also observed, for example regions 1770-kb region in Chr. II and 3320-kb in Chr. III, suggesting reduced fork progression rates are partially compensated for by firing cryptic origins [26]. The distribution of BrdU at genomic regions surrounding origins would be expected to become wider as S phase progressed (ultimately it would be flat at the end of S phase). Consistent with our hypothesis that replication fork progression is subtly delayed in rad18Δ cells (Fig 1E), we observed that deletion of rad18 resulted in a narrower distribution of BrdU later in S phase when compared to rad18+ control cells (S3A–S3E Fig). Control experiments where we allowed cells to progress into S phase in the presence of hydroxyurea confirmed that the two strains initiated S phase at the same origins and confirm that our sequencing methodology is reproducible (S3B Fig).
To examine further whether rad18Δ caused delayed replication in regions that replicate late, a meta-analysis was performed by computationally identifying replication origins and analysing the relatively late replicating inter-origin regions. As shown in Fig 1F the local replication extent of the early replicating origins was not perturbed in rad18Δ. In contrast, later replicating regions show a significant decrease in their extent of replication, even when adjusted for the global replication amounts. This effect was particularly striking in the regions that were amongst the last to be replicated (Fig 1G). Analysis of the specific loci that were most under-replicated in rad18Δ cells (S4A and S4B Fig) showed they correspond to those loci that we previously demonstrated to be the last to be replicated in wild type cells [5]. These data demonstrate that the lack of PCNA ubiquitylation delays replication fork progression, with the cumulative effect manifesting most obviously at late replicating regions.
PCNA is loaded during DNA replication, functions as the replicative clamp and remains chromatin associated until the polymerase has finished replication and ligation is complete. We speculated that PCNA ubiquitylation may contribute to PCNA retention on the chromatin. However, in native cell extracts PCNA is progressively deubiquitylated, compromising the ability to measure PCNA ubiquitylation during chromatin association assays. To overcome this limitation, we increased the level of PCNA ubiquitylation by engineering a strain, Purg1-rad18, where rad18+ is under the control of an inducible promoter (Fig 2A). Fractionation of cell extracts following rad18 induction revealed that ubiquitylated PCNA was preferentially associated with chromatin (Fig 2B) in a manner dependent on K164 ubiquitylation (Fig 2C and 2D). This suggests the modification contributes to the stability of PCNA chromatin association. Consistent with this, shut-off of rad18+ transcription from Purg1 when combined with induced Rad18 degradation resulted in rapid PCNA disassociation from chromatin, concomitant with deubiquitylation (S5 Fig). Because it is not practical to assay native fission yeast extracts for endogenous levels of ubiquitylated PCNA on chromatin due to its deubiquitylation by isopeptidase in native extracts we compared the total chromatin-associated PCNA in rad18+ and rad18Δ cells during S phase. In rad18+ cells, PCNA accumulated in S phase and gradually diminished towards the completion of replication. Comparatively, in rad18Δ cells, the amount of chromatin associated PCNA decreased during the late stages of replication (Fig 2E). This is reminiscent of the predominant effect of loss of PCNA ubiquitylation manifesting at late replicating regions (Fig 1F and 1G).
We verified the observed effect of Rad18 loss on PCNA chromatin association using a photo-activated localization microscopy (PALM)-based technique that directly visualises DNA-associated PCNA [27]. Briefly, this method exploits motion blurring to selectively eliminate signals arising from rapidly diffusing molecules, allowing visualisation of low mobility signals derived from DNA-associated molecules (Fig 2F). Previously we reported that low mobility PCNA (mEos3.1-Pcn1) is notably enriched during S phase [27]. Deletion of rad18 significantly reduced the fraction of these molecules (Fig 2F, right), thus confirming that PCNA-K164 ubiquitylation results in increased amounts of loaded PCNA during unperturbed S phase.
One possible explanation for the increased amount of chromatin-associated PCNA accompanying K164 ubiquitylation is that this contributes to the function of DNA polymerases during DNA replication. In unchallenged cells we could detect the association of Polδ, but not Polε, with PCNA by immunoprecipitation (S6A and S6B Fig). This would be consistent with the higher PCNA-dependency of Polδ function [28–30], but may equally reflect the lower levels of DNA-associated Polε during S phase when compared to Polδ. Increased PCNA ubiquitylation (by Rad18 overexpression via Purg1-rad18) increased Polδ co-immunoprecipitation with anti-PCNA without influencing cell cycle profiles (Fig 3A and 3B). PCNA ubiquitylation and co-immunoprecipitation were also both enhanced by hydroxyurea treatment of rad18+ and Purg1-rad18 cells. Thus, Polδ: PCNA co-immunoprecipitation intensity scaled with PCNA ubiquitylation (Fig 3A and 3B). We also noted that the PCNA which co-immunoprecipitated with Polδ was biased toward ubiquitylated forms (Fig 3C) and that the loss of poly-ubiquitylation (ubc13 deletion) showed an intermediate decrease in co-immunoprecipitation of Polδ when compared to loss of all ubiquitylation (pcn1-K164R) (Fig 3D). Using the PALM motion blurring assay (see Fig 2F) we did not detect a decrease in the Polδ immobile fraction in untreated S phase rad18Δ cells (Fig 3E), possibly because our assay is insufficiently sensitive. However, when rad18Δ cells were arrested within S phase by hydroxyurea treatment, the fraction of low mobility Polδ molecules decreased when compared to rad18+ controls, providing support for the contention that PCNA ubiquitylation contributes to Polδ function.
PCNA recruits DNA polymerases after it is loaded [31] and the affinity of PCNA: Polδ binding is not influenced by K164 ubiquitylation [32]. Thus, the increased Polδ-PCNA association could be accounted for purely by the increased amount of PCNA on DNA due to ubiquitylation inhibiting clamp unloading. This predicts that increasing PCNA chromatin association independently of its ubiquitylation status would lead to increased Polδ: PCNA co-immunoprecipitation. To address this, we examined Polδ-PCNA association in cells deleted for elg1, where PCNA chromatin association is enhanced due to inactivation of the Elg1 unloader (Fig 3F) [33]. Loss of Elg1 resulted in an increase in Polδ co-immunoprecipitation with PCNA in both rad18+ and rad18Δ backgrounds (Fig 3G). This result demonstrated that the amount of loaded PCNA relates to the level of PCNA-polymerase association, although we cannot rule out the possibility that additional factors that directly respond to PCNA ubiquitylation can also influence the association.
PCNA ubiquitylation is proposed to help ‘replace’ replicative polymerases with non-canonical polymerases. We therefore examined co-immunoprecipitation of several DNA damage tolerant polymerases, Polη, Polκ and Polζ, with PCNA (S6C Fig). Marginal Polη: PCNA co-immunoprecipitation was observed in Purg1-rad18 cells, where PCNA ubiquitylation levels were high, consistent with the ubiquitin-binding zinc-finger domain of Polη directing PCNA association. Co-immunoprecipitation of Polκ or Polζ with PCNA was not detectable, presumably due to sparse protein levels (S6C Fig). Taken together, these data indicated that the non-canonical polymerases do not appreciably outcompete Polδ for association with ubiquitylated PCNA. Consistent with this, neither of Polη, Polκ nor Polζ were responsible for the altered BrdU incorporation observed in rad18Δ cells during unperturbed S phase (S6D Fig).
To establish if PCNA modification influences Polδ and Polε function we examined synthetic genetic interactions between rad18Δ and temperature sensitive (ts) polymerase mutations (Fig 4A). For cdc6-23, (Polδ-ts), concomitant rad18Δ reduced the restrictive temperature, consistent with PCNA ubiquitylation enhancing Polδ activity. Importantly, this synthetic genetic interaction was also observed for pcn1-K164R and combining both rad18Δ and pcn1-K164R showed no additive effect (Fig 4B). For cdc20-m10 (Polε-ts) rad18Δ did not affect the restrictive temperature, suggesting Polε activity is not influenced by PCNA ubiquitylation. Consistent with this, when we examined the fraction of low mobility Polε in S phase cells using PALM motion blurring, we did not detect a significant change in when rad18 was deleted (S6E Fig). Interestingly, when we examined Polε mobility in a pcn1-K164R background, a significantly lower fraction of Polε displayed low mobility in S phase cells (S6E Fig). This phenomenon was not observed in a pli1 deletion mutant (S6F Fig). Thus, the K164R mutation has effects beyond that of PCNA ubiquitylation (c.f. S2 Fig) which are unlikely to be related to modification by small Ub-like molecules.
During DDT ubiquitylation of PCNA promotes ssDNA gap filling opposite DNA lesions [22, 34, 35]. We have confirmed (S1E Fig) that PCNA ubiquitylation is induced following dysfunction of Okazaki fragment synthesis and demonstrated that this increases the fraction of Polδ co-immunoprecipitating with PCNA (Fig 3A–3C) and can contribute to the chromatin association of this lagging strand polymerase (Fig 3E). Since Polδ repeatedly disassociates from and re-associates with the template during synthesis [32], relatively long lived ssDNA gaps may occur stochastically between Okazaki fragments. We reasoned that PCNA ubiquitylation could act to supress or repair such events via a DTT-like gap filling mechanism during unperturbed S-phase (Fig 4C). This predicts that the absence of PCNA ubiquitylation would result in ssDNA gap accumulation during DNA replication.
To estimate the extent of ssDNA gaps in vivo, we first utilised an S1 nuclease-based assay [36] previously developed for detecting ssDNA in replicated molecules (Fig 5A). By calculating the distribution of DNA fragment sizes from gel intensities (S7 Fig) we infer that rad18Δ cells displayed increased DNA fragmentation when compared to rad18+ cells, with small (< 1 kb) fragments accumulating in rad18Δ throughout S phase (Fig 5B–5D). As an alternative assay, we BrdUTP labelled ssDNA gaps in genomic DNA prepared in agarose plugs. When DNA from rad18Δ cells was compared to rad18+, increased signal was evident in mid to late S phase (Fig 5E–5G). These two experiments support a model where PCNA ubiquitylation occurs between Okazaki fragments (Fig 4C) and prevents the accumulation of ssDNA gap during unperturbed S phase (see Discussion).
Here we have used the fission yeast model to demonstrate that, in addition to its known role in DNA damage tolerance, PCNA K164-ubiquitylation contributes to the timely completion of unperturbed DNA replication. Our results show that PCNA association with chromatin is stabilised by PCNA-K164 ubiquitylation during S phase. We also observed an increased co-immunoprecipitation of Polδ with PCNA when PCNA is ubiquitylated and we provide evidence that the chromatin association of Polδ is promoted by PCNA ubiquitylation. In budding yeast, PCNA ubiquitylation is barely detectable in unperturbed S phase [22] and robustly induced in response to DNA lesions that block the canonical replicative DNA polymerases [10, 12]. Consequently, PCNA ubiquitylation has been studied almost exclusively in the context of its key role in DNA damage tolerance [37]. In contrast, in fission yeast PCNA is robustly ubiquitylated during unperturbed S phase [18] and this is not significantly further induced if DNA is damaged during S phase. Budding and fission yeast thus represent opposite ends of what appears to be a spectrum. We note that both yeasts have approximately similar genome sizes and there is no evidence to suggest that fission yeast suffers from elevated levels of spontaneous DNA damage. Interestingly, mammalian cells exhibit both S phase-dependent PCNA ubiquitylation and DNA damage induced PCNA ubiquitylation (see S8A and S8B Fig).
It is currently not known what underlies the differences between organisms in terms of PCNA ubiquitylation in unperturbed S phase. However, as Rad18 is activated by regions of single stranded DNA it is possible that PCNA ubiquitylation is reflecting the extent of ssDNA present when DNA replication is active. In support of this, in budding yeast a defect in short-flap Okazaki fragment processing caused by compromising the function of the Fen1 flap endonuclease, which normally processes the 5’end of Okazaki fragments, induced detectable levels of PCNA ubiquitylation [24]. This is explained by the accumulation of long 5’ ssDNA flaps that bind RPA and activate Rad18 ubiquitylation. However, when Okazaki fragment-processing is proficient, the vast majority of flap structures are cleaved by Fen1 when they are 1 or 2 nucleotide in length [38]. Thus, Okazaki fragment processing is unlikely to be a significant source of ssDNA during unperturbed S phase. We have shown here that the lack of PCNA ubiquitylation leads the accumulation of ssDNA gaps during S phase in fission yeast. We propose that the dynamics of Polδ disassociation from PCNA result in stochastic formation of transient gaps during lagging strand synthesis. These gaps trigger Rad18-dependent ubiquitylation of PCNA, which stabilises PCNA on the DNA, allowing association of Polδ and rapid gap resolution. In the absence of PCNA ubiquitylation, a proportion of these gaps persist and thus gaps are detected in our assays. The generation of transient gaps during lagging stand synthesis likely explains the fact that PCNA is ubiquitylated during S phase in this organism.
In support of fission yeast generating increased regions of ssDNA during unperturbed DNA replication (when compared to budding yeast) we note that the abrogation of recombination pathways in fission yeast (e.g. rad51Δ or rad52Δ mutants) causes a much more severe growth defect than the equivalent loss of recombination pathways in budding yeast and that the combination of rad51 deletion with rad18 or pcnl-K164R results in synthetic lethality (S9 Fig). This suggests that homologous recombination and DTT pathways cooperatively repair ssDNA gaps, which may be abundant compared to S. cerevisiae.
In considering the origin of ssDNA during S phase that we observe in S. pombe and the differential PCNA ubiquitylation between S. pombe and S. cerevisiae in unperturbed S phase, it is interesting to consider that the kinetics of Polδ holo-enzyme dissociation from PCNA. It has recently been reported [32] that the S. cerevisiae enzyme is more processive than its human counterpart: human Polδ dissociates more rapidly from PCNA than its budding yeast counterpart and it was estimated that ~14–31% of human Okazaki fragments are completed by two independent Polδ: PCNA association events. Conversely, >99% of budding yeast Okazaki fragments are predicted to be completed by a single Polδ: PCNA interaction. While the kinetics of S. pombe Polδ dissociation from PCNA has not been studied, the fact that PCNA ubiquitylation is strongly influenced by the intermediates of lagging strand DNA synthesis [21–23] (see S1E and S1F Fig) is consistent with the fission yeast PCNA ubiquitylation pathway, in addition to regulating translesion synthesis during DTT, functioning to maintain accurate Okazaki fragment synthesis in the face of frequent Polδ: PCNA dissociation.
Okazaki fragment synthesis is necessarily coupled, either directly or indirectly, to the movement of replication forks. Approximately 105 and 107 Okazaki fragments are synthesised per cell cycle in fission yeast cells and human cells, respectively. The potential for failure during this process as a consequence of premature Polδ dissociation would therefore need to be minimised by ensuring the re-association of Polδ and completion of Okazaki fragment synthesis. We propose that this is facilitated by PCNA ubiquitylation, which ensures that PCNA is not prematurely unloaded. The fact that we show that the loss of PCNA ubiquitylation results in the accumulation of ssDNA gaps during unperturbed S phase in S. pombe (Fig 5) supports our model. Intriguingly, preliminary analysis (S10 Fig) showed the positive effect of PCNA ubiquitylation on PCNA chromatin association is evident only when the Elg1 unloader complex is active, suggesting that PCNA ubiquitylation may inhibit its unloading by Elg1, a PCNA unloading factor currently characterised only in in S. cerevisiae [33].
In S. cerevisiae yeast, SUMOylated PCNA is the predominant modification during unperturbed S phase [12, 39]. Previous work showed that Elg1 preferentially interacts with SUMO-modified PCNA [40]. However, Elg1 unloads both unmodified and SUMOylated forms of PCNA, an event which in budding yeast requires the ligation of Okazaki fragments [41]. However, in fission yeast, and in human cells, SUMOylated PCNA is much harder to detect and the SUMO-interacting motifs identified in S. cerevisiae Elg1 are not conserved. Thus, the influence of PCNA SUMOylation is unlikely to be prominent and we propose that the effect of PCNA ubiquitylation on stabilising PCNA is more predominant in fission yeast cells and potentially higher eukaryotes. It has also been suggested that the unloading of PCNA in response to HU or MMS in S. cerevisiae is dependent on its ubiquitylation and concomitant activation of the DNA damage checkpoint [42]. One interpretation of this apparent contradiction could be that, under extensive replication stress, checkpoint activation changes the response to PCNA ubiquitylation. Alternatively, this may again reflect a difference between the two organisms in the regulation of PCNA unloading.
In fission yeast a significant proportion of PCNA is ubiquitylated during unperturbed S phase. To avoid a global engagement of error prone DNA polymerases, we propose that the replicative polymerases remain the preferred binding partners for ubiquitylated PCNA. However, when a replicative polymerase is stalled at a blocking lesion, the ubiquitin binding domain-containing polymerases are provided an increased opportunity to sample the damaged base. In budding yeast the situation is distinct: PCNA is not significantly ubiquitylated in unperturbed S phase, but is robustly ubiquitylated in response to a replicative polymerase arrested at a lesion. Thus, we would predict that the binding kinetics for the replicative and error-prone DNA polymerases will be different between the two organisms in order to maintain the same biological outcomes: an appropriate balance between unsuitable use of error prone DNA polymerases during unperturbed S phase (to minimise constitutive mutagenesis) and their appropriate use during DNA damage tolerance to maximise cell survival in response to DNA damage [43].
In summary, our analysis shows that PCNA ubiquitylation, in addition to controlling DNA damage tolerance pathway usage, also participates in the timely completion of unperturbed DNA synthesis. We propose that this function is related to the increased association of ubiquitylated PCNA with chromatin. We suggest that, when Polδ stochastically dissociates during Okazaki fragment synthesis, the consequent ssDNA results in PCNA ubiquitylation which ensures it remains DNA-associated to facilitate the recapture of Polδ and completion of Okazaki fragment synthesis.
Standard S. pombe genetic and molecular techniques were employed as described previously [44]. The BrdU-incorporating strains have been already reported [45]. Polδ-GFP cells were constructed by introducing the sequence encoding GFP into the N-terminal of the cdc6 gene on S. pombe genome based on the Cre-loxP method [46]. Polε-GFP cells were constructed by introducing GFP at the C-terminal of cdc20 gene using PCR-based integration [47]. Purg1-rad18 strains were based on rad18Δ cells in which ORF of the rad18 gene fused with the AID degron construct [48] was used to replace the endogenous urg1 ORF [49].
U2OS cells were cultured in Dulbecco’s modified Eagle’s medium supplemented with 10% foetal bovine serum (DMEM-FBS10%) in a 5% CO2 atmosphere. The medium was exchanged with one containing 400 ng/ml of nocodazol. Following 18 hr incubation, mitotic cells were detached by gentle shaking of the culture vessel and passaged in DMEM-FBS10%. Cells were then either UV-irradiated (254 nm peak; 20J/m2), or not, 2 hr prior to sampling. At the indicated time points cells were sampled and then subjected to immunoblotting with anti-PCNA antibody (mouse monoclonal, PC10 clone, Abcam). To determine the S-phase fraction of the synchronised cells, 5μM if EdU was added into an aliquot and EdU positive cells scored 2 hr after EdU addition [50]. 1BR3hTERT cells were cultured in DMEM-FBS10% and the medium was exchanged with DMEM without FBS. Following 15 days, cells were passaged into DMEM-FBS10%. Cells were UV-irradiated and scored for S-phase fraction as described. Cell lines from GDSC collection. Authenticated 2015 by STR profiling.
cdc25-22 cells harbouring the constructs for BrdU-incorporation were grown to exponential phase (0.2 x106 /ml) at 25°C and synchronised at G2 phase by incubation at 36°C for 3.5 hr. After adding bromodeoxyuridine (0.5 μM), cells were further incubated at 25°C. At relevant time points, 1x108 cells were pelleted and subjected to genomic DNA extraction. To detect total BrdU incorporation, dot blotting was performed as previously described [34]. The intensity of BrdU-incorporation was established by quantifying the signal using an ImageQuant LAS 4000 imager (GE Healthcare Life Sciences). Global replication rates for each time point after release from G2 phase were estimated by dividing signal intensities at each time-points by that for 150 min, at which genome replication was completed. Local replication rates were established from BrdU-IP-Sequencing.
Paired-end reads from high throughput sequencing were aligned to the S. pombe genome sequence (ASM294v2.23: chromosomes I, II and III, downloaded from 'PomBase’ website) using bowtie2–2.2.2. From the alignment data the position of the centre of each read was calculated and the number of reads in 300bp-bins across genome counted. The Perl program converting alignment data to count data: ‘sam-to-count.pl’ is available on the GitHub website (https://github.com/yasukasu/sam-to-bincount). The counts at the chromosome coordinate x, CB(t, x)–the BrdU-IP sample derived from cells at the t-min time point, CI(0, x)–the input sample derived from cells before release from G2 (t = 0), were normalised with the total number of reads: NB(t, x) = CB(t, x)/ΣCB(t, x), NI(t, x) = CI(t, x)/ΣCI(t, x). Enrichments for BrdU-incorporated fragments were calculated: E(t, x) = NB(t, x)/NI(t, x). As BrdU is an analogue of thymine and its enrichment is thus likely to be biased towards A/T rich regions, the dataset of enrichment was normalised using the A/T-ratio of each 300-bp bin AT(x): E’(t, x) = E(t, x)/AT(x). Moving average of E’(t, x) with 8 bins at both side were calculated and plotted (Fig 1D). To estimate the extent of local replication, enrichments across the genome were multiplied by the global replication amount G(t) determined from the dot-blot assay (Fig 1C): L(t,x) = E’(t, x) ×G(t). These were then normalised with that of the last time point, at which all the cells had completed genome replication: L’(t,x) = L(t,x)/L(160, x). To obtain a function of local replication extent, data of multiple time points at each 300 bp (L’(70, x), L’(75, x), L’(80, x), L’(85, x), L’(90, x)) were fitted with a cumulative normal distribution function in which the global replication amount is variable, F(G, x). Using this function, Local replication extent when the global replication was 25%, 50% or 75% completed was determined: F(0.25, x), F(0.5, x) and F(0.75, x). Fig 1E–1G and S4 Fig is derived from these datasets. The custom R scripts used for this computational analysis are available on request.
Whole cell extracts were prepared by spheroplast lysis using Zymolyase 100T (Seikagaku) and lysing enzyme (Sigma-Aldrich). Extracts were fractionated into soluble and chromatin-bound fractions by centrifugation through a sucrose cushion [51].
5 x 108 exponentially growing cells in 50 ml YE medium were treated with 1% formaldehyde for 15 min at RT under agitation. The crosslinking reaction was quenched by adding 2.5 ml of 2.5 M glycine. Cells were washed with ice-cooled PBS, pelleted and re-suspended in 700 μl pf RIPA buffer (50mM HEPES pH7.5, 1mM EDTA, 140 mM NaCl, 1% Triton X-100, 0.1% (w/v) sodium deoxycholate) supplemented with complete protease inhibitor (Roche), 1 mM AEBSF & 1μg/ml pepstatin (Sigma-Aldrich). After adding zirconia/silica beads (biospec), cells were ribolysed (6 bursts of 30 sec at speed 6.5 in a FastPrep ribolyser (MP-Biomedicals). 300μl of cell lysate was sonicated (7 cycles; 30 sec on, 30 sec off) using a bioruptor pico (diagenode), l μl of benzonase (novagen) added and incubated for 20 min on ice. The lysate was then centrifuged (14000 rpm for 30 min in a microfuge) and the supernatant transferred to new tube. 30 μl was kept as the ‘input’ sample. 2 μg of anti-GFP antibody (rabbit IgG, A11122, Life technologies) or anti-PCNA antibody [18] was added to the isolated cell extract. After a 3 hr incubation at 4°C with gentle agitation, 20 μl of magnetic G protein dynabeads (Life technologies) was added and incubated for a further 1 hr. Beads were washed twice with RIPA buffer and once with TE. Following addition of 60 μl of elution buffer, beads were incubated at 65°C for 15 min. Supernatant was isolated as the ‘IP’ sample. Laemmli buffer was added into both ‘IP’ and ‘input’ samples and western blots were interrogated with anti-PCNA or anti-GFP (mouse IgG clones 7.1 and 13.1, Roche).
1 x 108 cells were incubated in YE media containing 50 μg/ml of BrdU and subjected to genomic DNA extraction [44]. 2 μg of extracted DNA was digested with 1 μl of S1-nuclease (Life Technologies) using the manufactures buffer in a 20 μl reaction mixture. The reaction was stopped by the addition 2 μl of 0.5 M EDTA and heating to 70°C for 10 min. The complete reaction mixture was subjected to agarose (1.5%) electrophoresis. DNA was transferred onto GeneScreen Plus membrane (PerkinElemer) by neutral capillary transfer and the BrdU signal detected by the immunoblotting [34]. Normalisation of the BrdU incorporation intensities to the fraction of S1-digested fragments was performed as previously described for alkaline digested DNA [52].
4 x 107 cells were harvested and subjected to Zymolyase 100T (0.5 mg/ml, Seikagaku) and lysing enzyme (1mg/ml, Sigma-Aldrich) treatment in 1ml of spheroplasting buffer (20 mM citrate-phosphate buffer, 50 mM EDTA and 1.2M sorbitol). After spheroplasting, cells were re-suspended in 80 μl of spheroplasting buffer without enzymes, mixed with 80 μl of 2% agarose (SeaPlaque GTG agarose) and then 20 μl volume of agarose plugs were prepared. Plugs were washed with detergents and treated with Protease K as described previously [53]. Three plugs were subjected to treatment with T4 polymerase (6 units, New England Biolabs) and dNTP with BrdUTP (Sigma-Aldrich) instead of dTTP (200 μM each) in 100 μl at 37°C overnight. DNA was recovered from plugs by phenol/chloroform extraction and applied to dot blots (Scie-Plas Ltd.). BrdU signal was detected as described above.
Analysis of DNA binding in vivo by PALM was performed as previously described using a custom-built microscope system [27]. Photoconversion and excitation of mEos3 molecules was controlled by continuous wave illumination with 405nm and 561nm laser light. The intensity of the 405nm laser was modulated during the imaging such that the number of photoconverted molecules for any one frame was kept low to reduce the chances of overlapping static molecules, or the possibility of blurring molecules masking static localisations. Laser intensities at the sample were calculated as 0.1-1W/cm2 (405nm) and 1kW/cm2 (561nm). Camera EM gain was set at 250 and exposure time for each frame was 350ms. Typical data acquisition consisted of 3000–4000 frames and 6000–10000 frames for polymerases and PCNA respectively. Data sets were built from of a minimum of 3 biological repeats. Raw image data were processed using a custom ImageJ 2D-Gaussian fitting routine as previously described23. Code available on GitHub: https://github.com/aherbert/GDSC-SMLM and as a Fiji update site (GDSC SMLM). Scale bar 1.5 micometers.
Data files for BrdU-IP sequence have been deposited in the Gene Expression Omnibus database under accession number GSE70033.
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10.1371/journal.pntd.0005434 | Human Treponema pallidum 11q/j isolate belongs to subsp. endemicum but contains two loci with a sequence in TP0548 and TP0488 similar to subsp. pertenue and subsp. pallidum, respectively | Treponema pallidum subsp. endemicum (TEN) is the causative agent of endemic syphilis (bejel). An unusual human TEN 11q/j isolate was obtained from a syphilis-like primary genital lesion from a patient that returned to France from Pakistan.
The TEN 11q/j isolate was characterized using nested PCR followed by Sanger sequencing and/or direct Illumina sequencing. Altogether, 44 chromosomal regions were analyzed. Overall, the 11q/j isolate clustered with TEN strains Bosnia A and Iraq B as expected from previous TEN classification of the 11q/j isolate. However, the 11q/j sequence in a 505 bp-long region at the TP0488 locus was similar to Treponema pallidum subsp. pallidum (TPA) strains, but not to TEN Bosnia A and Iraq B sequences, suggesting a recombination event at this locus. Similarly, the 11q/j sequence in a 613 bp-long region at the TP0548 locus was similar to Treponema pallidum subsp. pertenue (TPE) strains, but not to TEN sequences.
A detailed analysis of two recombinant loci found in the 11q/j clinical isolate revealed that the recombination event occurred just once, in the TP0488, with the donor sequence originating from a TPA strain. Since TEN Bosnia A and Iraq B were found to contain TPA-like sequences at the TP0548 locus, the recombination at TP0548 took place in a treponeme that was an ancestor to both TEN Bosnia A and Iraq B. The sequence of 11q/j isolate in TP0548 represents an ancestral TEN sequence that is similar to yaws-causing treponemes. In addition to the importance of the 11q/j isolate for reconstruction of the TEN phylogeny, this case emphasizes the possible role of TEN strains in development of syphilis-like lesions.
| Treponema pallidum subsp. endemicum (TEN) is an uncultivable pathogenic treponeme that causes bejel (endemic syphilis), a chronic human infection mostly affecting children under 15 years of age, occurring mainly in several African and Middle East countries. In this work, we characterized a TEN 11q/j isolate from France that was obtained from an adult male with genital lesions, who was suspected of having syphilis and who received benzathine penicillin G. DNA sequencing of the isolate revealed two loci that were, rather than to TEN, related either to T. pallidum subsp. pertenue or to T. pallidum subsp. pallidum and likely resulted from recombination events. The recombination event in TP0488 as well as the recombination in TP0548, of the 11q/j, helped clarify the phylogeny of the TEN strains indicating that the recombination in TP0548 took place in a treponeme that was ancestral of Bosnia A and Iraq B, but was not an ancestor of the 11q/j isolate. In contrast, a recombination event in TP0488 appeared in the ancestor of the 11q/j isolate after separation of the ancestral treponeme of Bosnia A and Iraq B. This case also points to a possible role of TEN strains in development of syphilis-like lesions in countries with endemic syphilis.
| Treponema pallidum subsp. endemicum (TEN) is the causative agent of bejel (endemic syphilis), a chronic human infection usually affecting children under 15 years of age. The primary stage of endemic syphilis is often localized to the mucosa of the oral cavity or nasopharynx and frequently remains undetected. Secondary lesions often mimic syphilitic lesions and are found on both mucosal and skin surfaces including the oral cavity, pharynx, and larynx (for review see [1]). The tertiary stage is characterized by gummatous or destructive lesions of mucosa, skin, and bones. Recently reported cases of bejel have come from African countries with dry climates including Mauretania, Niger, Chad, Mozambique and from countries in the Middle East including Turkey, Saudi Arabia, and Iran [1]. Moreover, several imported cases of bejel have been described in France [2] and Canada [3] in children coming from countries where endemic syphilis has been reported.
Compared to the syphilis-causing Treponema pallidum subsp. pallidum (TPA) and the yaws-causing Treponema pallidum subsp. pertenue (TPE) (reviewed in [4, 5]), TEN is the least well characterized and least studied human pathogenic treponeme. There are few genetic studies on TEN strains [3, 6–14], which is likely due to a limited number of available TEN samples. In fact, most studies on TEN strains described one of the two reference strains, i.e., Bosnia A or Iraq B. The Bosnia A strain was isolated in 1950 in southern Europe (Bosnia) from a 35-year old male with several mucosal and skin lesions [15], and the Iraq B strain was isolated in 1951 in Iraq from a 7-year old girl who had oral mucous lesions and an anal condylomata [15]. Because of the low number of available reference strains, only a single complete genome sequence of TEN Bosnia A has been published to date showing a close relatedness (higher than 99.9%) to TPE strains and several sequences surprisingly similar to TPA strains [16].
First reported in 2013, an unusual 11q/j subtype (defined by enhanced CDC typing) [17, 18] was found among samples taken from a syphilis patient in Paris [19] who had returned from Islamabad, Pakistan, where he admitted having had sex with commercial sex worker. Based on a partial sequence type reported for TP0548, Mikalová et al. [20] pointed out that this sequence was more related to the yaws-causing strains rather than to syphilis-causing strains. Further analyses resulted in the classification of the 11q/j isolate as a TEN treponeme [21].
In this study, we characterized the 11q/j isolate in a set of 44 independent chromosomal regions. Sequencing of these loci revealed that the 11q/j isolate belongs to the T. pallidum subsp. endemicum with two loci having sequences that were related to either TPE (TP0548) or TPA (TP0488). The relevance of these findings is discussed here.
The study was approved by the institutional review board of the Comité de Protection des Personnes d’Ile de France 3 (S.C.3005).
The sample (a swab from an indurated genital ulceration) was collected from a 42-year-old heterosexual man who attended the outpatient STD clinic of Hôpital Saint-Louis (Paris) and was analyzed anonymously. Isolated DNA (20 μl) from this sample (referred as 11q/j) was obtained from the National Reference Center for Syphilis in France (CNR Syphilis, www.cnr-syphilis.fr) that had performed a routine analysis on DNA from clinical samples [22].
A nested PCR protocol for detection of the polA gene was performed using the previously described outer primers, polA_outer_F1 (5´-TTCTGTGCTCACGTCTGGTC-3´) and polA_outer_R1 (5´-TGCAACCATCGTATCGAAAA-3´), which resulted in a 637 bp amplicon [23–25] and inner primers for nested polA PCR, polA_F1 (5´-TGCGCGTGTGCGAATGGTGTGGTC-3´) and polA_R1 (5´-CACAGTGCTCAAAAACGCCTGCACG-3´), resulting in a 377 bp amplicon, were used as described in Liu et al. [26]. This nested PCR protocol was shown to be able to detect 1–10 copies of treponemal DNA in a 1 μl of sample [23–25] and was used for detection of the number of treponemal genome equivalents in 1 μl of DNA. The original 11q/j DNA sample (3 μl) was randomly amplified using a REPLI-g Single Cell kit (Qiagen, Hilden, Germany) according to the manufacturer's instructions. Randomly amplified sample of 11q/j was then used for (i) direct nested PCR amplification with specific primers (listed in S1 Table) according to a previously published protocol [25, 27], (ii) whole DNA sequencing using an Illumina MiSeq Next-gen sequencer (Illumina, San Diego, CA, USA), and (iii) the subsequent amplification with T. pallidum specific primers used in the pooled segment genome sequencing (PSGS) method [16, 28–30], which was followed by Illumina sequencing.
Regions successfully amplified from the 11q/j isolate were also amplified from another available DNA reference sample, i.e., TEN Iraq B. The TEN Iraq B DNA was provided by Dr. Kristin N. Harper from the Department of Population Biology, Ecology, and Evolution, Emory University, Atlanta, Georgia, USA, in 2005. The Iraq B DNA was amplified with PCR or the nested PCR protocol with the same specific primers used for nested PCR amplification of the 11q/j isolate (S1 Table).
The obtained partial sequences from the 11q/j isolate and TEN Iraq B were either assembled from Sanger and/or Illumina sequencing reads using SeqMan or SegMan NGen software (DNASTAR, Madison, WI, USA), respectively, with default assembling parameters. Genes were annotated according to the whole genome sequence of TEN Bosnia A (CP007548.1; [16]) and the 11q/j isolate and the TEN Iraq B genes were tagged with TEND11qj_ and TENDIB_ prefixes, respectively. The resulting sequences of the 11q/j isolate and TEN Iraq B were analyzed and compared to the following genomes: TPA Nichols (CP004010.2; [31]), TPA SS14 (CP004011.1; [31]), TPE Samoa D (CP002374.1; [29]), TPE CDC-2 (CP002375.1; [29]), TPE Gauthier (CP002376.1; [29]), TPE Fribourg-Blanc (CP003902.1; [30]), and TEN Bosnia A [16]. Alignments of treponemal sequences were performed using SeqMan software and MEGA7 software [32]. Phylogenetic trees were constructed in MEGA7 software [32] using the Maximum Likelihood method based on the Tamura-Nei model [33].
The following formula was used to calculate the probability that the observed nucleotide sequences were caused by accumulation of individual mutations instead of a recombination: pmut = (pmut_gen x pmut_nuc)n, where pmut = the end probability of mutations resembling recombinant events, pmut_gen = the frequency of mutation per single nucleotide, pmut_nuc = the probability of a nucleotide substitution into the nucleotide sequence in the putative recombinant region, n = the number of mutated nucleotides within the putative recombinant region. pmut_gen was calculated based on the number of variable sites identified within all available sequences of the 11q/j sample (total length of 29,753 bp, except for loci TP0488 and TP0548) and the corresponding sequences of TEN Bosnia A and TEN Iraq B. pmut_nuc had a constant value of 0.333 reflecting 3 possible substitutions changing the original sequence at each nucleotide site. Different probabilities of transitions and transversions were not considered in this analysis. The “n” was calculated based on the number of different nucleotide positions between the 11q/j isolate and one of the two TEN strains that matched either the TPA or TPE orthologous sequence.
The resulting sequences of the TEN 11q/j isolate and the TEN Iraq B with length ≥ 200 bp were deposited in the GenBank under following accession numbers: KY120774-KY120814 for TEN 11q/j isolate; KY120815-KY120855 for TEN Iraq B. The detailed overview of sequenced loci is shown in S2 Table.
The only available DNA-containing sample (20 μl) was obtained from the CNR Syphilis that performed the isolation of DNA from the original swab sample [22]. As revealed by the nested polA PCR reaction [23] with detection limit of less than 10 molecules [26], the sample contained undetectable amounts of treponemal DNA, i.e. less than 10 molecules of treponemal DNA per 1 μl. Following whole genome amplification with random primers, nested PCR protocol revealed positivity in a 10−2 dilution indicating, at least, 1x102 copies of treponemal genome equivalents per 1 μl in a total of 50 μl of amplified sample. This randomly amplified sample was used for further analyses.
The randomly amplified sample was used for direct Illumina sequencing and resulted in 1,786,712 individual reads. Of those, only 10 reads were mapped to the TEN Bosnia A genome indicating that the ratio of treponemal DNA to DNA from other species (mostly human) is less than 1:105. Subsequently, the randomly amplified sample was used for specific amplification with the PSGS technique [16, 28–30] and primer pairs from Pool 1 amplifying the first quarter of the treponemal genome (Fig 1). Specific amplification resulted in a total of 353,006 individual reads, of which 41,308 reads were mapped to the TEN Bosnia A genome. Consensus sequences from at least 2 individual reads represented sequenced DNA regions of the 11q/j isolate. All regions determined by Illumina sequencing are shown in Fig 1 and S2 Table. Altogether, 15 genomic loci were obtained for the 11q/j isolate with lengths ranging from 63–2,455 bp, with total length of 9,626 bp and with coverage ranging from 2–15,832x.
In addition to Illumina sequencing, a nested PCR of 31 chromosomal loci was performed from the randomly amplified 11q/j sample using 1 μl of the starting DNA template. The resulting amplicons were Sanger sequenced. Loci for nested PCR were selected based on whole genome comparisons of published TPE strains (Samoa D, CDC-2, Gauthier) and TEN Bosnia A. Preferentially, loci with accumulated single nucleotide variants (SNVs) and/or indels between TPE and TEN strains were selected as well as conservative genes suitable for unambiguous distinction between TPE and TEN subspecies. All regions amplified using nested PCR and sequenced using the Sanger method are presented in Fig 1 and S2 Table. The 16S and 23S rRNA loci were amplified from both genome positions [12]. The length of resulting sequences of the 11q/j isolate ranged from 352–2302 bp and represented a total of 23,979 bp.
Illumina and Sanger sequencing of the 11q/j isolate resulted in sequences obtained from 44 chromosomal DNA regions covering, altogether, 32,635 bp (2.87%) of the TEN Bosnia A genome length (S2 Table). Two genomic regions within TP0121 and TP0136 genes, where both sequencing techniques partially overlapped, revealed identical sequences. The average length of sequenced regions in the 11q/j isolate was 742 bp (range 63–2,455 bp). The sequenced chromosomal regions were dispersed throughout the entire chromosome with distances ranging from 0.1–124.7 kb (Fig 1). All sequenced genomic regions of the 11q/j isolate were also amplified and Sanger sequenced from the TEN Iraq B DNA and these regions are described in S2 Table.
Interestingly, sequencing of a short gene fragment (548 bp) of TENDBA_0488 between positions 684–1231 revealed that the sequence of the 11q/j isolate was very similar to the sequence in TPA Nichols, but not to TEN strains (Fig 2A); suggesting the occurrence of a recombination event at this locus. The minimal size of recombinant DNA sequence was 505 nucleotides (between coordinates 715–1219; Fig 2A). A set of 21 nucleotide positions of the 11q/j isolate were different from TEN Bosnia A as well at TEN Iraq B, but identical to TPA strains. A partial sequence of the TEND11qj_0488 from the 11q/j isolate, representing the recombinant part (505 bp long fragment), was used for construction of a tree (Fig 3A) that revealed clustering of the 11q/j isolate within TPA strains, not within TEN strains. The probability that the observed nucleotide sequence within this locus was caused by an accumulation of individual mutations instead of a recombination was tested using the following formula: pmut = (pmut_gen x pmut_nuc)n (see Materials and Methods). pmut_gen was calculated based on the number of variable sites identified within all available sequences of the 11q/j sample (22 variable positions in a total length of 29,753 bp from the 3 analyzed TEN genomes; 0.00074 nt differences per 1 bp). Loci TP0488 and TP0548 were not included in this calculation. The “n” was calculated based on the number of variable positions detected in the sequence alignment presented in Fig 2. In the TP0488 gene, there was a total of 23 nucleotide positions in the 11q/j sequence that differed from TEN strain Bosnia A but were identical to TPA strain SS14. With the assumption that the 11q/j isolate represents a TEN strain, the probability that the accumulated SNVs within the TP0488 of the 11q/j isolate were due to accumulation of individual mutations would be: pmut = (0,00074 x 0.333)23, i.e. pmut = 1.01987 x 10−83.
To rule out potential co-infection with TPA and TEN in this patient, Illumina sequencing reads of the 11q/j sample, especially in regions with positions that differ between TPA and TEN, were evaluated and revealed 20 informative sites with coverage ≥ 4x (range 4x–94x). However, there was no heterogeneity in these positions, excluding co-infection with multiple strains.
As shown previously, the 11q/j isolate within its 86 bp-long fragment of TP0548 gene revealed a new sequence type that is, in fact, related to TPE strains [20]. Analysis of a larger 2,302 bp-long region comprising TP0547, TP0547a, and TP0548 genes (positions 589,926–592,227 corresponding to the whole genome sequence of the TEN Bosnia A) revealed that the sequence of the 11q/j isolate, within the TP0548 gene, was very similar to TPE strains, especially to a sequence from TPE Samoa D (Figs 2B and 3B). The minimal size of recombinant DNA sequence was 613 nucleotides (between coordinate 69 of the TENDBA_0547a and coordinate 623 of the TENDBA_0548) and comprised 56 variable positions (Fig 2B). Thirty-seven of the nucleotide positions of the 11q/j isolate were different from TEN Bosnia A and TEN Iraq B, but identical to at least one of the TPE Samoa D or Gauthier strains (Fig 2B). Both TEN Bosnia A and Iraq B showed 23 nucleotide positions identical to at least one of the TPA strains, i.e., to Nichols or SS14, but different from the 11q/j isolate. A partial sequence of TEND11qj_0548 (613 bp) was used for construction of a tree (Fig 3B) and reveled clustering of the 11q/j isolate among TPE strains, but not among TEN strains. The probability that the observed nucleotide sequence within this locus was caused by an accumulation of individual mutations instead of a recombination was pmut = 1.12245 x 10−65 (pmut = (0.00074 x 0.333)18), since there was a total of 18 SNVs that differed from TEN strain Bosnia A but were identical to TPE strain Gauthier. Indels were omitted from the calculation.
The sequences of 42 chromosomal regions, excluding TP0488 and TP0548 sequences, were concatenated and used to construct a phylogenetic tree to visualize the relatedness of 11q/j isolate to other treponemal genomes (Fig 3C). The corresponding genome regions from the published whole genome sequences of two TPA strains (Nichols, SS14), four TPE strains (CDC-2, Gauthier, Samoa D, and Fribourg-Blanc), and TEN Bosnia A were used. Moreover, the dataset was supplemented with sequences of TEN Iraq B. All positions in the alignment containing gaps and missing data were eliminated resulting in a total of 29,447 positions in the final dataset having 509 variable sites. Overall, the 11q/j isolate clustered with both TEN Bosnia A and TEN Iraq B, indicating that most chromosomal loci of the11q/j isolate were consistent with TEN classification.
In this work, we analyzed an interesting human clinical isolate, 11q/j, that was first reported in 2013 as a case of syphilis [19], but due to an unusual sequence pattern at the TP0548 locus, similar to TPE, it was thought to be an imported case of yaws [20]. In 2016, the 11q/j isolate was further characterized in 7 genomic loci and classified as subspecies TEN [23]. Due to the unusual syphilis-yaws-bejel history of the 11q/j isolate we characterized larger genome regions of this clinical sample using different sequencing approaches.
The small amount of treponemal DNA within the only available sample of the 11q/j isolate (copy number less than 10 molecules of treponemal DNA per 1 μl) with an excessive amount of contaminating human DNA, which exceeded the treponemal DNA by at least 100,000 times, precluded the use of other techniques that have been recently reported to be effective in sequencing treponemal DNA directly from clinical samples [34, 35]. Efficient enrichment of treponemal DNA requires the number of treponemal copies > 1x104 per 1 μl [34]. In fact, enrichment of the TEN Iraq B DNA sample, containing 104 copies per 1 μl, revealed genome coverage less than 12.4% [35]. For these reasons, we mostly used nested PCR in this study. In all cases, amplification was done from samples containing at least 102 copies of treponemal DNA to avoid introduction of sequencing errors.
As reported in a previous study based on analysis of 7 chromosomal regions, classification of the clinical isolate 11q/j was consistent with T. pallidum subsp. endemicum [21]. In this work, we confirmed this finding based on analyses of 42 chromosomal regions (excluding the TP0488 and TP0548 loci), which were independently amplified and analyzed. The corresponding phylogenetic tree revealed a clear clustering of the 11q/j isolate with TEN strains (Fig 3C). In addition, the genetic distance between the 11q/j isolate and TEN Bosnia A and Iraq B was greater than the distance between Bosnia A and Iraq B, indicating that the ancestor of the 11q/j isolate diverged before TEN Bosnia A and TEN Iraq B diversified.
Although the sequenced portion of the 11q/j isolate represented less than 3% of the total genome length, the number of analyzed nucleotide positions informative for differentiation between TPE and TEN was much larger. Considering the extent of similarity of the genome sequences of available TPE and TEN strains (i.e., they are 99.91–99.94% similar), there were relatively few (711–970) variable sites between TEN Bosnia A and TPE strains Gauthier, CDC-2 and Samoa D [16]. Within the 11q/j isolate, 196 (20–28%) of these variable sites were sequenced and 98% of them revealed sequence similarity to TEN strains. Therefore, it is very likely that the classification of 11qj isolate as TEN strain will remain the same even after acquisition of additional genomic sequences.
Sequence analysis of TP0488 of the 11q/j sample revealed a sequence very similar to TPA strains. A similar situation has been previously reported in the genome of Bosnia A, where several chromosomal regions including TP0326, TP0488, TP0577, TP0858, TP0968, and TP1031 showed striking similarity to TPA treponemes [16]. However, the TPA-like sequences at the TP0488 locus of Bosnia A and Iraq B strains were different from the TP0488 sequence of the 11q/j sample and were located between positions 1175–1195 (Fig 2A), indicating that the 11q/j recombination event was independent of the recombination event at the TP0488 locus in the ancestor of TEN Bosnia A and TEN Iraq B. Interestingly, in the TPA Mexico A, TP0488 was found to contain a sequence very similar to that found in TPE strains, suggesting that the TP0488 locus is prone to gene recombination [36]. The TP0488 gene encodes a methyl-accepting chemotaxis protein (Mcp2-1) [37] and, as shown by expression profiling of treponemes isolated from rabbit infections, is highly expressed in TPA strains [38]. Moreover, the Mcp2-1 protein has been shown to elicit a humoral response [37]. In the TPA Mexico A genome, 8 out of 18 TPE-like changes were located in the Cache domain (domain binding small molecules) [39]. Similarly, 13 out of 21 amino acid replacements resulting from recombination in the 11q/j isolate were also located in the Cache domain, suggesting differences in binding properties of the Mcp2-1 protein. As discussed in a previous work [36], the observed sequence patterns are consistent with recombination events that have likely occurred during parallel human infections with both TPA and TEN or TPA and TPE treponemes.
Gene TP0548, on the other hand, was for the first time found to be recombinant. TP0548 from the 11q/j isolate appeared to be composed of sequences (in addition to TEN sequences) originating from TPE treponemes. Moreover, TEN Bosnia A and TEN Iraq B showed TPA-like sequences within this locus. For this reason, as well as the fact that the ancestor of the 11q/j isolate diverged before the ancestor of TEN Bosnia A and Iraq B strains, it is more plausible that the recombination event occurred in the common ancestor of Bosnia A and Iraq B rather than in the 11q/j isolate. The divergence of the ancestor of the 11q/j isolate before the ancestor of TEN Bosnia A and Iraq B strains is supported by greater genetic distances between the 11q/j isolate and both TEN strains (Bosnia A, Iraq B) compared to distances between Bosnia A and Iraq B (Fig 3). According to this scenario, the recombination occurred in a TEN strain that was ancestral to both the Bosnia A and Iraq B, which incorporated the TPA sequence into this locus (Fig 3C). The sequence of the 11q/j isolate thus represents the original TEN sequence that is similar to TPE strains. The TP0548 was predicted to encode for a rare outer membrane protein [40] and, as shown by molecular typing studies, is highly variable among syphilis isolates [18, 22, 25, 27]. The tendency of this locus to recombine, although shown only in TEN, should be considered during interpretation of data from both enhanced CDC and sequencing-based typing of syphilis-causing strains.
The calculated probability that the observed SNVs within TP0488 and TP0548 were caused by random mutations was extremely low, i.e. 1.01987 x 10−83 and 1.12245 x 10−65, respectively, suggesting that recombination occurred in these loci. Since mutation frequency per 1 bp within the TEN subspecies was calculated based on the sequences obtained for the 11q/j isolate, there was a potential bias in preferential sequencing of TEN variable regions. However, inclusion of additional chromosomal loci would likely lower the final probability even more. Moreover, the calculated SNV density in TEN strains (0.74 nt per 1000 bp) differed only slightly compared to densities within other treponemal subspecies (0.36 nt per 1000 bp in TPA; 0.14 nt per 1000 bp in TPE; [29]).
Both intra-genomic and inter-genomic recombination events have been identified in uncultivable pathogenic treponemes and are summarized in Table 1. While intra-genomic homologous recombination have been found in tpr genes [7, 10, 41, 42], several inter-genomic recombination events have already been described in the literature [16, 36].
The fact that the infection caused by the TEN 11q/j isolate resembled early syphilis with lesions located on the genitals supports previous findings that both TPA and TEN strains form similar, clinically undiscernible primary lesions. In a similar case, TEN Bosnia A was isolated from genital lesions of a 35-year old male, although in this case, lesions were also found in the oral cavity and pharynx and the patient showed secondary lesions on the face, trunk and extremities. The patient which was the source of the 11q/j isolate in this study, reported that he returned to France from Islamabad, Pakistan, where he admitted having had sexual contact with commercial sex worker. Since Pakistan is located close to countries that have recently reported cases of endemic syphilis, including Saudi Arabia and Iran [1], such an infection should not be surprising. Given the known number of repetitions in the arp gene, the restriction pattern of the amplified tprEGJ genes, and the TP0548 sequence, the enhanced CDC genotype [18] can be deduced for TEN strains. For TEN Bosnia A and Iraq B, the 10q/c and 8q/c genotypes can be predicted based on published data, respectively [8, 16, 20]. Interestingly, similar subtypes have already been identified among tested clinical isolates from China including 9h/c, 10h/c, and 9o/c [43]. In fact, the electrophoretic tpr pattern “h” differs from the pattern “q” by one fragment (i.e., 804 bp in pattern “h” vs. 726 bp in “q”) and “o” differs from pattern “q” by the absence of a 315 bp fragment [44]. Close similarity of identified subtypes of human syphilis isolates to predicted subtypes of TEN Bosnia A and TEN Iraq B suggests that TEN strains could and should be sporadically detected among human samples from patients suspected of having syphilis. In such situations, suspicious samples should be further analyzed to obtain an unequivocal classification of either a TPA strain or a TEN strain.
Taken together, analysis of the 11q/j isolate revealed a TEN genome seemingly containing two recombination events and highlights the fact that TEN strains could cause syphilis-like lesions in humans. A more detailed analysis revealed that the 11q/j isolate had just one recombinant locus, TP0488. The recombination of TP0548 took place in a treponeme that was the ancestor of both TEN Bosnia A and TEN Iraq B.
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10.1371/journal.ppat.1004378 | Myeloid Derived Hypoxia Inducible Factor 1-alpha Is Required for Protection against Pulmonary Aspergillus fumigatus Infection | Hypoxia inducible factor 1α (HIF1α) is the mammalian transcriptional factor that controls metabolism, survival, and innate immunity in response to inflammation and low oxygen. Previous work established that generation of hypoxic microenvironments occurs within the lung during infection with the human fungal pathogen Aspergillus fumigatus. Here we demonstrate that A. fumigatus stabilizes HIF1α protein early after pulmonary challenge that is inhibited by treatment of mice with the steroid triamcinolone. Utilizing myeloid deficient HIF1α mice, we observed that HIF1α is required for survival and fungal clearance early following pulmonary challenge with A. fumigatus. Unlike previously reported research with bacterial pathogens, HIF1α deficient neutrophils and macrophages were surprisingly not defective in fungal conidial killing. The increase in susceptibility of the myeloid deficient HIF1α mice to A. fumigatus was in part due to decreased early production of the chemokine CXCL1 (KC) and increased neutrophil apoptosis at the site of infection, resulting in decreased neutrophil numbers in the lung. Addition of recombinant CXCL1 restored neutrophil survival and numbers, murine survival, and fungal clearance. These results suggest that there are unique HIF1α mediated mechanisms employed by the host for protection and defense against fungal pathogen growth and invasion in the lung. Additionally, this work supports the strategy of exploring HIF1α as a therapeutic target in specific immunosuppressed populations with fungal infections.
| Due to the limited treatment options and severity of invasive fungal infections, a better understanding of fungal-host interactions is needed for the development of new therapies. Recent studies have implicated a role for hypoxia inducible factor 1-alpha (HIF1α) in the regulation of inflammation and host defense responses to microbial pathogens. In this study, we discover that HIF1α is required for protection and murine survival to Aspergillus fumigatus pulmonary challenge. First, we observed that nuclear HIF1α protein levels are reduced in the murine corticosteroid immunosuppressed model of invasive pulmonary aspergillosis, suggesting its involvement in disease outcome. We then tested the hypothesis that HIF1α is required by innate immune effector cells to control/prevent A. fumigatus growth and invasion. Surprisingly, we observed that the role of myeloid HIF1α is not to mediate innate effector cell A. fumigatus killing directly, but rather to induce and maintain a protective immune response that ensures proper effector cell recruitment and survival at the site of infection. These findings provide a better understanding of host mechanisms involved in thwarting fungal pathogenesis, have implications for host susceptibility, and reveal the potential for novel treatment strategies involving HIF1α mediated signaling in the lung in immune suppressed patients.
| Invasive fungal infections continue to take a toll on human health with high mortality and morbidity rates and increasing frequency [1]. The filamentous fungus Aspergillus fumigatus remains the most common cause of airborne invasive fungal infections and is the primary causal agent of invasive pulmonary aspergillosis (IPA) [2]. The persistence of sub-optimal IPA clinical outcomes is in part due to a less than optimal understanding of the Aspergillus-host interaction and toxicity associated with current antifungal drugs [3], [4], [5]. While much effort for treatment improvement is focused on identification of new antifungal drug targets and compounds, a complementary and important area of investigation is discovering therapies that improve host defense in immunocompromised patients [6].
Host defense to A. fumigatus challenge requires a functioning innate immune response with a strong dependence on neutrophils and other innate immune system effector cells, as their deficiency or defective function is a principal clinical risk factor for IPA [7], [8]. The timing and magnitude of neutrophil recruitment is pivotal for fungal clearance as a small delay in arrival leads to disease susceptibility [9], [10]. The recruitment of neutrophils to the site of infection requires a calculated interplay between multiple signaling chemoattractants and receptors that remain to be fully defined in the context of IPA. Alveolar macrophages, likely the first leukocyte exposed to conidia within the lung, induce the expression of pro-inflammatory cytokines, such as TNF, IL-1α/β, IL-6, GM-CSF, CXCL2, and CXCL1 in response to engagement of fungal PAMPs by macrophage PRRs such as toll-like receptors (TLRs) and dectin-1 [11], [12], [13]. Whether through MyD88-dependent or –independent signaling, the expression of these cytokines is mediated through the transcription factors NFκB, NFAT, and IRF's and are indispensible for proper clearance and preventing invasive disease [12]. In particular, the CXC chemokines defined by the amino acid sequence Glu-Leu-Arg (ELR) preceding the CXC motif, macrophage inflammatory protein 2 (MIP-2, murine CXCL2) and keratinocyte-derived chemokine (KC, murine CXCL1) are pivotal factors for neutrophil migration [14]. Neutrophils sense and respond to these inflammatory signals through surface expressed G-protein coupled receptors, cytokine receptors, adhesions (selectins and integrins), Fc-receptors, and innate receptors (TLRs and C-type lectins) [15]. Classical chemoattractant receptors expressed on the surface of neutrophils, including leukotriene B4, platelet-activating factor, and CXCR1 and CXCR2 are required for neutrophil recruitment and migration; mice deficient in these receptors are more susceptible to IPA [10], [16], [17], [18]. Once recruited to the lung and upon contact with hyphae, neutrophils induce degranulation, the respiratory burst, proteases, and antimicrobial peptides leading to both pathogen and host damage [9], [19], [20], [21]. The precise molecular mechanisms involved in neutrophil lung recruitment in response to A. fumigatus infection remain to be fully defined.
Pathogen driven inflammation and necrosis of tissue leads to development of microenvironments deficient in oxygen and nutrients, but there is a lack of knowledge regarding how host and pathogen responses to these deficiencies affect the outcome of pathogenesis [21]. Studies have implicated a role for hypoxia inducible factor 1 (HIF1α), a major regulator of the mammalian response to hypoxia, in the regulation of inflammation and host defense responses to microbial pathogens [22], [23], [24], [25]. However, the role of HIF1α in immune responses to lung microbial pathogens, and particularly fungi, is largely unknown. HIF1α is a heterodimeric protein whose α-subunit is stabilized under hypoxic conditions and translocated to the nucleus where it dimerizes with the β-subunit, referred to as the aryl hydrocarbon receptor nuclear translocator (ARNT, HIF1β) protein [26]. Once stabilized and in the nucleus, HIF1α binds to hypoxia response elements (HREs) of target hypoxia response genes including those involved in the processes of glucose metabolism, hypoxia, apoptosis, angiogenesis, and erythropoiesis [27]. HIF1α is regulated by prolyl hydroxylase's (PHD) through hydroxylation of its oxygen-dependent degradation domain and directs HIF1α for ubiquitin-dependent degradation by the von Hippel-Lindau (vHL) protein when oxygen is present [26], [28]. Activation of HIF1α requires basal levels of NFκB and in turn, NFκB activation is controlled by hypoxic inactivation of PHDs [29], [30]. Moreover, the activation of NF-κB in normoxia leads to up-regulation of HIF1α mRNA levels contrary to hypoxia increased protein levels [30]. This interdependence between HIF1α and NFκB supports a strong link between innate immunity and metabolism in controlling disease.
HIF1α plays an important role in innate immunity and host defense as myeloid cells have HIF-dependency for adaptation to hypoxic and inflamed microenvironments that develop during infection. For example, HIF1α is critical for regulating bactericidal activity of phagocytes against Group B Streptococcus [31]. In other pathogens, such as Group A Streptococcus and Staphylococcus aureus, presence and induction of HIF1α in myeloid cells, specifically macrophages and neutrophils, increases phagocytic activity and controls systemic spread of these pathogens [23], [25]. HIF1α has been suggested to be involved in the suppression of the angiogenic response by A. fumigatus in murine models of IA [32]. More recently, pulmonary HIF1α mRNA levels were observed to increase in response to the human fungal pathogen Coccidioides immitis [33].
Although the roles of HIF1α in myeloid cell bactericidal activity and inflammatory diseases are established, the function of HIF1α in the course of lung infections and particularly with fungi remains unclear. Therefore, we investigated the role of HIF1α following pulmonary challenge with A. fumigatus using a myeloid-specific lysozyme-M cre-recombinase driven HIF1α null mouse (HIFC) [22]. We observed that A. fumigatus challenge strongly induces HIF1α stabilization in wild-type immune competent murine lungs and macrophages; a process that is inhibited by administration of corticosteroids. Loss of myeloid HIF1α in otherwise immune competent mice resulted in a dramatic decrease in murine survival when challenged with A. fumigatus conidia. Surprisingly, rather than a role for HIF1α in mediating fungal killing by innate effector cells as previously observed with bacterial pathogens, the reduction in murine survival was in part mediated by a reduced number of neutrophils and innate immune effector cells early following fungal challenge. Reductions in these innate effector cells in the airways and lung in the absence of HIF1α were partially due to defective induction of the chemotactic signal CXCL1. These results support a role for HIF1α in initiating the correct inflammatory signal and immune response in order to prevent pulmonary fungal growth. Therefore, modulation of HIF1α signaling in specific immunocompromised patient populations is a potential area for therapeutic development.
To determine whether HIF1α stabilization is part of the pulmonary innate defense response to pathogenic fungi, levels of HIF1α mRNA abundance and protein were analyzed in an immune competent murine model of fungal bronchopneumonia initiated by A. fumigatus challenge. Consistent with a potential role for HIF1α in defense against pulmonary fungal disease, A. fumigatus induced a three-fold increase in HIF1α mRNA abundance in the lung compared to PBS inoculated controls (Figure 1A). Accordingly, stabilization of HIF1α protein and increased nuclear localization occurred in murine lungs exposed to A. fumigatus conidia with greater HIF1α protein levels occurring in the cytoplasmic fraction of the PBS inoculated mice (Figure 1B). These results demonstrate two distinct effects of A. fumigatus challenge on HIF1α in the murine lung, the increase of HIF1α mRNA and an increase in nuclear HIF1α protein localization. In addition, HIF1α stabilization occurs in vitro with cultured macrophages exposed to A. fumigatus conidia and germlings in standard tissue culture conditions (Figure S1A). Taken together, in a healthy immune competent murine lung, HIF1α is stabilized in response to A. fumigatus pulmonary challenge, suggesting an important role for this protein in resistance to pulmonary fungal growth and subsequent infection.
Consequently, we next sought to determine whether activation of HIF1α was inhibited under conditions known to enhance susceptibility to A. fumigatus pulmonary growth and infection. We determined the effects of corticosteroids on HIF1α stabilization in response to A. fumigatus [34]. Although the effect of steroids on host immune cells has been focused on the role of NFκB, recently, the glucocorticoid dexamethasone was found to abrogate the activation of HIF1α in response to inflammation induced hypoxia [35], [36]. Intriguingly, corticosteroid treatment of mice significantly reduced mRNA induction of HIF1α two-fold when exposed to conidia of A. fumigatus (Figure 1A). Corticosteroid treatment also resulted in decreased levels of HIF1α protein with overall reduced levels in nuclear extracts in both A. fumigatus inoculated and uninoculated mice compared to immune competent mice (Figure 1B–C). Reductions in nuclear levels of the p65 subunit of NFκB in the corticosteroid treated mice were also observed, which has been reported previously, validating the chosen murine model (Figure 1B–C) [37], [38]. These results suggest that reductions in HIF1α nuclear levels may contribute to susceptibility of corticosteroid treated mice to A. fumigatus.
To delineate the function of HIF1α in pulmonary host defense to A. fumigatus, we utilized a conditional knockout system to strongly reduce HIF1α levels in the myeloid compartment [22]. The conditional mice (HIFC) expressed Cre recombinase under the control of the lysozyme M promoter in combination with the loxP flanked exon2 of the HIF1α gene. HIF1α levels were significantly reduced in myeloid derived cells, especially macrophages and neutrophils (Figure S1B) [22]. Immune competent mice deficient in myeloid HIF1α (HIFC) were strikingly more susceptible to A. fumigatus pulmonary challenge compared to littermate controls, with 100% mortality occurring in HIFC mice by day 3 of the experiment and statistically different mortality by day 2 post- fungal inoculation (p = 0.0007) (Figure 2A, p<0.0001). HIFC mice had increased fungal burden at 24 and 48 hrs post inoculation compared to littermate controls that began clearing the conidial inoculum after 24 hrs as measured by quantitative real-time PCR analysis of the fungal 18S rDNA gene (Figure 2B) [39]. Dissemination to the liver and kidney was also increased in HIFC mice (data not shown).
Histopathology of the lungs 8 hrs post-fungal inoculation revealed no apparent difference in the number of conidia in the lungs of HIFC and littermate control mice (Figure S2A). However, decreased levels of cellular infiltrate and inflammation were apparent in HIFC mice A. fumigatus challenged mice (Figure S2A), and this phenotype was also observed at 12 hrs post inoculation (Figure 2C–D). However, the difference in the cellular infiltrate and inflammation was dramatically greater in the littermate controls at 12 hrs post inoculation consistent with a self-resolving fungal bronchopneumonia in these immune competent mice. At later time points post fungal inoculation, 24 and 48 hrs, littermate control mice were able to clear and contain the infection with mostly fungal debris remaining in the lung at 48 hrs (Figure 2C–D & Figure S2B), while the HIFC mice develop invasive disease with uncontrolled hyphal growth. Additionally, the HIFC mice develop increased levels of pulmonary damage, with significantly more lactate dehydrogenase (LDH) apparent in bronchoalveolar lavage fluid (BALF) at 24 hrs post challenge, but with no marked differences in vascular leakage determined by albumin BALF levels (Figure 2E–F). These results suggest that there is a defect in the innate immune response early during the response to A. fumigatus challenge in the HIFC mice that renders them unable to clear and prevent fungal growth and host tissue damage.
Previous reports on HIF1α's function during bacterial infection determined the importance of its activation and presence for neutrophil and macrophage-mediated pathogen killing [22], [23], [25]. We therefore sought to determine if the susceptibility of the HIFC mice to A. fumigatus challenge was due to an overall defect in innate effector cell mediated fungal killing. First, utilizing ex vivo bone marrow derived macrophages (BMDMs) from HIFC and littermate control mice, no difference in the ability of these cells to phagocytose conidia was observed between genotypes (Figure 3A). Additionally, there was no difference in the BMDM's ability to cause damage to phagocytosed conidia as measured by overall metabolic activity of the conidia phagocytosed by the BMDMs (Figure 3B).
To confirm these results in vivo in the context of appropriate inflammatory cues needed for full activation of innate effector cells, we generated a fluorescent Aspergillus reporter (FLARE) strain in the CBS144.89 (CEA10) wild-type background through ectopic insertion of TdTomato driven by the A. nidulans gpdA promoter. A single ectopic insertion of the construct by Southern blotting and FLARE based viability were confirmed as previously reported for the AF293 FLARE strain (Figure S3) [40]. A. fumigatus FLARE conidia contain two fluorophores that allow leukocytes to be distinguished based on their ability to engulf and/or kill conidia. The TdTomato fluorescence expressed by the conidia is lost upon conidia death, whereas the other fluorophore (AF633 or BV421) coating the conidia is stable even at low pH allowing for tracking of the conidia within leukocytes, whether dead or alive. Utilizing the FLARE strain, which allows real time measurement of conidial uptake and viability ex vivo and in vivo, we observed that HIF1α was not required in bone marrow derived neutrophil (BMDN) mediated uptake and killing of A. fumigatus conidia (Figure 3C–D) [40].
In agreement with ex vivo observations with single cell types derived from the bone marrow, inoculation of the FLARE strain into the immune competent murine model also revealed no significant difference in the ability of neutrophils or monocytes to engulf or kill conidia (Figure 3E–G). These surprising results demonstrate that HIF1α is not required for innate effector cell mediated killing following challenge with a fungal pathogen, which is in contrast to what has been determined with bacterial pathogens ex vivo and in skin models where HIF1α is critical for bactericidal activity of these effector cells [23], [25]. These results suggest unique HIF1α mediated mechanisms are employed by the host for protection and defense against fungal pathogen growth and invasion in the lung.
One factor determining the outcome of A. fumigatus lung infection is the timing and recruitment of neutrophils into the lung [8], [9], [41]. Due to the decrease in inflammation observed in the histology of the HIFC A. fumigatus challenged mice, we quantitatively analyzed the level of innate effector cells through analysis of BALF and lung cellularity at early time points following fungal challenge. Consistent with the qualitative histopathology observations, the overall BALF and lung cellular infiltrates of inoculated littermate mice was greater than the HIFC mice at all time points examined (Figure 4). At 4 and 8 hrs post fungal inoculation littermate mice displayed higher numbers of monocyte-like cells (CD11b+Ly6G−) and macrophages (CD11c+) in the BALF and at 8 hrs within the lung (Figure 4B–C,E). Interestingly, HIFC mock treated mice tended to have modestly higher levels of macrophages than littermate controls, though not always statistically significant (Figure 4C,E). More importantly, the main innate effector cells known to be required for clearance and defense against A. fumigatus, neutrophils and inflammatory monocyte-like cells, were consistently ∼2–3 fold lower in HIFC mice following fungal challenge at 4, 8 and 12 hrs in the BALF and at 8 hrs in the lung (Figure 4A,E p<0.02). These data suggest that HIFC mice are defective in overall effector cell population numbers early following fungal challenge in the lung and that this may contribute to their inability to control fungal growth and tissue damage.
Considering that early following fungal challenge there is precedence for the requirement of neutrophils and inflammatory monocytes in terms of infection outcome, we next sought to determine how HIF1α is involved in the neutrophil response following fungal challenge. During inflammation, the absence of HIF1α in effector cells is characterized by depletion of ATP stores due to a failure to switch from oxidative to glycolytic metabolism, which also results in an increase in toxic levels of ROS [42]. However, neutrophils rely strongly on glycolytic metabolism even though they have the capacity for aerobic respiration, supporting a major role for HIF1α in maintenance of normal neutrophil function [43]. Therefore, we sought to determine whether there was a defect in survival of HIF1α-deficient neutrophils in the presence and absence of fungal stimulation.
Agreeing with previously reported data [44], we determined that murine neutrophils deficient in HIF1α have increased cell death compared to wild-type control neutrophils with HIF1α deficient neutrophils exhibiting increased apoptosis at 5 hrs and increased necrosis at 22 hrs in the absence and presence of stimulation with the fungal β-glucan derivative curdlan (Figure 5A–B). Examination of the cellular infiltrates from BALFs of mice 8 hrs post A. fumigatus challenge revealed an increase in apoptotic neutrophils visualized by increased pyknotic nuclei and karyorrhexis of HIFC compared to wild-type neutrophils (Figure 5C–D) [45], [46], [47]. There was no difference in the number of wild-type and HIFC neutrophils with ruffled membranes, a marker of neutrophil activation (Figure 5C–D) [48]. These data correlate with the increase in LDH observed in the HIFC mice (Figure 2F).
Previous reports have identified a role for HIF1α in tissue adhesion, migration and invasion by neutrophils and macrophages at sites of infection and inflammation [22], [49]. In the HIFC mice, we observed a decrease in the level of margination on the vessel walls compared to the littermate mice (Figure 5E). Margination is a prerequisite for neutrophil transendothelial migration (TEM) from the capillaries into pulmonary tissue [50]. There is much controversy as to the requirement for selectin-mediated rolling in order for TEM to occur, but chemokine production due to stimulation from foreign agents is known to increase margination and TEM [51], [52]. In addition to the lack of margination, the migration of the inflammatory cells away from the vessels is minimal in the HIFC mice compared to littermate controls in which the cells have progressed into the alveoli (Fig. 2). These results demonstrate a defect in the HIFC neutrophils ability to sense and migrate towards conidia within the lung environment.
Therefore, we next sought to determine if there was a defect in neutrophil migration or chemotactic signaling in HIFC mice that could account for decreased neutrophil numbers in vivo in response to A. fumigatus challenge. In response to the general chemotactic signal fetal bovine serum (FBS), HIFC neutrophils were able to migrate to the same capacity as littermate neutrophils ex vivo (Figure 6A). Importantly, this assay was conducted within the time window during which there was no difference in apoptosis between WT and HIFC neutrophils. Consequently, the migration results indicated that a defect in the production of chemotactic/cell survival signals at the infection site or the neutrophil response to tissue-specific signals may be the cause of reduced PMN numbers in vivo.
Since HIFC neutrophils were competent to migrate in response to general chemotactic factors (Figure 6A), we next determined if there was a defect in the production of chemotactic signals during fungal pulmonary challenge that would result in decreased migration or cell survival. Utilizing the BALFs of A. fumigatus challenged HIFC and littermate mice as the source of chemotactic factors in the transwell migration assay, we observed that littermate and HIFC neutrophils were defective in migration towards the HIFC BALF, but not towards littermate control BALF. This result indicated there was a missing chemotactic component in the HIFC but not littermate control BALF (Figure 6B). These results support a defect in chemotactic or cell survival signal production in HIFC mice following A. fumigatus pulmonary challenge.
Cytokine/chemokine analysis of the 12 hr BALFs from A. fumigatus challenged mice indicated decreased production of the pro-inflammatory cytokines G-CSF, IL-1α, IL-6, and TNF with no difference in production of the anti-inflammatory cytokine IL-10 (Figure 6F). There was no observed difference in the production of IL-17 and IL-12p40 between the WT and HIFC challenged mice (Figure 6F). The overall response in the HIFC mice at 12 hrs post challenge did not deviate from the usual Th1 protective response with Th2 specific cytokines either unchanged (IL-10) or undetectable (IL-4, data not shown). The production of one of the major neutrophil chemotactic cytokines CXCL1 was decreased in the HIFC inoculated mice compared to littermate controls early following fungal challenge (Figure 6C). This reduction in CXCL1 correlated directly with the quantitative neutrophil defect found during early time points following fungal challenge (Figure 4A).
HIF1α is directly required for CXCL1 mRNA levels as the mRNA abundance in response to A. fumigatus is significantly decreased in macrophages deficient in HIF1α (Figure 6D). Whether the HIF1α regulation of CXCL1 mRNA levels is at the transcriptional or post-transcriptional levels remains unclear, however, analysis of the DNA sequence upstream of the CXCL1 start codon revealed multiple potential HRE elements (Figure S5A). Importantly, the production of CXCL2 [53] and CXCL5 [54], other neutrophil chemokines detected by CXCR2, was not different between the WT and HIFC inoculated mice (Figure 6E). Additionally, no statistically significant difference in the mRNA abundance of the receptors CXCR2, TLR4, and Dectin-1 were observed between the WT and HIFC neutrophils (Figure S5B). Taken together, these results support a direct role for HIF1α in production of cytokines early in the pulmonary response to A. fumigatus challenge. They also support the hypothesis that the neutrophil quantitative BALF defect in the HIFC mice is due in part to decreased levels of CXCL1 and/or other pro-inflammatory cytokines.
We next sought to determine if the HIFC phenotype was due, at least in part, to the marked reduction in CXCL1 levels. Mice deficient in CXCR2 (ligands CXCL1 (KC), CXCL2 (MIP2), CXCL5 (LIX)) develop invasive aspergillosis resulting from delayed neutrophil influx that allows conidial germination [10], [55]. Additionally, the transient expression of CXCL1 during invasive aspergillosis causes an earlier and increased number of neutrophils in the lung that results in improved host defense and outcome of Aspergillus infection [55]. In order to define the involvement of CXCL1 in neutrophil migration, LPS-free recombinant CXCL1 was added to the BALF of A. fumigatus challenged HIFC mice to the level that was determined in the littermate BALF (Figure 6C). The addition of physiological levels of CXCL1 largely restored the neutrophil migration defect observed with A. fumigatus challenged HIFC BALF (Figure 7A, p<0.001).
This significant restoration of neutrophil migration by CXCL1 led us to examine if the addition of recombinant CXCL1 to the HIFC A. fumigatus challenged mice could restore neutrophil levels and murine survival. Previously, kinetics of neutrophil recruitment after 4 hrs by the intratracheal instillation of varying amounts of CXCL1 to the lung was demonstrated [56]. Doses of 30 ng and 100 ng of CXCL1 were demonstrated to induce ∼1.5 and ∼3.5 fold increases in neutrophil recruitment over PBS treatment, respectively [56]. Based on these previously published results, we utilized a dose of 50 ng recombinant CXCL1 for intratracheal instillation. The addition of CXCL1 to the HIFC A. fumigatus challenged mice restored neutrophil numbers in the BALF back to levels of littermate inoculated mice and this restoration correlated with a significant increase in murine survival and improved outcome of infection based on the observed decrease in fungal burden and tissue damage (Figure 7B–C,E,F). The addition of CXCL1 also restored the inflammatory monocyte-like levels back to littermate controls, but not the CD11c+ macrophages, which was expected, as they are known to not respond to CXCL1 (Figure 7D, & Figure S4). These results demonstrate a unique requirement and mechanism for HIF1α control and induction of CXCL1 in response to A. fumigatus challenge in the lung.
Due to the multifunctional role of some cytokines in chemotactic and angiogenic responses and the observed increase in apoptosis/necrosis in the HIFC neutrophils, we determined if the addition of recombinant CXCL1 affected survival of the HIF1α deficient neutrophils. Adding physiologically relevant levels of recombinant CXCL1 to the HIFC neutrophils decreased their time-dependent level of apoptosis and necrosis ex vivo (Figure 7G). This demonstrates that CXCL1 signaling promotes and restores HIF1α neutrophil survival providing support for the requirement of HIF1α-dependent production of CXCL1.
In this study, we uncover a novel and essential function for myeloid HIF1α in protection against pulmonary A. fumigatus challenge. We present findings that myeloid HIF1α is required for initiating protective inflammatory signals and responses to control A. fumigatus growth and host tissue damage. These data strongly suggest a role for HIF1α in providing host protection to pulmonary fungal disease, provides a deeper understanding of the fungal-host interaction in the lung, identifies a new genetic factor critical for resistance to pulmonary murine fungal infections, and argues for further investigation into the therapeutic potential of HIF1α modulation for fungal disease.
Importantly, our results demonstrate a requirement for myeloid HIF1α in murine survival to A. fumigatus pulmonary challenge. The kinetics and final outcome of murine survival in the absence of myeloid HIF1α is striking. Mortality in HIFC mice does not appear to be due to an increased inflammatory response as there is less overall inflammation in the lung at the time points analyzed in our model. Upon examination of the lung, there appears to be no gross defects in the vasculature in the HIFC mice (also suggested by BALF albumin measurement), but as infection progresses and fungal burden increases an increase in the level of damage occurring in the lung in the absence of HIF1α occurs as evidenced by the increase in LDH levels. We cannot currently rule out systemic effects of HIFC loss on murine survival, however, there was a trend toward increased fungal dissemination to the kidneys and liver in the HIFC mice that could contribute to the rapid mortality (data not shown). The increased mortality therefore is likely due to the combination of the dysregulated host response and subsequent increased fungal burden following A. fumigatus challenge.
Perhaps most surprisingly, given previous studies in bacterial pathosystems, HIF1α was not required for direct killing of A. fumigatus conidia ex vivo or in vivo by innate effector cells. This is in contrast to the requirement for HIF1α in killing of multiple gram-negative and –positive bacteria in epidermal wound models of infection [22], [23], [25]. As HIFC neutrophils are not defective in their ability to elicit the respiratory burst [23], it is perhaps not surprising that no difference in their ability to kill A. fumigatus conidia was observed due to the known conidial susceptibility to reactive oxygen species [57], [58]. Additionally, we cannot rule out that the ability to kill conidia may be due to a compensatory HIF2α mechanism, as it has distinct and overlapping biological roles with HIF1α [59]. Importantly, these observations demonstrate the plasticity of HIF1α regulation between different tissue microenvironments and with fungal and bacterial organisms. As the phenotypes of innate cells differ between tissues within the host [60], it will be important to determine if the findings presented here occur with other pathogens such as the bacteria examined in the epidermal model that are also potent lung pathogens.
Though HIF1α does not seem to regulate innate mediated killing of A. fumigatus conidia per se, our data suggest a required role for HIF1α in controlling A. fumigatus infection through modulation of the infection microenvironment that drives the timing, recruitment, and survival of neutrophils at early critical time points following A. fumigatus challenge. Recruitment of myeloid cells during epidermal inflammation is known to require HIF1α and is reported to be due to decreased HIF1-dependent integrin expression [22], [49]. This may partially account for the decreased margination observed in the HIFC mice challenged with A. fumigatus, however, due to the increased percentage of neutrophils in the pulmonary capillary blood and the smaller size of the capillaries, the importance of integrin and selectin binding for pulmonary TEM to occur is debated [61], [62], [63]. Once at the site of inflammation, the ability of neutrophils to remain metabolically competent in low oxygen inflammatory environments is dependent upon the induction of the glycolytic pathway directly regulated by HIF1α [64]. In the absence of HIF1α and oxygen, neutrophils cannot maintain high ATP levels and as a consequence, undergo apoptosis [65]. The delay in the time-dependent recruitment of neutrophils and/or their apoptosis is partially responsible for the increased occurrence of conidial germination in the HIFC mice [9]. The HIFC A. fumigatus challenged mice also had decreased levels of CD11b+Ly6G− inflammatory monocyte-like cells, which have recently been implicated in inflammatory conditioning for neutrophil functions in the lung and conidial killing [41], and are also likely involved in the HIFC phenotype.
The effects of myeloid HIF1α loss on effector cell recruitment and survival appear to be driven in part by defects in the production of pro-inflammatory cytokines. Though our data strongly support a major role for the neutrophil chemoattractant CXCL1 in mediating the HIFC phenotype, other neutrophil chemoattractants/receptors that are known to be involved in neutrophil recruitment during various lung infections may be involved in the HIF1α phenotype including CCL3-CCL6-/CCR1 [66], C5a/C5aR [67], and LTB4/LTB4R1 [68], [69]. However, production of the other CXCR2 ligands CXCL2 [53] and CXCL5 [54] that are known to have roles in neutrophil recruitment were not different within the BALF of WT and HIFC mice. Untangling the signal transduction cascades mediated by HIF1α, including how it is activated in response to A. fumigatus challenge, is an important future direction of this research.
Downstream of pathogen recognition, regulation of CXCL1 transcription through NFκB is a known mechanism for CXCL1 induction in response to inflammation [70]. Given the dual regulation of multiple genes by HIF1α and NFκB, it is perhaps not surprising that a role for HIF1α in CXCL1 regulation exists. Loss of CXCL1 mRNA levels in HIF1α macrophages and the presence of putative HREs in the CXCL1 promoter further support a direct role for HIF1α in control of CXCL1 murine lung levels in response to A. fumigatus challenge. Importantly, it appears that in the context of lung infection that HIF1α may play a more dominant role in myeloid activation of CXCL1 and the amplification of the response. This is based on previous demonstration that amplification of CXCL1 levels occurred only in epithelial cells that had constitutively active HIF1α over NFκB activation alone [71]. Given the decrease in IL-1 family members in the HIFC mice, we hypothesize that IL-1 signaling may be play a critical function in these signal transduction cascades. Considering that transgenic expression of CXCL1 during the course of infection improved fungal burden and survival during infection with A. fumigatus [55], understanding the exact mechanism for HIF1α regulation of CXCL1 is of great importance.
Another important future direction is the cell compartment primarily producing CXCL1 and the effects of paracrine and autocrine signaling to regulate the observed effector cell phenotypes. Due to the ability of CXCL1 to reduce the apoptotic phenotype of the HIFC neutrophils, we hypothesize that the HIF-induced CXCL1 responses are not only required for chemotaxis of neutrophils, but also partially involved in inhibiting apoptosis during infection, perhaps consistent with the known angiogenic responses autocrine CXCL1 signaling has in epithelial cells [72], [73]. Recently, the implication for the requirement of neutrophil transmigration in the transcriptional imprinting of epithelial cells was demonstrated [74]. At the sites of transmigration, the depletion of oxygen by neutrophils in response to either a pathogen or tissue injury, resulted in the stabilization of HIF1α, which was required for mucosal protection and inflammatory resolution [74]. In addition, the release and sensing of VEGF in an autocrine and paracrine manner by monocytes, keratinocytes, and endothelial cells is required for wound healing responses to restore proper tissue homeostasis [75]. Tumors are notorious for exploiting this mechanism to aid in their growth and metastasis, as demonstrated by increased tumor growth with the paracrine signaling of CXCL1 in breast cancer [76]. The increased survival of the HIFC neutrophils with exogenous CXCL1 supports the possibility that decreases in cytokine production in the HIFC mice is due to decreased paracrine signaling responses between the deficient and less abundant myeloid cells and the epithelia/endothelia of the lung.
A direct translational outcome from our study that warrants further investigation is the observation that corticosteroids reduced nuclear localization and gene regulation of HIF1α. Given the phenotype of the HIFC mice, it stands to reason that steroid mediated suppression of HIF1α could contribute to aspergillosis susceptibility. Our results demonstrate that steroid treatment does not inhibit overall HIF1α protein levels, but rather reduces the nuclear levels of HIF1α and p65 NFκB. The decrease in nuclear p65 NFκB and subsequent HIF1α mRNA abundance did not cause a decrease in HIF1α protein accumulation in the cytoplasm, indicating that there may be a separate corticosteroid induced NFκB-independent mechanism that hinders the HIF1α induced response to A. fumigatus. Nuclear import of HIF1α is known to rely upon interactions with the septin SEPT9_i1, a product of transcript SEPT9_v1 that encodes isoform1, and importin-α [77]. These interactions have been established in the context of tumor and cancer progression, but the interactions during steroid treatment are unknown to our knowledge, and a mechanism for the steroid blockage of HIF1α cytoplasm-nuclear localization is not yet understood. There may also be differential posttranslational regulation of HIF1α under corticosteroid conditions that is impacting the localization, interactions, and nuclear stability of HIF1α in myeloid cells. Interestingly, corticosteroid treated mice were demonstrated to have a two-fold reduction in CXCL1 production compared to immune competent mice following A. fumigatus challenge [78] further supporting a role for HIF1α in steroid induced IPA susceptibility.
Taken together, our data support the conclusion that myeloid derived HIF1α is required by effector cells of the innate immune system to prevent A. fumigatus pulmonary infection. As control of metabolism and production of energy in inflammatory, low oxygen infection sites is dependent upon HIF1α, it is in accord that innate inflammatory responses required for clearing and preventing infection are also tied to this important transcriptional regulator. It will be of translational importance to determine if this idea is reflected in the mechanisms underlying the susceptibility of certain patients to A. fumigatus infection. Consequently, it is a high priority to determine if this response can be targeted to reverse the inactive and suppressed effects of the innate cells during IPA in the context of corticosteroid mediated immune suppression. The success of HIF1α agonists in the context of bacterial skin infections is promising in this regard [23], [25], [79].
Aspergillus fumigatus strain CBS144.89 (also known as CEA10) was used in all experiments except those involving the FLARE tdtomato strain generated from CEA17 (uracil auxotroph derived from CEA10). All strains were grown on glucose minimal medium with 1.5% agar at 37°C. Conidia were dislodged from plates with a cell scrapper, re-suspended in 0.01% Tween-20, and filtered through miracloth (EMD chemicals, CalBiochem).
A. fumigatus strain CEA17 was transformed with a construct consisting of an overlap PCR of the gpdA promoter driven-tdtomato from pSK536 (gift from Dr. Sven Krappmann) with A. parasiticus pyrG. The construct was ectopically inserted into the genome using the standard fungal protoplast transformation as previously described [80]. Transformants were initially screened by microscopy and flow cytometry for tdtomato expression. One of five transformants were selected for further use, based on bright fluorescence in all growth stages, comparable radial growth to parental strain, and comparable inflammatory TNF responses by BMDMs (Figure S3). Copy number was confirmed by Southern analysis with the digoxigenin labeling system (Roche Molecular Biochemicals, Mannheim, Germany) as previously described [81]. Generation of FLARE was done as previously described in [40] using Biotin conjugated AF633-streptavidin or Biotin conjugated BilliantViolet421-streptavidin (BV421) (BioLegend). For conidial kill assays, 2.5×105 FLARE conidia were incubated in 0.2 ml RP10 with 0–10 M H2O2 for 30 min at 37°C, washed, and analyzed by flow cytometry for TdTomato and BV421 fluorescence. Duplicate samples were plated (at 1∶1,000 dilution) to determine the cfu.
CD1 female mice, 6–8 weeks old were used in the corticosteroid (triamcinolone acetate, Kenalog) experiments. Mice were obtained from Charles River Laboratories (Raleigh, NC). For the corticosteroid model, mice were immunosuppressed with a single dose of Kenalog (Bristol-Myers Squibb Company, Princeton, NJ, USA) injected subcutaneously (s.c.) at 40 mg/kg 1 day prior to inoculation. For the immunocompetent experiments, mice 10–12 weeks old with targeted myeloid deletions of HIF1α created via crosses into a background of lysozyme M–driven cre (HIFC) expression and littermate controls (cre-/HIF1α floxed) were used as described in [22]. For infections, mice were lightly anesthetized and immobilized in an upright position using rubber bands attached to a Plexiglas stand for oropharyngeal aspiration. A blunt 20G needle attached to a 1 ml syringe was advanced into the trachea to deliver the indicated number of conidia (3–7×107) in a volume of 0.05 ml PBS or PBS with 0.025% Tween-20.
Nuclear and cytoplasmic proteins were isolated from lyophilized lung tissue, BMDMs, or J774.1 macrophages at indicated times. Cells were centrifuged at 1500 rpm for 5 min at 4°C. Supernatant was removed, and the pellet was washed with 5 packed cell volumes (PCV) of buffer A [10 mM Tris-HCl (pH 7.5), 1.5 mM MgCl2, 10 mM KCl supplemented with 1M dithiothreitol, 0.2 M PMSF, 1 mg/ml leupeptin, 1 mg/ml aprotinin, 1 mg/ml pepstatin, and 0.5 M Na3VO4], resuspended in 4 PCV of buffer A and incubated on ice for 10 min. The cell suspension and lung tissue were homogenized, and nuclei were pelleted by centrifugation at 10,000 g for 10 min at 4°C and the supernatant was collected as the cytoplasmic fraction. The cell pellet was resuspended in 3 PCV of buffer C [20 mM Tris-HCl (pH 7.5), 0.42 M KCl, 1.5 mM MgCl2, and 20% glycerol] and rotated for 30 min at 4°C. The suspension was centrifuged at 20,000 g for 10 min at 4°C. The protein concentration was determined using the Bradford method (Bio-Rad, Hercules, CA, USA). Nuclear and cytoplasmic samples were suspended in 6× SDS sample buffer, boiled for 10 min, and loaded onto a 10% mini-protein precast gels (Bio-Rad) for SDS-PAGE. After gel electrophoresis, protein was transferred to a PVDF membrane using the trans-blot turbo transfer system (Bio-Rad). HIF1α and NFκB (p65) were detected using polyclonal rabbit anti-mouse antibodies NB100-449 (1∶3000) and C-20:sc-372 (1∶1200), respectively and an anti-rabbit HRP-conjugated secondary antibody raised in goat (millipore) at a 1∶5000 dilution. Chemiluminescence was measured following incubation of blots with Clarity Western ECL substrate (Bio-Rad) using a FluorChem FC2 imager (Alpha Innotech). For loading controls, anti-tubulin (Sigma, T5192) (human) was utilized.
Tissue or BMDMs were re-suspended in Trizol reagent and chloroform to extract RNA. RNA was DNase treated with DNA-free kit (Ambion) and reverse transcribed with QuantiTect reverse transcription kit (Qiagen, USA). Primers for all murine genes of interest were designed with PrimerQuest (IDT) and manufactured by IDT, USA. Sequences are: hif1α fwd: ATGAGATGAAGGCACAGA, rev: CACGTTATCAGAAATGTAAACC, cxcl1 (kc) fwd: TGCACCCAAACCGAAGTCAT, rev: TTGTCAGAAGCCAGCGTTCAC, cxcr2 fwd: TGGCCTAGTCAGTCATCA, rev: CAATCCACCTACTCCCATTC, tlr4 fwd: GTGTGTGTGTGTGTGTTG, rev: AGCTGCTCTGTACACTATTT, dectin1 (clec7a) fwd: CCTAGTGTGATCTGTCTTGT, rev: TTTCTGCCCACATATTGATTAG, hprt fwd: GGAGTCCTGTTGATGTTGCCAGTA, rev: GGGACGCAGCAACTGACATTTCTA, rpl13a fwd: CTCTGGAGGAGAAACGGAAGGAAA, rev: GGTCTTGAGGACCTCTGTGAACTT. All reactions were performed on BioRad MyIQ real-time PCR detection system with IQ SYBR green supermix (Bio-Rad, Hercules, CA). The ΔΔCt method was used to assess changes in mRNA abundance, using either hprt or rpl13a as the housekeeping gene. Results presented are the mean and standard deviation from 3 biological and 3 technical replicates.
A. fumigatus challenged mice were euthanized at indicated times. For histological studies, the lungs were inflated with 10% buffered formalin, fixed, and embedded in paraffin to generate 4 µm sections stained with hematoxylin and eosin (H&E) or Gomori's Methenamine Silver (GMS) stain for microscopy by the Dartmouth Immunology COBRE core facility or at Montana State University. Contiguous tissue sections were imaged using a Zeiss Axioscope 2-plus microscope and imaging system (Zeiss, Jena, Germany) and a Leica upright DMRXA2 with Leica application suite software and DC500 camera (Leica Microsystems, Buffalo Grove, IL, US). Pathological examination was conducted for apoptosis, necrosis, and vasculature observation. Image analysis was performed using ImageJ software (v.1.46i).
For immunohistological studies, the left lung of each mouse was filled with OCT (frozen tissue matrix) and after embedding in OCT immediately frozen in liquid nitrogen. The lungs were stained as previously described in [21], [82] with a rabbit polyclonal antibody to Aspergillus (Abcam Inc., Cambridge, MA, USA) and detected with AlexaFluor488-conjugated goat Anti-rabbit (Invitrogen, Carlsbad, CA, USA) diluted 1∶400. After another washing step, prolong Gold antifade reagent with DAPI (Invitrogen, Carlsbad, CA, USA) was added to each section. Microscopic examinations were performed on a Zeiss Axioscope 2-plus microscope and imaging system (Zeiss, Jena, Germany). For each time point, a total of 2 to 4 mice were examined and experiments were repeated in triplicate.
To assess fungal burden in lungs, mice were sacrificed at 24, 36, or 48 hrs post inoculation, and lungs were harvested and immediately frozen in liquid nitrogen. Samples were freeze-dried, homogenized with glass beads on a Mini- Beadbeater (BioSpec Products, Inc., Bartlesville, OK, USA), and DNA extracted with the E.N.Z.A. fungal DNA kit (Omega Bio- Tek, Norcross, GA, USA) or phenol chloroform extraction. Quantitative PCR was performed as described previously [39].
Cellularity was analyzed on cells from the BALF at specific time points of 4, 8, or 12 hrs. Cells isolated from BALFs were enumerated and stained with the following Abs: anti-CD11b (clone M1/70), anti-CD11c (clone N418), and anti-Ly6G (clone 1A8) in staining buffer (PBS supplemented with 2% FBS). Neutrophils were identified as CD11b+CD11c−Ly6Ghi, macrophages as CD11c+CD11b−Ly6G−, and inflammatory monocyte-like cells as CD11b+CD11c−Ly6Glo (negative for NK1.1 staining, data not shown). A fourth population of cells staining with CD11c+CD11b+Ly6G+ were found, but further determination was not pursued. Flow cytometry data were collected on a MACSQuant 10 (Miltenyi Biotec) and analyzed with FlowJo, v.9.4.3 (TreeStar).
To assess the requirement of CXCL1 for in vivo infection, HIFC mice received 50 µL of 50 ng recombinant CXCL1 (BioLegend) 4 hrs following inoculation of 7×107 conidia and were compared to HIFC mice and WT infected mock mice receiving PBS. Separate experiments analyzing survival, cell recruitment by flow cytometry, and fungal burden were conducted.
Single-cell lung suspensions were prepared for flow cytometric analysis and classified as described in Hohl et al. (2009). Tissue processing did not result in leukocyte uptake of exogenously added FLARE conidia. Lung digest and, if applicable, BALF cells were enumerated and stained with the following Abs: anti-Ly6C (clone AL-21) anti-Ly6G (clone 1A8), anti-CD11b (clone M1/70), anti-CD45.2 (clone 104), and anti-Ly6B.2 (clone 7/4). PE- and APC-labeled Abs were omitted in FLARE experiments. Neutrophils were identified as CD45+CD11b+Ly6CloLy6G+Ly6B.2+ cells. Flow cytometry data were collected on a BD LSR II flow cytometer or MACSQuant 10 (Miltenyi Biotec) and analyzed with FlowJo, v.9.4.3 (TreeStar).
Lactate dehydrogenase (LDH) using the CytoTox96 non-radioactive cytotoxicity assay kit (Promega, Cat. No. 573702) and albumin assay (Albumin (BCG) Reagent Set, Eagle Diagnostics, Cedar Hill, TX, USA) were conducted on BALFs from WT and HIFC mice inoculated with 7×107 conidia or PBS according to manufacturers instructions with slight variation. Briefly, 100 µL of the BALF was added to equal volumes of the respective agents and incubated for either 30 min (LDH) or 5 min (Albumin) and read at 490 nm and 630 nm, respectively. Albumin levels were determined using a standard curve and LDH values for each time point are relative to the WT PBS BALF sample.
Bone marrow (BM) cells were eluted from tibias and femurs of 8–12 week old Littermate or HIFC mice, lysed of red blood cells, and cultured for macrophages in RP20 (RPMI, 20% FCS, 5 mM HEPES buffer, 1.1 mM L-glutamine, 0.5 U/ml penicillin, and 50 mg/ml streptomycin) supplemented with 30% (v/v) L929 cell supernatant (source of M-CSF) or neutrophils in murine neutrophil buffer (HBSS containing 0.1% FBS and 1% glucose). BM cells for macrophages were plated in a volume of 20 ml at a density of 2.5×106 cells/ml in 10 ml petri dishes. The medium was exchanged on day 3. Adherent BM-derived macrophages (BMDMs) were harvested on day 6. BM- derived cells for neutrophils (BMDNs) were suspended in 3 ml 45% percoll and isolated from a 30 min 1600× g percoll gradient (top to bottom: 3 ml 45% percoll containing BM cells, 2 ml 50%, 2 ml 55%, 2 ml 62%, and 3 ml 81%) in a Sorvall Legend Mach 1.6R benchtop centrifuge, with a BIOshield 600 rotor-75002005 (Thermo Scientific). BMDNs were collected from the 62/81% border and washed with HBSS before live cell counting (95% purity, determined by cytospin). For the cytokine mRNA abundance quantifying experiments, BMDMs were incubated with A. fumigatus conidia (strain CBS144.89) in a 10∶1 (effector∶target) ratio 8 hrs. Following the incubation, cells were directly re-suspended in Trizol reagent and chloroform to extract RNA for qRT-PCR.
BMDMs were incubated with A. fumigatus conidia in a 9∶1 (effector∶target) ratio 3 hrs (for CFU) and 16 hrs (for XTT). Following the 3 hr incubation, non-phagocytosed conidia were washed off the cells, serially diluted onto GMM plates in duplicate, and CFU was determined. Following the 16 hr incubation, BMDMs were cold water lysed and the percent damage was quantitated by measuring the OD at 450 nm following a 1 hr incubation with XTT as previously described [82].
Collected BALFs were assayed by ELISA and luminex. Commercially available ELISA kits for CXCL1 (Assay Biotech, OK-0189), CXCL2 (R&D systems, DY452), and CXCL5 (R&D Systems, DY443) were used according to the manufactures' instructions. The limit of detection was 15 pg/ml. Luminex analysis was carried out using Bio-Plex Pro Mouse Cytokine immunoassay on a Bio-Plex Array Reader (Bio-Rad Laboratories Inc., Hercules, CA) according to the manufactures' instructions. Bio-Plex Manager software with five-parametric-curve fitting was used for data analysis and procedure was carried out by the Dartmouth Immunology COBRE core. For BMDM cytokine analysis of TdTomato strain, cells were washed and plated in 0.2 ml TC medium at a density of 5×105 cells/ml in 96 well plates and co-incubated in a 9∶1 ratio with conidia for 8 hrs. Supernatants were collected for ELISA. A commercially available ELISA kit for TNF (eBioscience, San Diego, California, US) was used according to the manufactures' instructions. The limit of detection was 15 pg/ml for TNF.
BMDN migration was examined using Costar Transwell plates (6.5 mm diameter insert, 3.0 µm pore size, polycarbonate membrane, Corning Inc., Corning, NY). To determine if migration was defective, 10% FBS was added to the bottom chamber of these plates (media without FBS was used as a migration control). Isolated BMDNs were counted using trypan blue (Sigma), then placed in serum free medium (SFM). Cells were resuspended at 1×106/ml SFM and 250 µl were allowed to migrate for 3 hr at 37°C at 5% CO2. Following migration, the medium in the top chamber was aspirated and the membrane gently wiped with a cotton swab to remove the cells that did not migrate. The membranes were first rinsed with PBS, the cells were then fixed with 2% formaldehyde in PBS, permeabilized with 0.01% Triton X-100 (Sigma) in PBS and finally stained with crystal violet (Sigma). Cells that migrated across the membrane were counted. Ten random fields at 40× were counted for each condition using light microscopy. Each experiment was repeated three times. Results are expressed as mean cell migration normalized to media control ± SEM. To determine the role for cytokine signaling in migration defects, a BALF-switch experiment using BALFs from infected HIFC and littermate mice in the bottom chamber was conducted. Briefly, 250 µl BMDNs at 1×106/ml SFM were added to the top chamber of a transwell plate with FN coated membranes with 300 µl of BALF in the bottom chamber and were allowed to migrate for 3 hr at 37°C and 5% CO2. For add-back experiments, recombinant CXCL1 (Biolegend) was added back to the BALFs from infected HIFC mice to the concentration determined by ELISA in the infected littermate BALFs (600 pg).
Neutrophil apoptosis was measured using FACs analysis by staining BMDN's incubated at 37°, 5% CO2 in RP20 medium with annexin V-Pacific Blue (PB) (BioLegend, #640917) and PI (millipore). Staining was performed by following the manufacturer's instructions, with minor changes. Briefly, after isolation or incubation for the specified time points, neutrophils were washed twice with ice-cold PBS with 2% FBS and then resuspended in Annexin-V binding buffer (0.01 M HEPES, pH 7.4; 140 mM NaCl; 2.5 mM CaCl2). Annexin V-PB and PI were added into the culture tube and incubated for 15 min prior to direct analysis with flow cytometry. Viable neutrophils were defined as negative for annexin V-PB and PI staining; apoptotic neutrophils were defined as positive for annexin V-PB staining but negative for PI staining. Cells positive for both annexin V-PB and PI staining were considered necrotic cells. Cell survival/apoptosis was expressed as a percentage of neutrophils relative to the total number of counted neutrophils. In vivo neutrophils were quantified through analysis of BALF cytospins as previously described [45], [46], [47]. Briefly, apoptotic neutrophils were visualized by counting the number of neutrophils with pyknotic nuclei and karyorrhexis out of the total number of neutrophils per frame. Ruffled neutrophils were also quantified and depicted neutrophil activation. Ten random frames at 8 hrs post challenge were analyzed from four HIFC and four WT mice (analyzed two separate experiments).
This study was carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. The animal experimental protocol was approved by the Institutional Animal Care and Use Committee (IACUC) at Dartmouth College (protocol number cram.ra.1).
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10.1371/journal.pgen.1005128 | A Multi-layered Protein Network Stabilizes the Escherichia coli FtsZ-ring and Modulates Constriction Dynamics | The prokaryotic tubulin homolog, FtsZ, forms a ring-like structure (FtsZ-ring) at midcell. The FtsZ-ring establishes the division plane and enables the assembly of the macromolecular division machinery (divisome). Although many molecular components of the divisome have been identified and their interactions extensively characterized, the spatial organization of these proteins within the divisome is unclear. Consequently, the physical mechanisms that drive divisome assembly, maintenance, and constriction remain elusive. Here we applied single-molecule based superresolution imaging, combined with genetic and biophysical investigations, to reveal the spatial organization of cellular structures formed by four important divisome proteins in E. coli: FtsZ, ZapA, ZapB and MatP. We show that these interacting proteins are arranged into a multi-layered protein network extending from the cell membrane to the chromosome, each with unique structural and dynamic properties. Further, we find that this protein network stabilizes the FtsZ-ring, and unexpectedly, slows down cell constriction, suggesting a new, unrecognized role for this network in bacterial cell division. Our results provide new insight into the structure and function of the divisome, and highlight the importance of coordinated cell constriction and chromosome segregation.
| Bacterial cell division is a highly regulated process that must be coordinated with other cellular processes (i.e. DNA replication and chromosome segregation) to promote faithful reproduction. In Escherichia coli, this regulation is most often mediated through the polymerization of the prokaryotic tubulin homolog, FtsZ, which forms a ring-like structure (FtsZ-ring) at midcell. The establishment of the FtsZ-ring marks the site of division and enables the assembly of the macromolecular division machinery (divisome). Here we applied single-molecule based superresolution imaging to reveal the three-dimensional structure of FtsZ in the context of its regulatory proteins: ZapA, ZapB and MatP. We found that these four proteins exist in a multi-layered network that extends from the cell membrane to the chromosome. This layered organization not only helps to stabilize the FtsZ-ring, but also serves to coordinate division with DNA status by influencing constriction rate. Our results not only provide a comprehensive view of the divisome, but also allow new insight to be garnered regarding the structure and function of the divisome.
| Prokaryotic cell division is a conserved process that requires the formation of a multi-protein complex (divisome) at midcell [1]. Although many molecular constituents of the divisome have been identified [2], its in-vivo structural organization remains elusive. Understanding divisome architecture will help elucidate the mechanisms by which it achieves cytokinesis and coordinates with other cellular processes.
The central component of the divisome is FtsZ, a highly conserved prokaryotic tubulin homolog that polymerizes at midcell to form a ring-like structure [3]. The FtsZ-ring is not only required to serve as a stable scaffold for the assembly of all other division proteins [4], but may also generate a constrictive force during cytokinesis [5,6]. Based on in vitro polymerization studies, the basic structural units of the E. coli FtsZ-ring are believed to be single-stranded protofilaments that are on average 120 nm long [7]. These protofilaments are attached to the cytoplasmic membrane by binding to two membrane proteins, FtsA and ZipA [8,9].
Recently, high-resolution microscopy studies have revealed that the in vivo FtsZ-ring is a discontinuous structure, comprising a heterogeneous arrangement of FtsZ protofilaments [10–15]. Furthermore, FtsZ molecules within the ring have been shown to dynamically exchange with those in the cytoplasmic pool (half-time τ1/2 ≈ 10 – 30 s) [16,17]. These observations raise the question of how such a disordered, dynamic FtsZ-ring could provide the stable scaffold required for divisome assembly or generate a uniform constrictive force along the septum.
Recent work aimed at answering these questions have focused on a family of FtsZ-ring-associated proteins (Zaps), including: ZapA, ZapB, ZapC, ZapD and ZapE [18–23]. These proteins localize to the midcell in an FtsZ-dependent manner, and promote FtsZ-ring assembly in vivo. While no individual Zap is essential, single deletions lead to elongated cells, abnormal FtsZ-rings and irregular septum morphologies. Double and triple deletions result in synergistic defects [20,22]. These observations suggest that the Zaps carry out important, over-lapping roles in maintaining proper FtsZ-ring structure and function [20–23].
Among the five Zaps, ZapA and ZapB are best understood and are thought to work in concert to stabilize the FtsZ-ring [18,19,24]. We previously showed that in the absence of ZapA and/or ZapB, the FtsZ-ring dissociates into smaller, widely-dispersed FtsZ clusters throughout the midcell region, often leading to incomplete and abnormal septum formation [14]. These results support a model in which ZapA and ZapB stabilize the FtsZ-ring specifically at the division plane. In vitro characterization suggests that ZapA can stabilize the FtsZ-ring by cross-linking FtsZ protofilaments and possibly reducing its GTPase activity [24,25]. ZapB is a 100% coiled-coil protein that self polymerizes into large bundles in vitro [19]. ZapB does not interact directly with FtsZ, but associates indirectly through ZapA [26,27]. Hence, ZapB most likely exerts its stabilizing effect on FtsZ indirectly through ZapA. Interestingly, a recent confocal microscopy study observed that ZapB does not colocalize completely with the FtsZ-ring, but instead resides at the cytoplasmic face of the FtsZ-ring [26]. As such, the ZapB structure may have FtsZ-independent dynamics and functions, suggesting that the structural organization of the entire divisome is not simply dictated by the FtsZ-ring.
Further complicating the picture is the discovery of MatP, a DNA-binding protein involved in condensation and segregation of the terminus (ter) macrodomain of the chromosome [28,29]. MatP was found to interact with ZapB in a bacterial two-hybrid system [29] and recently shown to work with ZapB and ZapA to localize the divisome [30]. Thus, these data suggests a new, attractive model for the structural organization of the divisome—the divisome is extended from the membrane to the chromosome through a three-dimensional, extended protein network formed by these interacting proteins. This large protein network may provide the scaffold function previously attributed to FtsZ alone. Importantly, this network may also coordinate the progression of cell wall constriction with chromosome segregation. To test such a model, in this work we used quantitative superresolution imaging in conjunction with biophysical and genetic investigations to map the spatial organization and characterize the function of the three-dimensional cellular structures formed by FtsZ, ZapA, ZapB and MatP proteins in E. coli cells.
Using a single-molecule based superresolution imaging method, photoactivated-localization microscopy (PALM) [31], we first characterized the cellular structures formed by FtsZ, ZapA, or ZapB in live E. coli cells with a spatial resolution of ~45 nm [14]. We generated photoactivatable fluorescent fusion proteins mEos2-ZapA and ZapB-mEos2, and used an FtsZ-mEos2 construct described previously [10,14,32]. The mEos2-ZapA and ZapB-mEos2 fusions both rescued the elongated phenotypes of their respective deletion strains and localized to midcell in the absence of the endogenous protein (S1 Fig). To perform live-cell PALM imaging, we expressed these fusions ectopically in wild-type (wt) BW25113 cells and completed imaging in less than 30s. The estimated expression levels of FtsZ-mEos2, mEos2-ZapA and ZapB-mEos2 were ~30%, 45% and 10% of the total cellular concentration of the corresponding wt protein, respectively (Methods).
We found that ZapA and ZapB predominantly formed band-like structures, indicative of rings projected to 2-dimensional (2D) imaging planes (Fig 1, S1 Table). The band-like structure was observed in 41% of cells expressing mEos2-ZapA (ntotal = 229) and 59% of cells expressing ZapB-mEos2 (ntotal = 137), similar to the prevalence observed for FtsZ-mEos2 (51%, ntotal = 201). The remaining cells exhibited a number of different focal or non-planar morphologies, which were also observed previously for FtsZ [10,14] (S2 Fig). These alternate structures appear to precede the band-like structure as they were predominantly observed in shorter cells (S3 Fig, S1 Table). We observed similar morphologies when we applied superresolution imaging [33] to immuno-labeled, native FtsZ and ZapB structures, suggesting that the observed polymorphisms were not artifacts caused by the fused fluorescent proteins (S4 Fig).
While the three proteins displayed similar structural morphologies, we observed quantitative differences in the width (w) and diameter (d) of the band-like structures formed by each protein (Table 1, Methods). The mEos2-ZapA bands (w = 117 ± 4 nm, d = 686 ± 14 nm, n = 38; x- ± se) were similar (p > 0.3) to those of FtsZ-mEos2 (w = 115 ± 3 nm, d = 689 ± 11 nm, n = 54) (Table 1), but ZapB-mEos2 bands were significantly wider (161 ± 12 nm, p ≈ 1e-4, n = 34) and smaller in diameter (613 ± 16 nm, p < 5e-5). These structural differences are readily apparent in PALM images (Fig 1, iii), but obscured in the corresponding conventional fluorescence images (Fig 1, ii). The higher degree of structural similarity between ZapA and FtsZ relative to that of ZapB and FtsZ likely reflects the fact that ZapA binds FtsZ directly, while ZapB associates with FtsZ indirectly through ZapA [26,27].
Analysis of the single-color PALM images above indicated that ZapB structures could deviate from those of FtsZ and ZapA in vivo. Thus, to directly compare their spatial arrangements, we performed two-color PALM imaging in live E. coli cells. We expressed either Dronpa-ZapA or ZapB-Dronpa together with FtsZ-PAmCherry1 in the same cell. The functionality and expression level of these fusions were similar to those of the mEos2 constructs described above (S5 Fig).
We found that structures formed by Dronpa-ZapA and FtsZ-PAmCherry1 largely overlapped, and adopted similar structural morphologies in ~80% of cells, consistent with incorporation of ZapA into the FtsZ-ring (Fig 2Ai-iii, ntotal = 96). In the remaining cells, however, the two structures showed deviations significantly larger than our spatial resolution in two-color PALM imaging (~50 nm) (Fig 2Aiv-vi). On several occasions (n = 7), Dronpa-ZapA structures were sandwiched in between FtsZ-PAmCherry1 structures (Fig 2Av-vi), suggesting a possible bridging function for ZapA. We observed more substantial deviations between ZapB-Dronpa and FtsZ-PAmCherry1 (Fig 2B). In ~70% of cells (ntotal = 88), ZapB-Dronpa structures appeared to be encompassed by FtsZ-PAmCherry1 and located toward the inner surface of FtsZ-ring (Fig 2B).
The structural deviations we observed between the FtsZ-ZapA and FtsZ-ZapB pairs were not observed in a control strain where Dronpa-ZapA was co-expressed with PAmCherry1-ZapA and imaged under the same condition (Fig 2Ci-v). Multi-color fluorescent beads imaged in both color channels also showed complete overlap within our spatial resolution (Fig 2Cvi). These results suggest that the observed structural deviations were not imaging artifacts, nor caused by the fusion protein dynamics or photoproperties of Dronpa and PAmCherry1.
To quantify the degree of colocalization for the ZapA-FtsZ, ZapB-FtsZ and ZapA-ZapA pairs, we performed a coordinate-based cross-correlation analysis [34–36]. In each cell, we determined the cross-correlation value between two different species at midcell as a function of their displacement along the short axis of the cell. The displacement value at maximal cross-correlation value was assigned as the 'apparent displacement' for each cell (Fig 2D). We found that the average apparent displacement between Dronpa-ZapA and FtsZ-PAmCherry1 molecules was 55 ± 50 nm (n = 158; x- ± sd). Molecules of ZapB-Dronpa were displaced farther away from FtsZ-PAmCherry1 molecules with an average of 97 ± 70 nm (n = 132). Both of these displacements are significantly larger than that of the control ZapA-ZapA pair (33 ± 33 nm, n = 39) (Fig 2D). The latter reflects our spatial resolution in resolving two cellular structures. These results further support our qualitative observations that ZapA can appreciably deviate from the FtsZ-ring, and that ZapB assembles into a discrete structure internal to the FtsZ-ring.
In cells showing visible constrictions, we often observed that both FtsZ and ZapB formed ring-like structures. To investigate whether the relative spatial arrangement between FtsZ and ZapB changes during cell constriction, we measured the corresponding diameters of these ring-like structures in each individual cell. We found that the diameters of the FtsZ-ring and ZapB-rings were linearly correlated with each other, and the scatter plot can be best fit by a line with a slope of 1.0 and x-intercept at 95 nm (Fig 2E). This observation indicates that the radial separation (95/2 = 47.5 nm) between FtsZ and ZapB structures is maintained throughout constriction. These results suggest that there may be a specific mechanism to maintain the relative spatial separation between FtsZ and ZapB, and such a separation may be important for successful cell wall constriction.
While two-color PALM imaging allowed us to directly visualize the relative arrangement between two protein species in the same cell, the nature of 2D imaging of three-dimensional (3D) structures prevented us from accurately quantifying the relative spatial arrangement of FtsZ, ZapA and ZapB along the radial axis. Therefore, we performed 3D superresolution imaging using interferometric PALM (iPALM). iPALM employs the same principles as PALM imaging, but also utilizes the interference of light from the same molecule in two different paths to determine its z-position with high precision [37,38]. Under our imaging conditions we achieved a, x-, y-, and z-resolution of ~20 nm.
iPALM images of FtsZ-mEos2 and mEos2-ZapA showed similar, irregular punctate structures that curved along the cell periphery (Fig 3A). ZapB-mEos2, however, appeared much more cohesive, exhibiting large, contiguous structures that often lacked curvature. This observation is consistent with our previous observations that ZapB structures significantly deviate from FtsZ and ZapA.
To measure the relative radial displacement of FtsZ, ZapA and ZapB molecules, we compared their mean z-positions to that of the coverslip surface coated with Alexa Fluor 568 [38]. To restrict our analysis to midcell regions close to the coverslip, we excluded cells that showed visible indentation at midcell in corresponding DIC (Differential Interference Contrast) images, and only used molecules inside a user-defined region at the bottom of each non-constricting cell (Fig 3A). The mean z-position of these molecules was then calculated for each individual cell, and the corresponding distribution for each protein in different cells was plotted in Fig 3B. The distributions of the three proteins show substantial overlap, but clearly indicate that ZapB molecules are displaced internal to the FtsZ-ring. Quantifying the mean z-positions reveals that FtsZ and ZapA structures reside close to each other (z = 80 ± 2 nm, n = 125 for FtsZ, and 74 ± 1 nm, n = 250 for ZapA, respectively; x- ± se), while the ZapB structures are displaced significantly internal to the FtsZ-ring (z = 117 ± 2 nm, n = 226). We note that the ~40 nm difference between FtsZ and ZapB observed by iPALM is in a similar range with what can be estimated from single-color, 2D PALM measurements (343FtsZ radius – 306ZapB radius = 37 nm), or from the correlation between the diameters of ZapB and FtsZ band-like structures in two-color, 2D PALM imaging (x-intercept / 2 = 47.5 nm, Fig 2E).
To orient these structures in the context of the inner membrane (IM), we determined the z-position of mEos2 fused to the membrane-targeting sequence of MinD (V199-S219) from Bacillus subtilis (mEos2-MTSBs) (Fig 3A). mEos2-MTSBs distributed randomly and sparsely on the membrane; the mean z-position from the coverslip surface was measured at z = 67 ± 1 nm (n = 315), consistent with the expected value when the thickness of cell envelope was considered [39,40]. Thus, we used the z-position of mEos2-MTSBs as a proxy for the cytoplasmic face of the inner membrane and estimated that FtsZ is displaced ~13 nm away from the IM on average.
We note that our estimates of apparent displacement do not take into account the molecular size of the mEos2 label (~4 nm) [41]. To determine the true displacement between two mEos2-labeled proteins, one must know how mEos2 is oriented with respect to each labeled protein. Without this information, however, we can still estimate the error associated with our apparent displacement measurements by treating the size of mEos2 as an uncertainty (42+42=5.7 nm). Comparing this uncertainty to the measured apparent displacements of FtsZ-ZapA (6 nm), FtsZ-ZapB (37 nm) and FtsZ-IM (13 nm), we conclude that FtsZ and ZapA reside on a similar radial plane, and that ZapB is significantly displaced into the cytoplasm relative to FtsZ. The 13 nm distance between FtsZ and the IM requires more careful consideration because the 5.7 nm uncertainty level is relatively large. Nevertheless, by taking into account the size of mEos2 and how mEos2 is attached to FtsZ, the displacement between FtsZ and the IM measured from iPALM can be estimated (S1 Text). This estimate will allow us to assess the amount of force FtsZ protofilaments could exert on the membrane through specific force generation mechanisms.
Next, we investigated the role of MatP in maintaining the layered FtsZ-ZapA-ZapB structure. We previously showed that deletion of MatP resulted in dispersed, mislocalized FtsZ structures under fast growth conditions [14]. This effect can be explained by an indirect mechanism, in which the abnormal nucleoid architecture caused by deletion of matP [28] results in aberrant distributions of the nucleoid-occlusion factor SlmA [42], which consequently disrupts FtsZ-ring assembly at the midcell. Our previous observation of SlmA mislocalization in ΔmatP cells supported this mechanism [14]. To further test whether MatP also has a direct role in maintianing FtsZ-ring at the midcell through its interaction with ZapB and ZapA, we constructed a double deletion strain (ΔmatPΔslmA), which allowed us to observe the effect of ΔmatP independent of the nucleoid-occlusion effect caused by SlmA. We reasoned that if abnormally-distributed SlmA is solely responsible for mislocalizing the FtsZ-ring in ΔmatP cells, then deletion of slmA should revert cells to normal FtsZ-ring localization, as deletion of slmA alone does not show any detectable defect in FtsZ-ring localization (S6 Fig) [42]. In contrast to this expectation, we found that ΔmatPΔslmA cells displayed a similar FtsZ-ring mislocalization phenotype as ΔmatP, albeit with a slightly reduced frequency (S6 Fig). This observation indicates that MatP has a SlmA-independent role in stabilizing the FtsZ-ring at midcell, possibly mediated by the physical connection between FtsZ and MatP through ZapA and ZapB. This is consistent with a recent report in which similar FtsZ-ring positioning defect was observed in related mutant backgrounds [30].
To determine where MatP is positioned inside the cell with respect to ZapB, we performed live-cell PALM imaging on wt and ΔmatP cells ectopically expressing a MatP-mEos2 fusion protein (Fig 4Ai-iv). Consistent with previous studies [28,29], MatP-mEos2 typically appeared as one or two large clusters in both strains, suggesting that MatP-mEos2 localizes correctly. The MatP-mEos2 clusters we observed were on average 100 ± 58 nm in diameter (x- ± sd, n = 613, S7 Fig). Given our resolution, this size is much larger than a single molecule of MatP-mEos2 would appear, suggesting that the clusters likely comprise multiple closely-associated MatP molecules. This finding is consistent with the ability of MatP dimers to associate with a series of matS sites on the chromosome and further tetramerize to condense the ter macrodomain [43].
We measured the displacement of each MatP-mEos2 cluster from the cell center along the long and short axes of the cell (S8A Fig). We found that the long axis displacement exhibited a cell-length dependent decrease (S8B Fig), as expected from the directed movement of the ter macrodomain toward midcell during the cell cycle [28]. The short axis displacement, however, was independent of cell length (S8C Fig). The short axis displacement is the 2D projection of the radial displacement of each cluster from the cell center (S8A Fig). Assuming that MatP-mEos2 clusters are randomly distributed angularly and projected to the 2D imaging plane, the observed short axis displacement distribution of MatP-mEos2 clusters can be best described by a model in which MatP clusters are on average 280 ± 120 nm (x- ± sd, n = 613) radially away from the cell center (S1 Text, Figs 5B, S8–S9). Comparing this measurement with the typical diameter of ZapB band-like structures, we estimate that MatP resides ~30 nm (607ZapB radius ÷ 2–280) internal to ZapB on average. Thus, MatP forms another distinct layer internal to the FtsZ-ring. Given the wide distributions of MatP and ZapB z-positions (Fig 3B, 4B), our data suggest that MatP and ZapB are appropriately positioned to enable direct interaction, and that this interaction serves to stabilize the FtsZ-ring. We note, however, that we cannot exclude the possibility that other proteins may bridge the ZapB-MatP interaction.
As we have shown, MatP plays a direct role in stabilizing the FtsZ-ring through its interaction with ZapB. To further understand how MatP exerts this influence, we probed the turnover dynamics of FtsZ, ZapA, and ZapB in the presence and absence of MatP. Previous fluorescence recovery after photobleaching (FRAP) measurements showed that the FtsZ-ring and its associated proteins exchange dynamically with their cytoplasmic pools with a half-time of 10–30 s depending on growth conditions and strain background [16,17,25,44]. We reason that one way for MatP to stabilize the FtsZ-ring could be by promoting the stable formation of large, polymerized ZapB assemblies at midcell, which consequently stabilizes ZapA and FtsZ, and could be reflected by a reduction in the turnover rate of ZapB.
We ectopically expressed FtsZ-GFP, GFP-ZapA or GFP-ZapB in wt and ΔmatP cells, and used FRAP to measure their turnover half-time under our slow growth conditions. Interestingly, we found that in the wt background, the FRAP half-time for FtsZ-GFP and GFP-ZapA were comparable to each other at 12.3 ± 0.2 (n = 58, x- ± se) and 14.2 ± 1.1 s (n = 51), respectively, but that of GFP-ZapB was significantly longer (19.8 ± 1.0 s, n = 59, p < 1e-10) (Fig 5A, Table 2). The slower turnover rate of GFP-ZapB is consistent with the highly polymeric nature of ZapB assemblies observed in vitro [19,27], and also agrees with our in vivo observations that ZapB structures are generally larger and more cohesive compared to those of FtsZ and ZapA (Fig 3A). In the ΔmatP mutant, the turnover half-times of both GFP-ZapB and GFP-ZapA were significantly reduced by ~50% to 11.6 ± 0.4 s (n = 56, p < 0.001) and 5.7 ± 0.2 s (n = 59, p< 0.001), respectively, supporting an influential role for MatP on their turnover dynamics (Fig 5B, Table 2). The half-time of FtsZ-GFP turnover was also significantly reduced in the ΔmatP mutant (10.5 ± 0.3 s, n = 59, p< 0.001), but to a lesser degree (~15%). This smaller effect may be due to the fact that MatP’s interaction with FtsZ is more indirect than its interactions with ZapA or ZapB, and/or that FtsZ’s self-polymerization properties and interactions with other proteins may have a greater influence on its turnover rate than MatP does.
We note that these FRAP measurements were obtained under slow growth conditions, where the loss of MatP has little to no observable effect on the localization of FtsZ [14]. Nevertheless, here we observed significant effects on the turnover dynamics for ZapA and ZapB structures. These results suggest that MatP modulates the dynamic turnover of all three protein structures even in the absence of obvious mislocalization phenotypes.
The FtsZ-ring has long been regarded as not only the critical structural component of the divisome, but also a primary driving force for cell constriction [6,45]. If structural stability of the FtsZ-ring is important for active force generation, constriction may be slowed in cells where the structural stability of the FtsZ-ring is compromised by disruption of the FtsZ-ZapA-ZapB-MatP network. To test this idea we measured the constriction period, τc, in wt and ΔmatP cells ectopically expressing similar low levels of FtsZ-GFP under our slow growth condition (S10 Fig) using time-lapse fluorescence microscopy (Methods). We defined constriction initiation as the time when an indentation of cell wall was first visible in bright-field images, and the end of constriction as the time when FtsZ-GFP fluorescence completely disappeared from, and did not return to, the midcell (S11 Fig). Surprisingly, we found that the constriction periods of ΔmatP (40.3 ± 2.6 min, n = 37; x- ± se) and ΔmatPΔslmA cells (34.3 ± 2.2 min, n = 50) were significantly shorter than that of the wt cells (48.8 ± 3.0 min, n = 44, p < 0.04), while the doubling time remained similar (~200 min, Table 3). These results are contrary to our expectation that FtsZ-ring stability promotes constriction progress. As we will discuss below, two possible mechanisms coordinating the rate of constriction and nucleoid segregation could contribute to this observed phenomenon.
In this work, we provided a quantitative characterization of the spatial organization and function of the FtsZ-ZapA-ZapB-MatP network in E. coli cells. By taking advantage of the superior spatial resolution and detection sensitivity offered by single-molecule based superresolution imaging, we quantified the spatial arrangement of each protein and showed that they are positioned to form a large, multi-layered network extending from the membrane to nucleoid at the midcell. We found that the FtsZ-ring comprises a heterogeneous arrangement of FtsZ clusters and is displaced on average 13 nm away from the cytoplasmic face of the inner membrane. ZapA adopts a similar heterogeneous clustered arrangement to that of FtsZ and resides at a similar radial plane. Interestingly, ~20% of ZapA structures deviated from FtsZ structures, and in a few cases ZapA clusters appeared to be sandwiched in between FtsZ clusters. ZapB forms wider, larger and more cohesive structures that are displaced on average ~40 nm internal to FtsZ and ZapA. Finally, MatP forms puncta with an average diameter of ~100 nm and is located on average ~30 nm internal to ZapB, or ~280 nm away from the center of the cell. As MatP binds to the ter region of the chromosome, it is reasonable to expect that its position also reflects that of the ter region of the nucleoid at midcell. From these measurements a three-dimensional architecture of a multi-layered protein network begins to emerge (Fig 6). As we will discuss below, these quantitative measurements provide an important physical framework upon which mechanisms regarding constrictive force generation, divisome assembly, FtsZ-ring function, and the interplay between cell wall constriction and chromosome segregation should be considered.
Historically, the FtsZ-ring has been regarded as the main structural component of the divisome, serving as a scaffold for the assembly of all other division proteins at the constriction site [46]. Hence, its structural organization was thought to determine that of the other divisome components through a complex interaction network. We and others previously showed that the FtsZ-ring is not a smooth, uniformly organized structure, but rather a heterogeneously arranged assembly of FtsZ clusters [10–15]. Here, we show that ZapA also displays a heterogeneous organization that morphologically mimics the FtsZ-ring. This heterogeneity was in contrast to the largely uniform appearance of ZapB-mEos2, indicating that the heterogeneous nature of FtsZ and ZapA is specific to their assembly.
We additionally show that the structures of both ZapA and ZapB can significantly deviate from those of FtsZ using two-color PALM. These observations suggest that, although the midcell localization of ZapA and ZapB is dependent on FtsZ, their structures do not replicate that of FtsZ, and there is unlikely a strictly-defined protein complex with fixed stoichiometry between FtsZ, ZapA and ZapB. Note that in a recent superresolution study, structural deviations of FtsA and ZipA from FtsZ were also observed [47], suggesting that this may be a common feature of divisome assembly. Although no defined complex appears to predominate, we find it interesting that the radial separation between FtsZ and ZapB is conserved throughout constriction (Fig 2E). We reason that maintaining the relative spatial arrangement between FtsZ and ZapB structures may aid the efficiency of constriction.
What leads to the apparent structural deviations of ZapA and ZapB from FtsZ? It is likely that the inherent oligomerization properties of these proteins coupled with their interactions with other division proteins play a large role. In vitro biochemical studies have shown that FtsZ polymers exhibit remarkable polymorphism, assembling into single-stranded protofilaments, sheets, bundles, helices and toroids [48]. ZapA itself does not extensively polymerize but exists in a dimer-tetramer equilibrium [49]; ZapB, on the contrary, readily forms large, bundled polymers [19]. This self-polymerization capability of ZapB likely contributes to its ability to substantially deviate from FtsZ structures.
The protein interactions exhibited by FtsZ, ZapA, and ZapB are also consistent with their locations relative to FtsZ. Bacterial two-hybrid and in vivo FRET have shown that FtsZ mainly interacts with FtsA, ZipA, ZapA, and FtsK, while ZapA associates with a large number of inner membrane proteins that do not interact with FtsZ, including: FtsQ, FtsL, FtsB, FtsW and FtsN [50–52]. Notably, these latter proteins are involved in septum synthesis during cell wall constriction. The membrane-proximal location of ZapA is conducive to interactions between ZapA and these proteins, and these interactions may facilitate the constriction progress. It will be interesting to apply the tools developed in this study to investigate the degree of colocalization of other divisome proteins with FtsZ and ZapA. Different colocalization patterns may reflect specific roles for FtsZ and ZapA in supporting these proteins’ functions in cell wall constriction, further elucidating the relationship between the structural organization and function of the divisome.
ZapB does not exhibit the interaction promiscuity of ZapA, and has only been shown to interact with ZapA and MatP [26,29]. These limited protein-protein interactions are consistent with the fact that, compared to ZapA, ZapB is displaced an additional ~40 nm into the cytoplasm, near MatP. It is likely that the morphology and localization of FtsZ, ZapA and ZapB structures are greatly influenced by their respective interacting partners, and that these interactions are not uniformly distributed across the structures.
Correctly positioning the FtsZ-ring at the midcell in E. coli is largely attributed to two negative regulatory systems: MinCDE and SlmA [53]. MinCDE is a three-component system that oscillates from pole to pole, preventing polar FtsZ-ring formation [54]. SlmA is a DNA-activated FtsZ antagonist that prevents FtsZ-ring formation over the bulk nucleoid regions except the ter macrodomain [55]. Together, these two systems create a midcell zone where the FtsZ-ring can stably polymerize. Here we show that a third system, the ZapA-ZapB-MatP network, also contributes to the midcell positioning of the FtsZ-ring by providing a physical tether to the ter region of the chromosome. Disabling this linkage by deleting any one of the three proteins leads to dispersed FtsZ clusters in a large region around the midcell [14]. Importantly, the effect in the absence of MatP is not solely mediated by abnormally distributed SlmA, as FtsZ mislocalization persists in a ΔmatPΔslmA strain. We postulate that as the ter region moves toward the midcell at the beginning of cell cycle and resides there until the end of DNA replication [28], it provides a convenient positive positioning system to reinforce the midcell localization of the divisome—MatP promotes the localization of ZapB to midcell through its direct interaction with ZapB, which in turn influences the localization of ZapA and further FtsZ. This mechanism is supported by recent work from the Männik and Sherratt groups, who showed that the FtsZ-ring colocalizes with the MatP-bound ter macrodomain at the center of nucleoid in cells depleted of both MinC and SlmA [30]. Interestingly, this colocalization was diminished, but not completely abolished, in the absence of all the three systems, suggesting the presence of other positioning systems. The variety and redundancy of positioning systems highlights the importance of the FtsZ-ring and the robust nature of its highly-evolved regulatory system.
One unexpected finding of this study is that the deletion of matP leads to a faster cell wall constriction rate. This finding is counter-intuitive, because we have shown that the presence of MatP and the associated protein network helps to position and maintain the FtsZ-ring at the midcell, and the deletion of MatP leads to mislocalized FtsZ-ring and faster turnover dynamics of ZapA and ZapB. If the stability of the FtsZ-ring is indeed essential for efficient cell wall constriction, we should expect the deletion of MatP to slow down constriction, instead of speeding it up.
One possible explanation for this apparent paradox is that the cell wall constriction machinery has the ability to proceed much faster than nucleoid segregation. However, under normal conditions, cell wall constriction does not proceed at its maximum speed because some divisome constituents may exist to impede, or slow down cell wall constriction to allow time for nucleoid segregation. If the rates of the two processes are not balanced, a septum could form over an unsegregated nucleoid, which is detrimental for cell division.
How would MatP exert its influence on cell wall constriction rate? This influence could be transmitted through the network of FtsZ-ZapA-ZapB-MatP, or by the altered distribution of SlmA on the nucleoid. We showed in this study that the effects of ΔmatP on FtsZ mislocalization and constriction rate remained in the ΔmatPΔslmA double deletion strain, suggesting that the physical linkage of FtsZ-ZapA-ZapB-MatP indeed plays a direct role. We propose that there could be two different mechanisms mediated by the linkage to explain the influence of MatP on cell wall constriction rate.
In the first mechanism, the physical linkage between the membrane and nucleoid by the FtsA-FtsZ-ZapA-ZapB-MatP protein network could act as a steric hindrance to prevent cell wall constriction from proceeding too fast. As both ends of the linkage are anchored, this protein network effectively couples cell wall and nucleoid segregation mechanically so that cell wall constriction can only complete when the ter macrodomains are resolved at the end of DNA replication and moved outside of the midcell. If this linkage is disabled, unchecked cell wall constriction may not always allow sufficient time for complete nucleoid segregation, perhaps explaining the occasional nucleoid segregation defects observed in cells lacking MatP [28]. This mechanism is similar to one previously proposed for FtsK, which can interact with divisome proteins (FtsZ, FtsL, FtsQ) and the chromosome [56]. A recent study found that the ordered segregation of sister chromosomes by FtsK requires the presence of MatP, suggesting that these two proteins may coordinate with each other [57].
An alternative mechanism could be that the additional stability provided by the presence of the ZapA-ZapB-MatP network actually inhibits the ability of FtsZ to exert an active force to drive cell wall constriction. The inhibitory effect of an overly-stable FtsZ-ring is supported by the lethality of FtsZ overexpression [58]. In this mechanism, MatP facilitates the midcell localization of ZapA by interacting with ZapB, which further promotes the inhibitory bundling effect of ZapA on FtsZ. In vitro it has been shown that ZapA promotes FtsZ polymerization [18,24]. We and others showed that ZapA and ZapB promote FtsZ-ring assembly in vivo by aligning and corralling FtsZ polymers at the midcell. It may be possible that a less bundled, highly dynamic FtsZ-ring could be more active in directing/driving cell constriction.
One possible way to differentiate the above two mechanisms would be to examine the constriction rate in a ΔzapA or ΔzapB strain. If the physical, steric hindrance mechanism has a larger role, disabling the linkage by deleting zapA or zapB should have the same effect as deleting matP. If the activity inhibition mechanism has a larger role, deleting zapA or zapB should have a larger impact on cell wall constriction rate. This is because ZapA and ZapB can still localize to the midcell to inhibit FtsZ activity in the absence of MatP, albeit less efficiently. Previously we have observed that a subpopulation of ΔzapA and ΔzapB cells had much faster cell cycle time than wt cells, which we attributed to the ability of rapid reinitiation of previously primed division sites. We were unable to quantitatively verify whether the constriction rates of these cells were indeed faster than that in ΔmatP cells due to the highly dynamic nature of FtsZ-ring and abnormal septum formation in ΔzapA and ΔzapB cells. It would be interesting to design further experiments to examine these hypotheses.
Bacterial strains and plasmids are indicated in S2 Table. Construction of strains and plasmids is detailed in the S1 Text and primers used are listed in S3 Table. Prior to imaging, all cells were grown from a single colony in LB media overnight at 37°C. For our default slow growth condition, cells were then diluted in M9 minimal media supplemented with 0.4% Glucose, MEM Vitamins and MEM Amino Acids (M9+), and grown at room temperature (RT) for at least 20 hrs. For our fast growth condition, cells were diluted in EZ Rich Defined Media (Teknova) supplemented with 0.4% Glucose and incubated at 37°C. When appropriate, we added 150 μg ml-1 chloramphenicol, 50 μg ml-1 kanamycin, 50 μg ml-1 carbenicillin or 100 μg ml-1 spectinomycin. Expression of FtsZ-mEos2 (pJB042) and mEos2-ZapA (pJB051) was induced with 20 μM IPTG for 2 hrs. Expression of ZapB-mEos2 (pJB045) and MatP-mEos2 (pJB128) was induced with 5–10 or 33 μM IPTG for 1hr, respectively. The dual-labeled Dronpa-ZapA—FtsZ-PAmCherry1 (pJB089) and Dronpa-ZapA—PAmCherry1-ZapA (pJB090) strains were induced with 20 μM IPTG for 2 hrs. For the ZapB-FtsZ 2C-PALM sample, FtsZ-PAmCherry1 (pJB066) was induced with 0.2% Arabinose for 30 min, while ZapB-Dronpa (pJB073) was uninduced. For all constructs, induction was followed by a washing step and a 2–3 hr outgrowth at RT without inducer. Fixation for iPALM samples was performed using 4% (v/v) formaldehyde in PBS (pH 7.4) for 45 min at RT.
All ensemble and PALM image acquisition and sample preparation were performed as described previously [10,59]. All PALM images were constructed from 3,000 frames acquired at a frame rate of ~100 s-1 with a constant 405 nm activation (~5 W cm-2). Single-molecule identification and image reconstruction were described previously [59]. Images are displayed in pseudo-color ('Red Hot') via ImageJ with a pixel size of 15 nm. Measurements of cell length and band width were performed using custom MATLAB (The MathWorks, Inc., Natick, MA) software and are described elsewhere [10,59]. Diameters of band-like structures were determined by first projecting the band intensity along the short axis of the cell. Peak intensities were then identified using the 'findpeaks' MATLAB function and the distance between the two most distal peaks was calculated. Immuno-based superresolution (STORM)[60] imaging of FtsZ was performed with Alexa Fluor 568 Goat Anti-Rabbit IgG (Invitrogen, GAR-568), as described previously with α-FtsZ (a gift from H. Erickson). STORM imaging of ZapB was performed similarly with α-ZapB (a gift from K. Gerdes) and GAR-568 applied at 1:500 and 1:1,000, respectively.
Two-color PALM imaging utilized the OptoSplit II (Andor) device, which simultaneously projected the two emission signals onto separate halves of the CCD chip. We obtained 1,500 frames at a rate of 100 s-1 using 405-nm activation at ~500 mW cm-2 followed by a second 1,500-frame acquisition at ~5 W cm-2. Both acquisitions employed a constant 488-nm and 561-nm excitation at a power density of ~1 kW cm-2.
Image overlay of the two-color images employed a transformation step that was achieved by using the multi-colored emission spectrum of 100 nm TetraSpeck beads (Life Technologies, Inc.), as described previously [61]. Briefly, we acquired hundreds of snapshots of single TetraSpeck beads simultaneously in both channels at various positions across the imaging region, generating a large dataset of control points. We then calculated the transformation of two-color data using these control points and the 'cp2tform' function in MATLAB [62]. This type of global transformations resulted in ~18-nm registration error in our microscope setup.
iPALM imaging and sample preparation was performed as described previously [37,38] with the following exceptions. Gold-embedded coverslips were coated with 0.1% Poly-L-Lysine (Ted Pella) for 40 min, washed with PBS (pH 7.4), and dried with purified air. Alexa Fluor 568 carboxylic acid (Life Technologies, Inc.) was diluted (~2e-9) in PBS (pH 7.4), applied to the coverslip for 15 min, and then washed and dried as above. Fixed cellular samples were subsequently applied in a similar fashion. Each image was produced from 45,000-100,000 frames. The average spatial resolution obtained for the x-, y- and z-axes was 21 nm, 23 nm, and 17 nm, respectively.
We obtained at least three biological replicates for each fusion protein on different days. iPALM data were processed via the PeakSelector v9.3 software [37] to extract molecular coordinates and fitting errors. Further analysis was performed by custom MATLAB software. Specifically, for each iPALM image, we first determined the z-position of the coated coverslip surface by fitting the Alexa Fluor 568 signal to a Gaussian function. This fit was typically characterized by a FWHM of ~10 nm. We then drew a user-defined box centered at the bottom of the cell, which in general had a z-depth of 150 nm and width of 200 nm to avoid molecules along the curved regions (Fig 3). The z-positions of all molecules in this box were then averaged and the relative distance to the coverslip was taken as the mean z-position of the protein in the cell.
We used the fluorescence intensity of mEos2 after excitation with a 488-nm laser to quantify the relative expression levels of FtsZ-mEos2, mEos2-ZapA and ZapB-mEos2. We used custom MATLAB software to measure the integrated cellular intensity (A.U.) of each fusion protein: FtsZ = 46,000 ± 2,000, ZapA = 94,000 ± 5,000, and ZapB = 40,000 ± 3,000 (x- ± se). Taking into account the previously determined expression level of FtsZ-mEos2 under the same growth conditions (30% of FtsZtotal) [14] and the previously determined endogenous concentrations of the three proteins (FtsZ ≈ 5,000, ZapA ≈ 5,000 and ZapB ≈ 15,000 molecules cell-1) [10,14,18,19,63], we estimate that the fusions are present at 30%, 45% and 10% of the total (wt + fusion) protein concentration.
We used a previously published coordinate-based cross-correlation analysis to identify the apparent displacement between different protein pairs in 2C-PALM images [34,35]. Briefly, regions around the midcell of each 2C-PALM image were cropped and the cross-correlation between the two proteins of interest was calculated as a function of displacement along the short axis of the cell. Apparent displacement between a given protein pair in a single cell was defined as the displacement value that exhibited maximal cross-correlation. Fig 2D shows the distributions of this value for FtsZ-ZapA, FtsZ-ZapB and ZapA-ZapA pairs. The mean apparent displacement value for ZapA-ZapA (33 nm) reflects our resolution in determining the displacement between two protein structures.
Fluorescence recovery after photobleaching (FRAP) was carried out on wild-type BW25113 cells or ΔmatP mutants (JW0939) ectopically expressing FtsZ-GFP (pXY027), GFP-ZapA (pJB154) or GFP-ZapB (pJB150) in the absence of inducer. Imaging was conducted on cells immobilized on 3% Agarose gel pad (M9+ without MEM Vitamins) using an Olympus IX-71 inverted microscope with excitation from a 488-nm laser (OBIS, Coherent, Santa Clara, CA). The excitation laser was passed through a linear polarizer (Thorlabs, Newton, NJ), expanded to a 10mm diameter, and split with a polarizing beamsplitting cube (Thorlabs, Newton, NJ) to generate two excitation beams. The reflected beam was then focused with an achromatic doublet lens (f = 750.0 mm, Ø2", Thorlabs, Newton, NJ) to allow epi-fluorescence illumination and recombined with the transmitted beam by another polarizing beamsplitting cube. Both beams illuminated the sample through a 100×, 1.45 NA TIRFM objective. The transmitted beam was focused by the objective to a ~300 nm radius spot for photobleaching and controlled by an independent shutter. The epi-fluorescence imaging beam (reflected beam) was focused to a radius of ~50 μm. The intensity of the photobleaching beam and imaging beam were 2 kW/cm2 and 5 W/cm2, respectively.
For imaging, the focus of the photobleaching beam was positioned to the midcell periphery of an individual cell. Timelapse fluorescence images were then acquired with the imaging beam at a rate of 30 min-1 for 4 min. The photobleaching step occurred during the second timelapse acquisition, all other frames were acquired using epi-fluorescence illumination. The exposure time for all acquisitions was 50 ms.
For FRAP analysis, we cropped the bleached area and whole midcell region and plotted their average intensity in each frame against time: IBleach(N),IMidcell(N), where N is the frame number. We calculated the bleaching ratio as (IBleach(1)-IBleach(3))/IBleach(3))IBleach(1), and only used trajectories where this value was greater than 40%. We normalized individual trajectories to [0,1], with the first acquisition post-photobleach (IBleach (3)) set as 0. We typically observed large fluctuations at the tail end of the bleached area (IBleach(N)) trajectories, whereas trajectories of the whole midcell region (IMidcell(N)) were much more stable. Consequently, we used the average intensity of last 60 frames of the whole midcell region (< IMidcell(61:120) >) as the maximum to normalize each bleach trajectory (IBleach,Nor(N)).
We averaged the normalized trajectories of each dataset across all cells from at least two days of imaging and estimated their recovery rates by fitting the data to single exponential functions. These normalized trajectories were then resampled 3000 times by bootstrapping. The resampled trajectories were then averaged and fit to obtain the recovery half-time. We then calculated the standard error from the distribution of bootstrapped recovery half-time.
Imaging of FtsZ-GFP in BW25113, ΔmatP and ΔmatPΔslmA was performed using plasmid JW0093 [64]. Each strain was first inoculated from a fresh colony into LB, grown overnight at 37°C to saturation, then diluted 1:100 into M9+ media and grown overnight at 30°C to saturation. This saturated culture was then diluted 1:200 into fresh M9+ media and grown at RT until mid-log phase (0.2–0.4 OD600). This 3-step culture preparation ensured similar expression levels of FtsZ-GFP among all three strains (see S10 Fig). The midlog culture was deposited onto a 3% agarose gel pad made with M9+ lacking MEM vitamins. Bright-field and green fluorescence images were acquired at 5-minute intervals. Constriction onset was determined from bright-field images as the first frame with visible cell wall indentation (S11 Fig). To limit interference from adjacent cells, only cells located at the outer edge of the growing microcolony were analyzed. The end of constriction, and thus the end of the mother cell cycle, and beginning of daughter cell cycles, was determined by the complete and unrecovered loss of GFP fluorescence from midcell (S11 Fig). Measurement bias in identification of constriction onset and completion may lead to underestimation of constriction times because detection of cell wall indentation is diffraction-limited [65] and detection of fluorescence loss at constriction completion is limited by signal sensitivity. However, comparison of relative constriction times between the three strains remains valid as the same criteria were applied consistently between all three strains.
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10.1371/journal.pntd.0001561 | Reciprocal Tripartite Interactions between the Aedes aegypti Midgut Microbiota, Innate Immune System and Dengue Virus Influences Vector Competence | Dengue virus is one of the most important arboviral pathogens and the causative agent of dengue fever, dengue hemorrhagic fever, and dengue shock syndrome. It is transmitted between humans by the mosquitoes Aedes aegypti and Aedes albopictus, and at least 2.5 billion people are at daily risk of infection. During their lifecycle, mosquitoes are exposed to a variety of microbes, some of which are needed for their successful development into adulthood. However, recent studies have suggested that the adult mosquito's midgut microflora is critical in influencing the transmission of human pathogens. In this study we assessed the reciprocal interactions between the mosquito's midgut microbiota and dengue virus infection that are, to a large extent, mediated by the mosquito's innate immune system. We observed a marked decrease in susceptibility to dengue virus infection when mosquitoes harbored certain field-derived bacterial isolates in their midgut. Transcript abundance analysis of selected antimicrobial peptide genes suggested that the mosquito's microbiota elicits a basal immune activity that appears to act against dengue virus infection. Conversely, the elicitation of the mosquito immune response by dengue virus infection itself influences the microbial load of the mosquito midgut. In sum, we show that the mosquito's microbiota influences dengue virus infection of the mosquito, which in turn activates its antibacterial responses.
| Dengue virus is transmitted by Aedes mosquitoes. During their lifecycle, mosquitoes are exposed to a variety of microbes, and many of them inhabit the mosquito midgut, thereby sharing the same environment with ingested pathogens. The mosquito midgut is the site of multiple reciprocal interactions between the mosquito, its commensal bacteria, and ingested pathogens that will ultimately influence the level of pathogen infection and transmission. In this study the authors addressed the reciprocal interactions between the Aedes immune system, dengue virus and mosquito midgut microbiota using molecular and microbiological assays. The study showed that certain field-derived bacterial isolates of the mosquito midgut exert a detrimental effect on dengue virus infection. This effect is at least partly manifested through the action of the mosquito immune system which is activated by microbes. Conversely, dengue virus infection induces immune responses in the mosquito midgut tissue that act against the natural mosquito midgut microbiota. This study contributes to our understanding of dengue virus infection in Aedes mosquitoes, which may aid towards the development of novel biocontrol strategies to halt dengue transmission.
| Dengue has become one of the most important arboviral diseases, with infections rising at an alarming rate [1]. The dengue virus is transmitted by two highly anthropophilic mosquitoes, Aedes aegypti and Ae. albopictus. Although advances have been made toward the development of a vaccine, no cure for dengue is currently available [1]. Current methods are aimed at lowering the vector population through insecticide use, but there are concerns about the environmental impact of this approach as well as the rapid development of resistance in mosquitoes [2]. These setbacks have underscored the need for the development of additional methods to control dengue transmission.
In the past decade, there has been a notable increase in research aiming at the potential application of microbes to control the transmission of vector-borne pathogens [3]. These studies have been encouraged by the fact that pathogens and microbes inhabit the same environment prior to infection (the arthropod midgut) and on the observation that pathogen infection is decreased in vectors harboring particular bacterial symbionts.
In fact, the midgut is the site of multi-taxon interactions that include the arthropod vector (host), vertebrate blood factors, the pathogen (virus or parasite), and other symbiotic microbes. Although there is growing interest in these associations, our understanding of how these interactions at the molecular level and how they affect vector physiology and influence vector competence is still very basic. It has been shown that some of these interactions involve insect immune factors such as lectins, antimicrobial peptides, digestive enzymes, nitric oxide, and the prophenoloxidase complex [4]–[6]. Other factors and mechanisms that have been suggested to contribute to these interactions and to modulate vector competence include: bacteria-derived cytolisins (hemolysins), siderophores, proteases, anti-parasitic factors, and secondary metabolites [4].
The purpose of the present study was to analyze the cultivable endogenous microbial flora of field mosquitoes collected from dengue-endemic areas in Panama and to assess their influence on the mosquito immune system and dengue virus infection. The incidence of dengue in Panama is the fifth-highest in Central America, and all four dengue virus serotypes are currently present in the country [7]. Molecular and infection assays have revealed intricate reciprocal interactions among the mosquito, the dengue virus, and its microbiota, with some bacterial isolates significantly affecting vector competence by reducing dengue virus infection of the midgut. In turn, the activation of the mosquito immune system by dengue virus infection alters the mosquito's immune homeostasis in the midgut, thereby affecting its microbiota.
The mosquito Ae. aegypti Rockefeller strain used in this study was maintained on a 10% sugar solution at 27°C and 95% humidity with a 12-h light/dark cycle according to standard procedures. Sterile cotton, filter paper, and sterilized nets were used to maintain maximum sterility of the cages.
The Ae. aegypti mosquitoes for this study were collected outdoors with BG-sentinel mosquito traps and indoors with mosquito aspirators from three regions: Panama Centro (Panama City, Felipillo), Panama Oeste (Chorrera), and Chiriquí (David). These sites were chosen on the basis of their prevalence of dengue fever and dengue hemorrhagic fever cases in the last 3 years and on mosquito surveys conducted by the Center for Mosquito Surveillance, Ministry of Health (MINSA, from its Spanish acronym). Peridomestic collection of mosquitoes in selected areas was conducted in the early hours of the morning (5:30 to 6:30am) and late afternoon (6:00 to 7:30pm). At least 10 mosquitoes per site were collected and processed.
The collected mosquitoes were transported back to the laboratory, chilled on ice, and identified at the species level using a stereoscope and the taxonomic keys of Galindo and Adames [8] and Rueda [9]. Following species confirmation, mosquitoes were surfaced-sterilized by dipping and shaking them in 75% ethanol for 2 min and rinsing them with 1× PBS twice for 1 min each. Midguts were then dissected from each individual mosquito over a sterile glass slide containing a drop of 1× PBS, then transferred to a microcentrifuge tube containing 150 µl of sterile PBS and macerated for 30 sec. Three 10-fold serial dilutions were then plated on LB agar and kept at room temperature for 48 h. Initial isolation was based on morphology, color, and size of colony (Figure S1), and then followed by molecular identification via 16s rRNA gene sequencing. The primers used to amplify the 16s rRNA gene were those reported by Cirimotich et al [10] : forward, AGAGTTTGATCCTGGCTCAG; and reverse (degenerate), TACGGYTACGCTTGTTACGACT. PCR conditions were used according to the Platinum Pfx DNA Polymerase (Invitrogen) protocol. PCR amplification was done with an initial denaturation of 2 minutes at 94°C, and 40 cycles with a denaturation step at 94°C for 30 seconds, an annealing step at 58°C for 30 seconds and an extension step at 72°C for 1 minute.
Bacterial 16s rRNA gene sequences were manually curated and assembled from forward and reverse primer-generated sequences. Curated sequences were then aligned and compared to available bacterial sequences in GenBank and in the Ribosomal Database Project (RDP Release 10, http://rdp.cme.msu.edu/). A bacterial phylogenetic tree was constructed using the Ribosomal Database Project “Tree Builder” program, which uses bootstrap sampling and the Weighbor weighted neighbor-joining tree-building algorithm to best estimate the phylogenetic position of a sequence.
Mosquitoes were rendered free of cultivable bacteria (designated as aseptic) by maintaining them on a 10% sucrose solution with 20 units of penicillin and 20 µg of streptomycin from the first day post-eclosion until 2 days prior to challenge. They were then maintained for 1 day on sterile water and starved for 24 h prior to dengue virus infection. Effectiveness of the antimicrobial treatment was confirmed by colony forming unit (CFU) assays prior to blood-feeding or bacterial challenge.
Two types of bacterial reintroduction were tested: via blood meal and via sugar meal. Reintroduction of bacteria through the blood meal was accomplished by first treating the mosquitoes with antibiotics and then providing them with cotton balls moistened with sterile water for 24 h post-antibiotic treatment. Mosquitoes were starved overnight and fed on a mixture containing 50% of a given bacterium suspended in 1× PBS (final concentration: OD600 = 1, for controls only 1× PBS was added), 25% of MEM (devoid of any antibiotics), 25% human commercial blood, and 10% human serum. Mosquitoes were cold-anesthetized, and the fully fed mosquitoes were separated and provided with a dengue virus-infectious blood meal 4 days after bacterial reintroduction. Infection phenotype assays were performed as previously reported [11] and as described below.
Following the bacterial reintroduction via blood meal, a subset of bacteria showing an effect on dengue virus infection was further tested through reintroduction via a sugar meal, which would more closely resemble natural bacterial acquisition. The bacteria were reintroduced through a sugar meal by first treating mosquitoes with antibiotics for the first 2–3 days after emergence and then providing them with a sterile 10% sugar meal for 24 h after antibiotic treatment. Mosquitoes were then starved overnight and fed on cotton strips moistened with a bacterial suspension diluted in 3% sucrose solution and suspended in a 1.5-ml microcentrifuge tube. Proteus sp. and Pantoea sp. were used at an OD600 of 1.00. Bacterial concentrations used to infect mosquitoes were determined on the basis of the average bacterial load for each bacterial strain found in the midgut of field-collected mosquitoes. Initial assessment of sugar meal acquisition and the location of the sugar meal following ingestion were made by providing a group of mosquitoes with a sugar solution dyed with blue food colorant. Midguts and crops of exposed mosquitoes were dissected at 6 and 24 h.
Dengue virus serotype 2 (New Guinea C strain, DENV-2) was propagated in the C6/36 cell line according to standard conditions [11]. In brief, 0.5 ml of virus stock was used to infect a 75-cm2 flask of C6/36 cells at 80% confluence. Infection was allowed to proceed for 5–7 days, at which time the cells were harvested with a cell scraper and lysed by freezing and thawing in dry CO2 and a 37°C water bath, centrifuged at 800 g for 10 min, and mixed 1∶1 with commercial human blood. The infectious blood meal was maintained at 37°C for 30 min prior to feeding 5- to 7-day-old mosquitoes.
Infected mosquitoes were collected at 7 days post-infection and surface-sterilized by dipping them in 70% ethanol for 1 min, then rinsing them twice in 1× PBS for 2 min each. Midgut dissection was performed in one drop of 1× PBS under sterile conditions, and the midgut was transferred to a microcentrifuge tube containing 150 µl of MEM. Midguts were homogenized using a Kontes pellet pestle motor and stored at −80°C until used for virus titration.
Dengue virus titration of infected midguts was done as previously reported [11], [12].The infected midgut homogenates were serially diluted and inoculated into C6/36 cells in 24-well plates. After an incubation of 5 days at 32°C and 5% CO2, the plates were fixed with 50%/50% methanol/acetone, and plaques were assayed by peroxidase immunostaining using mouse hyperimmune ascitic fluid specific for DENV-2 as the primary antibody and a goat anti-mouse HRP conjugate as the secondary antibody. Also, where indicated, dengue virus titration of infected midguts was conducted in BHK-21 cells. At 5 days post-infection, the 24-well plates were fixed and stained with crystal violet. Plaques (formed by cells with cytopathic effect, CPE) were counted and analyzed.
Real-time PCR assays were conducted by first treating the RNA samples with Turbo DNase (Ambion, Austin, Texas, United States); they were then reverse-transcribed using M-MLV reverse transcriptase (Promega, USA). The real-time PCR assays were performed using the SYBR Green PCR Master Mix kit (Applied Biosystems, Foster City, California, USA) in a 20-µl reaction volume, and all samples were tested in duplicate. The ribosomal protein S7 gene was used for normalization of the cDNA templates. The primer sequences used in these assays are listed in Table S1.
RNA interference assays (RNAi-based gene silencing) were conducted as previously reported [11]. In brief, 69 nl of dsRNA (3 ug/µl) re-suspended in water was injected into the thorax of cold-anesthetized 3- to 4-day-old female mosquitoes using a nano-injector. Three days after injection and gene-silencing validation, the mosquitoes were allowed to feed on a dengue virus-laden blood meal. Dissection of midguts and virus titration were carried out as described above. The primer sequences used are listed in Table S2.
Real-time PCR assays were normalized and standardized according to Willems et al. [13]. Mann-Whitney U-tests and one-way ANOVA with Dunnett's post-test were used when appropriate. Statistical analyses were conducted using the GraphPad Prism statistical software package (Prism 5.05; GraphPad Software, Inc., San Diego, CA). Statistical significance is indicated with asterisks: *, p<0.05; **, p<0.01; ***, p<0.001.
To investigate the cultivable bacterial species composition of midguts from field-caught adult female Ae. aegypti, we conducted mosquito collections in dengue-endemic areas of Panama. The field-captured mosquitoes were surfaced-sterilized and dissected, and their midguts were homogenized and plated on rich culture medium. We isolated 40 distinct bacterial isolates on the basis of colony morphology and successfully characterized 34 of them. The bacteria isolated from the midguts of the field-collected mosquitoes were mostly Gram-negative, with no overrepresentation of a single genus (Table 1, Figure 1). Six bacterial genera have been previously isolated from mosquitoes, Asaia spp. [14], [15], Aeromonas spp., Enterobacter spp. [16], Paenibacillus spp. [16], Proteus spp. [17], and Comamonas spp. [18]. The isolated bacteria belonged to six phylogenetic classes, with the most dominant being the Gammaproteobacteria, the Betaproteobacteria, the Bacilli, and the Alphaproteobacteria (Figure 2A).
To investigate whether certain bacteria isolated from field mosquitoes might influence dengue virus infection of the midgut, we conducted bacterial reintroduction assays through a blood meal or sugar meal (Figure 2B and Figure 2C) prior to dengue virus infection. Recolonization of mosquito midguts, previously rendered aseptic through antibiotic treatment, with single-isolate bacteria through a blood meal led to a marked decrease in viral titers in the midgut at 7 days post-bloodmeal (PBM). Introduction of two bacteria species (Proteus sp. Prpsp_P and Paenibacillus sp Pnsp_P) separately into the mosquito midguts resulted in a significantly lower level of dengue virus infection, while introduction of other species (among them Pantoea sp. Pasp_P and Comamonas sp. Cosp_P) produced no significant difference in dengue virus titer from that of control group mosquitoes (Figure 3A).
Next we wanted to assess the impact of selected bacteria on dengue virus infection when introduced through a nectar meal, since this would be the most likely route of introduction in the field and exposure in a potential future symbiotic biocontrol strategy. The current perception is that the ingested nectar meal is stored in the mosquito crop and then relocated to the midgut for digestion [19], [20]. To determine the location of the ingested sugar meal in the mosquito's digestive system, we exposed mosquitoes to a food color-dyed sugar meal. Following a 6-h exposure to the dyed-sugar meal, the blue sugar meal could be observed in the crop and midgut of some mosquitoes, while the remaining mosquitoes showed the presence of the sugar meal only in the midgut (Figure S2). At the end of a 24-h exposure, all mosquitoes were found to have food color-dyed sugar meal in both the midgut and crop.
To assess the successful colonization of the mosquito midgut by the reintroduced bacteria, mosquito midguts were dissected, homogenized, and plated on LB agar at 3 days post-bacterial acquisition and prior to the time point at which dengue virus infection normally occurs. We observed a high prevalence of Proteus sp. Prsp_P (100%) and a somewhat lower prevalence (69%) of Pantoea sp. Pasp_P in the midgut of the mosquitoes (Figure 2B and Figure 2C).
Reintroduction of Proteus sp Prsp_P into the midgut through a sugar meal led to a significant decrease in dengue virus titers, but no significant effect on dengue virus infection was observed in mosquitoes colonized by Pantoea sp. Pasp_P (Figures 3B).
Reintroduction of isolated bacteria into the antibiotic-treated (aseptic) mosquitoes' midguts elicited changes in transcript abundace of a number of antimicrobial peptide genes, including cecropin, gambicin and attacin in the midgut (Figure 4A) and the abdominal fat body tissue (Figure 4B). This result suggests that modulation of immune gene transcript abundance by the reintroduced bacteria could have a detrimental effect on dengue virus infection.
Dengue virus infection of the mosquito's midgut led to significant decrease in the overall bacterial load (as assessed by 16s rRNA transcript levels) at 24 h, 7 days, and 14 days after ingestion of a dengue virus-supplemented blood meal. Interestingly, the difference in the bacterial 16s rRNA transcript levels between dengue virus-infected and uninfected mosquitoes was less prominent at 3 days post-infection (Figure 5A). Analysis of the relative transcript abundance of the antimicrobial peptide genes lysozyme C, and cecropin G revealed that cecropin G transcripts were significantly elevated in dengue-infected mosquitoes at 7 days post-infection but showed no difference from control levels at 10 days post-infection. Lysozyme C also showed a transient changes in transcript abundace, with no difference from control levels at 7 days but significant changes at 10 days post-infection (Figure 5B).
To assess the involvement of antimicrobial effector genes in regulating the midgut microbiota, we employed an RNAi-based gene silencing approach in conjunction with CFU assays. Although not statistically significant,silencing of several effector genes led to changes in the growth of the midgut bacterial populations compared to the control group (GFP dsRNA-injected mosquitoes) (Figure 6). This suggests that one function of these immune factors is to maintain a basal level of immunity to control microbial proliferation. Interestingly, we did not observe a significant increase in the midgut bacterial load after silencing the cecropin G and lysozyme C genes, suggesting that these factors may play more specialized roles in immunity (Figure 7A and 7B).
We used a RNAi-based gene silencing approach to assess the effect of selected antimicrobial peptide genes on dengue virus infection, some of which are known to be regulated by our field-derived bacteria. This treatment led to an overall increase in dengue virus titers in the mosquito midgut especially for lysozyme C, suggesting that this gene might exert a significant inhibitory effect, on dengue virus infectivity (Figure 8A). However, this effect was lost when the mosquitoes were maintained aseptically with antibiotics prior to receiving an infectious blood meal (Figure 8B). This might indicate that the infection phenotype observed upon lysozyme C–silencing reflects an indirect effect. It is possible that lysozyme inhibit the growth of bacteria that are beneficial to the virus, or, alternatively lysozyme may act against bacteria that compete with other bacteria that have a detrimental effect on the virus. The current analysis does not allow for a detailed mechanistic insight on this.
During their life span, insects harbor a variety of microbes in their intestine, some of which are needed for successful growth to adulthood, and some as aids in digestion, nutrition, and reproduction [21] as well as protection against pathogens [10], [22]–[25]. This situation is especially true for mosquitoes that, as larvae, develop in stagnant microbe-rich water, feeding on various bacteria and fungi, and that as adults are exposed to microbes, parasites, and viruses through plant nectars and ingested blood. For example, it has been shown that antibiotic treated aseptic Anopheles gambiae mosquitoes are more susceptible to Plasmodium infection and possess a lower basal level of immune gene transcripts than do An. gambiae with a normal microbial population [26], [27]. The basal level of immune activity appears to be critical in defining the level of susceptibility to Plasmodium infection [28].
With regard to virus-mosquito interactions, the intracellular bacterium Wolbachia spp. has been shown in several studies to affect dengue virus infection in Ae. aegypti mosquitoes [29] and infection with the Japanese encephalitis virus in aseptic Culex bitaeniorhyncus [30]. We have previously shown that mosquitoes with a reduced midgut bacterial load (as a result of antibiotic treatment) can support higher dengue infection levels than can septic mosquitoes [11]. Furthermore, the antibiotic-treated aseptic mosquitoes display a lower basal level of several Toll pathway-related genes transcripts. We have shown that the Toll pathway is involved in the anti-dengue defense [11]. We cannot, however, exclude other possible mechanisms by which the bacteria may hinder virus infection in the mosquito.
In order to assess this phenomenon in greater detail and select bacteria that can mediate potent anti-dengue activity and meet other criteria (easily cultivable and major representation in the midgut microbiome) for the development of dengue biocontrol strategies, we have now isolated and characterized cultivable bacteria from the midguts of field mosquitoes collected in dengue-endemic areas of Panama. Bacterial isolates from field collections belonged to several phylogenetic classes, but no predominant genus was observed. Many of these bacterial species have been previously isolated from mosquitoes and may be better adapted to the mosquito midgut environment. The diversity of microbes isolated from field mosquitoes suggests a complex mosquito midgut microbiome that is likely to affect the outcome of infection and the mosquito's midgut immune homeostasis. Our midgut bacteria discovery method identified only live, replicating bacteria that could grow aerobically on a rich culture medium, and this approach likely explains some of the discrepancies between our results and those of studies that have employed PCR-based amplification of bacterial DNA, much of which may have been derived from dead, minor, and/or transient microbial constituents of the midgut microflora [15], [31].
Reintroduction of some of these bacterial species through a blood meal led to changes in susceptibility of the midgut tissue to dengue infection. Furthermore, reintroduction of bacterial isolates via a sugar meal into the midgut of Ae. aegypti mosquitoes resulted in a significant decrease in dengue virus infection in the case of one bacterial isolate, Proteus sp. Prsp_P.
These bacteria may either indirectly exert an anti-dengue effect by boosting basal immunity or may directly influence the virus' infectivity. The bacteria could, for example, act prior to dengue virus infection of the midgut via bacterial metabolites that are detrimental to the dengue virus, or act as a barrier for the virus via steric hindrance, by growing along the midgut epithelium [15].
In contrast to the effects produced by Proteus sp. Prsp_P, reintroduction of Pantoea sp. Pasp_P had no effect on dengue virus infection, perhaps because of the inability of this bacterium to effectively colonize the mosquito's midgut.
This could partially offset the anti-dengue effects that derive from the elicitation of the mosquito's immune system by this bacterium. Alternatively, although Pasp_P shows a slightly higher immune induction than Prsp_P, our gene expression assays only addressed one time point of amp transcript abundance, and it is quite likely that Prsp_P may elicit an overall stronger induction of these genes over an extended time period. It is also possible that Prsp_P induces some other unknown anti-viral factor stronger than Pasp_P.
Furthermore, given that our introduction of bacteria was performed with a single bacterial species at a time, it is possible that lack of effect on dengue virus infection was because this bacterium needs to act in synergy with other microbes of the midgut. This type of synergistic effects may also alter some of our observed ant-dengue activities for the other studied bacteria, when combined with multiple bacterial species.
Our analyses of immune gene expression in mosquitoes exposed to the studied bacteria revealed responses that were similar in their direction of regulation but different in their magnitude. We observed elevated immune gene transcripts in both the midgut and fat body tissues, thus pointing to a local as well as a systemic immune response. These two compartment-specific responses could act in concert to limit dengue virus infection and dissemination in the mosquito host. The transcript abundance of the antimicrobial peptides we assayed has been shown to be regulated by the immune signaling pathways that govern the defense against dengue virus infection [11], [32], [33]. Thus, it is possible that mosquito immune responseselicited by the bacteria play a significant role in reducing the level of dengue infection in the mosquito midgut. In fact, recently, a cecropin-like peptide with anti-dengue virus properties was found to be elicited in the salivary gland of dengue virus-infected mosquitoes [34] and cecropin-D and defensin-C peptides have been shown to have anti-dengue activity in the mosquito midgut [35].
The mosquito can be considered a holobiont unit, in which the mosquito, its midgut microflora, and the dengue virus are involved in complex reciprocal tripartite interactions. Our analysis of these interactions has indicated that dengue infection in the mosquito is able to elicit an immune response involving the elevated transcript abundance of antimicrobial peptide genes such as cecropin, attacin, and lysozyme C [11], [32], [33]. Even though the antiviral activity of the mosquito's antimicrobial peptides have yet to be characterized, a cecropin-like peptide was recently found to have anti-dengue virus activity [34]. In addition, antimicrobial peptides are effective in controlling bacteria [36]–[38], and their elicitation by dengue virus infection can therefore modulate the mosquito's midgut microflora. Our observations agree with this assertion, in that dengue virus-infected mosquito midguts displayed a lower bacterial load (as measured by 16s rRNA) than did those of uninfected mosquitoes.
In summary, our analysis of the reciprocal interactions between the dengue virus, mosquito immune system, and bacteria isolated from midguts of field mosquitoes collected in Panama has revealed a marked decrease in viral load in mosquitoes infected with certain natural bacterial isolates. Transcript abundance analysis of selected antimicrobial peptide genes suggested that the mosquito's microbiota elicits an immune response that appears to act in part to control dengue infection. In turn, the activation of the immune system by dengue virus infection potentiates the mosquito's immune homeostasis and suppresses the microbiota of its midgut. A better understanding of these complex reciprocal interactions may facilitate the development of novel biocontrol strategies for dengue transmission.
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10.1371/journal.pbio.1001187 | Molecular Pathogenesis of EBV Susceptibility in XLP as Revealed by Analysis of Female Carriers with Heterozygous Expression of SAP | X-linked lymphoproliferative disease (XLP) is a primary immunodeficiency caused by mutations in SH2D1A which encodes SAP. SAP functions in signalling pathways elicited by the SLAM family of leukocyte receptors. A defining feature of XLP is exquisite sensitivity to infection with EBV, a B-lymphotropic virus, but not other viruses. Although previous studies have identified defects in lymphocytes from XLP patients, the unique role of SAP in controlling EBV infection remains unresolved. We describe a novel approach to this question using female XLP carriers who, due to random X-inactivation, contain both SAP+ and SAP− cells. This represents the human equivalent of a mixed bone marrow chimera in mice. While memory CD8+ T cells specific for CMV and influenza were distributed across SAP+ and SAP− populations, EBV-specific cells were exclusively SAP+. The preferential recruitment of SAP+ cells by EBV reflected the tropism of EBV for B cells, and the requirement for SAP expression in CD8+ T cells for them to respond to Ag-presentation by B cells, but not other cell types. The inability of SAP− clones to respond to Ag-presenting B cells was overcome by blocking the SLAM receptors NTB-A and 2B4, while ectopic expression of NTB-A on fibroblasts inhibited cytotoxicity of SAP− CD8+ T cells, thereby demonstrating that SLAM receptors acquire inhibitory function in the absence of SAP. The innovative XLP carrier model allowed us to unravel the mechanisms underlying the unique susceptibility of XLP patients to EBV infection in the absence of a relevant animal model. We found that this reflected the nature of the Ag-presenting cell, rather than EBV itself. Our data also identified a pathological signalling pathway that could be targeted to treat patients with severe EBV infection. This system may allow the study of other human diseases where heterozygous gene expression from random X-chromosome inactivation can be exploited.
| X-linked lymphoproliferative disease (XLP) is an immunodeficiency caused by mutations in the SH2D1A gene, which encodes a cytoplasmic component, SAP involved in a signalling pathway in certain populations of immune cells. The Achilles' heel in XLP is extreme sensitivity to Epstein-Barr virus (EBV) infection. Although EBV infection in normal individuals is generally innocuous, in XLP it can be fatal. Strikingly, individuals with XLP do not display this same vulnerability to other viruses, and here we investigate what immune defects underlie this specific susceptibility. We developed a system to examine the behaviour of immune cells that are identical with the exception of whether or not they have a functional SH2D1A gene. This approach uses human female carriers of XLP (one of their X chromosomes carries the mutation). Following the process of X-chromosome inactivation in female cells, which is random, individuals harbour T cells that express the normal SH2D1A gene as well as cells that express the mutated version. We found that SAP-deficient CD8+ T cells fail to be activated by antigen-presenting B cells, but are activated by other antigen-presenting cell types. Since EBV selectively infects B cells, the exquisite sensitivity in XLP to EBV infection results from the ability of the virus to sequester itself in B cells, which can only induce a cytotoxic T cell response in SAP-sufficient cells. Thus, the functional defect in SAP-deficient CD8+ T cells does not relate to a specific virus but rather to the nature of the target cell presenting viral epitopes.
| X-linked lymphoproliferative disease (XLP) is an inherited primary immunodeficiency caused by mutations in SH2D1A, which encodes the cytoplasmic adaptor protein SLAM-associated protein (SAP) [1]–[3]. SAP functions as an adaptor protein by associating with members of the SLAM family of surface receptors—SLAM (CD150), 2B4, NTBA, CD84, CD229, and possibly CRACC [4]–[7]—that are expressed on a variety of hemopoietic cells. A defining characteristic of XLP is extreme sensitivity to infection with EBV (reviewed in [7]–[9]). Thus, in contrast to infection of healthy individuals, which is self-limiting, exposure of XLP patients to EBV induces a vigorous and uncontrolled immune response involving polyclonally activated leukocytes. Despite such immune activation, XLP patients fail to control EBV infection, which results in severe and often-fatal fulminant infectious mononucleosis [7]–[9]. XLP patients who survive primary EBV infection can develop hypogammaglobulinemia and B-cell lymphoma, although exposure to EBV is not a prerequisite for these clinical manifestations [8],[9]. Strikingly, XLP patients do not display the same degree of vulnerability towards other herpes viruses—herpes simplex virus, cytomegalovirus (CMV), varicella zoster—which can cause life-threatening infections in individuals with other immunodeficiencies [10]. This highlights the unique role of EBV in the pathogenesis of XLP, and the critical—albeit undefined—role of SAP in anti-EBV immunity.
XLP is associated with a diverse range of lymphocyte defects including abolished NKT cell development [11],[12], compromised humoral immunity [13]–[15], and impaired functions of CD4+ T cells [13],[16]–[18], CD8+ T cells [19],[20], and NK cells [21]–[27]. This reflects the involvement of SAP in multiple signalling pathways. Given the complexity of the immunological abnormalities in XLP patients, it is unclear which of them underlies their unique susceptibility to EBV. While the defective response of NK cells following engagement of 2B4 or NTB-A may contribute to the susceptibility to EBV in XLP [22],[24],[26],[27], it is unlikely to be the predominant cause since a deficiency in either the absolute number of NK cells or NK cell cytotoxicity in the presence of intact T cell development and function in humans is associated with more generalised susceptibility to multiple viruses (reviewed in [28]). Similarly, while NKT cells may have a role in anti-viral immunity, the impact of an NKT cell deficiency on EBV sensitivity in XLP is unclear because patients with other immunodeficiencies have also been reported to lack NKT cells, yet they do not develop fulminant infectious mononucleosis [29]–[31]. Lastly, while several previous studies have investigated the function of CD8+ T cells in XLP [19],[20],[32], it is difficult to separate direct effects of SAP deficiency in these cells from indirect effects that may result from lack of “help” from either functionally impaired SAP-deficient CD4+ T cells or NK cells, or the absence of NKT cells, all of which can promote CD8+ T cell responses [33]–[36]. Furthermore, these studies of SAP-deficient CD8+ T cells have not provided an explanation as to why XLP patients are so vulnerable to infection with EBV, but not with other pathogens.
In addition to these issues, delineating the EBV-specific defect in XLP has been hindered by the lack of an appropriate experimental model. Thus, while SAP-deficient mice have proved key to elucidating mechanisms underlying some of the immunological defects in XLP [4],[7],[9], they cannot directly address the question of EBV susceptibility because neither EBV nor its close relatives in other primates infect mice, and no mouse virus can reproduce EBV's biology or its strictly B-lymphotropic means of persistence [37]. The question of EBV pathogenesis therefore can only be answered using a human model in which SAP-deficient immune cells develop in an otherwise intact immune system. Fortuitously, female carriers of XLP are healthy [38] and harbour both SAP-positive and SAP-negative T cells through random inactivation of the X-chromosome [11].
Here we demonstrate that such XLP carriers provide an ideal model for elucidating the role of SAP in anti-viral immune responses in humans. XLP carriers were shown to contain both SAP+ and SAP− T cells, which allowed us to determined which virus-specific responses were dependent on SAP. While both SAP+ and SAP− CMV or influenza-specific memory CD8+ T cells were able to respond to their cognate peptides, EBV-specific memory CD8+ T cells were exclusively restricted to the SAP+ population, revealing a specific requirement for SAP in anti-EBV immunity. Further analysis of the response of SAP− CD8+ T cells to different Ag-presenting cells (APC) showed that SAP is required for B cell-mediated CD8+ T cell responses but not for responses induced by other APCs. Our studies further demonstrated that an important function of SAP was to prevent the delivery of inhibitory signals downstream of SLAM family receptors on CD8+ T cells following interaction with their ligands on target B-cells. These data provide compelling evidence that the unique susceptibility to EBV infection in XLP patients is due to the inability of SAP− CD8+ T cells to respond to Ag-presenting B cells due to inhibitory signalling mediated by SLAM family receptors, rather than an inability to recognise and respond to EBV Ags.
We analysed seven female carriers of XLP, each of whom was confirmed as heterozygous at the SH2D1A locus by sequencing genomic DNA (Figure 1A,B). Analysis of lymphocyte subsets revealed that these carriers, unlike XLP patients [11],[15],[16], had normal frequencies of total and isotype switched memory B cells (Figure 1C,D,F) and NKT cells (Figure 1E,G). The proportions of memory CD8+ and CD4+ T cells were also within the range of healthy controls (unpublished data). This is consistent with XLP carriers being asymptomatic and lacking evidence of any obvious deficiency in anti-viral immune responses, including against EBV [38],[39].
Intracellular flow cytometric analysis using a SAP-specific monoclonal antibody (mAb) enabled us to identify SAP expression in different cell populations. SAP was expressed in CD4+ T cells, CD8+ T cells, and NK cells from normal donors (Figure 2A), but not in the same lymphocyte populations obtained from XLP patients (Figure 2B). Using this approach we confirmed heterozygous SAP expression (i.e., 40%–60% of the cells being SAP+/−) within the T and NK cell compartments of XLP carriers (Figure 2C,D). There was no significant difference in the frequency of CD8+ central memory (CD45RA−CCR7+) T cells (Figure 2C) or NK cells (Figure 2D) that were SAP− or SAP+. However, significantly more naïve CD8+ T cells were SAP− (p = 0.045), whereas more effector memory (CD45RA−CCR7−) and TEMRA (effector memory cells expressing CD45RA) cells were SAP+ (Figure 2C). The greater frequency of SAP− cells in the naïve compartment would be consistent with proposed functions for SAP in negatively regulating T cell responses in mice in vivo [40],[41] and in promoting apoptosis of human cells in vitro [42],[43]. In contrast to T and NK cells, >90% of NKT cells in XLP carriers were SAP+ (Figure 2E), consistent with the absolute requirement of SAP for their development [11],[12]. SAP was not detected in human B cells (Figure 2A,F) [15], supporting the concept that intrinsic defects in T cells, NK cells, and NKT cells, rather than B cells, are responsible for the XLP phenotype.
To determine the contribution of SAP+ and SAP− CD8+ T cells to antiviral immunity, we analysed SAP expression in populations of memory CD8+ T cells that were specific for EBV, CMV, and influenza (Flu), as detected by soluble peptide:MHC class I complexes (i.e., tetramers). Five of the XLP carriers had MHC class I types that allowed epitope-specific cells to be visualised by this approach. The frequency of CMV and Flu-specific CD8+ T cells within the SAP+ population (CMV: range 21%–72%; mean ± sem: 46.3%±12.3%, n = 4; Flu: 8% and 46%; mean: 27.0%±19%) was not significantly different from that within the SAP− population (CMV: 55.7%±12.3%, n = 4 [p = 0.78]; Flu: 73.0%±19%, n = 2) (Figure 3A,B). In stark contrast, almost all EBV-specific CD8+ T cells expressed SAP (95.0%±2.9% versus 5.0%±2.9% in SAP− cells, n = 4; p = 0.004; Figure 3A,B). The same clear-cut distinction was seen when the functional response of virus-specific CD8+ T cells to various antigenic peptide challenges was assessed in vitro. Following stimulation of PBMCs from XLP carriers with CMV or Flu Ags, both SAP+ and SAP− cells produced IFN-γ (Figure 3C,E) and expressed surface CD107a (Figure 3D,E), an indicator of the ability of cells to degranulate [44],[45]. However, when PBMCs were stimulated with various EBV peptides, including those from both lytic and latent Ags, only SAP+ CD8+ T cells responded (Figure 3C–E). Consistent with the recognition of EBV tetramers, the differences in the responses of SAP+ and SAP− CD8+ T cells to in vitro stimulation with EBV peptides were highly significant (p = 0.0001; Figure 3E). Taken together these data demonstrated that the CD8+ T cell response to EBV infection in healthy XLP carriers had been preferentially recruited from SAP+ T cells, whereas the CD8+ T cell response to other viruses showed no preference for SAP-expressing cells.
One explanation for the disparate responses of SAP− and SAP+ CD8+ T cells to EBV, but not to other viruses, may result from differential expression of co-stimulatory or inhibitory molecules in the absence of SAP. Thus, we determined the phenotype of SAP− and SAP+ cells with respect to expression of a suite of molecules known to regulate CD8+ T cell function. Expression of the co-stimulatory/activation/effector molecules CD27, CD28, CD38, OX40, ICOS, perforin, and granzyme B did not differ between SAP− and SAP+ CD8+ T cells, irrespective of whether the cells were of a naïve or memory phenotype. Similarly molecules known to inhibit lymphocyte function—PD-1, BTLA—were comparably expressed on SAP− and SAP+ naïve and memory CD8+ T cells (unpublished data). We also analysed the TCR repertoire of SAP− and SAP+ cells by determining expression of distinct TCR Vβ chains by flow cytometry to deduce whether the TCR usage was significantly different between these cells. Although this approach may not be sufficiently sensitive to detect restricted diversity, the TCR repertories of SAP− and SAP+ cells appeared to be generally similar (Table 1). The few biased TCR Vβ chains used in two carriers (#1, #3; Table 1) probably reflects the responses of different subsets of effector/memory cells to different viruses and their unique antigenic epitopes. Thus, lack of SAP expression does not appear to alter thymic selection of CD8+ T cells, or their ability to acquire expression of receptors involved in regulating lymphocyte function. Consequently, it is unlikely that perturbed selection or activation of SAP− CD8+ T cells through co-stimulatory and regulatory receptors underlies their poor responsiveness to stimulation with EBV. Rather, this is likely a direct effect of SAP deficiency.
The selective dependence of EBV-specific CD8+ T-cell-mediated immunity on SAP raised the question of which T-cell extrinsic mechanisms might explain the differences between the responses to EBV versus CMV and Flu. Since Ag presentation was a logical place to start, we developed an approach that would allow us to analyse the ability of SAP− T cells to respond to distinct types of APCs. Thus, multiple SAP− and SAP+ clonal pairs were established from different XLP carriers (Figure S1) and then tested for their ability to recognise cognate peptides presented on different APC targets, namely autologous EBV-transformed lymphoblastoid cell lines (B-LCLs), or HLA class I-matched monocytes or fibroblasts. SAP+ CD8+ T cell clones responded to their specific peptide regardless of the nature of the APC, as evidenced by enhanced IFN- γ production (Figure 4A, upper panels), acquisition of expression of CD107a (Figure 4B–E, Figure S2A upper panel) and lysis of Ag-presenting target cells (Figure 4F,G). In contrast, SAP− CD8+ T cell clones responded poorly upon stimulation with peptide-pulsed B-LCLs compared to SAP+ clones, irrespective of whether the clones were specific for CMV (Figure 4A,B, Figure S2A lower panels) or Flu (Figure 4C lower panel, Figure 4D,F). Importantly the defective responses of SAP− clones to specific Ag presented on B-LCLs did not reflect a generalised activation defect because these cells responded as well as SAP+ cells following PMA/ionomycin stimulation (Figure 4A–C, Figure S2A). Strikingly, the impairment was restricted to Ag presented in a B cell context. Thus, the same SAP− CMV-specific or Flu-specific clones responded as well as their SAP+ counterparts to peptides presented on HLA-matched monocytes (Figure 4B, Figure S2), or fibroblasts (Figure 4C,E,G).
We extended these studies by assessing induction of CD107a expression by SAP− and SAP+ CD8+ T cells within a CMV-specific T cell line in response to presentation of specific Ag by in vitro–derived dendritic cells (DCs) compared to B-LCLs. Although the frequency of total CD8+ T cells responding to CMV peptides was similar irrespective of whether B-LCLs or DCs were the APC (∼5%–6%), the SAP+ CD8+ T cells predominated the response when CMV-derived peptides were presented by B-LCLs (>90% of responding cells; Figure S2B). In contrast, both SAP− and SAP+ CD8+ T cells responded to Ag-presenting DCs (35% and 65% of responding cells, respectively; Figure S2B). These findings are entirely consistent with the data for Ag-specific paired SAP− and SAP+ clones (Figure 4, Figure S2A), and together provide compelling evidence for an important role for SAP in mediating CD8+ T cell recognition of B cell targets.
It would be ideal to also demonstrate that EBV-specific SAP-deficient CD8+ T cells are unable to respond to Ag endogenously presented by B cells. This could not be investigated using XLP carriers due to the extreme paucity of EBV-specific cells within the SAP− subset of CD8+ T cells in these individuals (see Figure 3). To address this, we generated EBV-specific CD8+ T cell lines from an XLP patient with a well-characterised loss-of-expression mutation in SH2D1A ([F87S], XLP#3 in [46]). This was achieved by repeatedly expanding their purified CD8+ T cells on autologous EBV-transformed B-LCLs, as performed previously for other SAP-deficient patients [19]. As expected, EBV-specific CD8+ T cells from normal donors efficiently lysed autologous B-LCL target cells. In contrast, there was a profound defect in the ability of XLP CD8+ T cells to lyse autologous B-LCLs (Figure S2C, panel [i]). For these experiments, the donor and XLP patient were HLA matched. This allowed assessment of the ability of EBV-specific CD8+ T cells to lyse B-LCL derived from a SAP-sufficient donor or SAP-deficient XLP patient, and thereby to determine whether the cytotoxic defect of XLP CD8+ T cells resulted from impaired presentation of EBV Ag by SAP-deficient B-LCL. When this experiment was performed, XLP CD8+ T cells proved to be equally defective in killing allogeneic B-LCLs, which contrasted the behaviour of EBV-specific CD8+ T cell lines from normal donors (Figure S2C panel [ii]). Importantly, the inability of XLP CD8+ T cells to lyse B-LCL target cells did not appear to result from altered expression of lytic effector molecules since acquisition of perforin and granzyme B by XLP CD8+ T cells was comparable to that of normal CD8+ T cells (Figure S2C panel [iii]). This is consistent with the reduced cytotoxicity of SAP-deficient cells resulting from impaired recognition of B-LCL targets, which subsequently compromises immune synapse formation between effector and target cells, and polarisation of lytic mediators [19],[47].
To begin to elucidate the mechanism underlying compromised SAP− CD8+ T cell recognition of peptide-pulsed B cell targets and explore ways in which function might be restored, we examined the expression of SAP-associating receptors on subsets of SAP− and SAP+ T cells. SAP associates with the cytoplasmic domains of SLAM, 2B4, CD84, NTB-A, CD229, and possibly CRACC [4],[7]. When expression of these molecules was assessed on lymphocytes from XLP carriers, we found no significant differences in their expression on SAP− and SAP+ CD8+ T cells within the naïve and TEMRA subsets (p>0.05; Figure 5A; Figure S3). Most of these molecules were also expressed comparably on SAP− and SAP+ central memory and effector memory CD8+ T cells. However, there were significant differences in the expression levels of 2B4 and NTB-A on SAP− and SAP+ central memory CD8+ T cells, and of 2B4 and CRACC on SAP− and SAP+ effector memory CD8+ T cells, with them being lower on SAP−, relative to SAP+, cells. While these differences were statistically significant, the net differences in expression were <2-fold. Thus, it is unknown whether this would translate to a biological effect; furthermore, it is important to highlight that CRACC has been reported to function independently of SAP, at least in the context of human NK cells [48]. Thus, the lower level of CRACC on SAP− cells will be inconsequential at least with respect to SLAM-receptor/SAP-dependent signalling and lymphocyte activation. These data generally imply that, at the cell surface, SAP− and SAP+ CD8+ T cells are similarly capable of interacting with relevant ligands of the SLAM family.
The next step was to examine expression of ligands of the SLAM family receptors on different APCs because expression of these molecules on APCs could also influence the outcome of CD8+ T cell-mediated recognition of target cells. While 2B4 interacts with CD48, the other SLAM family receptors are self-ligands [4],[7]. In contrast to SAP+ and SAP− CD8+ T cells, there were substantial differences in expression of SLAM family ligands by B-cell and non-B-cell APCs. NTB-A expression was highest on B cells and B-LCLs, while CD48 was highest on monocytes and B-LCLs (Figure 6A,B). B-LCLs also expressed higher levels of CD229, CRACC, and SLAM than resting B cells and monocytes (Figure 6A,B). Interestingly, NTB-A, CD48, and CD229 were all absent from in vitro–derived DCs; however, DCs did express CRACC, SLAM, and CD84 (Figure 6A,B). The relative levels of these molecules on DCs were similar to monocytes, with CRACC and SLAM being less, and CD84 being greater, than on B-LCLs (Figure 6A,B). Unlike APCs of hematopoietic origin, fibroblasts did not express any SLAM family ligands (Figure 6A,B). Thus, APCs exhibit substantial differences in their pattern of expression of SLAM family ligands.
The above findings implied that engagement of distinct arrays of co-stimulatory receptors on SAP− and SAP+ CD8+ T cells by ligands expressed on different APCs would modulate the acquisition of effector function of the responding CD8+ T cells. This would be consistent with the ability of SLAM family receptors to switch their function from activating or inhibitory depending on the presence of SAP [22],[24],[32]. We therefore explored the possibility that defined interactions between specific SLAM receptors on SAP+ or SAP− CD8+ T cells and their ligands on APCs differentially regulated cytotoxicity.
We first examined the ability of SAP+ and SAP− CD8+ T cells to respond to the Hodgkin's lymphoma cell line HDLM2. This line was chosen as a target cell because (a) it lacked expression of all SLAM family ligands with the exception of SLAM/CD150 itself (Figure 7A), (b) SLAM has been reported to enhance the cytotoxicity of human CD8+ T cells [49], and (c) SLAM was expressed at the highest levels on B cells relative to other APCs (Figure 6), revealing it as a candidate molecule to regulate CD8+ T cell function. Thus, if expression of SLAM on B cells, but not fibroblasts, controls the effector function of CD8+ T cells, then it would be predicted that SAP− CD8+ T cells would exhibit reduced cytotoxicity against HDLM2 cells than their SAP+ counterparts. When this was tested experimentally by pulsing either autologous B-LCLs or MHC class I–matched HDLM2 cells with CMV peptides and assessing the response of CMV-specific CD8+ T cells, both SAP− and SAP+ cells were equally capable of responding to HDLM2, as evidenced by acquisition of CD107a expression by a comparable proportion of cells (Figure 7B, lower panel), but not to B-LCLs, as expected (Figure 7B, upper panel). This dichotomy in recognising and responding to B-LCLs versus HDLM2 was not due to differences in expression of MHC class I by the target APCs (Figure 7A). This finding suggested that SLAM was unlikely to be the predominant receptor mediating the effector function of CD8+ T cells in the absence of SAP.
This led us to focus on NTB-A and 2B4 because their ligands (i.e., NTB-A, CD48) are highly expressed on B cells (Figure 6; [22],[50]) and they can deliver activating and inhibitory signals in the presence and absence, respectively, of SAP to human NK and CD8+ T cells [22],[24],[26],[27],[32]. Although CRACC was also more highly expressed on human B-LCLs than on monocytes (Figure 6), its role in regulating CD8+ T cell function was not explored because it functions independently of SAP [48],[51].
When interactions between NTB-A/NTB-A and/or 2B4/CD48 were blocked with specific mAbs [22],[52]–[54], activation of SAP+ CD8+ T cells by B cell targets was not significantly affected (%CD107a+ cells—no mAb: 51.3%±3.8%; + anti-NTB-A mAb: 56%±6.5%; + anti-2B4 mAb: 55.7%±5.6%; + anti-NTB-A/2B4 mAbs: 55.7%±7.3%; n = 4, p = 0.48 [27],[32]). By contrast, blocking interactions between NTB-A/NTB-A or 2B4/CD48 substantially improved the effector function of SAP− CD8+ T cells compared to when these cells were examined in the absence of added mAbs (Figure 7C,D). Importantly, combined blockade of both pathways could restore effector function of SAP− T cells to a level comparable to SAP+ clones (Figure 7C). These observations suggest that signalling through NTB-A and 2B4 impedes the effector function of SAP-deficient, but not SAP-sufficient, CD8+ T cell in response to Ag-presenting B cell targets.
To provide additional data that homotypic NTB-A interactions can suppress the function of SAP-deficient CD8+ T cells, we transfected fibroblasts to express NTB-A (Figure 7E) and compared the ability of SAP+ and SAP− clones to lyse the parental (i.e., NTB-A−) or transduced NTB-A+ cells in a 51Cr release assay. Consistent with the data presented in Figure 4, there was no difference in lysis of either parental fibroblasts by SAP+ and SAP− CD8+ T cell clones (compare Figure 7F and G; red lines), or lysis of NTB-A− and NTB-A+ fibroblasts by SAP+ CD8+ T cells clones (Figure 7F). However, the cytotoxic activity of the same SAP− CD8+ T cell clone was significantly reduced when NTB-A was ectopically expressed on fibroblasts (Figure 7G, p<0.05). Thus, these data provide evidence that in the absence of SAP, SLAM family receptors acquire inhibitory function which compromises the ability of CD8+ T cells to be activated by Ag-presenting B cells.
Primary immune deficiencies are characterised by increased susceptibility to infection by a range of pathogens [10]. The molecular mechanism underlying this heightened vulnerability is often explained by the nature of the genetic defect responsible for a particular immune deficient condition. Thus, a lack of B cells in X-linked agammaglobulinemia (XLA) a lack of T and NK cells in X-linked several-combined immunodeficiency (X-SCID) and impaired B-cell responses in X-linked hyper-IgM syndrome due to mutations in BTK, IL2RG, and CD40LG, respectively, predispose affected individuals to severe, recurrent, and often life-threatening infections [10],[55]. In contrast to these conditions, the explanation for why loss-of-function mutations in SH2D1A, resulting in SAP-deficiency, render XLP patients exquisitely sensitive to infection with EBV, but not other viruses, is enigmatic. Indeed, while previous studies that examined lymphocytes from XLP patients or Sap-deficient mice have clearly shed light on the role of SAP in different immune cells and allowed us to understand the complex nature of some of the clinical manifestations of XLP [4],[7], the question of why XLP patients are uniquely susceptible to EBV infection remains unanswered. Efforts to address this have also been hampered by the absence of appropriate animal models due to the specificity of EBV infection for humans. For these reasons, we developed a novel approach to answer this basic question relating to XLP.
Female carriers of several X-linked diseases, such as X-SCID, XLA, and Wiskott-Aldrich syndrome, display skewed X-chromosome inactivation with preferential expression of the wild-type (WT) allele in some lymphocyte lineages [56]–[58]. This occurs because expression of the WT allele in specific hematopoietic cells confers a survival advantage over cells expressing the mutant allele, which therefore fail to develop in the female carriers. In contrast to these X-linked diseases, normal numbers of T and NK cells are detected in XLP patients [11],[16], and lymphocytes from female carriers of XLP exhibit random inactivation of the X-chromosome [11]. These observations demonstrate that SAP is not required for lymphocyte development (with the exception of NKT cells [11]; Figures 1, 2). Consequently, female carriers of XLP represent an ideal model to assess the role of SAP in CD8+ T cell-mediated anti-viral immune responses because both SAP+ and SAP− cells with the same genetic background are generated at similar frequencies (Figure 2). This is essentially the human equivalent of a mixed bone marrow chimera in mice, and therefore eliminates any variability that may arise from comparisons of SAP-deficient CD8+ T cells from XLP patients with SAP-sufficient cells from unrelated normal donors, as has been performed in earlier studies [19],[20],[32]. Another feature of female XLP carriers is that they have an intact immune system and are not susceptible to any known infections [38],[39]. Thus, any secondary defects in the function of CD8+ T cells from XLP patients due to a lack of NKT cells or impaired NK cell function—which can all contribute to fine-tuning CD8+ T cell responses [33]–[36]—are circumvented by studying XLP carriers. These attributes of XLP carriers allowed us to perform a detailed analysis of the responses of SAP− and SAP+ CD8+ T cells from the one individual to not only EBV but other common viruses including CMV and Flu in the setting of a normal host immune response.
Previous studies using tetramers have demonstrated that EBV-specific CD8+ T cells could be detected in XLP patients (n = 2; [59]). These cells, however, exhibit poor in vitro responses to EBV Ags [19],[32]. Our phenotypic and functional analysis of Ag-specific CD8+ T cells from XLP carriers demonstrated that CMV or Flu-specific CD8+ T cells are distributed within both SAP+ and SAP− memory populations, however there was a dramatic, and highly significant, skewing of EBV-specific CD8+ T cells such that >95% of these cells were detected within the SAP+ compartment (Figure 3). By using peptides derived from both lytic and latent EBV Ag, we established that the exclusive SAP+ effector CD8+ T cells generated following EBV infection were not restricted to a single dominant antigenic epitope (Figure 3). This demonstrates that there is a selective advantage for SAP+ CD8+ T cells in anti-EBV immunity, but not in either anti-CMV or anti-Flu immunity. Thus, although SAP− cells are abundant within the pool of naïve CD8+ T cells, the SAP+ cells expressing a TCR with specificity for EBV vigorously outcompete their SAP− counterparts and subsequently become the predominant cell type that expands and is maintained following exposure to EBV. Thus, our studies reveal a strong requirement for SAP expression not only in mediating the effector function of CD8+ T cells in response to EBV infection but also in the expansion and survival of these cells. These findings underscore the obligate requirement for SAP, and by extension SLAM family receptors, at multiple stages in CD8+ T cells in mediating protection against EBV infection. The ability to examine competition between WT and gene-deficient cells ex vivo is another powerful feature of the carrier model, and a human equivalent of the studies performed in mice using mixed bone marrow chimeras to determine the intrinsic responses of WT versus mutant cells in a competitive environment.
The mechanism underlying this fundamental requirement for SAP expression during the generation of EBV-specific CD8+ T cells was revealed by investigating the ability of SAP− and SAP+ CD8+ T cells specific for the same CMV or Flu epitopes to respond to their cognate peptide when presented on B-cell or non-B-cell target APCs (monocytes, DCs, fibroblasts). The rationale for these experiments was 2-fold: first, one of the key differences between the three viruses studied here is the identity of the APC responsible for activating the CD8+ T cell response. CMV persists in immature myeloid cells and, on reactivation, is likely to be presented by infected monocytes/DCs [60], whereas influenza infects respiratory epithelial cells and can be cross-presented by DCs [61]. By contrast, EBV is a predominantly B-lymphotrophic virus and there is strong evidence to suggest that the CD8+ T cell response is driven by epitopes displayed on infected B cells themselves [37],[62]. Second, although the response of XLP CD8+ T cells to B cells is impaired, they can respond relatively normally to other types of target cells [19],[32]. Thus, it was possible that SAP-deficient CD8+ T cells failed to be activated when Ag was specifically presented by B cells. Indeed, SAP-deficient CD8+ T cell clones from XLP carriers were specifically defective in responding to their cognate epitopes when presented by B-cell, but not non-B-cell, targets irrespective of the viral origin of the specific Ag (Figure 4). Similarly, EBV-specific SAP-deficient CD8+ T cells expanded from XLP patients were severely compromised in their capacity to lyse B cells presenting endogenously processed EBV peptide Ags (Figure S2C). Our findings have several important implications. First, although EBV can presumably be presented by numerous non-B-cell types of APCs (e.g., tonsillar epitheilium, cross-primed DCs) [63],[64], and this may contribute to the initial generation of detectable EBV-specific CD8+ T cells in XLP patients [19],[59], the predominant APC involved in maintaining a robust anti-EBV CD8+ T cell–mediated immune response appears to be B cells. Second, the inability to control EBV infection in XLP is likely to result from a direct defect in CD8+ T cells. Defects in CD4+ T cells may contribute to impaired anti-EBV immunity in XLP because analysis of the CD4+ T cell compartment from XLP carriers revealed a predominant response by SAP+ CD4+ T cells to EBV lysate in vitro (Figure S4). Third, and most importantly, the exquisite sensitivity of XLP patients to EBV infection results from the ability of the virus to sequester itself in infected B cells which can only induce a cytotoxic T cell response in SAP-sufficient cells. In other words, the functional defect in SAP− CD8+ T cells does not relate to a specific virus but rather to the nature of the target cell presenting viral epitopes.
The finding of a requirement for SAP in CD8+ T cell–mediated lysis of Ag-presenting B cells, but not monocytes, DCs, or fibroblasts, predicted that expression of ligands of the SLAM family would differ between these populations of APCs. This was confirmed by demonstrating that while fibroblasts lacked expression of all SLAM family ligands, B cells, monocytes, and DCs expressed differing levels of some of these ligands (Figure 6). Signalling downstream of SLAM family receptors is regulated by SAP via several mechanisms. SAP can deliver activation signals via Fyn-dependent or Fyn-independent processes [6]. Alternatively, SLAM family receptors can alter their function to become inhibitory receptors in the absence of SAP [5],[6]. This appears to be mediated by the recruitment and/or activation of inhibitory phosphatases [22],[24],[65],[66]. We therefore reasoned that engagement of SLAM receptors delivered either activating signals to SAP-expressing CD8+ T cells or inhibitory signals to SAP-deficient CD8+ T cells. Our finding that (1) impeding NTB-A/NTB-A and 2B4/CD48 interactions with blocking mAbs [22],[52],[54] could improve the function of SAP− CD8+ T cells in the context of responses to Ag-presenting B cell targets and (2) ectopic expression of NTB-A on fibroblasts protected these cells from cytotoxicity induced by SAP-deficient Ag-specific CD8+ T cells favoured an inhibitory function for these receptors in the absence of SAP (Figure 7). This is reminiscent of early descriptions of inhibitory function of these receptors on SAP-deficient human NK cells [22],[24],[67],[68], and the recent demonstration of such a phenomenon for CD8+ T cell clones from XLP patients [32]. This conclusion is also consistent with the reported ability of NTB-A to associate with SHP-1 in the absence of SAP in human NK cells and T cells [22],[42], thereby suggesting a mechanism of how NTB-A exerts its inhibitory effect. Veillette and colleagues proposed that the SAP homolog EAT-2 mediates inhibitory signalling downstream of some SLAM family receptors in the absence of SAP [69]. Interestingly, EAT-2 associates with NTB-A in human lymphocytes [70], and SH2D1B (encoding EAT-2) was expressed at increased levels in memory CD8+ T cells from XLP patients compared to healthy donors (Figure S5). Thus, it is possible that in XLP heightened expression of EAT-2 mediates an alternative pathway downstream of NTB-A for inhibitory signalling in SAP-deficient CD8+ T cells following engagement of SLAM family receptors. Irrespective of these possibilities, it is clear that expression of SAP significantly alters the function of SLAM family receptors on human NK and CD8+ T cells such that these receptors inhibit cytotoxicity in the absence of SAP.
Previous studies established defects in SAP-deficient CD8+ T cells [19],[20],[32]. However, there have been major limitations to all of these inasmuch as they only examined responses of XLP CD8+ T cells to polyclonal (i.e., Ag non-specific) stimulation [19],[20], or only studied responses to EBV and not additional viruses [19],[32]. Thus, none of these earlier studies offered an explanation for the selective inability of XLP patients to respond to infection with EBV but not other viruses. We have now significantly extended these observations by providing mechanistic insight into the dysfunctional behaviour of SAP− CD8+ T cells by (1) revealing that the defect in anti-EBV immunity in XLP reflects the nature of the APC, rather than EBV itself, (2) proving that NTB-A is inhibitory for the function of SAP-deficient CD8+ T cells, and (3) excluding a role for SLAM itself in regulating the function of human Ag-specific CD8+ T cells, a scenario proposed by a previous study [49].
Our findings that SAP-deficient CD8+ T cells respond poorly to EBV-infected B cells, but not to monocyte, DC, or fibroblast APCs, parallel those reported recently for CD4+ T cells from Sap−/− mice. In that system no difference was found in the quality of interactions between DCs and either SAP-deficient or SAP-sufficient CD4+ T cells [17]. However, SAP-deficient CD4+ T cells exhibited greatly reduced interactions with cognate B cells, resulting in impaired help for T-dependent B cell responses [17]. Interestingly, mouse Ly108 (i.e., human NTB-A) is involved in the formation of stable conjugates between normal CD4+ T cells and B cells, while interactions with DCs were predominantly mediated by integrins [71]. The absence of NTB-A and CD48 from DCs potentially explains why DC-mediated Ag-presentation to CD8+ T cells is unaffected by SAP deficiency. While SAP was required in murine CD4+ T cells for NTB-A-mediated interactions with B cells [71], it appears that SAP functions in human CD8+ T cells to prevent the delivery of inhibitory signals downstream of NTB-A that probably involve the recruitment and/or activation of phosphatases or EAT-2 [22],[42],[70]. This apparent disparate function of NTB-A on murine CD4+ and human CD8+ T cells may be explained by the pattern of expression of EAT-2, inasmuch as it is detected in human CD8+ T cells (Figure S5) [72], but not murine CD4+ T cells [69]. Despite these potential differences, an emerging theme is that loss of SAP in T cells leads to altered interactions with B cells, while interactions with other APCs remain intact. This specific defect not only explains the molecular pathogenesis of the unique susceptibility to EBV infection in XLP patients but potentially explains their high incidence of B-lymphomas. Interestingly, EBV is the only known human pathogen that selectively infects B cells, which results in expression of high levels of SLAM family ligands to facilitate the T-B cell cross-talk necessary for immunity. Thus, our studies have identified a unique pathological signalling pathway that may be targeted to treat patients with severe EBV infection. Furthermore, the innovative XLP carrier model has allowed us to unravel the mechanisms of disease in the absence of a relevant animal model. This system may also allow the study of other human diseases, for instance XIAP deficiency, which also predisposes to EBV infection [8],[73], where heterozygous gene expression from random X-chromosome inactivation could be exploited.
Blood samples were collected from seven different XLP carriers and an XLP patient. PBMC were isolated and either used fresh or cryopreserved in liquid nitrogen. Genomic DNA was sequenced to confirm the heterozygous state of the carriers. Primers used for amplification of the four exons of SH2D1A are: Exon 1 sense: CAA CAT CCT GTT GTT GGG G, Exon 1 antisense: CCA GGG AAT GAA ATC CCC; Exon 2 sense: GCA ATG ACA CCA TAT ACG, Exon 2 antisense: GAA CAA TTT TGG ATT GGA GC; Exon 3 sense: GTA AGC TCT TCT GGA ATG, Exon 3 antisense: CAT CTA CTT TCT CAC TGC; Exon 4 sense: CTG TGT TGT GTC ATT GTG, Exon 4 antisense: GCT TCC ATT ACA GGA CTA C. All participants gave written informed consent and the experiments were approved by the Human Research Ethic committees of the Sydney South West Area Health Service (Royal Prince Alfred and Concord Zones) and St. Vincent's Hospital.
PBMC, CD8 T cell clones, B-LCLs, and fibroblasts were stained with fluorochrome-conjugated mAbs specific for cell surface receptors. The following mAbs were used to identify different lymphocyte populations: anti-CD3, CD4, CD8 (T cells), CD56 (NK cells), CD20 (B cells), CD14 (monocytes), CD1a, CD11c (DC) (BD Biosciences), and TCR Vα24/Vβ11 (NKT cells) (Immunotech, France) mAbs. CCR7 (R&D Systems), CD45RA (BD Biosciences), and CD27 (BD Biosciences) were used to identify subsets of naïve and memory T and B cells. CD83 (eBioscience), CD86, MHC class II, and MHC class I mAbs (BD Biosciences) were used to phenotype LPS-matured DCs. Expression of the SLAM family of receptors and ligands was determined using mAbs against CD84 (BD Biosciences), CD229, NTBA, CRACC (R&D Systems), 2B4 (Beckman Coulter), CD48 (Immunotech, France), and SLAM/CD150 (eBiosciences). TCR Vβ repertoire analysis was performed according to the manufacturer's instructions (Beckman Coulter). For degranulation assays mAb against CD107a (BD Biosciences) was used as previously described [44],[45] and for intracellular cytokine stains anti-IFN-γ (BD Biosciences) mAb was used. Stained cells were analyzed on either FACSCanto I or II flow cytometers (BD Biosciences) and the data processed using FlowJo software (Treestar, Ashland, USA).
MHC class I tetramers were prepared in-house, where the appropriate MHC class I heavy chain molecule was refolded with β2 microglobulin and the peptide and complexed with streptavidin-PE as described [74]. CMV epitopes used were the HLA-A*0201-restricted peptides NLVPMVATV from pp65 (UL83) protein, and VLEETSVML from IE-1 (UL122) protein; HLA-A*0101 restricted peptide, VTEHDTLLY from pp50 (UL44) protein. EBV epitopes used were HLA-A*0201-restricted GLCTLVAML from the lytic Ag BMLF-1, CLGGLLTMV from LMP2, HLA-B*4402-restricted peptides VEITPYKPTW from EBNA3B latent protein, and EENLLDFVRF from EBNA3C. The influenza A epitope was the HLA-A*0201-restricted peptide GILGFVFTL from matrix protein.
Cells were first stained for surface markers and then fixed with 2% paraformaldehyde, permeabilized with 0.5% saponin, and incubated with Alexa Fluor 647 (Invitrogen)-conjugated isotype control or anti-SAP mAb (Abnova, clone 1C9). Cells were washed and resuspended in PBS/1% FCS and analysed on a FACSCanto I or II flow cytometer (BD Biosciences).
1–2×106 PBMCs were stimulated with either an irrelevant peptide, specific MHC class I restricted synthetic peptide, or PMA/ionomycin as a positive control for 4–6 h in the presence of Brefeldin A (for IFN-γ production) or monensin (for CD107a expression). The capacity to respond to these peptides was tested by harvesting the cells and determining expression of IFN-γ or CD107a by SAP+ and SAP− CD8+ T cells.
DCs were generated from peripheral blood monocytes by culturing sort-purified CD14+ cells (5×105/ml) in human lymphocyte media [15],[16] supplemented with 500 U/ml of IL-4 (provided by Dr. Rene de Waal Malefyt) and 50 ng/ml GMCSF (Peprotech). After 5 d, monocyte-derived DCs were harvested, washed, and cultured (5×105/ml) in the presence of 1 µg/ml of LPS (Sigma) for a further 18 h. Monocyte-derived DCs were CD1a+ CD11c+ CD14−. Upon maturation with LPS, they upregulated expression of CD83, CD86, and MHC class I and MHC class II.
Virus-specific CD8+ T cell clones were established from PBMCs by sort-purifying tetramer positive cells and limiting dilution cloning as described [75]. Clones were established by seeding sort-purified tetramer+ CD8+ T cells at 0.3–3 cells/well into media containing 104 autologous B-LCLs and 105 feeder cells per well. CMV-specific clones were selected based on their recognition of the pp50 (UL44) epitope VTEHDTLLY (HLA-A1 restricted), while influenza-specific clones recognised the matrix protein epitope GILGFVFTL (HLA-A2 restricted). All clones were expanded and tested for specificity by staining with the appropriate tetramer and for SAP expression (see Figure S1). EBV-specific CD8+ T cell lines used in DC assays were generated by sort purifying tetramer-positive cells and expanding them in vitro on peptide-pulsed autologous B-LCLs and feeder cells. EBV-specific CD8+ T cell lines from XLP patients and normal donors were established by repeated stimulation of purified CD8+ T cells on autologous B-LCLs [19].
The ability of CD8+ T cell clones to respond to various target cells was measured either by intracellular IFN-γ staining or by staining for CD107a. Autologous B-LCLs were used as B cell targets. HLA-matched monocytes were sort-purified from buffy coats on the basis of CD14 (Immunotech) expression and used as APCs. DCs were generated as described above. HLA-matched human fibroblasts used were JuSt (HLA-A1 & A2) and MeWo cells (HLA A2) (ATCC). All APCs were pulsed with appropriate peptides (1 µg/ml) and used to stimulate CD8+ T cell clones. Where cytotoxicity was measured, APCs were sensitised with cognate peptide at a concentration of 1 µg/ml while loading with 51Cr. After washing, T cells were incubated at different APC∶T cell ratios and incubated for 5 h in standard cytotoxicity assay [75]. In some experiments, blocking mAbs against NTB-A (MA127) [22] and 2B4 (C1.7 [52],[53]) were used to prevent NTB-A/NTB-A and 2B4/CD48 interactions, respectively. B-LCLs were incubated with the relevant mAb at a final concentration of 20 µg/ml for 30–45 min prior to mixing with CTL clones. Cultures were incubated for 4–6 h in the presence of blocking mAbs and mAb to CD107a. Cells were then appropriately stained and analysed by flow cytometry. Fibroblasts were transfected using Lipofectamine with the pcdef3 plasmid containing cDNA encoding human NTB-A. Positive cells were initially selected in the presence of G418 and then isolated by sorting NTB-A+ cells. NTB-A+ transfected and untransfected parental fibroblasts were then used as targets in 51Cr release assay as described above.
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10.1371/journal.pntd.0002638 | Risk Factors for Disseminated Histoplasmosis in a Cohort of HIV-Infected Patients in French Guiana | Disseminated histoplasmosis is the first AIDS-defining infection in French Guiana. A retrospective cohort study studied predictive factors of disseminated histoplasmosis in HIV-infected patients between 1996 and 2008. Cox proportional hazards models were used. The variables studied were age, sex, last CD4/CD8 count, CD4 nadir, herpes or pneumocystosis, cotrimoxazole and fluconazole use, antiretroviral treatment and the notion of recent initiation of HAART. A total of 1404 patients were followed for 6833 person-years. The variables independently associated with increased incidence of disseminated histoplasmosis were CD4 count<50 per mm3, CD4 count between 50 and 200 per mm3, a CD4 nadir <50 per mm3, CD8 count in the lowest quartile, herpes infection, and recent antiretroviral treatment initiation (less than 6 months). The variables associated with decreased incidence of histoplasmosis were antiretroviral treatment for more than 6 months, fluconazole treatment, and pneumocystosis. There were 13.5% of deaths at 1 month, 17.5% at 3 months, and 22.5% at 6 months after the date of diagnosis of histoplasmosis. The most important predictive factors for death within 6 months of diagnosis were CD4 counts and antiretroviral treatment. The present study did not study environmental/occupational factors but provides predictive factors for disseminated histoplasmosis and its outcome in HIV patients in an Amazonian environment during the HAART era.
| Disseminated histoplasmosis is the first AIDS-related disease in French Guiana, and probably in the Amazonian area. In order to determine the factors that are associated with histoplasmosis, a retrospective looked at a cohort of HIV-infected patients between 1996 and 2008. Multiple models were used to study the relation of age, sex, last CD4/CD8 count, CD4 nadir, herpes or pneumocystosis, cotrimoxazole and fluconazole use, antiretroviral treatment and the notion of recent initiation of antiretroviral treatment with the occurrence of disseminated histoplasmosis. A total of 1404 patients were followed for 6833 person-years. The variables independently associated with the incidence of disseminated histoplasmosis were low CD4 counts, the lowest CD4 counts were most at risk; Patients with the lowest CD8 counts were also at increased risk; Antiretroviral treatment was generally associated with lower histoplasmosis incidence, but for the first 6 months following antiretroviral treatment initiation there was a transient period of increased risk of diagnosing histoplasmosis; Herpes was also associated with more histoplasmosis; Pneumocystosis and Fluconazole treatment were negatively associated with histoplasmosis. Of 156 patients with histoplasmosis, there were 13.5% of deaths at 1 month, 17.5% at 3 months, and 22.5% at 6 months after the date of diagnosis of histoplasmosis. The most important predictive factors for death within 6 months of diagnosis were low CD4 counts and no antiretroviral treatment. The present study did not study environmental/occupational factors but provides predictive factors for disseminated histoplasmosis and its outcome in HIV patients in an Amazonian environment during the HAART era. These results are useful to guide clinicians working in an area where this diagnosis is often overlooked.
| Histoplasma capsulatum var. capsulatum (HC) is found throughout the world, but there are great differences in the levels of endemicity [1]. On the South American continent, histoplasmin sensitivity studies showed proportions of the population with positive tests ranging from 7% to nearly 90%. On the Guiana Shield, the proportion of persons with positive tests is around 30%. Microconidia and mycelial forms of HC are present in the soil and aerial dispersion exposes persons to inhale these infective forms. In immunosupressed persons, HC yeasts then disseminate to various organs through phagocytes, notably macrophages where they can survive for lack of cellular activation by a robust proinflammatory immune response. Disseminated histoplasmosis has been an AIDS defining infection of HIV-infected patients since 1987 [2]. The disease may follow the resurgence of a previous infection due to immunodepression, or it may be newly acquired [3]. It often affects the most severely immunosupressed patients, and when untreated, usually leads to death. Approximately 10% of patients present with a septic shock-like syndrome [4] with high mortality. Even in the absence of initial shock, a significant proportion of cases of disseminated histoplasmosis (ranging from 22% to 47%) are severe and have a poor prognosis [5].
There have been few prospective studies on the predictive factors of disseminated histoplasmosis in HIV patients, mostly in the United States of America [3], [6]. Environmental and occupational aspects, and the patient characteristics that were associated with increased risk have been studied in the 1990's. In the Amazonian area and in the Guianas, some data about histoplasmosis suggest that this is a major –but dramatically underdiagnosed- AIDS defining illness [7], [8]. In the absence of diagnosis, the problem remains invisible, and therefore, in some endemic countries, standard drugs such as itraconazole are not available. There is thus a need to describe its epidemiology and to raise the awareness of clinicians and decision makers about this disease. The objective of the present study was thus to describe the predictive factors of disseminated histoplasmosis in a cohort of HIV-infected patients followed in French Guiana and to determine predictors of death.
HIV positive patients followed in Cayenne, Kourou, and Saint Laurent du Maroni Hospitals between January 1st 1996 and October 31st 2008 were enrolled in the French Hospital Database for HIV (FHDH). The data is entered in the FHDH database by trained technicians from the medical records. Diagnoses were coded according to the 10th international classification of diseases. Occupational and environmental data were not available in the FHDH. The variables usually used as prognostic factors [5] (LDH, haemoglobin, platelet counts, ferritine, liver enzymes, creatinine, albumine, symptoms) were not available in the database which was created to follow broader trends.
The diagnosis of histoplasmosis was performed by direct examination using May Grünvald Giemsa staining and culture of tissue and fluid samples for up to 3 months. Primary prophylaxis for disseminated histoplasmosis is not given.
All HIV patients in French Guiana can receive free antiretroviral treatments (including the most recent drugs) regardless of their origin or socio-economic level. Imagery, Viral loads, CD4 counts and genotyping and antiretroviral concentration measurements are available for routine care.
In this retrospective cohort study, incidence rates were obtained. Kaplan Meier curves were used to visualize the differences in the incidence of histoplasmosis between CD4 strata and between different CD4 and CD8 strata. Single failure multiple Cox proportional hazards models were used to evaluate the adjusted relationship between failure and a set of explanatory variables. Right censoring occurred after the last visit. For the first model, including 1404 patients, the failure event was a first episode of disseminated histoplasmosis. The main explanatory variables were for the time independent variables: sex, age, and a nadir of CD4 count<50/mm3, a prior history of herpes or pneumocystosis [6]; for the time dependent variables: last available CD4 cell count at the time of the visit (categorized 0–50, 51–200, 201–350, 350–500, and >500 cells per mm3), last available CD8 cell count at the time of the visit (dichotomous variable corresponding to CD8 values within the lowest quartile or not), cotrimoxazole and fluconazole use, the presence or absence of HAART and the notion of recent initiation of HAART (<6 months) [9]. A variable reflecting the annual frequency of visits was also added to the models. First the crude hazard ratios were obtained for each predictor, afterwards a multiple model with the relevant variables was constructed. Confounding was considered when the difference between crude and adjusted hazard ratios exceeded 20%. Different interaction terms were created between explanatory variables and added in succession to the full model and removed when non significant. Overall, none of the interaction terms was retained in the final model. The proportionality of the hazard functions was determined using Schoenfeld and scaled Schoenfeld residuals and the global proportional hazards test.
A second model was constructed in a subgroup of 156 patients with disseminated histoplasmosis with death within 6 months of diagnosis as a failure event and CD4 count, CD8 counts, age, sex and antiretroviral treatment as explanatory variables. Other treatments, such as fluconazole and cotrimoxazole were also explored.
The significance level was 0.05. The Data were analyzed with STATA 12.0 (College Station, Texas, USA).
Patients included in the FHDH gave written informed consent to the use of their data for the study. Their identity was encrypted before the data was sent to the Ministry of Health and the Institut National de la Recherche Médicale (INSERM) which centralize data from Regional Coordination for the fight against HIV (COREVIH) throughout France. This cohort is approved by the Commission Nationale Informatique et Libertés (CNIL) since Nov 27th 1991 and has led to numerous international publications.
A total of 1404 patients were included. This amounted to 30838 records and 6833 years at risk. There were 141 first episodes of disseminated histoplasmosis observed. The average time at risk was 4.04 years. The general characteristics of the patients at inclusion are shown in table 1.
The overall incidence rate for a first episode of disseminated histoplasmosis was 1.41 per 100 person years.
Figure 1 shows the Kaplan Meier curves for different CD4 strata, with a marked increase of the incidence of histoplasmosis in patients with CD4 counts <50 per mm3. Patients with both CD4 counts <50 per mm3 and CD8 counts under 643 had the highest risk of histoplasmosis (Fig. 2).
Table 2 shows the variables associated with disseminated histoplasmosis in the HIV cohort of French Guiana.
The incidence rate increased proportionally to the level of CD4 decline. Table 1 also shows that patients that had a CD4 nadir <50 had a greater risk of disseminated histoplasmosis. Patients in the lowest CD8 quartile had an increased incidence of disseminated histoplasmosis. There were important differences between the crude and adjusted hazard ratios suggesting confounding, notably by the CD4 nadir.
Antiretroviral treatment was associated with protection from histoplasmosis (models with different antiretroviral classes did not show any difference between classes, data not shown). However, the first 6 months following antiretroviral treatment initiation were a period of increased risk of diagnosing histoplasmosis (table 2).
After adjustments in Cox multiple models, cotrimoxazole was not associated with any protection from disseminated histoplasmosis while herpes was associated with an increased risk of disseminated histoplasmosis (table 2). When looking at the temporal relation between herpes and histoplasmosis 11 herpes cases (50%) were simultaneous with disseminated histoplasmosis, and 4 cases (18%) occured within six months before the diagnosis of disseminated histoplasmosis. There was a notable difference between the crude and adjusted hazards reflecting confounding.
On the contrary, after controlling for CD4 count, and cotrimoxazole use, a history of pneumocystosis (but not toxoplasmosis) was independently associated with a decreased risk of disseminated histoplasmosis. However, there were 3 simultaneous cases of pneumocystosis and disseminated histoplasmosis. Adjustments for the annual frequency of visits did not change the observed association between pneumocystosis and protection from histoplasmosis and thus, for the sake of parsimony, this variable was excluded from the final model. No other opportunistic infection was associated with histoplasmosis in the multiple single failure model. It is of note that tuberculosis was associated with disseminated histoplasmosis in an analysis with a single covariable and in a multiple failure model (but not in the single failure model) multiple model with the same covariables (Adjusted Hazard Ratio = 2.3 (95%CI = 1.1–4.6, P = 0.016). Altogether, 12% of first histoplasmosis cases had a concomitant opportunistic infection.
After adjustments in Cox multiple models, fluconazole treatment was associated with a reduction of the incidence of histoplasmosis. The crude incidence rate seemed higher in those receiving fluconazole (Table 3), but this reflected the underlying immunosuppression. When looking at the incidence in the <50 CD4 per mm3 strata, those having received fluconazole had a lower incidence of histoplasmosis than those who did not receive it (6.5 per 100 person-years vs 12.9 per 100 person-years, respectively). Unsurprisingly, curative treatments such as itraconazole, amphotericine B, or liposomal amphotericine were positively associated with histoplasmosis because they were initiated when the diagnosis of histoplasmosis was made, before that they were exceptionally prescribed. They were thus not included in the predictive models. Interaction terms between CD8 and CD4 counts were created but were not significant and thus removed from the Cox model.
Of 156 patients with disseminated histoplasmosis, there were 13.5% of deaths at 1 month, 17.5% at 3 months, and 22.5% at 6 months after the date of diagnosis of histoplasmosis. The factors associated with death are shown in table 4. Among the available variables, the most important predictive factors were CD4 counts and antiretroviral treatment. A history of oral fluconazole or cotrimoxazole treatment prior to disseminated histoplasmosis was not associated with any significant differences in mortality.
The present results show a markedly lower incidence of histoplasmosis in HIV infected patients in French Guiana (1.41 per 100 person years) than in the USA (4.7% per year) [3]. First, while the American study took place before highly active antiretroviral therapy (HAART) was available, the present study covered a period where over 80% of patients received HAART, which may have globally increased the level of immunity and reduced the incidence of disseminated histoplasmosis. In addition, histoplasmin skin test positivity studies suggest that histoplasma is much more Frequent in the middle west (60–90%) [10] than in French Guiana where, on the basis of local studies [11] and studies in neighbouring countries [8], [12], it is estimated to be around 30%.
HIV-positive men had a higher risk of histoplasmosis, and of death within 6 months if they had histoplasmosis, both of which had not been observed in the studies in the USA [3], [6]. However, histoplasmin skin test studies have shown a slight male bias [10], which presumably reflects the gender differences regarding their environmental and occupational niches. Previous studies have shown that males had a higher AIDS mortality in French Guiana [13] and elsewhere [14]; The present observation may also result from the same contextual determinants. The patients with the lowest CD4 counts were both at increased risk of histoplasmosis and death within six months for patients with histoplasmosis. In addition to CD4 counts around the time of diagnosis, the CD4 nadir was also an independent predictor of disseminated histoplasmosis as was demonstrated for other indicators of HIV disease progression [15].
A less straightforward finding was the observation that CD8 counts in the lowest quartile were independently associated with the incidence of histoplasmosis, and death within six months for patients with histoplasmosis. Some studies have shown that CD8 cell depletion affected the course of fungal infections [16], [17]. CD8 depletion could have resulted from the dissemination of the fungal pathogen, or from HIV itself [18], [19], [20]. CD8 counts often have a murky significance for clinicians. The present finding possibly offers a coarse glimpse on the nature of the immune response, but in practice seems unlikely to be very helpful for clinicians.
As observed elsewhere, after adjustments in Cox multiple models, antiretroviral treatment was independently associated with protection from disseminated histoplasmosis [6]. There was, as described before [9], a transient increase in the incidence within 6 months of antiretroviral treatment initiation presumably reflecting a surge of diagnoses following immune reconstitution. Oral fluconazole, although it is not as effective as itraconazole against HC, was also associated with decreased incidence of disseminated histoplasmosis but not with differences in mortality within 6 months of diagnosis. This is consistent with some previous observations [6] but not with other studies in the USA that did not observe any benefits of fluconazole in preventing histoplasmosis [3], [21], [22]. However, the present study involved a relatively large number of patients and may have had more power to detect moderate protective effects.
In 2001, Hajjeh et al. reported that pneumocystosis was associated with a lower risk of histoplasmosis and that herpes was associated with a poor outcome of histoplasmosis [6]. The present study also found that pneumocystosis was associated with a lower risk of histoplasmosis and found that patients with a history of herpes had an increased risk of histoplasmosis. The explanation for this is not clear. Perhaps pneumocystosis occurs earlier in the course of the HIV infection and may lead to initiate a better follow up and treatment, thereby preventing further loss of CD4 cells and the risk of severe immunodepression and disseminated histoplasmosis. Cotrimoxazole was not associated with a modified incidence of first episodes of disseminated histoplasmosis. Cerebral toxoplasmosis, which occurs at similar levels of immunodepression and often leads to similar prophylactic treatment, was not related to the incidence of disseminated histoplasmosis, or to its outcome as reported elsewhere [6]. Finally, pneumocystosis could influence the bronchial mucosal defences against Histoplasma, but this broad speculation should be tested in prospective studies.
The association of herpes with histoplasmosis may reflect the fact that clinical herpes lesions were triggered by latent histoplasmosis, or that clinical herpes reflected growing immunodepression. The single failure multiple Cox model using only the first episode of histoplasmosis did not show any link between a history of tuberculosis and histoplasmosis and was removed from the final model. However, the multiple failure model, showing relapses or reinfections showed that tuberculosis, as reported in the literature [5], was associated with disseminated histoplasmosis.
Previous studies in the American middle west had shown the occupational and environmental risk factors of HIV-associated histoplasmosis [3], [6] and some variables such as CD4 count [6], and past medical history and treatments [3]. The data collected for the FHDH does not include environmental and occupational data. Therefore, the present study could not explore these risk factors in the context of French Guiana. Most of the usual prognostic factors are not recorded in the FHDH, a cohort that does not go into fine clinical and biological detail. Therefore, the variables used are not of major importance for clinicians to identify prognostic elements influencing treatment [23]. However, the main objective of the present study was not to study prognosis. Despite these limitations, the present study provides additional information using longitudinal data from HIV patients in an Amazonian environment during the HAART era. The harzard ratios in the single models were often confounded, mostly by the CD4 count, as shown by the difference with the multiple models.
In conclusion, immunological factors such as low CD4 count, low CD8 count, low CD4 counts at the Nadir, the absence of antiretroviral treatment and/or oral fluconazole, and male gender were associated with an increased risk of histoplasmosis. Regarding mortality, low CD4 count, low CD8 count, absence of antiretroviral treatment, male gender and an age under 30 years were associated with death within 6 months.
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10.1371/journal.pntd.0002210 | Molecular Epidemiology and Genetic Variation of Pathogenic Vibrio parahaemolyticus in Peru | Vibrio parahaemolyticus is a foodborne pathogen that has become a public health concern at the global scale. The epidemiological significance of V. parahaemolyticus infections in Latin America received little attention until the winter of 1997 when cases related to the pandemic clone were detected in the region, changing the epidemic dynamics of this pathogen in Peru. With the aim to assess the impact of the arrival of the pandemic clone on local populations of pathogenic V. parahaemolyticus in Peru, we investigated the population genetics and genomic variation in a complete collection of non-pandemic strains recovered from clinical sources in Peru during the pre- and post-emergence periods of the pandemic clone. A total of 56 clinical strains isolated in Peru during the period 1994 to 2007, 13 strains from Chile and 20 strains from Asia were characterized by Multilocus Sequence Typing (MLST) and checked for the presence of Variable Genomic Regions (VGRs). The emergence of O3:K6 cases in Peru implied a drastic disruption of the seasonal dynamics of infections and a shift in the serotype dominance of pathogenic V. parahaemolyticus. After the arrival of the pandemic clone, a great diversity of serovars not previously reported was detected in the country, which supports the introduction of additional populations cohabitating with the pandemic group. Moreover, the presence of genomic regions characteristic of the pandemic clone in other non-pandemic strains may represent early evidence of genetic transfer from the introduced population to the local communities. Finally, the results of this study stress the importance of population admixture, horizontal genetic transfer and homologous recombination as major events shaping the structure and diversity of pathogenic V. parahaemolyticus.
| Infections caused by Vibrio parahaemolyticus have increased significantly over the last two decades, with cases now regularly reported globally. The emergence of cholera at global scale has brought the attention toward other Vibrio diseases in developing countries. This was the situation in Peru, where the investigation of V. cholerae in hospitals and regional public health laboratories after the dramatic emergence of cholera epidemic in 1991 enabled the identification of other pathogenic Vibrio throughout the whole country. The submission of all these bacteria to the Instituto Nacional de Salud (INS, Lima, Peru) for characterization generated an extraordinary repository of records and isolates which have been decisive for sizing the impact of V. parahaemolyticus infections on the population. The present study addresses, for first time, the impact of the arrival of a non-endemic population of V. parahaemolyticus on the genetic structure and virulence attributes of local populations. The detection of the pandemic clone of V. parahaemolyticus to Peru in 1997 changed not only the epidemic dynamics of this pathogen, but also the population structure and genetic variation of native populations through population admixture, horizontal genetic transfer and homologous recombination between native and introduced populations of pathogenic V. parahaemolyticus.
| Vibrio parahaemolyticus is a Gram-negative halophilic bacterium that naturally inhabits marine and estuarine environments throughout the world. While many strains of V. parahaemolyticus are strictly environmental, some groups are pathogenic and may cause gastroenteritis in human [1]. V. parahaemolyticus is the leading human pathogen of bacterial food-borne diseases associated with the consumption of raw or undercooked seafood [2].
Recently, V. parahaemolyticus has gained notoriety due to global dissemination of infections [3]. The rise of infections was initially linked to the emergence of gastroenteritis throughout Asia associated with a single clone of the O3:K6 serovar in 1996 [4]. The subsequent detection of this clone causing infections in Peru [5], [6], and Chile [7] in 1997 indicated the pandemic expansion of O3:K6 clone infections. Afterwards, gastroenteritis cases associated with the O3:K6 clone were reported in many other countries and different areas of the world, such as the United States, Russia, Mozambique, Mexico, and Spain [3]. In addition to rapid dissemination of the pandemic clone, a parallel rise of human cases associated with other genetic groups has been reported in recent years. Specific genetic groups have been described as being associated with infections in particular geographic regions of the world. Consequently, the presence of local clones and serovars prevail among clinical cases, such as O4:K12 in the Pacific coast area of the United States [8], O4:K8 in the Peruvian coast [5], O4:K13 in Africa [9], and O4:K11 in the northeast of Spain [10].
The majority of clinical cases of V. parahaemolyticus have been associated with strains bearing the thermostable direct hemolysin (tdh) and/or TDH-related hemolysin (trh). Therefore, the presence of one or both hemolysins has been considered to be a conventional marker of V. parahaemolyticus virulence [11], [12]. However, it has been reported recently that not only the presence of hemolysins but also other virulence factors such as the type III secretion system (T3SS) have been involved in the cytotoxicity and enterotoxicity of V. parahaemolyticus [13], [14]. Whole genome sequencing of the clinical V. parahaemolyticus strain RIMD2210633 revealed the presence of two sets of genes encoding two different T3SSs, named T3SS1 and T3SS2, distributed in each chromosome [15]. A functional analysis of these two T3SSs revealed that T3SS1 is involved in cytotoxic activity, while T3SS2 has been related to enterotoxicity [13]. Consequently, the presence of T3SS2 has been consistently associated with pathogenicity of V. parahaemolyticus in humans. The T3SS2 found in the second chromosome of V. parahaemolyticus (T3SS2α) is part of a large pathogenicity island (PAI) of approximately 80 kb that also includes the tdh-genes. In the trh-positive strain TH3996, a novel PAI that is inserted in a distinctive pathogenic island containing a homologous T3SS2 (T3SS2β) has recently been described [16].
Comparative genome analysis of V. parahaemolyticus predicted that RIMD2210633 pathogenesis is associated with the presence of eight pathogenicity islands (VpaI) [17]. However, to date, VpaI that include T3SS2 have only been functionally characterized in infection models [13], [16]. Genomic analyses also revealed that different types of pathogenicity islands and mobile elements are the major structural differences between trh-positive and tdh-positive strains, including the pandemic clone [18].
Vibrio parahaemolyticus has not been routinely investigated in Peru and infections caused by this organism are rarely reported to the surveillance system. The mandatory investigation of V. cholerae in clinical laboratories after the dramatic emergence of cholera epidemic in 1991 in Peru contributed to the identification of other pathogenic Vibrio species. Vibrio strains isolated from hospitals and regional public health laboratories throughout the country were shipped to the Instituto Nacional de Salud (INS, Lima, Peru) for final identification and characterization. This extraordinary repository has been decisive for identification and evaluation of the impact of V. parahaemolyticus infections on the population, especially in remote regions and small villages along the Peruvian coast where seafood consumption constitutes the nutritional base of population.
The arrival of the pandemic clone to Peru in 1997 resulted in a major shift in the epidemic dynamics of V. parahaemolyticus in the region, replacing the seasonal and local self-limited infections attributed to native genetic groups by the generalization of infections exclusively caused by pandemic strains distributed across the country [5], [6]. Due to the environmental nature of V. parahaemolyticus, this overturn in population dominance and the subsequent population admixture may be expected to lead to an unpredictable impact on populations of pathogenic V. parahaemolyticus in Peru. To test this hypothesis, we investigated the population genetics, genomic variation and pathogenic islands distribution in a complete collection of non-pandemic strains recovered from clinical sources in Peru over the pre- and post-emergence of the pandemic clone. Pandemic and non-pandemic clinical strains representative of the entire period of study were subjected to serotyping and genotyping analysis by Multilocus Sequence Typing (MLST) and Variable Genomic Region (VGRs) analysis.
Peruvian V. parahaemolyticus strains were recovered from the collection of the National Reference Laboratory for Enteropathogens at the Instituto Nacional de Salud (INS) in Peru. This collection comprised 46 strains from the previously studied period of 1994 to 2005 [6] and 10 additional strains from the period of 2006 to 2007. To extend the comparison, a Chilean group, comprised of 13 strains from Antofagasta and Puerto Montt [19], [7], and 20 Asiatic strains, corresponding to 18 from Japan, one from Bangladesh and one from Thailand [20], [21], were included in the analysis (Table 1). The reference strain to V. alginolyticus ATCC17749 was also added to the panel of strains.
DNA extraction of V. parahaemolyticus strains was performed using an overnight culture in trypticase soy broth at 37°C using the Chelex-100 method [22]. Strains were confirmed by the presence of the V. parahaemolyticus species-specific genes toxR as described previously [23]. Additionally, the presence of the genes tdh and trh was determined according to procedures described by Tada et al. [24]. Group-specific PCR for the detection of the toxRS sequence of strains belonging to the pandemic clone of V. parahaemolyticus was performed as previously described [6], [25]. Serotyping Lipopolysaccharide (O) and capsular (K) serotypes were determined by a commercially available antisera scheme (Denka Seiken Corp., Tokyo, Japan).
MLST analysis was performed as previously described [21], based on internal fragments of seven housekeeping genes: recA, gyrB, dnaE, dtdS, pntA, pyrC, and tnaA. Sequences of both strands were determined by custom sequencing (Macrogen Inc., Seoul, South Korea). All chromatograms were assembled, manually edited and trimmed in Bionumerics 5.1 (Applied-Maths, Kortrijk, Belgium). Allele numbers were assigned to each strain by comparing the nucleotide sequence at each locus to all known corresponding alleles available at the V. parahaemolyticus MLST Database (http://pubmlst.org/vparahaemolyticus/). Novel sequence variants and sequence types (ST) were deposited in this database, and ST assignment was performed using the MLST website tools (http://pubmlst.org). Nucleotide sequences of MLST locus corresponding to STs generated from the study were also deposited in GenBank under accession numbers KC542949–KC543109.
Population genetic relationships among V. parahaemolyticus strains included in this study were performed based on the MLST allelic profiles by a minimum spanning tree analysis implemented in BioNumerics 5.1 software (Applied-Maths, Sint Maartens-Latem, Belgium). Strains were grouped according to priority rules adopted from the BURST algorithm [26], with the highest priority given to profiles with the largest numbers of single locus variants or double-locus variants in the case of a match. Clonal complexes were defined as groups with a maximum neighbor distance of one change and a minimum size of two strains.
Variable genomic regions (VGRs) were identified by comparative genome analysis based on a complete genome sequence (RIMD2210633) and two draft genomes of V. parahaemolyticus (AQ4037 and AQ3810) retrieved from the NCBI database (http://www.ncbi.nlm.nih.gov/). Genomic sequence data were analyzed by MUMmer 3.0 [27] to identify non-redundant genomic sequences. The identified sequences were listed as VGRs and used in further analyses. To determine the presence and distribution of VGRs among the strains included in this study, we carried out 31 different PCR assays using specific primers targeting the 23 regions identified by comparative analysis (Table 2). The PCR assays were performed in a 50-µl reaction volume containing 10 ng of genomic DNA, 1 U of Taq DNA polymerase (Roche, Mannheim, Germany), 0.2 µM of each primer (Sigma-Aldrich, Sigma-Aldrich, St. Louis, MO), and 200 µM of each deoxynucleoside triphosphate (Roche, Mannheim, Germany). PCR cycling conditions consisted of an initial denaturation step at 94°C for 3 min followed by 30 cycles of denaturation at 94°C for 50 s, 50 s at the annealing temperature, which was variable for each region (see Table 2), and extension step at 72°C for 1 min. A final extension step consisted of 10 min at 72°C.
Phylogenetic relationships were inferred using ClonalFrame v1.1 software [28]. MLST loci sequences of unique STs were input into ClonalFrame using the default options. Two independent ClonalFrame runs were performed consisting of 500,000 iterations. The first 100,000 iterations in each run were discarded, and the phylogeny and additional model parameters were sampled every 100 generations in the last 400,000 iterations. The phylograms sampled from the two different runs were concatenated and summarized in a 50% majority rule consensus tree constructed by ClonalFrame GUI [28]. The convergence of the Markov Chain Monte Carlo (MCMC) in both runs was proven based on the Gelman-Rubin test as implemented in ClonalFrame [29].
To visualize potential associations between the phylogeny of housekeeping genes and the distribution of VGRs, we performed a transversal clustering analysis. For this purpose, the concatenated sequences of 67 strains were grouped by the mean of their phylogenetic relationship using ClonalFrame (50% majority consensus), while the VGR data were grouped using a UPGMA algorithm mean of their value (0 = absent and 1 = present), resulting in a data matrix ordered according to both phylogenetic relationship and VGR clustering.
A total of 324 V. parahaemolyticus strain records were retrieved from the INS database from 1994 to 2007. These strains were obtained from gastroenteritis cases that occurred in different regions of Peru and were subsequently submitted to the INS in Lima for identification and storage. The overall distribution of V. parahaemolyticus cases over the 14 years of study (Figure 1) showed a characteristic seasonal pattern with annual peaks of incidence concurring with the warmest months. This epidemic pattern was uniquely disturbed in the course of the austral winter of 1997 when V. parahaemolyticus cases dramatically increased coincidently with an anomalous rise of temperature. The highest incidence of cases was observed from July 1997 to May 1998 with two peaks during September 1997 and February 1998. The epidemic dynamics were restored from 1999 onwards.
From the 324 cases recorded over the period 1994–2007, only 56 V. parahaemolyticus strains could be recovered from the INS collection. Distinct serovar dominances were detected over different periods of the study. From 1994 to 1996, infections were associated with serovars O4:K8 and O5:KUT. This serovar dominance abruptly changed during the winter of 1997, with the emergence of infections caused by strains belonging to the pandemic clone. Finally, an undefined pattern of serovar dominance was detected after the emergence of the pandemic clone in 1997–98. In this post-pandemic period, pandemic strains were identified together with O4:K8 strains as well as multiple serovars not previously detected (O1:K33, O1:KUT, O3:K30, O3:K58, O3:KUT, O5:KUT, O6:KUT and OUT:KUT). Pandemic strains represented the largest group of strains (n = 38, 67.9%) detected from 1997 onwards, and O3:K6 was the most frequent serovar among the pandemic strains (n = 31, 55.4%), although serovars O3:K58 and O3:KUT were also identified in the pandemic group. Serovar O4:K8 was the second most frequent group of strains over the whole period (n = 11, 19.6%), whereas the remaining serovars (n = 14) represented 25% of the total strains.
The population genetic structure of V. parahaemolyticus strains representative of Peru, Chile, and Asian countries was analyzed by MLST (Fig. 2). MLST profiles of V. parahaemolyticus (n = 89) were categorized into 23 sequence types (STs). The recA gene of strains included within the ST265 (n = 8, O4:K8 serotype) showed an unexpected large PCR product of approximately 1500 bp (773 bp in the original) with a nucleotide identity diverging 18–19% from the characteristic sequence of V. parahaemolyticus.
The Peruvian group was split into 9 different STs (Fig. 2B). Minimum spanning tree analysis identified a single clonal complex consisting of ST88 (n = 3) and ST265 (n = 8) both of which included O4:K8 strains differing in a single locus. All of the strains belonging to the pandemic complex were grouped in ST3 (n = 24), whereas the remaining strains were included in 6 unrelated STs. Minimum spanning tree of the 23 STs resulting from the MLST analysis of the 89 strains showed a similar topography and group discrimination. Peruvian, Chilean and Asian pandemic strains shared the sequence of the 7 loci and were assigned to a single ST (ST3). Strains from Peru and Chile shared two additional STs: ST65 composed by O1:KUT strains and ST64 including strains belonging to O3:KUT and O1:KUT serovars. Three other clonal complexes (CC) were identified among trh+ Asian strains: one CC included STs 1, 83 and 82; a second group consisted of ST96 and ST91; and a third CC was shared by STs 87 and 14. The remaining strains were clustered in different and unrelated STs with differences in more than 5 loci.
The nucleotide sequences of 23 unique STs identified among the V. parahaemolyticus, as well as homologous sequences of V. alginolyticus (outgroup), were concatenated, and their phylogenetic relationships were inferred by ClonalFrame. The clonal genealogy inferred from the data revealed a star-like topology of V. parahaemolyticus delineated into two evident lineages, with the rest of the STs remaining unresolved (Figure 3). The pandemic lineage consisted of all of the pandemic strains from Asia, Chile and Peru (ST3), as well as other STs that included Asian pre-pandemic strains of diverse serotypes and the three variants of hemolysin-related genotypes. A second lineage consisted of trh+ strains of STs 1, 82 and 83, all of them from Asia.
The genealogy showed the influence of recombination on the genetic diversification of V. parahaemolyticus with a central node (node A) from which all of the branches diverged. This specific topology indicated an unresolved phylogeny due to the non-identification of a common ancestor. ST88 and ST265, comprising all the O4:K8 strains were grouped in a single lineage. ST89 and ST93, which included Peruvian strains, also shared a common linage, as well as ST92 and ST63, sequence types composed of Asian and Chilean strains, respectively. For each branch of the reconstructed genealogy, ClonalFrame identified fragments that were likely imported. The influence of mutation and recombination events in the generation of polymorphisms was further investigated in the two cluster nodes (A and B) indicated in Figure 3A. Node A showed a high probability of importing events consistent with recombination substitutions, while divergence in node B, which included the Pandemic lineage, most likely originated as a result of point mutations (Fig. 3B). ClonalFrame analysis shows that the relative impact of recombination versus point mutation expressed as a ratio (r/m) was approximately 1.45, and that the relative frequency of recombination in comparison to point mutation (ρ/θ) was approximately 0.20.
To extend our analysis of the phylogenetic relationships and to understand the impact of genomic variation in the evolutionary history of V. parahaemolyticus, the distribution of VGRs was assessed by PCR assays in the 67 strains. A transversal clustering analysis was performed to visualize the evolutionary significance of the presence of genomic regions within the strain collection (Fig. 4).
ClonalFrame majority-rule consensus tree building with the whole set of strains revealed the same group structure as resolved by the clonal genealogy. However, analysis of VGR data showed a specific clustering of VGRs with three different groups (A, B, and C) (Fig. 4). Cluster A grouped five different genomic regions almost exclusive for trh+ strains with the exception of two tdh+ strains (U5474 and AQ3810). The presence of TTSS2β was only detected in strains bearing the trh gene. This cluster also included regions HGT20 and HGT22 encoding for the Type I restriction system, which was only identified in trh+ Asian strains. Cluster B included 6 genomic regions; two of them (HGT3 and HGT3A) correspond to ORFs coding the type 6 secretion system (T6SS1), which were present in all of the clinical strains with the exception of 4 tdh+/trh− non-pandemic strains isolated in Peru, Chile, and Asia. T3SS2α was singularly detected in those strains with genotype tdh+/trh− but was not present in those strains positive for the tdh and trh gene. Cluster C consisted of the majority of VGRs (20 genomic regions), which were uniformly distributed among almost all the pandemic strains. Some of these regions were partially present in all Asian strains phylogenetically related to pandemic strains, while those were less frequent in the remaining non-pandemic strains and distributed with an undefined pattern. Non-pandemic Peruvian strains showed a shift in the distribution of the VGRs included within this cluster from the pre-pandemic period to 1999 and afterwards. Genomic regions such as HGT6A (ribonuclease R), HGT12 (VPaI2), HGT9 (site-specific recombinase), HGT14 (hypothetical protein), HGT16B (phage genes), HGT13 (type IV pilin), HGT18 (hypothetical protein) and HGT19 (hypothetical protein), which are characteristic of pandemic strains, were exclusively identified in those non-pandemic strains from Peru isolated after the arrival of the pandemic clone in 1997. Finally, none of the VGRs investigated were found in V. alginolyticus ATCC17749.
Vibrio parahaemolyticus represents an intriguing food-borne pathogen and poses a significant threat to public health in Peru. However, despite this concern for public health, the knowledge of the molecular epidemiology and genetic structure of this pathogen remains incomplete.
Epidemiology of V. parahaemolyticus in Peru have been historically associated with sporadic outbreaks linked with seafood consumption. Since 1983, clinical strains were dominated by the presence of local serovars such as O4:K8 that was isolated from both clinical and environmental samples [30]. These infections were characteristically related to auto-limiting outbreaks detected along the coastal regions over summer months. This epidemic pattern shifted in 1997 with the unexpected arrival of pandemic V. parahaemolyticus to Peru [5]. The presence of the O4:K8 serovar in pre- and post-pandemic periods allowed for the identification of this group as the dominant population among clinical strains in Peru and the cause of recurrent outbreaks during the warmest months [31], [5]. The totality of strains recovered from infections in 1997 and 1998 belonged exclusively to the pandemic clone. After the period of the infections associated with the pandemic clone in 1997–98, the dominance of this clone in clinical infections began to decline and a mix of different serovars began to emerge. This specific epidemiological trend of arrival and rapid decline of pandemic V. parahaemolyticus infection in Peru clearly contrasts with the infection dynamics found in Chile, the neighboring country where infections associated with the pandemic clone started first in 1997 and subsequently in 2004, and where the pandemic clone has dominated the clinical isolations since the second epidemic radiation in 2004 [32]. The arrival of the pandemic clone to Peru provided a unique opportunity for testing the potential impact of an introduced genetic group on the structure and genetic variability of local pathogenic populations. One particular feature of the epidemiology of V. parahaemolyticus in Peru after the appearance of the pandemic clone was the sudden apparition of diverse serovars not detected previously. Serovars O1:K33, O1:KUT, O3:K30, O3:K58, O3:KUT, O5:KUT, O6:KUT and OUT:KUT had not been isolated prior to 1998. Similar results were found in a previous retrospective study carried out in Peru covering the 1993–2002 period [6], which reported the presence of serovars O3:K68, O3:K58, OUT:K6, O6:K18, O11:KUT, O11:K15 and OUT:KUT after 1998. The flourishing of serovars and genotypes in Peru may be related to the particular vehicle for the introduction and propagation of pandemic strains. The epidemic dissemination of this pandemic clone along the coast of Peru corresponded with the expansion and dynamics of the poleward propagation and the receding of tropical waters linked to the 1997 El Niño event [5], [33].
Another important aspect to be considered to understand the possible genetic impact of the pandemic clone on Peruvian local populations is the comparative analysis of phylogeny inferred from MLST sequences and the distribution of variable regions in the panel of strains. The pattern of VGR distribution forming cluster C showed a conserved presence for all regions among strains belonging to the pandemic clone, which provides an additional evidence for the highly clonal nature of this phylotype [34], [18]. On the contrary, a sparse presence of VGRs in cluster C was observed, showing an undefined pattern among non-pandemic strains. A detailed analysis of variations and phylotypes showed that VGRs in cluster C are only present in strains isolated after the arrival of the pandemic clone in 1997. This specific pattern of distribution and the connection between these strains and the arrival of the pandemic clone may suggest a common origin for all of these groups. However, the presence of a region coding hypothetical genes (VPA0434 and VPA0435) in one O4:K8 strain also raises the possibility of a local horizontal transfer from pandemic strains to local Vibrio communities in Peru.
Horizontal gene transfer appears to be the major force shaping both the genomic variation and virulence of V. parahaemolyticus, as evidenced in previous studies linking the acquisition of pathogenicity islands with the emergence of the pandemic clone [17], [18]. The results of transversal clustering analysis revealed that the presence of T3SS, T6SS and mannose-sensitive hemagglutinin (MSHA) pilus were clustered together in most clinical isolates showing a clear association with the pathogenicity of these strains, suggesting that these regions are conserved in pathogenic strains and could be a good marker of pathogenicity.
The evolutionary history of pathogenic lineages of V. parahaemolyticus has been analyzed previously by different approaches [21], [17], [35]. However, the previous analysis of population structure was not sufficiently integrated within the epidemic dynamics prevailing in a specific region so that there could be an adequate evaluation of the shift in genotype and population dominance over different periods. ClonalFrame genealogy inferred a star-shaped tree with long terminal branches showing a primary diversification affecting all of the STs, likely as a result of recombination events [36]. A defined lineage grouped all of the pandemic strains as well as most of the Asian genotypes showing a hierarchical pattern recently evolved from a common ancestor, likely due to the course of successive point mutations. The overall results evidenced the influence of recombination events in the diversification of most pathogenic V. parahaemolyticus genotypes.
Homologous recombination in housekeeping genes has been found naturally in Vibrio, and it is an important driver of diversification in this genus [37]. ClonalFrame analyses of the whole concatenated dataset showed rates of recombination of 1.45 and 0.20 for r/m and ρ/θ, respectively. These data suggest an intermediate rate of recombination among the strains characterized [38]. This estimate of recombination frequency suggests that recombination is relatively rare compared to other species, such as Streptococcus uberis (ρ/θ, 9.0) [39] and Clostridium perfringens (ρ/θ, 3.2) [40], but it is approximately similar to that observed for other groups, such as lineage I of Listeria monocytogenes (ρ/θ, 0.13) [41], This particular feature may be related to the exclusive use of pathogenic strains in the study, which are characterized by a clonal diversification [38].
To conclude, the results of this study describe the epidemiological impact caused by the introduction of the pandemic clone in Peru on the epidemiology and structure of the local population of V. parahaemolyticus. The presence of genomic regions characteristic of the pandemic clone in other non-pandemic strains provides early evidence of genetic transfer from the introduced population to the local communities. Additionally, the genetic relationships based on MLST and VGR analyses support the epidemiological connection between pandemic and non-pandemic strains isolated in both Peru and Chile. The phylogenetic and genomic analysis performed allowed us to determine the recent origin of the pandemic clone lineage, probably caused by successive acquisition of genomic regions, as well as the influence of recombination events in the diversification of non-pandemic pathogenic V. parahaemolyticus. Ultimately, these results provide a preliminary framework about evolutionary history of V. parahaemolyticus. Recent advances in high throughput sequencing are revolutionizing the field of population genetics of human pathogens. Application of fine-scale analysis based on whole genome sequences in future studies of pathogenic bacteria will contribute to improve our knowledge of the epidemic dynamics and routes of dispersion of Vibrio diseases [42], [43].
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10.1371/journal.pgen.1004335 | Activating Transcription Factor 6 Is Necessary and Sufficient for Alcoholic Fatty Liver Disease in Zebrafish | Fatty liver disease (FLD) is characterized by lipid accumulation in hepatocytes and is accompanied by secretory pathway dysfunction, resulting in induction of the unfolded protein response (UPR). Activating transcription factor 6 (ATF6), one of three main UPR sensors, functions to both promote FLD during acute stress and reduce FLD during chronic stress. There is little mechanistic understanding of how ATF6, or any other UPR factor, regulates hepatic lipid metabolism to cause disease. We addressed this using zebrafish genetics and biochemical analyses and demonstrate that Atf6 is necessary and sufficient for FLD. atf6 transcription is significantly upregulated in the liver of zebrafish with alcoholic FLD and morpholino-mediated atf6 depletion significantly reduced steatosis incidence caused by alcohol. Moreover, overexpression of active, nuclear Atf6 (nAtf6) in hepatocytes caused FLD in the absence of stress. mRNA-Seq and qPCR analyses of livers from five day old nAtf6 transgenic larvae revealed upregulation of genes promoting glyceroneogenesis and fatty acid elongation, including fatty acid synthase (fasn), and nAtf6 overexpression in both zebrafish larvae and human hepatoma cells increased the incorporation of 14C-acetate into lipids. Srebp transcription factors are key regulators of lipogenic enzymes, but reducing Srebp activation by scap morpholino injection neither prevented FLD in nAtf6 transgenics nor synergized with atf6 knockdown to reduce alcohol-induced FLD. In contrast, fasn morpholino injection reduced FLD in nAtf6 transgenic larvae and synergistically interacted with atf6 to reduce alcoholic FLD. Thus, our data demonstrate that Atf6 is required for alcoholic FLD and epistatically interacts with fasn to cause this disease, suggesting triglyceride biogenesis as the mechanism of UPR induced FLD.
| Fatty liver disease (steatosis) is the most common liver disease worldwide and is commonly caused by obesity, type 2 diabetes, or alcohol abuse. All of these conditions are associated with impaired hepatocyte protein secretion, resulting in hypoproteinemia that contributes to the systemic complications of these diseases. The unfolded protein response (UPR) is activated in response to stress in the protein secretory pathway and a wealth of data indicates that UPR activation can contribute to steatosis, but the mechanistic basis for this relationship is poorly understood. We identify activating transcription factor 6 (Atf6), one of three UPR sensors, as necessary and sufficient for steatosis and show that Atf6 activation can promote lipogenesis, providing a direct connection between the stress response and lipid metabolism. Blocking Atf6 in zebrafish larvae prevents alcohol-induced steatosis and Atf6 overexpression in zebrafish hepatocytes induces genes that drive lipogenesis, increases lipid production and causes steatosis. Fatty acid synthase (fasn) is a key lipogenic enzyme and we show that fasn is required for fatty liver in response to both ethanol and Atf6 overexpression. Our findings point to Atf6 as a potential therapeutic target for fatty liver disease.
| The unfolded protein response (UPR) acts in most cells to maintain homeostasis within the protein secretory pathway during physiological conditions. During stress, the UPR becomes further induced to mitigate the accumulation of misfolded or unfolded proteins in the endoplasmic reticulum (ER). If UPR activation cannot relieve the excess protein load, the ER becomes dilated and dysfunctional, and all branches of the UPR remain chronically activated in a condition referred to as ER stress. Many studies have implicated ER stress in a range of pathologies, and there is a clear association between UPR activation and metabolic diseases such as fatty liver disease (FLD) [1]–[5]. However, it is not known whether factors that control the UPR can also directly impact lipid metabolism and it remains unclear how UPR activation causes FLD.
FLD is the most common hepatic pathology worldwide [6], and alcohol abuse is a leading cause. Even a single episode of binge drinking causes lipid accumulation in hepatocytes (steatosis) in over 90% of drinkers [7]. While acute steatosis can resolve, chronic steatosis can render hepatocytes susceptible to damage and is a prerequisite step in developing more severe liver disease, including steatohepatitis and cirrhosis. Most FLD etiologies are accompanied by impairment of protein secretion by hepatocytes that result in serum protein deficiencies, which are most apparent in chronic alcoholics and contribute to the systemic complications of alcoholic liver disease (ALD). These defects are reflected in many reports demonstrating that alcohol induces some UPR sensors and targets in the liver of mice [1], [8], rats [9], micropigs [10], and zebrafish [11]–[13]. Moreover, deleting ATF4 or CHOP, two key UPR effectors, reduces ethanol-induced liver injury [8], [14], indicating that UPR activation can contribute to ALD. However, it is not clear whether these genes, or other UPR effectors, contribute to steatosis.
There are three main UPR sensors that function through shared and independent mechanisms to maintain ER function by enhancing protein processing and folding in the ER, and by promoting degradation of terminally misfolded secretory proteins. Activating transcription factor 6 (ATF6) and inositol-requiring enzyme 1-alpha (IRE1α or ERN1) pathways generate the active transcription factors nATF6 and XBP1, respectively, that induce hundreds of UPR target genes [15], [16]. PRKR-like endoplasmic reticulum kinase (PERK or EIF2AK3) phosphorylates EIF2A to repress translation [17] and to promote production of ATF4 [18], which induces a subset of target genes [14], [18]. Each of these primary sensors has been evaluated for its contribution to FLD caused by a robust ER stressor [19], [20]. ATF6 knockout mice fail to resolve steatosis caused by acute stress due to tunicamycin injection [16], [21], [22], suggesting that loss of ATF6 promotes fatty liver. Our previous work using zebrafish confirmed the finding that steatosis caused by acute stress is augmented by atf6 loss, and also demonstrates that steatosis caused by chronic stress is reduced when Atf6 is depleted [23]. Thus, it appears that atf6 loss can alternatively enhance or reduce FLD, depending on the nature and duration of the stress.
The relationship between metabolic disease and UPR activation is under intensive investigation, yet several important questions remain unanswered. First, is UPR activation a cause or consequence of FLD? While some studies indicate that lipotoxicity and fatty acid accumulation can induce the UPR [24], [25], there is incontrovertible evidence that robust UPR activation is sufficient to induce steatosis [2], [26], [27]. These data are incorporated into a current model proposing that robust UPR activation can cause steatosis and, if the lipid burden is not resolved, this can further augment cellular stress and contribute to chronic UPR induction. The second question is which, if any, of the main UPR factors directly cause fatty liver? All studies to date that have addressed this question, including our work in zebrafish [23], utilize loss of function approaches whereby a key UPR gene is deleted and the effect on stress-induced steatosis is evaluated. However, these studies are complicated by the extensive crosstalk between UPR effectors, and by the ability of cells to adapt to changes in UPR capacity. Third, what is the mechanism by which UPR activation causes fatty liver? Activation of lipogenic genes by the sterol response element binding protein (SREBP) transcription factors has been implicated as an essential pathway in both alcoholic [28], [29] and non-alcoholic [30]–[32] FLD. Since SREBPs and ATF6 are activated by a shared proteolytic mechanism [33], it is possible that these two proteins are activated simultaneously in response to stress. However, the role of SREBPs in FLD caused by UPR activation has not been addressed.
Here, we use genetic and genomic approaches in a well-established zebrafish model of ALD [11]–[13], [34]–[37] to demonstrate that blocking Atf6 prevents alcohol-induced steatosis and that overexpression of the nuclear, active form of Atf6 (nAtf6) causes steatosis. We found that nAtf6 overexpression increases expression of genes in the lipogenic pathway and alters lipid flux to promote triglyceride synthesis by a mechanism that does not involve Srebps. Instead, we demonstrate an epistatic interaction between atf6 and fasn in driving alcoholic steatosis. These findings demonstrate the first clear and causative link between a central UPR sensor and FLD.
We previously reported that transcription of multiple UPR effector genes and translation of the major ER chaperone, Bip, are induced in zebrafish livers following alcohol exposure [11]–[13]. We have also demonstrated that xbp1 splicing, a direct measure of Ire1a activation, is only detectable within the first few hours of exposure to ethanol [13] yet UPR target gene induction persists long afterwards [12], suggesting that the Ire1a pathway is not entirely responsible for the UPR in ALD. Our previous finding that Eif2a is phosphorylated in the liver of zebrafish with ALD [13] suggests that Perk is activated by alcohol, but since Eif2a can also be phosphorylated by other kinases [38], this has yet to be resolved. The lack of reliable antibodies that recognize the active form of Atf6, Perk and Ire1a in zebrafish hampered direct measurement of the activation status of each pathway (not shown). However, quantitative real-time PCR (qPCR) analysis of liver samples from 5 day post fertilization (dpf) larvae treated with alcohol demonstrated that atf6 mRNA was significantly induced in the liver (Figure 1A) prior to the onset of steatosis (see Figure 1B and [13]). Since Atf6 has been shown to act in a positive feedback loop to induce its own expression [39], these data suggest that the increase in atf6 expression could reflect Atf6 activation in response to alcohol.
To test whether Atf6 was required for alcoholic steatosis, we injected a morpholino (MO) targeting either the translation initiation site (atf6MO-ATG) [23] or the boundary between coding exon 1 and intron 1 of atf6 (atf6MO-SPL) (Table S1 and Figure S1A), which blocked splicing (Figure S1B) and introduced an early stop codon (Figure S1C). Morphants and uninjected controls were treated with 350 mM ethanol for 32 hours, stained with the neutral lipid dye, oil red O and scored for the presence or absence of steatosis (Figure 1C and S2A). Consistent with previous findings [11]–[13], [23], [34], the percent of larvae with steatosis (i.e. steatosis incidence) was significantly higher in uninjected larvae exposed to 350 mM ethanol for 32 hours (13% in untreated larvae vs. 53% in ethanol treated larvae; p<0.05). atf6 morphants had reduced steatosis incidence (p<0.05, Figure 1C-D), but still displayed other gross morphological abnormalities caused by ethanol including increased liver circularity, an indication of hepatomegaly (Figure S2B-C), a common feature of liver disease. Thus, Atf6 is required for alcoholic steatosis.
To determine whether Atf6 is sufficient to cause FLD, we created a transgenic zebrafish line expressing the predicted nuclear, active fragment of zebrafish Atf6 (amino acids 1-366; Figure S3A) fused to mCherry under the hepatocyte-specific promoter fabp10, along with a cassette that expressed GFP in cardiomyocytes as a marker of transgenesis ((Tg(fabp10:nAtf6-cherry; cmlc2:GFP) hereafter called nAtf6 TG). There is high sequence identity between human and zebrafish Atf6 proteins in the transmembrane, leucine zipper and basic (bZIP) domains and the protease recognition sites are highly conserved (Figure S3A). To confirm its nuclear localization, the predicted nAtf6 fragment of the zebrafish protein was fused to GFP and transfected into HepG2 cells (Figure S3B).
Transgenics were selected based on GFP expression in the heart, appeared grossly normal throughout development (Figure S3C), and developed into viable, fertile adults. nAtf6-mCherry was not detectable using low-resolution fluorescence microscopy in larvae from all four of the transgenic lines we generated. However, we confirmed transgene expression by PCR and blotting for mCherry (Figures S3D-E). atf6 mRNA expression in the liver was ∼4 fold higher in nAtf6 TG larvae compared to wildtype (WT) larvae (Figure 2A and S4) at 5 dpf, and was persistently elevated in older fish (Figure S4D and not shown). We detected mCherry mRNA in 5 dpf nAtf6 TG larvae by PCR, and Western blotting for mCherry detected the transgene in the liver of adult fish, albeit at much lower levels than achieved in transgenic fish expressing nuclear-localized mCherry without fusion to nAtf6 (i.e. Tg(fabp10:nls-mCherry); [40] Figures S3D-E).
We confirmed that nAtf6 overexpression did not induce the other UPR branches by assessing xbp1 splicing (Figure S4A) and Eif2a phosphorylation (Figure S4B) in the liver of nAtf6 TG 5 dpf larvae. We found no difference in these markers in unstressed nAtf6 TG and WT larvae, however, xbp1 splicing was lower in transgenics than in WTs after exposure to stress (i.e. 1 µg/ml tunicamycin for 48 hours; Figure S4A). This suggests that nAtf6 overexpression adapts hepatocytes to withstand a robust, pharmacologically-induced ER stress.
We next examined the expression of UPR target genes in the liver of 5 dpf transgenic and control larvae. We found that bip (also called hspa5), a transcriptional target of Atf6, was elevated at the protein (Figure S4C) and mRNA levels (Figure 2A and S4D) in nAtf6 TG larvae. mRNA-Seq analysis comparing control larvae (Tg(fabp10:nls-mCherry); n = 2 pools of ∼40–50 livers each; see [40] and GEO dataset GSE52605), ethanol-treated (n = 2 pools of ∼40–50 livers each), and nAtf6 TG larvae (n = 1 pool of ∼40–50 livers) revealed 33 UPR target genes to be highly and significantly upregulated in nAtf6 TG livers (Tables S2-S4 and Figure 2A; GEO dataset GSE56498). These genes were identified as ATF6 target genes in other studies [16], [41] or by having the UPR or protein folding as a primary GO descriptor, and included ER chaperones (bip/hspa5, dnajc3, and grp94/hsp90b1), quality control effectors (canx, calret, calrl, and calrl2), protein disulfide isomerases (pdia3, pdia4) and ERAD components (edem1, derl1 and derl3). A subset of these genes was confirmed by qPCR at 5 dpf and at 14 dpf (Figure 2A and S4D-E). atf4 and derl1 were the only genes detected as significantly upregulated by qPCR but not by mRNA-Seq.
Analysis of the mRNA-Seq data from ethanol-treated larvae revealed a striking overlap between the “UPR-ome” induced by ethanol and by nAtf6 overexpression: of the 49 UPR target genes induced by ethanol, 30 were also induced in the liver of nAtf6 TG larvae (Figure 2B and Table S2). It is possible that the remaining 19 genes unique to the ethanol-induced transcriptome are attributed to the transient, early increase in xbp1 splicing [13] or to other transcription factors that regulate their expression. Collectively, these data indicate that Atf6 is a major regulator of the UPR in ALD.
We next asked whether nAtf6 was sufficient to cause FLD (Figure 2C-E). Induction of the fabp10:nAtf6-cherry transgene occurs at ∼2.5 dpf [42] and by 4 dpf, 44% of nAtf6 TG larvae developed steatosis which increased to 69% and 76% by 5 and 5.5 dpf, respectively (Figure 2C-D and S5A) and persisted until at least 14 dpf (Figure S5A), but there were no marked histological abnormalities in the nAtf6 TG liver at 5 dpf (Figure S5B). Hepatic triglyceride (TAG) levels were nearly doubled at 5 dpf, and six times higher in 14 dpf nAtf6 TG larvae (Figure 2E), clutch-to-clutch variability non-withstanding. However, TAG levels were the same in WT and nAtf6 TG adults (Figure 2E), despite persistent expression of the transgene (Figure S3E and not shown). We speculate that, over time, animals adapt to nAtf6 overexpression or that other metabolic parameters affect hepatic lipid accumulation in adult fish. Together, our data demonstrate that Atf6 is necessary for alcoholic steatosis and sufficient to cause steatosis in the absence of any other stress.
Steatosis is caused by elevated TAGs in hepatocytes resulting from increased lipid synthesis or uptake, or from decreased lipid utilization or export. We therefore analyzed pathways relevant to these processes in the mRNA-Seq dataset from nAtf6 TG livers to determine if any were dysregulated. TAGs are generated from linking free fatty acids (FFAs) to a glycerol backbone, which is generated by conversion of dihydroxyacetone phosphate (DHAP) to glycerol-3-phosphate (G3P) by G3P dehydrogenase (GPD1) (see schematic in Figure 3A). We found genes that participate in TAG synthesis (Figure 3A-B) or export (Figure S6) were dysregulated in nAtf6 TG livers at 5 dpf, albeit not to the same degree as observed for the UPR target genes (Figure 2). DHAP is generated via glycolysis or by glyceroneogenesis, a pathway that has significant overlap with gluconeogenesis (see [43] and schematic in Figure 3A). mRNA-Seq analysis demonstrated that several genes involved in glycolysis were downregulated in nAtf6 TG livers including aldob, glut2/slc2a2, gck, pgk1, and pklr (Figure 3A-B). Genes promoting glyceroneogenesis, particularly pck1 and got1, which generates oxaloacetate for conversion to phosphoenoylpyruvate by pck1, were upregulated, as was gpd1b, which drives glycerol formation. This suggests that nAtf6 overexpression increases factors regulating glyceroneogenesis preferentially over those that promote glycolysis (Figure 3A-B). In addition, ech1 and tecrb, two genes responsible for fatty acid elongation in the mitochondria and ER, respectively, were induced in nAtf6 TG livers by mRNA-Seq (Figure 3A-B). Some of these genes were confirmed by qPCR analysis of nAtf6 TG livers, with the median fold change from qPCR on 3 to 6 clutches of livers from 5 and 14 dpf larvae depicted in Figure S7.
TAGs are assembled in the hepatocyte ER and secreted in association with lipoproteins that are required for transport in plasma. Several genes that regulate lipoprotein assembly and export, including apom, apoeb, apoea, cetp and zgc:162608 and zgc:194131, two apolipoprotein-like genes, were downregulated in nAtf6 TG livers (Figure S7 and Table S3). pdia2, which interacts with MTP to transport lipids out of hepatocytes [44], was also downregulated. These data suggest that hepatic lipid transport may be impaired by nAtf6 overexpression. While these expression data do not capture the complex metabolic and post-translational regulation of the proteins encoded by these genes, they do suggest enhanced lipogenesis or suppressed lipoprotein export as potential mechanisms of steatosis in nAtf6 transgenics.
Acetate is a precursor to acetyl-CoA, the major building block of fatty acid and, therefore, TAG synthesis. To determine if nAtf6 overexpression increased de novo lipogenesis, we measured the incorporation of 14C-acetate into lipids in nAtf6 TG larvae (Figure 4A) and in HepG2 cells overexpressing nATF6 (Figure 4B-D) compared to their respective controls. Larvae were incubated with 14C-acetate from 3–5 dpf and we measured the amount of radiolabel present in the lipid fraction compared to that present in the lysate of the whole larvae. In an average of 5 individual pools of larvae, more 14C radiolabel was detected in the lipid fraction of nAtf6 TG larvae than in wildtype larvae (Figure 4A).
Since the liver in 5 dpf zebrafish larvae is too small to analyze incorporation in this organ exclusively, analysis of the whole larvae could not determine whether the label was preferentially incorporated into hepatocytes of nAtf6 TG larvae. We thus used human hepatoma (HepG2) cells transfected with human nATF6 to address this question. nATF6 overexpression causes accumulation of oil red O stained cytoplasmic droplets (Figure 4B) that are both greater in number and larger in size than in cells transfected with GFP (Figure 4B). These findings were confirmed in 293T cells (Figure S8) and correlate with our findings in nAtf6 TG larvae (Figure 2). Thus, nAtf6 overexpression causes lipid accumulation across cell types and species. Moreover, in eleven independent cell samples from two separate experiments, HepG2 cells overexpressing nATF6 had slightly more 14C radiolabel in the lipid fraction compared to GFP-transfected cells (Figure 4D). While we found that the incorporation tended to be higher in the nATF6 expressing cells, the average increase was moderate, at best. We attribute this to the transfection efficiency of HepG2 cells combined with experimental conditions used to label lipids, which likely act to select against nATF6 expressing cells.
Fatty acid synthase (fasn) mediates multiple steps in fatty acid synthesis that culminate in generation of palmitate from acetyl-CoA, a building block for TAG biosynthesis. fasn transcription in hepatocytes is primarily regulated by Srebp1c [45]–[47], and we previously demonstrated that fasn transcripts and other Srebp target genes are induced in zebrafish with ALD [12], [13]. Moreover, blocking Srebp activation by injecting a morpholino targeting the Srebp activating protein, Scap, reduced the incidence of alcoholic steatosis [12], [13]. Since Atf6 and Srebps are activated by similar mechanisms – they are both retained as inactive precursors in the ER and processed by the same Golgi-resident enzymes [33] – Srebp mediated lipogenesis has been proposed as a mechanism by which UPR activation causes FLD [27], [32]. We tested this using a genetic approach. First, we asked whether blocking Atf6 affected the induction of lipogenic gene expression in the liver of ethanol treated larvae. Both fasn (Figure 5A), and acc1 (acaca; Figure S9A) were upregulated by ∼2 fold in the liver of ethanol treated larvae, and this was blocked by atf6 MO injection (Figures 5A and S9A). However, induction of other Srebp target genes was not blocked by Atf6 depletion, including srebp1 (Figure S9A) and the Srebp2 targets hmgcra, hmgcs1 and mvk (Figure S9B). Since the entire Srebp target gene set was not uniformly affected by atf6 loss, we conclude that Atf6 does not interact with Srebps. Instead, Atf6 may affect expression of specific lipogenic genes (i.e. fasn and acc1) by a mechanism distinct from Srebp1. Interestingly, we found mild induction of Srebp2 target genes in untreated atf6 morphants (Figure S9B), supporting conclusions based on experiments in mammalian cultured cells where ATF6 was found to directly suppress SREBP2 activity [48].
Next, we asked whether nAtf6 was sufficient to induce fasn and acc1 by assessing their expression in livers of nAtf6 TG larvae. Both fasn (p<0.05; Figure 5B) and acc1 (Figure S9C) were increased by ∼2 fold at 5 dpf, and fasn expression was also higher at 14 dpf in nAtf6 TG larvae and in HepG2 cells overexpressing nATF6 (Figure 5B). However, other Srebp1 and Srebp2 genes were not significantly induced by nAtf6 overexpression in nAtf6 TG larvae (Figure S9C-D) or in HepG2 cells overexpressing human nATF6 (not shown), indicating that nAtf6 does not activate the entire Srebp target gene program. We then tested whether nAtf6 functioned upstream of Srebps by assessing whether scap morpholino injection suppressed steatosis in nAtf6 TG larvae. While scap morphants are resistant to ethanol-induced steatosis [12] we found no difference in steatosis incidence in nAtf6 transgenics (Figure 5B). Finally, to test whether atf6 and scap interacted to cause alcoholic steatosis we injected low concentrations of morpholinos targeting both genes and found that they did not synergize to suppress alcoholic steatosis (Figure S10). We thus conclude that lipogenic gene induction and steatosis caused by nAtf6 overexpression does not require Srebps.
The functional relevance of the finding that nAtf6 overexpression increases lipogenesis was tested by assessing the effect of morpholino-mediated fasn (Table S1) knockdown on steatosis incidence in nAtf6 TG larvae. Injection of high concentrations of fasn MO induced severe morphological defects, so we optimized the fasn MO concentration to have minimal toxicity. This had no effect on steatosis incidence in control larvae, but reduced steatosis incidence in nAtf6 TG larvae from 74% to 42% (Figure 5C). Furthermore, we found that atf6 and fasn interacted epistatically to cause alcoholic steatosis by co-injecting concentrations of fasn and atf6-SPL MOs that did not have any effect on alcoholic steatosis on their own but, together, significantly reduced steatosis incidence in response to ethanol (Figure 5D). Thus, Atf6 and Fasn function in the same genetic pathway. Taken together, our data suggest a model by which Atf6 causes steatosis, in part, by inducing fasn and TAG synthesis by a mechanism that is independent of Srebps (Figure 6).
There is a wealth of recent literature showing that the UPR is activated in FLD and that this aspect of the disease is conserved across species [4], [16], [21], [23], [49]–[51]. However, it has not been shown that any single UPR factor can directly cause this disease and the mechanism by which UPR activation causes lipid accumulation is unclear. Here, we use zebrafish genetics and biochemical analyses to show that Atf6 is both necessary and sufficient for steatosis by showing that: (i) Atf6 is required for alcoholic steatosis, (ii) activation of Atf6 is sufficient to cause steatosis, (iii) Atf6 activation induces expression of genes involved in glyceroneogenesis and fatty acid elongation and causes de novo lipogenesis, and (iv) Atf6 epistatically interacts with fatty acid synthase (fasn), a key enzyme involved in TAG biosynthesis, to cause FLD. This is among the first data to directly link a main UPR sensor and lipid metabolic pathways.
A number of studies have demonstrated that loss of one of the key UPR sensors – PERK [19], IRE1α [20] or ATF6 [16], [21], [23] – enhances steatosis caused by acute stress. Additionally, we previously demonstrated that Atf6 depletion suppresses steatosis caused by chronic stress [23]. Thus, it is clear that UPR activation is a conserved, common feature of FLD, but whether it functions to promote or prevent steatosis appears to depend on the nature and duration of the FLD-causing stress. Importantly, these loss of function studies have described a requirement for the UPR in FLD caused by tunicamycin injection, but not in the context of a stress that mirrors human conditions, such as obesity or alcohol abuse. Here, we show that Atf6 is induced in ALD, that its activation precedes steatosis, and that knocking down Atf6 reduces alcoholic steatosis. Thus, Atf6 is required for ALD. Moreover, since overexpression of nAtf6 is sufficient to cause FLD in the absence of any other stress, we conclude that this branch of the UPR is a pathophysiological mechanism of FLD.
Atf6 and Xbp1 are the primary transcription factors that both independently and cooperatively regulate expression of hundreds of UPR target genes. We previously found significant upregulation of many UPR target genes in zebrafish with ALD [11], [13] and hypothesized that since xbp1 splicing in the liver was only an early, transient response to alcohol [13], other transcription factors must participate in this robust UPR. Transcriptome analysis of the livers from nAtf6 TG and ethanol treated larvae enabled us to identify a set of target genes that are likely to be directly activated by Atf6. Many of these genes were also reported to be upregulated in mice overexpressing inducible ATF6 in the heart [41] and they were not induced by tunicamycin treatment of Atf6-/- MEFs [16]. Genes occupying the intersection of these different datasets are likely bona fide Atf6 targets and the transgenic larvae we generated provides a system to test this directly. In contrast, many genes that were downregulated when ATF6 was overexpressed in the mouse heart were unchanged in nAtf6 transgenic zebrafish livers and, conversely, genes such as canx, derl1, and atf4 were upregulated in the nAtf6 TG zebrafish liver samples but not in the mouse model. The variations between the two datasets may be due to inherent differences in the models or approaches used to detect changes in gene expression, or may reflect the ability of Atf6 to differentially regulate target genes depending on cell type: hepatocytes possess a significantly higher secretory capacity than cardiomyocytes, and thus the Atf6 transcriptome in hepatocytes may be more extensive in order to maintain ER homeostasis. Finally, comparative transcriptome analysis between nAtf6 TG and ethanol treated larvae identified significant overlap between the UPR target genes in these two datasets, indicating that Atf6 is the main transcriptional driver of the ethanol-induced “UPR-ome”.
What is the mechanism by which the UPR causes FLD? Our data showing that nAtf6 overexpression induces the expression of some lipogenic enzymes and increases lipid synthesis, and that blocking fatty acid synthesis reduces FLD, fits a model (Figure 6) whereby increased lipid production is, in part, the mechanism of steatosis in FLD caused by UPR activation. However, despite our finding that Atf6 depletion suppresses fasn expression and nAtf6 overexpression induces fasn, we have no evidence that fasn is a direct transcriptional target of Atf6. Indeed, the level of fasn induction is far less than the established Atf6 target, bip, and there are no canonical UPREs or ERSEs [15] in the fasn promoter (not shown), thus other pathways are also likely at play. The mechanism by which nAtf6 induces fasn and acc1 require further investigation, and the tools we describe here will facilitate such studies.
The SREBP transcription factors are well-characterized regulators of hepatic lipogenesis; SREBP1c functions by increasing the expression of the full panel of genes required for TAG biogenesis [52]. One possibility, suggested by the finding that the UPR and SREBPs are activated in parallel in some systems [26], [27], [32], is that nAtf6 causes Srebp activation, leading to increased expression of fasn and other lipogenic genes and promoting lipogenesis. Our data argues against this: (i) the full panel of Srebp targets are not induced by nAtf6 overexpression in zebrafish, (ii) Atf6 and Srebps did not epistatically interact to modify alcoholic steatosis and (iii) Srebp activation is not required for steatosis in nAtf6 TG larvae. While it does appear that Srebp activation is required for alcoholic steatosis [12], [28], [29], the current study shows that it is not the only important pathway that regulates hepatic lipid metabolism, as steatosis in nAtf6 TG larvae is independent of Srebps.
While Srebps are not required for the effects of Atf6 on steatosis, there does appear to be some interaction between Atf6 and Srebp2, consistent with in vitro data showing that ATF6 suppresses SREBP2 [48]. We found that Atf6 causes upregulation of Srebp2 target genes, and while this modest increase in Srebp2 target genes in atf6 morphants did not appear to have a functional impact on cholesterol biogenesis, since atf6 morphants do not develop steatosis but are protected from it, it does suggest that Atf6 may suppress Srebp2 activity.
We used mRNA-Seq analyses to identify potential pathways that are dysregulated in response to Atf6 overexpression, although it is clear that the complex post-translational regulation of these pathways are not captured by gene expression studies. Our data suggested that lipoprotein export may be impaired by Atf6 overexpression, but we did not find that the flux of 14C-acetate into the extracellular lipids was impaired in cells overexpressing nATF6. While there are some technical caveats to these biochemical studies, we speculate that decreased lipoprotein export is not the only mechanism by which nAtf6 overexpression causes steatosis. Future studies to optimize cell conditions to ensure maximal cell viability and sustained high levels of nAtf6 expression will be required to fully address this.
While steatosis is clearly a first step on the continuum to more severe liver disease, we also propose a different possibility: that lipid accumulation is a sign of an active and adaptive stress response. Protein folding in the ER is metabolically demanding, and when secretory cargo increases or the ER becomes full of unfolded proteins, increased lipid accumulation may enhance the ATP supply and thereby sustain the increased protein folding demands. Thus, the UPR may serve to enhance the protein folding capacity of the ER function in two ways: by activating genes required for protein folding, quality and export, and by promoting metabolic flux to TAGs as an energy source to withstand the challenge presented by a high level of secretory cargo. In this scenario, acute steatosis would not be a pathological response but, instead, would provide a protective role to sustain hepatocyte function during stress. However, if the stress response persists and steatosis becomes chronic, lipotoxicity could potentiate liver injury. Based on these data, it is tempting to speculate that enhancing protein folding in the ER by chemical chaperones, which have been used in both mouse models and humans to alleviate UPR activity, attenuate fatty liver disease and increase insulin sensitivity [3], [53], [54], could reducr the demand for energy and thus reduce the need to accumulate lipid. Whether Atf6 functions both as sensor of unfolded proteins and of metabolic demand remains to be elucidated.
Adult wildtype (WT, TAB14 and AB), Tg(fabp10:dsRed) [55] and Tg(fabp10:nls-mCherry) [40] zebrafish were maintained according to standard conditions. Larvae were exposed to 350 mM ethanol (Pharmco-AAPER, Brookfield, CT) in fish water starting at 96–98 hours post fertilization (hpf) for up to 32 hours as described [11], [12]. All zebrafish protocols were approved by Mount Sinai's Institutional Animal Care and Use Committee.
Morpholinos targeting the translation initiation ATG of atf6 (atf6MO-ATG) [23], an atf6 intron-exon boundary (atf6MO-SPL, Figure S1), a scap intron-exon boundary [12], and a fasn intron-exon boundary were ordered from GeneTools (Philomath, OR). Approximately 1–5 pmol were injected into 1–2 cell stage embryos. Morpholino sequences and amount injected per embryo (ng) are listed in Table S1.
The Tg(fabp10:nAtf6-cherry; cmlc2:GFP) transgenic line was created by injecting a vector containing 2813 bp of the fabp10 promoter [55] upstream of the predicted nuclear fragment of zebrafish Atf6 (amino acids 1–366) as identified via DNA and protein alignments with human ATF6 (NCBI Reference Sequence: NP_031374.2). A cassette driving GFP expression in cardiomyocytes was included for rapid screening of transgenics (cmlc2:GFP). The transgene was flanked by Tol2 sites and the vector was injected into fertilized eggs along with transposase mRNA. Larvae were selected for cmlc2:GFP expression and raised to adulthood, outcrossed to TAB14 adults and 4 germline founders were identified.
The nATF6-pcDNA-DEST47 plasmid was created using the Invitrogen Gateway System in which human nATF6 (amino acids 1–380) was amplified from a construct containing the full ORF (pEGFP-hATF6, from Dr. Aguirre-Ghiso) and ligated into pcDNA-DEST47. The nAtf6-GFP/pCI-Neo plasmid was generated by amplifying zebrafish nAtf6-GFP from a Tol2-generated plasmid (fabp10:nAtf6-GFP) with primers containing restriction sites for EcoRI and SmaI. The resulting PCR product was ligated into pCI-Neo via T4 ligase. pEGFP-C1 (Clontech) was used as a positive control for transfection.
293T and HepG2 cells were grown in 100 mm dishes (BD Biosciences) with DMEM (Corning Cellgro, Manassas, VA) containing 10% fetal bovine serum (Invitrogen) and penicillin/streptomycin (Cellgro), and housed in a humidified incubator at 37 C with 5% CO2. Cells were passaged at 80–90% confluency either into 100 mm dishes or 6 well culture plates (Corning). Each well was transfected with 1–2 µg plasmid using Xtreme GENE 9 (Roche) or Lipofectamine 2000 (Invitrogen) for 24 hours. Following transfection, cells were collected for RNA extraction using TRIzol (Invitrogen), used for oil red O staining, or labeled with 14C-acetate as described below.
HepG2 cells were washed with PBS 24–28 hours after transfection and kept in serum free high glucose DMEM (Corning Cellgro, Manassas, VA) containing penicillin/streptomycin (Cellgro), 100 nM insulin (Lilly USA, Indianapolis, IN) and 0.5 µCi 14C-acetate (PerkinElmer, Waltham, MA) for 17 hours. Media was harvested and spun to remove dead cells, while cells were scraped, washed twice and lysed in cold PBS. Protein concentration in the cell lysate was measured with a BCA protein quantification kit (Thermo Scientific, Waltham, MA). Fractions of the media and cell lysate portion were used for lipid extraction as described below.
WT and nATF6 5 dpf larvae were labeled with 1–5 µCi 14C-acetate for 48 hours. Larvae were washed twice and then sonicated in cold PBS. Protein concentration in the larval lysate was measured with a BCA protein quantification kit (Thermo Scientific). Fractions of the larval lysate were used for lipid extraction as described below.
Lipids from media, cells and 5 dpf larvae were extracted according to Bligh and Dyer [56] with the following modifications. Briefly, methanol and chloroform were added to 400 µl media or 50 µl of 100 µl total cell lysate in a ratio of 2∶1. Samples were vortexed and chloroform and 0.45% NaCl were added at a ratio of 1∶1, vortexed again and phases were separated by centrifugation. The aqueous phase was re-extracted with one part chloroform and combined with the lipid from the first second extraction, washed with methanol and 0.45% NaCl at a 1∶1 ratio, centrifuged and the lipid phase was recovered and dried under a stream of N2 gas. Lipids were reconstituted in a 2∶1 mixture of chloroform and methanol (Thermo Scientific, Waltham, MA), dissolved in Ultima Gold and counted using a 2460 MicroBeta2 LumiJET (PerkinElmer, Waltham, MA) liquid scintillation counter. CCPM were normalized to protein concentration to account for variations in cell number.
Total RNA isolated using TRIzol (Invitrogen) from pools of 15–20 dissected livers was reverse-transcribed using qScript cDNA SuperMix (Quanta Biosciences, Gaithersburg, MD). qPCR using A Light Cycler 480 (Roche) with PerfeCTa SYBRGreen FastMix (Quanta Biosciences) was used as previously described [11]. Values for target gene were normalized to reference gene rpp0, and dCts calculated using the comparative threshold method (dCt = 2−(Ct, gene – Ct, rpp0)). Primer sequences are listed in Table S1.
Approximately 20 livers from control and nAtf6 transgenic larvae were dissected and lysed in 0.5% Triton-X 100 and heated at 65°C for 5 minutes to inactivate hepatic lipases. Triglycerides were measured using the Infinity Triglyceride Liquid Stable Reagent (Thermo Fisher Scientific, Waltham, MA) following manufacturer's instructions, and were normalized to total protein concentration as determined by Bradford Assay (Bio-Rad, Hercules, CA).
Larvae were fixed in 4% paraformaldehyde (PFA; Electron Microscopy Sciences, Hatfield, PA) overnight at 4°C, stained with oil red O, and scored for steatosis as previously described [35]. Cryosections were stained by immersing slides in increasing concentrations of propylene glycol (85%, 100%) for 10 minutes followed by an overnight incubation in oil red O (0.5% in propylene glycol, Polysciences, Warrington, PA). Excess oil red O was removed the next day by sequential washes in 100% and 85% propylene glycol for 5 minutes. Nuclei were counterstained with hematoxylin.
HepG2 and 293T cells were stained as previously described [57] and counterstained with hematoxylin. Oil red O droplet area and number were calculated using ImageJ. In brief, images were split into the red, green, and blue channels and the green channel was further processed for quantification as described, as oil red O has an excitation at 510 nm [58]. Following this, the background was subtracted from each image to ensure only counting of oil red O droplets. Droplets equal or greater than five square pixels were counted and area quantified. The values were copied into Microsoft Excel and the average droplet area and number of droplets per nucleus were counted for each field. Twenty independent fields from nATF6 and GFP-transfected HepG2 and 293T cells were imaged at 60x magnification.
Total RNA was isolated using TRIzol (Invitrogen) from pools of ∼40–50 livers dissected on 5 dpf. Two clutches of Tg(fabp10:nls-mCherry) larvae that were either untreated (control) or treated with 350 mM ethanol for 24 hours and one clutch of nAtf6 TG larvae were used. polyA-tailed mRNA was selected using oligo-dT beads and then fragmented. cDNAs were created using random-hexamers and ligated with bar-coded adaptors compatible with HiSeq 2000. Single-end, 100 bp reads were sequenced at the Genomics Core of the Icahn School of Medicine at Mount Sinai. Custom-built software was used to map the reads to the zebrafish genome (Zv9/DanRer7) and estimate the coverage of each gene. Briefly, the reads were split into three 32 bp parts after trimming 2 bp at each end and mapped to the genome using a suffix-array based approach. The median of coverage across the transcript was used as an estimate of gene expression. The expression values were quantile normalized and log-ratios were calculated by comparing nAtf6 TGs to the average of the controls. Each ethanol treated sample was compared to its paired control (untreated siblings). Unique Gene Ontology terms (GO terms) were assigned to each gene by ranking the GO terms by relevance to the biology of the response, and using annotations from the human orthologues if the zebrafish annotations were lacking.
The distribution of expression values were plotted to identify the peak in the distribution, which is the level of noise in the system. The values were regularized by adding the noise to each gene's expression level before the log-ratios were calculated. This ensures that genes with low expression do not contribute to list of genes with large fold-changes. An absolute natural log ratio of 0.2 was used as the cutoff (known, non-responding genes are all below this threshold). The list of genes that show changes above this cutoff were analyzed for pathway enrichment using GO terms annotated as described above.
This data is available via the Gene Expression Omnibus (GEO) at accession number GSE56498. The controls for this dataset, untreated 5 dpf Tg(fabp10:nls-mCherry), also serve as controls in GEO dataset GSE52605 [40] and were included in both sets for ease of comparison between genotypes and treatments.
Images were cropped and minimally processed using Adobe Photoshop CS4 (Adobe Systems, San Jose, CA). Graphs were plotted using Prism 5.0c (GraphPad Software Inc., La Jolla, CA). Heat maps for mRNA-Seq data were generated using GENE-E (Broad Institute, Cambridge, MA). The global maximum and minimum for each gene set was set to orange and green, respectively, and zero was set to white. Metabolic pathway schematics were adapted from WikiPathways (URL: www.wikipathways.org).
Statistical tests were performed using GraphPad QuickCalcs (GraphPad Software, URL: http://www.graphpad.com/quickcalcs/index.cfm). For oil red O staining of whole larvae, chi-square with Fisher's Exact test were performed. For qPCR, radiolabeling, and oil red O staining of HepG2 and 293T cells we performed unpaired and one-sample t-tests as appropriate.
Methods for histological analysis, cryosectioning and staining, and liver circularity analysis are listed in Text S1.
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10.1371/journal.pgen.1002829 | GRHL3/GET1 and Trithorax Group Members Collaborate to Activate the Epidermal Progenitor Differentiation Program | The antagonistic actions of Polycomb and Trithorax are responsible for proper cell fate determination in mammalian tissues. In the epidermis, a self-renewing epithelium, previous work has shown that release from Polycomb repression only partially explains differentiation gene activation. We now show that Trithorax is also a key regulator of epidermal differentiation, not only through activation of genes repressed by Polycomb in progenitor cells, but also through activation of genes independent of regulation by Polycomb. The differentiation associated transcription factor GRHL3/GET1 recruits the ubiquitously expressed Trithorax complex to a subset of differentiation genes.
| Human epidermal keratinocyte differentiation provides a highly suitable system to understand how progenitor cells become specialized. Previous work has implicated resolution of repressive histone modifications in the activation of the terminal differentiation gene expression program. Our work shows that this mechanism only accounts for the regulation of a subset of the differentiation gene expression program and that activating histone modifications by Trithorax chromatin modifiers, acting alone or in combination with the release from repressive chromatin changes, is essential. Furthermore, we show that the Trithorax complex is recruited to a subset of differentiation gene promoters by the transcription factor Grhl3, an evolutionarily conserved regulator of the epidermal differentiation program. Altered differentiation is characteristic for several skin diseases, including skin cancer and inflammatory diseases such as psoriasis. While genetic abnormalities play a role in these diseases, the cellular and macro-environment may also alter the course of these diseases through chromatin changes (epigenetics). Understanding the epigenetic regulation of keratinocyte differentiation may in the future lead to the development of new drugs for skin diseases.
| Epigenetic control of cell fate by the opposing action of the repressive Polycomb group proteins (PcG) and the activating Trithorax group proteins (trxG) is a mechanism found throughout evolution [1]–[3]. These families of chromatin modifiers were first described as regulators of HOX gene expression in Drosophila [4], [5]. The mammalian counterparts of PcG and trxG have since been identified and their role in the regulation of multiple cellular processes, outside of HOX gene regulation, has begun to emerge [1]. Although trxG has been depicted as a de-repressor of PcG repressed genes in Drosophila, it remains unclear if in mammalian differentiation trxG mediated gene regulation is only through antagonizing PcG mediated gene repression, or if trxG can regulate gene activation independent of PcG [1].
The epidermis, the outermost skin layer, is a multi-layered epithelium containing proliferative progenitors at the base that migrate towards the surface while simultaneously undergoing differentiation to form an effective barrier which prevents dehydration and protects the organism against toxins and invasion of microorganisms [6], [7]. Epidermal differentiation involves the coordinated expression of numerous genes including those involved in protein cross-linking, lipid metabolism, and cell adhesion. As such this system represents an excellent model for dissecting the transcriptional and regulatory changes required for differentiation. Some key transcription factors regulating this process have been identified [8], including GRHL3/GET1 [9], [10], and more recently the contribution of epigenetic regulation has begun to emerge. DNA methylation [11] and histone deacetylation [12], as well as remodeling of chromatin by BRG1 [13] and Mi-2beta [14], have been described in regulating various stages of epidermal differentiation and homeostasis. Furthermore, the chromatin organizer Satb1, shown to be directly regulated by the transcription factor p63, regulates the expression of certain epidermal differentiation associated genes [15]. Epidermis-specific deletion of Ezh2, a PcG histone methyltransferase of the polycomb repressive complex 2 (PRC2) that catalyzes the methylation of Lys27 on histone 3 (H3K27), led to the premature expression of some but not all late differentiation genes [16]. Consistently, it was recently reported that epidermal deletion of the PRC2 related protein JARID2 leads to a loss of H3K27me3 at many of the Ezh2 affected epidermal differentiation genes [17]. Moreover, the PRC1 related protein Cbx4 was shown to maintain human epidermal stem cells in the proliferative state while also preventing them from senescence [18]. Complementing these findings, JMJD3, a histone demethylase that removes the repressive H3K27me3, promotes epidermal differentiation [19]. Thus, there is abundant evidence indicating that PcG-mediated H3K27 methylation maintains keratinocytes in the progenitor state and release from this repression contributes to epidermal differentiation. Yet, addition and removal of the H3K27me3 mark does not fully explain differentiation associated gene activation as many differentiation genes are not affected by interference with either Ezh2 or JMJD3 [16], [19]. We now show that the trxG components, histone H3K4 methyltransferase MLL2 and WDR5, play an important role in epidermal differentiation and, in part, act to regulate gene expression independent of PcG. Furthermore we demonstrate that a subset of epidermal differentiation genes are activated by GRHL3 mediated recruitment of trxG.
The highly conserved Grainyhead transcription factors control epidermal differentiation and barrier formation in organisms ranging from worm to human by directly or indirectly regulating the expression of key genes involved in these processes [9], [10], [20]–[22]. One Grainyhead homologue, the mouse Grhl3/Get1, is a critical regulator of the epidermal differentiation program [9], [22]. To study GRHL3 gene-regulatory mechanisms we utilized the in vitro calcium induced differentiation model of normal neonatal human epidermal keratinocytes (NHEK). In this system GRHL3 is expressed at low levels in undifferentiated cells and at higher levels when cells are induced to differentiate (Figure S1A), reminiscent of its high expression in the most differentiated layer of mouse skin. Likewise the GRHL3 target Transglutaminase 1 (TGM1), a Ca2+-dependent enzyme that functions in the formation of the cornified cell envelope by crosslinking proteins such as Involucrin and Filaggrin, is increased 3-fold upon calcium-induced differentiation (Figure 1A). The human TGM1 promoter contains a conserved GRHL3 binding site ∼800 bp upstream of the transcription start site (TSS) (Figure 1B) within a 2.5 Kb region that mediates correct temporal and spatial expression in transgenic mice [23]. To determine if TGM1 is a direct target of GRHL3 in NHEKs, we performed chromatin immunoprecipitation (ChIP) assays with a GRHL3 antibody in undifferentiated and differentiated NHEKs with three primer pairs tiling the TGM1 promoter (Figure 1B). Consistent with a differentiation-dependent increase in TGM1 expression, we observed a differentiation-dependent increase in GRHL3 occupancy at the predicted binding site in the TGM1 promoter (Figure 1C); this binding was specific as no binding was detected upstream or downstream (Figure 1C). Higher occupancy of GRHL3 on the TGM1 promoter in differentiated NHEKs correlated with increased H3K4me3, a histone modification associated with active promoters (Figure 1D, 1E). Additionally we found low levels of H3K27me3 in differentiated NHEKs compared to slightly higher levels in undifferentiated NHEKs and RT4 cells, a human bladder epithelial cell line which expresses GRHL3 but virtually no TGM1 (Figure 1A and 1D–1F, Figure S1A). RT4 cells also displayed very low levels of the H3K4me1 and H3K4me3 modifications consistent with the low level of TGM1 expression (Figure 1F). These experiments illustrate a distinct chromatin landscape in TGM1-expressing and non-expressing cell types and increased H3K4 methylation at the TGM1 promoter correlating with increased GRHL3 binding and TGM1 expression during differentiation.
To determine whether increased H3K4 methylation at the TGM1 promoter depends on GRHL3 binding, we utilized siRNAs to knock down GRHL3 in NHEK cells followed by calcium-induced differentiation for 48 hours. As expected there is a greater than 2-fold reduction in TGM1 mRNA levels upon knockdown of GRHL3 (Figure S1B). Correspondingly, we observed a strong decrease in H3K4 mono-, di-, and tri-methylation at the TGM1 promoter in GRHL3 depleted cells (Figure 2A). These results indicate that increased H3K4 methylation at the TGM1 promoter during differentiation of NHEK cells is facilitated by GRHL3.
As H3K4 methylation is mediated by the SET domain-containing trxG components, we examined the expression of these and core trxG complex members in NHEKs; all are expressed at a relatively stable level during human keratinocyte differentiation (Figure S2A). We also assessed expression of GRHL3, MLL1, MLL2, and WDR5 transcripts by qPCR in human skin (Figure S2B) and mouse skin separated into dermal and epidermal compartments (Figure S2C). Transcripts encoding all four factors are easily detected in human skin with WDR5 and MLL1 having the highest expression (Figure S2B). In mouse skin that was separated into epidermis and dermis, we found that Grhl3 and Mll2 were enriched in the epidermis while both Mll1 and Wdr5 are expressed at a similar level in the epidermis and dermis (Figure S2C). We also assessed the expression of these same proteins in human skin by immunofluorescence and observed expression of MLL1, MLL2, and WDR5 throughout the epidermis with MLL2 and WDR5 showing higher expression in the more differentiated cells and MLL1 expression being absent from the nucleus of basal cells (Figure S2D–S2H).
Upon siRNA knockdown of individual trxG members followed by differentiation, there were varying effects on the expression of TGM1. Knockdown of MLL1 and MLL2 (also known as KMT2B and ALR) caused a greater than 2 fold reduction in TGM1 expression, knockdown of MLL4 caused less of a reduction, and knockdown of ASHL1 caused a slight increase in TGM1 expression (Figure 2B, Figure S2I). TGM1 mRNA expression was also decreased upon knockdown of WDR5, a non-enzymatic core component of the methyltransferase complex, further supporting the role of trxG dependent histone methylation in TGM1 expression (Figure 2B). ChIP assays revealed that MLL2 but not MLL1 occupied the TGM1 promoter in differentiated NHEK cells (Figure 2C). MLL2 recruitment to TGM1 depends on GRHL3 as there is a significant reduction in both GRHL3 and MLL2 at the TGM1 promoter in GRHL3 depleted cells (Figure 2D). Conversely, when MLL2 was knocked down, GRHL3 recruitment to the TGM1 promoter was not significantly affected (Figure 2E). Together these findings indicate that TGM1 is a direct target of MLL2-mediated H3K4 methylation in NHEKs, and that this recruitment is GRHL3-dependent.
To better understand the broader roles of GRHL3 and MLL2 in keratinocyte differentiation we utilized microarrays to define the global differentiation gene expression program in NHEKs at various time points during differentiation; 2,583 genes are significantly differentially expressed (p<0.005, fold-change>1.25) (Figure S3A, Table S1). The most overrepresented Gene Ontology (GO) terms included, “epidermis development”, “regulation of cellular proliferation”, “regulation of cell motion”, “cornified envelope”, and “positive regulation of gene expression” (Figure S3B). K-Means clustering of these genes revealed four clear patterns (Figure 3A–3D). First, a “progenitor” cluster; genes most highly expressed in undifferentiated cells with falling expression during the time course (Figure 3A), including E2F3 and CDC6, both of which have been linked to differentiation induced cell cycle arrest in keratinocytes [24], [25]. Second, an “early” cluster; genes expressed at low levels in undifferentiated cells that were up-regulated most highly at one hour of differentiation (Figure 3B, Figure S4), including many key transcription factors like Jun and Fos, AP1 components that play an important role in epidermal differentiation [26]. Furthermore, this cluster contains KLF4 and HES1, both of which have been shown to play a role in the induction of terminal differentiation [27], [28]. Third, an “intermediate” cluster; genes up-regulated most highly at three to six hours post induction with many genes related to kinase activity, including SRF [29], and positive apoptosis regulators including SOCS3 [30] (Figure 3C, Figure S4). Fourth, a “late” cluster; genes most highly upregulated at 24 and 48 hours, at the end of the time course with an overrepresentation of genes related to epithelial differentiation, keratinization, desmosomes, and the cornified layer including KRT1, KRT10, and FLG, as well as members of the Epidermal Differentiation Complex (Figure 3D, Figure S3). This cluster also contains CDKN2A (Ink4a) a powerful inhibitor of cell cycle progression shown to play a role in inhibiting G1 to S transition in the epidermis, which is critical for epidermal terminal differentiation [31]. In summary, through gene expression profiling over a dense time course of NHEK differentiation, we are able to recapitulate key aspects of normal epidermis differentiation and classify novel genes in this process.
To further investigate the hypothesis that GRHL3 recruits MLL2 to target promoters to activate gene transcription during epidermal differentiation we studied the effect of loss of GRHL3 and MLL2 on global gene expression in differentiated NHEK cells. We found 323 and 4,281 differentially expressed genes (p<0.001) when we depleted GRHL3 and MLL2, respectively, indicating a more general role for MLL2 than GRHL3 in keratinocyte transcription (Figure S5A–S5B and Tables S2, S3). DAVID analysis of down regulated genes revealed over-represented GO terms such as “cornified envelope” and “epidermal differentiation” in the GRHL3 siRNA dataset, indicating that GRHL3 plays a similar differentiation-promoting role in human keratinocytes as in mice (Figure 3E). Down regulated genes in the MLL2 siRNA experiment were also enriched for “cornified envelope” and “epidermis development” supporting the idea that MLL2 plays a crucial role in epidermal keratinocyte differentiation (Figure 3F). Knockdown of MLL2 in undifferentiated NHEKs had no effect on proliferation, apoptosis or senescence while knockdown of the core component WDR5 lead to a slight but significant decrease in proliferation and no change in apoptosis or senescence (Figure 3G–3I). Comparing the lists of significantly down-regulated genes in each data set, we found a statistically significant overlap (Figure 3J), and using MotifMap [32], we detected a statistically significant over-representation of predicted GRHL3 binding sites in the down-regulated genes (Figure S5C), providing additional evidence that GRHL3 and MLL2 co-occupancy regulates some of these genes.
The GRHL3 regulated geneset overlapped significantly with both the early and intermediate clusters (p<0.001) from our time course study of NHEK differentiation. The intersection with the late cluster was even more significant (p<1×10−17), fitting with the idea that, as in mouse development, GRHL3 is an important regulator of terminal differentiation in human keratinocytes (Figure S5D). When the genes affected by MLL2 siRNA were overlapped with the same four clusters, the most significant enrichment was found with both the intermediate and late clusters although there was a significant overlap with all four clusters, suggesting a broader role for MLL2 in the differentiation process (Figure S5E). Further exploration of the differentially expressed genes in common between the GRHL3 and MLL2 siRNA experiments showed the strongest overlap with the late cluster of genes, indicating that these two factors converge on terminal differentiation genes (Figure 3K). These findings support our hypothesis that GRHL3 acts, in part, to regulate gene expression through collaboration with MLL2 and that MLL2 itself is a crucial regulator of epidermal differentiation.
To test whether GRHL3 and MLL2 co-occupy the promoters of epidermal differentiation genes other than TGM1, we identified a set of genes with the following criteria: 1) down regulated by siRNAs against both factors; 2) GO terms related to epidermal differentiation; and 3) containing a high scoring GRHL3 binding site within a −2 to +1 Kb region around the transcription start site. These putative GRHL3/MLL2 target genes were then tested for the presence of GRHL3, MLL2 and H3K4me3 by ChIP assays on chromatin from either scrambled or GRHL3 siRNA treated differentiated NHEKs. Consistent with our hypothesis many of the tested target genes were in fact bound by GRHL3 and MLL2 at their proximal promoters and displayed reduced H3K4me3 levels in GRHL3 siRNA treated cells, with the exception of SPRR2B which had no change in H3K4me3 (Figure 4A–4C). We also performed ChIP assays for MLL1 and SET1 occupancy and found that SPRR2B is also a target of both MLL1 and SET1 while EPHX3 is also a target of MLL1 (Figure 4D–4E). We also examined GRHL3 occupancy at these genes in cells depleted of MLL2 and found that with the exception of BLNK, GRHL3 occupancy was not significantly decreased, further supporting the hypothesis that GRHL3 contributes to MLL2 recruitment to target gene promoters and not vice versa (Figure 4F).
In order to test whether GRHL3 could directly bind trxG complex members, we performed Co-IP experiments on extracts from differentiated NHEK cells, and from 293T cells transfected with HA-GRHL3. While we could not detect GRHL3 in MLL2 immunoprecipitates in NHEK cells, we readily detected GRHL3 in extracts precipitated with WDR5, a core component of the trxG methyltransferase complex (Figure 5A). HA-GRHL3 was detected in both MLL2 and WDR5 immunoprecipitates in 293T cells, while WDR5, but not MLL1 or SET1, was readily detected in 293T extracts precipitated with HA antibody (Figure 5A, Figure S6A–S6B). Thus, GRHL3 appears to interact strongly with WDR5 and to a lesser degree with MLL2, consistent with the idea that GRHL3 can recruit trxG to target promoters.
The above results led us to perform ChIP-seq experiments to define genome-wide GRHL3 and WDR5 co-occupancy in differentiated NHEK cells, identifying 25,340 GRHL3 peaks and 48,269 WDR5 peaks (FDR<5%) (Figure S6C). For both proteins there is a statistically significant enrichment in occupancy at promoters compared to the average genomic distribution (Figure S6D). Strikingly, we found that 88 percent of genes that contained a GRHL3 peak also had an overlapping WDR5 peak, further supporting the hypothesis that GRHL3 recruits WDR5 to gene regulatory regions (Figure 5B). GO analysis of these co-occupied targets revealed enrichment for processes like “cell differentiation”, “positive regulation of gene expression”, “regulation of programmed cell death”, “cell-cell adhesion”, and “regulation of lipid biosynthetic processes”, all important components of epidermal keratinocyte differentiation (Figure 5C). Forty three percent of genes differentially expressed during keratinocyte differentiation had co-occupancy of WDR5 and GRHL3 (Figure S6E), and there was a relatively even distribution of WDR5, GRHL3, and WDR5/GRHL3 co-occupied genes in our four differentiation clusters (Figure S6F). There is also a statistically significant overlap of genes bound by GRHL3 that were downregulated upon GRHL3 depletion by siRNA, as well as between genes bound by WDR5 and downregulated upon MLL2 depletion, supporting the idea that many of these genes are direct targets of GRHL3 and MLL2, respectively (Figure 5D–5F). Together these findings indicate a role for trxG in human epidermal keratinocyte differentiation and a novel role for GRHL3 in recruiting this complex to gene promoters.
To understand the relationship between trxG and PcG in differentiation and to determine if there are genes that are regulated by trxG independent of PcG, we performed ChIP assays at one and three hours after calcium-induction to examine the dynamics of H3K4me3 and H3K27me3 at the differentiation associated genes we had identified as common targets of GRHL3 and MLL2. Interestingly, different sets of genes showed unique dynamics of these marks, one group, including the genes CRCT1, SBSN, and EPHX3 showed initially high levels of H3K27me3 followed by a drastic decrease, coupled with only a modest increase in H3K4me3 (Figure 6A, Figure S7A). A predominant mechanism of PRC recruitment, described in human ES cells, is through interactions with unmethylated CpG islands [33]–[35]. It is therefore intriguing that SBSN, whose promoter contains no CpG islands, had high levels of H3K27me3 in undifferentiated keratinocytes; there is a broad region of H3K27me3 with a peak ∼800 bp upstream from the TSS (Figure S7B). Taking advantage of the publically available ENCODE histone modification ChIP-seq data we studied globally the overlap of H3K27me3 with CpG islands and their 500 bp flanking regions. While in ES cells 43 percent of H3K27me3 regions did overlap with CpG islands, only 17 percent of H3K27me3 regions in undifferentiated NHEKs overlapped with CpG islands. We also carried out this analysis on H3K27me3 regions in Normal Human Lung Fibroblasts and found they also had a 17 percent overlap with CpG islands. This finding supports the hypothesis that in more differentiated cells types such as epidermal keratinocytes and lung fibroblasts, PRC recruitment and H3K27me3 are largely mediated by mechanisms unrelated to unmethylated CpG islands. A second group, BLNK and IVL, displayed much less change in H3K27me3, with a more prominent increase in H3K4me3 (Figure 6B, Figure S7C). These two groups are likely regulated by a combinatorial PcG/trxG mechanism. In contrast a third group of differentiation genes, including TGM1 and SCEL show nearly no H3K27me3 in progenitor or differentiated cells, but display a dramatic increase in H3K4me3 upon differentiation (Figure 6C, Figure S7D). These findings support a model where activation of differentiation genes in epidermal progenitors is either mediated through a joint, reciprocal PcG/trxG coregulation, or by trxG alone (Figure 6D).
Our study presents three major findings significant for understanding the control of epithelial cell differentiation. First we defined a role for Trithorax proteins WDR5 and MLL2 in activating the differentiation gene program in epidermal progenitor cells. Through siRNA knock down experiments, whole genome gene expression microarrays and ChIP experiments, we demonstrate that the histone methyltransferase, MLL2, activates many genes involved in different stages of epidermal progenitor cell differentiation. We also discovered, through ChIP-sequencing experiments, that a core component of the histone methyltransferase complex, WDR5, occupies promoters of a large subset of genes involved in epidermal progenitor differentiation. While previous work has demonstrated that WDR5 is involved in maintaining the ES cell state [36], our work shows that WDR5 is also important for promotion of terminal differentiation in the epidermal lineage. The most probable explanation for the diversity in WDR5 function is selective recruitment to the appropriate promoters by cell- and differentiation-specific DNA binding proteins. Thus, in ES cells trxG complexes are recruited to gene targets through direct interactions with Oct4, while in epidermal keratinocytes recruitment is through interactions with epidermal transcription factors such as GRHL3. The SET domain containing proteins in the trxG complex are the subunits that are enzymatically responsible for methyltransferase activity, therefore described as “writers”. These proteins include the MLL and SET proteins, the mammalian homologues to drosophila trx. The apparent non-redundant functions of the mammalian MLL proteins, as demonstrated by the embryonic lethality in mice upon deletion or truncation of individual family members [37]–[40], suggests that different MLL proteins may act as unique components in trxG complexes aiding in their gene specific recruitment. Identifying the specific SET domain containing MLL protein, MLL2, as having a role in promoting epidermal differentiation broadens our understanding of the role of MLL proteins in differentiation of a self-renewing tissue. We also observed MLL1 binding to some epidermal differentiation genes, suggesting that other MLLs and SET may play a role in activating the epidermal differentiation program. Recently mutations in MLL2 have been found with high incidence rate in patients with Kabuki syndrome, a syndrome with multiple congenital abnormalities and intellectual disabilities [41]–[47]. No skin abnormalities have been described in Kabuki syndrome which may be because the syndrome is caused by haploinsufficiency [41], [47] and the predicted fifty percent reduction in MLL2 expression may not be sufficient to affect epidermal differentiation in vivo.
Secondly we define a previously unknown role for the transcription factor GRHL3 in the recruitment of a trxG complex to promoters of genes, leading to increased H3K4 methylation and gene expression. The critical role for Grhl3 in epidermal differentiation was previously shown in the mouse [9], [10], and we now demonstrate a similar role in activation of the human epidermal differentiation gene expression program. Our genome wide gene expression analysis on cells depleted of either GRHL3 or MLL2 revealed 109 genes co-regulated by these two factors, most of which have an increase in expression late in epidermal progenitor cell differentiation. Furthermore, we determined that GRHL3 directly interacts with WDR5 in differentiated keratinocytes and through ChIP-sequencing experiments we established a global co-localization of these two factors, with a significant enrichment of occupancy at genes involved in processes crucial for proper epidermal progenitor differentiation. Thus, we hypothesize that GRHL3 recruits trxG to epidermal differentiation promoters through WDR5. While the expression of MLL2 and other trxG components do not change during epidermal differentiation, the expression of GRHL3 does increase, possibly explaining how trxG is directed to its differentiation associated gene targets in a differentiation dependent manner. As GRHL3 plays roles in the differentiation of other epithelia, including bladder epithelium [48], we speculate that trxG may be similarly involved in activation of other epithelial differentiation programs.
Lastly we propose models of trxG mediated regulation of differentiation that are dependent and independent of functional interactions with PcG. While previous studies demonstrated roles for the PcG complex in maintaining the progenitor state [16], and demethylation of H3K27 for de-repression of epidermal differentiation genes [19], the current work suggests that this mechanism alone cannot fully explain activation of the differentiation program. This finding is consistent with the aforementioned studies as they found that only a subset of differentiation associated genes are affected upon disruption of either Ezh2 or JMJD3 [16], [19]. Activation of differentiation genes that are suppressed by PcG in progenitor cells appears to be associated with recruitment of trxG and increased H3K4me3 at the gene promoters during the differentiation process. This mode of regulation for differentiation genes is reminiscent of the antagonistic actions of trxG and PcG in fate determination in Drosophila [49]. However, the promoters of a subset of differentiation genes, including TGM1, whose activation is associated with H3K4me3 are not marked by H3K27me3 in the progenitor state; these genes primarily rely on a trxG mediated mechanism for activation and their low expression in progenitor cells does not appear to be associated with PcG mediated H3K27me3. These findings are consistent with in vitro studies where trxG could activate transcription on a chromatin template without the presence of PcG proteins [50] and where during induced gene activation, MLL2 can associate transiently with the Myc locus [51]. However, it should be pointed out that the set of epidermal differentiation genes that we find to be independent of regulation by PcG in the NHEK differentiation model may have been regulated by PcG earlier in their lineage similar to that observed for GATA3 gene activation during T cell development [52].
In summary, our work supports a previously unappreciated role for trxG in promoting expression of the human epidermal progenitor differentiation program; reveals the role of a transcription factor GRHL3 in recruiting this complex to its gene targets; and uncovers a function for trxG mediated gene activation in differentiation that is independent of overcoming PcG mediated repression.
Neonatal Human Epidermal Keratinocytes (NHEK) were purchased from LifeLine Technologies and grown according to the manufacturer's instructions in Dermalife medium (LifeLine Tech) supplemented with Dermalife growth factors (LifeLine Tech). For ChIP-qPCR experiments, cells were grown for two days and for ChIP-seq experiments cells were grown for 24 hours in medium supplemented with a final concentration of 1.8 mM CaCl2 to induce differentiation. For timecourse experiment, cells were seeded into a 6 well plate and induced to differentiate at 50% confluency by addition of 1.8 mM CaCl2 and collected at 0,1,3,6,12,24,and 48 hours after induction. RT4 cell were maintained in McCoy's 5A medium (Gibco) supplemented with 10% FBS. 293T cells were grown in DMEM (Gibco) supplemented with 10% FBS.
For siRNA experiments, NHEK cells were subjected to reverse transfection using Lipofectamine RNAi max (Life Tech Inc.) per manufacturer's protocol. 15 hours post transfection, medium was changed to Dermalife supplemented with 1.8 mM CaCl2 and cells were allowed to grow for 48 hours. The following Silencer Select siRNAs (Ambion) were used at a final concentration of 25 nM: GRHL3 (cat#s33754) MLL1 (cat#s8819) MLL2 (cat#s15604) MLL4 (cat #s18831) AshL1 (cat #s31702) WDR5 (cat #s225470) Negative #1 (cat#4390843). For HA-GRHL3 overexpression, plasmid was transfected into 293T cells with Lipofectamine 2000 (Life Tech Inc.) per manufacture's protocol.
Proliferation assays were performed by quantifying total ATP content via the ApoSENSOR ATP Luminescence luciferase reporter assay (cat# K254-1000, Bio Vision Inc.). TUNEL assays were performed using In Situ Cell Death Detection Kit, Fluroscein (cat# 11 684 795 910, Roche). Senescence assays were performed using Senescence β-Galactosidase Staining Kit (cat# 9860, Cell Signaling Technology, Inc.).
Immunofluorescence was performed as previously described [53] using 4% PFA fixed tissue. The following antibodies were used: MLL1 (Thermo Scientific, cat#PA5-11264, 1∶100), MLL2 (Abcam, cat#AB32474, 1∶100), WDR5 (R&D Systems, cat#AF5810, 1∶100), K5 (Covance, 1∶1000), K10 (Covance, 1∶1000), p63 (Santa Cruz, sc-8431, 1∶200) AlexaFluor anti-Rabbit 488 (Invitrogen, cat#A11008, 1∶500), AlexaFluor anti-Goat (Invitrogen, cat#A11078, 1∶500) and AlexaFluor anti-mouse 594 (Invitrogen, cat#A11005, 1∶500).
Cells were collected and lysed in Trizol, followed by Chloroform extraction. RNA was extracted from the aqueous phase using Ambion PureLink RNA mini kit per manufacturer's protocol. RNA concentration and quality were quantified on a NanoDrop.
All experiments were performed with biological duplicates. Experiments were performed as previously described [54] except Affymetrix Human Gene 1.0 ST arrays (26,869 probe sets) were used and washed according to manufacturer's recommendations (Affymetrix, Santa Clara, CA). For the time course ANOVA was performed, using MeV software [55], [56] to analyze genes for differential expression. K-means clustering was performed on the differentially expressed genes as determined by ANOVA with a p<0.005 and greater than +/−1.5 fold change. siRNA microarrays were analyzed with Cyber-T [57]. The Neg siRNA was used as the control and either Grhl3 siRNA-treated or Mll2 siRNA-treated samples as experimental. Gene Ontology analysis was performed on all datasets using DAVID [58], [59]. Statistical significance of overlaps was calculated using the Fisher's exact test. The microarray data discussed in this publication have been deposited in NCBI's Gene Expression Omnibus [60] and are accessible through GEO series accession numbers GSE37570, GSE37049, and GSE38628.
ChIP assays were performed as previously described [48] with the following changes: 24 ug of sonicated chromatin was used for each IP and enrichment was calculated as a percent of input sample compared to an IgG control IP and normalized to a control genomic region (n≥3). The following antibodies were used : Grhl3 (Andersen Lab) MLL2 (AbCam cat# ab32474) MLL1 (Bethyl cat#A300-374A) SETD1A (Abcam cat# ab70378) IgG (Sigma cat#15006-10MG) H3K4me1 (AbCam cat# ab8895) H3K4me2 (AbCam cat# ab32356) H3K4me3 (Milipore cat#07-473) H3K27me3 (AbCam cat#ab6002) WDR5 (AbCam cat#ab56919)
Primer sequences available upon request.
For mRNA expression analysis cDNA was prepared using iScript cDNA kit (Biorad Laboritories) and RT-PCR was performed using SsoFast for Probes and SsoFast EvaGreen (Biorad Laboratories) master mixes in CFX384 Real-Time PCR Detection System (Biorad Laboratories). GAPDH or RPLPO were used as endogenous controls. RT-PCR was performed using the following primers or probes (n≥3): Taqman Probes: WDR5: Hs00424605_m1 TGM1: Hs00165929_m1
Krt10: Hs00166289_m1 Grhl3: Hs00297962_m1 Grhl1: Hs00227745_m1 Ppl: Hs00160312_m1
Primers: Brip1 Fwd: TTACCCGTCACAGCTTGCTAT Rv: TCCCACTAAGAGATTGTTGCCA Ets1 Fwd: AGACGGAAAAAGTCGATCTGGA Rv: TGCTTGGAGTTAATAGTGGGACA
Fos Fwd: CGGGCTTCAACGCAGACTA Rv: GGTCCGTGCAGAAGTCCTG
Jun Fwd: TGGAAACGACCTTCTATGACGA Rv: GTTGCTGGACTGGATTATCAGG
Klf4 Fwd: GCGCTGCTCCCATCTTTCT Rv: TGCTTGACGCAGTGTCTTCTC
Creb5 Fwd: CCCTGCCCAACCCTACAATG Rv: GGACCTTGCATCCCCATGAT
NHEK cells were differentiated for two days prior to cell collection. 293T cells were collected 2 days post transfection with HA-GRHL3. Cells were lysed in 1%NP-40 lysis buffer on ice for 1 hour with vortexing every 5 minutes. Protein extract was pre-blocked with protein A- agarose beads (Invitrogen) for 45 minutes at 4C with constant rotation. Protein extract was incubated with 5 ug of indicated primary antibodies overnight at 4C: IgG (Sigma), WDR5 (Abcam), MLL1 (Bethyl), MLL2 (Abcam), SETD1A (Abcam), HA (Covance), and Grhl3 (Andersen Lab). Samples were immunoprecipitated using pre-blocked protein A Dynabeads (Invitrogen) for 1 hour at 4C, followed by washing in PBS and elution in loading buffer at 100C for 10 minutes.
Protein samples were run on a 4–20% gradient gel (Invitrogen) and transferred to a PVDF membrane. The membrane was blocked in 5% milk, washed with 1xPBS-T and incubated in 1% milk with indicated antibodies: Grhl3 (Andersen Lab), MLL2 (AbCam), MLL1 (Bethyl) , SETD1A (AbCam), WDR5 (AbCam), HA (Covance) followed by incubation in secondary antibodies anti-rabbit HRP or anti-mouse HRP. Signal was detected using ECL per manufacturer's protocol (Denville).
Sequencing libraries were generated for the GRHL3, WDR5, and Input samples using the NEB Next reagents and Illumina adaptors and oligos, according to the Illumina protocol for ChIP-Seq library preparation, with some modification. After adaptor ligation, PCR amplification was performed prior to size selection of the library [61]. Clustering and 50-cycle single end sequencing were performed on the Illumina Hi-Seq 2000 Genome Analyzer. Resulting reads were aligned using Bowtie [62], and only uniquely aligning reads were retained. Peaks were called using MACS [63], and Galaxy [64]–[66] was used for further analysis.
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10.1371/journal.pgen.1002846 | The SCFDia2 Ubiquitin E3 Ligase Ubiquitylates Sir4 and Functions in Transcriptional Silencing | In budding yeast, transcriptional silencing, which is important to regulate gene expression and maintain genome integrity, requires silent information regulator (Sir) proteins. In addition, Rtt106, a histone chaperone involved in nucleosome assembly, functions in transcriptional silencing. However, how transcriptional silencing is regulated during mitotic cell division is not well understood. We show that cells lacking Dia2, a component of the SCFDia2 E3 ubiquitin ligase involved in DNA replication, display defects in silencing at the telomere and HMR locus and that the F-box and C-terminal regions of Dia2, two regions important for Dia2's ubiquitylation activity, are required for proper transcriptional silencing at these loci. In addition, we show that Sir proteins are mislocalized in dia2Δ mutant cells. Mutations in Dia2 and Rtt106 result in a synergistic loss of silencing at the HMR locus and significant elevation of Sir4 proteins at the HMR locus, suggesting that silencing defects in dia2Δ mutant cells are due, at least in part, to the altered levels of Sir4 at silent chromatin. Supporting this idea, we show that SCFDia2 ubiquitylates Sir4 in vitro and in vivo. Furthermore, Sir4 binding to silent chromatin is dynamically regulated during the cell cycle, and this regulation is lost in dia2Δ mutant cells. These results demonstrate that the SCFDia2 complex is involved in transcriptional silencing, ubiquitylates Sir4, and regulates transcriptional silencing during the cell cycle.
| Heterochromatin is important for the maintenance of genome stability and regulation of gene expression. Heterochromatin protein 1 (HP1), a protein that binds to histone H3 methylated at lysine 9 (H3K9me3) at heterochromatin loci in mammalian cells, is dynamically regulated during the cell cycle by phosphorylation of histone H3 serine 10 (H3S10ph). Compared to mammalian cells, transcriptional silencing at budding yeast silent chromatin requires silent information regulator (Sir) proteins, and H3K9me3 and H3S10ph are not present in budding yeast. Therefore, it is not known whether and how silent chromatin in budding yeast is regulated during the cell cycle. Here, we show that the SCFDia2 ubiquitin E3 ligase complex regulates transcriptional silencing. We show that SCFDia2 ubiquitylates Sir4, a structural component of yeast silent chromatin, and that Sir4 levels decrease during the cell cycle in a Dia2-dependent manner. Concomitant with the reduction of Sir4 at telomeric silent chromatin during the cell cycle, the expression of a telomere-linked gene increases. Therefore, we propose that transcriptional silencing at budding yeast silent chromatin is regulated during the cell cycle, in part by SCFDia2-mediated Sir4 ubiquitylation on chromatin.
| Chromatin structure governs a host of cellular processes, including gene expression, DNA replication, and DNA repair [1]. In higher eukaryotic cells, chromatin is classified into two major forms, euchromatin and heterochromatin, based on cytological staining. Euchromatin, the less dense and gene-rich form of chromatin, is associated with active gene transcription, whereas heterochromatin has a more compact structure and is associated with gene silencing. Though poor in genes, heterochromatin is important for development, centromere formation and the maintenance of genome integrity [2]–[4]. Therefore, it is important to understand how heterochromatin is formed and inherited during S phase of the cell cycle.
The budding yeast, Saccharomyces cerevisiae, has three heterochromatin-like loci: telomeres, the HM cryptic mating type loci (HMR and HML) and ribosomal DNA (rDNA) repeats. The initiation and maintenance of silent chromatin in budding yeast require Silent Information Regulator (Sir) proteins. While silencing at telomeres and the HM loci is regulated by many of the same factors, including Sir2, Sir3 and Sir4, only Sir2 is required for rDNA silencing. A stepwise model has been proposed for silent chromatin formation [5], [6]. For instance, at the HMR locus, Sir1 and Sir4 are recruited to the E silencer, a DNA sequence element containing binding sites for the origin-recognition complex (ORC) and transcription factors Rap1 and Abf1, through protein-protein interactions. Sir4 then recruits Sir2, a NAD+-dependent histone deacetylase, which deacetylates lysine residues on histones H3 and H4, including histone H4 lysine 16 (H4K16). This leads to the recruitment and binding of Sir3 and Sir4 to the adjacent nucleosome, as Sir3 and Sir4 bind hypoacetylated histones with higher affinity. This cycle of histone deacetylation and Sir protein binding to hypoacetylated nucleosomes leads to the spread of Sir proteins across the entire silent chromatin domain [5]–[7]. Despite the fact that protein factors and histone modifications involved in silent chromatin formation and maintenance in budding yeast are different from those in mammalian cells, this mechanism of step-wise formation of silent chromatin is likely to be conserved in higher eukaryotic cells [5], [8]. Importantly, despite advances made in understanding chromatin structure and transcriptional silencing, how silent chromatin is inherited and maintained during S phase of the cell cycle remains elusive.
During S phase of the cell cycle, nucleosomes ahead of the replication fork are disassembled to facilitate access of DNA replication machinery to DNA. Immediately following DNA replication, replicated DNA is reassembled into nucleosomes using both newly-synthesized histones and parental histones in a process called DNA replication-coupled nucleosome assembly. It is known that deposition of newly-synthesized H3–H4 requires histone H3–H4 chaperones, including CAF-1, Asf1 and Rtt106 [9]. Various studies in budding yeast indicate that these histone chaperones function in two parallel pathways in transcriptional silencing: an Asf1 dependent pathway and a CAF-1 dependent pathway [10], [11]. For instance, asf1Δ or rtt106Δ cells exhibit reduced silencing at both telomeres and the HM loci when combined with mutations in Cac1, the large subunit of CAF-1 [10]–[13]. In addition, Sir proteins are mislocalized in cells lacking both Rtt106 and CAF-1 [11]. On the other hand, rtt106Δ asf1Δ double mutant cells do not exhibit synergistic silencing defects, suggesting that Rtt106 and Asf1 function in the same genetic pathway in transcriptional silencing [13]. These results support the idea that nucleosome assembly factors are important for proper formation and inheritance of silent chromatin structure in budding yeast [6].
Dia2 is an F-box containing protein that serves as the substrate recognition component of a SCF (Skp1/Cullin/F-box protein) ubiquitin E3 ligase. Cells lacking Dia2 exhibit gross chromosomal rearrangements and sensitivity to cytotoxic agents, indicative of a role for Dia2 in maintaining genome integrity [14]–[16]. Furthermore, Dia2 has been shown to be important during DNA replication [15]–[17]. The F-box domain in each of 11 known F-box containing proteins in budding yeast interacts with the SCF component Skp1, enabling interactions between the ubiquitylation machinery and substrate [18]. In addition to the F-box domain, Dia2 has two additional important domains: a tetratricopeptide repeat (TPR) domain at the N-terminus involved in mediating Dia2's interaction with replisome components [14], [17] and a leucine rich repeat (LRR) region at the C-terminus. The LRR domain in other F-box containing proteins is known to be involved in substrate binding [19].
In a genetic screen designed to identify genes that function in parallel to RTT106 in silencing at the HMR locus, we discovered that DIA2, when deleted, enhances rtt106Δ silencing defects at the HMR locus. Structure-function studies revealed that the Dia2 F-box and LRR regions are important for transcriptional silencing. Furthermore, both Sir3 and Sir4 are mislocalized in dia2Δ cells, and Sir4 binding to the HMR locus is significantly elevated in dia2Δ rtt106Δ mutant cells. In addition, we show that Sir4 is ubiquitylated in yeast cells in a Dia2-dependent manner and that Sir4 levels on chromatin are cell cycle regulated, and this regulation is lost in dia2Δ mutant cells. Therefore, we suggest that the SCFDia2 E3 ligase functions in transcriptional silencing, in part through the regulation of Sir4 ubiquitylation.
We identified RTT106 in a screen for genes that function in parallel with PCNA in silencing at the HMR silent mating type locus [13]. Using a similar approach, we set out to identify genes that functioned in parallel with RTT106 in transcriptional silencing. Briefly, we used the synthetic genetic array (SGA) approach [20], [21] to combine the rtt106Δ single mutant containing the HMR::GFP reporter gene with each of ∼4700 yeast deletion mutants. The HMR::GFP reporter contains the green fluorescent protein (GFP) integrated at the HMR silent mating type locus within the a1 gene; thus, GFP is silenced. Once double mutants containing the HMR::GFP reporter gene were selected, flow cytometry was used to identify those genes from the collection of mutants that when combined with rtt106Δ, resulted in a significant elevation in the percentage of GFP expressing cells.
We identified five genes (CAC1, CAC2, SIR1, ARD1, and DIA2) that enhanced the silencing defects of rtt106Δ cells. Cac1 and Cac2 are two subunits of the histone chaperone CAF-1, and Sir1 is necessary for initiation of silent chromatin formation at the HM loci [22], [23]. Deletion of SIR1, CAC1, or CAC2 is known to enhance the silencing defects of rtt106Δ mutant cells [11], [13]. In addition, ARD1 is predicted to have a role in transcriptional silencing [24], [25]. These results affirm that our screen was effective for identifying factors that enhance silencing defects of rtt106Δ cells.
Dia2 is the F-box containing protein of the SCFDia2 ubiquitin E3 ligase involved in DNA replication, and it may also have a role in transcriptional regulation [16], [26]. Therefore, we decided to focus our studies on Dia2. To confirm our results, we deleted DIA2 from our standard genetic background (W303) and assessed transcriptional silencing at the HMR locus using the HMR::GFP reporter. Cells defective for transcriptional silencing at the HMR locus express GFP and exhibit a rightward shift in the flow cytometry profile as observed for sir3Δ cells (Figure 1A). Mutating DIA2 in rtt106Δ cells resulted in a rightward shift compared to single mutant cells (Figure 1A), indicating an increase in the percentage of GFP expressing cells, which was quantified in Figure 1B. Moreover, dia2Δ single mutant cells exhibited an elevated percentage of cells expressing GFP compared to wild-type cells, suggesting a role for Dia2 in HMR silencing. To validate the flow cytometry analysis, the percentage of cells expressing GFP was also determined using fluorescence microscopy. Among the strains tested, a similar trend was observed using both methods of determining the percentage of cells expressing GFP (Figure S1). To confirm the effect of dia2Δ on HMR silencing, we determined how the loss of Dia2 affected the expression of the silenced a1 gene at the HMR locus. RNA was collected from single and double mutant cells of the alpha mating type, reverse transcribed, and cDNA analyzed using real-time PCR. Upon normalizing the expression of a1 to ACT1, dia2Δ cells exhibited elevated a1 gene expression compared to wild-type cells (Figure 1C). Furthermore, dia2Δ rtt106Δ had an even greater a1 gene expression level compared to either the dia2Δ or rtt106Δ single mutant. These results are consistent with the idea that DIA2 and RTT106 function in parallel to regulate transcriptional silencing at the HMR locus.
Transcriptional silencing at telomeres and the HMR locus utilize similar mechanisms [6]. Moreover, it has been shown that Pof3, the homolog of Dia2 in S. pombe, is required for maintaining telomere length and telomeric silencing [27]. We, therefore, determined whether Dia2 in S. cerevisiae had a role in telomeric silencing using cells containing the reporter gene, URA3, integrated at the left arm of telomere VII. When plated on media containing 5-fluoroorotic acid (FOA) that is toxic to cells that express URA3, wild-type cells survive because of telomeric silencing of the URA3 gene. Cells with telomeric silencing defects (such as sir3Δ, used as a control) exhibit growth defects in media containing FOA [24]. We found that dia2Δ cells exhibited defects in telomeric silencing compared to wild-type cells (Figure 1D). We confirmed these observed telomeric silencing defects using RT-PCR to assess the expression of YFR057W, a gene found at the telomere on the right arm of chromosome VI and known to be silenced via a Sir-mediated mechanism [28]. Compared to wild-type and rtt106Δ cells, dia2Δ cells exhibited higher YFR057W gene expression, suggesting a telomeric silencing defect (Figure 1E). Interestingly, deletion of RTT106 in dia2Δ mutant cells did not significantly increase expression of YFR057W, suggesting that deletion of RTT106 does not exacerbate the telomeric silencing defect of dia2Δ cells. These results demonstrate that Dia2 is required for efficient silencing at both the HMR locus and telomeres and suggest that Dia2 functions in a pathway parallel to Rtt106 in transcriptional silencing at the HMR locus, but not at telomeres.
In addition to RTT106, mutations in ASF1, HIR1 and CAF-1 also result in silencing defects [12], [13]. We, therefore, tested how loss of DIA2 affected silencing at the HMR locus in the absence of each of these H3–H4 histone chaperones. Each double mutant, dia2Δ asf1Δ, dia2Δ cac1Δ or dia2Δ hir1Δ, had a higher percentage of cells expressing GFP compared to the respective single mutants (Figure 2A). These results suggest that Dia2 impacts transcriptional silencing at the HMR locus in a pathway parallel to each of the known H3–H4 histone chaperones.
In addition to defects in transcriptional silencing, histone chaperone mutants are sensitive to DNA damaging agents [10], [29], [30]. Moreover, dia2Δ cells also exhibit sensitivity to a number of DNA damaging agents [15], [16]. To determine whether the parallel action observed for DIA2 and H3–H4 histone chaperones extended beyond their function in transcriptional silencing, we tested the growth and DNA damage sensitivity of dia2Δ cells containing a mutation at each H3–H4 histone chaperone. The DNA damaging agents camptothecin (CPT) and methyl methanesulfonate (MMS) were used to assess genetic interactions in response to DNA damage. Synthetic interactions in growth and DNA damage sensitivity were observed for dia2Δ with each of the histone chaperone mutants tested (Figure 2B, 2C and Figure S2). The most dramatic effects were observed in dia2Δ asf1Δ and dia2Δ cac1Δ rtt106Δ mutants, with the interaction between DIA2 and ASF1 being the most pronounced. Furthermore, dia2Δ cac1Δ rtt106Δ cells grew slower on regular growth media (YPD) than dia2Δ and cac1Δ rtt106Δ cells and showed an increased sensitivity to CPT over dia2Δ, cac1Δ rtt106Δ and dia2Δ rtt106Δ cells. Taken together, these genetic interactions provide support for a role for Dia2 and the SCFDia2 complex in processes linked to nucleosome assembly.
Post-translational modifications on newly synthesized histones work in concert with histone chaperones to regulate nucleosome assembly [9]. For instance, histone H3 lysine 56 acetylation (H3K56Ac), catalyzed by the histone acetyltransferase Rtt109, is important for regulating the interaction between histones and the histone chaperones CAF-1 and Rtt106, and thus, H3K56Ac is a critical regulator of histone deposition [29], [31]. H3 N-terminal tail acetylation, catalyzed by Gcn5 and Rtt109, also serves as an important regulator of nucleosome assembly [32]. Finally, acetylation of histone H4 lysines 5, 8 and 12 (H4K5,8,12Ac), catalyzed by Hat1 and Elp3, has also been implicated in nucleosome assembly [33], [34]. Given the observed genetic interactions between dia2Δ and the H3–H4 histone chaperones involved in nucleosome assembly, we, therefore, determined how mutations in these important histone lysine residues affected the growth and CPT sensitivity of dia2Δ cells. For each histone mutant, the acetylated lysine residues were mutated to arginine to mimic the unacetylated state. We observed that dia2Δ cells showed significant growth defects and increased sensitivity towards CPT when combined with H3K56R, H4K5,12R and H3K9,14,18,23,27R (Table 1). Notably, we were unable to construct the dia2Δ H4K5,8,12R mutant via plasmid shuffling, suggesting that dia2Δ exhibited a synthetic lethal interaction with mutations at H4 lysines 5, 8 and 12 (Table 1 and Figure 2D). To confirm this result, wild-type and dia2Δ cells expressing wild-type histones H3–H4 from a uracil (URA3) containing plasmid were transformed with either wild-type or mutant forms of H3-H4K5, 8, 12 on a plasmid with a histidine (HIS) selection marker. The wild-type H3–H4 histone plasmid (URA) could not be lost in dia2Δ H4K5,8,12R cells (as no growth was observed when these cells were plated on FOA medium), whereas this plasmid was readily lost in dia2Δ or dia2Δ H4K8R cells (Figure 2D). This suggests that Dia2 functions in parallel with acetylation of H4 lysine residues 5, 8 and12 in maintaining cell viability. Together, these genetic analyses provide further evidence supporting the idea that the SCFDia2 E3 ligase has a role in a process linked to nucleosome assembly.
Dia2 contains three primary functional domains: an N-terminal tetratricopeptide repeat (TPR) region, an F-box domain and a C-terminal leucine rich repeat (LRR) domain (Figure 3A). The TPR region is important for Dia2's localization to the replisome [14], [17]. The F-box domain is required for Dia2 ubiquitylation activity, as the F-box, in general, facilitates interactions with other SCFDia2 components [15], [16]. The LRR region is proposed to be an interaction motif for substrates of the SCFDia2 complex [17]. To gain further insight into Dia2's role in transcriptional silencing, we deleted each of the three domains of Dia2, expressed these dia2 mutants in dia2Δ cells and analyzed transcriptional silencing at the silent HMR locus and the telomere. Expression of Dia2 in dia2Δ cells restored the silencing at the HMR locus, as the percentage of cells expressing GFP was similar to wild-type cells (Figure 3B). Expression of the Dia2-TPRΔ domain mutant also restored HMR silencing. Interestingly, deletion of the Dia2 F-box (F-boxΔ) or LRR domain (LRRΔ) did not rescue the HMR silencing defect. These results suggest that the Dia2 F-box and LRR domains are indispensable for silencing at the HMR locus.
Similar to the effects observed at the HMR locus, the expression of full-length Dia2 in dia2Δ cells containing the URA3 reporter at telomere VIIL rescued the telomere silencing defects of dia2Δ cells to that of wild-type cells, and expression of Dia2 lacking the TPR region (TPRΔ) resulted in an almost complete rescue of dia2Δ telomere silencing defects (Figure 3C). In contrast, expression of Dia2 lacking the F-box (F-boxΔ) or the LRR region (LRRΔ) was unable to rescue the telomere silencing defects of dia2Δ cells.
While the expression of the TPRΔ and F-boxΔ mutants was similar to that of full length Dia2 in dia2Δ cells (Figure S3), expression of the Dia2 LRRΔ mutant was much less than the other mutants (data not shown), most likely due to instability of the shortened form of the protein. Therefore, we made five additional Dia2 mutants, each with deletion of approximately 75 amino acids of the LRR domain: LRR (amino acids 347–424)Δ, 425–502Δ, 503–580Δ, 581–658Δ, and 653–737Δ. Expression of these five mutants was, for the most part, similar to full length Dia2 and other Dia2 mutant forms (Figure S3). Importantly, dia2Δ cells expressing each of the LRR mutants exhibited defects in HMR (Figure 3D) and telomere silencing (Figure 3E) similar to dia2Δ cells transformed with empty vector. Together, these results suggest that silencing defects displayed in cells expressing dia2 mutants lacking the LRR or F-box are unlikely due to reduced expression of these mutant proteins. Instead, the F-box domain, essential for Dia2's role in protein ubiquitylation, and LRR region, predicted to be important for substrate recognition, are indispensable for SCFDia2's role in transcriptional silencing, suggesting that SCFDia2's role in silencing is likely mediated through its ability to ubiquitylate a substrate involved in transcriptional silencing.
Sir proteins serve as structural components of yeast silent chromatin [6]. Sir3 and Sir4 form four to five foci at the nuclear periphery, which reflects the clustering of yeast telomeres [35]–[37]. Thus, we tested whether loss of Dia2 affected the localization of Sir3 and Sir4, and therefore, silent chromatin structure, using fluorescence microscopy. As reported, wild-type cells expressing Sir3-GFP or GFP-Sir4 formed foci at the nuclear periphery [11], [37]. While some dia2Δ cells had Sir3-GFP or Sir4-GFP foci patterns similar to wild-type cells (Figure 4A, dia2Δ, panel 1), a considerable percentage of dia2Δ mutant cells lost proper localization of Sir3 (Figure 4B) and Sir4 (Figure 4C) in dia2Δ or dia2Δ rtt106Δ cells. In the case of Sir3-GFP foci, some mutant cells had large areas of fluorescence without distinct foci (Figure 4A, dia2Δ panels 2 and 3). A commonly observed Sir4 pattern was multiple small foci (>8) that were relatively non-distinct (Figure 4A, dia2Δ panel 2), in addition to cells containing areas of fluorescence with no distinct foci (Figure 4A, dia2Δ, panel 3). Because the dia2Δ mutation did not affect the protein levels of Sir3 and Sir4 to a significant degree (Figure S4), the mis-localization of Sir3 and Sir4 observed in dia2Δ mutant cells is likely due to changes in telomeric chromatin structure.
To determine whether the localization of Sir3-GFP and Sir4-GFP was dependent upon particular Dia2 domains, we expressed the Dia2 mutants lacking specific domains (see Figure 3) in dia2Δ cells. Expression of full-length Dia2 or Dia2 TPRΔ restored the percentage of cells containing wild-type Sir3-GFP or Sir4-GFP foci closer to the percentage observed for wild-type cells, whereas cells expressing dia2 mutants lacking the F-box (F-boxΔ) or a portion of the LRR domain [LRR(425–502Δ) and LRR(581–658Δ)] did not (Figure 4D–4E and data not shown). Thus, the F-box and LRR regions are important for proper localization of Sir3 and Sir4, consistent with the idea that Dia2's role in transcriptional silencing is dependent upon its ubiquitylation activity.
To further analyze how the dia2Δ mutation affects the binding of Sir proteins to chromatin, chromatin immunoprecipitation (ChIP) assays were performed in unsynchronized wild-type, dia2Δ, dia2Δ rtt106Δ and control cells (sir3Δ) using antibodies against Sir4, Sir2, and Sir3. ChIP DNA was analyzed using real-time PCR with primers amplifying different positions at the HMR locus or the telomere region on the right arm of chromosome VI (Tel-VIR). Consistent with published results, Sir protein binding at silent chromatin loci was lost in sir3Δ cells, and more Sir proteins bound to silent chromatin than the corresponding active chromatin loci tested at both the HMR locus and telomere (Figure 5 and Figure S5) [11]. Compared to wild-type cells, dia2Δ cells exhibited a slight elevation in Sir4 binding at HMR silent chromatin (a1 gene, HMR silent) (Figure 5A), whereas dia2Δ rtt106Δ cells had a much larger and significant increase in Sir4 binding at the silent HMR locus than wild-type and dia2Δ cells (Figure 5A). Similarly, we also observed significantly more Sir2 binding at the HMR locus in dia2Δ rtt106Δ mutant cells compared to wild-type and dia2Δ mutant cells (Figure 5B). In contrast, Sir3 binding to the silent HMR locus was not altered to a significant degree in either dia2Δ or dia2Δ rtt106Δ cells when chIP was performed using an antibody against endogenous Sir3 (Figure S5A) or performed using IgG beads in strains in which Sir3 was tagged with a tandem affinity purification (TAP) tag (Sir3-TAP)(Figure 5C). Notably, the dia2Δ or dia2Δ rtt106Δ mutation did not affect mRNA levels of Sir4 or Sir2 (Figure S6), suggesting that the increase in Sir4 and Sir2 proteins at the HMR locus is not likely due to increased gene transcription in mutant cells. In addition, the overall protein levels of Sir3 and Sir4 were not altered to a detectable degree in cell extracts (Figure S4). Therefore, the observed elevation in Sir4 and Sir2 at the silent HMR locus in dia2Δ cells is not likely due to increased steady-state levels of Sir proteins and is likely due to elevated Sir2 and Sir4 levels on chromatin.
Sir protein binding was also assessed at telomeric silent chromatin. No significant change in Sir4 binding was detected at telomeric silent chromatin (Tel-VIR, silent) in dia2Δ cells compared to wild-type cells (Figure 5D). Sir4 binding to the telomeric silent chromatin in dia2Δ rtt106Δ cells was not increased significantly compared to wild-type (p-value for three independent experiments is 0.1). Thus, levels of Sir4 proteins are unlikely altered at telomeres in dia2Δ rtt106Δ mutant cells compared to wild-type cells. These results are consistent with the result in Figure 1 showing that deletion of RTT106 synergistically increases the silencing defect of dia2Δ cells at the HMR locus, but not the telomere. Sir2 (Figure 5E) and Sir3 (Figure S5B and S5C) binding to telomeric silent chromatin was not altered to a detectable degree in either dia2Δ or dia2Δ rtt106Δ cells compared to wild-type cells. Taken together, it is likely that mutations in Dia2 alter the chromatin binding of Sir4 and Sir2, but not Sir3, in dia2Δ rtt106Δ cells, and these imbalanced alterations in Sir protein binding to silent chromatin may contribute to the silencing defects observed for dia2Δ and dia2Δ rtt106Δ cells, especially at the silent HMR locus.
Overexpression of Sir4 is known to result in silencing defects, most likely due to disruptions in Sir protein stoichiometry [38], [39]. To further test whether the effect of dia2Δ on silencing is due to altered Sir4 protein levels on chromatin, we investigated the effects of exogenous expression of Sir4 on telomeric silencing, as transcriptional silencing at telomeres is perturbed more easily than that at the HMR locus [40]. Wild-type and dia2Δ cells were transformed with a centromere plasmid for expression of Sir4 under the control of its own promoter. RNA was then extracted for quantitative RT-PCR analysis of YFR057W expression. While ectopic expression of Sir4 in wild-type cells did not result in obvious changes in expression of YFR057W, Sir4 expression in dia2Δ cells resulted in over a two-fold increase in YFR057W expression compared to dia2Δ cells transformed with empty vector (Figure 5F). This suggests that telomeric silencing in dia2Δ cells is more sensitive to changes in Sir4 levels than that of wild-type cells. These results are consistent with the idea that altered Sir4 levels at chromatin contribute to the silencing defects observed in dia2Δ cells.
Since dia2Δ rtt106Δ cells exhibited elevated Sir4 binding at silent chromatin, we hypothesized that SCFDia2 may ubiquitylate Sir4. To test this idea, we first determined whether cell extracts prepared from cells expressing MYC-Dia2 or MYC-Dia2 F-boxΔ integrated at the endogenous DIA2 locus (Figure 6A, left panel) could ubiquitylate Sir4 purified from dia2Δ cells using TAP purification. Briefly, Flag–ubiquitin (Flag-Ub), E1, E2 (Cdc34) and the respective whole cell extract (E3) were incubated with Sir4-CBP (calmodulin binding peptide, part of the TAP tag). As negative controls, reaction mixtures containing all components except E1, E3 (cell extract) or substrate were assembled. Following ubiquitylation, Sir4 was pulled down using calmodulin beads, and ubiquitylated species were detected via Western blot using antibodies against the Flag epitope. More Sir4-associated ubiquitylated species were detected in the reactions using the extracts from full length Dia2 than from those using extracts from cells expressing Dia2 lacking the F-box domain (Figure 6A, right panel). No ubiquitylated species were detected in control reactions lacking E1, E3 or Sir4 (substrate). Thus, SCFDia2 ubiquitylates Sir4 and/or a Sir4-associated protein in vitro.
To test this idea further, the SCFDia2 complex was purified from yeast cells expressing MYC-Dia2 and MYC-Dia2 F-boxΔ and used to ubiquitylate Sir4 purified from yeast cells as described in Figure 6A. More ubiquitylated species were detected in reactions containing purified SCFDia2 (Dia2) than those containing the complex purified using the Dia2 F-box mutant (F-boxΔ) (Figure 6B). Taken together, these experiments indicate that SCFDia2 ubiquitylates Sir4, or a protein associated with Sir4, in vitro, and this ubiquitylation depends on the F-box domain of Dia2.
To provide additional evidence that SCFDia2 ubiquitylates Sir4, we tested whether Sir4 bound to SCFDia2 in vitro. Beads containing the MYC-Dia2 complex purified for in vitro ubiquitylation reactions in Figure 6B or control beads were incubated with two different amounts of in vitro translated 35S-methionine labeled Sir4. After the beads were washed, bound 35S-Sir4 was detected via autoradiography, and the presence of the MYC-Dia2 was detected via Western blot using antibodies against the Myc epitope. Compared to control reactions, more 35S-Sir4 signal was detected in samples containing the MYC-Dia2 complex (Figure 6C, compare lanes 3–4 to lanes 1–2 of lower panel) despite the fact that more MYC antibodies (IgG) could be detected by CBB staining in control samples (Figure 5C, middle panel) than samples containing Myc-Dia2 (Figure 5C, upper panel) These data further support the idea that Sir4 is a substrate of the SCFDia2 complex.
Next, we asked whether Sir4 is ubiquitylated in vivo and whether this ubiquitylation depends on Dia2 using our published procedures [41]. Briefly, Sir4-TAP was purified from wild type or dia2Δ cells transformed with a plasmid expressing HA-tagged ubiquitin (HA-Ub). Following TAP purification, ubiquitylated species were detected by Western blot with antibodies against the HA epitope. In wild-type (Sir4-TAP) cells, ubiquitylated protein species co-purified with Sir4, and these immunoprecipitated species were specific as no HA signal could be detected in controls (a strain without Sir4-TAP but containing HA-Ub or a strain with Sir4-TAP but no HA-Ub)(Figure 6D, lanes 1 and 2, respectively). Importantly, these ubiquitylated species were notably reduced in dia2Δ cells compared to wild-type cells, despite equal levels of purified Sir4 (Figure 6D, compare lane 4 to lane 3). These results suggest that the SCFDia2 complex ubiquitylates Sir4 in vivo. Interestingly, we observed similar amounts of Sir2 co-purified with Sir4 in wild-type cells and dia2Δ cells, suggesting that the Sir2-Sir4 interaction is not affected to a detectable degree in dia2Δ mutant cells (Figure S7).
To further confirm that Sir4 is ubiquitylated in vivo, we purified ubiquitylated species using a two-step purification procedure. First, Sir4-TAP was purified from wild-type and dia2Δ mutant cells expressing either HA-tagged ubiquitin or His-HA tagged ubiquitin (Figure 6E). The associated ubiquitylated species were then purified under denaturing conditions using Ni-NTA beads that bound to His-ubiquitin. A band with a similar size to Sir4 was detected from wild-type cells expressing His-HA ubiquitin, but not from dia2Δ cells expressing His-HA tagged ubiquitin. In addition, little signal was detected from cells expressing HA-ubiquitin (lanes 1 and 2), suggesting that co-purification of Sir4 with ubiquitin under denatured conditions was specific (Figure 6E). Together, these data strongly support the conclusion that Sir4 is a substrate for SCFDia2.
It has been shown that heterochromatin is dynamically regulated during S phase of the cell cycle in mammalian cells. During mitosis, phosphorylation of histone H3 serine 10 by Aurora B kinase displaces HP1 from heterochromatin [42], [43]. In S. pombe, this dynamic loss of HP1 protein from heterochromatin is proposed to facilitate transcription of siRNA required for the re-establishment of heterochromatin during S phase [44]. Therefore, we asked whether Sir4 proteins at chromatin are also dynamically regulated during mitotic cell division and whether this regulation depends on Dia2. Briefly, wild-type and dia2Δ mutant cells were arrested at G1 using α-factor and then released into the cell cycle. Cells were collected at 0, 30 and 60 minutes following release for analysis of DNA content by flow cytometry (Figure 7B), for ChIP assays using Sir4 antibodies (Figure 7C) and for analysis of telomere gene expression by RT-PCR (Figure 7D). Flow cytometry analysis indicated that both wild-type and dia2Δ mutant cells collected at 0, 30 and 60 minutes were predominantly at G1, S and G2/M phase of the cell cycle, respectively (Figure 7B). In wild-type cells, Sir4 binding at telomeric silent chromatin was significantly reduced as the cells proceeded from G1 to S and G2/M phases (Figure 7C). In contrast, Sir4 binding was not altered at telomeric silent chromatin in dia2Δ cells as the cells progressed through the cell cycle.
To determine whether the observed changes in Sir4 binding affect silencing at telomeres, RT-PCR was performed to assess the expression of the telomere gene, YFR057W, as the cells progressed through the cell cycle. Consistent with Figure 1E, dia2Δ cells exhibited higher expression of YFR057W at all time points compared to wild-type cells (data not shown). When normalized against the expression of YFR057W at G1, we observed that there was an increase in gene expression of the telomere gene, YFR057W as cells progressed from G1 through to G2/M phase in wild-type cells (Figure 7D). Interestingly, only minor fluctuations in YFR057W expression were observed in dia2Δ cells. Together, these results suggest that Sir4 proteins at silent chromatin in budding yeast are regulated during the cell cycle, and this regulation is dependent on Dia2. Given our observation of SCFDia2's role in Sir4 ubiquitylation, we suggest that Sir4 ubiquitylation, mediated by SCFDia2, regulates the binding of Sir4 at silent chromatin during the cell cycle.
In a yeast genetic screen for genes that function in parallel with RTT106 in transcriptional silencing, we found that deletion of DIA2, which encodes the F-box protein of the SCFDia2 E3 ubiquitin ligase complex with a role in DNA replication, enhances silencing defects of rtt106Δ mutant cells. Epistasis analysis suggests that DIA2 functions in parallel with CAF-1, Hir1 and Asf1, three other H3–H4 histone chaperones, in transcriptional silencing at the HMR locus, growth and the response to DNA damaging agents. In addition, localization of Sir proteins to silent chromatin loci is altered in dia2Δ mutant cells, and this alteration depends on Dia2's role in protein ubiquitylation. Furthermore, we show that SCFDia2 ubiquitylates Sir4 in vitro and in vivo and that Sir4 levels at silent chromatin are dynamically regulated during the cell cycle. Remarkably, this dynamic regulation is compromised in dia2Δ mutant cells. These results reveal a novel role for SCFDia2 in transcriptional silencing and suggest that SCFDia2 functions in silencing in part by ubiquitylating Sir4, which could serve as a mechanism to regulate Sir4 chromatin binding during the cell cycle.
The SCFDia2 complex is known to have a role in DNA replication [16]. However, how SCFDia2 functions in DNA replication is not clear. Our genetic analysis suggests that SCFDia2 may function in DNA replication-coupled nucleosome assembly. First, we show that the dia2Δ mutation exhibits synthetic defects in growth and sensitivity towards DNA damaging agents when combined with mutations in CAF-1, Asf1 and Rtt106, histone H3–H4 chaperones known to be involved in replication-coupled nucleosome assembly. Second, we show that the dia2Δ mutation exhibits synthetic defects with mutations at lysine residues of histones H3 and H4 known to be involved in the regulation of replication-coupled nucleosome assembly. Surprisingly, the dia2Δ mutation is synthetic lethal with mutations at H4 lysine residues 5, 8 and 12. Acetylation of these lysine residues occurs on newly synthesized H4 and is conserved from yeast to human cells [9]. Interestingly, DIA2 shares many genetic interactions with RTT101 [45], another ubiquitin ligase functioning in DNA replication [41], [46]. Genetic analysis using the epistatic miniarray profile (E-MAP) approach indicates that Rtt101 functions in the same genetic pathway as Rtt109, the histone H3 lysine 56 acetyltransferase known to be involved in DNA replication-coupled nucleosome assembly [29], [31], [47]. These results, combined with ours presented here, suggest that the SCFDia2 ubiquitin E3 ligase may function in nucleosome assembly. Further investigation is needed to determine Dia2's exact role in this process.
Using RT-PCR and reporter genes integrated at the HMR locus and telomeres, we show that Dia2 is needed for efficient transcriptional silencing at both the HMR locus and telomeres. Cells with defects in nucleosome assembly are known to affect transcriptional silencing [11], [12], [48]. Because genetic studies presented here suggest that Dia2 has a role in nucleosome assembly, it is possible that Dia2 impacts silencing through its role in nucleosome assembly. However, we have presented several lines of evidence supporting the idea that SCFDia2 impacts transcriptional silencing, at least partly through ubiquitylation of Sir4. First, we show that the role of SCFDia2 in transcriptional silencing depends on the Dia2 functional domains (the F-box and LRR) involved in protein ubiquitylation. Second, SCFDia2 ubiquitylates Sir4 in vitro and in vivo and interacts with Sir4 in vitro. Third, in wild-type cells, the levels of Sir4 at the silent telomere locus are reduced when cells proceed from G1 to S and G2/M phase of the cell cycle, and this regulation is not observed in dia2Δ mutant cells. Importantly, the reduction of Sir4 at telomere silent chromatin correlates with increased transcription of YFR057W, a gene located near a telomere, during the cell cycle. Thus, we suggest that SCFDia2 functions in transcriptional silencing in budding yeast, at least partly through ubiquitylation of Sir4.
It is not unprecedented for an E3 ubiquitin ligase to be involved in transcriptional silencing. In fact, several ubiquitin ligases and hydrolases have been shown to have roles in transcriptional silencing in budding yeast and other organisms. In S. pombe, the Rik1-Cul4 E3 ligase complex is important for the recruitment of the Clr4 histone methyltransferase for RNAi-mediated heterochromatin formation. Deletion of the Rik1-Cul4 complex results in silencing defects [49]–[51]. In budding yeast, Rtt101, a cullin protein of a ubiquitin E3 ligase complex, is important for telomeric silencing [52]. Rad6, involved in ubiquitylation of H2B, also impacts transcriptional silencing in budding yeast [53], [54]. Finally, Sir4 is known to interact with two ubiquitin hydrolases, Ubp3 and Dot4/Ubp10 [55], [56]. Ubp10 is proposed to regulate transcriptional silencing by deubiquitylating H2B [57]. How Ubp3 is involved in silencing is still unknown. Interestingly, loss of Upb3 results in improved silencing at telomeres and the HM loci (HML) [55], the opposite effect as that of loss of Dia2.
How does Sir4 ubiquitylation affect transcriptional silencing? Protein ubiquitylation, in general, is known to mediate two distinct functions. First, protein ubiquitylation marks proteins for degradation by the 26S proteasome. Second, protein ubiquitylation can also regulate protein-protein interactions [58], [59]. Interestingly, we did not detect significant changes in the steady-state levels of Sir4 in dia2Δ cells, suggesting that Sir4 ubiquitylation by SCFDia2 is not likely involved in regulating the steady-state level of Sir4. However, we did observe a significant increase in Sir4 proteins at the HMR silent locus in dia2Δ rtt106Δ double mutant cells. It has been reported that β-catenin levels on chromatin, but not steady state levels, are regulated by ubiquitylation through a protein complex containing the histone acetyltransferase component TRRAP and Skp1 [60]. Therefore, it is possible that a SCF ubiquitin ligase can regulate protein levels on chromatin. We have shown previously that Rtt106 binds Sir4 [11]. While the functional significance of the Rtt106-Sir4 interaction is not clear, it is possible that this interaction regulates Sir4 binding to silent chromatin. This could explain why loss of Rtt106 leads to aberrant accumulation of Sir4 at silent chromatin in dia2Δ rtt106Δ mutant cells. Therefore, we suggest that Sir4 ubiquitylation by SCFDia2 regulates Sir4 levels at chromatin, which in turn regulates silencing. Supporting this idea, we show that elevation of Sir4 levels using a centromere plasmid, while having no apparent effect on telomeric silencing in wild-type cells, reduces telomeric silencing in dia2Δ mutant cells.
It has been observed that heterochromatin proteins such as HP1 are dynamically regulated during mitotic cell division. For instance, in human cells, phosphorylation of serine 10 of histone H3 (H3S10ph) during mitosis reduces the binding affinity of HP1 towards H3K9me3, which propels the dissociation of HP1 from heterochromatin [42]. In S. pombe, dissociation of the sequence homolog of HP1, Swi6, from heterochromatin via H3S10ph results in transcription of siRNA during S phase. This, in turn, helps to maintain heterochromatin during S phase of the cell cycle [44]. These results highlight the fact that heterochromatin in S. pombe and mammalian cells is dynamically regulated during mitotic cell division. It was previously unknown whether silent chromatin in budding yeast was also regulated during S phase of the cell cycle. We observed that Sir4 binding to telomeric silent chromatin was significantly reduced as cells progressed from G1 to S and G2/M phase of the cell cycle. Concomitant with the reduction of Sir4 binding, the transcription of the telomere gene, YFR057W, increased when cells entered S phase. These results demonstrate that budding yeast silent chromatin is also dynamically regulated during S phase of the cell cycle. Because HP1 and histone modifications equivalent to H3K9me3 and H3S10ph are not present in budding yeast, we propose that perhaps SCFDia2-mediated ubiquitylation of Sir4 serves as a mechanism to regulate Sir4 proteins and silent chromatin structure during the cell cycle. Further investigation is warranted to address such a role for the SCFDia2 complex and Sir4 ubiquitylation.
In summary, our studies reveal a role for the SCFDia2 E3 ligase in transcriptional silencing. In addition, we show that the SCFDia2 E3 ligase binds and ubiquitylates Sir4. Furthermore, Dia2 is required for the regulation of Sir4 binding to chromatin during S phase of the cell cycle. These studies reveal a novel mechanism by which yeast silent chromatin is regulated during S phase of the cell cycle.
All yeast strains, except those for the SGA screen, were derived from W303 (leu2-3, 112 ura3-1, his3-11, trp1-1, ade2-1 can1-100) and constructed using standard methods and can be found listed in Table S1. The synthetic genetic array method to screen 4,700 viable yeast deletion mutants has been previously described in detail, along with the assay used to screen specifically for mutants that exhibit defects in HMR silencing [13], [21]. Detailed methods for the screen can be found in Text S1. Plasmids for Dia2 and Sir4 expression were constructed using standard methods in the vector, pRS313. Oligos used to construct the mutant Dia2 plasmids, as well as those used for analysis of mRNA expression and ChIP DNA using real-time PCR, are listed in Table S2.
Telomeric silencing was analyzed as described previously [13]. Briefly, yeast cells containing the URA3 gene at the left arm of chromosome VII (URA3-VIIL) were plated onto the indicated media in a 10 fold series dilution with a starting OD600 of 6.0 for growth on 5-fluoroorotic acid (FOA) and OD600 0.6 on media not containing FOA. Images were taken after four days of incubation at 30°C.
Assays for silencing at the HMR silent mating type locus were performed as described [13] in cells containing a GFP reporter integrated at the silent HMR locus. Cells were grown at 25°C to OD600 0.6–0.8 and washed three times with synthetic complete (SC) –TRP media. The percentage of cells expressing GFP was determined using flow cytometry with the GFP populations in wild-type and sir3Δ cells as standards.
Cells in which Sir3 or Sir4 were tagged with GFP were grown in YPD or selective media (SC-HIS) at 25°C and collected at OD600 0.6–0.8. Cells were washed three times with SC-TRP media and analyzed using a Zeiss fluorescence microscope. Images were taken with z-stack images captured at every 0.3 µm, and one z-stack image was shown in Figure 4A. At least 100 cells of each genotype were counted from at least two independent experiments, and the percentage of cells exhibiting foci similar to wild-type cells was reported.
ChIP assays were performed as described [11]. Cells were first fixed with 1% formaldehyde and then quenched with glycine. Cells were collected and homogenized using glass beads. Chromatin DNA was sheared to an average size of 0.5 to 1 kb by sonication and immunoprecipitated with specific antibodies against the protein of interest. The co-precipitated DNA was analyzed by real-time PCR using primers whose sequences are listed in Table S1. To analyze Sir4 levels during the cell cycle, cells were arrested for 3 hours with α-factor. After washing away α-factor with cold water three times, cells were released into fresh medium and collected at different time points for analysis of DNA content, gene expression and ChIP assays as described above.
Ubiquitylation assays were performed as described [41]. Briefly, Flag-ubiquitin, E1, E2, E3 (whole cell extracts prepared from cells expressing full length MYC-Dia2 or MYC-Dia2 F-boxΔ or purified MYC-Dia2 (full length and F-boxΔ). More details for both the in vitro and in vivo ubiquitylation assays can be found in Text S1.
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10.1371/journal.pntd.0005307 | GRAIL and Otubain-1 are Related to T Cell Hyporesponsiveness during Trypanosoma cruzi Infection | Trypanosoma cruzi infection is associated with severe T cell unresponsiveness to antigens and mitogens and is characterized by decreased IL-2 synthesis. In addition, the acquisition of the anergic phenotype is correlated with upregulation of “gene related to anergy in lymphocytes” (GRAIL) protein in CD4 T cells. We therefore sought to examine the role of GRAIL in CD4 T cell proliferation during T. cruzi infection.
Balb/c mice were infected intraperitoneally with 500 blood-derived trypomastigotes of Tulahuen strain, and spleen cells from control non-infected or infected animals were obtained. CD4 T cell proliferation was assessed by CFSE staining, and the expression of GRAIL in splenic T cells was measured by real-time PCR, flow cytometry and Western blot. We found increased GRAIL expression at the early stages of infection, coinciding with the peak of parasitemia, with these findings correlating with impaired proliferation and poor IL-2 and IFN-γ secretion in response to plate-bound antibodies. In addition, we showed that the expression of GRAIL E3-ubiquitin ligase in CD4 T cells during the acute phase of infection was complemented by a high expression of inhibitory receptors such as PD-1 and CTLA-4. We demonstrated that GRAIL expression during infection was modulated by the mammalian target of the rapamycin (mTOR) pathway, since addition of IL-2 or CTLA-4 blockade in splenocytes from mice 21 days post infection led to a reduction in GRAIL expression. Furthermore, addition of IL-2 was able to activate the mTOR pathway, inducing Otubain-1 expression, which mediated GRAIL degradation and improved T cell proliferation.
We hypothesize that GRAIL expression induced by the parasite may be maintained by the increased expression of inhibitory molecules, which blocked mTOR activation and IL-2 secretion. Consequently, the GRAIL regulator Otubain-1 was not expressed and GRAIL maintained the brake on T cell proliferation. Our findings reveal a novel association between increased GRAIL expression and impaired CD4 T cell proliferation during Trypanosoma cruzi infection.
| Chagas disease is caused by the protozoan parasite Trypanosoma cruzi and is endemic in Central and South America, where it affects about 10 million people. In addition, migration has led to the disease being established in non-endemic countries. Infection involves an acute stage that evolves to a chronic stage where infected individuals may or may not show clinical symptoms or suffer progressive heart disease. The relevance of T cells in the control of T. cruzi infection has been demonstrated in human infection and in experimental models. However, the T. cruzi parasite employs different strategies to downregulate the T cell function. These mechanisms can act at the initial time of T cell activation, leading to a state of anergy where lymphocytes do not respond. However, the molecular components that regulate this process during T. cruzi infection are not well understood. Our findings demonstrate for the first time that this T cell hyporesponsiveness could be linked to an increased expression of GRAIL. We propose that GRAIL expression induced by the parasite could be maintained by increased expression of inhibitory molecules, which blocked mTOR activation and IL-2 secretion. GRAIL could then play a key role in downregulating T cell functions by allowing the parasites to establish the chronic disease.
| Chagas disease, caused by the intracellular protozoa Trypanosoma cruzi, is one of the major human health problems in Latin America. It evolves from an acute to a chronic phase, where subjects may be clinically asymptomatic or show progressive heart disease and leads to an end-stage dilated cardiomyopathy in 20–30% of infected individuals. It is estimated that approximately 4 million chagasic individuals have developed heart disease, making Chagas disease the most frequent cause of infectious cardiomyopathy in the world [1,2].
The immune control of T. cruzi is complex, requiring the generation of a substantial antibody response and the activation of both CD4 and CD8 T cell responses. Even in cases in which these responses are sufficiently stimulated to be able to control the acute infection, T. cruzi is not completely eradicated, but instead persists in infected hosts for decades [3].
T. cruzi employs a variety of strategies to evade the immune system and remain in the infected host. The main method involves the inhibition of specific T-cell responses, and consequently, can favor the establishment of chronic infections [4,5,6,7,8]. Related to this, a number of both host-dependent and parasite-induced mechanisms have been previously shown to affect immune regulation [9,10]. Moreover, T cells from infected hosts are largely unresponsive to antigens and mitogens, resulting in reduced IL-2 synthesis [8].
IL-2 production initiates proliferation, effector functions, and clonal expansion via IL-2 receptor (IL-2R)-mediated signaling [11]. In the absence of a robust activation initiated by TCR and CD28 signaling, CD4 T cells fail to proliferate or to produce IL-2 and enter a state of unresponsiveness following immunogenic stimulation, referred to as “anergy” [11,12]. In the case of CD4 T cells, the development of anergy depends on the alteration of the expression of several genes [11,12,13].
Post-translational modification of proteins via ubiquitination also plays an essential role in the regulatory mechanism of CD4 T cell anergy [14,15]. GRAIL, also known as ring finger protein-128 (RNF-128), has been identified as a novel E3 ubiquitin-protein ligase that induces and maintains anergy in CD4 T cells [16,17,18]. It has been shown that GRAIL expression could be correlated with the inhibition of CD4 T cell proliferation and antigen-induced IL-2 transcription by disrupting the T cell stimulatory signaling [19]. In support of this observation, T cells from GRAIL knock-out mice were shown to be defective in anergy induction both in vitro and in vivo [17,20]. In particular, GRAIL (-/-) T cells hyperproliferated [17,21] and produced more cytokines [17] compared with wild type (WT) cells in response to TCR stimulation alone in vitro or with concomitant anti-CD28 costimulation. It has also been recently demonstrated that GRAIL, by mediating TCR-CD3 degradation, regulated naive T cell tolerance induction [20]. Furthermore, several investigations have shown how GRAIL interacts with T-cells, antigen presenting cell (APC) receptors and cytoskeletal proteins, thereby promoting their degradation [22,23,24,25].
GRAIL expression is regulated by Otubain-1 (Otub-1) [26], which is a member of deubiquitinating enzymes with the capability to cleave proteins at the ubiquitin-protein bond by using its cysteine protease domain [27]. It has been shown that Otub-1 is expressed and GRAIL degraded when naive CD4 T cells are productively activated to undergo proliferation [19]. Moreover, the loss of GRAIL was mechanistically controlled through a pathway involving CD28 co-stimulation, IL-2 production and IL-2R signaling, and ultimately, mTOR-dependent translation of select mRNAs. Blocking the mTOR by using CTLA-4-Ig, anti-IL-2 or rapamycin prevented Otub-1 protein expression and maintained GRAIL expression, which inhibited T cell proliferation [19]. Although the function of GRAIL in CD4 T cells has been studied extensively for the development of tolerance [17,20], with its participation having been demonstrated in the development of autoimmune diseases [20], only recently has its role been studied during T cell dysfunction in the course of infections [28,29,30,31]. Thus, the aim of this work was to search for a novel link between GRAIL and CD4 T cell unresponsiveness in the context of abnormalities of T cell proliferation observed during Trypanosoma cruzi infection. Our results provide evidence demonstrating that CD4 T cells from T. cruzi infected mice exhibited an increase in GRAIL expression during the acute phase of infection, which was correlated with defects in proliferation and immune responsiveness. In addition, we showed that high expression of CTLA-4 and low levels of IL-2 prevented mTOR activation and Otub-1 protein expression, and maintained GRAIL expression, which inhibited T cell proliferation during the acute phase of the infection. Our results therefore indicate that GRAIL is an important player in CD4 T cell anergy during the acute phase of Trypanosoma cruzi infection.
All the animal experiments were examined by the Institutional Experimentation Animal Committee, from Facultad de Ciencias Químicas, Univesidad Nacional de Córdoba, which approved the experimental procedures (authorization no. 2016–209). This committee follows the guidelines for animal care of “Guide to the care and use of experimental animals” (Canadian Council on Animal Care, 1993) and of “Institutional Animal Care and Use Comittee Guidebook” (ARENA/OLAW IACUC Guidebook, Nacional Institutes of Health, 2002).
BALB/c mice obtained from the Comisión Nacional de Energía Atómica (CNEA; Buenos Aires, Argentina) were inbred and housed according to institutional guidelines. BALB/c mice, when 6–8 weeks old, were intraperitoneally infected with 1x106 blood-derived T. cruzi trypomastigote forms from Tulahuén strain, which was maintained through intraperitoneal inoculation every 11 days [32].
Female BALB/c mice were infected intraperitoneally with 500 blood-derived T. cruzi trypomastigote forms diluted in saline solution. After different days post infection (p.i.), these mice were sacrificed by CO2 asphyxiation and spleens were extracted. Non-infected animals were processed in parallel. Trypomastigotes of the Tulahuen and Y strains were obtained from the extracellular medium of infected monolayers of Vero or NIH3T3 cells, respectively, and were collected by centrifugation at 4400 rpm for 10 min and resuspended in RPMI medium containing 10% FCS. Parasites were counted using a Neubauer chamber and used for in vitro infection experiments as described below.
Spleen cells were obtained from control or infected animals by homogenizing the organs in a cell strainer. Then, the spleen cell suspensions were centrifuged (1500 rpm, 5 min, 4°C) and the pellets treated with RBC lysis buffer (GIBCO). These cells were subsequently resuspended in complete RPMI medium containing 10% fetal bovine serum (FBS, PAA laboratories), L-glutamine (2 mM, GIBCO) and gentamicin (40 g/ml), and the isolated cell suspensions were passed through a 50-mm nylon mesh (BD Falcon) for cell culture, flow cytometry or cell isolation. Finally, the T cells were isolated from spleens using a CD4+ T cell isolation kit, according to the manufacturer’s instructions (Miltenyi Biotec), with the average purity found to be 95–98%.
HEK-293 cells (ATCC) were maintained in Dulbecco's modified Eagle's medium (Gibco) supplemented with 10% fetal bovine serum (FBS, PAA laboratories), L-glutamine (2 mM, GIBCO) and gentamicin (40 g/ml), and these cells were grown at 37°C under 5% CO2.
To examine PD-1, CTLA-4 and GRAIL expression, splenocytes or CD4 T cells from control and infected animals or in vitro infected CD4 T cells were washed with saline solution 2% FBS and incubated with anti-mouse CD32/CD16 antibody for 20 minutes at 4°C to block Fc receptors. Then, cells were incubated with APC labeled anti-CD4, PercP labeled anti-CD3 (BD Pharmingen), and with PE-labeled anti-PD-1 or anti-CTLA-4 (BD Pharmingen) for 20 min at 4°C.
For the assessment of intracellular GRAIL expression, cells were first stained with FITC-CD3 and APC-CD4 antibodies, and then fixed and permeabilized with Citofix/Citoperm (BD Biosciences,) for 30 min followed by reacting with rabbit anti-GRAIL primary Ab (Abcam) for 45 min. After washing, cells were stained for 20 min with PE–anti-rabbit IgG (Biolegend). Finally, cells were washed twice with saline solution of 2% FBS, and stored at 4°C in the dark until analysis using a FACS flow cytometer (FACS Canto II, BD Biosciences). The results were processed using Flow Jo software (version 7.6.2).
Cytokines were measured in culture supernatants using a capture enzyme-linked immunosorbent assay (ELISA). IFN-γ (eBioscience) and IL-2 (Biolegend) were used as paired monoclonal antibodies in combination with recombinant cytokine standards. All assays were performed according to the manufacturer’s guidelines.
CD4 T cells from control and infected animals were washed and lysed for 30 min at 4°C in RIPA buffer [1% Triton X-100 (v/v), 0.5% sodium deoxicolate (p/v), 0.1% sodium dodecyl sulfate (SDS)] containing a protease inhibitor cocktail (Roche), and the cell debris was spun down at 13,000 g for 15 min. Precipitates were removed, and aliquots of the cell lysates were diluted in SDS sample buffer, boiled at 100°C for 3 min, spun down, and applied to precast 10% acrylamide Tris-glycine gels at 40 μg protein/lane and run at 150 V for 1 h. Samples were transferred to nitrocellulose membranes (BioRad) at 100 V for 1 h, and these membranes were probed using rabbit anti-mouse GRAIL (Santa Cruz Biotechnology), anti-mouse Otub-1 or anti-mouse p-4EBP1 (Cell Signaling Technology) followed by peroxidase conjugated anti-rabbit antibody (Sigma Chemical Co.), before being visualized using enhanced chemiluminescence (Pierce) for detection. The protein loading was evaluated by actin expression.
RNA was extracted from splenocytes from infected or control animals by the Trizol reagent (Invitrogen) and reverse-transcribed into cDNA by using Revert Aid First Strand cDNA Synthesis (Fermentas). Transcripts were quantified by real-time quantitative PCR on an ABI Prism 7500 sequence detector (Applied Biosystems) with predesigned TaqMan gene expression assays and reagents (Applied Biosystems), according to the manufacturer´s instructions. Probes with the following Applied Biosystems assay identification numbers were used: Cblb, Mn01343092.m1; Rnf128, Mn00480990.m1; Rn18s, Mn03928990.g1. For each sample, mRNA abundance was normalized to the amount of 18S RNA and expressed as arbitrary units.
CD4 T cell proliferation was measured using the cell division tracking dye carboxyfluorescein diacetate succinimidyl ester (CFSE) (Molecular Probes, Eugene, OR). Spleen CD4 T cells isolated from infected or control animals were stained with CFSE dye at 5 μM concentrations. Cells were incubated at 37°C for 10 min, and then the reaction was stopped by adding 10 ml of RPMI medium containing 10% FBS. After washing, cells were resuspended in warm RPMI complete medium before being plated in anti-CD3/CD28 Abs (1 μg/mL of each Abs)-coated plates. After 72 h of incubation, cells were stained with PerCP/Cy5.5-CD4 Ab and acquired for FACS analysis. Unstimulated CFSE-labeled cells served as a non-dividing control. Data analysis was performed using a FACS flow cytometer (FACS Canto II, BD Biosciences) with FlowJo software (version 7.6.2), by setting a gate on the live cells to side-scatter versus forward-scatter dot plots and determining the expression of the CFSE.
CD4+ T cells were isolated from the spleen of control or infected animals and then cultured in complete RPMI with or without rmIL-2 (R&D Systems) at a concentration of 20 ng/mL for 3 days for the cell proliferation assays, or for 1 day for the assessment of GRAIL intracellular expression.
Splenocytes were isolated at 21 days p.i. and then cultured in complete RPMI medium in the presence of CTLA-4 blocking antibody or control antibody (10 μg/ml, eBioscience). Then, intracellular GRAIL expression was evaluated 48 h later by flow cytometry.
Statistical analyses were performed using the student’s t-test. Values of p < 0.05 were considered to be statistically significant.
To analyze the proliferative efficacy of the CD4 T cells, they were first isolated from the spleen of control and T. cruzi infected mice at different time points. Then, cells were stained with CFSE, stimulated with anti-CD3/CD28, and proliferation was analyzed 72 h later. CD4 T cells isolated from the spleen of infected animals showed a considerable decrease in their proliferation during the acute phase of infection compared to CD4 T cells from control animals (Fig 1A and 1B). However, proliferation of CD4 T cells from infected animals was recovered later on in the infection. In addition, stimulated CD4 T cells from infected animals produced less IFN-γ and IL-2 at an early time point of infection (21 p.i.) compared to control CD4 T cells (Fig 1C and 1D). These results coincided with the peak of parasitemia (Fig 1E).
Because CTLA-4 and PD-1 are known to inhibit T cell function, we examined the expression levels of these molecules in cells from the spleens of control and infected animals.
It has been shown that T. cruzi is able to modulate the expression levels of the negative coreceptor PD-1 in several immune cells [33]. However, in that study, different mice and parasite strains were used. Thus, to evaluate in our experimental model if T. cruzi infection upregulates PD-1 and CTLA-4 expression in CD4 T cells, flow cytometry was performed on spleen cells at several time points after infection, and the percentage of CD4+ T cells expressing PD-1 or CTLA-4 on the surface was determined, as shown in Fig 2A and 2C, with representative dot plots displayed in Fig 2B and 2D. We found that the infection led to an increase in the expression of PD-1 and CTLA-4 in spleen CD4 T cells from infected mice at 21 days p.i. compared to control cells (Fig 2A and 2C). In addition, we found that expression levels of CTLA-4 fell to normal levels at day 42 p.i., and PD-1 expression also decreased significantly (Fig 2B and 2D).
Considering that GRAIL is an inducer of impaired CD4 T cell proliferation during in vitro and in vivo tolerance [16,17], as well as being involved in CD4 T cell dysfunction during infection [28,29,30], we aimed to assess its expression in spleen cells from control and infected animals by real time PCR. We found a significant upregulation of its mRNA levels in splenocytes from infected mice, which were strongest at the earliest time point post-infection (Fig 3A). However, we did not observe upregulation of Cbl-b, which is another E3-Ubiquitin ligase shown to be involved in regulating T cell functions (Fig 3B) [34,35,36]. To confirm GRAIL expression in CD4 T cells, splenocytes were labeled with anti-CD4 and anti-CD3 antibodies, and then GRAIL expression was evaluated by intracellular labeling and analyzed by flow cytometry. A considerable upregulation of GRAIL protein was found in splenic CD4 T cells isolated from animals at 21 days p.i., compared to splenic CD4 T cells from control uninfected animals. However, GRAIL expression decreased in cells from animals at 42 days p.i. (Fig 3C). GRAIL expression in the HEK 293 cell line was used as a positive control (Fig 3C).
Next, to test if GRAIL upregulation in CD4 T cells is a general phenomenon of T. cruzi infection or is dependent on the strain used, we evaluated GRAIL expression by FACS in CD4 T cells cultured in vitro with two different T. cruzi strains. We observed that both T. cruzi strains induced GRAIL expression at similar levels (Fig 3D). Thus, our results showed that GRAIL expression is induced during acute phase of infection and correlates with the peak of parasitemia and with CD4 T cell hiporesponsiveness. In addition, we observed that GRAIL expression is induced directly by different parasite strains.
It has previously been shown that Otub-1 is expressed and GRAIL is degraded when naive CD4 T cells are productively activated to undergo proliferation [19]. In addition, Lin et al. demonstrated that the loss of GRAIL is mechanistically controlled through a pathway involving CD28 costimulation, IL-2 production and IL-2R signaling, and ultimately, by mTOR-dependent translation of select mRNA. Interference of this pathway using CTLA-4-Ig, anti-IL-2, or rapamycin prevents Otub-1 protein expression and maintains GRAIL expression, which inhibits T cell proliferation [19]. These findings implicate Otub-1 and GRAIL as important components governing T cell unresponsiveness. Given that we observed a reduced IL-2 production and increased CTLA-4 and GRAIL expression in CD4 T cells from the acute phase of T. cruzi infected mice, we evaluated GRAIL as well as Otub-1 expression and mTOR activation in CD4 T cells from T. cruzi infected mice at different time points after infection. An increased GRAIL expression was observed during infection, which was stronger at the acute phase of infection (Fig 4A) as shown also by real time PCR and FACS (Fig 3A and 3C), while GRAIL expression dropped at 42 days p.i. (Fig 4A). In addition, Otub-1 expression was not evident early on, although it increased as the infection progressed, with a peak occurring at day 36 p.i. Finally, GRAIL expression was not observed at 42 days p.i. while Otub-1 protein expression was observed (Fig 4A), showing that GRAIL downregulation happened later on in the infection and depended on Otub-1 expression.
As CD4 T cells require CD28 costimulation and IL-2R signaling to modulate GRAIL expression, we reasoned that the mTOR pathway might also control Otub-1 and GRAIL expression. On examining mTOR activity, we did not observe phosphorylation of 4EBP1 during the acute phase of infection although it was detected later on (Fig 4A), thereby allowing Otub-1 expression and GRAIL degradation. This effect could has been related to CTLA-4 expression (Fig 2A) since it has previously been shown that CTLA4-Ig treatment blocks CD28 costimulation and the resultant IL-2 expression. This in turn inhibits the mTOR-dependent translation of mRNA transcripts, including Otub-1, thus maintaining GRAIL expression and inhibiting CD4 T cell proliferation [19]. To test this hypothesis, we performed experiments by culturing splenocytes from infected mice at 21 days p.i. with blocking anti-CTLA-4 or control antibody and GRAIL intracellular expression was evaluated 48 h later by FACS. We observed a reduction in GRAIL expression in cells from infected mice cultured with anti-CTLA-4 compared to cells treated with control antibody (Fig 4B). This might indicate that the absence of co-stimulation during the acute phase of infection due to increased expression of inhibitory molecules, (Fig 2) such as CTLA-4, which may allow GRAIL expression to be maintained via a blockade of mTOR activation.
Taking into account that GRAIL expression during T. cruzi infection might be regulated by Otub-1 and that this depends on mTOR and IL-2 signalling, we hypothesized that the addition of exogenous IL-2 to CD4 T cells at 21 days p.i. may compensate for either diminished or delayed IL-2 production. We performed in vitro experiments by comparing CD4 T cell proliferation in control and in animals at 21 days p.i. after treatment with rmIL-2. CD4 T cells were stimulated with anti-CD3/CD28 ligands in the presence or absence of rmIL-2 for 3 days and then cell proliferation was assessed. It was found that CD4 T cells from control as well as 21 days p.i. animals had an increase in proliferation when treated with rmIL-2 together with the TCR stimulatory ligands (Fig 4C). In addition, we also evaluated GRAIL and Otub-1 expression as well as the phosphorylation of 4EBP1 in CD4 T cells with or without rmIL-2. In agreement with the increase in CD4 T cell proliferation from 21 days p.i., we found an increase in 4EBP1 phosphorylation and Otub-1 expression and a reduction in GRAIL expression (Fig 4D and 4E), indicating this E3 ubiquitin ligase to be a new player in T cell hyporesponsiveness during the acute phase of T. cruzi infection.
Several alterations of the immune response have been described in Chagas disease. Early investigations suggested that infection with T. cruzi was associated in both humans and mice with a severe T cell unresponsiveness to mitogens and antigens during the acute phase of the disease [37,38]. This immunosuppression was thought to facilitate the dissemination and establishment of the parasite in the infected host [9,39], which was ascribed to various mechanisms [5,8,40,41,42,43,44]. However, it has been widely demonstrated that the most affected cytokine in acute T. cruzi infection is IL-2, an important growth factor for T lymphocytes that is suppressed in several lymphoid organs such as thymus, mesenteric lymph nodes and spleen [45].
Many authors have shown that T. cruzi glycoproteins induce T cell anergy [37], cell cycle arrest [46] or inhibit T cell activation [5,6,8] by affecting IL-2 secretion or IL-2 receptor expression. In the present work, we found that CD4 T cells from acute T. cruzi infections in mice produced low levels of IL-2 when stimulated with anti-CD3/anti-CD28, and also had less capacity to proliferate, which could be related to the increase in GRAIL expression. In fact, the high expression of this gene alone is enough to convert a naïve CD4 T cell into an anergic phenotype [18]. Therefore, it is possible that T cell hyporesponsiveness caused by T. cruzi antigens such as mucins [8,37,46], and characterized by decreased IL-2 synthesis, might be mediated by GRAIL since expression of this E3 Ubiquitin Ligase correlates with the peak of the parasitemia. However, this still remains to be tested. Related to this, we have observed that GRAIL expression is induced directly by different T. cruzi strains.
It has been shown that parasite components such as mucins are able to inhibit early events in T cell activation and induce T cell anergy. These parasite components bind to L-selectin and inhibit different activation pathways that lead to inhibition of IL-2 secretion and T cell proliferation [8]. In addition, another work reported that parasite-derived mucins bind to Siglec-E (CD33) and inhibit mitogenic responses in CD4 T cells by inducing a cell cycle regulator that blocks the cell cycle [46]. It has also been shown that mannose-capped lipoarabinomannan (LAM) from Mycobacterium tuberculosis can inhibit CD4 T cell activation by downregulating the phosphorylation of key proximal TCR signaling molecules, which facilitates induction of anergy-related genes, and results in long-term CD4 T cell dysfunction [30]. In another study, engaging the TLR7 expressed on CD4 T cells resulted in complete anergy by inducing intracellular calcium flux, with the activation of an anergic gene-expression program being dependent on the transcription factor NFATc2. Then, T cell unresponsiveness was reversed by knockdown of TLR7 and restored the responsiveness of HIV-1+ CD4+ T cells in vitro [47]. Thus, it is possible that parasite derived factors might contact different receptors/molecules on T cells and induce T cell hiporesponsiveness directly by increasing the expression of anergy factors. We hypothesize that the high levels of parasites occurring during the acute phase of infection may induce GRAIL expression on T cells by a mechanism that we have not yet explored. However, additional studies are needed to understand how T. cruzi directly induces GRAIL expression on CD4 T cells.
Expression of the E3-Ubiquitin Ligase has been linked to CD4 T cell hyporesponsiveness during sepsis [29] and chronic murine schistosomiasis [31]. In addition, it has been reported to be induced by tegumental antigens from Fasciola hepatica and [28] LAM from Mycobacterium tuberculosis in CD4 T cells [30]. In fact, the T cell anergy observed during these infections is characterized by a lack of cytokine responses and reduced proliferative activity, which can be reversed by the addition of IL-2 and results in a reduction of GRAIL expression [29,30]. Although it has previously been shown that IL-2 is able to reverse the human T cell anergy induced by T. cruzi mucins [37], this is the first work that links the ability of IL-2 to reverse T cell hyporesponsiveness during T. cruzi infection to GRAIL regulation. During the acute phase of infection, we observed an increased expression of GRAIL with low Otub-1 and mTOR expression and activation in CD4 T cells. As Otub-1 is controlled by the Akt-mTOR pathway and is a negative regulator of the GRAIL function [19,25], this suggests that T. cruzi infection may disrupt the Akt-mTOR pathway resulting in Otub-1 downregulation, which in turn may induce GRAIL. In agreement with this, Sande et al. observed a downregulation of Otub-1 in LAM-treated T cells [30].
The absence of co-stimulation due to increased expression of inhibitory molecules such as CTLA-4 interferes with Otub-1 translation, with GRAIL expression being maintained via a blockade of the activation of the mTOR pathway [25]. In addition, most studies concerning T cell anergy have established that it results from TCR stimulation in an inhibitory environment, involving increased co-inhibition, decreased co-stimulation, or TCR engagement with a weak agonist peptide [48]. However, more recently, the role of mTOR and other related metabolic sensors and regulators has emerged as being of particular importance and has broadened our view of anergy-inducing signals [49,50]. Here, we observed differences in the expression of the co-inhibitory receptors PD-1 and CTLA-4 between CD4 T cells from acute and chronic phases of infection. An increased expression of CTLA-4 during the acute phase of infection may block mTOR activation, thus preventing protein translation, (including Otub-1) and leading to the maintenance of GRAIL expression with reduced T cell proliferation and cytokine production. Regarding this, we observed that CTLA-4 blockade in splenocytes from infected mice resulted in a reduction in GRAIL expression. Additionally, during several infections the expression of inhibitory molecules and E3 Ubiquitin ligases has been previously shown to be upregulated and to induce T cell hyporesponsiveness [51,52], with blocking CTLA-4 or neutralizing TGF-β during lymphatic filariasis restoring the ability to mount Th1/Th2 responses to live parasites and reversing the induction of anergy-inducing factors [51]. Furthermore, dendritic cells activated by tegumental antigens from Fasciola hepatica suppress T cells in vitro by inducing GRAIL and CTLA-4 expression [28]. Although the expression of inhibitory molecules has previously been observed during Chagas disease and in T. cruzi experimental infection [32,33,53], this is the first study that has demonstrated that CD4 T cell hyporesponsiveness may be caused by a combinatory effect of inhibitory molecules and GRAIL expression.
Considering the above results, we speculate that early upon infection parasite derived factors might contact receptors/molecules to induce directly GRAIL expression and T cell anergy. Later on, expression of inhibitory receptors such as CTLA-4 prevented Otub-1 protein expression and maintained GRAIL expression, which inhibited T cell proliferation. However, we have shown that GRAIL expression is reduced in T cells from acute infected mice cultured in the presence of CTLA-4 blocking antibodies or rm-IL-2. Therefore, these results may indicate that GRAIL expression during T. cruzi infection could be induced directly by the parasite and sustained by expression of inhibitory molecules.
GRAIL is not only expressed in naive T cells, but also in effector T cell subsets and controls their activation. Kriegel et al. reported that GRAIL-knockout Th1 effector CD4 T cells overproduce IFN-γ [17]. Related to this, we observed that CD4 T cells from acute infected mice, where GRAIL expression is increased, produced lower levels of IFN-γ than CD4 T cells from the later stages of T. cruzi infected mice. This is consistent with a recent report by Nunes et al. demonstrating that in vivo administration of T. cruzi mucin during murine experimental infection with T. cruzi parasites resulted in a lower number of splenic IFN-γ producing CD4 T cells [46], with these effects being accompanied by a greater susceptibility to infection, as shown by the higher levels of parasitemia [46].
CD3 is a known target of GRAIL, with the upregulation of GRAIL in T cells leading to degradation of CD3 [20]. In the present study, a lower CD3 expression in T cells was observed during the acute phase of infection that correlated with increased GRAIL expression (S1 Fig). Although additional experiments are needed to corroborate CD3-GRAIL interaction, it has been reported that immunosuppression during T. cruzi infection is due to defective T-Cell Receptor-CD3 functioning [54] and might be related to a lower expression of the CD3 molecule caused by degradation.
In summary, we have established a novel link between T cell hyporesponsiveness during T. cruzi infection and the expression and regulation of GRAIL in CD4 T cells. It is now important to extend these studies to evaluate additional GRAIL targets in T. cruzi-anergized T cells. In addition, it is necessary to determine if GRAIL can be detected in T cells from infected patients, and whether its expression can be induced directly in a dose dependent manner by purified antigens of T. cruzi. Finally, our data provide novel insights into why, despite the large immune cell activation by a wide variety of T. cruzi antigens, CD4 T cells may not respond optimally to their cognate antigens. In addition, T cell immune evasion strategies likely contribute to the host’s inability to eliminate T. cruzi and consequently permit survival and persistence of the parasite in the host.
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10.1371/journal.pntd.0006216 | Characteristics of inflammatory reactions during development of liver abscess in hamsters inoculated with Entamoeba nuttalli | Entamoeba nuttalli is an intestinal protozoan with pathogenic potential that can cause amebic liver abscess. It is highly prevalent in wild and captive macaques. Recently, cysts were detected in a caretaker of nonhuman primates in a zoo, indicating that E. nuttalli may be a zoonotic pathogen. Therefore, it is important to evaluate the pathogenicity of E. nuttalli in detail and in comparison with that of E. histolytica.
Trophozoites of E. nuttalli GY4 and E. histolytica SAW755 strains were inoculated into liver of hamsters. Expression levels of proinflammatory factors of hamsters and virulence factors from E. histolytica and E. nuttalli were compared between the two parasites. Inoculations with trophozoites of E. nuttalli resulted in an average necrotic area of 24% in liver tissue in 7 days, whereas this area produced by E. histolytica was nearly 50%. Along with the mild liver tissue damage induced by E. nuttalli, expression levels of proinflammatory factors (TNF-α, IL-6 and IL-1β) and amebic virulence protein genes (lectins, cysteine proteases and amoeba pores) in local tissues were lower with E. nuttalli in comparison with E. histolytica. In addition, M2 type macrophages were increased in E. nuttalli-induced amebic liver abscesses in the late stage of disease progression and lysate of E. nuttalli trophozoites induced higher arginase expression than E. histolytica in vitro.
The results show that differential secretion of amebic virulence proteins during E. nuttalli infection triggered lower levels of secretion of various cytokines and had an impact on polarization of macrophages towards a M1/M2 balance. However, regardless of the degree of macrophage polarization, there is unambiguous evidence of an intense acute inflammatory reaction in liver of hamsters after infection by both Entamoeba species.
| Entamoeba nuttalli is the phylogenetically closest protozoan to Entamoeba histolytica and is highly prevalent in macaques. Previous studies have indicated that E. nuttalli is virulent in a hamster model. In this study, we compared the immunopathological basis of formation of liver abscess in hamsters between E. nuttalli and E. histolytica. Mild liver tissue damage developed after intrahepatic injection of trophozoites of E. nuttalli, and lower expression levels of genes for host proinflammatory factors and amebic virulence proteins were detected at the edges of liver abscesses induced by E. nuttalli. In addition, alternatively activated macrophages were increased in E. nuttalli-induced liver abscesses in the late stage of disease progression. The lysate of E. nuttalli trophozoites also induced higher arginase expression than E. histolytica in vitro. Polarization of macrophages is likely to affect the degree of acute inflammatory reactions in liver in an animal model during E. nuttalli infection. Our data reveal new characteristics of abscess formation by E. nuttalli.
| The enteric protozoan Entamoeba histolytica causes an estimated 50 million cases of amebic colitis and liver abscess in humans, resulting in 40,000 to 100,000 deaths annually [1–5]. Entamoeba dispar is morphologically indistinguishable from E. histolytica, but is nonpathogenic. E. histolytica and E. dispar are also found in feces of nonhuman primates [6]. Recently, Entamoeba nuttalli, which is phylogenetically closer to E. histolytica than E. dispar, has also been identified in nonhuman primates [7], and there is a high prevalence of E. nuttalli infections in wild and captive macaques, including Macaca mulatta, M. fasciculalis, M. fuscata, M. thibetana and M. sinica, and other nonhuman primates in zoos [8–15]. Most macaques with E. nuttalli infections are asymptomatic, suggesting that the host-parasite relationship in macaques may be commensal in natural infection [12]. More recently, cysts of E. nuttalli were detected in a caretaker of nonhuman primates in a zoo [16]. The infected person was asymptomatic, but this finding raises the possibility that E. nuttalli is a zoonotic pathogen.
Fatal cases with liver abscess due to E. nuttalli have been reported in Abyssinian colobus and Geoffroy’s spider monkey in a zoo [17, 18], and inoculation of E. nuttalli trophozoites in liver of hamsters causes formation of abscesses and is lethal in some cases [7, 10, 12]. Hamsters inoculated with E. nuttalli are weakened and have decreased body weight. The liver lesions produced by E. nuttalli trophozoites are characterized by extensive necrosis associated with inflammatory reactions [7, 10]. These histological changes are similar to those caused by E. histolytica trophozoites, suggesting similar pathological mechanisms of tissue damage [7, 10, 19, 20]. However, E. histolytica infection in liver generally results in large single abscesses [1, 21], whereas E. nuttalli infection in hamsters induces small multiple abscesses [7]. Thus, the detailed mechanisms of how hosts with E. nuttalli develop a different pathogenic manifestation from that in E. histolytica infection are poorly defined.
E. nuttalli is as virulent as E. histolytica in animal models, but it remains unclear whether E. nuttalli is virulent in humans. These findings, coupled with in vivo observations that E. nuttalli causes histological lesions in similar conditions and has few sequence differences in some important genes [7, 14] in comparison with E. histolytica, have reinforced the idea that E. nuttalli is incapable of generating human lesions because of the host specificity of E. nuttalli and E. histolytica parasites. Therefore, it is important to evaluate the pathogenicity of E. nuttalli in comparison with that of E. histolytica to examine the molecular basis of the pathophysiology of amebic liver abscess (ALA) formation. In this study, expression levels of proinflamatory factors in hamsters and virulence factors from E. histolytica and E. nuttalli were compared between these parasites. The histopathological and immunopathological analyses of ALA provide valuable information on the pathogenicity of E. nuttalli.
All animal experiments were performed in strict accordance with the Regulations for the Administration of Affairs Concerning Experimental Animals (1988.11.1) and were approved by the Institutional Animal Care and Use Committee (IACUC) of our institutions (Permit Numbers 20110307–051 and 20160225–097). All efforts were made to minimize suffering.
Trophozoites of E. histolytica SAW755CR and E. nuttalli GY4 strains were grown under axenic conditions at 36.5°C in YIMDHA-S medium [22] containing 15% (v⁄v) heat-inactivated adult bovine serum. Trophozoites were harvested during the logarithmic growth phase (48 to 72 h) by chilling on ice for 5 min. RAW264.7 cells were cultured in DMEM (Thermo) supplemented with 10% fetal bovine serum (FBS) (Thermo), 100 U/ml penicillin, and 100 μg/ml streptomycin. CHO-K1 cells were cultured in Ham’s F12 nutrient medium (Thermo) supplemented with 10% FBS, 100 U/ml penicillin, and 100 μg/ml streptomycin. The mammalian cells were grown in a 37°C incubator with 5% CO2.
Six-week-old male hamsters were obtained from Shanghai Songlian Experimental Animal Factory. ALA was induced by direct inoculation of 1×106 axenic trophozoites of E. histolytica SAW755CR strain or E. nuttalli GY4 strain into liver, as previously described [23].
After intrahepatic inoculation of trophozoites, hamsters were euthanized at 3 h, 6 h, 12 h, 24 h, 48 h, 72 h and 168 h post-inoculation. At each time point, 6 to 7 hamsters were used. Liver tissues were harvested and fixed in 4% paraformaldehyde followed by paraffin embedding. Sections were stained with hematoxylin and eosin (HE) or periodic acid-Schiff (PAS) for histopathology [7]. Tissue damage and inflammatory cell infiltration were quantified in high quality images (2560×1920 pixels) captured using a Nikon light microscope. Areas of leukocyte infiltration and liver necrosis were measured using Image-Pro Plus 4.5.1 software (Media Cybernetics). Areas of interest are expressed as a percentage of the total tissue area.
Immunohistochemical staining was performed as described elsewhere [24]. Briefly, paraformaldehyde-fixed liver sections were deparaffinized, rehydrated by standard protocols and incubated overnight at 4°C with rabbit anti mouse IFN-γ, TNF-α, IL-1β and IL-6 polyclonal antibodies (Abcam). The slides were subsequently incubated with horseradish peroxidase-labeled goat anti-rabbit immunoglobulin and then with chromogen substrate (3,3′-diaminobenzidine) for 2 min before counterstaining with hematoxylin. The cytokine score (intensity area/total image area) was determined in areas of leukocyte infiltration using Image-Pro Plus 4.5.1 software (Media Cybernetics). Each cytokine score was determined by counting more than 50 high-power fields (×20).
Gene expression of IFN-γ, TNF-α, IL-1β, IL-4, IL-6, IL-10, nitric oxide synthase (iNOS), arginine enzyme I (Arg-1) and mannose receptor I (MRC-I) in liver tissues was examined by quantitative real-time PCR (qRT-PCR) using the primers listed in Table 1 [22, 25–26]. Briefly, total RNA (1 μg) of tissue from the edge of a liver abscess was purified with an RNeasy Plus Mini kit (Qiagen). cDNA was synthesized with a Primescript first-strand cDNA synthesis kit (Takara) using oligo(dT) primers. qRT-PCR was carried out in a final reaction volume of 20 μl on an ABI 7500 Real-time PCR system (Applied Biosystems). Reactions were performed in a 96-well plate with SYBR Premix Ex Taq (Takara, Japan) containing primers listed in Table 1. The amplification cycling conditions were as follows: 30 s at 95°C and 40 cycles of 5 s at 95°C and 35 s at 60°C. Analysis by qRT-PCR of gene expression was conducted during the log phase of product accumulation, during which Ct values correlated linearly with relative DNA copy numbers. Each experiment was performed at least three times.
Gene expression of heavy subunit of galactose/N-acetylgalactosamine lectin (Hgl), intermediate subunit of galactose/N-acetylgalactosamine lectin (Igl), cysteine proteinase 2 (CP2), cysteine proteinase 5 (CP5), amoebapore A (AP-A), and amoebapore B (AP-B) of Entamoeba trophozoites in ALA tissues were examined using the same RNA samples extracted from tissue at the edge of a liver abscess. Genes of virulence proteins of E. nuttalli GY4 strain were amplified and sequenced (S1 Text). Primers for these genes were designed using the identical sequence regions of E. histolytica SAW755CR strain and E. nuttalli GY4 strain, and are listed in Table 1. The primers for Hgl and Igl used in this study can amplify all known subtypes of Hgl and Igl genes. Reactions were performed as described above. Each experiment was performed at least three times.
To assess whether secretory proteins from E. histolytica and E. nuttalli cause polarization of macrophages, trophozoites were first incubated with CHO cells and then lysed. RAW264.7 cells were stimulated with the lysates of ameba trophozoites. Briefly, CHO-K1 cells (106) were cultured in a 35-mm dish (Costar), and then 5×105 trophozoites were added and coincubated for 30 min. Trophozoites were then harvested, and lysed by repeated freezing and thawing of 106 trophozoites per ml in PBS. After centrifugation at 20,000 g for 10 min, trophozoite lysates were used to stimulate RAW264.7 cells (5×105) in 24-well culture plates (Costar) overnight. The cells were stimulated with trophozoite lysates, LPS (1 μg/ml final conc.) (Sigma) or PBS. After incubation for 6 h, 12 h, 24 h and 48 h, culture supernatants of RAW264.7 cells were assayed for cytokines and NO production. Cells were collected and frozen for measurement of arginase activity and expression of iNOS and Arg-1 genes by qRT-PCR. Each experiment was performed at least three times.
Total RNA (1 μg) of treated RAW264.7 cells was purified with an RNeasy Plus Mini kit (Qiagen). cDNA was synthesized with a Primescript first-strand cDNA synthesis kit (Takara) using oligo(dT) primers. Gene expression of iNOS and Arg-1 was examined by qRT-PCR using the primers listed in Table 1. Reactions were performed as described above. Each experiment was performed at least three times.
A Griess assay was performed using 20 μl of culture supernatant of RAW264.7 cells mixed with 30 μl of distilled water and 50 μl of Griess reagent (Sigma). Absorbance was measured at 548 nm in a microplate reader [27, 28]. Experiments were performed at least three times.
Arginase activity in cell lysates was measured in RAW264.7 cells that were harvested and lysed with mammalian tissue lysis/extraction reagent (Sigma) for 15 min on a shaker and centrifuged at 13,000 g for 10 min to remove insoluble material. Sample supernatant (20 μl) was added to a well of a 96-well plate, 10 μl of substrate buffer was added, and the mixture was incubated at 37°C for 120 min for arginine hydrolysis. The reaction was stopped with 200 μl of urea in each well at room temperature for 30 min. Absorbance was measured at 430 nm in a microplate reader. One unit of arginase is the amount of enzyme that converts 1.0 mM of L-arginine to ornithine and urea per minute at pH 9.5 and 37°C. Each experiment was performed at least three times.
To assess whether secretory proteins from E. histolytica and E. nuttalli cause proliferation of macrophages, a cell proliferation assay was performed. RAW264.7 cells (5×104) were cultured in 96-well culture plates (Costar) overnight. Cells were stimulated with 5 μl of PBS, trophozoite lysates or LPS (1 μg/ml final). CCK-8 reagent (Dojindo) was added to each well at 4 h, 10 h, 22 h or 46 h, and optical density (OD) was measured at 450 nm using a microplate reader (Bio-rad) at 6 h, 12 h, 24 h or 48 h. Each experiment was performed at least three times.
A Luminex multiplex immunoassay was performed to determine the concentrations of inflammatory cytokines using a customized Milliplex Mouse Cytokine/Chemokine Magnetic Bead Panel (Merck Millipore) for IL-1β, IL-6 and TNF-α. Briefly, 25μl of cell supernatant, control or standard was added to a 96-well plate containing 25μl of capture antibody-coated, fluorescent-coded beads. Biotinylated detection antibodies and streptavidin-PE were added to the plate after the appropriate incubation periods. After the last washing step, 150μl of sheath fluid was added to the wells, and the plate was incubated and read on a Luminex100 instrument. Five-PL regression curves were used to plot standard curves for all analytes with xPonent 3.1 software by analyzing the bead median fluorescence intensity. Results are expressed in pg/ml. Samples with quantification below the detection limit were registered as “zero” and samples above the quantification limit of the standard curve were given the value equal to the highest value of the curve. Each experiment was performed at least three times.
Statistical analyses were performed using IBM SPSS (ver. 20, SPSS Statistics/IBM Corp., Chicago, IL, USA). qRT-PCR data were analyzed by two-tailed Mann-Whitney U test. Other data were analyzed with a two-tailed Student t-test. P < 0.05 was considered significant in all analyses.
After inoculation of trophozoites into hamster liver, both E. histolytica SAW755 and E. nuttalli GY4 caused ALA, with a clear boundary between the abscess and normal tissue. The main area of inflammatory cell infiltration and living trophozoites was located at the edge of the abscess. At 3 h post-inoculation, inflammatory cell infiltration (mainly neutrophils) was observed (Figs 1A, S1–S3), and then infiltration of inflammatory cells increased in liver tissues. These cells were mainly monocytes and macrophages. A clear liver abscess was seen from 24 to 168 h with E. histolytica and 48 to 168 h with E. nuttalli (Fig 1B). ALA areas increased to nearly 50% with E. histolytica at 168 h, whereas the average ALA area was only 24% at 168 h with E. nuttalli. These results indicate that mild liver tissue damage was induced by E. nuttalli GY4 strain. Similarly, the area of inflammatory cell infiltration with E. nuttalli was smaller than that with E. histolytica at each time point. At 168 h, the inflammatory cell infiltration area was significantly lower with E. nuttalli (7%) than with E. histolytica (12%) (Fig 1C).
To evaluate expression of cytokines during ALA formation by E. nuttalli trophozoites, hamster livers were used for immunohistochemistry at different time points, and expression levels of IFN-γ, TNF-α, IL-6 and IL-1β in liver abscesses were analyzed. The control group had low levels of these cytokines (Fig 2A) at each time point, whereas areas positive for TNF-α, IL-6 and IL-1β in liver were increased after inoculation with E. histolytica SAW755CR (Fig 2B–2E). These areas were also increased in liver inoculated with E. nuttalli GY4, but to a lesser extent compared with E. histolytica (Fig 2C–2E). These results indicate a smaller increase in expression of proinflammatory factors (TNF-α, IL-6 and IL-1β) in local tissues inoculated with E. nuttalli GY4. Little IFN-γ was detected in liver tissue after inoculation of either strain.
To quantify the changes in cytokines during ALA formation, qRT-PCR was used to amplify IFN-γ, TNF-α, IL-6, IL-1β, IL-4 and IL-10 genes, with β-actin amplified as a reference. The results for expression levels of TNF-α, IL-6 and IL-1β in tissue at the edge of liver abscesses were similar to the immunohistochemistry data. At most time points, IL-6 and IL-1β increased significantly with E. histolytica and E. nuttalli (Fig 3). qRT-PCR showed that expression of TNF-α, IL-6 and IL-1β with E. nuttalli GY4 was lower than with E. histolytica SAW755CR at 48 h, 72 h and 168 h, and expression of IL-6 was particularly significantly lower.
Macrophages are immune effector cells that play an important role in ALA development. Expression of iNOS and Arg-1 genes was analyzed by qRT-PCR to examine differences in macrophage polarization in liver abscesses induced by the two species of Entamoeba trophozoites. Expression of iNOS rose rapidly after inoculation, whereas that of Arg-1 decreased (Fig 4A and 4B). MRC-I is a highly expressed surface receptor on M2 macrophages, and expression levels of MRC-I and Arg-1 changed similarly in qRT-PCR. In the early stage of ALA formation, MRC-I expression increased in all damaged liver tissue (including in the control group). The MRC-I level with E. nuttalli GY4 continued to increase at 72 h and 168 h, but did not rise further in the control group or with E. histolytica SAW755CR (Fig 4C). The higher iNOS/Arg-1 ratio (macrophage polarization M1/M2) at 72 h and 168 h suggests macrophage polarization toward M1 with E. histolytica SAW755CR, but toward a M1/M2 balance with E. nuttalli GY4 at 168 h (Fig 4D). These results suggest that M2 macrophages increased at 168 h after inoculation of the GY4 strain, and the milder liver tissue damage caused by E. nuttalli GY4 strain might be attributable to the increase in these macrophages.
To compare changes of virulence proteins with the two Entamoeba species during ALA progression, qRT-PCR was performed to examine expression levels of Hgl, Igl, CP-2, CP-5, AP-A and AP-B genes. There were significant increases in Hgl (2- to 5-fold), CP2 (5- to 16-fold), CP5 (2- to 11-fold), AP-A (2- to 3-fold) and AP-B (2- to 7-fold) after inoculation in hamster liver (Fig 5). There were few differences between the expression levels of virulence protein genes of E. histolytica and E. nuttalli in vitro; only the CP5 level was half in E. nuttalli compared to E. histolytica (Fig 5D). However, significantly lower expression of CP2, CP5, AP-A and AP-B of E. nuttalli was found at 168 h, and this lower level of virulence proteins in vivo may contribute to the milder liver tissue damage caused by E. nuttalli GY4.
To study whether secretory proteins of Entamoeba play an important role in polarization of macrophages, in vitro stimulation of mice macrophage RAW264.7 cells was performed, and expression of iNOS and Arg-1 was analyzed by qRT-PCR. The levels of both of these genes rose rapidly after stimulation with trophozoite lysates of E. histolytica or E. nuttalli. Lysate of E. histolytica induced significantly higher iNOS expression than that of E. nuttalli at 48 h (1334-fold vs. 627-fold compared to PBS). In contrast, lysate of E. nuttalli induced higher Arg-1 expression than E. histolytica (122-fold vs. 61-fold compared to PBS) (Fig 6A and 6B).
To examine the effects of secretory proteins of Entamoeba on NO production, RAW264.7 cells were stimulated with PBS, trophozoite lysate of E. histolytica or E. nuttalli, and LPS. A stable oxidized product of NO in the cell culture supernatants was determined by the Griess assay. Nitrate was increased by stimulation with lysate of E. histolytica at 24 h (24.8 μM) and 48 h (36.9 μM) (Fig 6C). The effect of trophozoite lysates on arginase activity was also examined. Arginase activity induced by lysates of E. histolytica and E. nuttalli, and LPS was increased at 24 h and 48 h, with lysate of E. nuttalli inducing higher arginase activity (5.4 unit/L) than that of E. histolytica at 48 h (Fig 6D). These results indicate that secretory proteins of Entamoeba play important roles in the polarization balance of macrophages.
A cell proliferation assay indicated that RAW264.7 cells were capable of proliferation after stimulation with trophozoite lysates of E. histolytica and E. nuttalli. An additional 33% to 42% proliferation of RAW264.7 cells occurred in comparison with PBS-stimulated cells at 48 h. There was no significant difference between lysates of E. histolytica and E. nuttalli (Fig 6E).
Cytokine expression of RAW264.7 cells was determined using a Luminex multiplex immunoassay after stimulation with PBS, trophozoite lysate of E. histolytica or E. nuttalli, and LPS. Lysate of E. histolytica induced a significant increase in TNF-α (507.9 pg/ml), IL-6 (2573.0 pg/ml) and IL-1β (17.3 pg/ml) at 48 h. Lysate of E. nuttalli also caused a significant increase in TNF-α (435.5 pg/ml) at 48 h, but had no significant effect on IL-6 and IL-1β (Fig 7). These results are consistent with the lower expression levels of IL-6 and IL-1β with E. nuttalli GY4 in the hamster ALA model.
The aim of this study was to determine the histopathological features of ALAs that regulate host inflammatory immune responses following their interaction with parasites and to examine whether these features differ between E. nuttalli and E. histolytica. Our data show that both E. nuttalli and E. histolytica cause liver abscesses with a clear boundary between the abscess and normal tissue. Interestingly, lesions in hamster differed between E. nuttalli and E. histolytica, including the size of the abscess and inflammatory cell infiltration region. The ability of trophozoites to produce a liver abscess in hamsters also differs among strains of E. histolytica and E. dispar, and the SAW755 strain of E. histolytica used in this study is a highly virulent strain. Even in hamsters inoculated with E. nuttalli trophozoites, lethal cases occurred within 7 days using the NASA06 strain [10], and in hamsters inoculated with the E. nuttalli SSS212, the mean abscess size was >50% of the liver [12]. In liver tissue sections, the necrotic area with inflammatory reactions was highly extended. Therefore, the virulency of the E. nuttalli GY4 strain used in the present study may have been relatively low.
Tissue destruction during ALA formation is generally attributable to both the cytotoxicity of trophozoites and the resultant host inflammatory immune response [29]. A typical amebic lesion is characterized by a necrotic zone with edges consisting of cellular debris and inflammatory cell infiltration [30]. Such necrosis is produced by virulence factors of trophozoites, such as galactose/N-acetylgalactosamine lectin (Gal/GalNac lectin), APs and CPs [29–33]. In the present study, the expression levels of virulence factors of trophozoites in tissue was higher than that of axenically cultured trophozoites, but the levels of major CPs and APs differed between the two strains, with lower levels in E. nuttalli. Immunopathological effects also contribute to tissue destruction during liver abscess formation in the hamster model. The host inflammatory response suppresses invasive trophozoites, but also leads to severe tissue damage. During this infectious process, multiple types of inflammatory cells are recruited to the infected liver of hamsters. Infiltrating neutrophils are predominant in inflammatory regions in the initial phase of invasive liver amebic infection, followed by macrophages that accumulate rapidly during abscess formation.
Evidence from in vivo and in vitro studies suggests that macrophage-mediated anti-ameba activity is a major mode of host defense against E. histolytica infections, and has essential functions throughout ALA formation [34]. When pathogens attack, naïve macrophages can be polarized in a direction to classical activated (M1) macrophages that strongly express iNOS, which produces NO through catabolism of arginine, subsequently causing proinflammatory effects and tissue damage [35, 36]. During abscess formation, M1 macrophages release NO into infected tissue, and NO combined with toxic products from the oxidative burst then kill trophozoites. The macrophage-mediated anti-ameba activity is inhibited by arginase in a dose-dependent manner through competition with iNOS that depletes the common substrate, L-arginine [37, 38]. Macrophages can also be polarized into alternatively activated (M2) macrophages and induce Arg-1, which competes with iNOS by degrading arginine into ornithine and polyamines, giving rise to macrophages with anti-inflammatory effects and tissue repair functions [35, 36, 39]. The present study showed that E. nuttalli GY4 induced small liver abscesses at 168 h after inoculation compared with large abscesses driven by E. histolytica SAW755CR. Moreover, infiltration of inflammatory cells remained lower in abscess lesions of E. nuttalli compared to those caused by E. histolytica.
In amebic liver lesions, secretion of Gal/GalNac lectin, APs and CPs by trophozoites also results in destruction of neutrophils and liberation of their toxic products, which may play an important role in enlargement of abscess lesions [29, 30]. Immunosuppressive and tissue repair functions play critical roles in control of inflammation by producing anti-inflammatory mediators [36, 39]. In this study, both Entamoeba species caused increased levels of iNOS in liver lesions of hamsters and decreased arginine at the early stage of ALA formation, indicating elevation of M1 macrophages, which are involved in host defense and tissue damage. Significantly, the level of arginine increased with E. nuttalli at 168 h after trophozoite inoculation, which suggests greater elevation of M2 macrophages compared with E. histolytica infection. The increased proportion of M2 macrophages in liver abscess lesions might attenuate tissue damage through accelerated tissue repair, and this might explain the smaller abscesses and milder liver tissue damage in the animal model infected with E. nuttalli.
There are several key factors in macrophage polarization during infection, with pathogens and their virulence proteins being the fundamental regulators [39–41]. The current study indicated that proteins secreted by both Entamoeba species were able to induce macrophage polarization and skew differentiation towards M1 or M2 phenotypes. With E. histolytica, the macrophage polarization skewed towards the M1 phenotype, as shown by the significant increase in iNOS expression and multiple proinflammatory cytokines, such as TNF-α, IL-1 and IL-6, exerting immunoregulatory roles during infection. With E. nuttalli, the polarization trend of macrophages was not as clear, based on the lower levels of iNOS and cytokines and higher production of arginine, compared to E. histolytica infection. These results suggest an equilibrium in macrophage polarization. Several studies have shown amebicidal activity of macrophages mediated by iNOS mRNA expression and NO production [30, 42, 43]. There is also evidence of direct macrophage activity by amebic virulence factors, and E. nuttalli secreted fewer virulence factors than E. histolytica based on protein profiles. Taken together, these data indicate that virulence factors inducing macrophage polarization in hamster liver lesions switch to a protective M2 phenotype from a destructive M1 phenotype, leading to decreased NO production, which reduces immunopathological tissue damage.
T-cell cytokine responses can be divided into different classes based on the combination of cytokines produced. Th1 cells secrete cytokines including IL-2, IFN-γ and TNF-β that promote differentiation and activity of macrophages and cytotoxic T cells, and lead primarily to a cytotoxic immune response. In contrast, the Th2 cytokine response is characterized by IL-4, IL-5, IL-6, IL-9 and IL-10 production [44]. These cytokines, the levels of which correlate with the degree of tissue damage, are released by attacked host cells or effector cells. IFN-γ is a suppressive cytokine that can clear the parasite [45]. In the present study, there was no significant increase in IFN-γ during ALA development in hamsters, perhaps suggesting that persistent progression of lesions facilitates invasive amebiasis. In contrast, TNF-α and IL-6, which are inflammatory factors, were strongly sustained and expressed during progression of tissue damage. Macrophages were clearly the major effector cells secreting these cytokine mediators. M1 macrophages secreted proinflammatory cytokines, including TNF-α, IL-1β and IL-6, which activate phagocytes to kill pathogens, but also cause tissue damage [46, 47]. The results of immunohistochemistry and qRT-PCR indicated that the levels of multiple cytokines increased during ALA formation. These results suggest that macrophage polarization might profoundly affect the degree of tissue damage in ALA formation.
At the early stage of infection, TNF-α, IL-1β and IL-6 showed an increasing trend with both Entamoeba species, but with higher levels with E. histolytica than with E. nuttalli. The increased TNF-α and IL-6 during amebic tissue damage results in activation of macrophages to release NO and thereby exert an anti-inflammatory effect. Our data show that macrophage polarization by E. histolytica SAW755CR induced greater upregulation of iNOS expression at the transcriptional level, resulting in a higher proportion of M1 polarized macrophages, which then secreted higher levels of proinflammatory cytokines and aggravated amebic tissue damage. The released TNF-α and IL-1β feedback to further skew macrophage differentiation towards the M1 phenotype. Consequently, small multiple abscesses merge with each other and coalesce to form large single abscesses after infection with E. histolytica SAW755CR. M2 macrophages generally have high levels of mannose receptors and scavenger receptors, and play important roles in polarized Th2 reactions. For example, M2 macrophages promote the encapsulation and killing of parasites and have immunoregulatory and anti-inflammatory functions [48]. This macrophage population is also thought to play a critical role in negative regulation of host protective immunity against microbial infections. Thus, M2 macrophages modulate expression of anti-inflammatory cytokines such as MRC or transforming growth factor, and thereby modulate suppression of tissue inflammation and enhance tissue repair [49]. In E. nuttalli infection, expression levels of TNF-α and IL-1β decreased at 168 h after inoculation, whereas expression of MRC was upregulated with the same trend as that of Arg-1. The downregulation of TNF-α and IL-1β suggests that tissue damage might be slowed in E. nuttalli infection. Macrophage polarization tends to reach an equilibrium with the increase in the M2 phenotype. Repair-associated factors begin to take effect and inhibit ALA formation and development. Finally, this leads to formation of multiple small abscesses that are incapable of coalescing into larger lesions, consistent with the finding that E. nuttalli GY4 forms smaller liver abscesses than E. histolytica SAW755. Thus, our results show that differential secretion of amebic virulence factors in E. nuttalli infection may trigger lower cytokine secretion and promote polarization of macrophages towards a M1/M2 balance.
In consideration of intestinal immunity that is the first line of defense against amoeba trophozoites, a critical aspect in Entamoeba pathogenesis is to overcome the colonic epithelial barrier [50–53]. The intestinal bacterial microbiota is another important factor that influence the pathogenesis of E. histolytica. This could be interrelated to direct ingestion of intestinal bacteria that increase the expression of virulence proteins of E. histolytica [54–58]. The bacteria could also alter the immune status of host intestine to prevent or promote amoebiasis. For instance, the increasing of IL-23, IL-17, dendritic cells and neutrophil induced by segmented filamentous bacterium in the cecum mediated protection from E. histolytica [59, 60]. E. nuttalli trophozoites may elicit ALA formation with intense inflammatory reaction in human if the parasites translocate to liver. However, E. nuttalli is probably not adapted to intestinal microenvironment of human and unable to invade beyond the colonic epithelial barrier of human under natural conditions.
There is unambiguous evidence of an intense acute inflammatory reaction in hamster liver in infection by both Entamoeba species, but no evidence showing that these events in hamster liver also occur in human liver. Moreover, it is unknown whether E. nuttalli trophozoites produce intestinal ulcer in human and non-human primates. Additionally, as well known in genetic restriction, a T-cell receptor recognizes a particular antigenic peptide presented by a specific histocompatibility complex (MHC) molecule, and this interaction is associated with susceptibility or resistance to pathogen infection [61–64]. For instance, highly polymorphic HLA genes have an enormous capacity to bind to viral peptides associated with HBV infection [65] and a single MHC supertype confers qualitative resistance to Plasmodium relictum infections in avian malaria [66]. Consequently, host MHC molecule may also play a key role in determination of host susceptibility to E. nuttalli.
In conclusion, histopathological features and expression levels of proinflamatory factors in ALAs formed by E. nuttalli were identified in this study. The results also suggest that the difference of tissue damage in infection by E. histolytica and E. nuttalli is due to the levels of secretion of various cytokines, regardless of the extent of macrophage polarization. In any event, both Entamoeba species induced intense acute inflammatory reactions in liver of hamsters after infection.
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10.1371/journal.pcbi.1003681 | A Quantitative Comparison of Anti-HIV Gene Therapy Delivered to Hematopoietic Stem Cells versus CD4+ T Cells | Gene therapy represents an alternative and promising anti-HIV modality to highly active antiretroviral therapy. It involves the introduction of a protective gene into a cell, thereby conferring protection against HIV. While clinical trials to date have delivered gene therapy to CD4+T cells or to CD34+ hematopoietic stem cells (HSC), the relative benefits of each of these two cellular targets have not been conclusively determined. In the present analysis, we investigated the relative merits of delivering a dual construct (CCR5 entry inhibitor + C46 fusion inhibitor) to either CD4+T cells or to CD34+ HSC. Using mathematical modelling, we determined the impact of each scenario in terms of total CD4+T cell counts over a 10 year period, and also in terms of inhibition of CCR5 and CXCR4 tropic virus. Our modelling determined that therapy delivery to CD34+ HSC generally resulted in better outcomes than delivery to CD4+T cells. An early one-off therapy delivery to CD34+ HSC, assuming that 20% of CD34+ HSC in the bone marrow were gene-modified (G+), resulted in total CD4+T cell counts ≥180 cells/ µL in peripheral blood after 10 years. If the uninfected G+ CD4+T cells (in addition to exhibiting lower likelihood of becoming productively infected) also exhibited reduced levels of bystander apoptosis (92.5% reduction) over non gene-modified (G-) CD4+T cells, then total CD4+T cell counts of ≥350 cells/ µL were observed after 10 years, even if initially only 10% of CD34+ HSC in the bone marrow received the protective gene. Taken together our results indicate that: 1.) therapy delivery to CD34+ HSC will result in better outcomes than delivery to CD4+T cells, and 2.) a greater impact of gene therapy will be observed if G+ CD4+T cells exhibit reduced levels of bystander apoptosis over G- CD4+T cells.
| HIV infects and depletes the body's immune cells (CD4+T cells), and if untreated results in Acquired Immunodeficiency Syndrome (AIDS) and mortality approximately 10 years after initial infection. To protect the host against HIV induced immune depletion, either the main target cells (CD4+T cells) or the stem cells that produce the immune cells (hematopoietic stem cells) can be targeted for treatment with gene therapy. Gene therapy is the process of altering the genetic code of the host cell by the use of an integrative virus which has been modified to be safe and express the desirable genes. While a limited number of clinical studies have delivered gene therapy to either cellular target, the relative merits of each approach in terms of efficacy of AIDS treatment remain poorly understood. In the present analysis, we modelled clinical outcomes with gene therapy delivery to either CD4+T cells or to HSC. We found that delivery to HSC would result in better outcomes and the establishment of a persistent population of gene-modified CD4+T cells. These results provide important quantitative insights that may serve to optimize gene therapy delivery in upcoming clinical trials.
| Anti-HIV gene therapy represents a promising alternative treatment to combination antiretroviral therapy (cART) [1]–[5]. It involves the introduction of a protective gene into a cell, thereby conferring protection against HIV. While cART is a life-long systemic treatment that suffers from toxicity, co-morbidity, attendant compliance and viral resistance concerns [6]–[8], gene therapy may be envisaged as a full or partial replacement for cART that may help overcome these issues. A therapy that reduces or eliminates the need for continued systemic treatment holds significant advantages.
While genetic constructs may be introduced into a cell to inhibit various stages of the HIV infection pathway [9] (including pre-entry, pre-integration, and post-integration), several lines of evidence, including predictions from mathematical modelling [10], now indicate that inhibition of viral entry is most likely toachieve best clinical outcomes. Additionally, over 95% of HIV-induced cell death has been attributed to bystander apoptosis resulting from viral entry into a cell without viral integration into the cellular genome [11]. Suppressing viral binding to the CCR5 receptor induces additional benefits. Individuals with a 32 base pair deletion in their CCR5 gene (Δ-32) have reduced CCR5 expression on the surface of their CD4+T cells, and achieve full (homozygous) or partial (heterozygous) protection against HIV infection [12]–[15]. The importance of targeting the CCR5 mode of viral entry is further supported by the “curative effect” seen from transplantation of Δ-32 mutation hematopoietic stem cells to the “Berlin patient” with AIDS and leukaemia [16]. Collectively these observations have given strong impetus for gene therapy constructs that inhibit/target the entry stage of the HIV infection cycle.
Gene therapy can be delivered to a number of cellular targets including CD4+T cells [1] and CD34+ hematopoietic stem cells (HSC) [3]. While safety and indication of biological effect in HIV-infected individuals have been observed for delivery to CD4+T cells [17]–[24] and to CD34+ HSC [25]–[29], the clinical impact of each cellular target in terms of long-term preservation of total CD4+T cell counts and forestalment of AIDS remains uncertain. The relative merits of one cellular target over the other remain poorly understood.
In the present analysis we are concerned with a quantitative comparison of the merits of delivering an anti-HIV gene therapy into either CD4+T cells or into CD34+ HSC. We consider a dual anti-HIV genetic construct containing both a CCR5 entry inhibitor [1] and a C46 fusion inhibitor [23], [30], which will be delivered in-vivo in an upcoming phase I/II clinical trial conducted by members of our group [31]. While CCR5 inhibitors employing zinc finger nucleases have recently reported high-levels of protection against HIV in humanized mice studies [32], [33] and provided an indication of therapeutic effect in the ongoing phase I/II clinical trials SB-728-T [34]–[36], it is now also well-established that blocking or down-regulating the CCR5 co-receptor favours selection for CXCR4 (X4) tropic virus [37]–[39]. The emergence of ×4 virus is of concern, since ×4 viral strains are generally associated with accelerated progression to AIDS [40]–[42]. These observations of increased ×4 selection when the CCR5 co-receptor is blocked have provided strong impetus for anti-HIV gene therapy that, in addition to a CCR5 inhibitor, also employs an additional inhibitor to suppress ×4 viral strains [23], [30].
Previously we modelled the long-term in-vivo dynamics of an anti-HIV ribozyme (OZ1) [43], that was delivered autologously to CD34+ HSC in a recent phase II clinical trial conducted by members of our group [28]. In the present analysis we extend upon this previous modelling and investigate the long-term impact of delivering a dual anti-HIV gene construct (CCR5 entry inhibitor + C46 fusion inhibitor) to either CD4+T cells or to CD34+ HSC. Gene therapy delivery would be achieved through large-volume apheresis, cell selection for either CD34+ HSC or CD4+T cells, transduction with the gene therapeutics, followed by re-infusion [18], [44]. We evaluate the likely clinical impact in terms of preservation of total CD4+T cells, as well as in terms of forestalment of AIDS. The following points of interest were also investigated for therapy delivery to each of the two cellular targets:
The model employed here is depicted in Figure 1 and is described by the following differential equations, with all parameters listed in Table S1
where the index G-, G+ respectively denotes non gene-modified CD4+T cells ( G-) and CD4+T cells containing the anti-HIV dual gene construct (CCR5 entry inhibitor + C46 fusion inhibitor, G+). Gene-modified (G+) CD4+T cells are assumed to be less susceptible to viral infection than non-gene–modified (G-) CD4+T cells (see below).
The variables , and respectively denote the number of resting naive, activated and resting memory CD4+T cells at time (in unit of days). The variables and respectively denote the total number of productively infected cells of strain and the total number of viral particles of strain .
The terms denote thymic export of naive CD4+T cells, with and denoting the thymic export of G- and G+ CD4+T cells respectively. The term (here ) denotes the fraction of G+ CD34+ HSC in the bone marrow. Here denotes total thymic output in a healthy individual. The term (here ) models reduction of thymopoiesis with duration of untreated infection 45]. It is assumed that in a healthy individual. The term is governed by the equation (here are parameters), where denotes the total number of viral particles. In our model, the presence of substantial viremia ( 4 log10 HIV RNA copies/mL) reduces thymic supply (), but lower levels of viremia ( 4 log10 HIV RNA copies/mL) results in restoration of thymic supply ().
The parameters denote the net effect of homeostatic proliferation and of cell death in the compartment of resting naive and memory CD4+T cells respectively. and respectively denote death rates of activated and productively infected CD4+T cells.
The terms and denote normal activation rates (in a healthy individual). The terms and model HIV-induced activation of resting CD4+T cells [46], and are assumed to depend on the total viral load as well as the total number of CD4+T cells. Here . Since this total is mainly used in association with the number of target cells for infection and the pool of uninfected cells available for activation, we did not include the relatively small number of infected cells. These terms are defined by:
where and denote parameters, so that HIV-induced activation levels increase with higher total viral load and with lower total CD4+T cell count .
The term denotes clonal expansion following activation. Activated CD4+T cells expand by a factor of , resulting in approximately activated cells/ µL as observed during HIV infection [47]. The term denotes reversion from the activated to the quiescent/resting state. Virions are produced at rate per day per infected cell and removed at rate .
Here denotes the infection rate by viral strain (here ) of target cells of phenotype (here ), where denotes the infectivity of viral strain for target cells . The terms model increasing viral infection rates over the course of infection [48]–[50] and with accumulation of total viral load [51], resulting in higher viral loads with longer duration of infection:
The term models increased selection for ×4 virus with lower total CD4+T cell counts [40]–[42], reflecting increased availability of CXCR4-expressing activated target cells for productive infection by ×4 virus at lower total CD4+T cell counts [52], [53]. Here increases monotonically with decreasing total CD4+T cell counts , so that ×4 selection is driven by decreasing total CD4+T cell counts . The parameters and are selected so that ×4 emergence occurs with a median time of approximately 4 years post-infection. The time of ×4 emergence is defined in our model as the time at which the ×4 viral load exceeds a value of 100 HIV RNA copies/mL. The parameter is drawn randomly from a uniform distribution (see Table S1), so that the 5th and 95th percentiles of ×4 emergence times are approximately 1 and 8 years respectively, thereby capturing the observed variability in the time of ×4 emergence [40]–[42], [54]. The simulations with no ×4 virus are produced by setting .
The effective viral and cell population sizes in our simulations are taken as the total numbers of virions/cells in the body. When scaling to numbers and concentrations in peripheral blood (PB), we assume a 5L PB volume and also that 5.5% of total CD4+T cell reside in PB [55]. The value of 5.5% was obtained from analysis of peripheral blood data on CD4 and CD8+T lymphocyte concentrations after aphereses conducted during a previous gene therapy trial in humans. The number of virions per ml of PB is then estimated as 5.5% of the total body load divided by the 5,000 mls of PB. Although simulations for CD4+T cells and HIV RNA copies are shown per µL and per mL of PB respectively (in the Results section), all calculations are determined over total numbers in the body.
The course of infection is simulated over a 10 year period. We assume that time corresponds to the end of primary HIV infection (PHI), with a total CD4+T cell count in PB of 800 cells/ µL, such that initial levels of correspond to 60, 270 and 470 cells/ µL in PB respectively [56]
The number of R5 virions was initialized to a value of (corresponding to an R5 viral load of 4.5 log10 HIV RNA copies/mL in PB) and the initial number of productively infected cells of R5 tropism was initialized to a value of cells. The number of virions and number of productively infected cells for the ×4 virus were both initialized to zero.
The term modelling decline of thymopoiesis is initialized to , so that an individual at end of PHI (i.e. time ) has a 20% reduction in thymopoesis compared to a healthy individual.
The model parameters are given in Table S1, and were selected from the literature, with unknown model parameters determined by model calibration against known in-vivo dynamics of CD4+T cells and viral loads.
The model was calibrated to capture the following dynamical aspects of the in-vivo biology:
In the present modelling G+ CD4+T cells contain a dual gene construct (CCR5 entry inhibitor + C46 fusion inhibitor). The CCR5 gene therapeutic inhibits infection by R5 virus as a result of CCR5 down-regulation on the cell surface [1], and we assume it reduces HIV infection by R5 virus by 92.5% () in line with in vitro analysis [59], but test sensitivity of results relative to an efficacy range of 87.5% to 97.5%. The C46 gene therapeutic inhibits fusion of both R5 and ×4 virus, and has been shown to reduce infection against R5 virus by 1 to 2 logs [23], [30], [60] and also against ×4 virus [30], [61]. In line with our assumptions for the CCR5 gene therapeutic and compatible with this viral load decrease we assume the C46 therapeutic also reduces HIV infection by 92.5% (). Hence with this combination gene therapy G+ CD4+T cells exhibit reduced likelihood of infection from R5 by an amount and from ×4 by an amount .
For the case that the gene therapy is delivered as a one-off treatment to CD34+ HSC at time with of CD34+ HSC receiving the gene construct, we set for times after , where for . It is assumed that the percentage reflects the percentage of G+ CD34+ HSC in the bone marrow at steady state following engraftment. If the gene therapy is delivered repeatedly, then we assume that each repeated infusion results in of the G- CD34+ HSC in the bone marrow receiving the gene construct. In particular, for any time-point at which therapy delivery is performed, if denotes the fraction (of G+ CD34+ HSC in the bone marrow) just after delivery and denotes the fraction just before delivery, then .
Similarly for gene therapy delivered to CD4+T cells in PB with of CD4+T cells receiving the gene construct, we assume that of the G- CD4+T cell subpopulations (,,) receive the gene construct. If the gene therapy is delivered repeatedly, then we assume that each repeated infusion results in of the G- CD4+T cells receiving the gene construct.
The model assumptions from above represent the standard scenario (STD) considered in the present analysis. We also consider the impact of gene therapy subject to the following alternative assumption that acts to increase the impact of gene therapy in terms of preserving total CD4+T cell counts and decreasing viral loads:
As well as simulating the model with the parameters given in Table S1, we also performed a sensitivity analysis using 5,000 parameter sets determined from Latin Hypercube Sampling sampled uniformly with a ±10% variation of the parameters , and a ±5% variation of . The sensitivity analysis was performed in the scenario when ×4 virus was not present. The parameter was limited to a 5% variation since its value was highly correlated with final CD4+T cell counts, while was highly correlated with final CD4+T cell counts under Assumption A1. In both cases a 5% variation limited variation around the mean of CD4+T cell counts to 25%.
All simulations were implemented in Mathworks MATLAB 2012a.
To calibrate the model, we first simulated the course ofuntreated HIV infection where an individual is infected with R5-tropic virus and in whom no ×4 virus is assumed (Figure 2 A,B). Here the total CD4+T cell counts decline from a value of 800 cells/ µL to a value below 200 cells/ µL (AIDS) after approximately 9 years (Figure 2 A) in line with the clinical course of untreated HIV infection with R5 tropic virus [58]. The total CD4+T cell count at the end of the simulated 11-year period (1 year post-infection plus an additional 10 years) is approximately 173 cells/ µL. Over the course of infection, the viral load exhibits an increase from a value of approximately 4.5 log HIV RNA copies/mL at year 0 to a value of 5 log HIV RNA copies/mL after 10 years (Figure 2 B), reflecting increasing viral fitness/diversity over the course of infection that is independent of viral tropism [48]–[50].
We also reproduce the course of untreated HIV infection where an individual is initially infected with R5-tropic virus but in whom ×4 virus can emerge (Figure 2 C,D,E), with selection for ×4 virus driven by decreasing total CD4+T cell counts in our model (see Methods). We performed Monte Carlo simulations with 100 trials, with the parameter repeatedly sampled from a uniform distribution (see Methods and Table S1). Here ×4 emergence occurs with a median time of approximately 4 years post-PHI (Figure 2 E), resulting in accelerated progression to AIDS with a median time of AIDS of approximately 7.5 years post-PHI (Figure 2 C). Our model also captures significant variation in the time that ×4 emergence is observed (Figure 2 E) in line with clinical observations. At the end of the simulated 11 year period, the median total CD4+T cell count was 146 cells/ µL, which is considerably lower than the value of 173 cells/ µL with R5 virus only (Figure 2 A).
First we consider the case that gene therapy is delivered to CD4+T cells as a one-off treatment at 1 year post-PHI and with 20% of CD4+T cells receiving the gene construct (Figure 3). Simulation outcomes were determined with R5 virus only (Figure 3 A,B,C), and also when both R5 and ×4 viral strains were assumed in the simulations (Figure 3 D,E,F,G).
Under the standard scenario (STD), the total CD4+T cell counts and viral load are only marginally higher than for the untreated case from Figure 2, so that final median total CD4+T counts at the end of the 10 year period of 181 cells/ µL are observed if no ×4 virus is present will be the standard value for which we report (Figure 3 A,D, Table 1). Simulations with ×4 virus result in faster loss of CD4+T cells for an untreated individual and also less reconstitution of T cell counts especially with low levels of gene therapy (Table 2). The population of G+ CD4+T does not persist (Figure 3 B,E) due to their replacement with G- CD4+T cells from the thymus, resulting in negligible numbers of G+ CD4+T cells by 4 years post-PHI. The initial viral load decrease observed when therapy is delivered is not sustained for long (Figure 3 C,F).
Substantially improved outcomes are achieved under the assumption of decreased bystander apoptosis of G+ CD4+T cells (+A1). Here the G+ CD4+T cells persist at stable levels due to their relative advantage in terms of lower activation even against new G- CD4+T cells exported from the thymus (Figure 3 B,E). This scenario results in substantial preservation of CD4+T cell counts, with median total CD4+T cell counts of 355 cells/ µL after 10 years (Figure 3 A,D, Tables 1, 2). A marked and sustained reduction in R5 viral load (Figure 3 C,F), as well as strong suppression of ×4 emergence (Figure 3 G), are also achieved.
Next we consider the impact when therapy is delivered to CD34+ HSC at 1 year post-PHI and as a one-off treatment with 20% of CD34+ HSC receiving the gene construct (Figure 4). Simulation outcomes were determined with R5 virus only (Figure 4 A,B,C, Table 3), and also when both R5 and ×4 viral strains were assumed in the simulations (Figure 4 D,E,F,G, Table 4).
When gene therapy is delivered to CD34+ HSC under the standard scenario (STD), median total CD4+T cell counts of 211 cells/ µL are observed if ×4 virus is not present and 180 cells/ µL if it is (Figure 4 A,D, Tables 3, 4), both of which are higher than the corresponding values when therapy was delivered to CD4+T cells under the standard scenario STD. Furthermore G+ CD4+T cells persist at relatively constant levels (Figure 4 B,E) and do not decay as observed in the standard scenario when therapy was administered to CD4+T cells (Figure 3 B,E). A sustained viral load reduction is achieved in R5 virus (Figure 4 C,F).
Much higher impact was observed under the assumption of decreased bystander apoptosis of G+ CD4+T cells (A1), where median total median CD4+T cell counts of 432 cells/ µL were achieved at the end of 10 years (Figure 4 A,D).
In each of the two scenarios median total CD4+T cell counts at the end of the 10 year period were higher for one-off delivery to CD34+ HSC than for one-off delivery to CD4+T cells.
We also considered the scenario that gene therapy is delivered to CD4+T cells repeatedly every year starting at 1 year post-PHI (Figure 5). Here at each time of therapy delivery (i.e. every year starting from year 1), 20% of G- CD4+T cells become G+ CD4+T cells. Simulation outcomes were determined with R5 virus only (Figure 5 A,B,C), and also when both R5 and ×4 viral strains were assumed in the simulations (Figure 5 D,E,F,G).
Under the standard scenario (STD), the impact is more pronounced with repeated delivery of therapy to CD4+T cells than with one-off delivery, so that median total CD4+T cell counts of 226 cells/ µL are observed after 10 years (Figure 5 A D). This is in contrast to one-off delivery of therapy to CD4+T cells under the standard scenario STD, where final total CD4+T cell counts of 181 cells/ µL were observed.
Most impact was achieved under the scenario of reduced bystander apoptosis of G+ CD4+T cells (+A1), with median total CD4+T cell counts of 534 cells/ µL (Figure 5 A,D) and substantial viral load inhibition at the end of the 10 year period (Figure 5 C,F,G). Substantial inhibition of ×4 viral strains was also observed under this scenario (Figure 5 G). Repeated administration of therapy under this scenario (+A1) results in improved outcomes over one-off administration of 534 cells/ µL compared to 355 cells/ µL (Table 1).
We also considered the scenario that gene therapy is delivered to CD34+ HSC every year starting at 1 year post-PHI (Figure 6). Here, at each time of therapy delivery (i.e. every year starting from year 1), 20% of G- CD34+ HSC become G+ CD34+ HSC. Simulation outcomes were determined with R5 virus only (Figure 6 A,B,C, Table 3), and also when both R5 and ×4 viral strains were assumed in the simulations (Figure 6 D,E,F,G, Table 4).
Substantial preservation of total CD4+T cell counts was observed (Figure 6 A,D), resulting in median total CD4+T cell counts of 375 cells/ µL at the end of 10 years for the STD scenario and 687 cells/ µL under scenario +A1, when ×4 virus did not emerge. The population of G+ CD4+T cells persists and even expands under each scenario (Figure 6 B,E), albeit at different rates for each of the two scenarios. Under the +A1 scenario, both R5 and ×4 viral load were driven well below 10,000 HIV RNA copies/mL within 10 years.
These final total CD4+T cell counts are higher than the corresponding values for the case that therapy is delivered repeatedly to CD4+T cells. Repeated delivery to CD34+ HSC also resulted in improved outcomes over one-off delivery to CD34+ HSC.
To further explore the impact of gene therapy under the two scenarios of interest (STD, +A1), we also considered the long-term impact of gene therapy under the following cases:
Outcomes were determined in terms of total CD4+T cell counts 10 years after commencement of therapy, i.e. if therapy was first delivered at time , then outcomes were determined in terms of total CD4+T cell counts at years. The final total CD4+T cell counts, for therapy delivery to CD4+T cells and to CD34+ HSC, are shown in Tables 1 and 3 respectively (simulations with R5 virus only) and in Tables 2 and 4 (simulations including both R5 and ×4 virus). The differences, in outcomes for final total CD4+T cell counts, between therapy delivery to CD34+ HSC versus therapy delivery to CD4+T cells are given in Tables 5 and 6.
Under both scenarios (STD and +A1), therapy delivery to CD34+ HSC resulted in better outcomes in terms of final total CD4+T cell counts over therapy delivery to CD4+T cells (Tables 5 and 6). Even though this was observed for both the standard scenario STD and for scenario +A1, the effect was substantially more pronounced for the standard scenario STD.
Our modelling determined that even with only 10% of CD34+ HSC in the bone marrow receiving the gene construct as a one-off treatment, final total CD4+T cell counts of >360 cells/ µL could be achieved provided Assumption +A1 held (Tables 3 and 4). This was observed for therapy commencement at any stage of the infection (i.e. at 1, 4 or 7 years post-PHI) as well as when ×4 virus was included. These results indicate that substantial increases in total CD4+T cell counts can be achieved, even if therapy is first administered at later stages of the infection and even if only a small percentage of total cells receive the gene construct.
We observed that one-off administration of therapy to CD4+T cells under the standard scenario STD generally resulted in limited clinical impact (with final total CD4+T cell counts of <200 cells/ µL; see Tables 1 and 2), and only slightly better outcomes under scenario STD could be achieved with repeated therapy administration to CD4+T cells. Repeated therapy administration to CD34+ HSC (therapy delivery every 1 or 2 years) generally achieved much better results than single delivery to CD34, in contrast to the muted improvement for delivery to CD4 (Tables 3 and 4). Outcomes with repeated therapy administration under Assumption +A1 resulted in even better outcomes.
We observed that, under the standard scenario STD, commencement of therapy at earlier stages of the infection always resulted in higher final total CD4+T cell counts than commencement of therapy at later stages of the infection (Tables 1, 2, 3, 4). In contrast, under Assumption +A1, we observed that commencement of therapy at later stages could in some instances result in better outcomes (i.e. higher final total CD4+T cell counts) than commencement at early stages. This effect under Assumption +A1 was generally observed when therapy was delivered as a one-off treatment. When this “non-linear” effect was observed viral load (and also the viral load accumulation with time) was higher when therapy was commenced late than when commenced early, and resulted in increased selection for G+ CD4+T cells. This observed effect was however only substantially pronounced under Assumption +A1.
We also observed that, under Assumption +A1, in some instances final total CD4+T cell counts were higher when both R5 and ×4 viral strains were included in the modelling than when R5 virus only was included (compare Tables 1 and 2, also Tables 3 and 4). This effect in our modelling was again attributable to the fact that higher viremia (due to ×4 emergence) resulted in increased selection for G+ CD4+T cells for the same reasons as outlined above. The effect was most pronounced when therapy was commenced at a later stage of the infection (i.e. at 4 or 7 years post-PHI) and/or when the percentage of cells receiving the gene construct was low.
In summary Assumption A1 delivers a marked increase of CD4+T cell counts regardless of the delay before commencement of therapy. Delivery to CD34+T cells remains superior to direct gene delivery to CD4+T cells in all cases.
In the present analysis we evaluated the long-term impact on the course of HIV infection when a dual anti-HIV gene construct (CCR5 entry inhibitor +C46 fusion inhibitor) is delivered to either CD4+T cells or to CD34+ HSC. Previous computational studies have established that gene constructs that inhibit the early stages of the HIV infection cycle (i.e. pre-integration stages including entry/fusion steps) are more likely to achieve better long-term outcomes than those that inhibit the later stages [10], [67]–[69]. In the present study we determined the impact of delivering entry/fusion inhibitors to either CD4+T cells or to CD34+ HSC, in terms of preservation of total CD4+T cell counts, as well as in terms of inhibition of both R5 and ×4 viral loads, over a 10 year period.
Our modelling determined that gene therapy delivery to CD34+ HSC resulted in better outcomes than delivery to CD4+T cells in all circumstances (Tables 5, 6). When therapy was delivered to CD34+ HSC, a gradual accumulation of a sizeable, but persistent, population of G+ CD4+T cells (Figure 4, 6) was observed, resulting in the gradual exertion of protective effects by the gene therapy. In contrast, therapy delivery to CD4+T cells resulted in an immediate population of G+ CD4+T cells, but limited persistence/expansion of G+ CD4+T cells under this scenario (Figure 3, 5). The gradual accumulation of protective effects, when therapy was delivered to CD34+ HSC, is in agreement with previous reports that CD4+T cell export due to thymopoesis is a slow process [2], [5] estimated to contribute approximately 1 CD4+T cell/ µL/day in PB [70]. Thymic export rates are likely to be even lower in HIV-infected individuals, since thymopoiesis declines with duration of HIV infection [45], [71], as modelled in the present analysis, but that can also partially reconstitute at least with cART [72].
In the present modelling we determined that gene therapy delivery to CD34+ HSC can achieve greater clinical impact than with delivery to CD4+T cells, so that even a one-off gene therapy delivery to 20% of CD34+ HSC in the bone marrow resulted in final total CD4+T cell counts of 211 cells/ µL after 10 years under the standard scenario STD for the case that R5 virus only was modelled. If the uninfected G+ CD4+T cells (in addition to exhibiting reduced likelihood of becoming productively infected) also exhibited reduced levels of bystander apoptosis over G- CD4+T cells (i.e. under Assumption +A1), then with 10% of CD34+ HSC in the bone marrow receiving the gene construct as a one-off treatment, final total CD4+T cell counts of 354 cells/ µL. In contrast however, in-vivo studies to-date have reported very low levels of gene-marking in PB post-infusion with little or no clinical impact. These previous in-vivo studies have employed a number of anti-HIV gene constructs, including an anti-HIV OZ1 ribozyme [25], [28], a rev-responsive element decoy gene [27], a humanized dominant-negative REV protein (huM10) [29] and a triple construct that included a CCR5 ribozyme [26]. This discrepancy between the present modelling and previous in-vivo studies is most likely attributable to low engraftment levels (following infusion and equilibration) of gene-modified CD34+ HSC in bone marrow in those previous studies [73], possibly since no ablative regimens (cytoreduction) were performed. Bone marrow ablation was performed in one study in HIV-infected individuals with leukaemia [26], but in that study only a low percentage (<0.2%) of total CD34+ HSC received the gene construct, which resulted in persistent albeit low-level gene marking in peripheral blood (<0.2%) at 18 months post-infusion. In contrast, studies in mice employing bone marrow pre-conditioning using irradiation have demonstrated substantial expansion of gene-protected CD4+T cells and substantial anti-viral effect of therapy delivered to CD34+ HSC when CCR5 inhibitors were used [32], [74]. Hence higher engraftment levels should result in greater impact of gene therapy delivered to CD34+ HSC, as indeed observed in the present analysis. Myeloablation in the context of this modelling of delivery of gene-containing HSC would only be feasible for one-off delivery.
A recent study of repeated infusions of autologous CD4+T cells containing a lentiviral vector expressing an anti-sense gene complementary to HIV env, determined no additional persistence of the gene-containing cells with multiple infusions [75], unlike our calculations where repeated infusions always produced substantially higher CD4+T cell counts after 10 years. One possible explanation for this discrepancy relates to the function of the gene therapy. We and others have postulated, with support from mathematical modelling, that only Class 1 gene therapies that inhibit infection rather than only suppressing viral replication post-infection, will be effective [9], [67]. Both gene therapies modelled here are Class 1 whereas the therapy in the above study was not. However it may be that multiple infusions will be less effective than described here with the majority of effect achieved with the first infusion and decreasing returns from subsequent therapies.
In-vivo delivery of gene therapy to CD4+T cells can possibly provide an immediate protective/anti-viral effect, with any subsequent persistence/expansion of G+ CD4+T cells only likely to be observed if the G+ CD4+T cells are subject to substantially increased in-vivo selection over G- CD4+T cells. Recent results describing zinc finger nuclease CCR5 gene modification of autologous CD4+T cells showed an immediate impact on CD4+T cell levels [24]. Although these gene-modified cells decreased over time they did so at significantly slower rates than non-gene-modified CD4+T cells demonstrating a strong protective effect of this gene therapy. In the absence of a strong survival advantage, any expansion of G+ CD4+T cells would be expected to occur as a result of cell division/proliferation, which is a slow process that has previously been estimated at approximately 1 division every 3.5 years for naive T cells and 1 division every 22 weeks for memory T cells [76]. Previous modelling has demonstrated that in the absence of a strong selective advantage and sole reliance on cell division for expansion, G+ CD4+T cells are out-diluted and replaced by the thymic supply of G- CD4+T cells [68]. This is also in line with reports from clinical trials to-date, where gene-marking in PB was generally observed to decay with a half-life in the span of months following infusion [17]–[23], with recent studies reporting gene-marking detection at 10 years post-infusion but at extremely low levels [22] (0.01% to 0.1% of PBMCs expressing the gene construct). In our modelling we observed that if G+ CD4+T cells only exhibited reduced likelihood of productive infection (i.e. under standard scenario STD), then limited persistence/expansion of G+ CD4+T cells and little therapeutic impact was achieved with one-off delivery of therapy to CD4+T cells (Figure 3; Tables 1, 2). In contrast, if G+ CD4+T cells furthermore also exhibited reduced levels of bystander apoptosis (i.e. under Assumption +A1), then long-term persistence/expansion of G+ CD4+T cells and substantial preservation of total CD4+T cell counts were observed even with one-off therapy delivery to CD4+T cells. These results therefore indicate that any reduced levels of bystander apoptosis in G+ CD4+T cells can confer a strong selective advantage on G+ CD4+T cells, resulting in long-term persistence/expansion of G+ CD4+T cells and substantial preservation of total CD4+T cell counts. Inhibition of bystander apoptosis by these gene therapies is based on their ability to restrict HIV env binding and subsequent fusion with the cell membrane [11]. The ability of these gene constructs to achieve this additional aspect is supported by recent reports that C46 delivered to HSC of pigtail macaques provided positive selection of gene containing cells in peripheral blood and tissue, as well as enhanced CTL function and antibody responses [77].
That anti-HIV gene constructs containing a CCR5 entry inhibitor and a C46 fusion inhibitor can result in reduced levels of bystander activation and apoptosis in-vivo (as modelled by Assumption +A1 in the present analysis) is supported by a number of previous studies. It has been reported that levels of bystander apoptosis correlate with the surface expression of CCR5/CXCR4 [62]–[66]. It has also been reported that levels of bystander apoptosis correlate with the fusogenic activity of env [66], while recent results characterize high levels of depletion of non-productively infected cells through caspase-1-mediated pyroptosis [78], [79]. Consequently these previous studies predict a strong survival advantage for G+ CD4+T cells containing the dual construct (CCR5 entry inhibitor + C46 fusion inhibitor), since these have reduced CCR5 expression (due to CCR5 entry inhibitor) and inhibit the viral fusion step of the HIV infection cycle (due to C46 fusion inhibitor), and should therefore be less likely to undergo bystander apoptosis. Previous in situ labelling studies of lymph nodes from HIV-infected children and SIV-infected macaques have also reported that CD4+T cell depletion occurs predominately as a result of bystander apoptosis rather than as a result of productive infection of a cell [63], with over 95% of HIV-induced cell death attributable to bystander apoptosis resulting from viral entry into a cell prior to viral integration into the cellular genome [11]. Collectively therefore, entry/fusion inhibitors can result in reduced levels of bystander apoptosis from these processes and may achieve substantial in-vivo preservation of total CD4+T cell counts, as indeed observed in the present computational analysis. These therapies however would not ameliorate any increased activation and death associated with the heightened cytokine milieu, which would reduce the impact of Assumption A1.
Most impact with delivery of gene therapy is likely to be achieved in viremic patients, as opposed to patients with controlled/undetectable viral loads on stable cART. Previous in-vivo studies have reported increased selection for G+ CD4+T cells during standard treatment interruptions or in patients with substantial/detectable viral loads. This was observed for a number of anti-HIV genetic constructs in previous studies [20], [26], [29], [34]–[36]. Increased selection for G+ CD4+T cells under viremic conditions has also been reported in-vitro and in mouse studies employing CCR5 inhibitors [32], [33], [74]. While these studies have provided an indication of increased selection for G+ CD4+T cells due to viremia, the results of the present modelling now indicate that such effects are also likely to be observed in-vivo in the long-term. While in our modelling the reduced likelihood of productive infection in G+ CD4+T cells conferred a selective advantage over G- CD4+T cells in the presence of viremia, we observed strongest selection for G+ CD4+T cells if these cells furthermore also exhibited reduced levels of bystander apoptosis compared to G- CD4+T cells (i.e. under Assumption +A1). Collectively therefore these results indicate that the presence of viremia is likely to result in higher levels of cell death (following productive infection of the cell) and/or bystander apoptosis in G- CD4+T cells, resulting in the preferential depletion of this “unprotected" G- CD4+T cell subset and thereby driving the preferential expansion of the subset of G+ CD4+T cells.
A significant concern with gene constructs employing CCR5 inhibitors relates to the possibility of increased selection for ×4 viral strains, which are associated with accelerated progression to AIDS [40v42,52,53,80]. This concern is motivated by previous reports of increased ×4 tropism following administration of the CCR5 inhibitor CMPD 167 in three macaques [39]. The recent MOTIVATE clinical trials in HIV infected individuals also reported increased ×4 tropism following administration of the CCR5 inhibitor maraviroc [37], [38]. This particular aspect of increased ×4 selection when the CCR5 co-receptor is inhibited/down-regulated was not modelled explicitly in the present analysis (X4 selection in our model was driven by decreasing total CD4+T cell counts and not as a result of direct therapy pressure, see Methods). There are two reasons as to why this should not represent a substantial shortcoming of the present modelling. Firstly in the present modelling a dual construct (CCR5 entry inhibitor + C46 fusion inhibitor) was employed. Therefore, despite CCR5-downregulation in G+ CD4+T cells, ×4 selection is likely to be mitigated by the C46 fusion inhibitor that acts to inhibit ×4 viral entry into these G+ CD4+T cells. Secondly, strong selection for ×4 is only likely to be observed if the G+ CD4+T cells (containing the CCR5 inhibitor construct) constitute the majority of total CD4+T cells. The presence of a subpopulation of G- CD4+T cells at all times (as in the present modelling) is likely to sustain sufficient wild-type (and R5 tropic) viral replication in the population of G- CD4+T cells, thereby mitigating selection for ×4 virus [67], [68]. This bipolar partition into G+ and G- CD4+ T cells under gene therapy is in stark contrast to the scenario under traditional antiretroviral drugs (including the CCR5 inhibitor maraviroc) that bathe all cells in some inhibitory concentration of the drug thereby resulting in increased likelihood of selection for resistant mutants [67], [68]. Nevertheless the increasing likelihood of ×4 and dual-tropic virus with lower CD4+T cell count may add support for delivery of this combination gene therapeutic to early stages of infection.
Potential shortcomings of the present modelling relate to the additional effect of gene therapy on cell populations other than CD4+T cells, given that G+ CD34+ HSC also differentiate into macrophages and monocytes that are susceptible to HIV infection [3], [5]. Since this was not modelled in the present analysis, it is therefore likely that the present modelling outcomes represent an underestimate of the true benefit of gene therapy delivery to CD34+ HSC, as the establishment of a population of G+ macrophages/monocytes would result in additional protective benefits from therapy delivery to CD34+ HSC. Our modelling also did not include ×4 infection of CD34+ HSC. Previous studies reported that ×4 viral strains can infect CD34+ HSC [81], so that delivery of a protective gene construct (containing a C46 inhibitor that inhibits ×4 infection) to CD34+ HSC is likely to confer an additional survival advantage on G+ CD34+ HSC. However given the lack of quantitative data on HIV infection of HSC, this aspect was not modelled in the present analysis.
Finally, we did not model the emergence of viral strains that exhibit resistance to the present dual construct (however we did model ×4 emergence but this was as a result of lower total CD4+T cell counts and not as a result of direct therapy pressure, see previous paragraph). This should however not significantly impact on our conclusion, given that previous studies reported that the presence of a significant population of G- CD4+T cells at all times (as in our modelling) ensures sufficient wild-type virus replication, so as to mitigate the emergence of viral strains resistant to the gene therapy [67], [68]. Our previous modelling determined that 4 independent short-hairpin RNA (shRNA) anti-HIV constructs (acting independently and each with an 80% efficacy) are required to mitigate the emergence of viral mutants resistant to the gene therapeutic [67]. This implies that a 99.84% overall efficacy by the 4 shRNA constructs (here ) mitigates viral resistance. The dual construct employed in the present analysis however assumed a 92.5% mean efficacy of each construct, giving a 99.44% overall efficacy against R5 tropic virus assuming the two constructs act independently (here )). This figure for likely overall efficacy of the dual construct is comparable to the overall efficacy estimated previously with 4 shRNA, so that resistance to the present dual construct is likely to be mitigated sufficiently. Further to the point, the dual construct employed in our modelling inhibits a cellular process that is less susceptible to mutation than the viral processes targeted by the shRNA [67]. Hence the present dual vector will be a superior therapeutic to the 4 shRNA therapy that suppressed resistance in our previous modelling.
In conclusion we have demonstrated that gene therapy employing entry/fusion inhibitors can achieve substantial clinical impact in terms of long-term preservation of total CD4+T cells counts and forestalment of AIDS. Importantly, this was observed even if only a subset of total cells received the gene construct, indicating that full immune system ablation is not necessary (prior to delivery of the gene therapy) in order to achieve substantial clinical impact. We determined that therapy delivery to CD34+ HSC generally resulted in better outcomes than therapy delivery to CD4+T cells. Maximal impact in our modelling was observed if the uninfected G+ CD4+T cells, in addition to having reduced likelihood of productive infection, exhibited lower levels of bystander apoptosis over G- CD4+T cells. Under this scenario therapy delivery to either CD4+T cells or to CD34+ HSC resulted in substantial preservation of total CD4+T cell counts. The present mathematical modelling demonstrates that gene therapy employing entry/fusion inhibitors represents a promising and potent anti-HIV modality, and that further clinical investigation of these gene therapeutics is more than justified.
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10.1371/journal.ppat.1005021 | Should Symbionts Be Nice or Selfish? Antiviral Effects of Wolbachia Are Costly but Reproductive Parasitism Is Not | Symbionts can have mutualistic effects that increase their host’s fitness and/or parasitic effects that reduce it. Which of these strategies evolves depends in part on the balance of their costs and benefits to the symbiont. We have examined these questions in Wolbachia, a vertically transmitted endosymbiont of insects that can provide protection against viral infection and/or parasitically manipulate its hosts’ reproduction. Across multiple symbiont strains we find that the parasitic phenotype of cytoplasmic incompatibility and antiviral protection are uncorrelated. Strong antiviral protection is associated with substantial reductions in other fitness-related traits, whereas no such trade-off was detected for cytoplasmic incompatibility. The reason for this difference is likely that antiviral protection requires high symbiont densities but cytoplasmic incompatibility does not. These results are important for the use of Wolbachia to block dengue virus transmission by mosquitoes, as natural selection to reduce these costs may lead to reduced symbiont density and the loss of antiviral protection.
| Arthropods are commonly infected with heritable bacteria, and some of these symbionts can protect their hosts against infection and/or be reproductive parasites. Which of these traits evolves will depend on whether the trait is costly to the symbiont and the host. Using a panel of strains of the symbiont Wolbachia in the fruit fly Drosophila simulans, we found that the beneficial effect of antiviral protection and the parasitic phenotype of cytoplasmic incompatibility occur independently across the strains. We found that high antiviral protection is associated with high symbiont densities and strong reductions in other life-history traits affecting the fitness of both the symbiont and the host. In contrast cytoplasmic incompatibility did not induce costs on these traits. This trade-off between antiviral protection and other fitness components may select for reduced antiviral protection, which would endanger the long-term success of programs using Wolbachia to block the transmission of mosquito-borne viruses.
| Heritable symbionts are frequent in insects and their evolutionary success relies on various strategies. By sharing a common route of transmission with their host’s genes, they benefit from increasing host fitness. Consequently, numerous endosymbiotic bacteria evolved towards mutualism, for example by complementing their host diet [1,2], increasing tolerance to environmental stresses [3] or protecting against natural enemies [4–9]. However, because most of these heritable bacteria are maternally-transmitted, the evolutionary interests of host and symbiont are not perfectly aligned since only females transmit the symbiont. This has led to many symbionts evolving selfish strategies that consist of parasitic manipulation of their host’s reproduction by inducing female-biased sex-ratios or cytoplasmic incompatibility (CI) [10]. CI is a sperm modification that results in embryonic mortality in crosses between uninfected females and males harboring the symbiont, thus giving a competitive advantage to infected females that can rescue the sperm modification. Mutualism and reproductive manipulation are not mutually exclusive, and some symbionts display both [11]. However, the balance between the benefits and costs of these extended phenotypes to the symbiont’s fitness, as well as the genetic correlations between them, will determine which of these strategies is favoured by natural selection.
Wolbachia, which are common maternally-transmitted bacterial symbionts of arthropods, can be both parasites and mutualists. Wolbachia has been shown to protect Drosophila and mosquitoes against several RNA viruses—including Dengue and Chikungunya viruses [7,9,12–15]. Some strains also protect insects against filarial nematodes [16], Plasmodium parasites [12,17,18] and pathogenic bacteria [19]. Although it is unclear how important antiviral protection is in nature and whether it is under strong selection, some protective Wolbachia strains are able to invade host populations while inducing no other known phenotypes [20,21]. In addition, Wolbachia has the ability to spread rapidly through insect populations by parasitically manipulating reproduction, in particular by CI [22]. This combination of traits makes Wolbachia an attractive tool for blocking disease transmission by mosquitoes, as CI allows it to spread through vector populations while its antiviral effects can prevent them from transmitting arboviruses [23,24].
Levels of both antiviral protection and CI may evolve rapidly. During the 20th century in natural populations of D. melanogaster the Wolbachia strain wMelCS, which provides strong antiviral protection, was partially replaced by wMel [25,26], which provides weaker protection [27]. In North American populations of D. simulans, field and experimental data suggest that the strain wRi has evolved to produce weaker levels of CI within a few decades [28].
Efforts to use Wolbachia to block the transmission of viruses have focused largely on the mosquito Aedes aegypti, which is the primary vector of dengue virus. Wolbachia has been successfully introduced into two Australian populations of Aedes aegypti [29], and three years post-release it had reached a stable and high prevalence in the field despite having a negative effect on the fecundity of mosquitoes [30]. Both antiviral protection and levels of CI were maintained over time [30,31].
In the long-term, the presence of fitness costs is expected to select for both host genes and bacterial genes that reduce these costs [32]. In accordance with this prediction, the Wolbachia strain wRi evolved from reducing the fecundity of the flies to increasing it within two decades in North American populations of D. simulans [33]. It is possible that the evolution of lower costs could be achieved by a decrease in bacterial densities, as costly Wolbachia tend to have high bacterial densities [27,34,35]. Since a high Wolbachia density may be required for the expression of both antiviral protection [14,27,34,36–38] and CI [35,39–42], the evolution of reduced Wolbachia density might translate into a correlated decrease in the ability to block arbovirus transmission and invade insect populations.
To investigate these questions, we used sixteen Wolbachia strains in a common host genetic background to measure the level of CI induced and effects on other fitness-related traits, and have tested for correlations between these traits and antiviral protection. Our results demonstrate that antiviral protection is independent of CI but that it is associated with reduction on other fitness components. Furthermore, this trade-off can be explained by the density of the bacteria in the somatic tissues of the insect. Overall, our study suggests that newly introduced Wolbachia infections may evolve towards weaker protection in the field.
To compare multiple symbiont strains independent of host genetic effects, we used a panel of Wolbachia strains that had been transferred from different Drosophila species into a single inbred line of D. simulans (Fig 1F). To avoid effects of using an inbred fly line, we crossed these flies to a different inbred fly line and used the F1 progeny in our experiments. Vertical transmission rates were previously estimated and were 100% for all Wolbachia strains used in this study [14].
Cytoplasmic incompatibility causes an excess of embryonic mortality in crosses between symbiont-infected males and uninfected females. Therefore, in order to measure levels of CI induced by different Wolbachia strains, we crossed infected males of each strain with uninfected females and counted the number of eggs that hatched (9,432 eggs from 380 females). There was a significant effect of Wolbachia (Deviance = 681.81; df = 16; P < 0.0001) with a clear division between 10 strains that induce CI and six that do not (Fig 1B). The strength of CI also varied among the 10 CI strains, ranging from just 0.5% of the eggs hatching in incompatible crosses involving the wMel strain, to 38.7% of the eggs hatching with wStv.
We have previously shown that these strains provide varying levels of protection against the viruses DCV and FHV [14], and using this data we found that there was no correlation between CI and the antiviral effects of Wolbachia. This was the case regardless of which virus the flies are infected with or whether antiviral protection is measured in terms of increased survival (black line in Fig 2A and 2B) or reduced viral titre (black line in S1A and S1B Fig). This conclusion also holds if we only analyse the 10 strains that induce significant CI (red line in Fig 2A and 2B; S1A and S1B Fig). Since a decrease in hatch rate in incompatible crosses can be due not only to CI but also to an induced cost on male fertility, we also analysed the correlation between protection and levels of CI corrected for differences in male fertility (the hatch rates of infected females mated with infected males relative to hatch rates when mated with uninfected males). Similar to the uncorrected estimate, these corrected levels of CI did not show any significant correlation with antiviral protection, whether measured as survival after infection (Pearson’s correlation test: All strains: DCV: P = 0.28 and FHV: P = 0.86; CI-inducing strains: DCV: P = 0.67 and FHV: P = 0.71) or as viral titre (Pearson’s correlation test: All strains: DCV: P = 0.58 and FHV: P = 0.95; CI-inducing strains: DCV: P = 0.87 and FHV: P = 0.75).
As Wolbachia is vertically transmitted, reductions in the survival or fecundity of Wolbachia-infected females will reduce the fitness of both the host and the symbiont. To estimate these costs, we measured egg hatch rates (in parallel to the CI crosses, 16,469 eggs from 555 females), early-life fecundity (280,260 eggs from 1,548 females) and female lifespan (913 females) of flies infected with the 16 different Wolbachia strains.
We found significant variation in egg hatch rates between fly lines infected with different Wolbachia strains (Fig 1C; Deviance = 340,97; df = 16; P < 0.0001). When the father was uninfected, four strains caused a significant reduction in hatch rates, with three of them resulting in less than 40% of the eggs hatching (Fig 1C, grey bars). Additionally, when both the mother and father were infected, there was a trend towards even lower hatch rates, with two more strains becoming significant (Fig 1C, blue bars). This suggests that male fertility is also being reduced by Wolbachia or that rescue of CI is not perfect for some of the strains (ie the modification of sperm in males that is required for CI still causes embryonic mortality when the egg is infected).
Fecundity and lifespan are also affected by Wolbachia. For fecundity, two strains increased and two strains reduced the number of eggs laid (Deviance = 250.55; df = 16; P < 0.0001; Fig 1D). Wolbachia also affected female survival (Deviance = 52.37; df = 16; P < 0.0001), with five of the sixteen strains significantly shortening lifespan (Fig 1E).
The strains that provide the greatest protection against viruses (measured as survival) tended to cause the greatest reductions in the other life-history traits of the flies. Hatch rates of Wolbachia-infected females were significantly reduced in flies carrying the symbionts providing the highest levels of protection against both DCV and FHV, whatever the Wolbachia-infection status of males (Fig 3A and 3B; S2A and S2B Fig). Because the tested traits are not phylogenetically independent, we reanalyzed these correlations using phylogenetic independent contrasts (see methods). The correlations between hatch rates and level of protection were robust to the phylogenetic non-independence of the data (S1 Table). Higher levels of antiviral protection were also associated with reduced male fertility (Fig 3C and 3D) and lower fecundity (Fig 3E and 3F), but these correlations were only significant in case of DCV. Phylogenetic independent contrasts analyses also showed that correlations with male fertility and fecundity were significant but it strongly depended on the branch length used in the linear models (S1 Table). No correlation with the level of protection and female lifespan was detected (S2C and S2D Fig; note the smaller sample sizes for this trait). Interestingly, wAu, which is a native strain of D. simulans, provides high antiviral protection yet induced little reduction in hatch rates or fecundity.
If the antiviral effects of Wolbachia were measured as changes in viral titres rather than survival, most of the correlations became non-significant or marginally-significant, but the direction of the relationships remained the same, with low viral titres associated with stronger costs (S3A–S3J Fig). Again, costs induced by wAu on hatch rates were generally lower than expected by the correlations with viral titres.
Similar to antiviral protection, we tested for correlations between levels of CI and other components of host fitness. There was no significant correlation between the level of CI and male fertility, female fecundity, lifespan or the hatch rate of eggs from crosses between Wolbachia-infected females and uninfected males (Fig 4A–4C; S4B Fig). In crosses where both parents were Wolbachia-infected, the level of CI was negatively correlated with hatch rates (S4A Fig). This was only the case when both CI inducing and non-CI inducing strains were analyzed, and it may reflect incomplete rescue of cytoplasmic incompatibility.
We hypothesized that Wolbachia must infect the germline to induce CI and somatic tissues to provide antiviral protection, so differences in tissue tropism between symbiont strains may partly explain why they have different phenotypic effects on their hosts. To examine this, we measured Wolbachia density in somatic tissues (head and thorax of females), testes and freshly laid eggs (as a proxy for the female germline).
There were large between-strain differences in density (Fig 5A–5C). For example, in somatic tissues the Wolbachia copy number varies over a 19-fold range. Furthermore, the strains have different tissue tropisms, with a significant strain-by-tissue interaction (Fig 5A–5C). The density in the testes and head + thorax tended to be tightly correlated (Pearson’s correlation test: r = 0.89; P < 0.0001), and frequently differed from the density in eggs (Pearson’s correlation test: head + thorax–eggs: r = 0.63; P = 0.01; testes–eggs: r = 0.61; P = 0.013).
Variation in Wolbachia density can explain between-strain differences in antiviral protection but not differences in CI. Protection against DCV and FHV was positively correlated with Wolbachia density in head and thorax, whether measured as survival (Fig 6A and 6B) or viral titres (S5A and S5B Fig), even after removing potential phylogenetic effects (S1 Table). This holds when both the density in the soma and eggs are included as predictive variables: protection shows a significant partial correlation with density in the soma but not with density in the eggs (only marginally significant for FHV titre; S2 Table). On the contrary, there is no correlation between levels of CI and density in the somatic tissues (Fig 6C), in the testes or in the eggs (S6A–S6B Fig). The only exception to this was when only analyzing CI-inducing strains, levels of CI were positively correlated to the Wolbachia density in eggs (red line in S6B Fig; note eggs are uninfected in the CI cross).
The negative effects of Wolbachia on host life-history traits are related to the symbiont density, with hatch rates, male fertility and fecundity all negatively correlated to the Wolbachia density in the somatic tissues (Fig 6D–6F) but not with the density in the eggs (Pearson’s correlation test: Hatch rate with uninfected father: P = 0.08; hatch rate with infected father: P = 0.06; male fertility: P = 0.58; fecundity: P = 0.27). The same conclusion holds when controlling for the Wolbachia phylogeny (S1 Table), although for male fertility and fecundity significance depends on the branch length used for the linear model. When these traits are analyzed with a multiple regression, they show significant partial correlations with density in the soma but not with density in the eggs (S2 Table). There was no correlation between female lifespan and Wolbachia density in any of the tissues (Pearson’s correlation test: head + thorax: P = 0.73; testes: P = 0.32; eggs: P = 0.13).
Heritable bacterial symbionts have successfully colonized a wide range of arthropods by using a diversity of strategies ranging from mutualism to parasitism. Typically the evolution of these symbiont strategies has been considered in isolation, but this can be misleading if there are trade-offs between these traits and other components of host or symbiont fitness. Identifying these trade-offs is not only a prerequisite to understand the evolution of symbiosis, but will also inform the use of symbionts in applied programs. Using a set of Wolbachia strains that provide varying levels of protection against viral pathogens, we found that this mutualistic effect was independent of the ability to parasitically manipulate host reproduction. Antiviral protection relies on the bacteria reaching high densities in somatic tissues and is associated with strong reductions in several host life-history traits, while reproductive parasitism is not linked to symbiont density in somatic tissues and not costly to infected females.
While some symbionts are mutualists that spread through populations by increasing host fitness and others are parasites that manipulate host reproduction, others simultaneously have both effects [11]. It is already well known that in Wolbachia antiviral protection and CI are highly genetically variable traits [14,27,37,44]. However, to our knowledge, our study is the first to assess both traits in a wide array of strains in a common host genetic background. We found no correlation between the expressions of these phenotypes, with four strains only providing protection, two strains only inducing CI, eight strains inducing both protection and CI, and two strains showing neither phenotype. Therefore, these traits have independent evolutionary trajectories. Some strains may also rely on alternative strategies to be maintained in populations, such as enhancing the host fecundity or other fitness components [45]. For instance, two of the tested strains in our study were associated with increased fecundity.
Besides antiviral protection and reproductive manipulation, Wolbachia infections can induce fitness costs, with important life-history traits being affected such as lifespan, fecundity, egg viability or larval development and competitiveness [30,46–53]. In accordance with previous studies, we found Wolbachia-induced costs on several traits that should reduce both the fitness of the host and of Wolbachia. In some cases these costs could be very large–for example some strains result in the majority of infected eggs never hatching, suggesting that those strains might not be able to invade natural host populations.
We found that antiviral protection trade-offs with egg hatch rates, female fecundity and male fertility. In many cases highly protective strains induced substantial reductions in these fitness components. Because Wolbachia relies on host reproduction for its transmission, these trade-offs will affect both the host and symbiont, as both partners benefit from antiviral protection and both will suffer from reduced female reproduction. Further evidence that antiviral protection is costly comes from a comparison of the two main Wolbachia genotypes in D. melanogaster populations, which showed that the genotype that provided the greatest antiviral protection also shortened the lifespan of infected flies (Chrostek et al. 2013). Similarly, when wAu is transferred into D. melanogaster it reaches high densities, provides strong protection against viruses and shortens the lifespan of flies (Chrostek et al. 2014). Interestingly, using a similar experimental design to ours, another study showed that high levels of protection conferred by the symbiont Hamiltonella defensa against parasitoids in aphids are associated with less costly symbiont strains contrary to what we found [54]. While the mechanisms of protection in Wolbachia remain to be elucidated, in H. defensa it is known that protection relies on the presence of a bacteriophage encoding a toxin [55,56]. It is likely that different mechanisms of protection lead to different trade-offs with host life-history traits.
The reason that Wolbachia-mediated antiviral protection is so costly appears to be that it requires high symbiont densities. The density of Wolbachia in host tissues have been repeatedly shown to be involved in the ability of the bacteria to protect against viruses [12,14,27,36–38,57,58], and this was also the case in the present study with high protection being associated with higher densities in the somatic tissues of the flies. Using our sixteen Wolbachia strains we were able to test for a correlation between density and costs, and found that high densities of the bacteria in somatic tissues correlate with lower egg hatch rates, male fertility and fecundity. Harboring high loads of Wolbachia might be harming flies due to a re-allocation of resources from host to symbiont or pathological effects of the symbiont infection. Accordingly, wMelPop, a mutant strain that over-replicates causes a severe life-shortening effect [38,48,59] and other high density Wolbachia genotypes in D.melanogaster are associated with reduced lifespan [27,34]. The correlations between antiviral protection, costs on life-history traits and Wolbachia density remained when controlling for phylogenetic effects, which supports the hypothesis that there is a causal link between antiviral protection and costs that is mediated by symbiont density.
Contrary to antiviral protection, we did not observe any trade-off between the expression of CI and the other host fitness components. The explanation for this is likely that CI levels were not correlated to the density of Wolbachia in somatic tissues (note that our sample size is limited if considering just CI inducing strains). CI is thought to be the result of a sperm modification causing improper segregation of the paternal chromosomes after fertilization of the egg [60]. Rather than the overall density of Wolbachia in the somatic tissues, it is the ability of the bacteria to specifically colonize sperm cysts that is thought to allow the expression of CI [39,42]. For this reason we investigated whether differences in tissue tropism between strains might affect whether they cause CI. While tissue tropism did vary, there was no correlation between density in testes and CI, but it may be that this is a poor proxy for the number of sperm cysts that are infected. However, we found that, among CI-inducing strains, levels of CI were positively correlated with the bacterial density in eggs (our measure of female germline density), similar to what was found in another study [40]. It is possible that higher density in the eggs might correlate with bacteria targeting the germ line in developing male embryos. Alternatively, the egg is the site of the rescue activity that prevents the expression of CI in Wolbachia-infected embryos [60], so strains inducing high levels of CI may have evolved towards higher density in the egg to overcome the effect of the sperm modification.
Our findings have important implications regarding the evolution of Wolbachia symbioses, as trade-offs will act as a constraint on the evolution of mutualism (protection) but not reproductive parasitism (CI). Selection will act on both host and parasite genes to reduce the cost of Wolbachia infection, and alone this is likely to lead to the evolution of lower bacterial densities and therefore reduced antiviral protection. Thus, unless antiviral protection is sufficiently strongly selected for, it may reach lower levels or even disappear as the two partners coevolved towards less harmful Wolbachia infection. This prediction is supported by the partial replacement of the highly protective strain wMelCS by wMel, a lower density strain inducing lower protection, in populations of D. melanogaster [25–27]. Strikingly, in the pathogenic strain wMelPop, the symbiont density and the associated level of protection and costs on other life-history traits have been shown to evolve quickly, over a few host generations, suggesting that such changes may rapidly occur in nature [38]. As the most protective strains are very costly, they may only be favoured when there is very strong selection by viruses.
Over the long term, selection may sometimes be able to break a trade-off [61] and lead to the evolution of Wolbachia strains that provide the benefits of antiviral protection but without the associated costs. Because we transferred most of the symbiont strains from other species into D. simulans, the control of the bacterial density and associated costs is expected to be inefficient due to a lack of coevolution between the two partners. This situation therefore reflects new associations that have arisen by horizontal transmission (as frequently occurs during the evolution of Wolbachia). We had one highly protective strain that naturally occurs in D. simulans, and this strain induced little cost on egg hatch rates despite showing rather high bacterial density and strong protection. This strain does not induce CI and yet shows rapid spread in natural populations [20,21] suggesting that protection might be the selective force driving the evolution of this strain. While this suggests that natural selection may be able to break the association between antiviral protection and cost, this may not be inevitable as naturally occurring protective Wolbachia strains in D. melanogaster still reduce the lifespan of flies [27].
Whether CI or antiviral protection is favored by selection will depend not only on the costs of these traits but also the strength of selection favouring the trait. Selection on the symbiont to evolve CI may often be very weak–there is no selection for the phenotype in males in panmictic populations [32,62], and its evolution relies on population structure generating local relatedness [63] [64] (see [65] for an alternative explanation). Our observation that CI is not associated with costly changes in the phenotype of infected females (the transmitting sex) means there may often be little selection on the symbiont to reduce the strength of CI, making it stable over evolutionary time even when population structure is weak.
Finally, our results have implications for the control of vector-borne viral diseases by the introduction of Wolbachia into mosquito populations, as such efforts may fail if selection to reduce the cost of infection leads to reduced symbiont density and therefore the loss of antiviral protection [66]. This is even more likely if viruses cause little harm to its vector or are rare in the vector population, thus inducing little selective pressure on protection [23]. This is the case for the main target of these control efforts, dengue virus, which is thought to only decrease the fitness of mosquitoes by a few percent [67] and its prevalence in mosquito populations is low [68]. Therefore, the long-term maintenance of protection may rely on selection by the wider community of viruses favouring protection. The first releases of Wolbachia infected Aedes aegypti mosquitoes took place in 2011 [29], and one year later the Wolbachia strain still protected against dengue virus infection [31]. Only further monitoring over future years will determine whether this is truly an ‘evolution proof’ method of disease control.
All Wolbachia strains were in the D. simulans STCP line that was generated by six generations of sib matings [69]. Wolbachia was previously backcrossed or microinjected into the STCP line [14,44,69,70].
Flies were maintained on a cornmeal diet at 25°C, 12 hours light/dark and 70% relative humidity. To minimize inbreeding effects, before each experiment STCP females were crossed to males of a different Wolbachia-free isofemale line (14021–0251.175, Dsim\wild-type, San Diego Drosophila Species Stock Center). Groups of 30 first instar F1 larvae were then transferred to new vials to ensure a constant larval density. Measurements of fitness traits were carried out on emerging F1 adults. Except for the fecundity measurements, F1 larvae were raised on a standard cornmeal diet (agar: 1%, dextrose: 8.75%, maize: 8.75%, yeast: 2%, nipagin: 3%) with 100 μl of 15% liquid yeast on the top of the food. For the fecundity experiment (see below), F1 larvae developed on a diet depleted in maize (4.4%) and dextrose (4.4%) with no added yeast to create less favorable conditions. Two generations before the experiments, Wolbachia infection statuses were checked by PCR using primers wsp81F and wsp691R [71].
Virgin F1 male and female flies were collected and aged for 3 and 5 days respectively. Because multiple male matings can decrease the strength of CI [72,73], a male and female were placed in a vial for 4–8 hours. In D. simulans, remating does not occur within 8 hours after the first copulation (Nina Wedell, personal communication). Females were then placed individually on a 50 mm diameter Petri dish with standard cornmeal diet containing food coloring with 15 μl of 15% liquid yeast on the top of the food. Around 20 hours later, females were removed and eggs were counted. Females that laid five or less eggs were discarded. Hatch rates were estimated by counting unhatched eggs about 35 hours later. The compatible crosses between uninfected males and uninfected females showed a mean egg hatch rate of 98%, thus suggesting that most females in this experiment were mated. Moreover, for Wolbachia-infected lines, discarding potentially non-mated females for which none of the eggs hatched did not change the significance of correlations with the other traits as mean hatch rates with or without those females were strongly correlated (Pearson’s correlation test: r ≈ 0.99, df = 14, P < 0.0001).
F1 larvae were raised on our poor diet, and 0 to 2-day-old flies were placed on standard cornmeal food with live yeast on the surface to stimulate egg maturation. After 2 days (2- to 4-day-old), 3 males and 3 females were placed in petri dishes of colored poor diet. Over 6 days, flies were anaesthetized with CO2 and transferred onto a new dish every 24 hours. The number of eggs was recorded by photographing the Petri dish and counting eggs using a multi-point counter tool in ImageJ [74].
As for the hatch rate experiment, F1 larvae were raised on our standard cornmeal diet. Five male and 5 female freshly emerged flies were placed per vial on poor diet. Flies were tipped onto fresh food every 3 days and the number of dead female flies recorded daily for 72 days until all flies died.
To investigate Wolbachia tissue tropism, F1 larvae were reared on standard diet, and virgin males and females aged to 3 and 5-day-old respectively. Males were then anaesthetized on ice and dissected in Ringer’s solution [75]. For each Wolbachia strain, 10 pools of 5 pairs of testes were collected.
Five-day-old females were allowed to mate with 2- to 4-day-old virgin Wolbachia-free STCP males for 24 hours. Females were then isolated and 10 replicates of 3 females per strain were placed in Petri dishes onto grape agar food with 15 μl of 15% liquid yeast on the top. After 6 to 8 hours, 20 eggs were harvested from each Petri dish and transferred into a microcentrifuge tube. In parallel, the head and thorax was separated from the abdomen of 6-day-old females. For each Wolbachia strain, 10 replicates, each consisting of a pool of head and thorax collected from 10 females were transferred into microcentrifuge tubes. All tissues were frozen at -80°C for DNA extraction.
DNA was extracted from the tissue samples using EconoSpin All-In-One Silica Membrane Mini Spin Columns (Epoch Biolabs) and the QIAamp DNA Micro kit (Qiagen). Using the extracted DNA, quantitative PCR (qPCR) was used to determine the Wolbachia density in the carcasses (head and thorax), testes and eggs. For carcasses and testes, the amount of the Wolbachia gene atpD (atpDQALL_F: 5’-CCTTATCTTAAAGGAGGAAA-3’; atpDQALL_R: 5’-AATCCTTTATGAGCTTTTGC-3’) relative to the endogenous control gene actin 5C (Forward primer: 5’-GACGAAGAAGTTGCTGCTCTGGTTG-3’; Reverse primer: 5’-TGAGGATACCACGCTTGCTCTGC-3’) was quantified using the SensiFAST SYBR & Fluorescein kit (Bioline). The Wolbachia density was estimated as: 2ΔCt, where Ct is the cycle threshold and ΔC t = Ctactin5C-CtatpD. The PCR cycle was 95°C for 2 min, followed by 40 cycles of 95°C for 5 s, 55°C for 10 s, 72°C for 5 s. Since embryo mortality due to Wolbachia was observed in our experiment on hatch rate, the Wolbachia density in eggs was estimated as the amount of the gene atpD in a sample relative to the amount of the same gene in a positive control placed on every qPCR plate as follow: 2ΔCt, where Ct is the cycle threshold and ΔC t = Ctpositive control-CtatpD. For each sample, two qPCR reactions (technical replicates) were carried out and a linear model was used to correct for plate effects.
Statistical analyses were performed using R [76]. Hatch rates were analyzed using mixed effect generalized linear models with a logit link function and the effect of individual mothers treated as random (package lme4). Fecundity was analyzed using a linear model with the total number of eggs laid over 6 days as a response and a random temporal block effect. Female lifespan was analyzed with a generalized linear model with the Wolbachia infection status as a fixed effect and vial as a random effect. To test the effects of Wolbachia strain individually on these traits we performed multiple comparisons with the control cross (uninfected flies) using Dunnett’s test (package multcomp).
Wolbachia densities within tissues were log2-transformed and analyzed with a linear model including the effect of the Wolbachia strain, tissue, and their interaction. Between-strain differences in density were tested using multiple comparisons (Tukey’s HSD test, package multcomp).
Between-trait correlations were tested with Pearson’s correlation tests unless the assumptions of normality and homoscedasticity were not reached, in which case Spearman’s tests were used. In order to take into account the phylogenetic non-independence of the data, significant correlations were further analyzed using independent contrasts [77] with the function crunch (R package caper) [78] (See S1 Table).
|
10.1371/journal.pcbi.0030117 | Ligand Binding and Circular Permutation Modify Residue Interaction Network in DHFR | Residue interaction networks and loop motions are important for catalysis in dihydrofolate reductase (DHFR). Here, we investigate the effects of ligand binding and chain connectivity on network communication in DHFR. We carry out systematic network analysis and molecular dynamics simulations of the native DHFR and 19 of its circularly permuted variants by breaking the chain connections in ten folding element regions and in nine nonfolding element regions as observed by experiment. Our studies suggest that chain cleavage in folding element areas may deactivate DHFR due to large perturbations in the network properties near the active site. The protein active site is near or coincides with residues through which the shortest paths in the residue interaction network tend to go. Further, our network analysis reveals that ligand binding has “network-bridging effects” on the DHFR structure. Our results suggest that ligand binding leads to a modification, with most of the interaction networks now passing through the cofactor, shortening the average shortest path. Ligand binding at the active site has profound effects on the network centrality, especially the closeness.
| The cooperative movements within a protein concerning the binding and dissociation of the reactants and products could be important for protein function. Communication among the various parts of an enzyme can be achieved by the networks connecting amino acids through peptide backbone connections and nonbonded amino acid contact. We used dihydrofolate reductase (DHFR), a clinically important enzyme, as an example to explore the effects of amino acid communication on protein functions. We found that the peptide chain itself is an efficient “telephone wire” to transfer the communications. Breaking the telephone wire (peptide chain) at different points leads to differentiated behavior near the enzyme active site. The important points to keep the peptide chain communication are coupled with the place where protein folding occurs. On the other hand, ligand binding to the enzyme active site provides a “short cut” to the communication networks, with most of the interaction networks now passing through the added ligand and shortening the average communication path. We considered the short cuts to be “network-bridging effects” in the protein structure. The enzyme active site is the place where the short cut has the most dramatic effect in modifying protein communication networks.
| Extensive experimental studies of dihydrofolate reductase (DHFR) have provided rich data toward the structure–function relationship in proteins. Escherichia coli DHFR is a 159–amino acid, monomeric, two-domain protein that is well characterized in terms of structure and function. DHFR catalyzes the reduction of 7,8-dihydrofolate (DHF) to 5,6,7,8-tetrahydro-folate (THF) using the reducing cofactor nicotinamide adenine dinucleotide phosphate (NADPH). DHFR is a clinically important enzyme and is the target of a number of antifolate drugs.
Experimental kinetic analysis of various DHFR permutants has identified several loop regions important for catalysis [1,2]. Figure 1 illustrates the loop locations. The Met-20 loop (residues 10 to 23) directly controls the ligand binding to DHFR. The FG loop (residues 116 to 121) is behind the Met-20 loop. There is a network of hydrogen bonds connecting the Met-20 loop and the FG loop. The third GH loop (residues 142 to 149) is in contact with both the Met-20 and the FG loops. Based largely on the conformation of the Met-20 loop [3], the three states of the enzymatic reaction process (binding and release of cofactor, substrate, and product) can be defined using available crystal structures (Figure 1). In the open state (Figure 1, green ribbon) the Met-20 loop is flipped away from the binding site. In the closed state (Figure 1, blue), the Met-20 loop packs against the cofactor and seals the active site. In the occluded state, the Met-20 loop blocks the binding of the cofactor in the pocket. Simulations of the closed state also indicate changes in the other side of the binding pocket, in the helix region (residues 44 to 50), which binds the cofactor. Loop region 64–71, which contacts the helix, also presents large fluctuations.
The cooperative movements of these loops couple with the overall dynamics of the protein concerning the binding and dissociation of the cofactor, substrate, and product. The communications among the various parts of the DHFR can be achieved by residue interaction networks and through peptide backbone chain connections and nonbonded residue interactions. Agarwal et al. carried out genomic analysis of sequence conservation, kinetic measurements of multiple mutations, and theoretical calculations, observing that nonbonded residue interactions in DHFR form a network of coupled motions that are important for enzyme catalysis [4].
The effect of the peptide backbone chain connection on the protein dynamics is more complex, since chain connection is coupled with protein folding. Circular permutations of DHFR provide insight into chain connectivity, stability of the fold, and function. Circular permutation of a protein consists of connecting the native N- and C-termini covalently with a peptide linker and cleaving the peptide backbone at another specific site. Iwakura et al. have performed systematic circular permutation of the entire DHFR protein to investigate essential folding elements [5–8]. Other groups [9–11] have also circularly permuted the protein, selectively focusing on several permutations or cutting the backbone connection [11] to test fragment complementation. It was found that the peptide bonds in the protein could be grouped into two categories based on the effects of breaking the backbone connectivity. While cleavage of some peptide bonds results in less active variants or affects the enzyme function only slightly, suggesting that these make only minor contributions to the ability of the protein to fold, cleavage at certain other positions leads to a complete loss of the ability of the protein to fold. When such cleavage sites occurred sequentially in the primary sequence and formed a contiguous peptide segment, the region was named a “folding element,” which is crucial for a protein to be foldable [5–8]. Folding elements distribute throughout the sequence. It was proposed that a complete set of folding elements is necessary for a protein to fold [5]. By conducting a systematic circular permutation analysis in which the original N- and C-termini of a protein are connected by an appropriate linker and new termini are created sequentially, ten folding elements have been identified in E. coli DHFR, each of which contains two to 14 residues [5–8] (Figure 2). It was also found that although the positions of the folding elements do not appear to correspond to the secondary structure motifs or to binding sites, almost all of the amino acid residues known to be involved in early folding events of DHFR are located within the folding elements, suggesting a close relationship between the folding elements of a protein and early folding events.
In order to delineate the complex relationship among chain connectivity, protein folding, coupled networks, and catalysis by DHFR, we have carried out a systematic network analysis and molecular dynamics (MD) simulations of the native (closed state) DHFR and 19 of its circularly permuted variants. We first obtained average protein structures from MD simulations of the native DHFR and of the circularly permuted mutants. We investigated the relationship between small-world network behavior and chain connectivity using the crystal and MD average protein structures. Small-world network analysis of the protein structure uses graph theory to explore the bonded and nonbonded amino acid residue network. It was first used to identify key residues in protein folding, as these residues have high connectivity (betweenness) with respect to all possible network connections in the transition states of the protein structures [12]. The concept has been extended to the protein-folding process [13,14], the protein–protein interface [15], protein structure [16] and stability [17], protein dynamics [18], and key functional residues in enzymes [19]. By comparing the centrality of the residue interaction network, we found that ligand binding at the functional site has profound effects on the global network connections. Using the network connectivity index to distinguish between bonded and nonbonded connections, we found that breaking the chain connection in folding elements has a greater effect on active site loops than does breaking the chain in nonfolding regions. This leads us to conclude that the native sequence was selected to maximize the coupling between the protein fold and its functional dynamics. A folding–function interrelationship might particularly be the case for a fold like the DHFR, which currently has only been observed in the DHFR family, fulfilling a single function.
We have carried out a systematic network analysis and MD simulations of the native DHFR and 19 of its circularly permuted variants. Starting with the crystal structure of the native DHFR, we constructed circular permutations by linking the N-termini and C-termini with five glycine residues, and, following the experiment, introduced breaks in the chain connections in ten folding element regions and in nine nonfolding element regions. The 5-ns MD simulations provided average structures for the native DHFR and for the circularly permuted mutants that are able to fold according to experimental data [5]. For ten circularly permuted variants (five from the folding element group and five from the nonfolding element group), additional 5-ns simulations provided dynamics effects reflecting the changes in the closeness in the functional loop regions. Analysis of the structural variations illustrated that there are no distinctive structural features indicating whether particular circularly permuted mutants with cuttings within the folding elements will be able to fold. Simulations of experimentally determined permuted mutants that are unable to fold sample only the local minimum on the protein-folding energy landscape and do not reveal the true folding properties of such circularly permuted variants. Nevertheless, our combined MD and network studies with breaks in the chain connection in the folding element or nonfolding element regions present different patterns of perturbation of the network properties near the active site.
Our network analysis further leads us to propose that ligand binding induces network-bridging effects in the protein structure. We observed that substrate binding has profound effects on the DHFR network centrality, especially on closeness. The protein active site is near or coincides with residues through which the shortest paths in the residue interaction network tend to go. Our results suggest that cofactor binding leads to a modification of the interaction network, with most of the interactions now passing through the cofactor. The average shortest path is shorter with cofactor binding. Our findings are consistent with experimental observations that substrate binding increases DHFR folding stability [23,24].
In conclusion, our analysis demonstrates that active site dynamics of DHFR are communicated to the whole protein via both the peptide backbone and the nonbonded residue contacts. Such communication is indicated by the closeness change accompanying ligand binding, by breaking chain connections in the folding element region, and by breaking chain connections in the nonfolding element region. Even though the native and the circularly permuted proteins have similar overall folds, breaking the chain connections at different regions and ligand binding can change the properties of the network.
The structure of DHFR from E. coli (closed state) was taken from the Protein Data Bank (http://www.rcsb.org/pdb). Both crystallographic waters and substrates were deleted. Circular permutation of the protein consists of connecting the native N- and C-termini covalently with a peptide linker and cleaving the peptide backbone at one specific site. Because a five-glycine peptide was shown to be the most favorable linker in the circularly permuted DHFR [5], this peptide linker was used in all variants in our study. The MOE software (Chemical Computing Group, http://www.chemcomp.com) was used to obtain all the circularly permuted variants of the native DHFR, which now has 164 residues. A total of 19 variants were selected for simulation: ten in the proposed folding element region and nine in the nonfolding element region (Table 1).
The CHARMM program [25] (version 30b1) was used for all computations with the CHARMM force field version 22 using all atom representation [26]. The native DHFR and its circularly permuted variants were simulated in a 60 × 60 × 60 Å3 explicitly solvated periodic box. TIP3P water molecules were introduced. The simulations were carried out with a distance cutoff of 13 Å and a constant dielectric constant of 1. Each simulation was initialized with adopted basis Newton-Raphson (ABNR) minimization followed by 3 ps system heating and 17 ps system equilibration. A production simulation run was carried out for each of the protein structures described above with a 1-fs time step. The coordinates were saved at 1-ps time intervals. Each simulation was run at 300 °K for 5 ns. The average structures from the last 2.5-ns simulations were used for network analysis.
Patchdock [27], a geometry-based molecular docking algorithm, was used to generate clusters for the DHFR–ligand interactions. We kept those clusters with at least four docked conformations with RMSDs within 3.5 Å.
The amino acid network is defined by all residues within a contact distance. Residue interaction network analysis often uses uniformed distance as long as the contacting residues are within a cutoff distance. Such an approach does not distinguish between chain connection and nonbonded interaction. A recent network permutation analysis of DHFR based on such a definition led to two separate regions [22].
The nature of the chemical bond argues for a strong communication when two residues are sequentially linked by a peptide bond. In Figure 7, we show that the most connected residue Ile5 has two bonded connections with Leu4 and Ala6 and the two closely packed residues Ile94 and Tyr111. Since residues connected by a peptide bond have shorter Cα distances, a weighting of distance can distinguish between chain connection and nonbonded interaction. Thus, we define contact distances based on the Cα of all residues within 6.0 Å and use the integer of the distance as weight. Therefore, 6 is coded for a distance of 6.0 Å, 5 for a distance between 5.0 and 6.0 Å, 4 for a distance between 4.0 and 5.0 Å, and 3 for a distance between 3.0 and 4.0 Å. The chain connection can have a distance index of 3 or 4, and a nonbonded contact can have a distance index of 5 or 6. This definition can effectively reflect the effects of chain cleavage on the network properties. The algorithm by Pape [28] was used to calculate the shortest path lengths between nodes. Two network properties are computed to characterize network properties of a given protein structure.
The betweenness [29] is one of the standard measures of node centrality. The betweenness bi of a node i is defined as
where njk is the number of shortest paths connecting j and k, while njk(i) is the number of shortest paths connecting j and k and passing through i.
The closeness centrality of node x is the inverse of the average distance between x and other nodes:
The z-score of the closeness is calculated by z-score = (C (x) − μ) / σ, where μ is the average value of closeness and σ is the standard deviation.
The Protein Data Bank (http://www.rcsb.org/pdb) accession numbers for the structures discussed in this paper are open state (1ra9); closed state (1rx1); and occluded state (1rx5). |
10.1371/journal.pgen.1004931 | Phosphorylation of Elp1 by Hrr25 Is Required for Elongator-Dependent tRNA Modification in Yeast | Elongator is a conserved protein complex comprising six different polypeptides that has been ascribed a wide range of functions, but which is now known to be required for modification of uridine residues in the wobble position of a subset of tRNAs in yeast, plants, worms and mammals. In previous work, we showed that Elongator's largest subunit (Elp1; also known as Iki3) was phosphorylated and implicated the yeast casein kinase I Hrr25 in Elongator function. Here we report identification of nine in vivo phosphorylation sites within Elp1 and show that four of these, clustered close to the Elp1 C-terminus and adjacent to a region that binds tRNA, are important for Elongator's tRNA modification function. Hrr25 protein kinase directly modifies Elp1 on two sites (Ser-1198 and Ser-1202) and through analyzing non-phosphorylatable (alanine) and acidic, phosphomimic substitutions at Ser-1198, Ser-1202 and Ser-1209, we provide evidence that phosphorylation plays a positive role in the tRNA modification function of Elongator and may regulate the interaction of Elongator both with its accessory protein Kti12 and with Hrr25 kinase.
| tRNA molecules function as adapters in protein synthesis, bringing amino acids to the ribosome and reading the genetic code through codon-anticodon base pairing. When the tRNA contains a uridine residue in the “wobble position” of its anticodon, which base-pairs with purine residues in the third position of a cognate codon, it is almost always chemically modified and modification is required for efficient decoding. In eukaryotic cells, these wobble uridine modifications require a conserved protein complex called Elongator. Our work shows that Elp1, Elongator's largest subunit, is phosphorylated on several sites. By blocking phosphorylation at these positions using mutations, we identified four phosphorylation sites that are important for Elongator's role in tRNA modification. We have also shown that Hrr25 protein kinase, a member of the casein kinase I (CKI) family, is responsible for modification of two of the sites that are important for Elongator function. Phosphorylation appears to affect interaction of the Elongator complex both with its kinase (Hrr25) and with Kti12, an accessory protein previously implicated in Elongator function. Our studies imply that Elp1 phosphorylation plays a positive role in Elongator-mediated tRNA modification and raise the possibility that wobble uridine modification may be regulated, representing a potential translational control mechanism.
| Elongator is a conserved, multi-subunit protein complex containing six different polypeptides (Elp1-Elp6), first discovered in yeast in association with the elongating form of RNA polymerase II and initially proposed to play a role in transcriptional elongation [1], [2]. Although Elongator is non-essential in yeast, knockout of the mouse IKBKAP gene encoding Elongator's largest subunit leads to embryonic lethality and the protein is crucial for vascular and neural development [3]. The hereditary neuropathy Familial Dysautonomia results from human IKBKAP mutations, while mutations in other Elongator subunits have been associated with Amyotrophic Lateral Sclerosis [4] and Rolandic Epilepsy [5]. Elongator in Caenorhabditis elegans is also involved in neuronal function and development [6], [7], while in plants it plays a role in proliferation during organ growth [8].
While it is therefore clear that Elongator is important for neural function in higher organisms [9], [10], it has been proposed to have a bewildering range of seemingly unrelated functions. The Elp3 subunit of Elongator has a histone acetyltransferase (HAT) domain that can acetylate histones in vitro [11] and yeast Elongator mutants show changes in histone acetylation in vivo [12]. Elongator has also been proposed to acetylate α-tubulin and the neuronal protein Bruchpilot [7], [13], [14] and has been implicated in paternal DNA demethylation in mouse zygotes [15]. In yeast, Elongator mutants adapt slowly to changing growth conditions, show sensitivity to high temperature, rapamycin, caffeine, hydroxyurea and various other chemical stressors, and are resistant to zymocin [16], [17], a protein toxin secreted by the yeast Kluyveromyces lactis that kills other yeasts including Saccharomyces cerevisiae [18]. Yeast Elongator has also been implicated in transcriptional silencing, replication-coupled nucleosome assembly [17] and polarized secretion [19].
However, while these apparently diverse roles could imply that Elongator is multifunctional, work in yeast [20], C. elegans [6], plants [21] and mammals [22] has demonstrated that many if not all of these proposed functions reflect a primary role for Elongator in tRNA modification, specifically in the addition of mcm5 (5-methoxycarbonylmethyl) and ncm5 (5-carbamoylmethyl) groups to uridine when present at the ‘wobble’ position (U34) in tRNA anticodons. Eleven out of 13 such tRNAs in yeast contain either mcm5U, ncm5U or 5-methoxycarbonymethyl-2-thiouridine (mcm5s2U) in the wobble position and addition of the mcm5 and ncm5 moieties requires Elongator [20]. This role in tRNA modification explains the zymocin-resistant phenotype of Elongator mutants: zymocin is a tRNA anticodon nuclease that inactivates tRNAGlu(UUC) by cleaving the anticodon on the 3′ side of U34, but the mcm5 modification is necessary for tRNA recognition and cleavage [23], [24].
Wobble uridine-containing tRNAs read codons ending with a purine, and the mcm5/ncm5 modifications are needed to confer full decoding competence on these tRNAs [6], [20], [25]-[27]. Wobble uridine modification is most likely the primary role of Elongator, at least in yeast, because elevated expression of just two Elongator-dependent tRNAs, tRNALys(UUU) and tRNAGln(UUG), can suppress all the phenotypes associated with loss of Elongator function apart from zymocin resistance, which remains unaffected because elevated levels of the two tRNAs do not restore the tRNAGlu(UUC) modification required for cleavage by zymocin [28], [29]. Suppression of Elongator mutant phenotypes by elevated tRNA levels without restoration of wobble uridine modification strongly suggests that these phenotypes are caused by translational defects resulting from hypomodified tRNAs, a notion supported by findings that U34 modification promotes binding of tRNALys(UUU), tRNAGln(UUG) and tRNAGlu(UUC) to the ribosomal A-site [30]. Recent structural work indicates that Elp4, Elp5 and Elp6 are RecA-fold proteins that form a heterohexamer containing two copies of each polypeptide, which interacts with two copies of an Elp1-Elp2-Elp3 sub-complex [31]. The recombinant heterohexamer binds and hydrolyses NTPs and shows tRNA binding that is reduced when the NTP can be hydrolyzed [31], while a separate tRNA-binding motif in the C-terminal domain of Elp1 may also mediate tRNA interaction with the Elp1-Elp2-Elp3 sub-complex [32]. Thus while the existence of additional substrates cannot be excluded, it is now clear that Elongator plays a conserved role in wobble uridine modification [6], [21], [22] and that this role, through effects at the level of translation, is likely to underpin the majority of phenotypes resulting from Elongator deficiency, at least in yeast. Within Elongator, Elp3 is highly likely to catalyze the tRNA modification as it contains a ‘radical SAM’ domain [33] that in other proteins can mediate RNA modification reactions [34], and both its radical SAM and histone acetyltransferase (HAT) domains are required for wobble uridine modification [20], [29]. This is supported by the recent finding that recombinant archaeal Elp3 can catalyze modification of tRNA wobble uridines in an in vitro reaction containing SAM, acetyl-CoA, tRNA and Na2S2O4 [35].
Previously, we reported that the largest subunit of Elongator (Elp1) is a phosphoprotein and identified mutations in either HRR25 (encoding a casein kinase I) or SIT4 (encoding a protein phosphatase) that conferred zymocin resistance [36]-[38]. Elp1 was present as a hypophosphorylated isoform in hrr25 mutants and as a hyperphosphorylated isoform in sit4 mutants, whereas wild-type cells contained similar amounts of both isoforms [37], [39]. We therefore sought to investigate the potential functional significance of Elp1 phosphorylation by locating the phosphorylation sites on Elp1, identifying several such sites that are critical for Elongator-dependent tRNA modification. Our findings therefore raise the possibility that Elongator activity (and hence tRNA modification) could be regulated, potentially constituting a novel mechanism for translational control.
To identify sites of phosphorylation in Elongator we affinity isolated the complex from yeast cells expressing a TAP-tagged version of Elp1. Tryptic digests of the affinity-purified material were subjected to phosphopeptide enrichment involving a two-step procedure using Hypersep SCX and TiO2, followed by tandem mass spectrometry to locate sites of phosphorylation. To maximize the chance of detecting phosphopeptides, in addition to isolating Elongator from wild-type yeast cells we also prepared and analyzed the complex from a sit4Δ strain, in which lack of Sit4 phosphatase leads to Elp1 hyperphosphorylation [37], [39]. We also analyzed Elongator prepared from a kti12Δ mutant in which Elongator is hypophosphorylated [37], [39] in case additional phosphorylation sites could also be detected under these circumstances. In this way we identified eight phosphorylation sites in Elp1, one each in Elp2 and Elp4 and two in Elp5 (Table 1). With the exception of Ser-222 in Elp4 [40], all of these sites are novel and were not identified in any of the recent proteome-wide phosphoproteomics studies.
Since we previously showed that Elp1 phosphorylation state changes are associated with altered Elongator function [37], [39], we focused on the phosphorylation sites we identified in Elp1. S1 Fig. shows representative MS/MS spectra providing evidence for the eight sites identified in Elp1, which with one exception were identified within monophosphorylated peptides. Apart from Ser-1205/Thr-1206, where phosphorylation of the two sites cannot be unambiguously distinguished from the mass spectra, all phosphorylation sites can be identified with high confidence. All Elp1 sites identified in Elongator isolated from either the sit4Δ mutant or the kti12Δ mutant were also found in Elp1 from the wild-type strain (Table 1).
Since analysis of our mass spectrometry data also provided additional weak evidence for phosphorylation on Elp1 Ser-1209, we raised a phosphospecific antibody against a synthetic peptide carrying phosphate on this residue to examine whether it was a genuine phosphorylation site. As shown in Fig. 1B, when used to probe Elp1 by Western blotting, this phosphospecific antibody gave a strong signal that was lost when Ser-1209 was mutated to alanine, thereby demonstrating its phosphospecificity. Thus Elp1 Ser-1209 represents a ninth Elp1 phosphorylation site (Table 1). Fig. 1A shows that five of the nine sites are located centrally within Elp1. Four of these five sites map to the Elp1 amino-terminal domain, which is strongly predicted to form a β-propeller structure that may mediate interactions with other Elongator subunits or accessory proteins. The four remaining phosphorylation sites are tightly clustered close to the carboxy-terminus of Elp1, in a region that is located adjacent to its tRNA binding region [32] and predicted to be disordered (Fig. 1A). Phosphorylation sites are generally found to be enriched in disordered regions [41], in particular those sites that show dynamic variation in phosphorylation state [42]. Beyond Ser-1209, it is striking that every fourth residue between Thr-1212 and Thr-1230 is either threonine or serine (Fig. 1E). However, despite this intriguing pattern of phosphorylatable residues we were unable to detect phosphate groups in this region.
To determine whether any of the Elp1 phosphorylation sites we had identified were likely to be functionally significant, we mutated each in turn to non-phosphorylatable alanine. These mutants were assayed for zymocin sensitivity by eclipse assay, in which growth inhibition of an S. cerevisiae strain around a colony of zymocin-producing K. lactis indicates loss of Elongator-dependent mcm5 modification of tRNAGlu(UUC) [16], [24]. Fig. 1C shows that with the exception of the S1209A mutant, no alanine substitution at a single phosphorylation site conferred detectable resistance to zymocin and hence loss of Elongator function. In contrast, the S1209A substitution conferred complete zymocin resistance in this assay. Combinations of alanine substitutions at multiple sites were therefore also generated and tested (Fig. 1C and S1 Table). This analysis indicated that any mutants in which both Ser-1198 and Ser-1202 had been replaced by alanine were also zymocin-resistant. Thus Ser-1198, Ser-1202 and Ser-1209 define three phosphorylated residues in Elp1 that are required for Elongator function, but with Ser-1198 and Ser-1202 apparently showing some redundancy. Furthermore, these residues are highly conserved in Elp1 in both lower and higher eukaryotes (Fig. 1D). Since Elp1 becomes hyperphosphorylated in cells lacking Sit4 phosphatase that are defective for Elongator function [37], [39], we also examined whether any alanine substitutions could reverse the zymocin resistance shown by a sit4Δ mutant. However, none of the mutations tested altered the zymocin-resistance phenotype of the sit4Δ strain (S1 Table). Similarly, we also tried mimicking constitutive phosphorylation at many of the sites by making glutamate or aspartate substitutions alone or in combination (S1 Table), but failed to find any substitution(s) that conferred a significant loss of function phenotype.
To investigate the Elp1 C-terminal phosphorylated region, additional combinations of alanine, aspartate or glutamate substitutions at the phosphorylation sites were generated. In addition to using the eclipse assay, we monitored tolerance to intracellular expression of the zymocin tRNase (γ) subunit from the galactose-inducible GAL1 promoter as a more quantitative measure of zymocin resistance [43]. Since zymocin sensitivity provides a readout largely for the mcm5 modification state of just tRNAGlu(UUC), we also monitored Elongator function via efficiency of ochre suppression mediated by SUP4, a tRNATyr(UUA) that requires Elongator-dependent wobble uridine modification for efficient ochre (UAA) codon readthrough [20], [24]. This involved single copy integration of a plasmid that carried both SUP4 and a ura3 ochre allele [32], such that suppression (and hence Elongator's tRNA modification function) could be monitored by growth in the absence of uracil.
Fig. 2 shows that the S1209A substitution conferred complete resistance to intracellular expression of the zymocin γ subunit and greatly reduced SUP4-dependent ura3oc suppression, in each case conferring a phenotype comparable to that observed upon complete loss of Elongator function (elp1Δ). In contrast, an S1209D substitution showed considerable restoration of function, while the equivalent glutamate substitution (potentially a poorer mimic of phosphoserine due to its longer side chain) was less effective. The regain of functionality caused by the phosphomimic aspartate substitution therefore provides evidence that phosphorylation of Ser-1209 acts positively for Elongator function. Although the double S1198A S1202A substitution mutant conferred strong zymocin resistance it showed considerable residual Elongator function in the SUP4 assay, allowing for growth in the absence of uracil similar to that shown by the ELP1 wild-type control. However, concurrent glutamate substitutions at both positions largely restored zymocin sensitivity, supporting the notion that phosphorylation of these two sites acts positively for Elongator function. S1198A S1202A in combination with T1204A S1205A T1206A, a triple alanine substitution that on its own had essentially no effect on Elongator function in any of the assays, conferred stronger zymocin resistance than S1198A S1202A alone and dramatically reduced SUP4-dependent suppression efficiency, indicative of an additive Elongator defect in the quintuple mutant. The triple T1204A S1205A T1206A mutation was used here because phosphorylation at Ser-1205/Thr-1206 could not be unambiguously distinguished (see above). Mutants where acidic residues substituted combinations of these five positions, either alone or in combination with alanine substitutions, indicated that Elongator functionality was not greatly affected by acidic substitutions, although the T1204E S1205D T1206E triple substitution improved Elongator function when combined with the S1198A S1202A double mutant (seen most clearly by the eclipse assay). While the difference in the severity of phenotype observed between the zymocin- and SUP4-based assays for some elp1 mutants might reflect differential effects on tRNAGlu(UUC) and tRNATyr(UUA), we consider it more likely to reflect different loss of modification thresholds required to score positive in these assays; while as little as ∼40% reduction in modification may generate sufficient uncleavable tRNAGlu(UUC) to confer zymocin resistance [44], a much larger reduction in modification may be required before there is insufficient functional tRNATyr(UUA) to support effective ochre suppression. Our results therefore indicate that blocking phosphorylation at all four sites identified in this region of Elp1 leads to reduced Elongator function, with the S1209A mutant showing the greatest defect followed by S1198 S1202A and T1204A S1205A T1206A in decreasing order of severity, but with additivity between S1198 S1202A and T1204A S1205A T1206A leading to a defect as severe as that of S1209A. That acidic substitutions mimicking phosphorylation at each of the four sites conferred considerable Elongator function is consistent with the notion that phosphorylation at these sites functions positively for Elongator.
To look directly at the tRNA wobble uridine modifications, we prepared tRNA from selected elp1 mutant strains and used LC-MS analysis to quantitate the levels of modified U34 nucleosides. Fig. 3 shows that in the elp1Δ control strain, mcm5U and ncm5U were absent from tRNA as expected. The S1209A and quintuple S1198 S1202A T1204A S1205A T1206A strains showed almost no mcm5U or ncm5U, consistent with the major defect in Elongator-dependent wobble uridine modification indicated by the phenotypic assays (Fig. 2). The S1198A S1202A double mutant showed reduced levels of mcm5U and ncm5U consistent with a less severe Elongator defect, while strains with the S1198E S1202E double or S1209D single phosphomimic alleles, or with the triple T1204A S1205A T1206A allele, had levels of mcm5U and ncm5U intermediate between those of the S1198A S1202A mutant and ELP1 wild-type. Taken together, the profiles for the ncm5 and mcm5 modification nucleosides are consistent with our phenotypic analyses (Fig. 2) and our data therefore support the notion that phosphorylation of Ser-1209 plays a major, positive role in Elongator-dependent tRNA modification and that phosphorylation at Ser-1198, Ser-1202 and Ser-1205/Thr-1206 makes a similar but partly redundant contribution to Elongator activity.
Phosphorylation site mutations that alter Elongator functionality could in principle do so by affecting assembly of the holo-Elongator complex, either because they interfere with assembly or because phosphorylation of these sites could regulate assembly of the Elongator complex. When myc-tagged Elp2 was used to immunoprecipitate Elongator from cell extracts, elp1 phosphorylation site mutations at positions 1198, 1202, 1205, 1206 and 1209, alone or in combination, did not affect the recovery of Elp1, Elp3 and Elp5 in the Elp2 immunoprecipitates in comparison with strains where Elp1 was wild-type (Fig. 4). In particular, there was no evidence for any changes in the assembly of the complex when Elp1 carried the S1209A substitution, which is essentially null for Elongator function as discussed above. Thus similar co-immune precipitation of all four proteins was observed, regardless of whether these mutations affected Elongator functionality. Although we have not tested every mutant elp1 allele constructed in this study, assembly of the Elp1-Elp2-Elp3 subcomplex and its association with the Elp4-Elp5-Elp6 subcomplex appear essentially normal, irrespective of the consequences of the mutations for Elongator functionality.
Since Elongator interacts with both its accessory factor Kti12 and with Hrr25 kinase and because Elongator function requires both Kti12 and Hrr25 kinase activity [16], [37], [45], [46], we next used co-immunoprecipitation to examine the effect of selected phosphorylation site mutations on Elongator's association with Kti12 and Hrr25. Elongator complex in which Elp1 carries the double S1198A S1202A mutation showed reduced interaction with Hrr25 that was not seen with the corresponding phosphomimic allele (S1198E S1202E), but combining S1198A S1202A with T1204A S1205A T1206A did not further reduce interaction with Hrr25 (Fig. 5) despite the greater loss of Elongator function in the quintuple mutant. Conversely, mutation of Elp1 Ser-1209 to alanine led to enhanced interaction between Elongator and Hrr25 (Fig. 5). In all ELP1 mutants tested, Elongator retained its ability to interact with Kti12, but the elp1-S1209A allele also led to enhanced Kti12 interaction (Fig. 6A). We examined the effect of the Elp1 S1209A substitution on Kti12 association in more detail by tagging the genomic copy of ELP1 with GFP and carefully quantitating the recovery of HA-tagged Kti12 in strains expressing either the wild-type or mutant Elp1-GFP fusions. This confirmed that the S1209A mutation leads to enhanced Kti12 association: in comparison with wild-type Elp1, Elp1-S1209A was reproducibly associated with approximately twice as much Kti12 (Fig. 6B, C). Thus the S1198A S1202A and S1209A mutations have opposite effects on association with Hrr25 and the 1209A mutation enhances association with Kti12, suggesting that phosphorylation at these sites affects the interaction between Elongator and key proteins required for its functionality.
We next wished to identify the protein kinase(s) that mediate modification of the phosphorylation sites in the Elp1 carboxy-terminal domain that are important for Elongator function. To take an unbiased approach, we screened a library of 119 GST-protein kinase fusions [47] for their ability to phosphorylate Elp1 in vitro using TAP-purified Elongator as a substrate. Purified Elongator showed significant background phosphorylation specifically on Elp1 when incubated with [γ-32P]ATP in the absence of added kinase. However, five protein kinases were identified that could clearly phosphorylate Elp1 in vitro (Hrr25, Yck1, Yck2, Yck3 and Hal5: S2A Fig.). Given that Hrr25 interacts with Elongator [37], [48], [49] as seen in Fig. 5 and because Elp1 is already known to become hypophosphorylated in a hrr25 mutant [37], it is an excellent candidate for an in vivo Elp1 kinase. Hrr25, Yck1, Yck2 and Yck3 all belong to the casein kinase I (CKI) family of protein kinases [50], but while hrr25 mutants show clear defects in Elongator function [37], [38], Yck1, Yck2 and Yck3 are membrane associated via lipid modification [51], [52] and do not confer detectable zymocin resistance when the corresponding genes are deleted [38]. Thus we consider it unlikely that Yck1-3 mediate functionally important phosphorylation events on Elp1 in vivo and that they were identified in our in vitro screen due to shared substrate specificity with the related Hrr25 CKI isozyme. Preliminary analysis also failed to generate data supporting a role for Hal5 in Elongator function (S2B-S2C Fig.).
In further support of a role for Hrr25 as a direct Elp1 kinase, we next showed that the Elp1 kinase activity present in affinity-isolated Elongator preparations was due to Hrr25. This made use of a yeast strain dependent on an ‘analog-sensitive’ HRR25 allele (hrr25-I82G) in which the mutant Hrr25 kinase has acquired the capacity to be inhibited specifically by addition of the ATP analogs 1NM-PP1 or 3MB-PP1 [53]. When Elongator was isolated from the hrr25-I82G strain, the Elp1 phosphorylation observed upon incubation of the isolated complex with [γ-32P]ATP was blocked by addition of either 1NM-PP1 or 3MB-PP1 (Fig. 7A). This was in contrast to Elongator isolated from a control strain expressing wild-type HRR25, phosphorylation of which was refractory to these inhibitors. In fact the hrr25-I82G strain became zymocin resistant when grown in the presence of 1NM-PP1, emphasizing the positive role of Hrr25 kinase in Elongator's tRNA modification in vivo (Fig. 7B) and consistent with Elongator-minus phenotypes of hrr25 mutants [37], [38].
To determine whether Hrr25 phosphorylation of Elp1 occurred at any of the functionally relevant sites that we had identified, Elongator containing wild-type or mutant forms of Elp1 was purified and tested for incorporation of phosphate from [γ-32P]ATP both in the absence and presence of recombinant Hrr25. Fig. 7C shows that even when reactions were supplemented with Hrr25, Elp1 in which the C-terminal region had been deleted was not phosphorylated. Elongator in which both Elp1 Ser-1198 and Ser-1202 had been replaced by either alanine or glutamate showed negligible incorporation of radiolabel in comparison with the Elp1 in wild-type Elongator. In contrast, Elongator in which Elp1 Ser-1209 was substituted with either alanine or aspartate showed high levels of 32P incorporation into Elp1 in the presence of recombinant Hrr25. Since HRR25 is an essential gene we were unable to examine Ser-1209 phosphorylation with the anti-phophoserine-1209 antibody in the complete absence of Hrr25. However, in the kinase-dead hrr25-3 mutant, which shows loss of Elongator function and absence of the phosphorylated isoform detected in wild-type cells by gel shift assay [37], a similar level of Ser-1209 phosphorylation to that seen in a wild-type strain was observed (S3 Fig.). Although more complex interpretations are possible, these observations are therefore most simply explained if Ser-1198 and Ser-1202 are major sites of Hrr25 phosphorylation and Ser-1209 is not a major site for modification by the kinase. In the absence of added Hrr25, Elongator complex containing Elp1-S1209A still showed high levels of Elp1 phosphorylation that were not seen with the corresponding aspartate substitution or when Elp1 was wild-type, although after addition of recombinant Hrr25 the level of Elp1 phosphorylation of the Elp1-S1209A and Elp1-S1209D complexes was similar as noted above (Fig. 7C). These data are consistent with the enhanced interaction of Elp1-S1209A-containing Elongator with Hrr25 that was seen by co-immune precipitation (Fig. 5).
To complement these experiments, the ability of recombinant Hrr25 to phosphorylate synthetic peptides corresponding to the C-terminal phosphorylated region of Elp1 was tested, using both mass spectrometry to identify phosphorylated residues in reactions with unlabeled ATP and by monitoring incorporation of 32P-phosphate in reactions containing [γ-32P]ATP. When a peptide containing Elp1 residues 1193-1213 was phosphorylated by recombinant Hrr25 in vitro and analyzed by mass spectrometry, three types of phosphopeptide were detected: monophosphorylated peptide modified on either Ser-1198 (S4A Fig.) or Ser-1202 (S4B Fig.), and diphosphorylated peptide modified on both these residues (S4C Fig.). These data therefore confirm the identity of Hrr25 as a protein kinase that can directly phosphorylate Elp1 on serine residues that are important for Elongator function, and are consistent with a model in which phosphorylation at one of these two sites may then favor phosphorylation of the second site. When all phosphorylatable residues apart from Ser-1198 and Ser-1202 were changed to alanines, dual phosphorylation on Ser-1198 and Ser-1202 was still seen, indicating that phosphorylation of these two positions was not dependent on phosphorylation of Ser-1205 or any other downstream residues.
When the 1193-1213 peptide was incubated with Hrr25 together with [γ-32P]ATP, it readily incorporated radiolabelled phosphate (Fig. 8B and C; peptide 89). Although the related peptide in which all serine and threonine residues apart from Ser-1198 and Ser-1202 had been substituted by alanines was still an excellent Hrr25 substrate (Fig. 8C; peptide 92), incorporation of 32P at later times was reduced in comparison (Fig. 8B), consistent with the possibility of additional phosphorylation to the right of Ser-1202. Additional single alanine substitutions at either Ser-1198 or Ser-1202 in peptide 92 greatly reduced but did not completely prevent phosphorylation (Fig. 8C, peptides 1001, 1002). However, replacement of both Ser-1198 and Ser-1202 by either alanines or glutamates completely blocked phosphorylation of the peptide despite the presence of the five serine and threonine residues downstream (peptides 90 and 91). Another peptide encompassing Elp1 residues 1207-1229 was not phosphorylated at all following incubation with Hrr25 (Fig. 8D, peptide 95), supporting the notion that neither Ser-1209 nor any of the repeating threonine and serine shown in Fig. 1E can be directly phosphorylated by Hrr25 kinase. These data are therefore consistent with interdependence of Ser-1198 and Ser-1202 phosphorylation by Hrr25 and absence of Hrr25 phosphorylation on the downstream serines and threonines.
Since phosphorylation of specific Ser or Thr residues by CKIs such as Hrr25 is often primed by phosphorylation at another Ser or Thr residue 2-4 positions upstream [54], it was possible that Hrr25 might phosphorylate Ser-1205 once Ser-1202 had been modified, and that phosphorylation at Ser-1205 might then promote modification of Ser-1209. However, phosphomimic glutamate substitutions at 1198 and 1202, which supported essentially normal Elongator functionality in vivo (Fig. 2), also prevented phosphorylation of the 1193-1213 peptide on any other downstream site. Furthermore, phosphorylation of a related peptide (Elp1 1192-1212; Fig. 8E), which was also a good in vitro substrate for Hrr25, was also completely blocked for Hrr25 phosphorylation when synthesized with phosphoserine at the two positions corresponding to Elp1 Ser-1198 and Ser-1202. This again supports the model that Ser-1198 and Ser-1202 are the only major sites of Hrr25 phosphorylation and also indicates that it is unlikely that efficient priming of phosphorylation by Hrr25 on downstream residues, such as Ser-1205 and Ser-1209, occurs as a result of the upstream phosphorylation events at Ser-1198 and Ser-1202. Thus although these experiments do not as yet fully explain all the intricacies of Hrr25 phosphorylation in this region, taken together they nonetheless demonstrate that Hrr25 phosphorylates Elp1 directly on Ser-1198 and Ser-1202, two serine residues that we identified as functionally relevant in vivo phosphorylation sites.
We have identified nine phosphorylation sites on the Elp1 subunit of yeast Elongator complex and based on the phenotypes of non-phosphorylatable and phosphomimic mutations, provide evidence that phosphorylation on four sites near the Elp1 C-terminus (Ser-1198, Ser-1202, Ser-1205/Thr-1206 and Ser-1209) plays a positive role in Elongator function. Previously, we showed that a sit4 phosphatase mutant trapped Elp1 in a slower-migrating, hyperphosphorylated form whereas hrr25 kinase mutations led to presence of just a faster-migrating, hypophosphorylated Elp1 isoform [37], [39]. Both types of mutation cause loss of Elongator function [37], [39], suggesting that dynamic phosphorylation and dephosphorylation of Elp1 is needed in functional Elongator and predicting that mimicking constitutive phosphorylation on at least some of the sites we have identified should be inhibitory. It is therefore surprising that all of the phosphomimic alleles we tested conferred significant Elongator function. Possibly the acidic substitutions do not fully mimic constitutive phosphorylation and thereby allow for substantial residual function, even though constitutive phosphorylation might inhibit Elongator. Thus while our alanine substitution mutants lend support to the idea that phosphorylation at the mapped sites functions positively for Elongator activity, we cannot rule out a requirement for dynamic phosphorylation/dephosphorylation at the sites we have identified. It is also possible that additional, inhibitory phosphorylation sites exist that were not found in our study. For example, although we could not detect phosphorylation of Thr-1212 in vivo or in vitro, both alanine and glutamate substitutions at this site caused severe loss of Elongator function (S5 Fig.), consistent with the notion that dynamic phosphorylation and dephosphorylation of this site could be important if it is phosphorylated in vivo.
Despite conducting an unbiased, ‘kinome-wide’ screen to identify kinases responsible for Elp1 C-terminal domain phosphorylation, Hrr25, the yeast CKI that associates with Elongator and has already been implicated in Elongator function [37], [48], [49] was the sole convincing candidate to be identified. Through several lines of evidence, we have now established that Hrr25 directly phosphorylates Elp1 on two in vivo phosphorylation sites that we have mapped and shown to be relevant for Elongator function: Ser-1198 and Ser-1202. Thus purified Elongator shows Hrr25-dependent phosphorylation that requires the presence of these two residues, while peptides derived from the Elp1 C-terminal region show direct, interdependent phosphorylation by recombinant Hrr25 on Ser-1198 and Ser-1202. Conversely, we found no evidence for direct phosphorylation of Ser-1209 by Hrr25 using three different synthetic peptides containing Ser-1209, or using purified Elongator complex in which Ser-1198 and Ser-1202 were mutated but Ser-1209 was intact.
Two types of consensus phosphorylation site have been proposed for CKIs: pS [X]1-3 [ST] and [DE]2-4 [X]0-2 [ST], where pS indicates an upstream phosphoserine residue that is needed to prime phosphorylation at the downstream Ser or Thr (shown in bold and underlined) but which can be substituted by an acidic patch in the second class of motif [54]. Ser-1198 conforms to the latter type of motif and is closely related to other mapped Hrr25 phosphorylation sites [55]-[57] in yeast proteins (S6 Fig.). In contrast, Ser-1202 matches the former motif, suggesting that priming-independent phosphorylation at Ser-1198 by Hrr25 might then prime phosphorylation by Hrr25 at Ser-1202 and predicting that Ser-1198 should still be efficiently phosphorylated when Ser-1202 is replaced by alanine. However, the latter mutation largely blocked phosphorylation at Ser-1198 in our in vitro peptide kinase assays and instead we observed interdependent phosphorylation at these two residues. Furthermore, replacement of one of the two upstream aspartate residues with alanine did not obviously interfere with phosphorylation of Ser-1198 and Ser-1202. Thus although the sites are direct targets of Hrr25 and match the accepted consensus for CKI phosphorylation, dependency of Ser-1202 phosphorylation on prior Ser-1198 modification remains to be demonstrated. Similarly, Ser-1205 and Ser-1209 both fit the pS [X]1-3 [ST] consensus and might be modified once Ser-1202 phosphorylation has occurred, and yet we could not demonstrate Hrr25-dependent incorporation of phosphate at these sites even using a peptide where the upstream sites were already phosphorylated. Given the known substrate specificity of CKIs, it is therefore surprising that we can find no evidence for such priming of phosphorylation at Ser-1205 and Ser-1209 following modification of Ser1202. Furthermore, since the repeating pattern of phosphorylatable residues shown in Fig. 1E also fits the pS [X]1-3 [ST] motif we are even more surprised that we failed to detect phosphorylation of these sites either in vivo or in vitro.
In spite of the strong evidence we have provided that Hrr25 is a direct Elp1 kinase, inability to detect direct phosphorylation of either the key residue Ser-1209 or of Ser-1205/Thr-1206 by Hrr25 implies that at least one additional Elp1 kinase is involved in Elp1 phosphorylation. While these sites might become better substrates for Hrr25 in the context of fully assembled Elongator complex rather than within synthetic peptides, the fact that we detect normal levels of Ser-1209 phosphorylation in a hrr25 mutant that is defective for Elongator function strongly supports the involvement of a different kinase at Ser-1209.
Although all phosphorylation site mutants examined showed essentially normal assembly of the Elongator complex and retained the ability to interact with the accessory protein Kti12, the S1209A mutation enhanced association of Kti12 with Elongator in comparison with cells expressing either the wild-type protein or the S1198A S1202A mutant. Kti12 stoichiometry is important – either too much or too little interferes with Elongator function [39], [43], [45] – and thus Ser-1209 phosphorylation may regulate Elongator's interaction with its accessory protein. Furthermore, the S1209A mutation caused increased Hrr25 association with Elongator whereas the double S1198A 1202A mutation led to reduced Hrr25 association. Thus Hrr25 not only directly phosphorylates Elp1 but may also regulate its own interaction with Elongator through doing so. Although the S1209A single and S1198A S1202A T1204A S1205S T1206A quintuple mutants both conferred strong loss of Elongator function, their differing effects on Hrr25 association implies that they are defective for different reasons – phosphorylation at S1198A S1202A may stabilize Hrr25 binding whereas phosphorylation at Ser-1209 may be required to displace the kinase. Since the interaction between Hrr25 and Elongator is dependent on Kti12 [37] it is also possible that the enhanced interaction of both proteins with Elongator seen when Elp1 cannot be phosphorylated on Ser-1209 are directly related.
Intriguingly, enhanced interaction of both Kti12 and Hrr25 with the Elongator complex is also seen in hrr25 mutants that cause Elp1 phosphorylation defects and zymocin resistance [37 and S7 Fig.]. However, these properties of hrr25 mutants are more similar to those of the elp1-S1209A mutant, which is mutated at a site apparently not directly phosphorylated by Hrr25, rather than mirroring the properties of the elp1-S1198A, S1202A mutant that removes the only sites in Elp1 that we have shown to be direct Hrr25 targets. This suggests an as yet undiscovered connection between phosphorylation at Ser-1209 and Hrr25 kinase. Regardless of this, our data nonetheless indicate that Elp1 phosphorylation by Hrr25 and other kinases could modulate the interaction between Elongator and Kti12.
Although there is clearly more to learn about the role of phosphorylation in Elongator function, two types of model can be proposed. On the one hand, phosphorylation might regulate Elongator, turning its wobble uridine modification activity up or down in response to growth conditions and the demand for protein synthesis, or perhaps in response to cellular stresses. Given that the translation of some mRNAs is particularly dependent on wobble base modification and Elongator function [58]-[60] and that tRNA modifications (including Elongator-dependent ones) oscillate during the cell cycle and in response to stress signals [61], [62], this raises the interesting possibility that Elongator may be part of a translational control mechanism functioning through tRNA modification. Our proposal that Elp1 phosphorylation operates in a largely positive sense for Elongator, based on the properties of the phosphorylation site mutants that we have examined, is consistent with such a regulatory role. The Elp1 kinase Hrr25 is needed for a wide range of cellular functions [49], [56], [57], [63]-[65] that do not appear to provide a clear insight into the signals that might regulate Elp1 phosphorylation. However, the Hrr25 requirement for full functionality of two different components of the translation machinery - wobble uridine-containing tRNAs [37, this work] and ribosomes [49], [57] - might reflect a role in regulation of the cell's capacity for protein synthesis. In addition, both hrr25 mutants [66] and Elongator-deficient yeast [17] are sensitive to hydroxyurea and methyl methanesulfonate. Since efficient translation of the ribonucleotide reductase gene RNR1 requires mcm5-modified tRNAs [62], [67], Elp1 phosphorylation may also be linked to the known role of Hrr25 in expression of genes such as RNR2 and RNR3 in response to DNA damage [66].
Alternatively, Elp1 phosphorylation might be dynamic, with sequential phosphorylation and dephosphorylation of specific residues driving the biochemical mechanism through which Elongator carries out the tRNA modification reactions. Such a dynamic role might operate through modulation of Elongator's interaction with factors such as Kti12 or with tRNA. It is intriguing in this context that the C-terminal phosphorylated region in Elp1 is immediately adjacent to a basic region that binds specifically to tRNA and that mutation of the tRNA binding domain leads to reduced interaction with Kti12 [Fig. 6C and ref. 32]. Thus phosphorylation of Elp1 could potentially influence how Elp1 interacts with tRNA, perhaps through interaction between the acidic phosphate groups and the basic residues present in the tRNA binding domain.
In conclusion, while there is still much to learn about Elp1 phosphorylation and its involvement in Elongator function, our work clearly demonstrates the importance of at least four in vivo phosphorylation sites in the C-terminal domain of Elp1 for Elongator-dependent tRNA wobble uridine modification, shows that Hrr25 kinase directly modifies two of these sites and provides evidence that phosphorylation regulates the association between Elongator and both its accessory protein Kti12 and its kinase Hrr25.
Basic yeast methods, growth media, and routine recombinant DNA methodology were performed as previously described [68], [69]. All plasmids used in this study are listed in Table S2 and yeast strains are listed in Table S3. To generate yeast strains dependent on wild-type or mutant forms of ELP1, ELP1 was first deleted from BY4741 and WAY008 (ELP3-TAP) using pFA6a-KanMX6 and pCORE-UH deletion cassettes to obtain the elp1Δ knockout mutant strains WAP034 and WAY037, respectively. Wild-type or mutant ELP1 variants carried on the low-copy plasmid YCplac111-ELP1-6HA (Table S2) were then introduced into these elp1Δ strains as the sole source of Elp1. The mutant versions were made by either site directed mutagenesis (Qiagen QuikChange) using YCplac111-ELP1-6HA as template, or by replacing the relevant region using standard cloning procedures and synthetic DNA carrying the desired mutations. The majority of plasmids made using the latter approach were produced by DNA2.0, Inc (Menlo Park, CA, USA). All elp1 mutants made by site-directed mutagenesis were verified by DNA sequencing of the entire ELP1 ORF plus approximately 200 base pairs upstream and downstream. When mutations were introduced by cloning, the region that had been replaced was sequenced to exclude the possibility of gene synthesis errors. The wild-type and mutant versions of YCplac111-ELP1-6HA were transformed into WAY034, WAY037, or elp1Δ::KanMX6 ELP2-myc3 strains carrying additional epitope-tagged components (Table S3) for phenotypic screening, Elongator-complex purification or Western blot analysis, respectively. pFA6a-CTAP4-HIS3MX6 was made by ligating the 1322 bp BglII-PmeI fragment from pFA6a-3HA-HIS3MX6 to the 3603 bp BglII-PmeI fragment from pFA6a-CTAP4-NatMX6 [70] such that the NatMX marker was replaced by HIS3MX. Using this template, a C-terminal TAP tag was added to ELP1 in RL-343-F0 and RL-343-F1 (Table S3) using standard one-step tagging methodology [71]. sit4Δ::LEU2 and kti12Δ::LEU2 knockouts were made as previously described [39], [43], as was introduction of elp1Δ::KanMX6 in strains used for co-immune precipitation with myc-tagged Elp2 [16]. Construction of ELP1-HA6::KlTRP1 utilized the pYM3 tagging plasmid and S2/S3 primer set described by Knop et al. [72].
For phosphorylation site mapping, Elongator complexes were purified from WAY009, WAY010, WAY011, WAY-H-P1T and WAY-Has-P1T (Table S3) by two-step tandem affinity purification of Elp1-TAP from 2-12 liter cultures in YPAD medium grown to OD600 1.0-1.5, as described previously [73]. All purifications were done in the presence of PhosSTOP and Complete protease inhibitor cocktail (Roche). Elongator complexes in which Elp1 contained phosphorylation site mutations were similarly purified from WAY037 (elp1Δ::pCORE-UH ELP3-TAP::HIS3MX: Table S3) harboring YCplac111-ELP1-6HA or its mutant derivatives (Table S2), growing cells as above but in SCD-Leu to select for retention of the plasmid.
Elongator protein preparations were digested in solution with Trypsin (Trypsin Gold, mass spectrometry grade; Promega V5280). The resulting peptides were cleaned over Hypersep C18 columns (Thermo Scientific) to remove buffer contaminants, eluting the peptides in 70% acetonitrile, 0.1% trifluoroacetic acid (TFA). Phosphopeptide enrichment was done in a two-step procedure using a Hypersep SCX column (Thermo Scientific) followed by TiO2 enrichment of mono-phosphorylated peptides from the SCX flow-through. SCX binding and washing buffer contained 10 mM KH2PO4, 25% acetonitrile (pH 3.0) and the elution buffer contained 10 mM KH2PO4, 25% acetonitrile, 350 mM KCl (pH 3.0). The flow-through was reduced in volume to ∼100 µl, supplemented with 100 µl 80% acetonitrile/2% TFA containing 200 mg/ml 2,5-dihydroxybenzoic acid (DHB), pH 2.0, then the sample applied to 5 micron Titansphere TiO2 beads (GL Sciences) and rotated gently at room temperature for 1 h. The beads were washed twice with 80% acetonitrile/2% TFA, 200 mg/ml DHB (pH 2.0), then 3 times with 80% acetonitrile/2%TFA (pH 2.0), before eluting with 60 µl 400 mM NH4OH (pH 11.0) and then supplementing with 2 µl of 100% formic acid. All phosphopeptide fractions were cleaned over C18 and submitted for mass spectrometry in 0.1% TFA.
Phosphopeptide fractions were analyzed by LC-MS/MS using a Dionex U300 system (Dionex California) with a PepMap C18 column coupled to either an Orbitrap XL or Orbitrap Velos (Thermo Fisher Scientific). Peptides were eluted using a 45 min 5%-90% acetonitrile gradient, sequencing the top 5 most intense ions with the following settings: CID, FTMS 335-1800 Da, 60,000 resolution, MS/MS charge state 1+ rejected,>2+ accepted. Peak picking, recalibration and peptide mass fingerprinting was done using MaxQuant software [74], [75], searching masses against the Saccharomyces Genome Database orf_trans_all database (January 5th 2010 release) with 10 ppm MS error, ≤ 2 missed cleavages, Trypsin/P enzyme, variable modifications set to Acetyl (protein N-term), Oxidation (M), Phospho (ST) and Phospho (Y) and a fixed modification of Carbomidomethyl (C). The MS/MS tolerance was set to 0.5 Da and the false discovery rate for site, protein and peptide identification was set to 0.01. All phosphorylation sites identified in this way were verified by hand annotating the spectra. The analysis was done on four biological replicates of the finally optimized SCX-TiO2 protocol.
An anti-Elp1 phosphoSer-1209 antibody was raised in a rabbit by BioGenes GmbH, using a peptide antigen (H2N-CTSTQE-pS-FFTRY-CO.NH2, where pS indicates phosphoserine) and their standard procedures (see http://www.biogenes-antibodies.com). The phosphospecific fraction of the final bleed was purified by serum depletion using immobilized non-phosphorylated peptide (H2N-CTSTQESFFTRY-CO.NH2), then phosphospecific antibodies were isolated from the depleted serum using immobilized phosphopeptide. The purified phosphospecific antibody fraction was tested for phosphospecificity by Western blotting, showing loss of signal when protein extracts were prepared from an elp1-1209A mutant and following competition with the phosphopeptide. The antibody was stored at -20°C in 50% (v/v) glycerol and used at a 1 in 3000 dilution. A small excess of the non-phosphorylated peptide was added to the primary dilution before use to prevent any cross-reaction with the non-phosphorylated epitope.
To test the effect of extracellular zymocin on wild-type and mutant strains, killer-eclipse assays were performed as described previously [76] using the K. lactis zymocin producer strain NCYC1368 and YPD plates prepared using Kobe I agar (Roth 5210). To test the effect of intracellular expression of zymocin's γ subunit on growth, wild-type and mutant elp1 strains were transformed with pAE1, which expresses the γ subunit from the galactose-inducible GAL1 promoter [43]. The response to zymocin γ-toxin induction was monitored on galactose plates after 3-4 days at 30°C. SUP4 suppression efficiency was measured following integration of pSB3 in single copy at the his3Δ0 locus and then monitoring growth on SCD-Leu-Ura after 3 days at 30°C as described previously [32]. To test the effect of Hrr25 inhibition on Elongator function, strains dependent on w.t. or analog-sensitive Hrr25 were grown overnight in YPAD medium, diluted to 1.0 A600/ml and then 10-fold dilutions were spotted onto YPAD agar with or without 1%(v/v) zymocin and containing 10 µM 1NM-PP1 or an equivalent volume of DMSO as drug vehicle control. Growth was documented after 2 days growth at 30°C. Zymocin was prepared using cell-free culture medium from K. lactis NCYC1368 which had been grown at 30°C for 2 days in YPAD medium, by 50-fold concentration using Amicon Ultra-15 Centrifugal Filter Units (Millipore) followed by sterilization by filtration.
elp1Δ yeast strains containing YCplac111-ELP1-HA6 or its mutant derivatives were grown in SCD-Leu to select for the plasmid and then total tRNA was prepared by RNA extraction and LiCl precipitation as described previously [32]. Prior to LC-MS/MS analysis, 5 µg of each tRNA sample were digested into nucleosides by incubation at 37°C for 2 h in the presence of 1/10 volume of 10× nuclease P1 buffer (0.2 M NH4OAc pH 5.0, ZnCl2 0.2 mM), 0.3 U nuclease P1 (Sigma Aldrich, Munich, Germany) and 0.1 U snake venom phosphodiesterase (Worthington, Lakewood, USA). Next, 1/10 volume of 10× fast alkaline phosphatase buffer and 1 U fast alkaline phosphatase (Fermentas, St. Leon-Roth, Germany) were added, and samples were incubated for additional 60 min at 37°C. The digested tRNA samples were analyzed on an Agilent 1260 HPLC series equipped with a diode array detector (DAD) and a triple quadrupol mass spectrometer (Agilent 6460). A Synergy Fusion RP column (4 µm particle size, 80 Å pore size, 250 mm length, 2 mm inner diameter) from Phenomenex (Aschaffenburg, Germany) was used at 35°C column temperature. The solvents consisted of 5 mM ammonium acetate buffer adjusted to pH 5.3 using acetic acid (solvent A) and pure acetonitrile (solvent B). The elution was performed at a flow rate of 0.35 ml/min using a linear gradient from 0% to 8% solvent B at 10 min, 40% solvent B at 20 min and 0% solvent B at 23 min. For additional 7 min, the column was rinsed with 100% solvent A to restore the initial conditions. Prior to entering the mass spectrometer, the effluent from the column was measured photometrically at 254 nm by the DAD for detection of the 4 canonical nucleosides. The triple quadruple mass spectrometer, equipped with an electrospray ion source (Agilent Jet Stream), was run at the following ESI parameters: gas (N2) temperature 350°C, gas (N2) flow 8 L/min, nebulizer pressure 50 psi, sheath gas (N2) temperature 350°C, sheath gas (N2) flow 12 L/min and capillary voltage 3000 V. The MS was operated in positive ion mode using Agilent MassHunter software and modified nucleosides were monitored by multiple reaction monitoring (dynamic MRM mode). Identification of ncm5U and mcm5U peaks was performed as described previously [77]. Peak areas were determined employing Agilent MassHunter Qualitative Analysis Software. In the case of the major nucleosides, peak areas were extracted from the recorded UV chromatograms in order to avoid saturation of the mass signals. For inter-sample comparability of the detected modifications, the peak areas of the modified nucleosides were normalized to the UV peak area of uridine.
Detection of tagged proteins used anti-TAP (Thermo Scientific, CAB1001), anti-myc and anti-HA antibodies (Roche) and was performed as previously described [16], [45]. Protein concentrations were determined using Quick StartTM Bradford Protein Assay (BioRAD) [78] and checked with anti-Pfk1 antibodies recognizing yeast Pfk1 (1:50,000, kindly provided by Dr. J. Heinisch) or anti-Cdc19 serum (1:10,000, kindly provided by Dr. J. Thorner) so as to ensure equivalent loadings. For detection of the Hrr25 kinase in total yeast extracts and in immune precipitates, a generic anti-Hrr25 antibody [64] was used (1:10,000 dilution). Antibody cross-linking to Dynabeads M-270 Epoxy (Invitogen), preparation of protein extracts and immune precipitation were performed according to the manufacturer's instruction and as described previously [16], [79]. In general, 1 µg of antibody coupled to Dynabeads was used per 1 mg total cell extract in B60 buffer.
All 119 GST-protein kinases constructs in the library described by Zhu et al. [47] were expressed in yeast and purified as described previously [80]. Yeast Elongator complex was isolated as described in the main paper and used as a substrate for the GST-kinases, which were initially assayed in 23 pools of 5 kinases under similar conditions to those described below. Reactions were separated by SDS-PAGE and radiolabelled Elp1 detected by autoradiography. Eight out of the 23 pools showed phosphorylation occurred above the background level observed due to co-purification of Hrr25 with Elongator (see Results). From these eight pools, 40 individual kinases were individually screened for their ability to phosphorylate Elp1. Reactions in which Elongator was omitted controlled for any radiolabeled bands that co-migrated with Elp1 and were therefore due to kinase autophosphorylation or co-purified kinase substrates.
E. coli BL21 (DE3) pLysS (Novagen) was transformed with pTrcHis-HRR25 for overexpression and purification of His6-Hrr25 using HisPur cobalt resin (Thermo Scientific) according to the manufacturer's instructions. To analyze Elp1 phosphorylation in vitro, purified wild-type or mutant Elongator complex (1 µg) was incubated for 30 min at 30°C in 20 µl P-buffer (50 mM HEPES-KOH (pH 8.0), 5 mM MgCl2, 50 mM KCl) containing 100 µM ATP and 0.5 µCi of radiolabelled [γ-32P]ATP (3000 Ci/mmol), in the absence or presence of purified recombinant His6-Hrr25 (1 µg). Phosphorylation reactions were stopped by the addition of NuPAGE 4× LDS Sample Buffer (Life Technologies) and heated for 5 min at 95°C. Samples were run on a NuPAGE (4-12%) polyacrylamide/Bis-Tris SDS gel (Life Technologies) at 200 V for 1 h. Gels were stained with Bio-Safe Coomassie (BioRad), dried and subjected to autoradiography. To examine the effect of chemical inactivation of yeast Hrr25 on Elp1 phosphorylation in purified Elongator complex, Elongator was purified as above from strains WAY-H-P1T (wild-type HRR25) and WAY-Has-P1T (allele-sensitive hrr25 I82G) and assayed as described above in the presence or absence of 10 µM 3-MB-PP1 or 1-NM-PP1.
Time course phosphorylation reactions of synthetic peptides were carried out in 150 µl P-buffer containing 500 pmol synthetic peptide (Biomatik), 400 pmol recombinant His6-Hrr25, 1 mM ATP and 10 µCi [γ-32P]ATP (6000 Ci/mmol) and incubated at 30°C. Samples (5 µl) were collected at time intervals, spotted on Whatman Grade P81 Ion Exchange Cellulose Chromatography Paper, washed 3 times in 1% phosphoric acid, dried and quantitated by liquid scintillation counting [81]. To visualize phosphorylated peptides, 10 µl reactions were assembled at the above described stoichiometry, incubated for 30 min at 30°C and then terminated by addition of 4× SDS loading dye and heating at 95°C for 5 min. After separation by SDS-PAGE at 200 V using a NuPAGE 12% polyacrylamide/Bis-Tris gel (Life Technologies) with MES running buffer (50 mM MES, 50 mM Tris-base, 0.1% SDS, 1 mM EDTA, pH 7.3), phosphorylated peptides were visualized by autoradiography. For mass spectrometric analysis of phosphorylated peptides, 150 µl reactions were conducted as above but omitting the radiolabelled ATP.
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10.1371/journal.ppat.1002840 | CD160 and PD-1 Co-Expression on HIV-Specific CD8 T Cells Defines a Subset with Advanced Dysfunction | Chronic viral infections lead to persistent CD8 T cell activation and functional exhaustion. Expression of programmed cell death-1 (PD-1) has been associated to CD8 T cell dysfunction in HIV infection. Herein we report that another negative regulator of T cell activation, CD160, was also upregulated on HIV-specific CD8 T lymphocytes mostly during the chronic phase of infection. CD8 T cells that expressed CD160 or PD-1 were still functional whereas co-expression of CD160 and PD-1 on CD8 T cells defined a novel subset with all the characteristics of functionally exhausted T cells. Blocking the interaction of CD160 with HVEM, its natural ligand, increased HIV-specific CD8 T cell proliferation and cytokine production. Transcriptional profiling showed that CD160−PD-1+CD8 T cells encompassed a subset of CD8+ T cells with activated transcriptional programs, while CD160+PD-1+ T cells encompassed primarily CD8+ T cells with an exhausted phenotype. The transcriptional profile of CD160+PD-1+ T cells showed the downregulation of the NFκB transcriptional node and the upregulation of several inhibitors of T cell survival and function. Overall, we show that CD160 and PD-1 expressing subsets allow differentiating between activated and exhausted CD8 T cells further reinforcing the notion that restoration of function will require multipronged approaches that target several negative regulators.
| HIV infection is widely known to cause generalized immune activation and immune exhaustion ultimately leading to HIV disease progression. Several studies have suggested over the years that the accumulation of inhibitory signalling proteins on the surface of responding cells is linked to immune exhaustion in HIV. It has become paramount to distinguish functionally exhausted CD8 T cells from activated HIV-specific CD8 T cells because both cell types have different fates. Using specific cell surface markers, we were able to identify these different cell types and show that HIV-infected patients accumulate dysfunctional CD8 T cells over time. Importantly, we show that this dysfunction is reversible.
| Mounting evidence supports the notion that CD8 T cells contribute to the control of HIV viral replication [1]. The emergence in early infection of viral variants bearing escape mutations within sequences targeted by HIV-specific T lymphocytes is consistent with CD8 T cells exerting selective antiviral pressure [2]–[3]. However, HIV viral replication outpaces the adaptive immune response leading to the establishment of chronic infection partly because CD8 T cells are progressively deleted [4] and/or become dysfunctional [5]–[6].
Functionally exhausted T cells were originally described in a murine model of acute and chronic lymphocytic choriomeningitis virus (LCMV) infection whereby virus-specific CD8 T cells persist but lack effector function [7]. Exhausted CD4 and CD8 T cells have since been described in cancer [8] and chronic viral infections such as SIV [9], HIV [10]–[12], Hepatitis C (HCV) [13]–[14] and Hepatitis B (HBV) [15]. Functional impairment of antigen-specific responses has been shown to occur in a stepwise and hierarchical manner with proliferation, IL-2 and TNFαproduction being the first functions lost followed by IFNγ [16]–[18]; eventually cells die by apoptosis [19]. Of note, in chronic LCMV infection inhibitory receptors such as PD-1, LAG-3, CD160, CTLA-4, 2B4/CD244, GP49 and PirB are all upregulated on exhausted LCMV-specific CD8 T cells compared to functional effector or memory cells [20]–[21]. Blocking the interaction of PD-1 and LAG-3 with their natural ligands restored virus specific T cell proliferation and effector cytokine production (IFNγ, TNFα and CD107a) in this mouse model. However, functional restoration was only partial suggesting the involvement and cooperation of several negative regulatory pathways [20] in the programming of T cell exhaustion.
Several mechanisms that lead to antigen-specific CD8 T cell dysfunction in HIV infection have also been described; they include the lack of CD4 help [22] as well as the upregulation on HIV-specific and total T cells of several negative regulators of T cell activation including PD-1, CTLA-4, CD160, 2B4 and Tim-3. PD-1 [23]–[25], CTLA-4 [26] and Tim-3 [27]–[28] have also been associated to HCV and HIV-specific CD4 and CD8 T cell dysfunction as the expression levels of these molecules correlated positively with plasma viral load and negatively with absolute CD4 T cell counts while there expression declined in subjects treated with highly active antiretroviral therapy (HAART) [23], [26]–[27].More recently, studies have shown in cancer [29] and HIV [30]–[31] that the co-expression of several immune inhibitory molecules on antigen-specific CD8 T cells leads to a more severe dysfunction. Of note, expression of these molecules is a by-product of T cell activation as they play an important role in T cell homeostasis. Therefore, the mechanisms that determine the functions of these molecules in T cell activation and in functional T cell exhaustion have been difficult to decipher, as most of these molecules are also upregulated upon T cell activation.
We therefore analyzed the expression and function of CD160 and PD-1 on CMV and HIV-specific CD8 T cells during different stages of infection and identified 4 functionally distinct subsets of CD8 T cells (CD160−PD-1−, CD160−PD-1+, CD160+PD-1−, CD160+PD-1+). Our data identified a unique CD160+PD-1+ subset at an advanced stage of exhaustion mostly found during chronic HIV infection (CHI).
Microarray analysis of sorted CMV and HIV-specific CD8 T cells in 27 HIV-infected subjects, showed a significant increase (1.7 fold and p<0.05) in levels of expression of CD160 mRNA in HIV-specific CD8 T cells when compared to CMV-specific CD8 T cells (Figure S1). To confirm the results generated by microarray, we measured the levels of CD160 expression on CMV and HIV-specific CD8 T cells as well as total CD8 T cells isolated from 7 HIV-uninfected individuals and 38 HIV-infected subjects divided into 4 groups: acute HIV infection (AHI; n = 7), chronic/progressing infection (CHI; n = 9), successfully treated and aviremic individuals (ST; n = 12) and Elite controllers (ECs; n = 10) (Table 1, 2). HIV and CMV-specific MHC class I tetramers were used to identify these cells. We also measured the expression of PD-1 on cells from individuals in these different groups as this molecule has also been shown to be upregulated on HIV-specific T cells as well as total CD8 T cells in HIV infection.
Results illustrated in Figure S2A and S2B showed that significantly higher levels of CD160, as monitored by Mean Fluorescence Intensity (MFI), were found on HIV-specific CD8 T cells when compared to CMV-specific CD8 T cells in CHI subjects (CMV: MFI = 1817; HIV: MFI = 4636; P = 0.0001) and ECs (CMV: MFI = 2428; HIV: MFI = 3610; P = 0.009) (Figure S2B; left panel). As well, the frequency of HIV-specific CD160+ T cells was significantly higher than that of CD160+ CMV-specific T cells in CHI (CMV = 42.5%; HIV = 70.7%; P = 0.0001) and ECs (CMV = 56.1%; HIV = 74.1%; P = 0.009) (Figure S2B; right panel). The increased expression of CD160 on HIV-specific CD8 T cells was observed only during the chronic phases of HIV infection. Importantly, HIV and CMV-specific CD8 T cells expressed similar levels of CD160 in the acute phase of infection. The MFI and frequencies of CD160 on antigen-specific CD8 T cells did not differ between ST and ECs. As shown previously [23]–[24], the frequency of PD-1+ HIV-specific T cells as well as the levels of expression of this molecule were greater on HIV than CMV-specific CD8 T cells during CHI (Figure S2C,D).The MFI and frequency of PD-1 on HIV-specific T cells were significantly lower in ECs (54.2%; MFI = 3479) compared to AHI (69.8%, P = 0.0004; MFI = 5742, P<0.0042),CHI (86.0%, P<0.0001; MFI = 8377, P = 0.0002) and ST patients (76.1%, P = 0.03; MFI = 5097, P = 0.006).
Total CD8 T cells in CHI subjects and ECs also included a significantly higher proportion of cells expressing high levels of CD160 compared to levels observed in uninfected controls (MFI = 1251 and 24.4% in uninfected individuals; MFI = 1340 and25.3% in AHI; MFI = 1983and 35.4% in CHI; MFI = 1788 and 36.0% in ECs) (Figure S3). As shown for antigen-specific responses, total CD8 T cells upregulated CD160 expression only during the chronic stage of infection. CD160 expression was restricted to terminally differentiated (CD45RA+CD27−CCR7−) and memory CD8 T cells (CD45RA−CD27+/−CCR7+/−) whereas naïve CD8 T cells (CD45RA+CD27+CCR7+) did not express this protein (Figure S4). Of note, while PD-1 levels were upregulated on HIV-specific and total CD8 T cells in AHI and CHI compared to uninfected subjects, elevated CD160 levels were limited to CHI. Our results highlight a difference in the timing of expression of these molecules during the natural history of HIV infection.
We measured the co-expression of CD160 and PD-1 on HIV and CMV-specific CD8 T cells in the groups of subjects described above. Four distinct cell subsets were identified: CD160−PD-1− (DN), CD160−PD-1+ (SP-PD-1), CD160+PD-1− (SP-CD160) and CD160+PD-1+ (DP) (Figure 1A, Figure S5A,B). Frequencies of DN and DP subsets within HIV-specific T cells showed significant differences when comparing cross-sectionally HIV-infected subjects at different stages of disease (AHI vs CHI). The percentages of HIV-specific DN cells were highest in AHI (20.2% range, 4.7–31.3) and lowest in CHI (3.2% range, 0.7–19.1) (P = 0.0002)(Figure 1B; upper left panel). In contrast, frequencies of HIV-specific DP cells were highest in CHI (59.5% range, 21.5–78.2) and their numbers were the lowest in AHI (24.8% range, 9.4–37.0) (P = 0.0001)(Figure 1B; upper right panel). In this cross-sectional analysis, the frequencies of DN and DP subsets within CMV-specific T cells did not vary between acute and chronic infection (Figure 1B).Elite controllers also showed high frequencies of HIV-specific T cells with a DP phenotype; however frequencies were significantly lower compared to CHI (P = 0.004) (Figure 1B; upper right panel). As for the two SP subsets, their frequencies significantly shifted when comparing individuals at different stages of disease. The frequency of SP-PD-1 HIV-specific CD8 T cells was highest during AHI (47.1%) and significantly decreased in CHI (25.5%; P = 0.0001) and ECs (14.0%, P = 0.008) while the frequencies of SP-CD160 CD8 T cells were higher in ECs compared to other study groups (Figure 1B; lower panels). The frequencies of SP-PD-1 and DP subsets from ST subjects did not significantly differ from those observed in CHI and EC. Interestingly, the frequency of SP-PD-1 HIV-specific CD8 T cells did not differ from that of SP-PD-1 T cells recognizing CMV epitopes at all stages of HIV infection (Figure 1B; lower right panel). These results indicated that HIV-specific CD8 T cells expressing PD-1 encompass multiple subsets. Importantly, the phenotype of these cells was shown to be different when comparing CD8 T cells in acute and chronic HIV infection. Moreover, the level of PD-1 expression was highest on CD160+CD8 T cells(Figure S5C), further reinforcing the notion that DP cells are at an advanced stage of exhaustion and raising the possibility that SP-PD-1 cells encompass recently activated T cells [32]–[33]
The lower frequencies of HIV-specific DP CD8 T cells in ECs compared to CHI subjects (Figure 1) suggested the involvement of CD160 and PD-1 co-expression in antigen-specific T cell dysfunction as previously shown in mice models of chronic viral infection [20]–[21] and suggested recently in HIV-infected subjects [27], [30].
A longitudinal analysis was performed to confirm the dynamic evolution of these phenotypes during different disease stages. We assessed the frequencies of CMV, EBV and HIV-specific CD8 T cells expressing CD160 and/or PD-1 in 5 HIV-infected subjects during AHI (<3 months on infection) and CHI (>6 months of infection) (Figure 2A). The frequency of HIV-specific PD-1 expressing CD8 T cells (SP-PD-1 and DP) remained stable over time(Figure S6). However, when we assessed the two subsets of PD-1+ cells, we observed a significant decrease in the frequencies of SP-PD-1 HIV-specific CD8 T cells (P = 0.016) over the course of infection whereas the frequencies of DP CD8 T cells significantly increased (P = 0.016) from AHI to CHI (Figure 2B; right panels) confirming results obtained in the cross-sectional analysis. In contrast, the proportion of CD160+ HIV-specific CD8 T cells (SP-CD160 and DP) increased with disease progression (Figure S6). Percentages of DN and SP-CD160 HIV-specific CD8 T cells increased from AHI to CHI (P = 0.016) (Figure 2B; left panels). The distribution of CD160 and PD-1 subsets on cells specific for CMV and EBV epitopes did not significantly change from AHI to CHI (Figure 2C). Taken together, these results showed a dynamic evolution of the frequency and distribution of CD160 and/or PD-1 expression on HIV-specific CD8 T cells during infection. Both cross-sectional and longitudinal results confirmed that the distribution of CD160 and PD-1 expressing subsets was predominantly SP-PD-1 in AHI and DP in chronic HIV infection. Our results strongly indicated that the simultaneous expression of CD160 and PD-1 could constitute a marker of T cell exhaustion and disease progression. A recent study published by Youngblood et al. [34] reinforced this observation by showing that persistent TCR signalling results in sustained PD-1 expression by maintaining PD-1regulatory regions accessible to transcription factors.
We next assessed the effector function of these different subsets by intracellular cytokine staining (ICS) for IFNγ and TNFαsecretion and measured the expression of CD107a following stimulation with SEB, CMVpp65 and HIV peptides in viremic HIV-infected subjects. Figure 3A depicts the percentage of cytokine secreting cells within the total CD8 T cell population upon SEB stimulation. Frequencies of CD8 T cells producing TNFα, IFNγ or upregulating CD107a were significantly higher in DN, SP-PD-1 and SP-CD160 compared to frequencies observed in the DP subset (P<0.05 and P<0.005, #or ## represents significant change in cytokine production compared to DP). Furthermore cells with a DN phenotype had higher frequencies of functional CD8 T cells when compared to SP-CD160 and SP-PD-1 following stimulation with SEB. In summary, the expression of either CD160 or PD-1 on CD8 T cells identified a T cell subset having lower levels of effector function (IFNγ, TNFα) or degranulation (CD107a). Importantly, our results indicated that cells expressing both CD160 and PD-1 were more dysfunctional than cells expressing either one of these two molecules.
The same hierarchy of functionality was observed when comparing the four different subsets of T cells following stimulation with individual CMV peptides (Figure 3B). As observed above, DN cells showed the highest frequencies of cells that produced TNFα (19.2%), IFNγ (11.5%), and upregulated CD107a (17.6%) when compared to DP cells (TNFα0.4%, IFNγ0.5%, CD107a 0.5%). SP-PD-1 (TNFα = 1.7%, IFNγ = 2.9%, CD107a = 2.5%)and SP-CD160 (TNFα = 1.3%, IFNγ = 1.5%, CD107a = 3.8%) also showed lower frequencies of cytokine producing cells than DN and importantly higher frequencies than DP cells.
Triggering of HIV-specific cells yielded similar results to SEB and CMV stimulated T cells. As noted above, DP (TNFα: 0.8%, IFNγ:1.2%, CD107a:2.6%) and DN (TNFα:3.5%, IFNγ:5.4%, CD107a:13.4%) cells showed the most significant difference in the frequency of cytokine producing cells with a downregulation of TNFα, IFNγand CD107a expression (Figure 3C). Following stimulation with HIV peptides, the SP-PD-1 (TNFα:1.9%, IFNγ:5.4%, CD107a:7.0%) subset included significantly higher frequencies of IFNγ, TNFα secreting cells as well as CD107a+ degranulating cells as compared with the DP subset (Figure 3C; ##represents significant change in cytokine production compared to DP). As observed for SEB and CMV peptide stimulated CD8 T cells, DP CD8 T cells were consistently the least functional subset.
Together these results showed that antigen-specific CD8 T cells with a DP phenotype were less functional than SP-PD-1 expressing CD8 T cells as shown by their lower responses to all three T cell activation signals. These results provide evidence that co-expression of CD160 and PD-1identified dysfunctional CD8 T cells. This degree of dysfunctionality progressively increases with the co-expression of additional immune inhibitory markers on CMV and HIV-specific T cells.
HVEM is the natural ligand of CD160 [35]. We therefore performed experiments aimed at determining whether interfering with CD160 engagement by HVEM allowed dysfunctional T cells to recover their effector T cell function. PBMCs were stimulated with HLA-restricted CMV and HIV optimal peptides in a 6-day CFSE assay in the presence or absence of αHVEM together with or without αPD-L1 blocking antibodies (Figure 4A). We measured the expression of HVEM on monocytes, mDCs and pDCs and observed a significant upregulation of HVEM surface expression on monocytes and pDCs from CHI individuals compared to healthy controls. Similar findings were observed when measuring the expression of PD-L1 on monocytes (P<0.05) (Figure S7). Our results showed that blocking CD160 interaction with HVEM significantly enhanced CMV and HIV-specific CD8 T cell proliferation (Figure 4B,C). We observed a median fold increase of 10.1 (P<0.0001) and 4.9 (P<0.0001) in CMV and HIV-specific CD8 T cell proliferation respectively, compared to isotype controls. Blocking the PD-1/PD-L1 pathway led to a statistically significant enhancement of HIV-specific CD8 T cell proliferation by a factor of 1.2 (P = 0.02); hence CD160/HVEM blockade was more potent than PD-1/PD-L1 blockade at restoring HIV-specific CD8 T cell proliferation. PBMCs cultured with both blocking antibodies (αPD-L1 and αHVEM) also significantly enhanced T cell proliferation however, the effect was not synergistic compared to using αHVEM alone (P<0.0001) (Figure 4B,C).We analyzed the coexpression of BTLA and CD160 on HIV-specific CD8 T cells during chronic HIV infection and found that the frequency of CD160 (35.5%) was significantly greater than BTLA (3.9%; P<0.0001) suggesting that using αHVEM preferentially disrupts the CD160/HVEM axis (Figure S8). Controls showed that the αHVEM blocking antibody did not induce T cell activation in the absence of the T cell cognate peptide.
Supernatants harvested following the 6-day CFSE assay were used to assess cytokine production in the presence or absence of PD-1 and or CD160 engagement by their respective ligands. We found that levels of IFNγ, IL-4 and IL-10 were significantly increased compared to isotype controls in conditions where αHVEM was added to the T cell cultures (IFNγ, P = 0.001; IL-4, P = 0.03; IL-10, P = 0.003) (Figure S9). Although TNFα production was increased in conditions where αHVEM was present, the levels were not statistically different compared with the isotype control (TNFα, P = 0.054). The levels of IL-2 production did not significantly increase upon antigen-specific stimulation in the presence of HVEM blockade most likely due to the consumption of this cytokine by proliferating T cells. These results confirmed that CD160 was implicated in HIV-specific CD8 T cell exhaustion. Blocking its interaction with HVEM restored the proliferation and cytokine production of antigen-specific CD8 T cells.
Gene array profiling was performed on sorted CD8 T cell subsets based on CD160 and PD-1 co-expression in 4 HIV viremic individuals to determine if the functional defects observed in DP cells were the consequence of a distinct gene expression signature that was associated with signal transduction pathways that regulate T cell survival, turnover and function. Unsupervised cluster analysis showed that both DP and SP-PD-1 subsets clustered apart to create two statistically significant populations with unique transcriptional profiles (Figure 5A). The heatmap lists the top 39 genes expressed at significantly different levels between both subsets (P<0.05).
The results showed that genes upregulated in DP cells include those involved in the inhibition of several survival pathways. Importantly, SUMO2 (Small Ubiquitin-like modifier) was upregulated in DP CD8 T cells compared to SP-PD-1. This enzyme upregulates the activity of PIAS (protein inhibitor of activated STAT) molecules which are responsible for the inhibition of STATs (Signal Transducer and Activator of Transcription) including STAT5, a molecule directly downstream of γ chain cytokine receptors such as IL-7 and IL-15 [36]–[38]. The inhibition of the STAT5 pathway was confirmed by the downregulation of bcl-2 in DP cells (Figure 5B). Moreover DP cells upregulated the expression of KIF7, an antagonist of hedgehog the positive regulator of Wnt signaling [39] and several cell surface negative regulatory molecules including KIR2DL3 known to express ITIM motifs in the cytoplasmic tail [40]. In contrast, SP-PD-1 CD8 T cells upregulated the expression of several T cell activation markers including HAVCR2 (Tim-3), CTLA-4, LAT, CCR1 and TNFRS25 [26]–[27], [41]–[42]. In addition, positive regulators of Wnt/Notch signalling including Wnt7A and AXIN2 were upregulated in SP-PD-1 compared to DP CD8 T cells [43].
A network analysis of differentially expressed genes between DP and SP-PD-1 subsets showed a significant downregulation of signal transduction pathways enriched in genes that regulate T cell survival (IL-15, IL-7R, PIM3, bcl-2, all part of the STAT-5A pathway) and T cell effector function (LTβ, IL-18RAP, IL-18R1, CXCL16) in the DP subset (Figure 5B). Interestingly, the expression of NFκB was downregulated in DP compared to SP-PD-1 further confirming the advanced state of exhaustion and the unique identity of this novel subset in chronic HIV infection. Of note, TNFα production, which triggers NFκB [44], was downregulated in CD160+ CD8 T cells. Moreover, this network analysis confirmed the upregulation of several molecules with inhibitory functions in the DP subset such as the inhibitory KIR family (KIR2DL1, 2DL2, 2DL3, 2DL4, KIR3DL1, 3DL3) [40], 2B4 as well as members of the KLR family of proteins which are all associated to senescence. We confirmed by flow cytometry the increased expression of inhibitory KIR2DL2/KIR2DL3 in the DP compared to SP-PD-1 subset (p = 0.038) (Figure 5C). Taken together, gene expression analysis of CD160+PD-1+ CD8 T cells showed a gene expression signature that comprises several inhibitors of survival signal transduction pathways (STAT-5 and Wnt/Notch) and the increased expression of multiple immune inhibitory molecules. In contrast, SP-PD-1 CD8 T cells showed a transcriptional profile reminiscent of recently activated T cells.
The results presented here show that CD160 was upregulated on CD8 T cells during HIV infection. We identified 4 distinct subsets of CD8 T cells: DN, SP-PD-1, SP-CD160 and DP. Importantly, only CD8 lymphocytes that co-expressed CD160 and PD-1 had functional features and transcriptional profiles of exhausted cells. Recent studies have described an accumulation of inhibitory molecules on HIV-specific CD8 T cells. However, we show here that the distribution of cells expressing one or more of these molecules significantly shifted during the course of HIV infection. We show that SP-PD-1 cells increased in numbers in AHI while cells co-expressing CD160 and PD-1 were the dominant cell subset in CHI. This increased frequency of DP cells was associated with HIV disease progression and T cell dysfunction. Cells within the CD160+PD-1+ subset were less functional than SP-PD-1 and SP-CD160 as shown by the reduced frequency of cytokine secretion upon TCR triggering. Our gene array data confirmed the unique exhausted phenotype of the DP subset as these cells expressed transcriptional programs that were highlighted by the downregulation of the NFκB transcriptional node, strongly associated to T cell survival, and the upregulation of cell surface inhibitory KIR expression. This allowed us for the first time to provide molecular evidences for differences that demarcate cells expressing these inhibitory molecules as a consequence of T cell activation or those that express these molecules when they are functionally exhausted. We also show that CMV and HIV-specific CD8 T cell proliferation and cytokine secretion were rescued after blocking the engagement of CD160 with its natural ligand (HVEM). These results confirmed that functional exhaustion of T cells results from the progressive accumulation of several molecules that negatively impact on T cell activation. The temporal accumulation of these negative regulators results from chronic exposure to HIV and other molecules that trigger hyper-immune activation [45] since CD160 expression on T cells is observed mostly during the chronic phase of infection
Along with LIGHT, LTα, HSVgD, and BTLA, CD160 is also a ligand of HVEM. The interaction of LIGHT with HVEM delivers a co-stimulatory signal by triggering NFκB whereas the binding of CD160 or BTLA with this ligand delivers an inhibitory signal to CD4 T cells [35], [46], most probably by competing with LIGHT [47] for binding to HVEM. Although conflicting results regarding the function of CD160 have been reported [48]–[49], [50]–[51], recent findings are consistent with an inhibitory function for this molecule when expressed on T cells [20]–[21], [35]. The fact that SP-CD160 and SP-PD-1 subsets are expressed at comparable levels on HIV and CMV-specific T cells suggest that these cells are still functionally competent. Future work will compare the phenotype of HIV-specific CD8 T cells to other acute and chronic viral infections with the aim of understanding whether the observed phenotypic distribution is unique to HIV-specific CD8 T cells. Moreover since SP-PD-1 can still mount polyfunctional responses upon TCR triggering with cognate antigen, and the observation that HIV-specific T cells in ECs exhibit mostly an SP-CD160 phenotype further confirms the functionality of these subsets.
DP cells express the highest levels of PD-1 when compared to SP-PD-1.DP CD8 T cells are hence a unique dysfunctional T cell subset, as highlighted by the downregulation of several transcriptional nodes (STAT5, Notch-Wnt, NFκB) that regulate T cell survival and effector function as confirmed by flow cytometry and T cell functional assays. Differences in the functionality of DP and SP-PD-1 subsets could not be accounted for by their distribution in different memory or effector T cell compartments. Indeed our results (Figure S4) showed that DP and SP-PD-1 cells were found in TTM,TEM and late differentiated T cells all known to be endowed mostly with effector functions thereby confirming the data by Yamamoto et al. [30]. These results are consistent with those generated in the LCMV model whereby the frequency of CD160+PD-1+CD8 T cells increases during CHI leading to an accumulation of dysfunctional HIV-specific CD8 T cells [20]–[21]. The expression of PD-1 in ST patients is significantly higher than that observed in ECs highlighting the ongoing viral replication in tissues (Figure S2). The higher levels of PD-1 on HIV-specific CD8 T cells from ST subjects most probably contribute to the dysfunction of these cells while DP cells from EC subjects still remain functional [17].
Previous studies have shown that CD160 and PD-1 interact with molecules downstream of the TCR [35], [52]. Following the activation of CD4 T cells with αCD3/CD28, ligation of CD160 with HVEM reduced the phosphorylation of tyrosine residues on several substrates such as CD3ζ. This decreased expression and phosphorylation of CD3ζ has been associated to T cell anergy and dysfunction [53]–[54]. Our results confirm that the presence of both CD160 and PD-1 on the surface of cells is required for the inhibition of TCR mediated signalling. In that context, we observed higher frequencies of functional antigen-specific CD8 T cells in lymphocytes negative for both CD160 and PD-1, whereas subsets that expressed either molecule alone were significantly less functional. It is important to note that both CD160 and PD-1 are upregulated upon T cell activation [55]. Hence it is more than likely that SP-PD-1 and SP-CD160 cells correspond to recently activated T cells that have upregulated those inhibitory receptors (PD-1, CD160) to control T cell activation as part of a homeostatic T cell response.
Blocking the interaction of CD160 and HVEM significantly enhanced CMV and HIV-specific proliferation and cytokine production further reinforcing the notion that CD160 acts as a negative regulator of T cell function. The magnitude of increase in CMV responses upon addition of αHVEM was more important than HIV-specific CD8 T cell responses (Figure 4). This is most probably due to the higher frequencies of DN and SP-CD160 cells within the CMV-specific T cell pool as compared to HIV-specific T cells. In addition, HIV-specific T cells include higher frequencies of DP cells compared to CMV-specific CD8 T cells. We found that the increase in proliferation that resulted from blocking CD160 engagement with HVEM was greater than that observed upon blocking PD-1 engagement with PD-L1 [24], [30]. As we have shown that CD160 is expressed at much higher levels than BTLA (the other ligand of HVEM), it is most likely that the rescue of HIV-specific CD8 T cell responses observed after addition of αHVEM targets mostly the interaction of HVEM with CD160. As previously shown, PD-1 is mostly expressed on DP cells during CHI. In CHI, the frequency of DP HIV-specific CD8 T cells is significantly higher than DP CMV-specific CD8 T cells (p<0.0001). HIV-specific DP cells are at an advanced stage of exhaustion and simultaneously express other negative regulatory molecules (elevated expression of KIR receptors), which could also contribute to T cell dysfunction. These cells, as shown from our transcriptional profiling, are hence truly exhausted and are at an irreversible stage of T cell dysfunction.
Several studies have observed that restoration of HIV-specific CD8 T cell proliferation and cytokine secretion of T cells specific for different epitopes [23]–[25] was variable. Our results suggest that the presence of multiple subsets of T cells expressing PD-1 with other negative regulators could account for this variability. For instance, in the murine LCMV infection model and during chronic HCV infection, subsets of PD-1hi and PD-1intexpressing cells have been identified. Blocking experiments have shown that only CD8 T cells that expressed intermediate levels of PD-1 were responsive to PD-1/PD-L1 blockade suggesting that not all specificities reverted towards a functional phenotype [56]–[57].
As noted above, PD-1 and CD160 are upregulated upon T cell activation. Transcriptional profiling helped elucidate the differences between cells that upregulate PD-1 as a result of T cell activation and PD-1hi exhausted T cells. Indeed SP-PD-1 cells also expressed several other T cell activation markers (CTLA-4, Tim-3, CCR1, TNFRSF25) as well as other molecules associated with T cell survival (AXIN2, Wnt7A). In contrast, the KIR family of cell surface receptors clearly demarcated PD-1hi (DP cells) exhausted cells from SP-PD-1 activated T cells [58]. Interestingly, only KIR genes with ITIM motifs (KIR2DL1, 2DL3, 2DL4, 3DL1, 3DL2, and 3DL3) were found upregulated on CD8 T cells which co-expressed CD160 and PD-1. The DP phenotype was associated with the down regulation of the NFκB survival pathway. As noted above, NFκB activity is triggered by LIGHT that competes with CD160 for binding to HVEM. These results support the view that DP lymphocytes express a large array of immune inhibitors and prevent CD8 T cells from acquiring a fully functional state. Our gene array results further confirmed the significant differences that demarcate DP from SP-PD-1 CD8 T cells. The former represent exhausted T cells while the latter are characterized by the expression of T cell activation markers and evidence for the induction of several pathways of T cell activation.
Our findings demonstrate that T cell exhaustion during chronic viral diseases results from the progressive temporal accumulation of multiple negative regulators of T cell activation and their interaction with their ligands. Understanding the contribution of these multiple inhibitory signals will be essential to properly define exhaustion and to determine whether these pathways converge to inhibit T cell activation by targeting multiple cellular pathways. In that context, system biology and transcriptional profiling have provided essential tools to dissect the functional status of cells expressing the different negative regulators of T cell activation. Functional restoration of exhausted T cell subsets will require combination therapies that target distinct sets of receptors at different stages of infection.
Written informed consent was provided by study participants and approved by the University of Montreal Health Center ethics review board (CRCHUM). Research conformed to ethical guidelines established by the ethics committee of the University of Montreal Health Center.
The study population included 38 HIV-1 subtype B infected individuals at various stages of infection and 7 HIV-uninfected donors (Table 1 and 2). HIV-infected patients were categorized into 4 subgroups: Elite controllers (ECs; n = 10) infected for more than 7 years with undetectable viremia, successfully treated (ST; n = 12) and aviremic individuals, subjects with acute HIV infection (AHI; n = 7) analyzed within 3 months of infection [59] and 9 chronically progressing subjects (CHI) infected for more than 6 months based on CD4 T cell counts under 500/mm3or declining CD4 T cell counts. All HIV infected subjects with the exception of ST, were naïve to antiretroviral therapy at the time of testing. Plasma viral loads were measured with the Amplicor HIV-1 Monitor Ultrasensitive method with a limit of detection of 50 HIV-1 RNA copies/mL of plasma (Roche Diagnostics, Mississauga, Canada). Absolute CD4 counts were quantified by the BD Multitest (CD3/CD4/CD8/CD45RA)using a FACSCanto (BD).
Soluble pMHC monomers were generated as previously described (Montreal, Canada) [60]. The peptides and tetramers used to analyze the CMV, EBV and HIV-specific CD8 T cell responses were: NLVPMVATV (A*02 CMV), TPRVTGGGAM (B*07 CMV), GLCTLVAML (A*02 EBV), RAKFKQLL (B*08 EBV), FLGKIWPSYK (A*02 Gag), ILKEPVHGV (A*02 Pol), SLYNTVATL (A*02 Gag), RLRPGGKKK (A*03 Gag), AIFQSSMTK (A*03 RT), QVPLRPMTYK (A*03 NEF), RYPLTFGWCF (A*23 NEF), RPGGKKKYKL (B*07 Gag), TPGPGVRYPL (B*07 NEF), SPAIFQSSM (B*07 RT), GEIYKRWII (B*08 Gag), FLKEKGGL (B*08 NEF), DCKTILKAL (B*08 Gag), RRWIQLGLQK (B*27 Gag) and YPLTFGWCF (B*35 NEF).
PBMCs were resuspended in PBS containing 2% FCS and stained with Tetramer-PE at 0.3 µg per 106 cells. The following cocktail was used for phenotyping CD8 T cells: αCD160FITC (BD), αCD3Alexa 700 (BD), αCD8 PB (BD), αCD45RA ECD (Beckman Coulter), αCD27 APC-Cy7 (eBioscience), αCCR7 PE-Cy7 (BD), αCD158b PE (KIR2DL2, KIR2DL3) (BD) andαPD-1 APC (eBioscience). Monocytes, mDCs and pDCs were labelled using αCD3, αCD16 and αCD19 Alexa700 (BD), αCD14 PB (BD), αHLA-DR APC-Cy7, αCD11c APC, αCD123 PE (BD), αPD-L1 PE-Cy7 (BD) and αHVEM FITC (MBL). Dead cells were eliminated with an amine-reactive viability dye (LIVE/DEAD, Invitrogen). We acquired a minimum of 1×106 events for all cytometry-related experiments using a BD LSRII flow cytometer and analyzed the results with FlowJo 9.1 (Treestar).
Optimal peptides used for ICS and CFSE assays were identical to the ones folded in the pMHC monomers. PBMCs were stimulated with 5 ug/ml of CMV and HIV-specific peptides as described previously [24]. The cocktail used for ICS was: Tetramer PE, αCD160 FITC (BD), αTNFα Alexa 700 (BD), αIFN-γ PE-Cy7 (BD), αPD-1 APC, αCD3 PB and αCD8 ECD (Caltag). For CFSE, we stimulated PBMCs with CMV and HIV peptides for 6 days in the presence of 10 µg/ml of αPD-L1 (eBioscience), αHVEM (R&D systems)and polyclonal goat or monoclonal mouse IgG1 isotype controls (R&D systems).As described in the manufacturer's protocol, we used a cytokine bead array(CBA) (BD) assay to measure the concentrations of IL-4, IL-2, IL-10, TNFα and IFNγ in the supernatants harvested at the end of the CFSE assay.
CD8 T cells subsets expressing CD160 and/or PD-1 were sorted from 4 HIV chronically infected individuals using BD FACS ARIA, lysed in RLT buffer and stored at −80°C. Total RNA was purified using RNA extraction kits (RNeasy Micro Kit, Qiagen).Quantification was performed using a spectrophotometer (NanoDrop Technologies) and RNA quality was assessed using the Experion automated electrophoresis system (Bio-Rad). Total RNA was amplified using the Illumina TotalPrep RNA Amplification kit [61]. Biotinylated cRNA was hybridized onto Illumina Human RefSeq-8 BeadChips and quantified using Illumina BeadStation 500GX scanner and Illumina BeadScan software.
Gene expression data was analyzed using Bioconductor(www.bioconductor.org) [62]. The R software package was used for pre-processing to filter out genes with intensities below background in all samples, minimum-replace (a surrogate-replacement policy) values below background using the mean background value of the built-in Illumina probe controls as an alternative to background subtraction, reduce “over inflated” expression ratios and finally quantile-normalize the gene intensities. Out of the 24526 initial probe set, 9070 probes were left after the filtering steps. The resulting matrix was log2 transformed and used as input for linear modeling using Bioconductor's limma package which estimates the fold-change between predefined groups by fitting a linear model and using an empirical Bayes method to moderate standard errors of the estimated log-fold changes for expression values from each gene [63]–[64]. P values from the resulting comparison were adjusted for multiple testing according to the method of Benjamini and Hochberg.
Gene networks were generated using Ingenuity Pathway Analysis (www.ingenuity.com). A dataset containing gene identifiers and corresponding statistical values were uploaded to the application. Each gene identifier was mapped to its corresponding gene in the Ingenuity Pathways Knowledge Base. Genes obtained from this analysis were overlaid onto a global molecular network. Networks of these focused genes were then algorithmically generated based on their connectivity.
Statistical analysis and graphical presentation was performed using GraphPad Prism 5.0c (GraphPad software, San Diego, CA) FlowJo 9.1 (Treestar) and SPICE 5.1 (http://exon.niaid.nih.gov) [65]. Two-tailed Unpaired tor Mann-Whitney tests were used to assess between-group differences (Figure 1). Two-tailed Wilcoxon matched pairs test was used to assess differences in the relative frequency of subsets over time (Figure 2), in the functionality between CD160 and PD-1 expressing subsets (Figure 3) and to assess differences in proliferative responses following co-culture with blocking antibodies (Figure 4). To determine if the variables analyzed came from a Gaussian distribution we applied the D'Agostino and Pearson's normality test. P-values less than 0.05 were considered statistically significant.
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10.1371/journal.pntd.0003558 | Epilation for Minor Trachomatous Trichiasis: Four-Year Results of a Randomised Controlled Trial | Trachomatous trichiasis (TT) needs to be managed to reduce the risk of vision loss. The long-term impact of epilation (a common traditional practice of repeated plucking of lashes touching the eye) in preventing visual impairment and corneal opacity from TT is unknown. We conducted a randomized controlled trial of epilation versus surgery for the management of minor TT (fewer than six lashes touching the eye) in Ethiopia. Here we report the four-year outcome and the effect on vision and corneal opacity.
1300 individuals with minor TT were recruited and randomly assigned to quality trichiasis surgery or repeated epilation using high quality epilation forceps by a trained person with good near vision. Participants were examined six-monthly for two-years, and then at four-years after randomisation. At two-years all epilation arm participants were offered free surgery. At four-years 1151 (88.5%) were re-examined: 572 (88%) and 579 (89%) from epilation and surgery arms, respectively. At that time, 21.1% of the surgery arm participants had recurrent TT; 189/572 (33%) of the epilation arm had received surgery, while 383 (67%) declined surgery and had continued epilating (“epilation-only”). Among the epilation-only group, 207 (54.1%) fully controlled their TT, 166 (43.3%) had minor TT and 10 (2.6%) had major TT (>5 lashes). There were no differences between participants in the epilation-only, epilation-to-surgery and surgery arm participants in changes in visual acuity and corneal opacity between baseline and four-years.
Most minor TT participants randomised to the epilation arm continued epilating and controlled their TT. Change in vision and corneal opacity was comparable between surgery and epilation-only participants. This suggests that good quality epilation with regular follow-up is a reasonable second-line alternative to surgery for minor TT for individuals who either decline surgery or do not have immediate access to surgical treatment.
| Trachoma causes visual impairment through the effect of in-turned eyelashes (trichiasis) on the surface of the eye. Epilation is a common traditional practice of intermittent plucking of lashes touching the eye, however, its long-term effectiveness in preventing visual impairment is unknown. We conducted a randomized controlled trial of epilation versus eyelid surgery (the main treatment option) in 1300 people with mild trichiasis in Ethiopia. We defined mild trichiasis as fewer than six lashes touching the eye. We have previously reported results to two years and have now re-assessed these individuals at four years. Overall, we found no difference between the epilation and surgery groups in terms of change in vision and corneal opacity between baseline and four years. Most mild trichiasis participants randomised to the epilation arm continued epilating and controlled their trichiasis. This suggests that good quality epilation is a reasonable second-line alternative to surgery for mild trichiasis for individuals who either decline surgery or do not have immediate access to surgical treatment.
| Trachoma is the leading infectious cause of blindness worldwide [1]. Trachomatous trichiasis (TT) is the late stage scarring sequelae of repeated conjunctival Chlamydia trachomatis infection and inflammation in which the upper eyelid is distorted and rolled inwards (entropion) and the eyelashes turn towards the eye [2]. Trachoma leads to visual impairment through the damaging effect of trichiasis on the cornea. The risk of sight loss is directly correlated with disease severity, becoming more frequent with increasing severity of trichiasis [3–6]. The clinical phenotype ranges from a single aberrant eyelash touching the eye (without entropion) to the whole eyelid rolled inwards [7]. Some lashes may scratch the cornea directly while others are peripheral. Trichiasis is usually grouped, based on the number of eyelashes touching the eye into minor TT (1–5 lashes touching the eye) and major TT (>5 lashes touching the eye) [5,8]. Globally, the most recent World Health Organisation (WHO) estimate suggested 8 million people had trichiasis in 2009 [1]. Updated disease estimates will become available in the next few years from the Global Trachoma Mapping Programme.
Eyelid surgery is performed to correct the anatomical abnormality, in the expectation that this reduces the risk of sight loss [4,9]. The WHO advises that “all patients should be offered surgery for entropion trichiasis” [9]. However, up to half of the individuals with trachomatous trichiasis may not have significant entropion [7]. Therefore, there is a degree of ambiguity about how programmes should manage patients with non-entropic trichiasis, particularly those with only a few lashes touching the eye.
Despite considerable efforts to scale-up surgery programmes only around 150,000 people per year have been reported as treated surgically in recent years worldwide [10]. It has been estimated at the current rate the trichiasis backlog (ignoring incident cases) would not be dealt with until 2032, twelve years after the 2020 target for controlling trachoma [11]. Given the current surgical rate in Ethiopia, the country with the greatest burden of trichiasis, it will take more than 10 years to clear the backlog [12].
Many individuals with trichiasis, particularly those with mild disease, decline surgery, even when this is provided free and close to home [13–16]. Lack of time and fear of surgery are leading reasons for poor surgical uptake, suggesting a need for non-surgical, community-based management strategies for those declining surgery [8,13,17]. Poor surgical outcomes (recurrent trichiasis or an unsatisfactory cosmetic appearance) may also deter people from accepting surgery [5,18–20].
Epilation is a widespread traditional practice in many trachoma endemic societies, with up to 70% of people with trichiasis using this treatment strategy [3,6,13,21]. It involves the repeated plucking of lashes touching the eye with forceps [3,4,8]. Many individuals who decline surgical treatment consider epilation an acceptable alternative [13]. In view of the problems in delivering the necessary volume of surgery, the high rate of refusals in some areas and concerns about the quality of programmatic surgical outcomes, we conducted a randomized controlled non-inferiority trial of epilation versus surgery for minor trichiasis in Ethiopia; the two-year follow-up results have been reported [22]. With respect to the primary outcome of progression to major trichiasis there was an inconclusive result, relative to the predetermined non-inferiority margin of 10%. However, at two years there was no difference in the change in visual acuity or change in corneal disease between the two groups. At the two-year time-point all individuals who had been randomized at baseline to epilation were offered free surgery, however, only one third accepted. Here we report the four-year outcomes of study participants.
This study was approved by the National Health Research Ethics Review Committee (NHRERC) of the Ethiopian Ministry of Science and Technology, the London School of Hygiene and Tropical Medicine Ethics Committee and Emory University Institutional Review Board. Potential participants were provided with both written and oral information in Amharic about the trial. For those agreeing to participate, written informed consent in Amharic was required prior to enrolment. If the participant was unable to read and write, the information sheet and consent form were read to them and their consent recoded by witnessed thumbprint. The detailed Trial Protocol is described in S1 Text and the CONSORT statement in Text S2 of the report of the two-year results [22].
The trial methods and results up to two-years have been published previously [22]. Briefly, 1300 individuals aged 18 years or over with previously un-operated minor trichiasis were recruited in West Gojjam, Amhara Region, Ethiopia from March to June 2008. At baseline, unaided and pinhole LogMAR visual acuities were measured at 4 metres using an ETDRS equivalent Tumbling-E LogMAR chart and the eyes were examined using 2.5x magnification loupes by a single ophthalmologist (SR), and graded according to the detailed WHO FPC Trachoma grading system. Standardised high-resolution digital photographs were taken of each of the clinical features. In individuals with bilateral trichiasis one eye was randomly designated as the “study eye” although both eyes were treated. Following baseline assessment, participants were randomised to one of two intervention groups: (1) posterior lamella tarsal rotation surgery, or (2) repeated epilation using high quality, machine-manufactured epilation forceps (Tweezerman). Surgery was performed by five experienced Integrated Eye Care Workers (IECWs), chosen on the basis of the quality of their surgery. The surgeons received refresher training and underwent a standardisation process. Individuals randomised to the epilation group were each given two pairs of epilation forceps; the participant and an accompanying adult (“epilator”) with good near vision were trained to perform epilation. The procedure was explained and demonstrated to them by a field worker, who then in turn watched and checked the technique of the relative / patient in performing epilation.
Participants were followed-up at 6, 12, 18 and 24 months and re-assessed using the same protocol. Participants who showed evidence of disease progression during the follow-up period, defined as five or more lashes touching the eye or corneal changes related to observed lashes, were immediately offered primary surgery (epilation arm) or repeat surgery (surgery arm) to be performed by a senior surgeon. New epilating forceps were provided for epilation arm participants as required. Individuals with other ophthalmic pathology (e.g. cataract) were referred to the regional ophthalmic service in Bahirdar. At the end of the trial at two-years all epilation arm participants were offered free trichiasis surgery in the community. Some individuals accepted this, but the majority chose to continue epilating.
About four years after enrolment participants were invited for a follow-up assessment (March to August 2012). They were notified by a letter sent out through the village administration teams, which explained the purpose, date and place of follow-up assessment. People not able to come to the health facilities for assessment were assessed in their homes. Reasons for loss to follow-up were identified and documented. Participants were interviewed in Amharic about their vision, ocular symptoms, epilation forceps retention and history of epilation and/or surgery since the two-year follow-up. Individuals were considered to be “frequent epilators” if they performed epilation at least once in two months. Participants enrolled into the epilation arm were asked about their views on epilation and epilation practices.
Unaided and pinhole LogMAR visual acuities were measured at 4 metres. Ophthalmic examinations were conducted in the same manner as the previous follow-ups by a single observer (EH) who had also conducted the 6 and 18-month follow-ups. Grades of trichiasis, entropion, and corneal opacity were documented and the eyes were photographed. The examiner was masked to the intervention allocation. The treatment allocation code had been previously broken for the two year analysis. Recurrent trichiasis was defined as one or more lashes touching the eye or evidence of epilation or repeat surgery. Clinical evidence of epilation was identified by the presence of broken or newly growing lashes, or areas of absent lashes. Change in corneal opacity was assessed by direct comparison of the baseline and four-year cornea photographs. Photographs were viewed on a computer screen at about 10x magnification by a single masked ophthalmologist (MJB). These were graded as improved, no change, or worse.
Patients initially randomised to epilation were sub-divided according to whether or not they had surgery during the four-year follow-up period. These are subsequently referred to as epilation-to-surgery and epilation-only groups, respectively. Baseline and four-year follow-up demographic and clinical characteristics, and the change in clinical phenotypes during follow-up were compared between the surgery-only, epilation-only and epilation-to-surgery participants using X2 tests. The Wilcoxon rank-sum test was used to test for significant differences in number of lashes. Logistic regression was used to assess factors associated with trichiasis progression within the epilation-only group, to identify predictors of surgery uptake in all epilation arm participants and corneal opacity deterioration in all study participants. Ordinal logistic regression was used to assess factors associated with reduction in visual acuity by four-years in all study participants. Variables that were associated with the outcome on univariable analyses at a level of p<0.05 were retained in the multivariable logistic regression models.
At baseline, 1300 individuals were recruited, of whom 650 were randomised to immediate surgery and 650 to epilation (Fig. 1). The baseline demographic and clinical characteristics of all 1300 participants have been previously described along with the results up to two-years follow-up [22]. At four-years 1151 (88.5%) were re-examined: 579 (89%) participants from the surgery arm and 572 (88%) from the epilation arm. The baseline demographic and clinical characteristics of these individuals are shown in Table 1 (columns A and B). They were all Amharan with an average age of 50.3 years (SD 14.4; range 18–95) at baseline. The majority were female (767, 66.6%) and illiterate (1034, 89.8%). Of those initially randomised to the epilation arm, 189 (33%) had undergone trichiasis surgery by the four-years follow-up, while the other 383 (67%) were still epilating only (Fig. 1).
There were 149 participants who were not re-examined at four-years. The reasons for not being re-examined are shown in Fig. 1. In summary, at baseline this group was slightly older (p <0.001), had worse presenting LogMAR visual acuity (p <0.001) and had slightly more corneal opacification (p = 0.04) than the 1151 re-examined at four-years. Other variables such as sex, literacy and other baseline clinical characteristics including trichiasis severity and corneal opacity were comparable between those re-examined and not re-examined at four-years.
Amongst those re-examined at four-years, there were more female participants randomised to surgery compared to those randomisation to epilation (p = 0.03), however, there was no difference in age or literacy status (Table 1). Baseline clinical characteristics were balanced between the randomisation arms (Table 1), with the exception of central corneal opacity (CC2/CC3), which was more frequent in the epilation group (128; 22.4%) than the surgery group (95; 16.4%).
The 383 epilation arm participants who were still only epilating at four-years (epilation-only) were slightly older than both the 579 surgery arm and 189 epilation-to-surgery participants (mean ages 50.0, 49.0 and 46.4 years respectively; p<0.002, Table 1). Within the epilation arm, the epilation-only group had slightly less baseline trichiasis than the epilation-to-surgery group (p<0.001). The epilation-only group had slightly less baseline entropion than both the surgery arm (p = 0.02) and epilation-to-surgery participants (p = 0.001). The baseline LogMAR visual acuity of the epilation-only group was slightly worse than both the surgery-arm (p = 0.007) and the epilation-to-surgery arm (p = 0.02), Table 1.
During the four-year period, recurrent trichiasis developed in 122 (21.1%) of the 579 participants randomised to surgery. Of these 122, 61 (50.0%) were practicing epilation at four years. Twenty-one (3.6%) had undergone repeat surgery during the four years. Among the 189 epilation-to-surgery participants, 42 (22.2%) had failed surgery (recurrent trichiasis), of whom 27 (64.2%) were epilating.
At four-years, among the 383 epilation-only participants, 207 (54.1%) were successfully epilating (had no lashes touching the eye), 166 (43.3%) had minor trichiasis (<6 lashes) and 10 (2.6%) had major trichiasis (>5 lashes). Overall, the epilation-only group had more trichiasis, entropion and lid margin conjunctivalisation than either the surgery arm or the epilation-to-surgery group (Table 2).
Changes in clinical phenotype between baseline and four-years are shown in Table 3. The outcomes of surgery in terms of trichiasis, entropion and conjunctivalisation were mostly very good. In the epilation-only group, the number of lashes touching the eye increased in 82 (21.5%), however, this was mostly a minor increase, with progression from minor to major trichiasis in six (1.6%) of the 383 epilation only patients. The majority of individuals had less or the same level of trichiasis. Independent risk factors for 5+ lashes touching were baseline age ≥50 years, ≥3 lashes at baseline, and infrequent epilation (Table 4).
At four-years the surgery arm and epilation-to-surgery participants had slightly better LogMAR visual acuity than the epilation-only group (Table 2). However, this difference is attributable to the pre-existing difference in baseline vision (reported above, Table 1), as there was no difference between the different groups in terms of change in visual acuity between baseline and four-years (Table 3). Age ≥50 years, male gender, detection of other visually impairing conditions (e.g. cataract), baseline corneal opacification (CC2/CC3) and incident/progressive corneal opacification were independently associated with deterioration in visual acuity (Table 5).
Overall, few individuals had a change in corneal opacification, determined by the comparison of baseline and four-year photographs (Table 3). There was no difference in change of corneal opacification between the surgery arm and the epilation-only group or the epilation-to-surgery group (Table 3). Incident or progressive corneal opacification was independently associated with age ≥50 years and the presence of some baseline corneal opacification (CC2/CC3), Table 6.
Among the epilation-only group 259 (67.6%) were “frequent epilators” (at least once in two months) between the two and four-year follow-ups. They were asked about their experience: 185 (72%) reported “no problem”, 37 (14.3%) did not always find the trained epilators when needed, 17 (6.6%) had found epilation uncomfortable, the trained epilators of 9 (3.5%) reported difficulty epilating, and 7 (2.7%) had found people unwilling to epilate them. Among the 124 who were not frequently epilating, 119 (96%) did not have a specific reason for not epilating other than not needing to; the other five had nobody to perform epilation. Epilation frequency was not associated either with age (p = 0.31) or gender (p = 0.60). Compared to the “infrequent epilators”, the “frequent epilators” had a slightly higher lash burden at baseline (Median: 1 vs 1, Wilcoxon rank-sum test, p = 0.19), but lower lash burden at four-years (Median: 0 vs 1, Wilcoxon rank-sum test, p = 0.073).
The epilation-only group were asked if they had tried to obtain trichiasis surgery at any time between the two and four-year follow-up: 352 (92%) replied “Never” and 325 (85%) reported that they were happy epilating. There was no statistically significant difference in the average lash burden at four-years between those who were happy epilating and those that were not (1.11 v 1.31, p = 0.30). Participants who were not happy epilating were more likely to have tried to obtain surgical treatment for their trichiasis between two and four-year follow-ups (Fisher’s exact test, p = <0.001).
At the two-year follow-up, 589 / 603 (98%) of epilation arm participants still had their epilation forceps. At the four-year follow-up, 351 / 383 (92%) of the epilation-only group had retained at least one pair of epilation forceps. Females were more likely to have retained their forceps than males (OR 2.38; 95%CI 1.15–4.96; p = 0.020). At four-years, new forceps were provided to those who had lost their forceps, and did not want surgery.
Univariate and multivariable associations with having surgery in epilation arm participants at any point during the four years of follow-up are shown in Table 7. Having surgery was independently associated with age less than 50 years, ≥3 lashes or corneal lashes at baseline, and frequent baseline epilation.
At the four-year follow-up, all participants with recurrent trichiasis in the surgery arm and all participants in the epilation arm who had not previously had surgery were offered free surgery: only 17 / 383 (4.4%) of the epilation only participants accepted surgery, the remaining 366 (95.6%) preferred to continue epilating.
Trachomatous trichiasis has a wide disease spectrum, with many individuals having relatively few lashes touching the eye [4,7]. This may partly explain the observation in this study that at two-years, despite being offered surgery free of charge and close to home, more than two-thirds of people practicing epilation declined surgery. Most (92%) of the epilation-only patients had not sought trichiasis surgery during the two to four-year follow-up period, and the majority (85%) reported that they were still happy epilating. This was also reflected in 96% of the epilation-only patients declining the offer of free community-based surgery at the time of the four-year follow-up. This finding is consistent with two Gambian cohort studies, in which 50–70% of individuals with major trichiasis declined trichiasis surgery [8,13]. Presence of symptoms interfering with work was a predictor for accepting surgery [13]. In our study, younger patients and those with higher baseline lash burden, corneal lashes and frequent epilation at baseline were more likely to accept surgery. It seems likely that these individuals are more symptomatic and therefore more motivated to find a potential long-term solution in surgery. It is encouraging to note that patients with corneal lashes and higher lash burden are more willing to accept surgical management, as these are strong indications for surgery.
In this study, surgery was better than epilation at correcting entropion and controlling trichiasis. However, it should be noted that at four-years 76.2% of the epilation-only participants had mild or no entropion, and 63.4% had no entropion progression. The epilation-only group generally controlled their trichiasis well by epilation, with only a few showing signs of significant progression. At four-years 2.6% of this group had major trichiasis (>5 lashes), which is low compared to the Gambian longitudinal study, in which 37% of the eyes progressed from minor to major trichiasis over four years [8]. However, in the Gambian study participants used low quality traditional epilation forceps without training. In our original trial analysis with follow-up to two-years, the primary endpoint was the presence of 5+ lashes touching or a history of surgery. At four-years only 6.8% of the epilation only group had 5+ lashes. This is somewhat less that the cumulative total of 13.9% individuals who had reached the primary endpoint by two-years, many of whom had accepted surgery at two years.
The proportion of participants in the surgery arm with recurrent trichiasis at four-years was relatively low compared to other trials and longitudinal studies [18,23,24]. This is because the risk of recurrence is heavily influenced by pre-operative disease severity; all the participants in our study had minor trichiasis at baseline; other studies have enrolled patients with more severe disease [5,22,25,26].
The epilation-only group had poorer baseline visual acuity compared to both the surgical arm participants and the epilation-to-surgery group. However, there was no difference in visual acuity change (baseline to four-years) between the epilating and surgery groups, which is similar to what we reported at two-years [22]. Several studies have reported an overall improvement in visual acuity after trichiasis surgery [5,27]. However, participants in these studies, unlike those in our present study, had a wider range of baseline trichiasis severity and were therefore more likely to have an improvement in vision following trichiasis surgery. Consistent with other studies, older age, presence of other blinding conditions, baseline corneal opacity and progressive corneal opacification were associated with deterioration of vision [8]. It is likely that much of the reduction in visual acuity over the four years is due to age related changes such as cataract. Interestingly, we found that female participants had 33% less risk of loss of vision than males. The explanation for this is unknown. The proportion of participants with visual impairment and blindness increased markedly at four-years in all groups, pointing to a major burden of blindness in the study area from other causes such as cataract in this older group of people.
The epilation arm as a whole and the epilation-only sub-group had more baseline corneal opacity than the surgery arm participants. However, there was no significant difference in change of corneal opacity between the different groups at four-years. This result is consistent with our findings at two-years and a report from a longitudinal study in The Gambia, which compared change in corneal opacity in individuals with minor trichiasis who had undergone surgery with those who had declined surgery and practiced epilation [8]. In farming communities, corneal opacity can occur from other causes such as corneal infections and injuries. Similarly, new corneal opacity has been reported after surgery without the presence of recurrent trichiasis [18]. Corneal opacity development or progression was associated with old age and the presence of some pre-existing opacification, similar to other studies [5,6].
More than two-thirds of the epilation-only participants reported frequent epilation. Most reported no problems. Difficulty of getting the trained epilator when they needed help was cited as the main problem, encountered by 14%. However, this could be addressed by training more than one family member. The high retention rate of forceps in this study is encouraging, suggesting that the forceps are valued.
This study has a number of limitations. This four-year follow-up and analysis was not pre-specified in the original trial protocol, which covered the period up to the two-year follow-up. The study ceased to have a fully randomised treatment allocation at two-years when all the epilation arm participants were offered free surgery, and hence we have adjusted follow-up outcomes for baseline imbalances. Those not examined at four-years were older and had worse baseline presenting visual acuity than those seen at four-years. This could have underestimated change in vision over time as older age is associated with greater visual impairment. However, this is unlikely to introduce bias in vision change between the surgery arm and epilation only group as those lost to follow-up were equally distributed between these groups.
We found that surgery was more effective for controlling trichiasis than epilation; however there was no difference in change in visual acuity and corneal opacity. The progression of minor trichiasis can be effectively mitigated with frequent epilation. We found low rates of surgery uptake among people with mild disease, even with free community-based surgery. There is a need for clear guidelines on how programmes should manage patients with a few non-entropic lashes who refuse surgery. Trichiasis in general and particularly major trichiasis warrants surgical treatment. However, the results of this study and the reality of low surgical uptake in many regions, suggest that good quality epilation, in the context of regular follow-up by a service that can provide surgery if subsequently needed, is a reasonable second-line alternative to surgery for minor trichiasis for individuals who either decline surgery or do not have immediate access to surgical treatment.
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10.1371/journal.pntd.0000513 | Dynamics of Socioeconomic Risk Factors for Neglected Tropical Diseases and Malaria in an Armed Conflict | Armed conflict and war are among the leading causes of disability and premature death, and there is a growing share of civilians killed or injured during armed conflicts. A major part of the civilian suffering stems from indirect effects or collateral impact such as changing risk profiles for infectious diseases. We focused on rural communities in the western part of Côte d'Ivoire, where fighting took place during the Ivorian civil war in 2002/2003, and assessed the dynamics of socioeconomic risk factors for neglected tropical diseases (NTDs) and malaria.
The same standardized and pre-tested questionnaires were administered to the heads of 182 randomly selected households in 25 villages in the region of Man, western Côte d'Ivoire, shortly before and after the 2002/2003 armed conflict.
There was no difference in crowding as measured by the number of individuals per sleeping room, but the inadequate sanitation infrastructure prior to the conflict further worsened, and the availability and use of protective measures against mosquito bites and accessibility to health care infrastructure deteriorated. Although the direct causal chain between these findings and the conflict are incomplete, partially explained by the very nature of working in conflict areas, the timing and procedures of the survey, other sources and anecdotal evidence point toward a relationship between an increased risk of suffering from NTDs and malaria and armed conflict.
New research is needed to deepen our understanding of the often diffuse and neglected indirect effects of armed conflict and war, which may be worse than the more obvious, direct effects.
| Armed conflict and war and infectious diseases are globally among the leading causes of human suffering and premature death. Moreover, they are closely interlinked, as an adverse public health situation may spur violent conflict, and violent conflict may favor the spread of infectious diseases. The consequences of this vicious cycle are increasingly borne by civilians, often as a hidden and hence neglected burden. We analyzed household data that were collected before and after an armed conflict in a rural part of western Côte d'Ivoire, and investigated the dynamics of socioeconomic risk factors for neglected tropical diseases (NTDs) and malaria. We identified a worsening of the sanitation infrastructure, decreasing use of protective measures against mosquito bites, and increasing difficulties to reach public health care infrastructure. In contrast, household crowding, the availability of soap, and the accessibility of comparatively simple means of health care provision (e.g., traditional healers and community health workers) seemed to be more stable. Knowledge about such dynamics may help to increase crisis-proofness of critical infrastructure and public health systems, and hence mitigate human suffering due to armed conflict and war.
| Infectious diseases and violence have been among the leading causes of premature death and disability throughout history [1]. In 2000, for example, the global burden of armed conflict and war – defined as a more or less organized, large-scale form of violence – was estimated at 26.1 million disability-adjusted life years (DALYs). This burden accounted for 1.9% of the total global burden of disease and injury at the beginning of the new millennium. It is anticipated that armed conflict and war will further rise in the ranking of leading causes of global burden; from position 16 in 1990 to position eight in 2020, with an estimated burden of 41.3 million DALYs or 3.0% of the global burden. An alarming 25.2 million DALYs – or 61% – of this predicted burden is expected to occur in sub-Saharan Africa [2]. It is informative to put these worrying statistics into context: the global burden due to the so-called neglected tropical diseases (NTDs) and malaria, in 2002, were estimated at 56.6 and 46.5 million DALYs, respectively [3],[4]. Taken together, the NTDs and malaria currently account for 6.9% of the total global burden of disease.
Armed conflict and infectious diseases may have a mutually reinforcing impact on each other leading to an even more devastating situation for human health. On the one hand, an adverse public health situation may cause or exacerbate social tensions and contribute to the outbreak of armed conflict [5],[6]. On the other hand, more soldiers died because of infectious diseases than of the enemies' weapons throughout history. Advances in the medical sciences though reduced the risk of infection over the past decades, at least for combatants [1]. The civilian deaths, measured as a proportion of all deaths in armed conflict, have been constantly rising over time [7] and many of these deaths may be due to infectious diseases [7]–[10]. However, the impact of conflict on infectious diseases in the civilian population, even though being significant, is still insufficiently studied [1],[11].
To further our understanding of the complex interactions between armed conflict and risk factors for infectious diseases threatening mainly civilian populations, our study carried out in a rural part of western Côte d'Ivoire investigated the impact of an armed conflict on socioeconomic risk factors for NTDs and malaria. We employed two datasets obtained from interviewing a random sample of households shortly before the outbreak of an armed conflict and again after military hostilities ceased one and a half year later. Previous research in the same area confirmed that socioeconomic and demographic factors had a strong leverage on parasitic disease distribution, even stronger than environmental determinants [12].
The consequences of armed conflict and war on public health can be classified as either direct or indirect. The direct effects consist of immediate physical harm to the human body, mainly injuries and killings.
Indirect effects can occur immediately as well as delayed and affect rather the physical, social, and socioeconomic environment of people than directly the people themselves [1],[7],[11]. Indirect effects include, for example, individual [13],[14] and collective socioeconomic losses [15],[16], destroyed infrastructure and biosphere [9],[17], the disruption of public health systems [8]–[10], social dislocation, migration and displacement [18],[19] or psychological trauma [20],[21]. By changing risk profiles, indirect effects may substantially contribute to increased morbidity and mortality rates during and after an armed conflict [22]. It should be noted, however, that the complexity of all interactions may lead to counter-intuitive outcomes. For example, a marked decline in case fatality among hospitalized children was reported during a war in Guinea-Bissau, which was partly explained by improved treatment as a result of better access to drugs funded and distributed by humanitarian aid and non-governmental organizations [23].
As illustrated in Figure 1, health, wealth and the environment may have a backlash on war by influencing the available resources and conditions for fighting.
Our study was carried out between August 2002 and February 2004 in the mountainous region of Man, western Côte d'Ivoire. The area is part of the Ivorian rainforest zone, which belongs to the Western African forest belt. Ecological transformations have occurred in this region over the past decades, spurred by increased demographic pressure due to internal and foreign immigration and the influx of Liberian refugees [24],[25].
In 2002, approximately 250,000 people lived in the study area with about half of them in the regional capital, the city of Man [26],[27]. Most inhabitants belong traditionally to the Yacouba ethnic groups [28], which are involved in a longstanding conflict on land property rights with their southern neighbors, the Krou ethnic groups. Rural households, which are in the main focus of this study, make their living mainly by subsistence farming (rice, cassava, maize, plantain, and yam), the cultivation of a few cash crops (coffee and cocoa), and the keeping of goats, cattle, and poultry. Additionally, there is a small timber industry in the region and some inland fish farming [26],[27].
The area has been subject to intensive fighting during the Ivorian civil war between September 2002 and July 2003. The conflict officially ended by a cease-fire, negotiated with the help of the United Nations (MINUCI/ONUCI mission), but it divided the country in a northern part dominated by the Forces Armées des Forces Nouvelles (FAFN) and a government-held south. After the officially negotiated cease-fire, violence still flared up occasionally. However, at the time this manuscript was drafted (late 2008/early 2009), the borders appeared to be dissolving as demobilization, disarmament, and reintegration efforts were under way even though post-war elections have been postponed several times since the constitutionally scheduled date in 2005.
We focused on the effects of the armed conflict on infrastructure and the health systems, with an attempt to quantify the impact on population density per household, access to clean water and improved sanitation, use of preventive measures, and access to health care. Hence, major risk factors for a number of NTDs, including schistosomiasis, soil-transmitted helminthiases (ascariasis, trichuriasis and hookworm disease), intestinal protozoa infections (entamoebiasis, giardiasis), and malaria were investigated [29]–[32]. NTDs are widespread in our study area and malaria is holoendemic [25]–[27], [33]–[40]. Furthermore, epidemiologic surveys indicated that polyparasitism is the norm rather than the exception in the region of Man [33],[37],[39].
Two cross-sectional surveys were carried out; the first took place in August 2002 and the second in late 2003/early 2004. Thus, the data were collected shortly before the outbreak of the armed conflict and after the officially negotiated cease-fire in an area close to the military front-lines.
Data were obtained using standardized, pre-tested, and locally adapted questionnaires. They were filled out by trained assistants, who interviewed the heads of seven to nine randomly selected households from each of 25 randomly selected villages in the region of Man in the first survey (total 203 households). The seven to nine households interviewed per village equaled approximately 10% of all households in the respective villages. Our study complemented a larger cross-sectional survey done in 55 villages with more than 4,000 schoolchildren screened for Plasmodium, Schistosoma, and soil-transmitted helminth infections [34]–[37],[40]. In the second survey, the same households were tried to be re-interviewed. Overall, 182 households could be identified again based on (i) location, and (ii) household members.
In both surveys, the heads of households were asked the same questions. The questionnaires started with general demography and some socioeconomic wealth indicators, followed by the main water supply in the village, availability of functioning toilets, and the use of soap. Access to preventive measures was mainly evaluated by asking about the use and availability of protective measures against mosquito bites. For estimating the accessibility of health care, the studied households were asked about the required time and the estimated travel distance to reach a traditional healer, a community health worker, a dispensary, a pharmacy, or a health care centre. An estimated travel time of 5 min was uniformly assigned if the respective household mentioned the existence of the relevant health care infrastructure in the village of residency. Furthermore, if a household reported neither a specific travel time nor the existence of the respective health care infrastructure in the village, the travel time was computed by using the reported distances and information on the generally used means of transportation. In this last case, estimated travel velocities for walking, cycling, and motorized transportation (motorbike, car, or tractor) were 4 km/h, 10 km/h, and 25 km/h, respectively.
The study was part of a research project with the objective to contribute to the reconciliation process of Côte d'Ivoire. The project was funded by the Swiss Agency for Development and Cooperation (SDC) and was jointly implemented by the Centre Suisse de Recherches Scientiques (CSRS) and the Université de Cocody-Abidjan (Abidjan, Côte d'Ivoire). The purpose and procedures of the surveys were discussed with local authorities. Oral informed consent was obtained from study participants as the majority of them were illiterate. This was done in the presence of district and regional health and education authorities, with detailed explanations given in the local languages by trained field assistants and local witnesses. This is the usual procedure when administering questionnaires without concurrent collection of biological samples (e.g., blood, stool, and urine) in Côte d'Ivoire and was approved by the institutional research commissions of the Swiss Tropical Institute (STI; Basel, Switzerland) and CSRS. The study was cleared by the national health and education authorities of Côte d'Ivoire. Further details on institutional and organizational background have been presented elsewhere [41].
Data were double entered and cross-checked in EpiInfo version 6.04 (Centers for Disease Control and Prevention; Atlanta, GA, USA). All analyses were done in STATA version 10 (STATA Corporation; College Station, TX, USA). Statistical tests were carried out at 5% significance level. With the exception of the demographic data, all analyses were performed at the household level.
A household asset-based approach was adopted in order to stratify the households into socioeconomic wealth quintiles [42]. Following the instructions of O'Donnell and colleagues [43] (see also revised former technical notes provided by the World Bank's Poverty Reduction and Economic Management (PREM) group and Vyas & Kumaranayake [44]), we used principal component analysis (PCA) and binary information on asset possession and housing characteristics. This approach proved to be valid in rural Côte d'Ivoire and other African settings [45]. The same assets were selected as in our previous work in the same study area [27], [34]–[38].
Poisson and logistic regression models, including random effects accounting for clustering at the village level, and likelihood ratio tests were used in order to check for statistically significant changes and associations.
Overall, 203 heads of households in 25 villages were interviewed in the first survey carried out in August 2002. We were able to re-interview 182 household heads one and a half year later. The 21 missing households were explained as follows: inhabitants of eight households fled because of the armed conflict, seven households still existed but no inhabitants were present during our interviews, and two households each were in hide-out near the village, abandoned or disappeared with unknown fate (Figure 2). An attrition analysis, comparing the characteristics of the 21 households that were lost to follow-up with the 182 households that could be re-identified, revealed no differences between these two groups.
Table 1 summarizes demographic findings from the final cohort of 182 households. The number of inhabitants decreased from 1,749 in the first survey to 1,625 in the second. This decrease of 124 individuals (7.1%) was due to migration (n = 74) and natural population changes (n = 59). Potential misreporting, identified by comparing the reported number of household members in the second survey with the projected number using first survey data and reported information on death, births, and migration, accounted for only nine individuals (0.5% of the original population).
Overall, our data revealed a more mobile male population, but at the end the gender distribution remained virtually unchanged (difference (diff.): 0.2%; 95% confidence interval (CI): −3.1%, 3.5%). Also, the age structure of the studied population did not vary significantly between the two surveys (diff. = −2.9%; CI: −6.2%, 0.4%). However, young people were more likely to have left the study area rather than to in-migrate. In general, an increased rural-urban migration is supported by the fact that 66% of all people who left their villages were supposed to have migrated to an urban setting nearby or far away.
Adjusting the natural population change to a calendar year and assuming no temporal variation, we estimated a birth rate of 13.0 per 1,000 persons per annum. The respective death rate was 34.3 per 1,000 persons per annum. Hence, our data imply a birth rate, which was over three times lower, and a death rate, which was almost three times higher than the latest pre-conflict birth and death rates reported for Côte d'Ivoire by two different sources (United Nations Statistics Division [46] and Institut Nationale de la Statistique (INS) of Côte d'Ivoire [47]). These differences are striking, even after taking into consideration the limitation of our own data, and the fact that the latest United Nations' and INS' statistics from 2000 are neither stratified into rural and urban settings nor specific for our study area. However, as shown in Figure 3, only 3 deaths (3.2%) were directly attributed to the armed conflict by the interviewed heads of household. Conversely, diseases were the most frequent causes of death (83%) with gastrointestinal infections (16%), fever (5.3%), and meningitis (5.3%) being the most frequently reported illnesses. More than a quarter of all disease-related deaths lacked further specification.
When asked about the three most important problems since the fighting started, 48% of all households mentioned health-related issues, followed by the lack of food (29%) and the interruption of public services (13%). Most of the households considered the situation of malaria (78%), helmintic diseases in general (72%), and schistosomiasis in particular (51%), as worse than before the armed conflict.
Asset possession followed an intuitively logical trend in both surveys. Possession of ‘goods’ (high-quality housing, electronic devices) increased with increasing wealth and possession of ‘bads’ (low-quality housing) decreased. The absence of land and/or house ownership is often explained as the equivalent of employed work (e.g., teaching) [48] and thus, as it was also the case in our study, plausibly associated with higher socioeconomic status. Interestingly, overall wealth as measured by the sum of assets of all households showed only little variation between the two surveys.
The number of individuals per sleeping room was used as a proxy for crowding. Figure 4 shows the quantile-quantile plot of the number of individuals per sleeping room for the two cross-sectional surveys. Only small changes occurred in population densities in the most crowded households between surveys. The overall decrease in population by 7.1% was almost offset by a decrease of 6.8% of the total number of available sleeping rooms. Using Poisson regression models revealed no statistically significant difference between the population density in the first and in the second survey (p = 0.989), but significantly decreasing numbers of individuals per sleeping room with increasing wealth was observed in both surveys (p<0.001 for each survey). However, there was no association with the educational level of the head of household (first survey: p = 0.570; second survey: p = 0.652).
Table 2 illustrates the use of different sources of water supply in the villages. The water supply was primarily based on traditional wells in both surveys. Theses traditional wells are typically constructed like draw wells, and hence consist of a simple dug hole in the ground and a bucket on a rope to pull up the groundwater. Other important ways of communal water supply include the collection of rain water, stagnant surface water bodies such as stagnant arms of rivers, multipurpose small dams, and ponds. There are also fountains, which are normally hand-operated groundwater pumps. Less common sources of water supply were use of surface water, natural springs, and tap water. The more sophisticated techniques of fountains and tap water, as well as the use of flowing surface water were significantly less often mentioned in the second survey.
Virtually all households used soap (98% in the first and 96% in the second survey). Neither the armed conflict nor the socioeconomic turbulences influenced the availability of soap.
The analysis of the availability of functioning toilets was significantly associated with household wealth in both surveys, as summarized in Table 3. However, in the wake of the armed conflict, the overall proportion of households with cemented toilets significantly decreased and the overall proportion of households with no toilet significantly increased. Two-third of all households reported having only an uncemented latrine or no toilet at all in the second survey. In both surveys, there was no statistically significant association between the availability of different types of toilets and the heads of households' educational level (all p>0.05).
The main protective measures used by the studied households to avoid mosquito bites included indoor insecticide repellents, mosquito nets (either treated with an insecticide or left untreated), fumigating coils, and natural repellents (plant leaves). The results also revealed that some households used more than just one protective measure and others, which took no action at all.
Table 4 shows that fumigating coils and plant leaves were the most common measures, while insecticide-treated mosquito net coverage was very low in the region in both surveys (<5%). In total, the application of insecticides and fumigating coils was significantly lower in the second survey, whereas the percentage of households mentioning no protection at all increased significantly to an overall level of 39%. The total number of households using multiple protective measures decreased significantly from 25% to 10% (p<0.001). Meanwhile, the total percentage of households using mosquito nets, either insecticide-treated or not, and plant leaves did not change significantly.
Socioeconomic position was only associated with using no protection in the second survey. Educational level of the respective heads of households had no influence (all p>0.05).
Before the outbreak of the armed conflict, two-third of all heads of households knew a place in their village of residency where they could purchase an insecticide repellent, and a third knew a place where to buy a mosquito net (Table 5). At least one head of household reported a point of sale of insecticide repellent in all 25 villages included in the study and in all but three villages a point of sale of mosquito nets. However, knowing such an opportunity was not associated with wealth in the first survey.
After the outbreak of the armed conflict, the known opportunities for preventive measures against mosquito bites decreased dramatically. In the second survey, only half of the interviewed heads of households mentioned an opportunity to purchase an insecticide repellent in their village and only every fifteenth an opportunity to buy a mosquito net. The availability of insecticide spray was confirmed in 23 villages, but the availability of mosquito nets in only six villages. Socioeconomic wealth was not significantly associated with the knowledge of existing selling points in the second survey.
Educational level of the heads of households did not show any significant association with knowing an opportunity to buy an insecticide repellent (first survey: p = 0.738; second survey: p = 0.780), as well as with knowing an opportunity to buy a mosquito net (first survey: p = 0.257; second survey: p = 0.404).
Overall, 19% of all households mentioned insufficient medical care as one of the most severe problems since the beginning of the armed conflict. Table 6 summarizes average travel times to different types of health care structures, stratified by socioeconomic status, in order to better understand accessibility. An arbitrary cut-off point of one hour was used for categorizing and further analyzing the outcomes.
Traditional healers and community health workers showed comparatively good accessibility in both surveys regardless of a household's socioeconomic status, as most households reached them within one hour. Furthermore, the presence of a traditional healer and a community health worker was confirmed by at least one household in all except one village in both surveys. However, about a fifth of all households gave no answer; a fact that may indicate either that these households did not know where the next healer or community health worker was at that time or that they were not able to guess.
Most households reported to reach a dispensary within one hour. However, this proportion decreased significantly between the two surveys. Socioeconomic status seemed to play no role. Longer travel times were noted with respect to pharmacies and their accessibility became worse in the second survey. The proportion of households requiring no more than one hour to reach the nearest pharmacy significantly decreased to an overall level of 28% and the proportion requiring longer significantly increased to 53%. The influence of socioeconomic status became more important in the second survey.
Finally, on average, less than every second household reached a health care centre within one hour in the first survey (40%). This proportion was still twice as high as the proportion of households which needed more than one hour (19%), but about 40% of all households were not able to guess or did not know where the next health care centre was. Both the proportions of households which reported less or more than one hour decreased in the second survey with the latter decrease showing statistical significance. Consequentially, the proportion of households giving no answer increased significantly to 59%. There was no statistically significant association with socioeconomic status.
The educational level of the heads of households was not significantly associated with accessibility in both surveys and for all types of health care structures.
We used two micro-level datasets, originating from cross-sectional household surveys carried out before and after an armed conflict in Côte d'Ivoire, to determine the dynamics of socioeconomic risk factors for NTDs and malaria in the face of military hostilities. Reliable data for such analyses are often lacking due to missing baseline data, security issues, and military sensitivities [49]. Our data are consistent, as could be shown by a high recapture rate of households (90%) and accurate population data (misreporting of only 0.5% of the original population in the second survey).
Several particularities of the present study warrant further discussion. First, a remarkable decrease in the number of available sleeping rooms offset the decrease in the number of household members, and hence the number of individuals per sleeping room remained unchanged. Adaptation of housings could be due to a rearrangement of available rooms as well as war-related collateral damage.
Second, the decrease of safe water provision by tap or fountain [50] increased the households' dependency on traditional wells, stagnant surface water, rain water, and natural springs. Interestingly, the use of flowing surface water decreased, which is likely to result in a reduced risk of schistosomiasis transmission. However, especially traditional wells and stagnant surface water may be at a particular risk of local contamination. In combination with the deteriorating toilet standards, water-related NTDs such as intestinal protozoa infections may further spread. In addition, the anticipated increase in open defecation leads to increased soil contamination, and hence to conditions favorable for the transmission of soil-transmitted helminths [29].
Third, while the use of the most expensive (i.e., insecticide-treated and untreated mosquito nets) and the least expensive (i.e., burning of plant leaves) protective measure against mosquito bites did not vary significantly between the two surveys, the reduced use of insecticides, fumigating coils, and multiple protection as well as the increased proportion of households using no protection at all suggests an increased risk of being bitten by mosquitoes and consequently an increased risk for malaria transmission. Furthermore the availability of insecticide repellents or mosquito nets in the respective villages seemed to have become restricted. A shortfall on the supply side of malaria protection may be an instructive example also with regard to other protective and health improving measures.
Fourth, the evaluation of accessibility of the different types of health care structures revealed that the travel times to reach traditional healers and community health workers were comparatively short, equitably distributed over wealth quintiles, and stable over the two surveys, which may also indicate robust resilience to crisis. Thus, one might consider the integration of traditional healers and community health workers into the health systems.
Previous studies confirmed a worsened accessibility, especially of more sophisticated health care infrastructures [51] and emphasized the importance of more equity-balanced health care, which could serve as a strategy for poverty alleviation in the region of Man and elsewhere in sub-Saharan Africa [35].
There are several aspects of our study that need to be discussed in further depth. Most important, the study was only looking at one dimension, namely accessibility, of the broader concept of access to health care as described by Penchansky & Thomas [52]. Furthermore, it did not take the quality of the respective health care infrastructure or the health provision efforts of “exogenous” organizations such as the International Committee of the Red Cross (ICRC) or Médecins Sans Frontières (MSF) into consideration. In fact, the presence of organizations providing emergency relief can have major positive effects on human health in armed conflict settings [23].
Additionally, while measuring accessibility in terms of reported travel times has some advantages, it also has important drawbacks. On the one hand, reported travel times may better reflect effective distances between a household and a health care delivery centre than shortest straight-line distances derived from remote sensing and geographic information systems (GIS). This should be especially true in difficult-to-access areas like our mountainous study area. However, previous remote sensing and GIS analysis already showed significant association between shortest straight-line distances to the nearest facility and household wealth in the study area, demonstrating the validity even of this rather simple approach [35].
On the other hand, the used approach resulted in a high proportion of households giving no answer. This outcome is difficult to interpret and was previously explained by households, which are uninformed about the location of the relevant health care structures or which are simply not able to reasonably guess. However, giving no answer was not significantly associated with education and this result may support the hypothesis that rather the missing information about the location than the inability to guess was the problem. If this is the case, the high proportion of households giving no answer could be interpreted as a lack of knowledge and consequently a risk factor for ill-health.
The average educational level of the household heads, often reported as an additional risk factor [29], did not significantly change over time and also showed no association with the other risk factors under investigation. This may be explained by a low educational attainment with approximately 60% of the heads of households reporting to have never attended school.
Wealth, in contrast, had a positive effect, which means that richer households could constrain at least some of the risk factors (e.g., less crowded living conditions, enhanced availability of functioning toilets, higher proportion of people using protective measures against mosquito bites, and shorter travel times to pharmacies). Pre-conflict studies conducted in the same study area also revealed a somewhat lower level of hookworm infection among richer households [35],[36]. However, households of all wealth quintiles seem to be negatively struck by the armed conflict, and hence confronted with aggravated risk profiles.
A survey conducted in the same area by the Ministry of Health, UNICEF, and WHO described similar patterns of water supply and toilet standards as the present study, but found no changes in these variables over time. In the aforementioned report, the precarious sanitarian conditions were emphasized, along with lower mortality rates and a lower proportion of disease-related deaths in the face of the armed conflict [53]. Possible explanations for the somewhat differing findings include study design issues and differences in the exact classification of water supply and sanitation facilities. The present study used two consecutive cross-sectional surveys of the same rural households, whereas the other study was based on one cross-sectional survey with retrospective questions, which leads to a higher risk of recall bias. Also, data about mortality and causes of death included figures from rural as well as urban areas. Nevertheless, the derived recommendations from the two studies are congruent and include the securing of the livelihoods of the local population, the improvement of the water supply, toilet standards, use and availability of preventive measures, as well as the rehabilitation of the health systems on all administrative levels by taking into account the experiences made during the armed conflict.
It follows that the risk for NTDs and malaria has considerably increased in the rural part of the region of Man. Given that the surveys are separated by a violent conflict, and the second survey carried out in an environment of socioeconomic turmoil, it seems likely that the occurrence of violent conflict, and not some other cause, is at the origin of this observation. Although the study did not investigate the actual direct causes of a riskier situation for the population of Man, e.g., why tap water stopped flowing or why overflowing cemented latrines were replaced by simple earthen ones, it is conceivable that the conflict situation caused an excess risk for NTDs and malaria. First, the conflict has been the most dominant aspect of daily life in western Côte d'Ivoire between the two surveys. Second, the pace at which the changes have occurred points clearly to a severe disruption of the socioeconomic tissue, which has been caused by the conflict. Third, our findings are consistent with the perceptions of the household heads about increased health-related problems in the households, the spread of schistosomiasis, other helminthic diseases and malaria, the interruption of public health services and the given information about increased socioeconomic risk factors since the fighting started. Hence, the excess cases of death among the rural civilians might be attributable to indirect effects of the armed conflict and the indirect character of this association may explain the very low fraction of deaths attributed to war by the interviewed heads of households. However, to unambiguously prove the causality of the above mentioned facts is very difficult, even though a plausible causal chain emerged. During the troubled times of an armed conflict, it is impossible to account for all potential confounders, especially if one thinks about the many theoretical and practical challenges that researchers face under such highly dynamic and unpredictable circumstances.
An important lesson from our case study is the need for additional micro-level research investigating not only direct but also indirect effects of armed conflict and war, as indirect effects may be at least as important as the direct ones and often affect particularly the civilian population. A growing body of literature demonstrates the feasibility of war-related research [54]–[56]. Additional information may help to design critical infrastructure and public health systems that are more crisis-proof. The potential gains of crisis-proof public health systems in terms of reduced human suffering are immense and should provide enough motivation for future scientific efforts.
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10.1371/journal.ppat.1004355 | Structural Correlates of Rotavirus Cell Entry | Cell entry by non-enveloped viruses requires translocation into the cytosol of a macromolecular complex—for double-strand RNA viruses, a complete subviral particle. We have used live-cell fluorescence imaging to follow rotavirus entry and penetration into the cytosol of its ∼700 Å inner capsid particle (“double-layered particle”, DLP). We label with distinct fluorescent tags the DLP and each of the two outer-layer proteins and track the fates of each species as the particles bind and enter BSC-1 cells. Virions attach to their glycolipid receptors in the host cell membrane and rapidly become inaccessible to externally added agents; most particles that release their DLP into the cytosol have done so by ∼10 minutes, as detected by rapid diffusional motion of the DLP away from residual outer-layer proteins. Electron microscopy shows images of particles at various stages of engulfment into tightly fitting membrane invaginations, consistent with the interpretation that rotavirus particles drive their own uptake. Electron cryotomography of membrane-bound virions also shows closely wrapped membrane. Combined with high resolution structural information about the viral components, these observations suggest a molecular model for membrane disruption and DLP penetration.
| Non-enveloped viruses (viruses lacking a lipid-bilayer membrane) require local disruption of a cellular membrane to gain access to the cell interior and thereby initiate infection. Most double-strand RNA viruses have an outer protein layer that mediates this entry step and an inner-capsid particle that transcribes their segmented dsRNA genomes and extrudes the capped mRNAs into the cytosol. Removing the two rotavirus outer-layer proteins inactivates the virus, but recoating with recombinant outer-layer proteins restores infectivity. We have labeled the recombinant proteins with distinct fluorophores and the stripped inner-capsid particle with a third fluorophore and reconstituted fully infectious particles from the labeled components. We have followed by live-cell imaging the binding and engulfment of the labeled particles and studied the kinetics of inner-capsid particle release. We have interpreted these events in structural terms by examining images of entering particles from conventional electron microscopy and electron cryotomography. When analyzed in view of our previously determined high resolution structures of the virus particle and its constituents, and of information about conformational changes in the outer-layer components, our data lead to a molecular description of the observed entry steps and of the mechanism of membrane disruption.
| Non-enveloped viruses must breach a membrane to enter and infect a cell. Various groups of viruses have evolved distinct molecular mechanisms to carry out this penetration step, which leads to translocation of the infecting particle from an endocytic vesicle or other intracellular compartment to the surrounding cytosol. For example, picornaviruses and reoviruses release a myristoylated peptide, which forms pores in a lipid bilayer [1]. Structural and mutational evidence suggests that rotaviruses penetrate by disruption of an endocytic membrane, driven by a conformational change in one of its outer-surface proteins; the transition has some similarities to the fusion-promoting conformational change of certain enveloped-virus glycoproteins [2], [3]. In no case, however, do we yet have a detailed description of conformational changes in viral proteins couple to changes in the membrane being disrupted nor can we confidently place these events in the context of an intracellular compartment.
The double-stranded RNA (dsRNA) viruses offer particular advantages for analyzing cell entry by individual virions. An infectious rotavirus particle encapsidates eleven distinct double-stranded RNA segments within a three-layer protein coat (a “triple-layered particle”, or TLP: Fig. 1A) [4]. The inner two layers, composed of viral proteins 2 and 6 (VP2 and VP6) respectively, remain associated with the RNA as a “double layered particle” (DLP), even after penetration. The outer layer, composed of two proteins, VP4 and VP7, is the agent that delivers the DLP into the cytosol. VP7, a Ca2+-stabilized trimer [5], clamps onto the VP6 trimers [6], [7], which form a T = 13 icosahedral array on the DLP surface [8]. The VP7 lattice thus generated holds the sixty trimeric VP4 “spikes” in place [9]. Tryptic cleavage of VP4 into two fragments, VP8* and VP5*, is an activation step that primes the protein for conformational changes linked to penetration (Fig. 1B) [10]–[13]. The final, stable conformation of VP5* is probably the folded-back structure shown in the last panel of Fig. 1C, which illustrates a model for membrane disruption generated by conformational changes in VP5*. The model derives from structural studies of VP4 and its fragments [2], [14]–[16]; the experiments we describe here test some of the model's predictions.
Rotaviruses infect enterocytes of the small intestine; in culture, they grow in a variety of epithelial-origin cells [4]. For the best characterized strains, attachment and internalization depend on a surface glycan. Animal rotaviruses generally require sialic acid, either at a terminal or subterminal position (the corresponding viruses then being neuraminidase sensitive or insensitive, respectively [17]), but some human rotaviruses do not [4]. Recent work shows that one such virus interacts with a non-sialylated glycan, the A-type histo-blood group antigen, which contacts VP8* at the position usually occupied by sialic acid [18].
The glycans identified as rotavirus receptors probably mediate uptake when they are headgroups of glycolipids. Knockdown with RNAi of enzymes required for ganglioside biosynthesis in the MA104 epithelial cell line reduced infectivity of several different human and animal rotaviruses, all of which bind sialic acid [19]. The same viruses appeared to attach adequately to the surface of the RNAi-treated cells, but failed to penetrate; knockdown of ganglioside biosynthesis would not have affected the presence of similar glycans on glycoproteins, which therefore could have been sites of the observed non-productive attachment. Conflicting evidence concerning the role of particular routes of uptake in rotavirus infection may come from the potential for more than one productive pathway of entry and from the choice of a particular preferred pathway by any specific viral strain. Thus, one paper reports that of four strains tested in MA104 cells, three showed a dependence on components of clathrin-mediated endocytosis, while the fourth (RRV, the one we have used for the experiments in this paper) did not [20].
Previous efforts to detect entering virions directly by optical microscopy have relied on immunofluorescent staining with antibodies specific for specific conformational states of VP4 (or VP8* and VP5*), VP7, and VP6 [21]. Use of fixed and permeabilized cells, as required for immunofluorescence, precludes tracking of individual virions, and kinetic inferences are therefore indirect. The functional recoating procedure that allows us to add back recombinant VP4 and VP7 to DLPs stripped of their outer layer [22] provides an effective means to label each component with a distinct fluorophore and then to follow entry by live-cell imaging. We report in this paper a series of such experiments, from which we derive the following conclusions. Virions bind tightly upon contact with a cell, becoming relatively immobile on the cell surface in less than a minute. Within about five minutes of attachment, many of the fluorescently tagged rotavirus particles have become sufficiently engulfed that they are inaccessible to antibodies and insensitive to elution with EDTA. Clathrin or the AP-2 clathrin adaptor colocalize only rarely with entering particles, and infectivity in these cells is dynamin independent. Most particles that release a DLP into the cytosol – an event marked by separation of the DLP label from the labels on VP7 and VP4 – have done so by ten minutes post attachment. At later time points, we find virions in Rab5-labeled early endosomes, but these particles rarely penetrate. Moreover, we find that infectivity is insensitive to overexpression of Rab5 mutants. Images from thin-section electron microscopy (EM) and electron cryotomography (cryoET) are consistent with these live-cell observations and suggest that direct contacts between virion and membrane drive engulfment.
We can monitor individual behaviors and roles of the DLP, VP4 and VP7 during rotavirus infection by combining previously optimized recoating techniques [22], [23] with amine-specific, fluorescent labeling of these components (Materials and Methods). We produced reconstituted rhesus rotavirus (RRV) TLPs with two (“doubly-labeled”) or three (“triply-labeled”) structural components linked to spectrally distinct fluorescent dyes, which were discernible in confocal microscopy or upon electrophoresis (Figs. 2A,B). The modifications had no effect on viral infectivity, as assessed by fluorescent focus assays (Fig. 2C). RRV requires cell-surface sialic acid for attachment and subsequent infection. We verified that our labeled, assembled particles had the same requirements when infecting the BSC-1 cells used here, by comparing virion binding to neuraminidase treated and untreated cells. The results (Fig. S1) confirmed that removal of sialic acid from surface glycans greatly reduces viral attachment.
To visualize rotavirus cell entry in vivo, we infected BSC-1 cells with reconstituted, labeled virus; we then imaged the course of infection by spinning-disk confocal microscopy. BSC-1 cells, which are permissive for various rotaviruses [24], spread on the coverslip to generate thin, broad periphery, placing much of the non-adherent cell surface within the focal depth of the microscope and allowing us to capture in one image a large area of plasma membrane. We added doubly- or triply- labeled virus to cells and followed over time the fate of the various components. Over a 10–30 min period after addition of labeled virions, we observed gradual accumulation in the cytoplasm of rapidly moving, singly labeled particles, corresponding to dye-linked DLPs – i.e., virus particles that have lost their outer coat (Fig. 2D). Careful tracking of individual particles revealed a loss in the intensity of the outer-layer proteins preceding an abrupt separation of DLP label from residual outer-layer protein label and rapid diffusional motion of the DLP away from the position of initial attachment (Fig. 2E,F). We interpret this event as release of the DLP into the cytosol. In some cases, the rapidly moving DLP left behind a distinct residue of VP7 and VP4 fluorescence close to the plasma membrane (Fig. 2E); in the majority of cases, the fluorescence intensity associated with the outer coat of the virus had decreased to the threshold of detection by the time of DLP release. The motion of the released DLP was random and much faster than that of TLP from which it derived (Fig. 2E,F; Fig. 3C,D) and consistent with free diffusion in the cytosol for a particle of radius ∼350 Å (Fig. 3C,D; see caption to Fig. 3C). By 30 minutes after addition, the fraction of released particles had reached a plateau of about 20% (Fig. 3A). (In principle, our observations on DLP release do not exclude the possibility that the DLP has “budded away” from the other components, retaining some surrounding membrane. There is no clear molecular basis for such a mechanism, however: VP5* binds membranes, and DLPs do not. The properties of VP4 mutants make an interpretation other than access to the cytosol very unlikely – see below.)
The distribution of times to DLP release in the experiments we analyzed is shown in Figure 3B. The mean time from binding to penetration is approximately 10 minutes; the shortest times are between 6 and 8 minutes.
Upon binding to the cell surface, some particles appeared to move about relatively rapidly on the cell surface (>4 µm/min: “lateral motion”), before becoming nearly stationary (<1 µm/min: “capture”); the majority (>70%) appeared to not to have a lateral-motion phase at all. A correlated, centripetal motion of captured particles in a given region of the cell surface probably arose from retrograde flow of the plasma membrane on which they were bound (e.g., capture phase in Fig. 2C). For the subset of captured virus particles that ultimately uncoated, the fluorescence intensity of the labeled outer-layer protein(s) VP7 and/or VP4 often slowly declined, while the particles remained more or less fixed. The event we designate as “release” is characterized by the sudden onset of rapid motion of the uncoated DLP (average speed >7 µm/min, with frequent change of direction), leaving in place any residual fluorescence from outer-layer proteins.
Figs. 3E and 3H show distributions for the durations of the various phases of entry just described. For the majority of viruses that had a lateral-motion phase (<30% of the total), capture occurred within 5 minutes; the mean duration of this phase for all particles was less than a minute (∼0.7 minutes). The mean interval between capture and onset of uncoating (decline of fluorescence intensity for VP4 and/or VP7) was ∼2.5 minutes, and the mean duration of the “uncoating” phase (time between onset of uncoating and decline either to undetectable VP4 or VP7 signal or DLP release, whichever came first) was ∼4.5 minutes. For about half the particles (∼50% frequency), release coincided with the end of apparent uncoating; for the remaining particles, the DLP appeared to be relatively stationary after the outer-layer fluorescence had declined maximally. For all particles, the mean interval between loss of detectable VP4 or VP7 and release of the DLP was ∼3.5 minutes. During this time, there may have been some outer-layer protein continuing to dissociate from these particles, but with a total fluorescence below the detection threshold of our microscope configuration.
The VP7-directed neutralizing antibody, m159, inhibits rotavirus entry by preventing uncoating of trimeric VP7 [25]. We used fluorescently labeled m159 to determine when in the time course of entry just described the virion becomes inaccessible (Fig. 4A). We added antibody 5–7 minutes after adding virus. Some cell-attached virions bound the antibody, but a number failed to do so. Many in the latter group proceeded to uncoat and release, with the characteristic sequence of stages described above (Fig. 4A). We measured the intervals between addition of antibody and onset of uncoating and time DLP release. The mean time between antibody addition and the onset of uncoating of antibody-inaccessible particles was about 2 minutes, with a range between 0 and ∼7 minutes; release followed by about 4 minutes the onset of uncoating (Fig. 4B). Virus particles exposed to the medium on the surface of the cell bound antibody within seconds of its addition and never uncoated (data not shown), in keeping with the known characteristics of m159; controls with particles bound to the coverslip of the imaging chamber confirmed that all viruses that are exposed to the medium bind the antibody quickly and efficiently (Fig. 3A and data not shown).
Chelation of Ca2+, by EDTA or similar agent, dissociates the VP7 trimer and strips the outer layer from TLPs [5]. When cells are pulsed with EDTA-containing medium, any virus not internalized loses both VP7 and VP4 and dissociates from the cell surface (Fig. 4C, top panel). We exposed the cells to EDTA-containing medium for no longer than 10 seconds before reintroduction of medium containing calcium, thus minimizing damage to the cells (Fig. 4C, middle panel). As a control, viruses that were bound directly to the coverslip were monitored to verify that all viruses exposed to medium had lost their outer layer (Fig. 4C, bottom panel). By about 5 minutes, ∼50% of cell-bound viruses were EDTA resistant (Fig. 4D), in good agreement with the time between attachment and the onset of uncoating. Comparison of the overall time to uncoating (Fig. 3B) with the kinetics of internalization as determined by EDTA resistance indicates that in our experiments there was on average a ∼1–3 minute lag between time to internalization and time to cytoplasmic release. Thus, sequestered virions spend only a relatively short time in an uptake compartment before release of the DLP into the cytosol.
Previously characterized mutations in the hydrophobic loops of VP5* block infection but do not interfere with binding or uptake [3]. These same mutations also prevent trimeric VP5* from associating with membranes, when the trimer is prepared in vitro and in the presence of liposomes, by successive treatment with chymotrypsin and trypsin (which generates the species designated “VP5CT”: [26]). They also fail to release α-sarcin into the cytosol [3]. We followed the uptake of particles recoated with one of these VP4 mutants, V391D, which reduces infectivity by a factor of about 104 [3]. The data in Fig. 5 show that although these particles acquire EDTA resistance with normal kinetics, they fail to release DLPs, even after 45 minutes. Combining this observation with loss of α-sarcin release by the same mutant strengthens our conclusion that DLP release corresponds to cytosolic access. The particles recoated with mutant VP4 also exhibit a somewhat longer lateral motion phase than do wild-type TLPs (Fig. S2), suggesting that the hydrophobic loops might participate in engulfment as well as in membrane disruption.
Disulfide crosslinking of VP7 trimers also blocks infection, by preventing VP7 dissociation [25]. Particles recoated with this modified VP7 have normal uptake kinetics, which we have followed by accessibility to the m159 antibody (data not shown), but like the VP4 V391D mutation, the VP7 disulfide crosslinks prevent DLP release (Fig. 4). Thus, the effects of these mutations on infectivity correlate closely with their effects on the entry pathway detected by live-cell imaging.
Viruses can exploit a variety of cellular uptake mechanisms. We looked for colocalization of entering rotavirus particles with markers for particular intracellular compartments. We followed each particle for long enough that we could confidently classify its entry as “productive” or “non-productive” – the former defined by the release step described above. Less than 20% of the productive events involved particles that had co-localized at any time with the plasma-membrane clathrin adaptor, AP-2, with dynamin (required for budding of clathrin-coated vesicles and probably for caveolin-associated membrane remodeling), or with Rab5, a marker for early endosomes (Fig. 6A). Moreover, non-productive events correlated positively with Rab5 (Fig. 6B). We ruled out more directly a requirement for Rab5 by ectopic expression of two Rab5 mutants – a dominant negative, inactive form (Rab5DN, bearing the mutation S34N) and a constitutively active form (Rab5CA, bearing the mutation Q790L). We transfected BSC-1 cells, seeded on prepared coverslips, with vectors encoding the Rab5 mutants fused to eGFP. One day later, we added RRV at various MOI, allowed infection to proceed overnight, fixed the cells, and assayed by immunofluorescence for VP7 expression. Each coverslip, corresponding to a particular mutant Rab5 and a particular MOI, had transfected and untransfected cells. We could therefore determine for each MOI the proportion of cells infected, both for those expressing the Rab5 mutant and for those expressing only endogenous, wild-type Rab5. The data in Fig. S3 show that neither of the two mutant Rab5s influenced the efficiency of infection.
We conclude, from the low frequency of escape from Rab5 endosomes and from the negligible effects of Rab5 mutants, that rotavirus entry in BSC-1 cells does not require transport to early endosomes and that particles with a Rab5-endosomal fate may have reached a dead end. This conclusion is consistent with the rapid time course of uncoating and release documented above. We also found that rotavirus infectivity in BSC-1 cells is insensitive to hydroxy-dynasore, a second generation dynamin inhibitor (Fig. S4).
We followed stages of viral entry by conventional thin-section electron microscopy, to complement the live-cell imaging studies with visualization of cellular ultrastructure and membrane morphology. We exposed cells to virus for various times, then chemically fixed the cells and prepared them for EM by standard techniques (see Methods). Fig. 7 shows three apparent stages of viral uptake, which we label “bound”, “engulfing”, and “enclosed”. (We cannot, of course, rule out a residual membrane neck, oriented away from the plane of the section, connecting an “enclosed” particle with the cell surface.) We interpret the first and last of these stages as corresponding, respectively, to the “lateral-motion/capture” and “capture/uncoating” phases detected by live-cell imaging, with engulfment as the transition between them; the asynchrony of events at the cell surface and the times required for fixation prevent any direct experimental correlation of the two methods.
Heavy-metal staining of virions is somewhat irregular, with a strong concentration of stain on the RNA interior and relatively weak and spotty staining of the protein shells. Weak but perceptible bridges between the virus particles and the cell membrane are consistent features of all the images. The most striking property of the engulfing and enclosed particles is the relatively uniform distance – roughly 550 Å – between the surrounding membrane and the center of the virion. There are no evident specializations on the cytosolic side of the membrane, such as membrane-bound molecules or cytoskeletal assemblies (Fig. 7), except for some infrequent examples of clathrin-mediated uptake (Fig. 7B, right-hand panel). The most straightforward interpretation is that the particles are driving their own engulfment through direct contacts with the surrounding membrane. We show in the Discussion that the dimensions and structural properties of the VP4 spikes on infectious TLPs are consistent with this interpretation. The fully enclosed particles are generally within 100–200 nm of the plasma membrane, as measured in the plane of the section, consistent with the relative immobility of sequestered virions up to the time of DLP release.
The periphery of BSC-1 cells is thin enough in some regions to allow recording of a tomographic tilt series of rapidly frozen cells preserved in a nearly native state. We grew BSC-1 cells on carbon-coated, gold grids (see Methods). At 24 hours after depositing the cells on the grid, we added RRV at high concentration to maximize the likelihood of finding attached virus particles and recorded tilt series from positions at which the edge of a cell was thin enough for transmission EM. The high virus concentration often yielded clusters of particles at the cell surface. We concentrated our analysis of cryo-tomograms on isolated, attached or partially engulfed virions, in order to correspond as closely as possible to the events we followed by live-cell imaging, which always had the fluorescence intensities of single recoated TLPs. We detected various states of attachment and engulfment, as illustrated in Fig. 8. Unattached particles showed clearly defined VP4 spikes; icosahedral averaging of 18 virus particles (1080 repeats) produced a tomographic 3D reconstruction with ∼4 nm resolution (Fig. 8D,G). Membrane-attached particles appeared to have induced various degrees of membrane wrapping, with the bilayer at a uniform distance from the surface of the particle. We detected two classes of virion-membrane contacts – those for which the separation of the particle surface from the membrane corresponded to the full extent of the VP4 spike (Fig. 8B) and those for which the separation was substantially smaller (Fig. 8C). We computed subtomogram averages of just the icosahedral repeats facing the membrane. Contacts in the former class had VP4 spikes similar in appearance to those on free virions, but with some indication of disorder at their tips (Fig. 8E, H); contacts in the latter class had largely disordered spikes (Fig. 8F, I), which we infer had undergone a conformational change. We do not yet have enough images to describe this apparent spike reorganization in more detail, but we suggest that it could reflect VP8* dissociation from the tips of the spikes and interaction of the VP5* hydrophobic loops with the membrane bilayer. The well-preserved particles and the uniformity of membrane invaginations around them reinforce our interpretation of the images from conventional EM in Fig. 7.
Functional recoating of rotavirus DLPs with tagged, recombinant VP4 and VP7 enables direct observation, by live-cell imaging, of the stages and kinetics of RRV entry into BSC-1 cells. We have labeled all three components independently with distinct fluorophores without compromising infectivity of the recoated particles and followed them through a reproducible succession of stages leading to DLP release into the cytosol. When added to cells, virions attach and immobilize rapidly, sometimes with an initial short period (<1 min) of lateral motion on the cell surface. Within about 5 minutes of attachment, the particles become inaccessible to EDTA (which releases accessible virions from the cell by dissociating VP7) and to VP7-directed antibody. Abrupt release of the DLP follows within a further 3–5 minutes. The sequestered virions lose VP4 and VP7 at variable rates until DLP release, but any residual VP4 and VP7 at the time of release remain at the site of penetration. The sequence of events leading to DLP release does not require association with clathrin adaptors or dynamin, and under the conditions of our experiments, association with Rab5 endosomes appears to be a dead end. Mutations in VP4 and VP7 that block infectivity prevent DLP release without affecting particle sequestration.
The particle to infectious unit ratio is high for nearly all viruses that infect animal cells, and the low efficiency of infectious outcome can affect interpretation of experiments on mechanisms of entry. We use the phrase “productive entry” here to mean release of the DLP into the cytosol – the step on which we focus – not synthesis and assembly of progeny virions. Various factors influence whether a particle released into the cytosol will then initiate infection. Indeed, we expect the probability of infectious outcome to be low, even for rotavirus particles containing a perfect assortment of eleven genomic segments that have entered by the principal infectious route, because a sequence of potentially inefficient steps follows DLP release. Evasion of host-cell innate defense mechanisms and transport of the released particle to a location in the cell appropriate for viral RNA and protein synthesis are both likely to reduce the chance that a released particle will produce detectable progeny. For example, if each of only two subsequent events were to have the same relatively high efficiency (∼20%) as does DLP release in the experiments reported here, and if the proportion of genetically competent particles and the efficiency of attachment of incubated virus to cells were each 50%, the overall particle to infectious unit ratio would be about 500∶1, consistent with published estimates [27] and with our own estimates for fresh RRV preparations. Thus, the route of penetration with the highest frequency is very likely to be the one taken by any particle that ultimately generates an infectious outcome.
The release events we describe here have all the properties we expect for functional penetration. They are of much higher frequency than any other mode of productive release we can detect by following individual virions; they deliver DLPs to the cytosol, as monitored using distinguishable fluorophores for DLP and outer-layer proteins; they are sensitive to mutations with known structural and functional consequences. In particular, we expect the VP4 hydrophobic-loop mutant, which has greatly impaired infectivity, to be defective in membrane disruption and particle release, because that mutant also fails to mediate release of α-sarcin into the cytosol [3]. We likewise expect that the disulfide cross-linked VP7 mutant, which also has impaired infectivity, will not release DLPs, because it prevents loss of VP7 from the DLP surface [25].
Conventional thin-section electron microscopy of cells exposed to rotavirus particles shows at least three apparent morphological stages of viral uptake – particles bound at the cell surface, particles partly engulfed in a surface invagination, and particles apparently fully enclosed within a tightly surrounding vesicle. In all three cases, the distance between the densely stained center of the virion and the closest segment of the cell surface appears to be about 500–600 Å. For fully enclosed particles, the surrounding membrane vesicle is generally concentric with the virion. The cryoET analysis yields more accurate dimensions, without distortion from fixation and sectioning, and shows two distinct states of the virion-membrane contact. The distance from the center of the particle to the outer surface of the membrane is 480–500 Å for the “long” contacts – just as expected for sialic-acid binding by spikes in the conformation seen by single-particle analysis. The corresponding distance for the “short” contacts is about 410 Å.
The images suggest that the virus drives its own engulfment, by multiple contacts with the target-cell membrane. RRV binds sialic acid in a pocket on the outward-facing surface of VP8* [14], and the functional receptor on the cell surface is probably a sialylated ganglioside [19]. The distance from the center of a rotavirus particle to the sialic-acid binding sites on the outward facing surfaces of VP8* is about 480 Å, and the sialic acid of a ganglioside such as GD1a, at the outer tip of the glycan, can project 10–20 Å above the mean plane of phospholipid polar headgroups. Thus, the radial position of the receptor-binding site and the length of a typical glycolipid glycan account for the observed distance between the center of the virus particle and the surface of the membrane that contacts or surrounds it, and the micrographs are compatible with the notion that most or all of the VP8* lectin domains on a particle bind a glycosphingolipid sialic-acid group. At normal glycolipid compositions, a spherical shell of membrane 1000 Å in diameter will present several hundred glycans to an attached virus, more than enough to saturate the 120 VP8* domains at the interface.
Proposed non-ganglioside receptors for rotaviruses include integrins (αVβ3 for a site on VP7 and α2β1 for a site on VP4) [28]–[30] and Hsc70 [31]. We have explained elsewhere [6], [7] that the hypothesized attachment sequence on VP7 is buried at the VP7-VP6 contact, so any potential role for αVβ3 integrin would have to be at a post-uncoating step. The DGE sequence on VP5* suggested to be a site for α2β1 [30] is at the base of the protruding spike, near the surface of VP7, and so oriented that the Asp and Glu of DGE (the potential MIDAS-site interacting residues) face away from the surface of the subunit, both on the A and B subunits and on the C subunit [9]. Thus, like the VP7 site, access to α2β1 would probably require a post-uptake conformational change. Hsc70, which can bind almost any protein, is cytoplasmic; if small amounts of a related Hsp 70 were to appear on the cell surface, it would not be an abundant molecule. The uniformity of wrapping, shown by both cryoET and by conventional EM, and the direct contact of spikes with membrane, shown by cryoET, rule out any irregular, elongated, or low abundance receptor, at least for the engulfment step we have examined.
The short class of membrane contacts shown in Fig. 8 requires a conformational change in the spike. The relatively flexible tethering of the VP8* lectin domain to the “foot” of the spike through its extended, N-terminal linker [9] would enable VP8* to move away from the tips of VP5* without dissociating from the foot (Fig. 1C), exposing the VP5* hydrophobic loops. If these loops then insert into outer leaflet of the membrane bilayer, the observed separation agrees well with estimates from the structure. Tomogram sections such as the one in Fig. 8C suggest that this conformational change can occur progressively as the particle engulfs, potentially also explaining why a mutation in a VP5* hydrophobic loop extends the interval of lateral motion on the cell surface following initial attachment.
The current observations cannot rule out participation of cellular proteins in the stages of entry we have outlined, but they limit the requirements in the combination of virus strain and host cell we have studied. Electron microscopy shows occasional images of virions captured by a clathrin-coated pit or vesicle (Fig. 7). Although this route of uptake is clearly rare in BSC-1 cells, as we could not detect colocalization with AP-2 by live-cell imaging, it is reasonable to expect that in other cells – and for other rotavirus strains – a clathrin route might predominate [20], [32]. The tightness with which VP8* binds its ganglioside receptor, the abundance of the receptor on the cell surface, and the membrane tension (which affects the free energy of invagination) could all determine whether invagination needs assistance from the clathrin machinery. Clathrin coats disassemble within 10–15 secs of pinching off [33], [34], and the resulting uncoated vesicle resembles closely the virion-generated vesicles we find in our experiments; the diameter of the vesicle itself would also be about the same (Fig. 7B). Thus, release from vesicles derived from clathrin uncoating would have the same mechanism and potentially a similar time course as release from the vesicles detected in the experiments reported here.
Our finding, that in BSC-1 cells, RRV does not enter from Rab5 endosomes, appears at first to be at variance with published conclusions that the virus traffics to Rab5 comparments in MDCK cells [35] and MA104 cells [36]. In the former study, overexpressing the same Rab5 mutants used in our experiments gave only a twofold increase for constitutively active Rab5 and a twofold decrease for inactive Rab5; in the latter study, inactive Rab5 had roughly a fourfold effect, while constitutively active Rab5 had no effect at all. These relatively modest differences, compared with those of one or more orders of magnitude produced by mutations in VP5* [3] and VP7 [25], might reflect variation of principal entry routes in the cell types used, but they might instead reflect indirect effects of perturbations in membrane traffic under the conditions of cell growth in the different experiments. Linkage among pathways of membrane traffic makes inferring mechanism from indirect readout particularly challenging. For example, “knock-down” with siRNA of various endosome-associated proteins affects entry and infectivity (twofold to fourfold relative to cells transfected with irrelevant siRNA) [36], [37], but the cells have had several generations to adjust to the loss of function. We also detect virus in Rab5 endosomes, but those particles rarely uncoat and penetrate (Fig. 6). It is certainly possible that in other cells or under different conditions, virions might bud into endosomal membranes and penetrate from that compartment – for example, if the relevant glycan receptor for the strain in question were abundant on the endosomal luminal membrane. Nonetheless, the subsequent mechanism of membrane disruption, rupture of a small vesicle, would be the same as studied here.
The kinetics of entry we have analyzed agree well with earlier, less direct observations. Measurements of RRV internalization kinetics in MA104 cells, with protection from neutralization by mAb 159 as a readout, showed roughly 50% protection within 3–5 minutes of warming cells from 4°, at which virus attached but did not internalize, to 37°C [38]. Particles that had not been treated with trypsin attached efficiently, but internalized much more slowly than did trypsinized particles, with a half-time of 30–50 minutes. These observations suggest that entry into MA104 cells proceeds by stages similar to the ones we have followed in BSC-1 cells and that rapid, clathrin-independent internalization requires VP4 cleavage. There is ample trypsin in the part of the gut in which rotavirus propagates, and trypsin treatment of virions harvested from cell culture is probably a good surrogate for a normal, in vivo event.
The polyomaviruses, in particular SV40 and murine polyoma virus, have glycolipid receptors [39] and appear to enter at the cell surface by a process that closely resembles the “wrapping” we infer from images such as those in Fig. 7B [40], [41]. SV40 can induce tight-fitting invaginations even in ganglioside-containing, giant unilamellar vesicles with no cellular proteins, but scission, which presumably occurs during infectious entry, may require cellular factors in addition to the glycoplipids [41]. Dynamin recruitment appears not to be important for scission of the virus-containing invaginations and formation of the “autoendocytic vesicles” we describe here for rotavirus; whether other cellular proteins have any role remains an open question.
Even for penetration-incompetent particles (e.g., those bearing specific mutations in VP4 or VP7), the overall kinetics of sequestration from EDTA or antibody are the same as for particles recoated with wild-type proteins. Thus, the functions disrupted by the mutations – VP5* membrane interaction and VP7 dissociation – affect only the membrane-disrupting steps. Close inspection of the TLP structure [9] suggests that with VP7 in place, the spike could reorganize to allow the hydrophobic loops of all three VP5* subunits to engage the target membrane (step 1 in Fig. 1c), but that VP7 would hinder the folding back we postulate drives bilayer disruption (step 2). Loss of Ca2+ from the vesicle that encloses the virion is presumably the event that induces VP7 dissociation. Transient Ca2+ leaks in the membrane should allow the few thousand Ca2+ ions in a vesicle of the observed diameter (including those bound by VP7) to move rapidly down their concentration gradient into the cyotosol. Such leaks might be produced by the inserted VP5* loops, either by perturbations in the membrane bilayer from the loop insertion or by fluctuations of the VP5* trimer toward its folded-back conformation.
Our results bear on the molecular details of membrane disruption and DLP release. The critical observation is that release is from a relatively small vesicle that conforms closely to the outer diameter of the virion, not from a much larger endosome. A potential molecular consequence of this observation is illustrated by the right-hand panel in Fig. 1C. We have proposed previously that the conformational change in VP5* leading to the stable, folded-back structure generates the disruptive force that breaks the membrane [2]. The transition couples to the membrane through insertion of the hydrophobic loops at the tip of the β-barrel domain [3], [23]. A transition from an extended to a folded back structure will inevitably force the membrane to expand in area, because the local “bubble” created by any one trimer must be at the expense of membrane elsewhere. The bursting point for a lipid bilayer undergoing lateral expansion is about 3%. Distortion of the membrane as shown in Fig. 1C, even by a VP5* trimer released from its underlying DLP, will produce an approximately 0.5% expansion for each VP5* trimer that attempts to fold back, implying that membrane-coupled conformational reorganization of even a modest fraction of the 60 spikes on a virion will be enough to disrupt a small, tightly fitting vesicle as seen in the experiments reported here. A larger membrane-bound compartment, such as a Rab5 or Rab7 endosome, can withstand many more local impositions of sharp curvature, by compensating elsewhere in its extended and potentially pleated surface. Thus, if folding back of VP5* is indeed the mechanism of membrane disruption, DLP escape is very likely to be from a small, membrane vesicle, closely wrapped around the particle, rather than from a much larger one.
The picture we have acquired for uptake and penetration of a non-enveloped virus and its interpretation in molecular terms has depended both on detailed structural information from x-ray crystallography and cryoEM and on tracking of large numbers of individual particles by live-cell imaging. To confirm the proposed sequence of molecular events, we need to determine the stage at which the hydrophobic loops of VP5* engage the cellular membrane and the timing and location of the VP5* fold-back step, relative both to dissociation of VP7 and to release of the DLP. On-going enhancement of imaging sensitivity and use of context-dependent labeling (e.g., Ca2+ sensitive fluorophores) should make it possible to resolve these issues and thus to connect the molecular events sketched in Fig. 1c even more intimately with the cellular steps outlined in Figs. 2 and 7.
BSC-1 cells, and the derived cell line stably expressing α2-eGFP [42], were maintained at 37°C and 5% CO2 in DMEM (Invitrogen Corporation), supplemented with 10% fetal bovine serum (Hyclone Laboratories). To obtain cells expressing dynamin2-eGFP or Rab5-eGFP, approximately 60,000 BSC-1 cells were seeded into 6-well plates and transfected with 0.5 µg of plasmid encoding either rat dynamin2-eGFP (gift of Dr. Sandra Schmid) or 0.5 µg Rab5-eGFP (Addgene), with the aid of FUGENE 6, used according to the manufacturer's instructions (Roche Diagnostics). Cells were then trypsinized and re-seeded in T25 mL flasks (Corning) in the presence of G418 for at least 48 h, to select for cells that expressed the tagged protein at levels not detrimental to cell growth.
TLPs, DLPs, VP7, VP7 disulfide mutant, and m159 antibody were purified as previously described [7], [23], [25]. For TLP and DLP production, MA104 cells were grown in 10-stack cell-culture chambers (Corning), and confluent monolayers were infected with rhesus rotavirus (RRV; G3 serotype), at MOI of 0.1 focus-forming unit (FFU)/cell in M199 medium supplemented with 1 mg/mL porcine pancreatic trypsin (Worthington Biochemical). We collected the cell culture medium 24–36 h post infection, when cell adherence was <5%, and purified the TLPs and DLPs by freeze-thawing, ultracentrifuge pelleting, Freon-113 extraction, and cesium chloride gradient centrifugation. WT and C-C VP7 were expressed in Sf9 cells infected with a baculovirus vector and purified by successive affinity chromatography with concanavalin A and monoclonal antibody (mAb) 159, which is specific for VP7 trimer (elution by EDTA). DLPs and VP7 were dialyzed into amine-free buffers containing 25 mM Hepes pH 7.5 (VP7: 25 mM Hepes pH 7.5, 100 mM NaCl, 1 mM CaCl2; DLP: 25 mM Hepes pH 7.5, 100 mM NaCl, 0.1 mM EDTA). We expressed WT and V391D VP4 in baculovirus-infected insect cells [2]; the harvested cells were flash frozen and lysed by thawing; PMSF was added to 1 mM when thawing was complete. The lysate was clarified by centrifugation, and VP4 was precipitated by addition of AmSO4 to 30% saturation. The AmSO4 pellet was resolubilized in a volume of 25 mM Tris pH 8.0, 10 mM NaCl, 1 mM EDTA that gave a conductance matching that of Phenyl HP Start Buffer (25 mM Tris pH 8.0, 3.5M NaCl, 1 mM EDTA) and loaded onto a Phenyl HP column (GE Healthcare). Following elution with 25 mM Tris pH 8.0, 10 mM NaCl, 1 mM EDTA, fractions containing VP4 were pooled, dialyzed against the same buffer, loaded onto a HiTrap Q column (GE Healthcare), and eluted in Phenyl HP Start Buffer. Pooled fractions containing VP4 were then concentrated to 1–2 mL with a Centriprep 50 concentrator (Millipore) and subjected to a final purification on S200 (GE Healthcare) in 25 mM Hepes pH 7.5, 100 mM NaCl, 0.1 mM EDTA.
DLPs, VP7, VP4 and m159 antibody in amine-free buffer were conjugated to amine-specific Atto dyes (ATTO-TEC) as follows. NaHCO3 (pH 8.3) was added separately to each of the components listed to a final concentration of 0.1 M, and Atto dyes, suspended in anhydrous DMSO (Sigma) to 2 mg/mL, were then added to obtain the following final dye concentrations: for DLP labeling, 20 µg/mL; VP4, 16 µg/mL; VP7, 20 µg/mL; m159, 50 µg/mL. For recoated particles, dye combinations were varied according to the objectives of the experiment (e.g., doubly vs. triply labeled particles). DLPs. Proteins were incubated with dye for 1 h in the dark at room temperature, and the reactions were quenched by adding Tris pH 8.0 to a final concentration of 200 mM. Labeled components were then dialyzed into buffers for ensuing recoating reactions (see below for recoating methods). For recoated particles, dye combinations were varied according to the objectives of the experiment (e.g., doubly vs. triply labeled particles).
Recoating was carried out as previously described [22], [23], using recoating components labeled as described above. Briefly, Atto-labeled, recombinant VP4 was added to purified DLPs in at least 5-fold excess in buffer adjusted to pH 5.2 with sodium acetate, and the pH of the mix was adjusted to pH 5.2 by stepwise addition of sodium acetate and testing by pH paper. After incubation at room temperature for 1.5 h, mutant or WT VP7 was added in at least 3-fold molar excess in buffer supplemented with calcium, and the mixture was incubated for a further 30 minutes at room temperature. Recoated particles were separated from excess labeled components by cesium chloride gradient centrifugation and dialyzed into appropriate buffers (25 mM Hepes, 100 mM NaCl and 1 mM CaCl2). Titers of recoating reactions were determined by infectious focus assays as described [25].
BSC-1 cells, plated on 25 mm No. 1.5 coverslips at a density of 150,000 cells/coverslip, were grown overnight at 37°C. The cells were washed twice with HEPES pH 7.0, 140 mM NaCl, 1 mM CaCl2; 300 µl of α-MEM supplemented with 1 mM CaCl2, with or without 100 mU/ml Vibrio cholera neuraminidase (Sigma), was then added and the plates incubated for 1 hr. at 37°C. Recoated TLPs (RcTLPs), prepared with Atto 565 labeled VP7, unlabeled VP4, and Atto 647N labeled DLPs, were activated by 1∶10 dilution in 5 µg/ml trypsin in TNC buffer at 37°C for 30 min and then placed on ice until use. Trypsin-treated RcTLPs (30 µl, added to 70 ul of α-MEM and mixed) were added directly to the medium on the plates (final MOI∼15). After 15 min of incubation of cells and RcTLPs, confocal z-stack images were collected using transmitted light (cell) or laser excitation at 561 nm (VP7 Atto 565). For focus-forming assays, confluent BSC-1 monolayers were incubated for 1 hr. at 37°C in α-MEM supplemented with 1 mM CaCl2, with or without 100 mU/ml V. cholera neuraminidase. RcTLPs or native TLPs were then added at an MOI of 15 and allowed to bind at 4°C for 2 hrs. Following incubation, cells were washed three times in PBS, freeze-thawed three times, and the amount of infectious virus bound determined by focus-forming assay on fresh, confluent BSC-1 cells.
Approximately 1×105 BSC-1 cells were grown on 25 mm No. 1.5 coverslips as described above. Medium was exchanged immediately before imaging with pre-warmed MEM–α, without phenol red, supplemented with 25 mM Hepes (pH 7.4) and 2% FBS (Hyclone). Labeled recoated virus particles were added to cells at MOI of 0.1–0.2. For experiments in Figure 2, images were acquired every 1 minute (Fig. 2D) or 1 s (Fig. 2E,F) using 100 ms (Fig. 2D) or 5–30 ms (Fig. 2E, F) exposure times (no binning) with a previously described laser and confocal microscope configuration [43]. Image and data analysis was performed using Slidebook 4.2 (Intelligent Imaging Innovations, Denver CO). For experiments in Figures 3–6, cells were grown on No. 1.5 coverslips as described above and mounted on a Prior Proscan II motorized stage on a Nikon Ti inverted microscope equipped with 100× Plan Apo NA 1.4 objective lens and the Perfect Focus System. The microscope was enclosed in a custom built, heated chamber warmed to 37°C; the sample was supplied with 5% CO2. All images were collected with a Yokagawa CSU-X1 spinning disk confocal with Spectral Applied Research Borealis modification Excitation with solid state lasers was controlled by an AOTF; images, acquired with a Hamamatsu ORCA-AG cooled CCD camera controlled by MetaMorph 7 software, were collected with a 405/491/561/642 band pass dichroic mirror (Semrock) at the following wavelengths: 491 nm line with a 525/50 emission filter; 561 nm line with a 620/60 emission filter; 642 nm with a 700/75 emission filter (Chroma). For time-lapse experiments, images were collected every 3–6 s depending on the objectives of the experiment (described in respective figure legends), using an exposure time of 5–30 ms and 2×2 binning, with illumination attenuated by the AOTF between acquisitions. Gamma, brightness, and contrast were adjusted on displayed images (identically for compared image sets) using MetaMorph 7 software. Analysis was performed using built-in functions provided by Slidebook 4.2 and MetaMorph 7. EDTA flow-in experiments were performed during imaging experiments without interruption of image collection by quickly pipetting away the MEM-a/FBS medium bathing the cells and replacing the medium by gently layering pre-warmed MEM-a/FBS medium containing 4 mM EDTA onto the culture plate; we were careful not to disturb the plane of focus. Although EDTA treatment caused cells ultimately to detach from the coverslip, they remained in place long enough to determine whether a bound virus particle resisted dissociation. m159 flow-in experiments were performed similarly; the replacement medium contained 25–50 µg/mL fluorescently labeled m159 antibody rather than EDTA.
BSC-1 cells, seeded in complete growth medium (DMEM supplemented with Pen/Strep and 10% FBS) at 70% confluence on 6-well plates, were transfected with plasmids expressing GFP-Rab5DN(S34N) and GFP-Rab5CA(Q79L) as described [44]. After 24 h at 37°C, the cells were trypsinized and replated at ∼30% confluency on polylysine-coated glass coverslips placed in 6-well plates. After another 24 h at 37°C, cells were washed twice with warmed phosphate buffered saline (PBS), and RRV TLPs, freshly treated with trypsin in α-MEM, were added to each well at the indicated MOI. Cells were incubated with virus at 37°C for 1 hr, after which the medium was replaced with DMEM supplemented with Pen/Strep and neutralizing mAb m159 at 3.6 µg/ml, and the infection was allowed to proceed for 16–18 hours at 37°C. After washing the cells with serum-free α-MEM at 37°C, transferrin labeled with Alexa-Fluor 647 (Tf-647) was added at 50 µg/ml. The cells were incubated with the Tf-647 for 10 min at 37°C, washed with PBS, fixed for 10 min with 3.7% formaldehyde in PBS, and prepared for immunofluorescence as described [22]. Infection was assayed by staining with anti-VP7 mAb m60 and counterstaining with Alexa Fluor 568 labeled goat anti-mouse IgG. Images were acquired with a Mariana system (Intelligent Imaging Innovations, Denver, CO) based on a Zeiss AxioVert 200M inverted microscope (Carl Zeiss Microimaging, Inc., Thornwood, NY) equipped with a CSU-22 spinning-disk confocal unit (Yokogawa Electric, Tokyo, Japan), a piezo-driven Z-translation, and linear encoded X&Y translations and controlled with SlideBook V5.0 (Intelligent Imaging Inc., Denver, CO). Excitation wavelengths were 491, 561, and 660 nm (lasers from Cobolt, Solna, Sweden); the emission filters ranges were 525–550 nm, 620–660 nm, and 680 nm long-pass (Semrock, Rochester, NY). We collected Z-scans at 10–12 positions in each sample, imaging the entire cell volume in 0.5 micron steps, with exposure times per step of 30 ms at 491 nm (GFP-Rab5) and 50 ms at 561 nm (Alexa-Fluor 568 goat anti-mouse IgG) and 660 nm (Tf-647). For each field of view (10–12 fields/experimental condition), we scored cells for Rab5 expression, RRV infection, and Tf uptake, the last as a control for clathrin-based receptor mediated endocytosis. As each coverslip had transfected and non-transfected cells, we could score both expressing and non-expressing cells for RRV infection in the same fields.
Authentic, trypsin-treated TLPs were added at 280 ffu/cell to BSC-1 cells that had been pre-incubated with MEM-a (without FBS) for 10 min. The cells were fixed 5–10 min after adding virus by incubating for 1 hr in fixative (1.25% formaldehyde, 2.5% glutaraldehyde, 0.03% pictric acid in 0.1 M sodium cacodylate, pH 7.4). Fixed cells were stained successively with osmium tetroxide (1%) and uranyl acetate (1%), washed, dehydrated with successive washes in 75%, 90% and 100% ethanol, soaked in propyleneoxide for 1 hr, and infiltrated and embedded in Epon (polymerized for 24–48 hr at 60°C). Sections about 50 nm thick were examined in the FEI Tecnai G2 Spirit BioTWIN electron microscope of the Harvard Medical School Electron Microscopy Core Facility.
Freshly glow-discharged EM gold-grids coated with a holey carbon film (Quantifoil R 2/2 200 mesh, Quantifoil MicroTools GmbH, Germany) were set on the bottom of a sterile Petri dish and sterilized with 70% ethanol for 10 minutes. The sterilized grids were rinsed twice with 0.2 µm filtered water, submerged in 0.1% poly-l lysine hydrobromide overnight, and then rinsed once in 0.2 µm filtered water and twice in unsupplemented MEMα media. BSC-1 cells were plated over 6 prepared grids at a density of 9×104 cells/ml in a total of 2 ml MEMα supplemented with 10% FBS in a 35 mm diameter glass bottom dish (MatTek). The cells were incubated for 24 hrs. at 37 deg. C and 5% CO2. They attached to the grids at low density (about one cell per three grid squares), which we verified by DIC light microscopy. Grids with cultured host cells, held by self-closing tweezers at the edge of the grid, were washed with three drops of medium, and TLPs at 9.4×109 FFU/ml in 5–10 µl were added to the cells on the grid. The cells were incubated at 37°C for 30 min, after which 1.5 µl of 10-nm colloidal gold solution (Sigma-Aldrich, St. Louis, MO) was added to create fiducial markers for use during tilt series alignment and tomogram reconstruction. Excess fluid was blotted with filter paper just before rapid freezing by plunging into liquid ethane, using a manual plunge freezing device. The frozen grids were stored in liquid nitrogen.
For imaging, we used a Tecnai F30 transmission electron microscope (FEI, Inc., Hillsboro, OR) operating at an accelerating voltage of 300 kV. The microscope was equipped with a field emission gun, a high-tilt stage, a post-column energy filter (Gatan Inc., Pleasanton, CA) and a 2k×2k charge-coupled device camera (Gatan). We recorded low-dose images at −8 µm defocus and at a nominal magnification of 13,500×, giving a pixel size of 9.86 Å. Single-axis tilt series were recorded by tilting the specimen from −60° to +60° in 1.5–2° increments using SerialEM control software [45]; the total electron dose per tilt series was kept below 150 e/Å2.
We used IMOD [46] for fiducial alignment of the tilt series images and for tomogram reconstruction by weighted back-projection. Virus particles were picked from the raw 3D cryo-tomograms. Bound and unbound particles were identified by visual inspection. Subtomogram averaging was performed with PEET [47], using the published cryo-EM structure (EMDB 5199) [9], filtered to 25 Å resolution, as an alignment reference. Icosahedral symmetry was applied as described by the Boulder Laboratory for 3D Electron Microscopy of Cells online protocol (http://www.youtube.com/watch?v=c9LqABmRd7Q&list=PLGggUwWmzvs_Q8j05yw2B2vVstVZaT9at). We averaged 18 unattached particles (18×60 = 1080 repeats), and 7 membrane-attached particles (7×60 = 420 repeats). For the class of virons bound to the host cell membrane, we also calculated a “membrane-preserving” average using only those repeats that contained spikes in contact with the membrane (78 repeats from 5 particles). We used IMOD for 3D visualization and isosurface rendering of the averaged particles.
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10.1371/journal.ppat.1004057 | Functionally Redundant RXLR Effectors from Phytophthora infestans Act at Different Steps to Suppress Early flg22-Triggered Immunity | Genome sequences of several economically important phytopathogenic oomycetes have revealed the presence of large families of so-called RXLR effectors. Functional screens have identified RXLR effector repertoires that either compromise or induce plant defense responses. However, limited information is available about the molecular mechanisms underlying the modes of action of these effectors in planta. The perception of highly conserved pathogen- or microbe-associated molecular patterns (PAMPs/MAMPs), such as flg22, triggers converging signaling pathways recruiting MAP kinase cascades and inducing transcriptional re-programming, yielding a generic anti-microbial response. We used a highly synchronizable, pathogen-free protoplast-based assay to identify a set of RXLR effectors from Phytophthora infestans (PiRXLRs), the causal agent of potato and tomato light blight that manipulate early stages of flg22-triggered signaling. Of thirty-three tested PiRXLR effector candidates, eight, called Suppressor of early Flg22-induced Immune response (SFI), significantly suppressed flg22-dependent activation of a reporter gene under control of a typical MAMP-inducible promoter (pFRK1-Luc) in tomato protoplasts. We extended our analysis to Arabidopsis thaliana, a non-host plant species of P. infestans. From the aforementioned eight SFI effectors, three appeared to share similar functions in both Arabidopsis and tomato by suppressing transcriptional activation of flg22-induced marker genes downstream of post-translational MAP kinase activation. A further three effectors interfere with MAMP signaling at, or upstream of, the MAP kinase cascade in tomato, but not in Arabidopsis. Transient expression of the SFI effectors in Nicotiana benthamiana enhances susceptibility to P. infestans and, for the most potent effector, SFI1, nuclear localization is required for both suppression of MAMP signaling and virulence function. The present study provides a framework to decipher the molecular mechanisms underlying the manipulation of host MAMP-triggered immunity (MTI) by P. infestans and to understand the basis of host versus non-host resistance in plants towards P. infestans.
| Phytophthora species are among the most devastating crop pathogens worldwide. P. infestans is a pathogen of tomato and potato plants. The genome of P. infestans has been sequenced, revealing the presence of a large number of host-targeting RXLR effector proteins that are thought to manipulate cellular activities to the benefit of the pathogen. One step toward disease management comprises understanding the molecular basis of host susceptibility. In this paper, we used a protoplast-based system to analyze a subset of P. infestans RXLR (PiRXLR) effectors that interfere with plant immunity initiated by the recognition of microbial patterns (MAMP-triggered immunity - MTI). We identified PiRXLR effectors that suppress different stages early in the signaling cascade leading to MTI in tomato. By conducting a comparative functional analysis, we found that some of these effectors attenuate early MTI signaling in Arabidopsis, a plant that is not colonized by P. infestans. The PiRXLR effectors localize to different sub-cellular compartments, consistent with their ability to suppress different steps of the MTI signaling pathway. We conclude that the effector complement of P. infestans contains functional redundancy in the context of suppressing early signal transduction and gene activation associated with plant immunity.
| Plants possess innate defense mechanisms to resist microbial infection [1], [2]. Efficient plant disease resistance is based on two evolutionarily linked layers of innate immunity. One layer involves cell surface transmembrane receptors that recognize invariant microbial structures termed pathogen- or microbe-associated molecular patterns (PAMPs/MAMPs), hereafter referred to as MAMPs [3]–[5]. MAMPs are not only shared by particular pathogen races, but are broad signatures of a given class of microorganisms. They constitute evolutionarily conserved structures that are unique to microorganisms and have important roles in microbial physiology. Typical MAMPs include lipopolysaccharides (LPS) of Gram-negative bacteria, bacterial flagellin and fungal cell wall-derived carbohydrates or proteins, some of which were shown to trigger plant defense in a non-cultivar-specific manner [3], [6]. The best-studied MAMP receptor in plants is FLAGELLIN-SENSITIVE 2 (FLS2) from Arabidopsis, a receptor-like kinase (RLK) with extracellular leucine-rich repeat domains [7]. The 22 amino acid peptide (flg22) corresponding to the highly conserved amino-terminus of flagellin is sufficient to trigger immune responses in Arabidopsis, tomato, tobacco and barley but not in rice [8]–[12]. Although different MAMPs are perceived by different receptors, convergent early-signaling events, including MAP kinase activation and specific defense-gene induction, have been observed in Arabidopsis plants and protoplasts [13]–[15].
Suppression of flg22-induced defenses by bacterial virulence effectors suggests that manipulation of MAMP-triggered immunity (MTI) in plants is a key strategy for successful pathogens to grow and multiply (reviewed in [16]–[19]). A major target of bacterial effectors is the plant MAP kinase cascade, probably because of the central role of MAP kinase signaling in MTI. The Pseudomonas syringae effector HopAI1 displays phosphothreonine lyase activity and inactivates MPK3, MPK6, and MPK4 in Arabidopsis by dephosphorylating them [20]. P. syringae effector HopF2 blocks MAMP-induced signaling by targeting MKK5, a MAP kinase activating MPK3/MPK6, through a different mechanism of action i.e. ADP-ribosylation [21]. Bacterial effectors can also suppress MAP kinase signaling by targeting the pattern recognition receptor complex as illustrated by the P. syringae effectors AvrPto and AvrPtoB that block FLS2-mediated signal transduction in Arabidopsis and tomato [22]–[24]. Other effectors appear to act downstream of the activation of the MAPK cascade by blocking the expression of defense-associated genes in the nucleus. Such an effector is XopD from Xanthomonas campestris that inhibits the activity of the transcription factor MYB30, resulting in suppression of basal immune responses and promotion of pathogen growth [25], [26].
Unlike bacterial effectors, little is known about the molecular functions of effectors from eukaryotic plant pathogens. It remains to be demonstrated whether these pathogens have evolved effectors that subvert early-induced MTI signaling above, at, or immediately downstream of MAP kinase cascades. Oomycetes, including downy mildews and Phytophthora species, establish intimate association with host plant cells through structures such as appressoria, infection vesicles and haustoria, which are believed to facilitate the delivery of effectors into the host cytoplasm [27]. The genome sequences of Phytophthora sojae, P. ramorum, P. infestans and Hyaloperonospora arabidopsidis are published [28]–[30]. Each genome encodes several hundred putative RXLR effectors. Most oomycete Avirulence (Avr) proteins characterized so far carry a signal peptide followed by a conserved motif centered on the consensus RXLR-(EER) sequence, where X is any amino acid [31]. It has been shown that the RXLR peptide motif acts as a host-targeting signal for translocation into plant cells [31], [32].
Amongst the best-characterized oomycete RXLR effectors are AVR3a, AVRblb2 and PITG_03192 from P. infestans, AVR1b and AVR3b from the soybean pathogen P. sojae and ATR1 and ATR13 from H. arabidopsidis [33]–[47]. P. infestans Avr3a alleles encode secreted proteins of 147 amino acids that differ in two residues which determine recognition; only the isoform AVR3aKI is recognized by the potato resistance protein R3a, whereas AVR3aEM evades detection by R3a. When expressed in Nicotiana benthamiana cells, AVR3a suppresses host cell death induced by the elicitin INF1, a typical MAMP [35], [37]. It has since been shown to suppress cell death elicited by perception of a range of pathogen molecules by direct interaction with, and stabilization of, the plant E3 ligase CMPG1 [36], [42]. The Avrblb2 gene family is highly polymorphic and different forms/alleles are present in different P. infestans isolates. Sequence alignment of the deduced amino acid sequences of the Avrblb2 family members showed that the C-terminal effector domain undergoes positive selection, which is strong evidence for co-evolution with host resistance and/or target proteins [44]. The amino acid residue at position 69 was shown to be crucial for recognition by the cognate resistance protein Rpi-blb2 [44]. AVRblb2 was shown to block the secretion of a C14 cysteine protease that is involved in plant resistance against P. infestans [38]. Recently, the RXLR effector PITG_03192 has been shown to enhance P. infestans colonization of N. benthamiana by its interaction with NAC DNA binding proteins at the host endoplasmic reticulum, preventing their re-localization into the nucleus following pathogen perception [43]. Suppression of MTI has also been reported for ATR1 and ATR13 in Arabidopsis [47]. Nevertheless, for the majority of RXLR effectors, their biological functions and potential host targets are unknown.
Transient expression in protoplasts has proven fast and reliable for studying the function of bacterial type III effectors that suppress early MAMP signaling [48], [49]. Moreover, the assay allows the measurement of synchronized responses and it does not require the use of bacteria for protein or DNA transfer into the host cell. In addition, the protoplast system offers the possibility to test large sets of effectors in a medium-high throughput manner. In this study, we have used tomato mesophyll protoplasts to screen a library of 33 P. infestans RXLR effector candidates (PiRXLRs) for their ability to suppress flg22-triggered defense signaling. Our additional aim was to test whether PiRXLRs that suppress early MTI signaling in the host plant tomato retain that ability in the distantly-related non-host plant Arabidopsis. For the experimental read-out we measured the abilities of these effectors to suppress: i) flg22-induced promoterFLG22-INDUCED RECEPTOR-LIKE KINASE 1 - LUCIFERASE (pFRK1-Luc) reporter gene activity; ii) flg22-induced post-translational MAP kinase activation; and iii) flg22-induced gene expression. In addition, we performed sub-cellular localization studies of fluorescent protein-tagged PiRXLR effectors by confocal microscopy. Finally, we tested the potential of the PiRXLR effectors suppressing early MTI signaling to enhance N. benthamiana susceptibility to P. infestans. Unraveling the mode-of-action of PiRXLR effectors within plant cells will help to gain insight into the specific mechanisms that coordinate different signaling and metabolic pathways to ensure proper plant development and response to environmental changes or stresses.
A prerequisite to performing a screen that would allow us to identify PiRXLR effector candidates suppressing early events of MAMP signaling pathways in both a host (tomato) and a non-host (Arabidopsis) of P. infestans was to develop comparative bio-assays. Several components of the flg22-triggered signaling pathway are conserved in Arabidopsis and tomato. SlFLS2, the ortholog of AtFLS2, binds flg22 [50]. The MAP kinase orthologs of AtMPK3 and 6 in tomato are SlMPK3 and 1, respectively [51].
We adapted most of the techniques and materials that were generated for the identification and functional characterization of the P. syringae type III effector AvrPto, a well-studied suppressor of early MAMP signaling in both Arabidopsis [48], [49] and tomato [52]. Figure S1 shows that we could reproduce the AvrPto-mediated suppression of early MTI signaling observed in Arabidopsis protoplasts [48]. Moreover, we were able to extend this assay to tomato, and the induction of luciferase activity under control of the flg22-responsive promoter of FRK1 (pFRK1-Luc) was strongly impaired in Arabidopsis and tomato protoplasts expressing AvrPto with a C-terminal Green Fluorescent Protein (GFP) fusion (Figure S1A, B). An inactive AvrPto in which the Gly residue in position 2 is replaced by an Ala (AvrPto G2A-GFP), preventing the myristoylation and membrane localization of the effector protein [53], could not suppress pFRK1-Luc activation by flg22 (Figure S1A, B). Furthermore, we confirmed that AvrPto-GFP but not the AvrPto G2A-GFP mutant blocks post-translational activation of flg22-responsive MAP kinases in both protoplast systems (Figure S1 C, D).
We searched for PiRXLR effectors interfering with flg22-induced early immune responses in protoplasts of tomato, a host for P. infestans. Thirty-three PiRXLR effector genes, most of which were selected on the basis of their up-regulation during the biotrophic phase of infection [28], [32], [44], were cloned without the native secretion signal peptide into pDONR Gateway vectors (Table S1). We sub-cloned these sequences into Gateway destination vectors of the p2GW7 series to allow transient expression with/without an N-terminally fused GFP tag.
For the initial read-out, we measured pFRK1-Luc activity upon flg22 treatment. Of the 33 PiRXLR effectors screened, 8 (PITG_04097, PITG_04145, PITG_06087, PITG_09585, PITG_13628, PITG_13959, PITG_18215 and PITG_20303) reduced consistently and reproducibly flg22-induced pFRK1-Luc activation in tomato protoplasts, when compared to control protoplasts expressing only GFP (p-value<0.05 - Figure 1: S.lycopersicum). We named these effectors Suppressor of early Flg22-induced Immune response (SFI) 1 to 8, respectively. Protoplast staining with vital dyes, 24 h after plasmid transformation, showed that the percentage of dead cells is, with the exception of a higher (but non-significant) value for SFI6, similar for each of the tested PiRXLR effectors and the GFP control (Figure S2). Therefore, the suppression of reporter gene activity is not the consequence of a toxic or a programmed cell death process in transformed protoplasts.
Five PiRXLR effectors (SFI1 and SFI5-8) reduced pFRK1-Luc activation by flg22 with an efficiency comparable to the bacterial effector AvrPto (+flg22/−flg22≅1). Among PiRXLR effectors with a reported avirulence function in potato, only AVRblb2 (SFI8) [44] was able to suppress flg22-induced pFRK1-Luc activity. SFI8 is a representative member of a large family of AVRblb2-related proteins but it bears a Phe residue at position 69 in its sequence and, therefore, is predicted not to be recognized by Rpi-blb2 [44]. Thus, we extended our analysis to three more AVRblb2 family members with either an Ala (PITG_20300 and PITG_04090) or Ile (PITG_04085) at position 69 and crucial for Rpi-blb2-mediated HR (Table S2). Both predicted Rpi-blb2-recognized and -unrecognized isoforms of AVRblb2 equally suppressed reporter gene activation (Figure S3A). Other PiRXLR effectors identified as avirulence proteins such as AVR1 [28], AVR3a [34], AVR4 [54] and AVRblb1/IPI-O1 or IPI-O4 [55], [56] did not interfere with early flg22-induced responses in our assay (Figure 1: S. lycopersicum). In the case of AVR3a, both R3a-recognized AVR3aKI and R3a-unrecognized AVR3aEM had no effect on flg22-induced pFRK1-Luc activity (Figure 1: S.lycopersicum).
Using quantitative real-time PCR (qRT-PCR) we monitored the expression levels of the eight PiRXLR effector genes that suppressed pFRK1-Luc activation in tomato protoplasts at different stages of potato infection, relative to their expression in sporangia. Previous expression analyses of P. infestans RXLR effector genes showed that, when detected by either qRT-PCR [24] or in microarray experiments [28], [57], they are up-regulated in the first 48–72 hours of infection, i.e. during biotrophy. Transcripts of SFI1-8 accumulated during the first 48 hours post-inoculation (Figure S4), consistent with a potential role in effector-triggered susceptibility.
We extended our analyses to determine whether PiRXLR effectors that suppress pFRK1-Luc activity in the host tomato are able to also suppress such responses in the non-host plant Arabidopsis.
The pFRK1-Luc reporter gene assay, which turned out to be more sensitive in Arabidopsis than in tomato, showed that four effectors (SFI1, SFI2, SFI5 and SFI8/AVRblb2) were also able to attenuate activation in Arabidopsis (p-value<0.05 - Figure 1: A. thaliana). As observed in tomato, each tested AVRblb2 isoform suppressed reporter gene activation by flg22 in Arabidopsis protoplasts (Figure S3B), whereas AVR3a had no effect (Figure 1: A. thaliana). We found a further four effectors (PITG_00821, PITG_05750, PITG_16737 and AVRblb1/PITG_21388) that attenuated the flg22-dependent pFRK1-Luc activation only in Arabidopsis (p-value<0.05 - Figure 1: A. thaliana). Like in tomato, transient expression of PiRXLR effectors in Arabidopsis protoplasts did not cause significant cell death (Figure S5). One effector, PITG_18670, significantly induced a stronger flg22-dependent pFRK1-Luc activity than did the GFP control (p-value<0.05 – Figure 1: A. thaliana), but did not do so in the host plant tomato (Figure 1: S. lycopersicum). This effector was not pursued further in this work.
The observation that 4 PiRXLR effectors suppress flg22-mediated pFRK1-Luc induction in the non-host plant Arabidopsis, but not in the host plant tomato, was unexpected. This prompted us to test whether all 8 PiRXLR effectors that suppress pFRK1-Luc induction in Arabidopsis also inhibit the endogenous expression of early MAMP-regulated genes. First, we measured the level of endogenous FRK1 in Arabidopsis following flg22 treatment. This experiment confirmed the data obtained in the reporter gene assay with 3 PiRXLR effectors (SFI1, SFI2 and SFI8/AVRblb2) attenuating the up-regulation of FRK1 expression by flg22 (Figure 2A). In contrast, SFI5, as well as PITG_00821, PITG_05750, PITG_16737 and AVRblb1/PITG_21388, failed to suppress flg22-induced FRK1 expression (Figure 2A). We extended our analysis to an additional MAMP-induced gene, WRKY DNA-BINDING PROTEIN 17 (WRKY17), and observed that its up-regulation was also notably diminished by SFI1, SFI2 and SFI8/AVRblb2 (Figure 2B), whereas SFI5, PITG_00821, PITG_05750, PITG_16737 and AVRblb1/PITG_21388 again had no effect. The expression of the housekeeping gene ELONGATION FACTOR 1A (EF1α) was generally not altered. Only with SFI2 did we observe a 2–3 fold decrease of the EF1α transcript level, possibly as a consequence of reduced cellular fitness due to effector expression (Figure 2C). Indeed, the expression of all genes tested was barely detectable in the presence of this effector.
Together, our initial results revealed a set of 8 PiRXLR effectors that are candidate suppressors of early flg22-mediated MTI signaling in tomato, and assigned a novel function to the previously described AVRblb2 effector family. Moreover, our data predict that 3 of these PiRXLR effectors target processes contributing to MTI that are conserved in Arabidopsis and tomato. We proceeded to study all 8 effectors that suppress flg22-inducible reporter gene activation in tomato in more detail.
From the initial screen for MTI signaling suppression we hypothesized that the function of 3 PiRXLR effectors (SFI1, SFI2 and SFI8/AVRblb2) may be conserved in both tomato and Arabidopsis while 5 effectors (SFI3, SFI4, SFI5, SFI6 and SFI7) may function specifically in tomato. We expected that the sub-cellular distribution of PiRXLR effectors might provide additional important information about their function in the cell. Therefore, these PiRXLR effectors, N-terminally fused to GFP, were transiently expressed in tomato (all SFI effectors) and Arabidopsis (only SFI1, SFI2 and SFI8/AVRblb2) protoplasts, and in N. benthamiana leaves for comparison, and visualized by confocal microscopy (Figure 3). We performed immunoblot analysis to confirm protein expression and stability of intact GFP-fusion proteins (Figure S6), and verified that GFP-tagged PiRXLR effectors were still functional and effectively suppressed pFRK1-Luc activity in protoplasts (Figure S7). Most of the GFP-tagged PiRXLR effectors were as active as the un-tagged proteins. Notably, GFP-SFI8/AVRblb2 functioned only weakly or not at all in Arabidopsis, but retained its function in tomato (Figure S7). SFI8/AVRblb2, C-terminally fused to GFP (SFI8-GFP) was also unable to suppress pFRK1-Luc activity in Arabidopsis protoplasts (Figure S8).
The sub-cellular localizations of the 3 PiRXLR effectors (SFI1, SFI2 and SFI8/AVRblb2) affecting pFRK1-Luc/MAMP gene activation in both tomato and Arabidopsis are similar in each plant species (Figure 3A). GFP-SFI8/AVRblb2 showed nuclear-cytoplasmic localization whereas GFP-SFI1 and GFP-SFI2 localized predominantly in the nucleus, and were also apparent in the nucleolus (Figure 3A). In the case of GFP-SFI1, additional fluorescence signal was observed in the cytoplasm (and possibly at the plasma membrane [PM]) (Figure 3A). The 5 PiRXLR effectors (GFP-SFI3, -SFI4, -SFI5, -SFI6 and -SFI7) with a tomato-specific effect showed different subcellular localizations. GFP-SFI3 was enriched in the nucleus/nucleolus, GFP-SFI4 showed nuclear-cytoplasmic localization, and GFP-SFI5, -SFI6 and -SFI7 showed differing degrees of cytoplasmic localization and association with the PM (Figure 3B), with GFP-SFI5 almost exclusively localized to the PM.
Additional sub-cellular localization studies performed upon Agrobacterium-mediated expression in N. benthamiana leaves confirmed the results obtained in protoplasts, suggesting that protoplasts are accurate in reflecting sub-cellular localizations of these effectors in planta. Confocal microscopy revealed distinct sub-nuclear localization patterns for the 3 PiRXLR effectors (GFP-SFI1-3) that were predominant in this compartment. GFP-SFI1 appears to localize in the nucleolus, GFP-SFI3 forms a ring around the nucleolus, whereas GFP-SFI2 showed a range of sub-nuclear localizations (Figure S9). The obvious differences in sub-cellular localization between effectors imply that different steps and/or pathways may be targeted by individual effectors that have in common the suppression of flg22-triggered pFRK1-Luc activity.
We performed an epistatic analysis to find out which step of the flg22-triggered signaling pathway in tomato or Arabidopsis is affected by the PiRXLR effectors that suppressed pFRK1-Luc/MAMP responsive gene activation. We conducted immunoblot assays using the p44/42 antibody, raised against phosphorylated MAP kinases, to assess the impact of our effectors on the activation by flg22 of endogenous SlMPK1/3 and AtMPK3/6 in tomato and Arabidopsis protoplasts, respectively. AvrPto was used as a positive control, as it is known to block MTI signaling upstream of the MAP kinase cascade at the FLS2/BAK1 receptor complex [23], [24], [48].
In tomato, 3 effectors (SFI5-SFI7) consistently suppressed flg22-dependent post-translational MAP kinase activation (Figure 4A). We confirmed this result by performing transient expression of HA-tagged SlMPK1 and SlMPK3 in protoplasts followed by immunoprecipitation and in vitro MAP kinase assay (Figure 4B). In contrast, none of the 8 SFI effectors attenuated flg22-dependent post-translational MAP kinase activation in Arabidopsis (Figure 4C). This suggests that the effectors (SFI1, SFI2 and SFI8/AVRblb2) that were shown to attenuate flg22-induced gene activation in both tomato and Arabidopsis are most likely doing so downstream of MAP kinase activation. In the case of SFI5, the demonstration that it attenuates MAP kinase activation only in tomato (Figure 4A, 4C) is consistent with the observation that, although this effector suppressed pFRK1-Luc activation in Arabidopsis, it failed to suppress flg22-mediated up-regulation of endogenous FRK1 in that plant.
To further elucidate the molecular mechanism(s) underlying the mode of action of SFI5-SFI7 in suppressing flg22-induced post-translational MAP kinase activation in tomato, we performed gain-of-function experiments using components that activate the MAP kinases SlMPK1 and SlMPK3 in the absence of flg22 signal. The ectopic expression of known key players of MAMP-signaling pathways, such as MAPK kinases and MAPKK kinases [48], [58] have helped to elucidate the steps at which bacterial effectors such as AvrPto interfere with MTI in Arabidopsis [48], [59].
In tomato and other solanaceous plants, MAP kinase signaling cascades are best studied in the context of programmed cell death (PCD) associated with effector-triggered immunity [51], [60], [61]. In N. benthamiana, PCD triggered by perception of the P. infestans MAMP INF1 requires NbMKK1 and its interaction with SIPK (salicylic acid-induced protein kinase; an ortholog of SlMPK1) [62]. The role of MAPKK kinases in tomato immunity is only documented for SlMAP3Kα and SlMAP3Kε [60], [61] and the best characterized MAPK kinases are SlMEK1 and SlMEK2 [60]. Whether these kinases contribute to flg22-triggered signaling in tomato is unknown. As shown in Figure S10, transient expression in tomato protoplasts of a constitutively active SlMEK2 (SlMEK2-DD), or the kinase domain of SlMAP3Kα (SlMAP3Kα-KD), led to post-translational activation of SlMPK1 and SlMPK3 in the absence of flg22. The constitutively active SlMEK1 (SlMEK1-DD) and kinase domain of SlMAP3Kε (SlMAP3Kε-KD) did not activate SlMPK1 and SlMPK3. The expression of the constitutively active SlMEK2 (SlMEK2-DD) and the kinase domain of SlMAP3Kα (SlMAP3Kα-KD) overrode the suppression of flg22-dependent activation of SlMPK1 and SlMPK3 by SFI5-SFI7 (Figure 5A, 5B). These results indicate that the three effectors suppress the signaling cascade very early; either upstream of MAPKK kinase activation, or specifically at the MAPK- and/or MAPKK kinase(s) involved in flg22 signaling. This is consistent with association of these effectors with the plant plasma membrane, where they may interfere with the earliest components of MAMP perception or signal transduction.
Since in N. benthamiana PCD triggered by perception of the MAMP INF1 [62], or perception of Cladosporium fulvum effectors Avr4/9 by tomato Cf-4/9 receptors [60], [61], involves MAP kinase cascades, we tested whether SFI5-SFI7 were able to suppress either PCD event. In contrast to AVR3a, which is known to suppress PCD triggered by INF1 or by co-expression of Cf-4/Avr4 ([42]); Figure 6a, 6b – p-value<0.01), GFP-SFI5 and GFP-SFI6 did not attenuate PCD triggered by either recognition event (Figure 6A, 6B). However, whereas GFP-SFI7 also failed to suppress Cf-4/Avr4-mediated PCD (Figure 6B), this effector significantly attenuated INF1-mediated PCD, albeit less efficiently than AVR3a (Figure 6B – p-value<0.01). Our results indicate that SFI5 and SFI6 display functional specificity by targeting the flg22/FLS2 MAP kinase cascade, but not suppressing MAP kinase cascades leading to Cf-4- or INF1-mediated PCD, whereas SFI7 has a broader suppressive effect which includes INF1- but not Cf-4-mediated PCD.
The 8 PiRXLR effectors suppressing early MTI signaling in tomato are assumed to contribute significantly to the virulence of P. infestans. N. benthamiana was further used to explore the role of the 8 selected PiRXLR effectors in host colonization. Agrobacterium tumefaciens strains containing the PiRXLR effector construct were infiltrated into leaves of 2–3 week-old N. benthamiana plants. Leaves were challenged with P. infestans 1 day after agro-infiltration and lesion size (Figure 7A), as well as disease symptoms (Figure 7B), were recorded after an additional 7 days. Except SFI2, whose overexpression in N. benthamiana leaves caused cell death that interfered with the pathogen assay, we found that the remaining 7 PiRXLR effectors enhanced colonization of N. benthamiana by P. infestans (Figure 7A, 7B). Compared to the empty vector control, the expression of the PiRXLR effectors caused a two- to five-fold increase of the lesion size (p<0.001) due to enhanced P. infestans colonisation. The strongest effect was observed when expressing GFP-SFI1. Interestingly, this is one of the effectors that localizes predominantly to the nucleus/nucleolus and suppresses flg22-mediated induction of MTI response genes in both Arabidopsis and tomato, but does not suppress MAP kinase activation, suggesting that it may act downstream of this step. We were thus prompted to look further at the significance of the nuclear/nucleolar localization of SFI1 on its virulence function.
We attempted to address the importance of the nuclear localization for the function of SFI1 and hypothesized that mis-targeting of the effector away from the nucleus could impact its virulence function. We generated a construct introducing a myristoylation site at the N-terminus of GFP-SFI1. Transient expression of myr-GFP-SFI1 in planta showed that the myristoylation site was functional in targeting SFI1 to the plasma membrane in Arabidopsis protoplasts (Figure 8A) and N. benthamiana leaves (Figure 8B). Both GFP-SFI1 and myr-GFP-SFI1 fusion proteins were stable and intact in planta (Figure 8F). Strikingly, whereas the flg22-dependent induction of pFRK1-Luc activity was suppressed by GFP-SFI1 in Arabidopsis protoplasts, no such suppression was observed in the presence of myr-GFP-SFI1 (p-value<0.05 - Figure 8C). Notably, myr-GFP-SFI1 lost its ability to enhance P. infestans colonization of N. benthamiana (Figure 8D, 8E), providing strong evidence that suppression of MAMP-induced immune responses by this effector in both host and non-host plants requires its localization to the nucleus/nucleolus.
In this study, we used a protoplast-based system to assess the potential for RXLR effectors from P. infestans (PiRXLRs) to manipulate MAMP-triggered early signaling in both a host and non-host plant species. Of 33 PiRXLR effector candidates, selected on the basis of up-regulation during the biotrophic phase of late blight infection, 8 (SFI1-SFI8) were able to suppress flg22-mediated induction of pFRK1-Luc activity in protoplasts of the host plant tomato (summarized in Table 1). Of these, three (SFI5-SFI7) were shown to suppress flg22-dependent MAP kinase activation at - or upstream of - the step of MAPK- and/or MAPKK kinase activation, indicating that they target the earliest stages of MTI signal transduction in tomato (Table 1). As P. infestans does not possess flagellin, the ability of these effectors to attenuate flg22-mediated MAP kinase activation and early defense gene expression indicates that these events are likely stimulated following perception of as yet undefined oomycete MAMPs. We confirmed that 7 of the 8 PiXRLR effectors that suppress early MTI signaling in tomato also enhance colonization by P. infestans in the host plant N. benthamiana (Table 1).
We found that 3 PiRXLR effectors (SFI1, SFI2 and SFI8/AVRblb2) suppress flg22-mediated induction of pFRK1-Luc activity in protoplasts of both the host plant tomato and the non-host plant Arabidopsis. We confirmed that suppression by all 3 effectors attenuates transcriptional activation of endogenous MAMP-induced marker genes in Arabidopsis (Table 1), indicating that some effectors may function efficiently across diverse (host and non-host) plant species. Interestingly, we found another set of 4 PiRXLR effectors that suppressed pFRK1-Luc activation only in the non-host Arabidopsis. This was a surprise, albeit the assay is potentially less sensitive in the host plant tomato. However, none of these effectors were able to prevent the activation of endogenous (Arabidopsis) MAMP-induced marker genes (Table 1). Therefore, additional experiments are necessary to determine to what extent suppression of flg22-induced post-transcriptional or translational processes may account for the activity of these effectors on the pFRK1-Luc reporter system in this plant.
While 3 PiRXLR effectors (SFI5-SFI7) suppressed MAMP-dependent MAP kinase activation in tomato, no PiRXLR effector had a similar effect in Arabidopsis (Table 1). This is an important finding, consistent with the hypothesis of Schulze-Lefert and Panstruga [63] that non-host resistance in plants (in this case Arabidopsis), which are distantly related to the host of P. infestans, is likely to include failures in effector-triggered susceptibility, due to effectors that are not sufficiently adapted to adequately manipulate plant immunity. Each of these observations will be discussed below.
The large number of RXLR effector gene candidates in Phytophthora genomes complicates their functional analysis by reverse and forward genetics. Thus, the development of a medium/high- throughput system to explore their function in plants is strongly desired. Other large-scale effector functional screens have been conducted recently. A study by Fabro et al. [64] identified 39 out of 64 RXLR effectors from Hyaloperonospora arabidopsidis that enhance P. syringae growth in Arabidopsis Col-0 when delivered via the type III secretion system (T3SS). A majority of these effectors was additionally able to suppress callose deposition in response to bacterial MAMP perception. Thirteen of the H. arabidopsidis RXLR effectors promoted bacterial growth in turnip (Brassica rapa), a member of the Brassicaceae that is a non-host of H. arabidopsidis, indicating that they likely retain their virulence function in this closely related plant. Although the authors did not provide molecular evidence of the influence of these RXLR effectors on MTI in turnip, their results are in line with our conclusions, in that the activity of some RXLR effectors is not restricted to the pathogen's host(s). Nevertheless, a number of H. arabidopsidis RXLR effectors that promoted P. syringae growth in Arabidopsis either failed to do so (44 effectors) in turnip, suggesting that they fail to function in the non-host plant, or reduced P. syringae growth (7 effectors), suggesting that they had activated ETI. Whereas we have identified a set of PiRXLRs that suppress early MTI signaling in tomato but not in Arabidopsis protoplasts, none of the tested PiRXLRs in our study significantly promoted cell death in Arabidopsis protoplasts. In apparent contradiction to the molecular evolutionary concept of non-host resistance [63] we have also identified three PiRXLR effectors that potentially attenuate early flg22-mediated MTI signaling events in Arabidopsis. In order to demonstrate whether failure to suppress MTI has the potential to contribute to non-host resistance to P. infestans in Arabidopsis, it would be necessary to extend the analysis to all PiRXLR effectors and provide an in-depth study of their precise function in both host and non-host plant.
Our primary goal was to identify and ascribe functions to PiRXLR effector proteins that interfere with early plant defense responses. Interestingly, AVRblb2 family members (such as SFI8), but not AVR3a, were among effectors suppressing flg22-induced pFRK1-Luc activity. This apparently contrasts with the results obtained from the screen for suppression of cell death mediated by the MAMP INF1 in N. benthamiana, in which AVR3a but not AVRblb2 family members acted as a suppressor [35], [37], [44]. Similarly, PITG_14736/PexRD8 also suppressed INF1-mediated PCD [44] whilst failing to attenuate flg22-mediated responses in this study, and SFI5/PexRD27 suppressed flg22-mediated MAP kinase activation here, whilst failing to suppress INF1-mediated PCD ([44]; Figure 6). Possible explanations would be that AVR3a and PexRD8 disable components located downstream of the MAMP signal transduction and early transcriptional changes studied here, or that these effectors act specifically on alternative signal transduction events related to INF1-mediated cell death, but not the FLS2/flg22 pathway. The opposite may be true for SFI8/AVRblb2 and SFI5. Moreover, SFI7 suppresses flg22/FLS2-mediated signal transduction and attenuates INF1-mediated PCD, but not Cf-4-mediated PCD, whereas AVR3a attenuates both INF1-mediated and Cf-4-mediated PCD. Evidence is thus emerging of effectors with overlapping functions, at the phenotypic level, that are likely mediated by distinct modes of action at the mechanistic level.
The epistatic analysis of the MAP kinase signaling cascade showed that SFI5-SFI7 presumably act upstream of the activation of the SlMPK1/SIPK and SlMPK3/WIPK MAP kinases in tomato protoplasts following flg22 perception. These effectors potentially function at the FLS2 receptor complex, or upon MAPKKK or MAPKK activity, or upon alternative regulators associated with this signal transduction pathway. As P. infestans does not possess flg22, and is thus unlikely to activate FLS2, the activity of any effectors upon the receptor complex must involve targets that are associated with bacterial and oomycete MAMP detection. Nevertheless, the absence of any suppressive activity of these effectors against CF4-mediated cell death and the modest suppression of INF1-mediated PCD only by SFI7 – two defense pathways that utilize alternative MAPKK kinases - imply specificity in the signal transduction pathways targeted by these effectors. It is important to note that all three effectors, to differing degree, associate with the plasma membrane, consistent with a potential action at the level of signal perception. Mukhtar et al., [65], postulated that an overlapping subset of host proteins, so-called hubs, are targeted by oomycete (H. arabidopsidis) and bacterial (P. syringae) effectors that have arisen independently through convergent evolution. Therefore, future work will focus on identification of host proteins with which SFI5-SFI7 interact to better elucidate the molecular mechanisms underlying the action of these effectors.
An effect of AVRblb2 on early MAMP signaling in solanaceous plant species has not been reported before, but it is has been shown that AVRblb2 affects plant immunity by inhibiting the secretion of C14, an apoplastic papain-like cysteine protease [38]. It is worth noting that in that study, AVRblb2 was exclusively localized at the plasma membrane, whereas in our experiments SFI8/AVRblb2 appeared mainly in the nucleus and cytosol. Yet, as AVRblb2 forms a large family and it is not clear which AVRblb2 isoform was exactly tested for the inhibition of C14 secretion [38], any apparent discrepancies in our results raise the possibility that different members of the AVRblb2 family have distinct or multiple cellular activities. Nevertheless, in our study all tested members of the AVRblb2 family were able to significantly suppress flg22-mediated induction of pFRK1-Luc activity in protoplasts of the host plant tomato.
As the effectors SFI1, SFI2 and SFI8/AVRblb2 interfere with transcriptional up-regulation of MAMP-responsive genes in both host and non-host plants, we presume that they target conserved processes upstream of the earliest transcriptional responses. None of these effectors prevented MAP kinase activation, suggesting that they act downstream of such signal transduction. The nuclear localization of SFI1 and SFI2 in Arabidopsis, tomato and N. benthamiana may indicate that they directly manipulate regulatory processes leading to transcriptional up-regulation. For SFI1 we showed that its mislocalization to the plasma membrane, via addition of a myristoylation signal, prevented both its ability to suppress flg22-mediated MTI gene activation in Arabidopsis and its ability to enhance P. infestans colonization of the host plant N. benthamiana. This strongly implicates the nucleus as the site of effector activity for SFI1. It also indicates the importance of determining subcellular localization of effectors, as mis-targeting them provides a strategy for investigating their virulence function. The fact that SFI1 activity is apparently conserved in the non-host plant Arabidopsis indicates that we may draw on the wealth of genetic resources available in the model plant to further dissect the functions of this effector. Future work will employ additional mis-targeting approaches, for example nuclear export (NES) and nuclear localization (NLS) signals, to better elucidate the potential contributions of SFI1-SFI8 activities, either within or outside the nucleus, to suppress early MTI signaling.
Three of the PiRXLR effectors, SFI5-SFI7, suppressed flg22-mediated post-translational MAP kinase activation in tomato but not in the non-host Arabidopsis. A further two effectors, SFI3 and SFI4, were shown to suppress specifically pFRK1-Luc activation in tomato, although we need to confirm their inhibitory effect on the expression of endogenous MAMP-responsive genes. Nevertheless, each enhanced P. infestans colonization when transiently expressed in N. benthamiana, consistent with a role in MTI suppression. Functional characterization of all these effectors is thus better pursued in host plants within the Solanaceae. The availability of genome sequences for potato [66], tomato [67] and N. benthamiana [68], the genetic tractability of the diploid tomato [67], and the range of functional analyses that can be performed in N. benthamiana [52], considerably broaden opportunities to do this. Moreover, the adaptation of the Arabidopsis protoplast-based system [48], [49], [58] to investigate the earliest stages of MTI in tomato, presented here, further enhances capabilities to study the functions of effectors from pathogens that infect solanaceous hosts. Future work will employ transgenic host and nonhost plants expressing the effectors revealed here, and additional RXLR effectors from P. infestans, to more specifically investigate their precise mechanistic action. Such studies will also reveal those effectors which may act downstream of the earliest signaling events in order to suppress MAMP-triggered immunity.
Ectopic expression in N. benthamiana of 7 of the 8 SFI effectors selected through the protoplast-based screen enhanced plant susceptibility toward P. infestans infection. This result suggests that the suppression of early signaling events triggering basal immunity is an important step toward successful host colonization by this pathogen. P. infestans itself offers the possibility to further study functional aspects of PiRXLR effectors, and gain- and loss-of function experiments may confirm the importance of our candidate effectors for virulence. However, it should be noted that the functional redundancy of the PiRXLR effectors studied here in suppressing early FLS2/flg22 MTI signaling suggests that silencing of these effector genes in P. infestans may not lead to clear virulence phenotypes, as has been shown by deletion studies with type III effectors in P. syringae [69]. Nevertheless, silencing of single PiRXLR effector genes Avr3a [36], or PITG_03192 [43] compromised P. infestans pathogenicity, indicating that (at least some of) the functions of these effectors are not redundant.
In conclusion, the tomato protoplast system provides a new medium/high-throughput tool to identify effectors that modulate the earliest stages of MTI signal transduction. We have identified 8 PiRXLR effectors that suppress early flg22-mediated MTI in tomato. Three of these reveal association with the plant plasma membrane and act at, or upstream of, MAPKK activation specifically related to flg22-mediated MTI signal transduction. Two of these effectors, SFI5 and SFI6, apparently do not act on other MAP kinase-mediated signal transduction events studied in this investigation. In addition, five of the effectors act downstream of the MAP kinase cascade, 3 of which also clearly suppress early flg22-mediated gene induction in Arabidopsis. This demonstrates that the effector complement of P. infestans contains functional redundancy in the context of suppressing early MTI signal transduction and gene activation. It remains to be established why such functional redundancy is necessary, or is selected for, and it is consistent with studies of bacteria such as P. syringae [69] that plant pathogens evolve multiple means of confounding the host immune system.
Solanum lycopersicum cv. Moneymaker was kept in a greenhouse under controlled growth conditions: 16 h light at 24°C/8 h dark at 22°C, 40%–45% humidity, ∼200 µE m−2 s−1 light intensity. They were grown on soil containing a 4.6∶4.6∶1 mixture of type P soil, type T soil (Patzer, Germany) and sand. Leaves from 3 to 4 week-old plants were used for experiments.
Arabidopsis thaliana plants of the Col-0 ecotype were cultivated in a phytochamber under stable climate conditions: 8 h light at 22–24°C/16 h dark at 20°C, 40%–60% humidity, ∼120 µE m−2 s−1 light intensity. They were grown on soil composed of a 3.5∶1 mixture of GS/90 (Patzer, Germany) and vermiculite. Leaves from 4 to 5 week-old plants were used for protoplast preparation.
Nicotiana benthamiana was grown as described previously [36].
Phytophthora infestans putative RXLR effector genes (PiRXLR) were amplified minus the signal peptide from gDNA of the sequenced isolate T30-4 in a two-step nested PCR reaction in order to add flanking attB sites to the RXLR coding sequence. The cloning primers are shown in Table S1 and Table S2. The PCR products were recombined into pDONR201 or pDONR221 vectors (Invitrogen) to generate entry clones, which were further recombined into the vectors p2GW7, p2FGW7 (N-terminal GFP fusion), pB7WGF2 (N-terminal GFP fusion), p2GWF7 (C-terminal GFP fusion) or p2HAGW7 (N-terminal hemagglutinin-tag; derived from p2GW7) (VIB, Ghent University, Belgium) using the Gateway recombination cloning technology (Invitrogen). The myristoylation signal sequence MGCSVSK was added to the amino-termini of the GFP-PiRXLR fusions using PCR with modifying primers and restriction cloning into pENTR1a (Invitrogen) before recombination into p2GW7 or pB2GW7 (VIB, Ghent University, Belgium). The Gateway destination vectors used are designed for transient 35S promoter-driven gene expression in protoplasts or, in the case of pB7WGF2 and pB2GW7, in N. benthamiana plants.
To generate the constructs used for epistasic analysis of the MAP kinase signaling cascade, four primer combinations: SlMEK1-attB1/SlMEK1-attB2, SlMEK2-attB1/SlMEK2-attB2, SlMAP3Kα-attB1/SlMAP3Kα-attB2 and SlMAP3Kε-attB1/SlMAP3Kε-attB2 (listed in Table S3) were used to amplify by PCR SlMEK1-DD, SlMEK2-DD, SlMAP3Kα-KD and SlMAP3Kε-KD from pER8 plasmid constructs, respectively. Subsequently, Gateway attB linkers were added via PCR using primers attB1-adapter and attB2-adapter. The obtained PCR products were introduced into pDONR201 to generate entry clones using the Gateway recombination cloning technology (Invitrogen). The genes were further recombined into the vector p2GWF7 (C-terminal GFP fusion – VIB, Ghent University, Belgium). The resulting plasmid constructs, p35S-SlMEK1-DD-GFP, p35S-SlMEK2-DD-GFP, p35S-SlMAP3Kα-KD-GFP and p35S-SlMAP3Kε-KD-GFP were used for protoplast transfection as described below.
Plasmid DNA was isolated from E. coli DH5α liquid cultures by column purification using the PureYield Plasmid Midi-prep system (Promega) following the manufacturer's instructions. For selected candidate gene, control genes and reporter gene constructs, higher amount of the corresponding plasmids were purified on a cesium chloride density gradient.
S. lycopersicum mesophyll protoplasts were prepared as described by Nguyen et al., [52] with slight modifications. The lower epidermis of fully expended leaflets was gently rubbed with grated quartz, rinsed with sterile water and leaf strips were floated on enzyme solution containing 2% cellulose ‘Onozuka’ R10 (Yakult Pharmaceutical Industry), 0.4% pectinase (Sigma) and 0.4 M sucrose in K3 medium. After 30 min vacuum-infiltration and 3 h incubation at 30°C in the dark, the enzyme-protoplast mixture was filtered through a 45–100 µm nylon mesh. Viable protoplasts were collected by sucrose gradient centrifugation and washed in W5 buffer. After recovery on ice for 2 h, protoplasts were harvested by centrifugation and suspended to a density of 6*105 cells/ml in MMG buffer prior polyethylene glycol-mediated transfection. 100 µg plasmid DNA/ml protoplast suspension was used during transfection. Protoplasts samples were then incubated in W1 buffer at 20°C in the dark for 12 to 16 h allowing plasmid gene expression.
The preparation of A. thaliana mesophyll protoplasts was performed according to the protocol from Yoo et al., [70] with minor changes. Briefly, thin leaf stripes were dipped into 1.5% cellulose ‘Onozuka’ R10 – 0.4% macerozyme R10 solution (Yakult Pharmaceutical Industry), vacuum-infiltrated for 30 min and digested for 3 h at 20°C in the dark. After two subsequent washing steps with W5 buffer Arabidopsis protoplasts were suspended in MMG buffer to a concentration of 2*105 cells/ml. Arabidopsis protoplast transfection was performed as for tomato.
Luciferase and GUS reporter gene assays were conducted to screen for immunity-suppressing effector genes. For this, A. thaliana or S. lycopersicum protoplasts were co-transfected with pFRK1-Luc, pUBQ10-GUS and an effector gene construct (or empty p2FGW7 serving as GFP control). For the luciferase assay, luciferin was added to 600 µl transfected protoplast solution to a final concentration of 200 µM. Protoplasts were transferred to an opaque 96-well plate (100 µl per well). For each sample, flg22 was added to 3 wells to a final concentration of 500 nM (+flg22). The remaining 3 replicates were left untreated (−flg22). The luminescence reflecting the luciferase activity was measured at different time-points using a Berthold Mithras LB 940 luminometer. For the GUS assay, 50 µl transfected protoplast solution of each sample was treated with 500 nM flg22 (+flg22) and 50 µl were left untreated (−flg22). Protoplast pellets were collected 3 or 6 h after flg22 elicitation. The cells were lysed in 100 µl CCLR solution (cell culture lysis reagent, Promega). For each sample, 3 technical replicates of 10 µl were incubated with 90 µl MUG substrate (1 mM 4-methyl-umbelliferyl-β-D-glucuronide, 100 mM Tris-HCl pH 8.0, 2 mM MgCl2) for 30 min at 37°C. The reaction was stopped with 900 µl 0.2 M Na2CO3. The fluorescence was monitored in an opaque plate using a MWG 96-well plate reader with λex = 360 nm and λem = 460 nm.
Raw data of Luciferase and GUS assays were processed using Microsoft Excel. First the mean value of the +flg22 and the −flg22 triplicates was calculated for each sample in both assays of an experiment. Next, the +flg22/−flg22 ratio was calculated using the values from the 3 or 6 h time-point of the Luciferase assay and divided by the corresponding +flg22/−flg22 ratio of the GUS assay for normalization. Statistical analysis was performed using one-way ANOVA followed by Dunnett's multiple comparison test.
Total RNA from 400 µl A. thaliana protoplasts was extracted with TRI reagent (Ambion) and treated with DNase I (Machery-Nagel) following the suppliers' protocols. Poly A-tailed RNA (1 µg) was converted to cDNA using the RevertAid reverse transcriptase (Fermentas) and oligo-dT primers. qRT-PCR reactions were performed in triplicates with Maxtra SYBR Green Master Mix (Fermentas) and were run on a Biorad iCycler according to the manufacturers' instructions. Relative gene expression was determined with a serial cDNA dilution standard curve. The Actin transcript was used as an internal control in all experiments. Data was processed with the iQ software (Biorad).
qRT-PCR to measure PiRXLR gene expression was carried out on a time-course of potato leaves (cv Desiree) infected with P. infestans isolate 88069. Total RNA from infected leaf discs was extracted with RNeasy Plant mini kit (Qiagen) and treated with DNase I (Qiagen) following the suppliers' protocols. Poly A-tailed RNA (1 µg) was converted to cDNA using the Superscript II reverse transcriptase (Invitrogen) and oligo-dT primers. qRT-PCR reactions were performed in triplicate with Power SYBR Green Master Mix (ABgene) and run on a Biorad Chromo4 cycler according to the manufacturer's instructions. Relative gene expression was determined using the ΔΔCT method, and P. infestans ActA gene was used as an internal control in all experiments, as described in Whisson et al [32]. Data was processed with Opticon monitor software (Biorad). Primers used in qRT-PCR reactions are listed in Table S3.
To monitor the activation of MAP kinase, transfected protoplasts were challenged with 500 nM flg22. Pellets from 100 µl protoplast solution were collected 0, 15 and 30 min after treatment and denatured in protein loading buffer. Proteins were loaded onto a 13.5% SDS-polyacrylamid gel and separated by electrophoresis (SDS-PAGE) using the Biorad MiniProtean equipment according to the manufacturer's instructions. PageRuler Prestained protein ladder (Fermentas) was used as a molecular weight marker. Proteins were blotted onto nitrocellulose membranes (Hybond–ECL, Amersham) and stained with 0.1% Ponceau S to visualize equal sample loading. The membranes were blocked for 1 h at room temperature in 5% skimmed milk in TBS-T buffer (50 mM Tris-HCl pH 7.5, 150 mM NaCl, 0.1% Tween 20), incubated overnight at 4°C in primary antibody solution (anti-phospho-p44/42 MAPK antibody, dilution 1/1000 in 5% BSA in TBS-T, Cell Signaling Technology) and finally incubated for 1 h at room temperature in secondary antibody solution (alkaline phosphatase-coupled anti-rabbit IgG antibody, dilution 1/3000 in TBS-T, Sigma). The immunoblot was revealed in NBT/BCIP detection solution.
The expression of GFP-tagged PiRXLR effectors was assessed by immunoblotting using polyclonal anti-GFP antibody produced in rabbit or in goat (Acris Antibodies) at a 1/3000 dilution in 5% BSA in TBS-T. For this, protoplast samples were collected 12 (for S. lycopersicum) or 24 h (for A. thaliana) after transfection and SDS-PAGE and immunoblotting were carried out as described above.
The MAP kinase in vitro kinase assay was carried out as described by He et al., [48]. Briefly, 1 ml transfected protoplasts were lysed in 1 ml of immunoprecipitation (IP) buffer (150 mM NaCl, 50 mM HEPES pH 7.4, 1 mM EDTA, 1 mM DTT, 0,1% Triton X-100, 1× phosphatase inhibitor cocktail [PhosphoSTOP, Roche Applied Science] and 1× protease inhibitor cocktail [Complete EDTA-free, Roche Applied Science]). HA-tagged SlMPK1 and SlMPK3 kinases [52] were immunoprecipitated from lysates after adding 20 µl anti-H antibody-coupled beads (Roche Applied Science) and incubated for 3 h at 4°C with gentle shaking. After centrifugation at 500 g for 1 min, the immunoprecipitated material was washed with IP buffer followed by a wash with kinase buffer (20 mM Tris-HCl pH 7.5, 20 mM MgCl2, 5 mM EDTA and 1 mM DTT). The kinase reaction was performed by adding 25 µl of kinase buffer (0.25 mg/ml MBP, 100 µM ATP and 5 µCi [γ-32P] ATP) for 30 min at RT. The reaction was stopped with 4× SDS-PAGE loading buffer. The 32P-labeled MBP was separated by SDS/PAGE (15%) gel and visualized by autoradiography.
To determine the cell death rate after transfection (percentage of dead cells/total number of cells), 100 µl protoplast samples were incubated for 24 h and subsequently stained with 1 µg propidium iodide. Stained protoplasts were counted using a Nikon Eclipse 80i epifluorescence microscope with the following filter: TRITC EX 540/40, DM 565, BA 605/55. For sub-cellular localization studies protoplasts were monitored 12 h post-transfection and N. benthamiana leaves at 2 days post-infiltration. Imaging was performed using Leica TCS SP2 AOBS confocal microscopes with HCX PL APO lbd.BL 63×1.20 W, L 40×0.8 and L 20×0.5 water immersion objectives. Samples were excited by an argon laser and fluorescence emission was detected at 496–552 nm for GFP and 620–726 nm for chloroplasts. The pinhole was set to 1.5 airy units for protoplasts and 1 airy unit for leaf cells. Single optical section images were acquired from protoplasts and z-stacks were collected from leaf cells and projected and processed using the Leica LCS software and Adobe Photoshop CS3.
A. tumefaciens transformed with pB7WG2 or pB7WGF2 vector constructs were grown overnight, pelleted, re-suspended in infiltration buffer (10 mM MES pH 5.6, 10 mM MgCl2 and 200 µM acetosyringone) and adjusted to the required OD600 before infiltration into N. benthamiana leaves.
A. tumefaciens cultures were grown as above and subsequently mixed together to a final optical density at 600 nm (OD600) of 0.3 for each construct except Cf4, which was used at 0.6, N. benthamiana plants were infiltrated using a 1 ml needleless syringe through the lower leaf surface. Three leaves on six plants were used for each biological replicate. Cell death was scored at 7 d post-infiltration (dpi). An individual inoculation was counted as positive if >50% of the inoculated area developed clear cell death. The mean percentage of total inoculations per plant developing cell death of combined data from at least two biological replicates was calculated. One-way ANOVA was performed to identify statistically significant differences.
A. tumefaciens Transient Assays (ATTA) in combination with P. infestans infection were carried out as described [36]. Briefly, Agrobacterium cultures were re-suspended in infiltration buffer at a final concentration of OD600 = 0.1 and infiltrated in N. benthamiana with the bacteria harboring the vector control on one side of the leaf midrib and the bacteria harboring the PiRXLR effector constructs to be tested on the other. P. infestans strain 88069 cultured on Rye Agar at 19°C for 2 weeks was used for plant infection. Plates were flooded with 5 ml cold H2O and scraped with a glass rod to release sporangia. The resulting solution was collected and sporangia numbers were counted using a haemocytometer and adjusted to 30,000 sporangia/ml. After 1 day, each agro-infiltration site was inoculated with 10 µl droplets of sporangia. Three leaves per plant for 4–6 intact plants were used for each biological replicate. Lesions were measured and photographed at 7 days post-infection and data of at least two biological replicates were combined. Statistically significant differences in lesion size were identified by one-way ANOVA with pairwise comparisons performed using the Holm-Sidak method.
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10.1371/journal.pgen.1007336 | Transcription factor HAT1 is a substrate of SnRK2.3 kinase and negatively regulates ABA synthesis and signaling in Arabidopsis responding to drought | Drought is a major threat to plant growth and crop productivity. The phytohormone abscisic acid (ABA) plays a critical role in plant response to drought stress. Although ABA signaling-mediated drought tolerance has been widely investigated in Arabidopsis thaliana, the feedback mechanism and components negatively regulating this pathway are less well understood. Here we identified a member of Arabidopsis HD-ZIP transcription factors HAT1 which can interacts with and be phosphorylated by SnRK2s. hat1hat3, loss-of-function mutant of HAT1 and its homolog HAT3, was hypersensitive to ABA in primary root inhibition, ABA-responsive genes expression, and displayed enhanced drought tolerance, whereas HAT1 overexpressing lines were hyposensitive to ABA and less tolerant to drought stress, suggesting that HAT1 functions as a negative regulator in ABA signaling-mediated drought response. Furthermore, expression levels of ABA biosynthesis genes ABA3 and NCED3 were repressed by HAT1 directly binding to their promoters, resulting in the ABA level was increased in hat1hat3 and reduced in HAT1OX lines. Further evidence showed that both protein stability and binding activity of HAT1 was repressed by SnRK2.3 phosphorylation. Overexpressing SnRK2.3 in HAT1OX transgenic plant made a reduced HAT1 protein level and suppressed the HAT1OX phenotypes in ABA and drought response. Our results thus establish a new negative regulation mechanism of HAT1 which helps plants fine-tune their drought responses.
| Drought stress is a key environmental factor that severely reduces crop yield all over the world. The phytohormone abscisic acid (ABA) is known to mediate drought responses through regulating drought-responsive genes expression and stomatal closure, but the mechanisms that negatively regulate this process and prevent the adverse effects of excess drought responses on plant growth is less well studied. Here, we show that a HD-ZIP II transcription factor HAT1 negatively regulates ABA-mediated drought responses through suppressing ABA biosynthesis and signaling. The hat1hat3 mutant showed ABA-hypersensitive and drought-tolerant phenotypes, whereas the HAT1-overexpressing lines were insensitive to ABA and less tolerant to drought. Furthermore, we found SnRK2.3 kinase, a positive component of ABA signaling, interacts with and phosphorylates HAT1 to destabilize and suppress its binding activity. Overexpression of SnRK2.3 reduces HAT1 protein level and inhibits HAT1OX phenotypes in ABA and drought responses. Our results revealed a HAT1-mediated negative regulatory mechanism in attenuating the ABA signaling and drought response.
| As sessile organisms, plants need to respond and adapt to environmental stress to survive adverse conditions. Plants respond and adapt to stresses through a complex network of factors involved in stress hormone signaling and regulation of gene expression. The phytohormone abscisic acid (ABA) plays a key role in plant responses to biotic and abiotic stress, in particular drought and salinity [1–3].
Since the discovery of ABA receptors, PYRABACTINRESISTANCE1 (PYR1)/PYR1-LIKE (PYL)/REGULATORYCOMPONENTS OF ABA RECEPTORS (RCAR) [4,5], a core ABA signaling pathway has been proposed. In the absence of ABA, group A protein phosphatases type 2C (PP2Cs) interact with subclass III SNF1-related protein kinases (SnRK2.2, 2.3 and 2.6) which keeps the kinases inactive by blocking their catalytic cleft and by dephosphorylating the activation loop [6]. In the presence of ABA, ABA binds to the PYL receptors, forming a PYLs-ABA-PP2C complex and inhibiting phosphatase activity of PP2C [7,8]. This binding and inhibition of the PP2Cs releases the SnRK2s from PP2C-SnRK2 complexes, and the released SnRK2s are activated through autophosphorylation. The activated SnRK2s can then phosphorylate downstream effectors and activate ABA signaling [7,9,10]. Various transcription factors function in ABA signaling-mediated drought response [2,11]. The basic leucine zipper (bzip) family transcription factors including ABF1, ABF2 (AREB1), ABF3, and ABF4 (AREB2), which bind directly to ABREs of stress-responsive genes and stimulate their transcriptional activities, function in the ABA-dependent pathway and are major targets of SnRK2 protein kinases in the ABA core signaling pathway [12–14]. Additionally, some members of the MYB and MYC (bHLH) classes, the No Apical Meristem/Cup-Shaped Cotyledon (NAC), and WRKY families have also been shown to be induced by ABA or abiotic stress or to regulate stress responses, underscoring the importance of transcriptional regulation in plant stress responses [2,15,16]. Transcriptional regulation is one of the most essential mechanisms in the acquisition of stress tolerance [2,17].
However, in many cases, stress adaptation is exchanged for growth and productivity, therefore, it is necessary for plants to develop a resilient system to obtain the optimal trade-off for survival and growth. To this end, plants use elaborate mechanisms associated with posttranscriptional modulation [18] and posttranslational regulation [19,20], as well as transcriptional regulation. In particular, the appropriate control of transcription factors regulating plant growth and development genes is important, because these transcription factors negatively affect plant stress tolerance while being essential for increased productivity. The environmental conditions surrounding plants are constantly changing; thus, posttranslational regulation to control the protein levels of these transcription factors is considered an important mechanism to avoid adverse effects on plant survival. However, the negative components involved in regulation to efficiently coordinate ABA-dependent stress responses are less well known.
The homeodomain-leucine zipper protein (HD-ZIP) family constitute a large family of transcription factors that are unique to plants and is divided into four subfamilies (HD-ZIP I–IV) on the basis of the additional conserved domains, structures and physiological functions [21–23]. HD-ZIP proteins contain homeodomain (HD) that is responsible for specific DNA binding and the closely associated leucine zipper (LZ) domain which acts as a dimerization motif [24]. HD-ZIP proteins can bind to partially inverted repeats such as CAAT(A/T)ATTG (BS1 site), CAAT(C/G)ATTG (BS2 site) or as lightly modified version TAAT(C/T)ATTA for AtHB2/HAT4 [25]. Arabidopsis thaliana homeodomain-leucine zipper protein 1 (HAT1) and its close homologs belong to Class II HD-ZIP of transcription factors that mainly act as repressors by binding to their target genes promoters and play important roles in plant development and in response to the environment [25,26]. Previous works have shown that several members of the family, HAT1, HAT4/AtHB2 and AtHB4, are induced by shade avoidance and overexpression of HAT1 or HAT4 resulted in a similar effect in promoting cell elongation [23,25–27]. HAT2 expression is rapidly induced in response to auxin, and AtHB4 was also reported to modulate auxin, BRs and gibberellin responses [28,29]. It was recently reported that HAT1 is a substrate of BIN2 (BRASSINOSTEROID-INSENSITIVE 2) kinase and appears to function redundantly with other family members such as HAT3 to positively mediate BR responses [30]. HAT1 was also reported to participate in anti-CMV defense response in Arabidopsis and negatively regulates this process [31]. Collectively, these studies indicate that HAT1 is involved in the complex signaling and transcriptional networks coordinating plant growth and stress response. HAT1 promotes plant growth and development by BR signaling or other pathway.
In this study, we demonstrate that HAT1, which was previously reported as a critical regulator in BR-mediated plant growth and in viral defense response, is involved in ABA regulation of drought response by suppressing the ABA biosynthesis and signaling. We found that HAT1 and its homolog HAT3 act redundantly, as the expression of both HAT1 and HAT3 were repressed by ABA and drought, and the double mutant hat1hat3 displayed a reduced ABA sensitivity and enhanced drought tolerance phenotype that was stronger than the single mutants alone. HAT1-overexpressing transgenic plants exhibit a hyposensitive response to ABA and drought. Furthermore, we found that HAT1 physically interacts with and can be phosphorylated by SnRK2.3 in vitro and in vivo. SnRK2.3 phosphorylation of HAT1 decreased its protein stability and binding activity. Overexpressing SnRK2.3 in HAT1OX transgenic plant can suppress its phenotype in ABA and drought responses. Therefore we identified a new substrate of SnRK2.3 and established a novel negative regulation mechanism by which plants can efficiently coordinate drought responses.
From public data (http://bbc.botany.utoronto.ca/efp/cgi-bin/efpWeb.cgi), we found that HAT1 expression was reduced after ABA and osmotic stress treatment in seedlings, implying that HAT1 may be implicated in ABA and stress responses. To test the hypothesis, we examined the expression of HAT1 in different tissues and in seedlings treated with exogenous ABA or osmotic stress. Consistent with the public data, the expression level of HAT1 was highest in root, and lower in stem, leaf, and inflorescence (Fig 1A), and was significantly repressed by exogenous ABA and osmotic stress (Fig 1B). We further generated GUS reporter lines using HAT1 native promoter and examined the responsiveness of HAT1 expression in the presence of ABA and osmotic stress. As shown in S1A Fig, after ABA and osmotic stress treatments, GUS signals were reduced in cotyledons, leaves and roots as well as guard cells. To determine the subcellular localization of HAT1, we generated constructs that introduced the GFP sequence at the C-terminus of HAT1. The 35S:GFP and 35S:HAT1-GFP constructs were used to transfect Arabidopsis thaliana protoplasts. As shown in S1B Fig, 35S:GFP fluorescence was observed in the entire cell, while HAT1-GFP fusion protein localized in the nucleus. The expression of HAT3, its homolog, was similarly regulated, whereas the expression of HAT2 was not changed by ABA and osmotic stress (S3A and S3B Fig).
To investigate the role of HAT1 in the ABA response and in osmotic stress tolerance, we obtained T-DNA insertion mutants of HAT1, HAT2 and HAT3, hat1, hat2 and hat3, respectively. Then we created the double mutant hat1hat2, hat1hat3 and triple mutants hat1hat2hat3. The RT-PCR results showed that HAT1 expression was hardly detected in hat1, hat1hat2, hat1hat3 and hat1hat2hat3 mutants. Similarly, transcript of HAT2 was not observed in hat2, hat1hat2 and hat1hat2hat3 mutants, and HAT3 transcript was abolished in hat3, hat1hat3 or triple (hat1hat2hat3) mutants (S2A Fig). Western blotting using an anti-GFP antibody showed that HAT1-GFP accumulated in the two HAT1OX lines (S2B Fig). Next, we analyzed ABA sensitivity with regard to seedlings growth in Col-0, HAT1OX lines and knockout mutants. The 4-day-old seedlings grown on 1/2 MS medium were transferred to 10 μM ABA-containing medium for 10 days. As shown in Fig 1C and 1D and S3C and S3D Fig, root growth of double mutant hat1hat3 or triple mutant hat1hat2hat3 was dramatically retarded under ABA conditions compared with that of wide-type Columbia-0 (Col-0) and was similar among Col-0, hat1, hat2, hat3 and hat1hat2 with or without ABA treatment. To analyze the function of HAT1 in osmotic stress tolerance, 4-day-old Col-0 and knockout mutants were treated with mannitol, a stress treatment commonly used to mimic osmotic stress tolerance in the laboratory. The double mutant hat1hat3 or triple mutant hat1hat2hat3 displayed less inhibition on growth in the medium containing mannitol compared with Col-0, while the single mutant hat1, hat2, hat3 and double mutant hat1hat2 showed little difference after mannitol treatment in comparison with Col-0 (Fig 1C, 1E and 1F and S3C, S3E and S3F Fig). In contrast, the two HAT1-overexpressing lines (HAT1OX#11 and HAT1OX#13) showed significantly reduced ABA sensitivity and osmotic stress tolerance (Fig 1C bottom, Fig 1D, 1E and 1F). Together, these data indicate that HAT1 plays a negative role in ABA signaling and in osmotic stress tolerance, and it is functionally redundant with HAT3 in ABA and osmotic stress response.
ABA regulation of stomatal movements is a well established model system for the study of plants response to drought stress. Thus, we measured the stomatal aperture from epidermal peels of Col-0, HAT1OX lines and knockout mutants. Overexpression of HAT1 suppressed ABA-mediated stomatal closure, while double mutant hat1hat3 and triple mutant hat1hat2hat3 exhibited an accelerated ABA sensitivity in stomatal closure and single mutants(hat1, hat2, hat3) or hat1hat2 showed little difference after ABA treatment in comparison with Col-0 (Fig 2A and 2B and S4A and S4B Fig), indicating that HAT1 and HAT3 function redundantly in regulating ABA-mediated stomatal closure. As H2O2 acts as an important signal molecular in ABA-induced stomatal closure, H2O2 accumulation in guard cells was measured by a fluorescence dye, 2,7-dichlorodihydro fluorescein diacetate (H2DCF-DA) [32,33]. As shown in Fig 2C and 2D, H2O2 accumulation in guard cells was less in HAT1OX lines, more in hat1hat3 double mutant, compared to Col-0 and hat1 after ABA treatment, suggesting that HAT1-impaired stomatal closure may be caused by changed H2O2 in guard cells. Next, we tested whether HAT1 plays a role in the drought stress response. When exposed to dehydration stress by withholding water for 10 days, HAT1OX lines displayed a withered phenotype, while the hat1hat3 double mutant largely remained turgid and single mutant hat1 showed little difference in comparison with Col-0 (Fig 3A). Measurement of leaves water loss showed that HAT1OX lines lost water much faster, while hat1hat3 displayed reduced water-loss rate than Col-0 and hat1 (Fig 3B). As a result, overexpression of HAT1 markly reduced plant survival under drought stress, whereas hat1hat3 showed enhanced survival compared to Col-0 and hat1 (S5A–S5C Fig).
To study the responses of different genotypes to controlled soil water deficit drought, Col-0, HAT1OX lines and knock-out mutants (hat1, hat1hat3) were grown for 3 weeks under well-water condition (2.2g H2O/g dry soil) and then subjected to mild drought stress (Fig 3D). After grown under mild drought condition (0.7g H2O/g dry soil) for 9days, the biomass of both drought-treated and well-watered plants was measured and then the change in biomass was calculated. As shown in Fig 3C and 3E, HAT1OX lines showed more reduction in biomass compared to the Col-0 which is considered drought sensitive genotype, while the double mutant hat1hat3 displayed less reduction in biomass and hat1 showed similar reduction in biomass in comparison to Col-0. Altogether, these data demonstrate that HAT1 and HAT3 function redundantly and negatively to regulate ABA-mediated stomatal closure and drought response.
As HAT1 is a negative regulator of ABA signaling and drought response (Fig 1 and Fig 2), the expression of ABA or drought stress inducible marker genes were tested in different genotypes. We first determined the transcript levels of ABA response maker genes which were also ABA biosynthesis genes. These genes include ABA1 [34], AAO3 [35], ABA3 [36], and NCED3 [37]. Among the four genes, the expression of ABA3 and NCED3 were significantly reduced in HAT1OX lines and up-regulated in hat1hat3 double mutant under both control and osmotic stress conditions (Fig 4A and 4B). To determine whether or not ABA levels were affected, we quantified the ABA content in different genotypes. Under normal conditions, ABA level in HAT1OX seedlings was found to be lower than that in Col-0 and hat1 single mutants, whereas it was elevated in hat1hat3 double mutant. When exposed to 15% polyethylene glycol (PEG) 6000 that mimics a drought stress, HAT1OX lines had a reduced ABA level, and the hat1hat3 double mutants accumulated higher level of ABA compared with Col-0 and hat1 (Fig 4E). In addition, the induction of RD29A and RD22, which are well established drought-induced marker genes [16,38], was also tested in different genotypes. As expected, the expression of these two genes were reduced in HAT1OX lines and elevated in hat1hat3 double mutant compared with Col-0 and hat1 (Fig 4C and 4D). Furthermore, the expression of HAI1, HAI2 and PP2CA, which belong to PP2Cs, negative regulators of ABA signaling, was diminished by down-regulation of both HAT1 and HAT3 and enhanced by HAT1 overexpression (Fig 4F–4H). Taken together, these results indicate that HAT1 repressed drought-responsive genes and induced PP2C genes, which may account for the repressed drought tolerance in HAT1OX lines.
The SnRK2 kinases are integral positive component of ABA signaling, and phosphorylate S/T residues in the RXXS/T motif in their substrates. There are 7 potential phosphorylation sites for SnRK2 kinases in the predicted HAT1 protein, which prompted us to test whether HAT1 was a substrate of SnRK2 kinases. First, we tested if subclass III SnRK2s could physically interact with HAT1. Bimolecular fluorescence complementation (BiFC) analysis was performed to examine the interaction of HAT1 with SnRK2.2, SnRK2.3, and SnRK2.6 in plants. We found that HAT1 interacts with all subgroup III SnRK2s in the nucleus and no fluorescence signal was detected in the negative controls (Fig 5A). Quantitative analyses of BiFC signals showed strong SnRK2.3-HAT1 interactions and weak signals for HAT1 interaction with other subgroup III SnRK2s (Fig 5B). GST pull-down experiment confirmed this interaction in vitro (Fig 5C). GST-SnRK2s, but not GST alone, pulled down a significant amount of MBP-HAT1 protein, demonstrating a direct interaction between SnRK2s and HAT1. Consistent with the result of BiFC assays, the interaction between SnRK2.3 and HAT1 is the strongest (Fig 5C). The in vivo interaction of SnRK2s with HAT1 were corroborated by co-immunoprecipitation (Co-IP) assay using Arabidopsis protoplasts co-expressing Myc-SnRK2s and HAT1-Flag fusion constructs (Fig 5D). We also generated a series of truncated HAT1 fragments (HAT1-1F (135–282), HAT1-2F (192–282), HAT1-3F (234–282)) which were fused with the C-terminal half of YFP and transformed them individually with SnRK2.3-nYFP into tobacco leaves. When deleted to amino acid 134 in HAT1, only a weak fluorescent signal was detected, while deletions to amino acid 191 and 233 in HAT1 totally abolished the interaction with SnRK2.3 (S6B Fig). Several truncated MBP-HAT1 (N-terminal region, HD, LZ, and C-terminal region of HAT1) were further used to map the specific domain of HAT1 required for the interaction with SnRK2.3. As shown in S6C Fig, HAT1 interacts with Snrk2.3 with its N-terminal region. Taken together, the N-terminal region in HAT1 mediates the interaction between HAT1 and SnRK2.3. Further, we conducted in vitro kinase assays to test whether SnRK2.3 can phosphorylate MBP-fusion HAT1 protein and found that SnRK2.3 can phosphorylate HAT1, but not MBP (Fig 5E). The kinase dead form of SnRK2.3 (SnRK2.3K51N) was used as a negative control and it totally abolished the phosphorylation of SnRK2.3 on HAT1 (Fig 5E). We further found that the homeodomain of HAT1 (MBP-HAT1-HD) can be phosphorylated by SnRK2.3 rather than the other regions (Fig 5F). In addition, the interaction of SnRK2.3 with HAT3 was also examined by BiFC analysis. As shown in S7A Fig, HAT3 interacts with SnRK2.3 in the nucleus, suggesting that SnRK2.3 may regulate HAT3 through a similar manner as HAT1.
To test whether phosphorylation of HAT1 by SnRK2.3 in vivo, the HAT1-GFP was immunoprecipitated from HAT1OX or SnRK2.3OX/HAT1OX transgenic seedlings treated with/without ABA or MG132 and detect the phosphorylation/dephosphorylation form using phos-tag gel blot analysis with an anti-GFP antibody (Fig 6A). Two faster-migrating bands can be detected in untreated plants. We found that ABA treatment or SnRK2.3 overexpression resulted in the appearance of a slower-migrating HAT1 in HATOX transgenic plants (Fig 6A). When subjected to phosphatase [calf-intestinal alkaline phosphatase (CIP)] treatment, all three bands disappeared and a new lower band which is likely the unphosphorylated form of HAT1 appeared, indicating that HAT1 exists mostly as phosphorylated forms in plants and an elevated phosphorylation of HAT1 is formed by ABA treatment or SnRK2.3 overexpression (Fig 6A and 6B). Furthermore, when treated with MG132, the phosphorylation level of HAT1 was significantly increased in ABA-treated HAT1OX or SnRK2.3OX/HAT1OX seedlings, indicating that super-phosphorylation form of HAT1 was instable (Fig 6A).
To investigate the function of the SnRK2.3 phosphorylation on HAT1 protein stability, we detect HAT1-GFP protein level in transgenic plants. First, we expressed HAT1-GFP fusion proteins in Nicotiana benthamiana epidermal cells and examined the effects of ABA and the proteasome inhibitor MG132 on GFP fluorescence. Time-course microscopic observation revealed that the HAT1-GFP fluorescence intensity was substantially reduced in leaves treated with ABA alone, whereas HAT1-GFP was more stable after application of ABA plus MG132 (Fig 6C). Similarly, HAT3-GFP fluorescence intensity was also rapidly reduced in response to ABA treatment and only slightly altered in response to the control stimulus (solvent used for ABA) and combined ABA and MG132 (S7B Fig). We then examined HAT1-GFP protein level in HAT1-GFP transgenic plants. As shown in Fig 6D, HAT1 protein increased in the liquid one-half MS (Murashige and Skoog) medium without ABA treatment (Fig 6D top panel). However, in the presence of ABA, HAT1-GFP protein clearly decreased in relation to the mock treatment after 3 h of treatment (Fig 6D middle panel). When we treated plants with ABA and MG132 together, the HAT1 protein level significantly increased as mock (Fig 6D bottom panel), suggesting that ABA triggers proteasome-mediated HAT1 degradation.
To investigate whether ABA-induced HAT1 degradation is mediated by SnRK2.3 phosphorylation in plant, we detected HAT1-GFP protein level in HAT1OX or SnRK2.3OX/HAT1OX transgenic seedlings. The transcriptional level of HAT1 was same in HAT1OX and SnRK2.3OX/HAT1OX (S8 Fig). As shown in Fig 6E, HAT1 protein was clearly degraded in SnRK2.3OX/HAT1OX transgenic plants, while this degradation was blocked by addition of MG132. We further examined the ubiquitination level of HAT1 in ABA-treated HAT1OX and in SnRK2.3OX/HAT1OX transgenic plants. As shown in Fig 6F, the ubiquitinated level of HAT1 was significantly increased in HAT1OX plants after treatment with ABA, or in SnRK2.3OX/HAT1OX transgenic plants. Taken together, these results indicated that SnRK2.3-mediated HAT1 phosphorylation facilitates the degradation of HAT1 via stimulating its ubiquitination.
HAT1 acts as a regulator by binding to HB site within its target genes promoters. First, we analyzed promoter sequences of four ABA or drought-responsive genes (ABA3, NCED3, RD29A, RD22) and found that there were two HB-binding sites within the ABA3 and NCED3 promoter regions respectively (Fig 7A). To determine whether or not HAT1 bind to the ABA3 and NCED3 promoter, electrophoresis mobility shift assays (EMSAs) were conducted. The MBP-HAT1 fusion protein can bind to A1 fragment of ABA3 promoter and N1 fragment of NCED3 promoter, but this binding was abolished by mutation of HB sites in the probes (Fig 7B and 7C). The addition of GST-SnRK2.3 fusion protein was able to slightly inhibit the ability of HAT1 binding to the A1 fragment and N1 fragment (Fig 7B and 7C). When HAT1 was phosphorylated by SnRK2.3 in vitro, the binding affinity of phosphorylated HAT1 was dramatically reduced (Fig 7B and 7C). These data indicate that HAT1 protein can bind to the A1 fragment of ABA3 promoter and N1 fragment of NCED3 in vitro, and its binding ability is repressed by SnRK2.3 phosphorylation.
To further test the effect of SnRK2.3 on the binding ability of HAT1 in vivo, we performed chromatin immunoprecipitation (ChIP) assays. We immunoprecipited HAT1-GFP protein from HAT1OX transgenic seedlings treated with/without ABA or ABA in combination with MG132 with anti-GFP antibody. TA3, a retrotransposable element, was used as the internal control [39]. ChIP-qPCR results indicated that HAT1 specifically bound to the A1 region of ABA3 and N1 region of NCED3, and other genomic fragments containing HB sites were not targeted by HAT1 (Fig 7D and 7E). The binding ability of HAT1 was reduced by both ABA treatment and ABA plus MG132 treatment (Fig 7D and 7E). Furthermore, HAT1 binding ability was significantly diminished by SnRK2.3 overexpression, and it cannot be recovered by addition of MG132 (Fig 7D and 7E). Altogether, these results support that SnRK2.3 represses the binding ability of HAT1 by phosphorylation.
To confirm the regulation of HAT1 by SnRK2.3, we examined whether or not overexpression of SnRK2.3 can suppress HAT1OX phenotypes in ABA and drought responses. SnRK2.3OX/HAT1OX double overexpressing line displayed an enhanced ABA sensitivity in seedlings growth and was more tolerant to drought stress compared with HAT1OX, which was similar to Col-0 (Fig 8A–8D and S9 Fig). Moreover, SnRK2.3OX/HAT1OX showed less reduction in biomass under mild drought conditions compared to HAT1OX (Fig 8E and 8F). Then, we tested the influence of SnRK2.3 overexpression on HAT1 in the regulation of ABA or drought inducible marker genes expression. As shown in Fig 8G–8J, the expression of ABA3, NCED3, RD29A, and, RD22, were significantly up-regulated in SnRK2.3OX/HAT1OX, compared to HAT1OX, which reached to the expression level of Col-0. These data together with phenotype tests indicated that SnRK2.3 overexpression suppressed the ABA-insensitivity and drought-hypersensitivity of HAT1OX.
Currently, the most thoroughly understood in transcriptional regulation of ABA-mediated drought responses is AREB/ ABFs pathway, which activated the expression of drought-responsive genes in an ABA-dependent manner [40], however, the components involved in compromising drought response were less well studied. In this study, we identified SnRK2.3 interaction transcription factors HAT1 and HAT3 as important components to regulate ABA-mediated drought response. As negative regulators, HAT1 and HAT3 suppressed ABA sensitivity and drought tolerance. Furthermore, we found HAT1 was a substrate of SnRK2.3 and SnRK2.3 phosphorylation decreased HAT1 protein stability and binding activity. Our results identified a new negative component that regulates ABA signaling in Arabidopsis in response to drought and established a novel mechanism to attenuate stress response.
HAT1 plays important roles in phytohormone-regulated developmental processes and stress response [23,25]. HAT1 interacts with BES1, a central regulator in BR signaling pathway, and functions as a BES1 co-repressor to inhibit BR-repressed genes and thus optimizes BR-regulated plant growth [30]. In addition, HAT1 acts as a repressor in plant defense response to CMV infection [31]. Thus, HAT1 may function as a transcriptional regulator to modulate plant growth and stress response. Several lines of evidence support the role of HAT1 as a negative regulator in ABA-mediated drought response. First, the expression of both HAT1 and its close homologs HAT3 is repressed by ABA and osmotic stress, indicating that these genes are ABA or stress-responsive factors. Second, HAT1 can bind to specific DNA sequences (HB binding sites) on promoter of NCED3 and ABA3, two key ABA biosynthesis genes, and represses these genes expression, leading to a reduction of ABA synthesis. In addition, drought-responsive genes like RD22 and RD29A, were also suppressed by HAT1. Third, consistent with the role of negative regulators for ABA signaling under stress conditions, HAT1OX displayed reduced sensitivity to ABA and less tolerance to drought stress, whereas the double knockout mutant hat1hat3 showed an enhanced ABA sensitivity and increased drought tolerance phenotypes. Finally, the modulation of HAT1 by SnRK2.3 kinase further suggests that HAT1 forms part of ABA signaling network to regulate ABA-dependent stress response.
Besides the repression by ABA at transcription level, HAT1 is regulated by ABA-activated SnRK2 kinases through a post-transcriptional modification mechanism. Post-translational modifications of transcription factors fine-tune their functions to effectively and precisely implement the stress response. SnRK2s-mediated phosphorylation of target proteins triggers most of the molecular actions of ABA signaling pathway [14,41,42]. In addition to the originally identified bZIP transcription factors AREBs (ABA-Responsive Element Binding factors) that function in ABA-responsive gene regulation, 58 putative substrates of ABA-activated SnRK2s were identified through mass spectrometry-based global phosphorylation profiling, which include components involved in flowering time regulation, RNA and DNA binding, miRNA and epigenetic regulation, signal transduction, chloroplast function, and many other cellular processes [41]. In this study, we identified an additional substrate for SnRK2.3 kinase. In contrast to bZIP transcription factors AREBs, which are stabilized by SnRK2s phosphorylation [43,44], SnRK2.3 phosphorylation promotes the degradation of HAT1. In addition to destabilizing HAT1 protein, we found that SnRK2.3 phosphorylated HAT1 on its homeodomain, which is responsible for specific DNA binding, leading to the reduction of its binding ability to the HB sites on the promoter of target genes. Our results thus suggest that SnRK2.3 phosphorylation of HAT1 can have different functional consequences, inhibiting both its DNA binding and protein accumulation. However, the mechanisms how phosphorylation by SnRKs mediates HAT1 degradation remain to be determined in future studies.
HAT1 belongs to Class II HD-ZIP transcription factors, which have been shown to regulate plant growth and development [45–47]. For example, ATHB4, ATHB2 and HAT3 are required for normal leaf development and blade growth [45]. ATHB4, a shade signaling component, acts redundantly to other members of the HD-Zip class-II subfamily to integrating shade perception and hormone-mediated growth [29]. HAT2 is an auxin inducible gene and modulates auxin-mediated morphogenesis [48]. In addition to the regulation of plant growth and development, several of the class II HD-ZIP transcription factors have been also reported to participate in plant responding to exogenous ABA and drought stress. ATHB17 has been characterized as a positive regulator of ABA response and multiple stress responses [46,49]. ABIG1/HAT22 is induced by ABA and drought stress, and relays ABA signaled growth inhibition and drought induced senescence [50]. HDG11 can promote main root elongation and lateral root formation in Arabidopsis and was able to confer drought tolerance in Arabidopsis, tobacco, rice, sweet potato, cotton and woody plant poplar (Populus tomentosa Carr.) [51–55]. It seems likely that a general role for HD-ZIP II proteins is to link environmental and developmental signals to growth control. As noted above, these class II HD-ZIP transcription factors share many similar characteristics though they have different expression patterns. Expression pattern of HAT1 and HAT3 in response to BR and ABA is analogous and functions in BR-mediated hypocotyl elongation and ABA-induced drought stress tolerance are redundant. So it proposed that HAT1 together with HAT3 played essential roles in balancing plant growth and stress responses. However whether ABA regulates HAT1 and HAT3 function and stability in a similar manner is unclear and further study will be needed.
Our results strongly indicate that HAT1 is an important part of mechanisms that functions to control basal ABA signaling and drought response. HAT1 can suppress ABA synthesis and signaling through down-regulating the expression of ABA3 and NCED3 via directly binding to their promoters, and ABA/drought-responsive genes, RD29A and RD22. In contrast, HAT1 promotes the expression of PP2Cs which negatively regulate the ABA response, enhancing the negative regulation of ABA signaling (Fig 9). When exposed to drought conditions, stress-induced ABA led to activation of SnRK2s, which in turn negatively regulates HAT1 functions by posttranslational regulation of its stability and binding ability. The suppression of HAT1 at both transcriptional and protein level appears to be an adaptive strategy of plant responses to water deficit, facilitating plants survival under drought conditions (Fig 9). When the environmental conditions are favorable, HAT1 and its homologous function to suppress drought response, prevent unnecessary activation of stress response, and ensure the normal growth of plants. HAT1 thus can be considered as a brake to fine tune ABA signaling and drought response (Fig 9).
In summary, this study revealed the mechanism of the negative regulatory function of HAT1 in ABA-mediated drought response (Fig 9). We found that ABA biosynthesis and signaling were repressed by HAT1. We also establish that HAT1 is phosphorylated by SnRK2.3 kinase and that SnRK2.3 phosphorylation promotes the proteasome-mediated HAT1 degradation and represses the binding ability of HAT1. The identification of negative regulators, like HAT1, and elucidation of the regulatory mechanism will lead to a better understanding of ABA signaling mechanism and drought response, which has potential in manipulating crop plants for drought tolerance.
Arabidopsis thaliana ecotype Columbia-0 (Col-0) was used as the WT control. The HAT1-overexpressing lines (HAT1-OX#11 and HAT1-OX#13) were described previously [30]. T-DNA insertion mutants hat1, hat2 and hat3 were obtained from ABRC (Arabidopsis Biological Resource Center) [56], corresponding to line SALK_059835, SALK _091887 and SALK_056541. We performed cross to create the double mutant hat1hat2, hat1hat3 and triple mutant hat1hat2hat3. HAT1OX and mutants were identified (S2 Fig). All the plants were grown on half-strength MS plates and/or in soil under long-day conditions (16 h light/8 h dark) at 22°C.
Gene-specific primers HAT1 were used to isolate HAT1, from a cDNA library by PCR. To generate the pZP211-HAT1-GFP, full-length HAT1 was amplified and cloned into the pZP211 vector with a GFP tag using the BamHI and SalI sites [57]. To generate the Myc-SnRK2.2/2.3/2.6, the coding regions of SnRK2.2/2.3/2.6 were cloned into pCAMBIA1307-63 Myc vector [58]. To generate HAT1-Promoter::GUS, 1.3-kb fragments upstream of HAT1 were amplified by PCR using primers HAT1p-F/R and inserted into the binary vector pBI121-GUS using HindIII and BamHI sites [59]. For BiFC assays, SnRK2.2/2.3/2.6 were cloned into the pXY103 vector fused to the C terminus of YFP, and HAT1 and its fragments were fused into the pXY104 vector fused to the N terminus of YFP [60]. For the recombinant protein and GST pull-down assay, the HAT1 coding region was amplified from Col-0 cDNA and various deletion constructs were incorporated into the pETMALc-H vector (MBP, BamHI/SalI) [61]. The coding regions of SnRK2s were inserted into the binary vector PGEX-6P-1(GST, BamHI/SalI). All primers are listed in S1 Table. The construct of HAT1-GFP driven by 35S promoter were transformed into Agrobacterium tumefaciens (strain GV3101), which were used to transform plants by the floral dip method. Transgenic lines were selected on half-strength MS medium that contained 50 μg ml-1 kanamycin. Transgene expression was analyzed by western blotting.
Rosette leaves of 4-week-old A. thaliana plants grown under short day conditions were used for the isolation of protoplasts [62]. The relevant vectors 35S:HAT1–GFP, and 35S:GFP were used for protoplast transformation. A fluorescence microscope was used to observe GFP signals (Kim et al., 2001; Bae et al., 2008). For GUS staining, the transgenic plants with or without ABA and osmotic stress treatment were immersed in a staining solution (100 mM sodium-phosphate buffer, pH 7, 1 mM K4Fe(CN)6, 1 mM K3Fe(CN)6, 0.1% Triton X-100, 2 mM X-Gluc) overnight at 37°C in the dark followed by two times washes with 70% ethanol to remove chlorophyll. Samples were photographed using a stereoscope (Leica) equipped with a CCD camera. To test for GUS expression before and after ABA and osmotic stress, plants were treated with 100 μM ABA for 3 h and mannitol treatment for 6 h, respectively.
For ABA sensitivity, different genotype seeds were grown vertically on 1/2 MS medium for 3–5 days and then transplanted to normal 1/2 MS medium or 1/2 MS medium containing 10μM ABA. The root growth was observed after about 10 days [63]. For the osmotic stress treatment, 4-day-old seedlings grown on half-strength MS medium (0.5% agar) were transferred to new agar plates containing 200 mM mannitol, and the primary root length and 30-seedlings fresh weight were measured after 10 days. The primary root lengths were measured with ImageJ (National Institutes of Health, Bethesda, MD, USA). Three independent experiments were performed.
To study the promotion of stomatal closure by ABA, fully expanded young leaves of 4-week-old Arabidopsis plants were harvested and incubated in MES-KCl buffer (50 mM KCl, 10 mM MES-KOH, pH 6.15), at 22°C and exposed to light for 2 h. Once the stomata were fully open, leaves were incubated in MES-KCl buffer alone or containing 50 μM ABA. Control treatments involved the addition of DMSO, an appropriate solvent with ABA. After treatment for 3h under light conditions, the epidermal strips were immediately peeled carefully from the abaxial surface of leaves, and stomatal apertures were measured with an optical microscope (Nikon, Optiphot-2) fitted with a camera lucida and a digitizing table linked to a personal computer [64]. The stomatal aperture sizes were analysed by the software image J. To avoid any potential rhythmic effects on stomatal aperture, experiments were always started at the same time of the day. Blinded stomatal aperture experiments were conducted by another group in the laboratory who are not aware of any information about the control group (WT) and test group (mutants and transgenic plants) (S2 Data Blinded experiments). For the ROS accumulation assay in guard cells, prepared epidermal peels with or without ABA treatment were loaded with 50 μM 2,7-dichlorofluorescin diacetate for 10 min (H2DCF-DA; Sigma-Aldrich) in dark, as described previously [65]. Fluorescence emission of guard cells was analyzed using image J. Three independent experiments were performed.
To measure leaf water loss, rosette leaves of similar developmental stages from 4-week-old plants were excised from their roots, placed in open Petri dishes, and kept on the lab bench for the indicated time, and then their fresh weights were monitored, with three replicates per time-point [66]. Water loss was expressed as a percentage of weight loss at the indicated time versus initial fresh weight.
For the progressive drought treatment experiment, 10-day-old plants were transferred from 1/2 MS medium to water-saturated soil and the plants were grown in the same glasshouse with 120 μmol m-2 s-1 under a 16 h: 8 h, light: dark photoperiod (23°C) for 2 weeks, then the plants were deprived of water for 14 days and the survival rates of plants were determined 5 d after re-watering (rehydration) [67]. Relative electrolyte leakage rates were measured as described by Julieta V. Cabello et al. [68]. Three independent experiments were performed.
The mild drought treatment was conducted as previously described [69,70], with a slight modification. Briefly, 12-day-old Arabidopsis seedlings of different genotypes grown on 1/2 MS medium were transferred to pots. Before transfer, the relative water content of the pots was set at 2.2 g water g-1 dry soil. The plants were kept to grow for 10 days. During this growth period, the water content of the soil was kept constant until 10 days, after which it was lowered daily to target 0.7 g water g-1 dry soil and mild drought stress treatment began. Control soil water content (well water) was maintained at a constant value of 2.2 g water g-1 dry soil during the entire experiment. Fig 3D showed the water loss from the peat pellets during the duration of the experiment. After mild drought treatment for 9 days, images of each genotype were taken. To quantify the biomass change of each genotype, the dry weights of detached rosettes of both the drought-treated and the well-watered control were measured. The reduction in biomass was calculated using the following equation:
Reduction in Biomass (RB) = (Biomass of Well Watered Control–Biomass of Drought Treated) / (Biomass of Well Watered Control)
Polyethylene glycol (PEG) 6000 was used to mimic drought stress [66]. Arabidopsis seedlings grown on 1/2 MS medium plates were transferred to 1/2 MS liquid medium (CK) and 1/2MS liquid medium containing 15% PEG (drought stress treatment) for indicated time, and then the seedlings were harvested for gene expression analysis or ABA content assay.
For ABA content assay, 0.5g 12-day-old seedlings with or without 15% PEG treatment were homogenized in 2 mL of 80% methanol, and incubated with additional 3 mL of 80% methanol overnight at 4°C. After centrifugation (4000 r/min for 10 min, 4°C), the supernatant was passed through a C18-SepPak classic cartridge (Waters, Milford, USA) [71]. ABA content measurement was performed by using a Plant hormone abscisic acid (ABA) ELISA Kit (BIOSAMITE, CK-E90047). Three independent experiments with different biological repeats were done.
12-day-old seedlings grown under long-day conditions were used for qRT-PCR analysis of ABA or drought stress-responsive genes. Total RNA extraction, cDNA synthesis and qRT-PCR were performed as described by Zhang et al. (2010) [72]. Briefly, total RNAs were extracted using RNAprep pure Plant Kit (from Transgene Biotech Co. Ltd. of Qiagen, Beijing) according to the manufactures’ protocols. Total RNAs treated with DNase I (Transgene Biotech Co. Ltd. of Qiagen, Beijing) were converted into cDNAs using M-MLV Reverse Transcriptase Kit (Invitrogen, USA). Real-time qPCR analysis was carried out using the SYBR® Premix Ex TaqTM II (TAKARA) on a BIO-RAD CFX ConnectTM Real-Time System, following the manufacturer’s instruction. Three independent experiments were performed, and three technical replicates of each experiment were performed. Actin2 genes was used as an internal control for normalization of transcript levels [73]. All primers used for gene expression analysis are shown in S1 Table.
For GST pull-down assay, HAT1 and HAT1 fragments fused with MBP were purified with amylose resin (NEB). SnRK2.3 fused with GST was purified with glutathione beads (Sigma, G4510). GST pull-down assays were performed as described Yin et al. [74]. The assays were repeated three times with similar results.
For the BiFC assay, SnRK2s were cloned into the pXY103 vector and fused to the C terminus of YFP, and HAT1 and its fragments were fused into the pXY104 vector and fused to the N terminus of YFP. The resulting plasmids were introduced into Agrobacterium tumefaciens (strain GV3101), and then infiltrated into young leaves of Nicotiana benthamiana. Infected leaves were analyzed 48h after infiltration. YFP fluorescence was observed under a fluorescence microscope (Leica).
For the Co-IP assays in the Arabidopsis protoplasts, full-length coding sequences of HAT1 and SnRK2.3 were individually cloned into tagging plasmids behind Flag or Myc tag sequences in the sense orientation behind the cauliflower mosaic virus 35S promoter. Flag-fused HAT1 and Myc-fused SnRK2s were then transformed into Arabidopsis protoplasts. After overnight incubation at 23°C, the protoplasts were lysed, sonicated, and centrifuged. Co-IP assays were performed using transiently expressed proteins as described previously [75]. Briefly, the protein extracts were mixed with Myc agarose beads (Sigma-Aldrich) and then incubated at 4°C for 2 h. After being was hed at least five times, the agarose beads were recovered and mixed with the SDS sample buffer. The samples were detected by immunoblotusing anti-Myc antibody, and the coimmunoprecipitated protein was then detected using an anti-Flag antibody.
The in vitro kinase assay was performed as previously described as Yin et al. [74]. MBP, MBP-HAT1, and truncated MBP-HAT1 were incubated with GST-SnRK2.3 kinase in 20 μL of kinase buffer [20 mM Tris (pH 7.5), 100 mM NaCl, and 12 mM MgCl2] and 10 μCi 32P ATP. After incubation at 37°C for 60 min, the reactions were stopped by adding 20 μL of 2×sodium dodecyl sulfate (SDS) buffer and boiling for 5 min. Proteins were resolved by polyacrylamide gel electrophoresis (PAGE) and phosphorylation was detected by exposing to a phosphor screen, and signals were obtained by a Typhoon 9410 phosphor imager. The in vivo phosphorylated HAT1 was examined by Phostag reagent (NARD Institute) with or without CIP treatment as described Guan et al [76].
Total protein was extracted from Arabidopsis using extraction buffer as described previously [77]. Briefly, plant material was ground in the Eppendorf tube using 2×sodium dodecyl sulfate (SDS) sample buffer, centrifuged at 13,000g for 10 min, and the supernatant was saved. For immunoblot analysis, total protein was separated by 10% SDS-polyacrylamide gel electrophoresis (PAGE) and transferred to PVDF membranes. The membrane was blocked for 1 h in TBST buffer (10 mM Tris, pH 7.6, 150 mM NaCl, 1.0% Tween20) with 5% skim milk powder at room temperature and then incubated with specific primary antibodies in TBST buffer for 1 h. After the membrane washed by TBST buffer for several times, the blot was incubated with horseradish peroxide-conjugated secondary antibody (goat anti-rabbit IgG, Thermo fisher) at a dilution of 1/10000 for detection by the enhanced chemilumine scence assay.
EMSA was performed using an Electrophoretic Mobility-Shift Assay (EMSA) Kit *with SYBR Green and SYPRO Ruby EMSA stains* (MolcularprobesTM, E33075). The binding reactions were carried out in 20 μL binding buffer [25 mM HEPES-KOH pH 8.0, 50 mM KCl, 1 mM dithiothreitol (DTT) and 10% glycerol] with approximately 1 ng probe (10000 cpm) and recombinant proteins purified from E. coli. After 30 min incubation on ice, the reactions were resolved by 5% native polyacrylamide gels with 1×TGE buffer (6.6 g L-1Tris, 28.6 g L-1 glycine, 0.78 g L-1EDTA, pH 8.7). The assays were repeated three times with similar results.
ChIP was performed as previously described [78]. Briefly, 14-day-old seedlings of HAT1OX and SnRK2.3OX/HAT1OX seedlings were treated as above described. 1.5 g of the samples were cross-linked with formaldehyde and nuclei were isolated using sucrose gradients. Chromatin was sonicated to generate fragments with the average size of 300 bp and precipitated using anti-GFP antibody. Immunocomplexes were harvested by protein A beads, washed and reverse cross-linked by boiling in the presence of Chelex resin (Bio-Rad, http://www.bio-rad.com/). The level of precipitated DNA fragments was quantified by RT-qPCR using specific primer sets (S1 Table). Col-0 was the negative control and the values in control plants were set to 1 after normalization against TA3 for qPCR analysis. Three biological replicates were carried out through the whole process.
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10.1371/journal.pntd.0003521 | The Relative Contribution of Immigration or Local Increase for Persistence of Urban Schistosomiasis in Salvador, Bahia, Brazil | Urbanization is increasing across the globe, and diseases once considered rural can now be found in urban areas due to the migration of populations from rural endemic areas, local transmission within the city, or a combination of factors. We investigated the epidemiologic characteristics of urban immigrants and natives living in a neighborhood of Salvador, Brazil where there is a focus of transmission of Schistosoma mansoni. In a cross-sectional study, all inhabitants from 3 sections of the community were interviewed and examined. In order to determine the degree of parasite differentiation between immigrants and the native born, S. mansoni eggs from stools were genotyped for 15 microsatellite markers. The area received migrants from all over the state, but most infected children had never been outside of the city, and infected snails were present at water contact sites. Other epidemiologic features suggested immigration contributed little to the presence of infection. The intensity and prevalence of infection were the same for immigrants and natives when adjusted for age, and length of immigrant residence in the community was positively associated with prevalence of infection. The population structure of the parasites also supported that the contribution from immigration was small, since the host-to-host differentiation was no greater in the urban parasite population than a rural population with little distant immigration, and there had been little differentiation in the urban population over the past 7 years. Public health efforts should focus on eliminating local transmission, and once eliminated, reintroduction from distant migration is unlikely.
| Urban transmission of schistosomiasis is becoming more recognized as rural disease is becoming less common and urbanization increases. Characteristics of infection of the immigrant population to cities and genetic characteristics of the parasite population itself indicate local transmission is the most important factor for the presence of the parasite rather than arrival of infected immigrants. While there is 70% coverage of adequate sanitation, this was insufficient to interrupt transmission. If eliminated, this focus is unlikely to readily reappear due to immigration.
| Urbanization, the concentration of regional populations in cities, has been the great global demographic trend of the last 100 years, and the urban context has influence the nature and distribution of parasitic diseases, such as schistosomiasis. While the disease is usually thought of as a rural problem, it is increasingly recognized in large urban areas of Brazil [1,2,3,4,5,6]. In Brazil, urbanization has been rapid, and today 86% of Brazilians live in cities [7] compared with 80% of the US population [8]. The city of Salvador, the capital of the state of Bahia, is a good example of this growth. The city’s population has increased by 300% in just 20 years, mostly due to migration from rural areas. At this pace, city services have not been able to keep up in the neighborhoods with the highest proportion of informal housing [9,10]. Schistosomiasis is ultimately a disease of inadequate sanitation, so that the pace of migration may also influence the presence or persistence and even spread of schistosomiasis in the urban environment. The relative contribution of migration or local transmission, therefore, becomes an important public health issue when considering control measures.
The presence of schistosomiasis in the city of Salvador is not a new problem. In fact, the city has had a historic role in the study of schistosomiasis. Schistosoma mansoni was conclusively differentiated from S. haematobium in 1908 by the Brazilian scientist, Pirajá da Silva, based on patients resident in Salvador [11]. Between 2000 and 2006, the state required reporting of all cases of schistosomiasis identified in municipal areas. All regions of the city reported cases from clinics (Fig. 1A), and multiple hospitals. Still, prevalence for the whole metropolitan area of Salvador in the last 10 years has only been between 2 to 5% [12]. Hospital cases were concentrated in the central and northern regions of the city consistent with the Municipal Health Department reports of positive stool exams and predictably included areas with the greatest combination of low income, high population density and new migration [13,14].
Some degree of local transmission has been demonstrated in the city. In 2004, 30% of school-aged children in the São Bartolomeu neighborhood of Salvador were found to have schistosomiasis [15]. Some children had stool egg counts in the thousands, where 400 eggs per gram of feces (epg) is considered heavy. Three of the 298 children examined had splenomegaly. Further, a 2011 cross-sectional survey of major water bodies in Salvador [16] found S. mansoni infected snails in 7 of 158 locations. What percentage of infections in the city is due to migration from endemic rural areas and what percentage represents local transmission is not known, but it is an important question when considering public health measures. We studied the distribution of infections within the human population and the genetic epidemiology of S. mansoni in São Bartolomeu to better understand the risks, sources and persistence of the infection within a large metropolitan area.
The Committee on Ethics in Research of the Oswaldo Cruz Foundation of Salvador, Bahia, the Brazilian National Committee on Ethics in Research and the Institutional Review Board for Human Investigation of University Hospitals Case Medical Center, Cleveland, Ohio approved the study design. All subjects provided written informed consent or in the case of minors, consent was obtained from their guardians. All aspects of the study have been conducted according to the principles expressed in the Declaration of Helsinki.
The neighborhood of São Bartolomeu (12o 54′4" S, 38o28′31" W) is located in the northwestern part of the city between the Cobre Reservoir with its nature park and the All Saints Bay. The Cobre River drains from a large reservoir into a small mangrove swamp and then out to sea. The neighborhood surrounds this outlet of the Cobre River (Fig. 1B) and is home to nearly 5,000 inhabitants. Socially, São Bartolomeu is considered a low-income and high-crime area with housing that dates from the 1970's, but informal new housing is continually being added. A recently established unit of the Federal Family Health Program has divided the region into 6 microareas. Each has approximately 800 residents, and each has an assigned health agent who is a liaison between the community and the clinic. For comparison we used data from our study on the distribution of schistosomiasis and its genetic epidemiology in 2 rural communities 200 km southwest from Salvador [17,18] in the municipality of Ubaíra (13o 14’ 59” S, 39o 43’ 60” W).
The 3 microareas with the highest prevalence of S. mansoni infection in school-aged children in 2004 [15] were selected for study. All households were visited, numbered and their location registered with a hand-held Trimble. Nomad GPS unit (Model 65220–11). Responses to a questionnaire were recorded for each resident. Interviewers solicited the respondent’s age, sex, race, years of residence in Salvador, previous residence, travel outside of the city of Salvador, education, occupation, major household goods, use of city services, frequency of flooding, history of S. mansoni infection, treatment for schistosomiasis, surface water contact sites commonly visited and activities performed at these sites. Parents and guardians responded to some questions for minors <12 years of age. Water contact information was obtained directly from each resident. The 8 major sites where residents came into contact with surface water were identified by community members and confirmed in the survey. All contact sites were located along the Cobre River (Fig. 1B).
All residents >4 years of age were asked to provide 3 stools on different days for parasitologic examination. We also analyzed stool eggs from 9 infected residents of São Bartolomeu collected in 2004 for comparison. For fecal examination, the stools were weighed to the nearest 0.1 g with a digital balance (Startools, China), and then a single slide was processed by the Kato-Katz method and examined microscopically to identify ova of S. mansoni and other helminths. The number of S. mansoni ova were quantified, recorded and finally expressed as eggs per gram of feces (epg). Total egg counts were calculated as stool weight X epg. Each stool positive for S. mansoni ova was homogenized, filtered and sedimented as described [18] to obtain a sample enriched for these ova. A standard phenol-chloroform extraction was followed by treatment with hexadecyltrimethylammonium bromide (CTAB) to remove PCR inhibitors [19].
Primers for 15 microsatellite markers [20] were used to amplify S. mansoni DNA. The specificity of these markers has been demonstrated for this parasite in stool samples as have the utility [18,20,21,22] and limitations [23] of the approach for schistosomes as well as other parasites [24,25,26]. The intensity of resultant amplicons was measured by automated sequencer and analyzed with the program Peak Scanner (Applied Biosystems, Waltham, Massachusetts). Allele frequencies were calculated by dividing the peak height for each allele by the sum of all peaks for the microsatellite. Peaks providing less than 5% of the total intensity were excluded as were markers where no peaks were greater than 100 pixels using the program’s default settings. The number of alleles for each marker was calculated by multiplying the allele frequency by sample total egg counts.
Demographic data and water contact were analyzed for their association with infection status using logistic regression or for infection intensity with forward conditional linear regression in SPSS. Independent variables were age, sex, migration status, percent of lifetime spent in the community, income, household size, type of sanitation, history of household flooding, number and location of water contact sites visited, previous S. mansoni infection and history of treatment. Age groups at 5-year intervals were compared for prevalence of infection. Adequate sewage disposal was defined as the presence of a connection to the municipal sewer system or a septic tank. Household density was evaluated as number of occupants per number of bedrooms. Persons born outside of the city of Salvador were considered immigrants. p values <0.05 were considered statistically significant if less than the value obtained after Bonferroni correction for multiple comparisons. Infrapopulations were defined as all of the parasites within one host. Component populations were all the parasites within a host species usually within a limited geographic area. We also stratified parasites as component populations based on host demographic characteristics.
Genetic differentiation based on Jost's D [27] was used to compare pairwise the infrapopulations of infected individuals (Di). The mean for all Di comparisons was calculated for stratified subsets of infected individuals based on demographic, geographic or parasitologic characteristics (Fig. 2). When the combined allele numbers for infrapopulations from one group of hosts were compared to a different group, this comprised the component population differentiation (Dc). Each individual's parasite infrapopulation was compared to the component population to yield the Dic. The Dic produces a single number representing how differentiated an individual's population is from the population infecting all humans in São Bartolomeu. Jost’s D and its confidence interval were calculated with the Diversity Index function in the Species Prediction and Diversity Estimation program (SPADE) (http://chao.stat.nthu.edu.tw, Chao, A. and Shen, T.-J., last accessed 6–9–2013). Finally, the effective allele number (AE), a measure of genetic diversity, was calculated as described [16,28]. Means for continuous variables were compared by a bootstrapped Student's t-test using SPSS for Windows (Version 19.0). Egg counts were normalized by log-transformation. Supplemental data (S1 Dataset) containing demographic data and infrapopulation allele frequencies have been deposited with Case Western Reserve's online source for curated digital content, Digital Case (http://library.case.edu/digitalcase/DatastreamListing.aspx?PID=ksl:blanton2).
A total of 1,335 residents were identified in the 3 selected microareas (MA1, MA3, and MA6) of São Bartolomeu, and 91% were enrolled in the study. The mean per capita income for the study population was R$325 ($140) compared to a mean of R$1496 ($643) for the city of Salvador [29]. Municipal water was supplied to 99.9% of the homes and 67% used either a septic tank or the municipal sewer system (Table 1). Although the whole area of São Bartolomeu is less than 1 km2, the sample characteristics of MA3 were significantly different than the other MAs. A higher proportion of residents of MA3 were born outside of Salvador, had lower coverage with adequate sanitation, and flooding was more common (Table 1). Of the current residents, 22% were born outside of the metropolitan area of Salvador. The majority of immigrants were from within the state (92%) and came from a median distance of 178 km from Salvador. Immigrants were significantly older (44.1 ± 16.7) than natives (24.6 ± 15.4).
Out of a population of 1,335, 92% (1,225) participated in the parasitologic survey. Of these 82% provided 3 stools, 10% furnished 2 stools and 8% gave 1 stool. In total, 300 (24.7%, Table 1) had S. mansoni ova on examination. Univariable analyses showed that male sex, age group, prior infection or treatment for schistosomiasis and number of water sites visited were risk factors for current schistosome infection. Trips outside of Salvador, an adequate sewer system, low household density and reported lack of contact with surface water collections were negatively associated. The prevalence and intensity of infection with S. mansoni was highest in the 11–15 age group, and when compared to the 5-year-old age group, prevalence remained significantly higher in older groups up to the age of 45 (Fig. 3). In multivariable analysis, sex, household density and number of water contact sites visited remained independently associated with infection, while increasing number of visits outside Salvador and immigrant status were not (Table 2).
Immigrants were considered those not born in Salvador. They were substantially older than natives (17 years on average), and had lower prevalence (18.2% vs 26.5%, p<0.01) and intensity of infection (40.2 vs 65.7 epg) than those born in Salvador. Controlling for age and sex, the percent of lifetime immigrants spent in Salvador was significantly associated with infection, although the effect size was small (p = 0.011, OR 1.019, CI 95% 1.004–1.034).
Three water contact sites were significantly associated with infection prevalence (Table 3). The OR’s were 1.91–2.15 for contact with these sites. Contact site 1 is at the outlet for the Cobre Reservoir Dam. The site is relatively shallow and is used for netting fish. Site 2 was used for crossing small streams within an area used for small-scale commercial vegetable production along the flood plain of the river. Contact site 5 is located at a bridge that spans the river on a road that joins MA’s 1 and 6. The bridge is a gathering point for young people as well as used for fishing. Point 7 is a vegetation-covered wetland near a soccer field. A kernel density map suggests that risk of infection increases with distance from the major avenue that borders the neighborhood (Fig. 4), although the population density and absolute number of those infected is higher near this thoroughfare.
The average differentiation between replicate samples is 0.007. There was little genetic differentiation (Dc) between the parasite populations when stratified into component populations based on host sex, age, infection intensity or geographic location within São Bartolomeu (Table 4). Geographically, the Dc between the parasite population in São Bartolomeu and the rural area of Ubaíra Dc was 0.103. A temporal comparison was made between samples collected in a 2004 pilot study from 9 infected children in São Bartolomeu and the current component population. The Dc for these samples was 0.007.
Di—pairwise Jost's D for all members of the group. Dic—mean Jost's D for each infrapopulation in the group compared to the village component population. Ae—mean effective allele number. Bootstrapped Student's t-test was used to compare group means for these indices. Dc—Jost's D for the component population formed by the total allele numbers for the group. Comparisons significant after Bonferroni correction are indicated in bold. Other variables tested but without significant differences were trips outside the region, co-infection with other helminths, all other water contact points, number of water contacts visited, a history of past infections. MA1, MA3, MA6—microarea divisions of São Bartolomeu. See Fig. 1. * epg—mean count of S. mansoni eggs per gram of stool.
Within São Bartolomeu, the Di different for all comparisons. The large number of eggs makes p values less useful and confidence intervals will not overlap. The largest difference, however, is for intensity of infection greater or less than 400 epg (Table 4). The Dic was statistically smaller for those more heavily infected, those visiting site 2 and for those living in MA6 compared to MA1. While there was no statistically significant difference for the mean prevalence and intensity between these microareas, MA6 had 5 of the 10 most heavily infected individuals, whereas, MA1 had only 1 of the top 10 intensities. The effective allele number (Ae) was significantly higher for those more heavily infected.
The Dc treats all the parasites in 2 groups of hosts as a single population. The DC in urban São Bartolomeu was very low between natives and immigrants suggesting infection from a common source. In the rural area, the wider DC between natives and immigrants may indicate some contribution from different sources. The rural area, however, does include 2 sites separated by 8 km. The Di and Dic, which represent comparisons based on individual infrapopulations, were significantly different between parasite populations of native born and immigrant residents of São Bartolomeu (Table 5), but not between those of natives compared to immigrants of the rural area. A positive correlation between the average pairwise Di and the log epg was weak but significant (r2 = 0.08, p<0.01) and stronger between the Dic and log epg (r2 = 0.23, p<0.01). The Ae was lower in the urban site compared to the rural area, but did not differ between natives and immigrants for either area.
Multiple Brazilian cities have seen outbreaks of schistosomiasis [4,6,30,31,32,33]. In some cities like Salvador, this is not so much a new introduction as it is a low level continuation of a pattern of infection present for some time [34,35,36]. Although Bahia is a state where schistosomiasis is endemic, Salvador, its capital, is considered a non-endemic area. This would be a valid designation if the infection were not transmitted and only found in immigrants to the city, but this is not the case. The overall prevalence is only 2–3% [36], but it can exist in islands of transmission where the community-wide prevalence is >20% and even cases of hepatosplenomegaly can be identified [15]. Given the rapid growth of the city, it is important to understand the relative contributions of immigration and local transmission to the presence of the infection.
Several epidemiologic indicators suggest local transmission is a more significant driver for S. mansoni infection in the neighborhood of São Bartolomeu than immigration. Guimarães et al. [15] found in 2004 that most infected children in São Bartolomeu had never left the area even for visits or vacations and also identified infected snails. In this study we extended this analysis to include all inhabitants in selected sections (microareas) of the neighborhood. Immigrants were less likely to be infected than natives (Table 2), and infection in immigrants increased with increasing time spent in São Bartolomeu.
Parasite population genetic structure and history also suggest local transmission was the major source of parasites. We and others have previously observed a great diversity in rural populations of schistosomes and a degree of reproductive isolation over even short distances [18,37]. Immigrants, particularly newer immigrants, infected at distant sites would be expected to carry strains that are genetically differentiated from those transmitted at their current residence. For São Bartolomeu, the overall mean pairwise Di was 0.063, which was lower than that we observed in the rural area of Ubaíra where the mean Di's were 0.097 [18]. Comparing the Di for those born in Salvador with those who immigrated there (Table 5), there is a nearly 2-fold difference, but this is likely due to the immigrant’s lighter infection and not to the introduction of a heterogeneous population. Factors associated with an increase in the relative sample size of parasite eggs tend to be associated with a relative decrease in Di and Dic. The younger age, higher prevalence and intensity for the native born in the urban area are consistent with having lower Di and Dic than immigrants. By contrast, immigrants to the rural area did not differ for these characteristics or for these differentiation indices. The Dc, however, indicates that both natives and immigrants were infected from the same pool of parasites, especially in the urban area. The Dc in the rural area was higher than the mean differentiation between replicate samples (0.007) and may indicate a higher degree of introduction of parasites from a distant population or populations.
Finally, comparing the parasite population in 2004 to that in 2011, the Dc is near the replicate error rate. Its genetic composition has changed little in 7 years. The lack of Dc differentiation by age also suggests that the parasite population is stable and little influenced by migration. The combined genetic characteristics of S. mansoni in São Bartolomeu indicate there is a single parasite population with no internal obstacles to gene flow and few new introductions of genetically diverse parasites. It is likely that most of today’s children as well as adults became infected in São Bartolomeu, and the prevalence there is not being sustained by the arrival of people infected far from Salvador. A drawback for using pooled samples is that null alleles cannot be accounted for. This should have little effect on differentiation, since the risk of these should be equally distributed between the comparison groups.
The age distribution, prevalence of infection, association with a perennial water source and poor sanitation make transmission in the urban site similar to many rural sites in Brazil. Having more than 3 people per bedroom as a risk factor may reflect socio-economic development, although crude household income itself was not associated with infection status. The number of water contact sites regularly visited was used as a proxy for the amount of water contact. This relies on recall and could be subject to recall bias. The kind of recall is relatively coarse and was not influenced by interviewers knowing infection status. In the urban and the previously studied rural area [17], increasing numbers of sites visited was associated with increasing risk for being infected.
One of the factors that makes infections like schistosomiasis unexpected in cities is the presence of city services and sanitation. Worldwide, drinking water is a first priority in municipal development before sanitation, and essentially everyone in São Bartolomeu has municipal water piped to his or her home. For Salvador, at the turn of the 20th century, a series of reservoirs were constructed outside what was then the city limits. As the city expanded rapidly after World War II, these became surrounded by new housing with poor sanitation such that many of these collections became polluted and the waterways used to occupy new areas became open sewers. São Bartolomeu is downstream from one of these early development projects. The Cobre Reservoir is still relatively protected, while it’s outflow, the Cobre River, is not. It is a recipient of raw sewage not only from the São Bartolomeu community itself but all from the densely populated hills surrounding it. The city of Salvador has grown so rapidly that attempts to keep up with the infrastructure requirements around it can be described as heroic. In 2004, a citywide program for the introduction of sewer systems increased the coverage from 40% to 70% in São Bartolomeu. The Bahia Azul Project, as it was known, was demonstrated to have an enormous effect on reducing the incidence of diarrheal diseases in the city [38]. The effect on schistosomiasis in this area, however, appears to have been negligible. The prevalence of infection is the same in children today as in 2004 despite a degree of coverage by the municipal sewer system superior to many emerging countries of the world. Nevertheless the 70% coverage is not sufficient where raw sewage makes its way to waterways that large numbers of people use for recreation and commercial activities. The persistence of schistosomiasis represents a failure of city services. Fortunately, our analysis indicates transmission in the city is focal and elimination of these islands of infection should produce long-term control despite migration [16].
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10.1371/journal.pcbi.1000765 | The Brain's Router: A Cortical Network Model of Serial Processing in the Primate Brain | The human brain efficiently solves certain operations such as object recognition and categorization through a massively parallel network of dedicated processors. However, human cognition also relies on the ability to perform an arbitrarily large set of tasks by flexibly recombining different processors into a novel chain. This flexibility comes at the cost of a severe slowing down and a seriality of operations (100–500 ms per step). A limit on parallel processing is demonstrated in experimental setups such as the psychological refractory period (PRP) and the attentional blink (AB) in which the processing of an element either significantly delays (PRP) or impedes conscious access (AB) of a second, rapidly presented element. Here we present a spiking-neuron implementation of a cognitive architecture where a large number of local parallel processors assemble together to produce goal-driven behavior. The precise mapping of incoming sensory stimuli onto motor representations relies on a “router” network capable of flexibly interconnecting processors and rapidly changing its configuration from one task to another. Simulations show that, when presented with dual-task stimuli, the network exhibits parallel processing at peripheral sensory levels, a memory buffer capable of keeping the result of sensory processing on hold, and a slow serial performance at the router stage, resulting in a performance bottleneck. The network captures the detailed dynamics of human behavior during dual-task-performance, including both mean RTs and RT distributions, and establishes concrete predictions on neuronal dynamics during dual-task experiments in humans and non-human primates.
| A ubiquitous aspect of brain function is its quasi-modular and massively parallel organization. The paradox is that this extraordinary parallel machine is incapable of performing a single large arithmetic calculation. How come it is so easy to recognize moving objects, but so difficult to multiply 357 times 289? And why, if we can simultaneously coordinate walking, group contours, segment surfaces, talk and listen to noisy speech, can we only make one decision at a time? Here we explored the emergence of serial processing in the primate brain. We developed a spiking-neuron implementation of a cognitive architecture in which the precise sensory-motor mapping relies on a network capable of flexibly interconnecting processors and rapidly changing its configuration from one task to another. Simulations show that, when presented with dual-task stimuli, the network exhibits parallel processing at peripheral sensory levels, a memory buffer capable of keeping the result of sensory processing on hold. However, control routing mechanisms result in serial performance at the router stage. Our results suggest that seriality in dual (or multiple) task performance results as a consequence of inhibition within the control networks needed for precise “routing” of information flow across a vast number of possible task configurations.
| A ubiquitous aspect of brain function is its modular organization, with a large number of processors (neurons, columns, or entire areas) operating simultaneously and in parallel. Human cognition relies, to a large extent, on the ability to perform an arbitrarily large set of tasks by flexibly recombining different processors into a novel chain (e.g. respond with the right hand to the red square) [1]–[3]. Yet this flexibility does not happen without a cost. Chaining individual computations is done at a very slow pace (100–500 ms per step) and with a considerable temporary tying-up of the brain's resources, generating what is known as “dual-task interference” – the inability to perform several tasks at once [4]–[8]. Several cognitive theories support this view, arguing that while most mental operations are modular and parallel, certain specific processes which establish flexible links amongst existing processors impose a serial processing bottleneck [3], [9]–[15].
The psychological refractory period (PRP) provides a classic and clear demonstration in experimental psychology of the coexistence of parallel processing and serial processing bottlenecks within a cognitive task. When performing two tasks in rapid succession on two successively presented targets T1 and T2, delays are observed in some but not all of the T2 processing stages. Analysis of these delays suggests that a “central decision stage” suffers from seriality while perceptual and response operations occur in parallel [4], [6], [7], [16], [17]. Despite the fact that the PRP has been one the most widely studied paradigms to investigate dual-task interference, no network implementation had been proposed which provides a plausible implementation of its underlying mechanisms. Boxological and schematical models of the PRP [4], [18], [19] have successfully determined a theoretical framework which provides a synthesis of two basic aspects of cognitive architecture: 1) its chronometric organization, 2) its components that can act in parallel and those that impose seriality. According to these models, each task involves three successive stages of processing: a perceptual, a central, and a motor component. The perceptual stage of sensory processing - which is performed in a modular (parallel) fashion - does not provide a major contribution to temporal variability. A subsequent stage of serial processing involves a stochastic integration process, traditionally used to model decision making in single tasks [20]–[23] and is a main source for the variability in response time. In contrast, the last motor processing stage has only a small contribution to response variability and can be performed in parallel without interfering with other processing stages from concurrent tasks. Despite their simplicity, these models have been very successful in explaining a broad range of behavioral data, including the complex response time distributions of dual-task experiments, which can be precisely predicted only after untangling the serial and parallel stages of each task [18].
Until now, the modeling of dual tasks is only specified at a level of mathematical description and functional cognitive architecture [4], [18], [24], [25]. At the neurophysiological level, understanding what kind of collective neural organization leads from massively parallel single-unit processing to a serial unfolding of two successive decisions has not been established. This situation is, to a large degree, due to the fact that there have been detailed monkey electrophysiology of single-task decision making [26], [27], but no comparable investigation of dual-tasks. Here we present an effort to bridge this gap between an abstract mathematical description and the underlying complex neurophysiology. We present a detailed model, based on realistic properties of spiking neurons which is capable of flexibly linking processors to form novel tasks. As a consequence of this flexibility, the network exhibits a functional serial bottleneck at the level of the “router” circuit needed to link processors. The model presents detailed predictions for future electrophysiological studies of dual-tasks and serial computations in the human and non-human primate brain.
In accordance with previous theoretical proposals [28], [29] here we propose that seriality in dual (or multiple) task performance results as a consequence of inhibition within the control networks needed for precise “routing” of information flow across a vast, virtually infinite, number of possible task configurations. To examine this hypothesis, we will explore dual-task performance in a recurrent network of spiking neurons capable of performing flexible routing of information according to specific task instructions. Contrary to previous computational work addressing flexible mapping [30]–[33], our objective is not to study flexible behavior per se but to understand the conditions under which a computational model capable of flexible sensory-motor mapping shows patterns of interference when two tasks have to be performed simultaneously or in close succession [17], [18], [34].
Following classic experimental procedures of the PRP [35], the interference experiments we address here involve different sensory modalities, to avoid sources of interference in early sensory processing (with the exception of the last section, where we investigate the effects of masking). The model that we simulate is described in detail in the Materials and Methods section and in Figure 1. It includes two sensory modalities organized in a hierarchy in which each successive layer receives inputs from neurons of the previous layer thus generating progressively complex receptive fields. Within each hierarchical level, for simplicity we explore in detail only two distinct neural populations for each sensory modality, which correspond to the neural coding of the two task-relevant dimensions (red and orange populations in Figure 1 representing, for example, a high and low pitch sound, respectively). Other task-irrelevant stimuli were encoded by a large pool of non task-selective excitatory neurons (pink populations in Figure 1), as done in many other spiking networks modeling decision-making [36].
Each element in this sensory hierarchy is a canonical cortical circuit comprising excitatory pyramidal cells and local inhibitory cells, previously shown to be capable of performing elementary functions of working memory and decision making [36]–[38]. Only excitatory pyramidal cells project with long-range connections to neurons higher and lower in the sensory hierarchy, while inhibitory neurons only project locally. Feedforward and feedback connections in the model differ both in the properties of the receptors that mediate the transmission as well as in their specificity [39]–[42]. Feedforward connections are highly specific: Each neuron projects to a single homogeneous population in the next higher level. For simplicity, they are assumed to be all mediated by fast AMPA receptors, although in reality a small fraction of NMDA receptors would be expected. In the reciprocal direction, feedback connections are more broadly connected: each neuron sends non-specific feedback connections to all excitatory cells in the previous level [40], [41]. Again, for simplicity we assume that feedback transmission is mediated by slow NMDA receptors. Since the contribution of NMDA receptors to synaptic transmission varies with the level of postsynaptic depolarization, this ordering of glutamate receptors between the feedforward and feedback streams broadly assigns a driving role to the feedforward input and a modulatory one to the feedback, as in previous models [43].
Both sensory modalities project to a router which connects the sensory representations to a set of possible responses. Neurons in the router integrate sensory evidence and trigger a response when their activity reaches a threshold [44].
An explicit instruction - presented before the stimulus – sets the task for a given trial, i.e. specifies the specific mapping which indicates which response has to be executed when the stimulus is presented. The network that stores task instructions is referred throughout this work as the task-setting network. Excitatory populations in this network are activated by the presence of task-relevant stimuli in sensory areas and, through their patterns of projection to “router” neurons (see below), encode different stimulus-response mappings. As with the sensory modalities, we only simulate two task-setting populations which are sufficient for the experiments considered here.
An important aspect of our model is a circuit which we refer as the “router”. As in previous models of flexible decision making that do not rely on synaptic plasticity to dynamically adjust their behavior [33], [45], [46], task-setting neurons affect the decision process by gating a specific subset of “router” neurons, which implement the possible mappings between stimuli and responses. Here we assume a reduced ensemble of stimuli and responses and simply model as many selective populations in the router as there are combinations of stimuli and responses [33], [47]. Simulating a completely flexible network capable of mapping arbitrarily large stimulus and response sets, would require a high degree of overlap in the cortical representation implemented by task-setting and routing neurons. We will come back to this possibility and its possible implications for serial processing in the discussion.
As with all other neurons in the network, task-setting neurons are entailed with self excitation and lateral inhibition. Excitatory neurons in the task-setting network are connected to the router through NMDA connections. When an excitatory population of the task-setting network is in an “active” state it excites the subset of neurons in the router receiving inputs from task relevant sensory populations and connecting them to the appropriate motor populations. A neuron in the router which receives excitation from task-setting neurons is set in a mode of integration in which it can accumulate sensory information (Text S1,A). This architecture also serves as a selection mechanism, assuring that task-irrelevant stimuli that are represented in sensory cortex do not elicit any output (Figure 2).
Response execution is triggered in response selection networks (motor 1 and 2 in Figure 1) by a set of bursting neurons that signal a threshold-crossing of the input received from the integrating neurons, modeled as in previous work by Wang and collaborators [44].
To ensure that the network did not enter in a response perseveration mode (Figure S1), we implemented an inhibition of return mechanism [48] typical of a control network. After response execution, response neurons feed back to inhibit the sensory, routing and task-setting neurons involved in the task (similar to the “termination” signals in Dehaene and Changeux, 1997 [49] and recently observed in single-cell recordings in awake behaving monkeys performing a sequential task [50]).
This architecture ensured that the network did not respond spontaneously, to irrelevant stimuli or to mappings different than those set by the explicit task-instruction and that it did not show perseveration of responses to task-relevant stimuli. We emphasize that here we have not investigated how a large repertoire of tasks can be encoded with a finite number of neurons. Rather, we ensure that the network has stable performance for a small number of tasks and then explore the operation of this network during dual-task performance.
Our simulations of dual task experiments showed that when both tasks were close together in time, response order could be reversed on a fraction of trials so that the first response was given to the stimulus that was presented second (Figure S2). This coincides with experimental observation in task-interference experiments when the response order is not fixed [51]. Here we wanted to explore a comparatively simpler situation, typically studied in psychophysical experiments, in which participants are explicitly told to respond to two tasks in a specific order, as fast as possible. This required the implementation of a task-setting network [52] that determined the order of the tasks. The task-setting network was bistable. It was composed of two excitatory populations that projected to the inhibitory population of the other task. Three hundred milliseconds before the presentation of the first stimulus, excitatory neurons in the order-setting network are activated by a brief (100 ms) external input. Due to the strong self-recurrent connections, the network maintains high levels of activity after removal of the external input and tonically inhibits T2 neurons in the task-setting network. When the response to T1 is emitted, inhibition from the router resets the order-network permitting the activation of T2 task setting-neurons (Text S1,B).
In summary, we generated a network based on a large-scale implementation of simple canonical neuronal circuits endowed with self-recurrence and lateral inhibition. The network has a hierarchical sensory organization which ultimately feeds stochastic evidence to “router” neurons which (if activated by a specific task-setting context) both accumulate evidence towards a motor decision and route sensory input to the relevant motor neurons.
Each stimulus has four features. The four populations encoding low-level features of a stimulus receive a brief pulse of constant current during stimulus presentation (100 ms). This initial impulse generates a transient response in the earliest input neurons (Figure 2A–D), which increase their firing rate from the default level of around 2 Hz to around 40 Hz. This transient response initiates a wave of activation that propagates through the network [47], [53], [54]. Each layer works as an integrator of the previous layer and thus the neural response becomes increasingly expanded in time as one progress in the hierarchy. At the highest level, recurrent connections are strong enough to assure a very low decay rate of stimulus information, resulting in an effective form of working memory as observed in several areas of occipito-temporal and frontal cortex [55]–[57].
The last stage in the sensory hierarchy projects to the router using AMPA receptors. Neurons in the router also receive currents from task-setting neurons, but these projections use NMDA receptors. These NMDA currents control the recurrence in the router, and they determine the degree of integration of AMPA currents. As a result of this architecture, neurons in the router act as detectors of the conjunction of stimulus presence and task relevance as observed in [58]–[60]. A neuron which receives task-setting currents integrates the sensory input rapidly (Figure 2B), while a neuron that does not integrates the input only partially (Figure 2E–H). Thus, task-setting neurons accomplish their role by assuring that the wave in the sensory system initiated by an irrelevant stimulus does not trigger a response. The integration process continues until a threshold is crossed, which is signaled by a nonlinear response: a powerful burst of spikes in the motor network (Figure 2D). The activation of these response neurons, in turn, initiates a cascade of feed-back inhibition that resets activation in task-related neurons [50].
The principal aim of this paper is to explore the operation of the model in a classic dual-task paradigm: the psychologically refractory period (PRP), widely studied in the psychophysical literature. We explored the response of the model with two different stimuli, presented simultaneously or at a short stimulus onset asynchrony (SOA). When the separation between stimuli (SOA) is much longer than the response time to the first task (RT1), the neural activations associated with the first and second task do not interfere with each other and the observed dynamics is similar to that observed during single-task performance (Figure 2A–D).
The most interesting situation is for SOA values close to or shorter than RT1 (Figure 3A–D, SOA = 100ms) in which case the two waves of activation evoked by each stimulus partially interfere. In the model, this interference does not occur at the sensory level: even at short SOA, while a first target T1 is being processed, sensory neurons associated with the second target T2 still initiate a wave of activations which is very similar to that in the single-task condition. However, due to competition between task-setting neurons, the routing neurons of T2 are not gated and hence do not integrate sensory information while T1 is being processed. In this instance there is a very interesting dissociation: local-recurrence in the sensory hierarchy is sufficient to maintain T2 stimulus information, but this information is not piped to the motor response and awaits liberation of the router. This constitutes a key aspect of this network – during a temporary waiting period, T2 has to be maintained in a “local memory” which does not propagate throughout the network. After the response to the first task has been executed, the T1 pathway is reset and Task 2 setting neurons activate, gating the router neurons of T2 and allowing them to begin to integrate information about the second incoming stimulus. Thus, the shift in the locus of “task-related attention” (which information is amplified in sensory areas and routed to response networks) is the natural consequence of the progression of the task in the router and task-setting network.
Note that the second key aspect of our network is that routing neurons of T1 and T2 cannot be simultaneously activated. In our network this is controlled through a competition between task setting neurons, but a similar result would be obtained if this competition would be implemented by lateral inhibition between routing neurons. This would occur, for example, if the number of possible mappings largely exceeds the number of neurons in the router so that routing can only occur by a distributed assembly of active cells. We will come back to this possibility in the discussion.
In the interference regime, the network includes groups of neurons with very different response properties (Figure 3E); the existence of these different types of neuronal firing patterns constitutes a key prediction of our simulations. Early sensory neurons show a response which is essentially unaffected by interference, reflecting fully parallel behavior. In contrast, the motor and task-setting neurons are strictly serial, only showing strong activation after task 1 has been completed. The behavior of the router neurons is intermediate; they are mostly serial, but can undergo moderate integration (insufficient to boost a response) before completion of T1. Interestingly, late sensory neurons act as a buffer. They have an onset which is locked to the stimulus and are active until the response, so that they hold a memory of T2 which is retrieved when the router becomes available. This population of neurons is therefore engaged in different components of the task; first, a transient response which results in stimulus encoding, and second, a later memory trace which is eventually broadcasted to the motor neurons involved in the second task.
All the previous analysis relied on spiking activity. Recently, much effort has been devoted to understand the relevance of complementary measures of brain function such as synaptic currents, local field potentials, and induced oscillations. Our neuronal network has the potential to study these measures.
We first explored whether input currents in the router could be more informative than spiking activity of T2 processing stages. We measured input currents to the router at different processing stages of T2: Spontaneous activity, S2 queuing (memory phase), and S2 routing. During queuing, currents in the router reflected a steady level of activity which was significantly larger than during spontaneous activity (Figure S3). Thus, during this regime, subthreshold activity in the router is tightly coupled to spiking activity of late sensory neurons. During the routing stage, synaptic current activity ramps, coupling to the progression of spiking activity in the router. An interesting observation was that this pattern was virtually identical for all receptor currents (NMDA, AMPA and GABA). Although the input from the task-setting network is carried by NMDA-receptors, the local amplification in the router circuit also engages AMPA currents and the NMDA specificity is lost very rapidly (Figure S3).
The task-switching circuit was endowed with high efficiency inhibition to achieve rapid switching from one task-setting program to another. This endowed the task-setting circuit with high frequency oscillations as can be seen in the raster plots of Figure 2. Since the task-setting circuit drives the router, we asked how these oscillations propagate into the network and whether measures of oscillatory activity could be more informative than simply spiking activity to identify distinct processing stages from neuronal responses. We analyzed the spectrogram of sensory, routing and task setting T2 neurons throughout the trial (Figure S4). Responses were locked to RT1. Both router and task setting neurons showed clear event-related spectrograms, as seen for firing rates. The spectral content of the responses of both populations are quite distinct: task-setting circuit activity occurs in high-frequency bands (peaking around 70 Hz) while router neurons, which act as slow integrators, display low-frequency responses (∼20 Hz). Router neurons do not inherit high frequency oscillations of the driving task-setting neurons because these connections are mostly mediated through NMDA receptors which have a slow time constant.
Rhythmic activity in the sensory neurons showed distinct oscillatory activity during buffering and routing (Figure S4, left panel). During routing, responses of sensory neurons showed high power in the 40–60 Hz range while during routing they were more broad band and showed an increase in lower-frequency activity. Firing rates of sensory neurons during buffering and routing were not different (Figure 2). Spike density coherence between sensory and router neurons also showed distinct profiles during distinct phases of task processing: phase coherence was not-significant during spontaneous activity, it showed significant coupling for low frequencies during routing and broad-band coherence during T2 queuing (Figure S5).
An appealing aspect of the PRP paradigm (Figure 4A–B) is that it is associated with a large number of chronometric observations. We explored whether the network shows a behavior in accordance with these observations including the dependence of mean RT (and RT distributions) with SOA and the differential effects of pre and post-bottleneck manipulations.
Specifically, the main experimental characteristics of the PRP phenomenon are [18], [34], [35]:
We first explored the main effects of the PRP (without specific task manipulations) by simulating an experiment in which two stimuli were presented at an SOA which varied between 0 and 800 ms, sampled at [0, 50, 100, 150, 200, 250, 300, 400, 500, 600, 700, 800] ms (Figure 4C). Response times were defined as the time interval between the onset of the stimulus signaling each task and the peak of the motor burst. The network virtually made no mistakes (error rates were less than 0.1% for both tasks), which was expected given that the two different stimuli have non-overlapping representations in each sensory modality. We observed that the network behavior captured all the predictions listed above (Figures 4 and 5). RT1 was unaffected by SOA (Figure 4C–G). Although, the presentation of the second stimulus provides input to the task-setting neurons of T2, this network is configured in a winner-take-all mode and the top-down control of T1 over the router neurons is virtually unaffected by the incoming stimuli. Thus, S2 was never strong enough to overwrite T1 in the task setting network as long as this task was ongoing.
Second, we observed the classic RT2 profile with varying SOA values: An initial decrease with a slope of −1 (Figure 4C). This indicates that T2 completion is strictly serial even though some aspects of T2 processing are carried out in parallel with T1 (Figure 3). As SOA increased and reached the average value of RT1, the two tasks became increasingly independent. The stochasticity of the system (see below for an analysis of RT distributions) assured that this elbow –i.e. the regime in which RT2 becomes independent of SOA was not sharp and thus RT2 showed a curved decay which reached a horizontal asymptote after about 300 ms, as observed in human psychophysics (Figure 4C).
Based on typical experimental procedures, we then explored the effect of different manipulations on the first and second task on mean response times, and their interaction with SOA (Figure 4D–G).
First we investigated the effect of changing the complexity of sensory processing. In a number comparison task, changing the notation (for instance replacing the digit 3 by the word three) results in an increase in response time which is absorbed during the PRP (i.e., more elaborate sensory processing of S2 can occur while central processing for T2 is blocked by the processing of task 1, therefore not increasing RT2 at short SOA) [18]. A simple model of word recognition predicts that complex combinations of characters are encoded in successive layers of a feed-forward scheme [53], [61]. To model this experimental factor in our network, we simply added an additional processing level in the sensory hierarchy. We first applied this manipulation to task 1, and observed an additive effect on RT1, which did not depend on the SOA (Figure 4F). This effect propagated to RT2 in the interference regime. This shows that the network functions strictly in a first-come first-served basis. Manipulating the second task affected RT2 for long SOA values, but had no effect at short SOA (Figure 4D), indicating that the additional sensory processing can be carried out in parallel with T1 processing. This absorption of pre-bottleneck manipulations constitutes one of the critical predictions of theoretical models of the PRP (Text S1,C).
We then explored another important manipulation which affects the complexity of the sensory-motor mapping, i.e. the amount of sensory evidence in favor of the correct decision. In experiments in which a decision is taken on an analog variable (movement, intensity, numerosity, size etc…) the two competing stimuli can be made arbitrarily close, rendering the decision progressively more difficult. This results in increased errors and RTs, and attractor dynamic networks have been very successful in modeling these phenomena [37], [62]. This distance manipulation in a PRP setup results in a bottleneck manipulation which is not absorbed in the PRP. Here, as conventionally done, we modulated the amount of evidence by changing the relative input currents of each of the two competing sensory populations (Figure 4E,G). We applied this manipulation to the first task, and observed an increase in RT1 unaffected by SOA (Figure 4G). This effect propagated to RT2 in the interference regime. When the manipulation was applied to the task performed second (Figure 4E), the first task was unaffected but the second task showed an additive effect not absorbed at short SOA values. This effect is what would be expected from bottleneck manipulations. The statistical significance of these observations was evaluated with a series of ANOVAs using the R software package (http://www.r-project.org/) (Table S1).
The response times histogram for SOA = 0 ms is displayed in Figure 5 (A, B). The results of the model capture an important experimental observation that the variability in RT2 is higher at short SOA, as RT2 accumulates the variability of both tasks. Response times for T2 become faster and less variable as SOA increases, as seen by plotting the cumulative response time distributions for varying SOA (Figure 5F) [18]. Interference and seriality are also observed in the scatter plots of RT1 vs. RT2, for different SOA values: for short SOA values RT2 is tightly correlated to RT1 indicating that RT2 is sequentially locked to Task 1 completion. For long SOA values, RT1 and RT2 become independent measures (Figure 5 C–E).
The previous results showed that our model can explain the precise shape of response time distributions in dual-task performance. Here we investigate the underlying physiological markers which result in such distributions, i.e. the relation between neuronal and response time variability. All neurons in the model receive strong background Poisson inputs, which assures a spontaneous activity of 2–5 spikes/s. We hypothesized that in trials in which input noise in the sensory neurons coincides with stimulus presentation (presented for 100 ms) response times would be faster. We also hypothesized that in the case of low-frequency noise (∼5Hz), the coincidence effect of external-stimulus and internal noise fluctuations, should manifest in a phase-locking relation of stimulus presentation to internal rhythms, as observed in both psychophysical [63], [64] and neurophysiological [65] experiments.
We first used a general linear regression model to investigate how noise fluctuations affected response times in the PRP. The explanatory (independent) variables were external noise fluctuations for each population group and temporal bin, and the response (dependent) variable was either RT1 (Figure 6A) or RT2 (Figure 6B).
We simulated 900 trials of the PRP for an SOA of 50 ms. For each trial, the population average of - dynamic gating variable mediating background AMPA currents (see Materials and Methods section) - was measured every 1 ms, assigning a value of 0 if its value exceeded the median value over all trials, and a value of 1 otherwise, independently for each population and time step. Independent variables were obtained by averaging these values within windows of 100 ms. Similar populations - for example, all neurons in the first level of the sensory hierarchy selective to the same stimulus - were averaged together. A positive regression coefficient means that higher activity of a group of neurons leads to faster responses.
The time-course of the coefficients of the regression (Figure 6) showed a very clear temporal organization. For Task-1 sensory neurons (Figure 6A), fluctuations in the first sensory level which were coincident with stimulus presentations were highly predictive of RT1. On the contrary, fluctuations beyond this window were essentially independent of response time. In successive stages of the hierarchy the window of correlation was delayed.
As we showed previously, RT2 variability accumulates RT1 variability (due to changes in the onset of the routing of T2) and intrinsic variability of the T2 routing process. To understand the impact of noise on each of these processes, we measured the time-course of the noise input to Task-2 responding neurons locked to the response to Task 1 (Figure 6B). Significant noise contributions were observed before the integration onset (Figure 6B, upper panel), suggesting that although sensory integration is delayed during the PRP, fluctuations in the memory trace of S2 during T2 queuing or before have an influence on RT2.
Thus, spontaneous Poisson-noise fluctuations were effective when they coincided in time with external stimulus currents. If noise currents were carried by low-frequency oscillations [66] this effect could result in phase locking of RTs to the rhythmic oscillatory activity. We tested explicitly this possibility by running single-task simulations where excitatory neurons in the first sensory level received a low-frequency (5 Hz), low-amplitude (0.06% of the external background noise), oscillatory input. This additional input resulted in a small synchronous fluctuation on top of the large external background input. The phase of the stimulus onset relative to the background rhythm was varied across trials in order to study its effects on average response times and their distributions (Figure 6C). The relative phase between stimulus onset and rhythmic background activity had a marked effect on response times, compatible with recent experimental findings [65] and theoretical proposals [66] linking low-frequency oscillations to attentional selection. Our model provides a simple physiological explanation of why phase-locking stimulus to low-frequency oscillations may result in shorter response times. When the phase is such that the peak of noise fluctuations coincides with stimulus presentation, the stimulus is enhanced and this reduces response time. On the contrary, when stimulus presentation coincides with the valley of noise oscillations, input to the router is less effective and response times are longer.
Behavioral experiments which have combined the basic features of different manifestations of central processing such as the PRP (two rapid responses) or the attentional blink (extinction of a second rapidly presented stimulus) have suggested that both forms of processing limitations may arise in part from a common bottleneck [67]–[70]. The main differences between the PRP and the AB is that in the PRP a speeded response is required to the first target and, most importantly, that in the AB the visibility of the second target is reduced, generally by masking it or by embedding it in a rapid visual serial presentation (RSVP).
To evaluate whether our model could, without modification, also account for AB experiments, we studied the effect of a mask applied after T2. The mask was modeled as a brief stimulation of non-specific excitatory cells in the first layer of the sensory hierarchy, thus modeling the activation of a neural representation competing with the target T2 [71]. The mask lasted 100 ms and was presented immediately following T2. In the majority of AB experiments, both T2 and the T1 are masked. Here, for direct comparison with the PRP simulations, we considered a special AB case in which the T1's fleetingness is obtained by virtue of its weak strength, rather than masking [72].
We simulated 100 trials at each SOA value, varying the SOA between 50 and 500 ms at 50 ms intervals. In contrast to the previous PRP simulations, when the SOA between T1 and T2 was short we observed a small (but significant) number of errors and, most importantly, a large number of trials in which the network failed to respond to T2 (Figure 7A). For simplicity and to follow the convention of prior experimental work, we refer to trials in which the network responds correctly as seen, and those in which it fails to respond as unseen. For example, at SOA = 50 ms we obtained 49±5% seen trials, 47±4.99% unseen trials, and 4±1.96% errors; for SOA = 500 ms, we obtained 90±3% seen trials, 9±2.86% unseen trials, and 1±0.01% errors. As observed in the Attentional Blink and in mixed AB-PRP paradigms, the brief mask after T2 is only effective when T2 is presented within a short temporal window – typically of around 500 ms – following T1 presentation.
For short SOA values, the network exhibits a highly stochastic behavior: the same configuration of stimuli and SOA may lead to seen or unseen responses depending on the inner state of the network. Figure 7B–D shows the time-course of activity of a representative seen and unseen trial and reveals the cause of the blink. In the unseen trial, RT1 was longer and thus at the moment in which inhibition of T2 task-setting neurons was released, T2 sensory activation had faded out. As a consequence, T2 task-setting neurons failed to respond and this impeded the integration and routing of T2. This can also be seen when averaging across all trials (for an SOA of 100 ms) according to whether the network responded or failed to respond to T2 (Figure 7E). T2 non-responded trials resulted – on average - from a delayed response of the T1 task setting neurons. This observation establishes a concrete prediction for the dynamics of routing neurons in a AB experiment and is consistent with physiological and behavioral experiments which have shown that the extent of T1 processing has an impact on T2 visibility [69], [73], in accordance with the behavior of the sequential bottleneck model.
The interpretation of our results is that the mask results in an accelerated exponential fading of the representation of T2 stimulus in short-term memory [74], [75]. As a result, if the waiting time of T2 is too long, due to the concurrent processing of T1, the remaining activation is insufficient to ignite the router and task-setting neurons and the network fails to respond to T2. Consistent with this interpretation, we verified that early responses evoked by the second stimulus in seen trials showed a small, but significant effect in the amplitude – but not in the latency - of the transient responses when compared to unseen trials (Figure 7E). These small fluctuations are strongly amplified in the router and task-setting neurons, which show an almost all-or-none difference (Figure 7E). This result is consistent with electrophysiological experiments of the blink and the PRP which have observed a modest effect in early sensory components and a massive all-or-none effect in late P3 components [73], [76], [77].
A series of experimental observations have shown that the AB is attenuated (i.e. the probability of seeing T2 increases) with increased T1 strength. For example, the blink is attenuated when a blank is placed after T1, i.e. masking is delayed [10]. This observation is in contradiction with pure T1–T2 competition models of the AB since these models predict the opposite effect: increased T1 strength should result in a reduced likelihood of perceiving T2 [78], [79]. However, it seems compatible with our network operation, since a stronger T1 stimulus should result in a faster conclusion of Task 1, increasing the probability of retrieving the second stimulus before it has fade out.
We examined this hypothesis performing two different simulations. First, we increased the strength of T1 by 10% relative to the previous PRP and AB simulations. This resulted in an attenuated AB for the second task (76±4% correct vs. 49±5% correct without the manipulation; p-value <0.0005; 100 trials at a fixed SOA of 50 ms). Despite perfect performance for T1 in these simulations, RT1 was smaller when T1 was stronger (with strong T1: RT1 = 318±5 ms; without the manipulation: RT1 = 396±9 ms; p-value<0.0005). Thus increasing T1 strength decreases RT1 and increases the probability of retrieving the second stimulus.
The second manipulation, conversely, involved masking the first target T1, simulating the most typical AB paradigm in which both T1 and T2 are masked. As for the first manipulation, 100 trials were simulated at a fixed SOA of 50 ms and we now added a mask identical to the one previously used for T2. In this condition, performance in the first task was still accurate (92±3% correct) while T2 visibility was decreased significantly (26±4% correct). This effect can be understood by the increased latency of the inhibitory signal following routing of T1, which increased RT1 from 396±9 ms in the unmasked condition to 869±50 ms when T1 was masked.
In summary, our simulations show that T1 manipulations that facilitate the first task and therefore reduce its duration have the effect of reducing the attentional blink for T2, as experimentally observed [5], [80]. Since RT1 is typically not measured in most AB tasks, where the task is to covertly commit T1 to memory for delayed report, only the reduced blink for T2 would have been noticed experimentally – but our network suggests that, if RT1 was measured by an on-line task, then the reduced AB would be replication and would be mediated by a faster RT1.
The present model constitutes, to our knowledge, the first spiking-neuron model of a global architecture capable of simulating the entire sensory-motor chain of processing in a dual-task setting. We could explain the detailed dynamics of behavior (including both mean RTs and RT distributions) during dual-task-performance, by simulating a large-scale network of realistic neurons, comprising about 20.000 spiking neurons and 46.000.000 synaptic connections. For consistency with the majority of previous PRP experiments, we simulated an experimental design in which stimuli involve distinct sensory modalities and the responses distinct effectors. Under these circumstances, interference occurs exclusively at the routing stage, commonly referred to in psychology as the response selection stage [4]. The central aspect of our model is a detailed neuronal implementation of this flexible “routing” and how it manages to change from one task to another in hundreds of milliseconds, using an area that maps stimuli onto responses which we have termed the router network. The model capitalizes on a number of existing elements: (1) perceptual attractor networks capable of encoding stimuli and maintaining them in an exponentially decaying buffer [62], [71], (2) an accumulation-to-threshold mechanism, comprising both recurrent neuronal assemblies [36] and a thresholding device inspired by the architecture of basal ganglia [81]; (3) a control network comprising rule-coding units capable of modulating other areas in a top-down manner [32], [45], [55], [82]–[85]; (4) the concept of a routing circuit implemented by neurons with broad connectivity, capable of transiently interconnecting other brain processors in a flexible manner [33], [47], [86]–[89]. The novel aspect of the present simulations is to integrate these theoretical constructs into a global functional architecture. We observed that the interplay between these control and routing mechanisms resulted in a central limitation during dual-task processing, which manifested itself either as a delay in the second task (PRP), or a complete interruption of the processing of a second target (Attentional Blink).
Based solely on the known dynamics of neurotransmitter receptors, the model reproduces, in a quantitative manner, a large number of behavioral observations of dual-task interference (see [17], [18], [35]):
These results are in full accordance with the central interference model [17], [35], [90], by which certain processes are carried out in parallel and routing and accumulation are intrinsically serial. Our model provides a detailed neuronal implementation of this classical psychological model and makes many new predictions for the neurophysiological correlates of the PRP.
Several brain-imaging experiments implicated a number of cortical systems in the PRP phenomenon. The cerebral basis of processing bottlenecks has been investigated with Event Related Potential studies (ERPs), which have shown that the PRP results in reduced and/or delayed components [91]–[97]. Using time-resolved fMRI [98]–[100], Dux and collaborators showed a slight delay in the peak fMRI activity in prefrontal cortex during a PRP paradigm [101], implying that the PFC was one of the fundamental nodes responsible for the central bottleneck of information processing. Recently, using both time-resolved fMRI and high density ERP recordings we could fully parse the execution of two concurrent tasks in a discrete sequence of processing stages. The ERP analysis demonstrated that a late P3-like complex is in fact delayed by an amount comparable to the PRP effect on RTs, and time-resolved fMRI confirmed that the PRP delayed parietal and prefrontal activation by several hundreds of milliseconds [77]. The notion that the global P3 indexes a late capacity-limited central stage fits with results from the AB. As we could show in the simulations the main difference between the PRP and the AB can be accounted for solely by the masks used to produce the AB, which interfere with the local memory of T2. The result is that T2 processing is not merely delayed (PRP), but erased and it therefore escapes from consciousness. During AB, the initial ERP components up to about 270 ms are essentially intact, but the P3 component is essentially abolished [73], [76], [102], [103]. The P3 component can only be detected in seen trials, in an all-or-none fashion [73], [104]. We observed this precise dependence for the activity of routing neurons and the onset of task-setting neurons, suggesting that the P3 is likely to constitute a large-scale electrophysiological marker of the router system. Also, as indicated by our simulations, increased latencies in T1 processing resulted in higher probability of the second target being blinked [73], [105], [106]. Direct comparison of AB and PRP paradigms suggests that both affect the same P3 component [95].
The spatial resolution of EEG is very imprecise and thus a better characterization of the locus of central processing bottlenecks in the brain comes from fMRI studies, which have pinpointed a broad parietofrontal network that exhibits various manifestations of central capacity limits [67], [107], including the AB [67], [105], [108] and the PRP [77], [101], [109], [110]. This network is ubiquitously activated by a large variety of goal directed tasks [107] suggesting that it plays an important role in flexible routing information between remote neuronal representations.
Our network postulates a hierarchical organization of this system: neurons controlling the whole-task structure (order network) gate neurons controlling the individual tasks (task-setting network), which, in turn, gate the routing from the sensory representations to the motor intention stage. Such a hierarchical organization has been demonstrated in humans in the prefrontal cortex as the Broca region and its homologue in the right hemisphere implement executive processes that control start and end states as well as the nesting of task segments that combine in hierarchically organized action plans [52], [111]–[114]. A hierarchical organization involved in planning of complex sequential tasks has also been found in non-human primates [113], [115].
Understanding the emergence of serial behavior in the human brain is an important and central theoretical question in cognitive psychology as modularity and parallel processing are hallmarks of brain computations. Different authors have proposed cognitive architectures that can explain how components of the mind work to produce coherent cognition [14], [24], [86], [116]–[118]. Concrete implementations of these ideas have shown that these coherent states which transiently bind together existing modular processors naturally result in serial behavior [14], [43].
Here we have tentatively proposed that seriality in dual (or multiple) task performance results from the necessity to establish a task set through the activation of a “router” network. This router network is shared by all sensory-motor mappings and its activity can, potentially, code for a virtually infinite number of possible tasks. A task-setting program acts as a gate, permitting routing neurons to propagate information if they receive the appropriate sensory input. This system acts as a control mechanism that avoids erroneous, conflicting or unwanted stimulus-response associations. We showed that a concrete implementation of such a control system results in serial behavior of the routing process when probed in dual-task situations.
In our network, seriality and its behavioral manifestations, the PRP and the Attentional Blink, emerged from competition between task-setting neurons which, through a lateral inhibition process, prevented the simultaneous activation of two task settings. This form of control is necessary to ensure correct task performance in conflicting mappings - as classically demonstrated in the Stroop paradigm in which the same stimulus may lead to distinct responses according to task requirements [119]. While this mechanism is strictly required only in conflicting response mapping situations, which is not the case in our present simulations, it is possible that it has emerged as a ubiquitous mechanism in control networks to assure correct task performance. Seriality in non-conflicting tasks would therefore emerge as a consequence of the need for a flexible mechanism linking stimuli with multiple responses according to context [28], [29].
Another possible origin of seriality relates to the coding properties of the router (for a simple illustration see Figure S6). Here we have explored a comparatively simplified situation of a small number of tasks, stimuli and responses in which all possible routings were coded by distinct neural populations. This mechanism would result in a combinatorial explosion in a more realistic setup, arguing that the code of router neurons should be distributed, i.e. each routing scheme should be encoded in a large population of neurons. This is consistent with many findings in prefrontal cortex neurons which have found that a large fraction of neurons respond to virtually all tasks [83]. In this scheme, the precise pattern of active and inactive neurons determines the code and thus superposing two routing configurations (of two distinct tasks) should result in a mixture leading to erroneous mapping properties. Avoidance of incorrect mappings in a combinatorial router can be implemented by the same mechanism shown here, leading to serial routing in the composition of flexible task settings (Figure S6).
Previous modeling efforts have established cognitive architectures which can account for human complex problem solving [14], [24], [116]. The adaptive control of thought–rational (ACT-R), for example, proposes a theory of distinct modules that interact with each other to produce coherent cognition [14]. While ACT-R is based on a sequential scheme, the temporal constant of the sequential step in ACT-R and in the PRP are not comparable: in ACT-R, productions (if-then structures representing procedural knowledge) fire approximately every 50 ms, about five times faster than the PRP delay. The 50 ms delay of individual productions is consistent with other experimental approaches which have suggested a discrete organization of cognition at a frequency close to 13 Hz [120]. These observations of ∼50 ms productions and the comparably slower ∼300 ms PRP delay can be reconciled by modeling the entire routing program as a sequence of productions, as in the ACT-R implementation of the PRP of Byrne and Anderson [25]. Sensory modules in the ACT-R involve a two-layer structure, a visual module (mapped to occipital/temporal regions) and a visual buffer (mapped to parietal regions). The visual buffer incorporates a selection mechanism that determines the contents of the visual system which will be available to other processors. Our model provides a concrete neuronal implementation of these mechanisms. In our model, the sensory hierarchy acts as a module which can select and maintain information locally (unless a subsequent element such as the mask overrides the buffer). This information can be broadcasted to the rest of the network. Similarly, in ACT-R the selection of actions is achieved by a loop that mimics the Basal-Ganglia- cortical connections. By building up on previous architecture for thresholding and gating sensory information through striatal-cortical interactions [44] our model provides a neuronal implementation of these mechanisms.
The router circuit in our model builds on previous computational models which have studied the role of contextual signals on transient sensory-motor mappings [30], [33], [121], [122]. Salinas (2004) showed that a linear read-out of sensory input could result in arbitrary sensory-response mappings if sensory responses are modulated by (a non-linear) contextual influence. A concrete implementation of flexible mapping by rule-setting contextual signals was developed by Deco and Rolls [47], [123].
In the present model, the router binds sensory and motor representations. Similar conceptions of flexible routing circuits have been applied to other instances of information binding such as, linking the attributes of an object in pattern recognition [89] or linking discrete objects to temporal contexts through distributed representations as recently proposed by Wyble and Bowman [124]. Olshausen and colleagues implemented a routing scheme in a set of control neurons which rapidly modify the strength of intra-cortical connections to implement the attentional gating of information flow from early visual representations to a higher level object-centered reference frame [89], [125]. The SAIM model of selective attention [88], [126] has shown how this ‘dynamic routing’ model can be extended to account for a wide range of results of visual experiments with competing stimuli in space, i.e. neglect [127] or in time, i.e. inhibition of return [88] in both normal and impaired subjects. The SAIM model [88] shares many features with our network: it implements a routing neuron which is modulated by a control (task-setting) network and thus acts as a coincidence-detector of a task-setting program and current sensory state. Recently, Heinke and collaborators showed how the SAIM model can be implemented with spiking units [126].
Our network provides an implementation of simple boxological models of dual-task execution in the PRP [17], [34], [35]. While very simple, these models have established a vast range of predictions in behavioral experiments regarding the precise functional dependence of RTs with SOA and how these functions should change with different manipulations. By incorporating ideas of models of decision making, we previously generated a schematic model that accounts for the entire distribution of RTs and how it changes in the interference regime [18]. Here we have shown that these ideas can be implemented robustly in realistic network architecture.
A critical aspect of our network is that while the router is occupied by T1, the T2 stimulus was maintained in the recurrent activity of high-level sensory units, thus forming a memory which remains local because it cannot activate the router. This coexistence of parallel mechanisms – a cascade of sensory processes which encode the stimulus - and of serial bottlenecks – queuing by the routing process - constitutes a hallmark of PRP observations. Our network implemented this local memory as a local attractor showing progressive integration and exhibiting a metastable form of memory that could be maintained for a few hundred milliseconds. According to this proposed mechanism, the memory trace remains stored in a local network and is relatively fragile as it can readily be overridden by a mask. The critical observation is that the mask can only override processing of T2 if it the router is occupied by T1.
To our knowledge, our model is the first one to propose a concrete neural implementation of the mechanisms leading to the PRP. In contrast, several computational models have been recently proposed for the attentional blink [43], [78], [128]–[130]. Two current explanations include the simultaneous type serial token (ST2) model [78] which proposes that access of sensory representations to working memory is gated by an episodic-driven attentional signal and the boost and bounce model [130] which suggests that a target initiates an attentional boost which is interrupted when the trailing task-irrelevant stimulus is accidentally boosted. Our model shares with the ST2 model the idea of gating of a router-system and with the boost and bounce model that task-setting activation is not a phasic event, but rather, can stay active until it is inhibited by a termination signal.
We emphasize that our model does not intend to give a detailed account of all the findings from attentional blink experiments, but instead to show how the same mechanisms that lead to delayed responses in the PRP can lead to missed targets in the AB. Recent reviews of the extensive AB literature argue for a multifactor origin in this processing deficit [131], and thus it might be impossible to pinpoint a single mechanism behind the full diversity of experimental findings (although see [132], [133]). Nevertheless, our results show that limited capacity operations – as the one implemented by our router/task-setting network – may play a central role in the attentional blink [72], [134].
One aspect of the attentional blink phenomena which our model fails to replicate is the relative increase in performance observed at very short SOA (∼100 ms), an effect known as lag-1 sparing [5]. This effect is not observed when T1 and T2 involve different modalities [135] (as in our simulations of the AB) or spatial locations [136]. Recent experiments show that the sparing can even be spread to several targets presented rapidly without intervening distractors [137], [138], suggesting that the unit of selection of a serial attentional process is not the individual target but an extended event which may include several rapidly presented targets [132], [139], [140]. This grouping does not happen without a cost, since order swapping and performance tradeoffs between different targets do occur [78], [141]. In our model, the task-setting configuration is sustained until information is routed to the motor system, and thus it might be possible to extend the present model such that more than one target in a RVSP benefits from the same task-setting configuration. Processing a temporally extended event encompassing several targets would require broadening – in feature space - the action of the task-setting network as well as making the router/task-setting complex capable of flexibly routing information not only to motor areas but also to mnemonic [142] or sensory areas in order to achieve recursive computations.
In fact, we see the extension of the present model along the lines just discussed: the different types of neurons used in our implementation (briefly reviewed in the next section) have been found in the awake behaving monkey and may serve as a basis from which to construct complex cognitive programs, as those implemented in systems like ACT-R [3] or SOAR [143] - but with a stronger grounding on neurophysiological findings [144]. In this implementation, we see router neurons as capable of accumulating evidence not only towards a motor response, but implementing a full production system [145], [146] where stochastic rules are selected according to the information contained in different mnemonic systems which are in turn updated by external stimuli and by the action of the productions themselves. These ideas will form the basis for a future extension of the present model to flexible series of chained tasks.
Most, if not all, types of neurons used in our implementation have been observed in studies that measured single-neuron activity in awake behaving monkeys during single-task performance. Here we will briefly mention the main types of neurons in the various areas of our model and compare them to neurophysiological data, a comparison that will have to remain somewhat superficial as we cannot attempt to discuss the precise relationships between the variety of tasks employed in the neurophysiological studies and the PRP task implemented here. Firstly, the properties of the sensory areas of our model are consistent with what is known about representations in areas of sensory cortex. Neuronal activity in low level sensory cortex is largely (but not entirely) determined by the incoming sensory information [147], while neurons in higher areas carry information about the behavioral relevance of stimuli, as well as traces of stimuli to be remembered [148]. Secondly, neurons in areas of parietal and frontal cortex have response properties consistent with the routing process proposed by our model. Many of these cells are tuned to categories of stimuli that are associated with a particular behavioral response [149]–[151] and integrate evidence in favor of one of a number of possible actions until a threshold is reached, just as is required by the model's router [152]–[154]. Thirdly, some neurons in the frontal cortex only respond if a particular stimulus maps onto a particular motor response, but not when the same stimulus or response is part of a different stimulus-response mapping [60], and yet other prefrontal neurons code abstract rules [84]. Clearly, the response properties of these neurons are in accordance with the model's task-switching network. Finally, neurons in the motor response selection stage of our model have either a gradually increasing activity before the response or they respond with a sharp burst at the time of the response. Neurons with gradually increasing activity before the motor response and cells with a motor burst are indeed observed in areas of the motor cortex [155], [156] as well as in the basal ganglia [157]. These results, taken together, indicate that the types of units required by our implementation are broadly consistent with the types of neurons that are observed in neurophysiological experiments.
Our network can also explain timing and latencies of the sequence of events identified in single-task physiological experiments in monkeys [158]–[160] and humans [161]. Accumulation of information about the upcoming response influences the firing rate of routing neurons at a latency of about 200 ms, a latency that may be relatively fixed for a given task [162]. This latency cannot be explained solely by synaptic delays, since measurements of conduction velocity of cortical feedforward and feedback connections showed that they can be rapid, even faster than intrinsic connections within a cortical area [163], [164]. A previous neurophysiological study showed that the onset of response modulation in the visual cortex depends of the sequencing of subtasks, with later modulation for subtasks that occur later in a sequence [165]. Our model grasps this observation: the latency of the response of routing neurons depends on the order in which the two subtasks are executed (Figure 3B–C). The present results suggest that the latency of feedback modulation may reflect the time required by the network to settle into a brain-scale state of coherent activity [18], [87], which in our model is reflected by a coherent pattern of activity across sensory, router, and task-setting networks coding different aspects of the same subtask.
Our observations also raise a note of caution on the interpretation of processing latencies from physiological data. A concrete example is conveyed in our model by the measurement of activity in the routing neurons. Spiking activity shows a clear sequential scheme: routing neurons of T2 start integrating only once routing of T1 has completed (Figure 3B). Thus, the latency at which spiking activity exceeds a certain threshold constitutes a physiological marker of the PRP effect. The picture is quite distinct if one would measure synaptic router activity (Figure S3). During the time in which T1 is being routed and T2 is being buffered, T2 sensory neurons spike and project silently (i.e. without evoking spiking responses) to router neurons. Hence synaptic activity in T2 router neurons increases during T2 compared to baseline. A consequence of this observation, which may be of relevance beyond the specifics of this study, is that timing analysis based on synaptic or spiking activity yield qualitatively different observations. Various studies have simultaneously measured different markers of neurophysiological activity such as multi-unit activity (MUA), laminar current-source density (CSD) and local field potentials (LFP) [166] and fMRI [167] or EEG [168]. Multimodal interactions have been shown to display such a mixed effect in response latencies. Primary auditory cortex shows a clear CSD response to somatosensory stimulation, without observable changes in the spiking response as measured by MUA [169]. Computational models may be a useful link to bridge information gathered at different scales.
Our data showed that fluctuation in response time could be accounted by the dynamics of noise fluctuations in relation to the timing of stimulus routing (Figure 6). When noise is oscillatory, this is determined by a precise phase relation. Our model does not explain how this relation can be entrained. Neurophysiological data of multi-sensory integration suggests that somatosensory stimuli can reset the phase of ongoing oscillations in primary auditory cortex such that auditory stimuli are boosted if presented during the high excitability phase [169], [170]. Also, it has been shown that neuronal oscillations can entrain to environmental rhythms improving discriminative performance and decreasing response times [65], [66]. As mentioned, these aspects lie outside the scope of the present model.
The correlates of the bottleneck have yet to be studied at the single cell level and our simulations therefore generated a number of new predictions that could be tested in future neurophysiological experiments. First the model establishes the existence of routing and task-setting neurons with well distinct dynamics and connectivity with different neuronal populations. At the anatomical level, routing neurons should receive inputs from all sensory modalities and from task setting neurons. At the functional level, they should be characterized by their firing in response to specific conjunctions of stimuli and responses, a preference which may change dynamically according to task context, on a time scale of about 100 ms or more (for supporting evidence, see [60], [113]). Task-setting neurons should engage in a competition such that two task-setting programs or routing schemes cannot coexist in time. This should avoid unwanted mappings but also causes an inertia which results in relatively slow switching (>100 ms) from one task-setting to another leading to seriality in the routing process. In a PRP experiment, neurons coding for the memory T2 stimulus should show a characteristic temporal profile, comprising (1) a phasic sensory response, time-locked to actual stimulus presentation, (2) a sustained response exhibiting a slow exponential decay, and (3) a late amplification at the time when task 1 routing is completed and the router neurons of task 2 become active. On the contrary, the onset of router and task-setting neurons of Task 2 should be delayed at short SOA, with a delay that should decrease with SOA because task 2 router neurons are released from the inhibition of task 1 as soon as it is completed. In trial-by-trial comparisons, at short SOA values, the onset of router and task-setting neurons of T2 should be locked to the response time of the first task. While sharing the onset, the model predicts distinguishable time-courses of activations for router and task-setting neurons. Task-setting neurons should show sustained high-levels of activation throughout the duration of the task while router-neurons activity should ramp to a critical threshold. In an AB experiment task-setting neurons of T2 should be active both in seen and unseen trials. Only in unseen trials should the memory of T2 fade below a threshold (either due to fluctuations in transient response or in the durations of the memory due to the extension of T1) impeding routing and broadcasting to the rest of the network. These predictions will become testable once an awake animal model of dual-task performance is defined.
The model contains 21,000 neurons and 46,634,400 synapses. Neurons were either excitatory or inhibitory. All neurons were modeled as conductance-based leaky integrate and fire units. The membrane potential of each cell below the threshold for spike generation is described by:(1)where is the total synaptic current flowing into the cell, = −70 mV is the resting potential, is the membrane capacitance (0.5 nF for pyramidal cells and 0.2 nF for interneurons), and is the membrane leak conductance (25 nS for pyramidal cells and 20 nS for interneurons). The threshold for spike generation was set to −50 mV. The reset potential after spike generation is −55 mV, and the refractory period is 2 ms for pyramidal cells and 1 ms for interneurons.
All neurons receive large amounts of background synaptic activity which determines the level of spontaneous activity. External inputs and background activity are mediated exclusively by AMPA receptors.
Recurrent excitation is mediated by AMPA and NMDA receptors, and inhibition is mediated by GABA receptors. The total synaptic currents are given by:(2)in which(3)(4)(5)(6)where = 0 mV and = −70 mV. The extracellular magnesium concentration = 1 mM controls the voltage dependence of NMDA currents [171]. and are the number of excitatory and inhibitory inputs, respectively. The values of the synaptic efficacies g are given below. The dimensionless factor w controls the strength of recurrent connections between neurons with similar response properties (see below). in equations 3–6 is the gating variable - or fraction of open channels –updated according to the activity of the presynaptic neuron j and the identity of the receptor mediating the transmission. The dynamics of the gating variables are as follows. When a neuron receives a presynaptic action potential the appropriate gating variable s is increased. Otherwise, these variables decay exponentially. For AMPA and GABA receptors:(7)
For NMDA receptors:(8)where is the time of presynaptic spike k and α = 0.63 controls the saturation properties of NMDA channels. The decay time constants are τNMDA = 100 ms, τAMPA = 2 ms, and τGABA = 10 ms.
Neurons are grouped into homogeneous populations. A total of 84 unique populations were included in the simulations. In sensory and routing areas these homogeneous populations were grouped into larger groups, forming local modules as used in previous studies [36], [37].
The proposed network simulates a generic PRP experiment. Observers (and the network) must perform two tasks as fast as possible, in a pre-specified order. Each task involves a simple two-alternative decision. In the network, the set of possible task-related stimuli in each modality is restricted to two, as is often the case in real PRP experiments.
All neurons receive background Poisson input to maintain a spontaneous activity of a few Hertz. The presentation of a task-relevant stimulus increased the external input of the four selective populations in the first level sensory network, from the background level of 2,400 Hz (as may result from 800 afferent neurons spiking at a spontaneous rate of 3Hz) to 2,717 Hz, for 100 ms (thus ). All external inputs, both background and stimulus-related, are mediated exclusively by AMPA receptors.
In Figure 4 we investigated the effect of changing the complexity of sensory processing. This was implemented by adding one additional module in the sensory hierarchy, between levels two and three. This additional module had the same number of neurons and recurrent, feedforward, and feedback parameters as the other sensory modules, with w = 1.94. In the same figure we also showed the effect of changing the amount of sensory evidence in favor of the correct decision. In this case, the input to the stimulus projecting to the correct response was and to the other , with f = 0.92 in the high ambiguity case (f = 1 in all other simulations).
In the attentional blink (AB) simulations, a mask is presented after the task-relevant stimulus. This was modeled as in previous studies [71]. After the stimulus is removed, the external input to the non-selective cells in the first level sensory network is increased, from the background level of 2,400 Hz to 2,880 Hz, during 100 ms (thus ).
Each simulated trial lasted 3400 ms. The first stimulus was presented at 700 ms, and the second stimulus was presented according to the SOA. The code was written in C++, and simulations were performed in the CECAR computer cluster (Buenos Aires University). Equations were integrated with the first-order Euler method, with a time step of 0.05 ms. When run on a Linux 3.16 Ghz Pentium IV PC, each trial takes about 3 minutes to complete.
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10.1371/journal.pntd.0002820 | Epidemiologic Features and Environmental Risk Factors of Severe Fever with Thrombocytopenia Syndrome, Xinyang, China | Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease discovered in rural areas of Central China in 2009, caused by a novel bunyavirus, SFTS virus (SFTSV). The disease usually presents as fever, thrombocytopenia, and leukocytopenia, with case-fatality rates ranging from 2.5% to 30%. Haemaphysalis longicornis was suspected to be the most likely vector of SFTSV. By the end of 2012, the disease had expanded to 13 provinces of China. SFTS patients have been reported in Japan and South Korea, and a disease similar to SFTS has been reported in the United States.
We characterized the epidemiologic features of 504 confirmed SFTS cases in Xinyang Region, the most severely SFTS-afflicted region in China from 2011 to 2012, and assessed the environmental risk factors. All cases occurred during March to November, with the epidemic peaking from May to July. The patients' ages ranged from 7 to 87 years (median 61 years), and the annual incidence increased with age (χ2 test for trend, P<0.001). The female-to-male ratio of cases was 1.58, and 97.0% of the cases were farmers who resided in the southern and western parts of the region. The Poisson regression analysis revealed that the spatial variations of SFTS incidence were significantly associated with the shrub, forest, and rain-fed cropland areas.
The distribution of SFTS showed highly significant temporal and spatial heterogeneity in Xinyang Region, with the majority of SFTS cases being elderly farmers who resided in the southern and western parts of the region, mostly acquiring infection between May and July when H. longicornis is highly active. The shrub, rain-fed, and rain-fed cropland areas were associated with high risk for this disease.
| Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease discovered in rural areas of Central China in 2009, caused by a novel bunyavirus, SFTS virus (SFTSV). The disease usually presents as fever, thrombocytopenia, and leukocytopenia, with case-fatality rates ranging from 2.5% to 30%. By the end of 2012, the disease had expanded to 13 provinces of China. SFTS patients have been reported in Japan and South Korea, and a disease similar to SFTS has been reported in the United States. Here we characterized the epidemiologic features of 504 confirmed SFTS cases in Xinyang, the most severely SFTS-affected region in China from 2011 to 2012, and identified the environmental risk factors. We found the distribution of SFTS cases showed highly significant temporal and spatial heterogeneity, with the majority of SFTS cases being elderly farmers who resided in the southern and western parts of the region, mostly acquiring infection between May and July when H. longicornis is highly active. The shrub, forest, and rain-fed cropland areas were strongly associated with high risk for SFTS.
| Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease discovered in middle-eastern China [1]. The disease usually presents as fever, thrombocytopenia, and leukocytopenia, with case-fatality rates ranging from 2.5% to 30%. In 2009, the causative agent was identified as a novel bunyavirus in the genus of phlebovirus, family Bunyaviridae, and designated as the SFTS virus (SFTSV) [1]–[4]. Immediately after noticing the epidemic, the Chinese Ministry of Health initiated a national surveillance program [5]. By the end of 2012, SFTS cases had been reported in 13 provinces of China [6]. Most recently, SFTS patients have been reported in Japan and South Korea and a disease similar to SFTS has been reported in the United States [7]–[9].The potential for SFTS to spread to other countries of the world, in combination with its high fatality rate, possible human-to-human transmission, and extensive prevalence among residents and domesticated animals in endemic regions [10]–[16] make the disease a severe threat to public health.
SFTSV has been detected and isolated from Haemaphysalis longicornis ticks in the endemic areas. The high sequences homology between viruses isolated from ticks and those from patients suggested this tick species as the most likely vector [1]–[4]. It's long been recognized that many tick-borne diseases such as Lyme disease, tick-borne encephalitis, and rickettsiosis are zoonotic and have shown strong associations with environmental elements [17]–[21]. We hypothesize that the environmental factors might also contribute to the occurrence and distribution of SFTS. However, no report has explored the environmental factors associated with this emerging infectious disease. The role of environmental factors in human infection with SFTSV remains unclear. The objectives of this study were to characterize the epidemiologic features and to identify the environmental risk factors of the disease in one of the most severely affected regions by the disease.
The study was performed in Xinyang, an administrative region of Henan Province in middle-eastern China located between113°42′–115°56′E and 31°23′–32°40′N (online Technical Appendix Figure S1). Xinyang reported 99% of SFTS cases in Henan Province [22] and 48% of SFTS cases in China [23]. The region includes 200 administrative townships of 10 counties and districts, with a total area of 18,819 square kilometers and a population of 6,108,683 residents. Xinyang has a humid subtropical climate with annual precipitation of around 1,100 millimeters. The region is characterized by its distinct natural landscapes, with the northern part mainly comprising plains and the southern part stretching across the Dabie Mountain range.
From January 1, 2011 to December 31, 2012, laboratory-confirmed SFTS cases in Xinyang Region were included in the analysis. According to the national guidelines [5], a laboratory-confirmed SFTS case was defined as meeting one or more of the following criteria: 1) a positive SFTSV culture; 2) a positive result for SFTSV RNA by molecular detection; 3) seroconversion or ≥4-fold increase in specific antibody to SFTSV between acute and convalescent serum samples. Information regarding age, sex, occupation, onset date of symptoms, and residential address were collected. Each case was geo-referenced to a digital map of Xinyang Region according to his or her residential addresses assuming they had never left their living place in the last two weeks before onset of symptoms.
To explore the relationship between the SFTS incidence and the environmental factors, the data regarding land cover, normalized difference vegetation index (NDVI) and elevation were collected and processed. Land cover data were derived from a raster version of “GlobCover 2009 land cover map” (available at http://ionia1.esrin.esa.int), which was processed by the European Space Agency [24]. Land cover types were classified as follows: irrigated cropland, rainfed cropland, orchard, forest, shrub, built-up land and water body. For each type of land cover, its covering proportion of each township was calculated using ArcGIS 9.3 software (ESRI Inc., Redlands, CA, USA). NDVI, which represents the amount and productivity of vegetation [25], was derived from “Free Vegetation Products” (http://free.vgt.vito.be), then the average value in each township was calculated in ArcGIS 9.3. Elevation data were obtained from Shuttle Radar Topography Mission (SRTM) archives (http://www.srtm.csi.cigar.org). Demographic data were obtained from the Xinyang Bureau of Statistics from the sixth national census in 2010, and the average population density for each township was calculated.
The research protocol was approved by the human ethics committee of hospitals where the study was performed (including the 154 Hospital of People's Liberation Army, the Shangcheng People's Hospital, the Xinxian People's Hospital) and the institutional review board of State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology. All participants provided written informed consent, for the cases of children, parents or guardians of eligible children were informed and asked to provide written informed consent on behalf of their children. The study-related information was analyzed anonymously.
We applied Poisson regression to explore the association between SFTS incidence and environmental factors at the township level, using STATA 10.0 software (StataCorp LP, College Station TX, USA). The variables considered in the analysis included land cover, elevation, NDVI and population density.
Univariate Poisson analysis was employed for each variable. The variables with a P-value <0.10 in the univariate analysis were included in the multivariate analysis. For all continuous variables, we also presented trisection categorical results to inspect whether or not the assumption regarding continuous variables was justified [26]. A scale parameter was applied to compensate for the over-dispersion, and the collinearity between covariates was assessed. The percentage change (PC) in incidence in response to the change of a variable by a given amount, 95% confidence intervals (CIs), P-values were estimated after correction for over-dispersion, and a P-value <0.05 was considered to be significant.
A total of 504 laboratory-confirmed SFTS cases (193 in 2011 and 311 in 2012) were reported. All of them occurred during March to November (Figure 1), with epidemic peaking from May to July (71.6%, 361/504). In 2011, case number peaked in July (32.1%, 62/193), while in 2012, the peak occurred in May (37.9%, 118/311).
The patients' ages ranged from 7 to 87 years (median 61 years) old, and the mean (±SD) age was 59.4 (±12.9) years. Age distribution demonstrated that the annual incidence increased with age (χ2 test for trend, P<0.001) (Figure 2).The female-to-male ratio of cases was 1.58. Overwhelming majority of confirmed cases lived in rural areas, and 97.0% (489/504) of the cases were farmers being engaged in agriculture activities. All the recruited cases in the current study did not report infection through human-to-human transmission.
The annual incidence tremendously varied from township to township ranging from 0 to 64.9 per 100,000 people, with an average of 4.2/100,000 people in the study site. The geographic distribution of annual SFTS incidence is displayed in the thematic map (Figure 3), twenty-nine of 200 townships in the southern and western parts of Xinyang Region had the annual incidences over 20.0/100,000. The 5 townships with highest incidences were Gaoliangdian, Wanggang, Guanmiao, Hefengqiao and Yanghe. No case was found in 102 townships in northern Xinyang.
Based on the univariate analysis, six variables (shrub, forest, irrigated cropland, rainfed cropland, orchard, and elevation) were significantly associated with SFTS incidence (Table 1). The multivariate analysis revealed that SFTS incidence was raised with increases in proportion of shrub and forest (Table 1). The association between SFTS incidence and proportion of rainfed cropland showed an inverted-U pattern relationship. With the rainfed cropland proportion increasing, SFTS incidence rose to the peak and then dropped (Table 1). Elevation was removed from the multivariate analysis because of its collinearity with forest (r = 0.89).The results coincided with the spatial distribution shown in Figures 3 and 4, where SFTS cases predominantly occurred in the southern and western forest, shrub, and the surrounding rainfed cropland areas, while cases were rarely reported in the northern and eastern plains.
In the current study, we provide an overview of the epidemiologic features of the novel human bunyavirus infection in Xinyang, the most severely SFTS-affected region in China. Highly significant temporal and spatial heterogeneity of the disease was identified, with the majority of SFTS cases being elderly farmers who resided in the southern and western parts of the region, mostly acquiring infection between May and July. The shrub, forest, and rainfed cropland areas were significantly associated with high risk for SFTS.
Since the disease was discovered in 2009, ticks have been considered to be the most likely vector. People who live in mountainous or hilly rural areas were suggested to be the high-risk populations [1], [2], [4]. Our epidemiologic results corroborated the current knowledge on the epidemiology of SFTS. According to our results, 97.0% of confirmed patients were farmers being engaged in agriculture activities, with some reporting tick bites within 2 weeks before the symptom onset. We also observed a high incidence of SFTS among people over the age of 60 years old, and more females than males among the cases. We hypothesized the age and gender specific distribution of the disease might be related with exposure characteristics of the local population. In Xinyang Region, most young people take industrial work, instead of farming activity. In contrast, the elderly take the main agriculture activities (preparing land for cultivation, planting crops, pasturing cattle, and clearing weeds, etc), especially tea-picking activity, which was performed mostly by elderly women from May to July when H. longicornis is highly active in this region [2], [22], [27]. This high exposure experience in elderly, especially in female could remarkably increase the risk for SFTSV infection. On the other side, we could not determine whether the waning immunity of the elderly might also play a role in the age specific distribution of the disease, since the population immunity level was not evaluated. Based on this hypothesis, people living in the endemic regions should be aware of the main causes of exposure, and self-protective measures should be taken to avoid being bitten by ticks accordingly.
Using the Poisson regression analysis, we found that shrub, forest, and rainfed cropland showed strong associations with SFTSV infection. These findings may help us to explain spatially-clustered distribution of SFTS cases. The risk of SFTS incidence rose linearly with increasing shrub and forest areas. However, the relationship between SFTS incidence and rainfed cropland area showed an inverted-U pattern relationship (Table 1). This finding was consistent with the previous survey in Xinyang [27]. Liu et al. described shrub and forest areas as ideal habitats for H. longicornis. It seems quite possible that geographic expansion of this tick population plays a role in SFTS spreading in the region. Despite of these associations, the exact role of ticks and possible wild animal reservoirs for SFTSV merit future well-designed tick transmission competence studies.
We also recognize limitations of the study. First, the hospital-based surveillance captured data only from patients with SFTSV who sought medical care. As patients with subclinical infection might have been missed, our data do not offer complete SFTSV disease spectrum and epidemiological characteristics. Second, we only considered major potential environmental factors into our statistical analysis. Climatic factors were not studied because meteorological data were unavailable. Furthermore, data for other potential factors such as population immunity, economic conditions, ticks density, etc were not included in the study, which need further investigation.
In conclusion, we characterized the epidemiologic features of SFTS cases in Xinyang Region, and demonstrated that shrub, forest, and rainfed cropland areas were associated with high risk of SFTS incidence. As no vaccine against SFTS is available, and fatal outcomes are common, our findings can be used to identify high risk areas and populations, which might assist public health officials in developing and targeting educational programs and other interventions to reduce the disease incidence.
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10.1371/journal.ppat.1000981 | PPARγ and LXR Signaling Inhibit Dendritic Cell-Mediated HIV-1 Capture and trans-Infection | Dendritic cells (DCs) contribute to human immunodeficiency virus type 1 (HIV-1) transmission and dissemination by capturing and transporting infectious virus from the mucosa to draining lymph nodes, and transferring these virus particles to CD4+ T cells with high efficiency. Toll-like receptor (TLR)-induced maturation of DCs enhances their ability to mediate trans-infection of T cells and their ability to migrate from the site of infection. Because TLR-induced maturation can be inhibited by nuclear receptor (NR) signaling, we hypothesized that ligand-activated NRs could repress DC-mediated HIV-1 transmission and dissemination. Here, we show that ligands for peroxisome proliferator-activated receptor gamma (PPARγ) and liver X receptor (LXR) prevented proinflammatory cytokine production by DCs and inhibited DC migration in response to the chemokine CCL21 by preventing the TLR-induced upregulation of CCR7. Importantly, PPARγ and LXR signaling inhibited both immature and mature DC-mediated trans-infection by preventing the capture of HIV-1 by DCs independent of the viral envelope glycoprotein. PPARγ and LXR signaling induced cholesterol efflux from DCs and led to a decrease in DC-associated cholesterol, which has previously been shown to be required for DC capture of HIV-1. Finally, both cholesterol repletion and the targeted knockdown of the cholesterol transport protein ATP-binding cassette A1 (ABCA1) restored the ability of NR ligand treated cells to capture HIV-1 and transfer it to T cells. Our results suggest that PPARγ and LXR signaling up-regulate ABCA1-mediated cholesterol efflux from DCs and that this accounts for the decreased ability of DCs to capture HIV-1. The ability of NR ligands to repress DC mediated trans-infection, inflammation, and DC migration underscores their potential therapeutic value in inhibiting HIV-1 mucosal transmission.
| Heterosexual transmission is the primary mode of HIV transmission worldwide. In the absence of an effective vaccine, there is an increasing demand for the development of effective microbicides that block HIV sexual transmission. Dendritic cells (DCs) play a critical role in HIV transmission by efficiently binding virus particles, migrating to lymph nodes, and transmitting them to CD4+ T cells, a process called trans-infection. In addition, DCs secrete proinflammatory cytokines that create a favorable environment for virus replication. DC maturation by pathogen-encoded TLR ligands or proinflammatory cytokines dramatically increases their capacity to capture HIV, migrate to lymphoid tissue, and trans-infect T cells. Here, we report that signaling through the nuclear receptors PPARγ and LXR prevents DC maturation and proinflammatory cytokine production, as well as migration. In addition, PPARγ and LXR signaling prevents efficient DC capture and transfer of infectious HIV by increasing ABCA1-mediated cholesterol efflux. Our studies suggest that PPARγ and LXR may be targets for drugs that can inhibit specific aspects of HIV mucosal transmission, namely inflammation, migration, and virus capture and transfer. These findings provide a rationale for considering PPARγ and LXR agonists as potential combination therapies with conventional anti-viral microbicides that target other aspects of mucosal HIV transmission.
| Worldwide, heterosexual transmission accounts for most new HIV-1 infections, with a majority of these occurring in developing countries [1], [2]. Clearly, controlling heterosexual transmission of HIV-1 would be a significant step toward reducing this global pandemic. To achieve this goal, it will be important to delineate the cellular and molecular events that promote or restrict virus transmission and dissemination.
Immune cells within the vaginal, cervical, or rectal mucosa are thought to be the primary targets of infection in the sexual transmission of HIV-1 [1], [3], [4]. These target cells include sub-epithelial CD4+ T lymphocytes, intra-epithelial Langerhans cells, macrophages, submucosal plasmacytoid DCs (pDCs), and myeloid (or conventional) DCs (mDCs) located within the lamina propria [4], [5], [6], [7], [8], [9], [10], [11]. DCs, in particular, play a central role in HIV-1 transmission. DCs are thought to capture cell-free HIV-1 particles from the intralumenal space or from the mucosa after transcytosis across or leakage of HIV-1 particles through the epithelial barrier or by contacting HIV-1-infected cells introduced into the mucosa through abrasions or ulcerative lesions [6], [12], [13]. In addition, studies examining vaginal transmission of SIVmac in a rhesus macaque model of AIDS have implicated DCs in virus dissemination from the mucosa to draining lymph nodes [6], [14]. Moreover, DCs are the predominant infected migratory cell type harboring HIV-1 from virus exposed cervical tissue explants [15] supporting the idea that they are involved in virus dissemination. Upon capture, DCs can deliver infectious HIV-1particles to draining lymph nodes that contain large numbers of CD4+ T cells [16], [17]. The close contact between virus-laden DCs and CD4+ T cells facilitates cell-to-cell transmission and viral spread [18], [19]. In addition to their roles in virus transmission and dissemination, DCs can produce proinflammatory cytokines that create a microenvironment that favors virus replication [20], [21], [22]. Recent reports have demonstrated that DCs matured by exposure to pathogens encoding Toll-like receptor (TLR) ligands or to proinflammatory cytokines are capable of enhanced HIV-1 trans-infection [23], [24], [25] and chemokine-directed migration [26], [27], suggesting that agents capable of preventing inflammation and DC maturation may be able to limit HIV-1 transmission and dissemination.
NRs are a superfamily of ligand-activated transcription factors that includes classic hormone receptors, as well as the so-called orphan receptors and adopted orphan receptors whose natural ligands are either unknown or recently discovered [28], [29]. Included in these latter two families are peroxisome-proliferator activated receptors (PPAR) and liver X receptors (LXR). Ligand-activated PPARγ and LXR are bifunctional modulators of gene expression, capable of either activating or repressing transcription in a promoter-specific manner. Importantly, PPARγ and LXR are potent inhibitors of inflammation and are capable of repressing cytokine and chemokine production by Toll-like receptor (TLR)-activated macrophages and DCs through trans-repression mechanisms involving the failure to clear co-repressor complexes from promoters or through direct antagonism of transcription factors such as the p65 subunit of NF-κB, AP-1, STATs, and IRF3 [30], [31], [32], [33], [34], [35], [36], [37], [38]. The effects of PPARγ and LXR on TLR signaling are complex and a number of studies have demonstrated that each NR inhibits different subsets of inflammatory genes [32], [34]. For example, LXR signaling represses TLR4-induced expression of iNOS, COX-2, and IL-6 in murine macrophages, while PPARγ signaling represses IL-1β, GCSF, MCP-1, MCP-3, and MIP-1α expression [32]. Here, we show that PPARγ and LXR signaling acutely prevents TLR-activated expression of the proinflammatory cytokines TNF-α, IL-6, and IL-8, which have been implicated as co-factors for enhanced mucosal transmission of HIV-1. Moreover, PPARγ and LXR signaling inhibit the expression of the chemokine receptor CCR7, thereby preventing DC chemotaxis in response to gradients of CCL21, a process thought to be involved in DC migration from mucosal surfaces to draining lymph nodes.
As opposed to their inhibitory effects on inflammatory gene expression, ligand-activated PPARγ and LXR induce expression of genes involved in lipid and cholesterol metabolism, as well as cholesterol transport, including ABCA1 and ABCG1 [29], [39], [40], [41]. Importantly, many studies have demonstrated that cholesterol plays an essential role in HIV-1 biology. Cholesterol must be present in both the target cell membranes and HIV-1 particles for efficient virus binding and fusion [42], [43], [44], [45], [46]. In addition, nascent HIV-1 particles bud through cholesterol-rich lipid rafts [47], [48] and infectious particles enter target cells through cholesterol-rich lipid rafts [42], [49], [50]. Finally, studies using the cholesterol chelator, methyl-β-cyclodextrin, demonstrated that cholesterol is required for DC binding of virus particles [51]. Interestingly, PPARs and LXR are expressed at high levels in HIV-1 target cells such as macrophages and DCs [28], [29]. Therefore, we hypothesized that PPARγ and LXR-mediated changes in cholesterol metabolism and trafficking might contribute to their ability to inhibit the transmission of HIV-1 from DCs to T cells. Our results demonstrate that PPARγ and LXR signaling inhibit the capture of HIV-1 by DCs, and its subsequent transfer to CD4+ T cells. These effects are due to up-regulation of ABCA1-dependent cholesterol efflux, a mechanism distinct from the effects of PPARγ and LXR signaling on DC migration and proinflammatory cytokine production. Collectively, our data suggest that the bifunctional activities of ligand activated PPARγ and LXR can be exploited to inhibit multiple distinct steps in HIV-1 mucosal transmission and dissemination.
TLR signaling induced by sexually transmitted pathogens is thought to enhance HIV-1 mucosal transmission in part by promoting local inflammation. Inflammation not only activates HIV-1 target cells but, importantly, it also induces DC maturation and the subsequent migration of HIV-1-carrying DCs to local lymph nodes where they can contribute to virus dissemination [16], [17]. We were therefore interested in determining whether the anti-inflammatory activities of ligand-activated PPARγ and LXR [34], [52], [53] could be exploited to limit DC functions involved in HIV-1 transmission and pathogenesis. To examine the effects of PPARγ and LXR signaling on DC maturation, human monocyte-derived DCs (MDDCs) were treated with E. coli K12 LPS, a TLR4 ligand, in the presence or absence of ligands for PPARγ and LXR. As expected, LPS treatment upregulated the expression of surface markers associated with maturation, such as HLA-DR, CD80, CD86, and CD83, downregulated the expression of surface markers associated with an immature phenotype, such as the C-type lectin DC-SIGN, but had no effect on the expression of the pan-DC marker CD11c (Figure 1A and data not shown). Notably, treatment of MDDCs with the PPARγ ligand ciglitazone or the LXR ligand TO-901317 inhibited LPS-dependent upregulation of cell-surface expression of HLA-DR, CD80, and CD86 (Figure 1A). Similarly, we found that ciglitazone or TO-901317 treatment inhibited human MDDC maturation in response to the TLR2 ligand PAM3CSK4 (data not shown).
We next examined the effects of ciglitazone and TO-901317 treatment on TLR-induced proinflammatory cytokine and chemokine production. We found that treatment with these PPARγ and LXR ligands prevented the release of proinflammatory cytokines and chemokines such as TNF-α, IL-6, and IL-8 by PAM3CSK4-activated MDDCs (Figure 1B). In addition, PPARγ and LXR treatment also prevented the release of the chemokines MIP-1α and RANTES, which are important for the recruitment of CD4+ T cells to sites of infection, both from MDDC in response to the TLR4 ligand LPS (Figure 1C) and from plasmacytoid DCs (pDCs) in response to the TLR7 ligand CLO97 and the TLR9 ligand CpG ODN 2006 (Figure 1D). Importantly, PPARγ and LXR signaling inhibited TLR-induced proinflammatory cytokine and chemokine production coincident with TLR ligation (data not shown), suggesting that NR-mediated inhibition most likely acts through a trans-repression mechanism [34]. The concentrations of the PPARγ ligand ciglitazone and the LXR ligand TO-901317 necessary to see a reduction in DC maturation and the production of pro-inflammatory cytokines and chemokines did not affect MDDC viability as measured by LDH release or mitochondrial activity (Figure S1 and data not shown).
In addition to transmitting HIV-1 to T cells with high efficiency, DCs can also contribute to HIV-1 pathogenesis by binding virus and then migrating from mucosal sites of infection to regional lymph nodes. In this way, DCs can contribute to viral dissemination. Studies have shown that mature DCs have a greater migratory capacity than immature DCs [26], [27]. This led us to examine whether NR signaling would also inhibit MDDC migration through a 5 µm pore size Transwell insert in response to the chemokine CCL21, which has been shown to be important for DC migration in vivo [27]. We found that LPS-matured MDDCs (mMDDCs) migrated in response to a CCL21 gradient and that co-treatment with PPARγ or LXR ligands repressed this migration approximately 2-fold (Figure 2A). In contrast, immature MDDCs (iMDDCs) migrated quite poorly in response to CCL21 and, consequently, NR ligand treatment had a limited effect. Expression of CCR7, a receptor for CCL21, is upregulated in DCs in response to TLR engagement [26], [54]. Notably, treatment with PPARγ and LXR ligands prevented the LPS-induced upregulation of CCR7 (Figure 2B), which may partly explain why NR ligand-treated MDDCs migrate poorly in response to CCL21. Together, these data suggest that PPARγ and LXR signaling inhibit DC migration by preventing TLR-induced DC maturation.
DCs are thought to play a critical role in virus dissemination by capturing HIV-1 and transferring it to T cells [5], [24], [55]. We therefore examined whether NR ligands could modulate DC-mediated HIV-1 trans-infection. iMDDCs were treated with ciglitazone or TO-901317 for 48 hours, extensively washed, and then incubated for four hours with either a single-round replication-competent HIV-1 reporter virus packaged with an R5-tropic envelope or with wild-type HIV-1. Following incubation with the virus, MDDCs were washed extensively to remove unbound virus and then cultured directly with autologous T cells or in the upper well of a Transwell insert separated from the T cells by a 0.4 µm membrane. Although HIV-1 replicated very poorly in immature MDDCs (Figure 3), we found that DCs were able to mediate T cell infection when directly cultured with the T cells or when separated from them by the Transwell insert (Figure 3), suggesting that a portion of the MDDC-mediated trans-infection is mediated by either exosome-associated HIV-1 [56] or virus shed from the surface of MDDCs [57].
Most importantly, we found that PPARγ and LXR ligands inhibited trans-infection up to 5-fold underscoring their potential to limit HIV-1 transmission (Figure 3). NR signaling inhibits trans-infection of T cells by both single-round replication competent virus (Figure 3) and wild-type replication competent virus (Figure 4A), suggesting that the majority of virus transferred to T cells is due to virus captured by the DC and not due to newly synthesized virus. Because mature DCs capture and transfer HIV-1 to T cells with higher efficiency than immature DCs [23], [24], [25], we next determined whether PPARγ or LXR ligands could inhibit trans-infection mediated by LPS- or PAM3CSK4-matured MDDCs. PPARγ and LXR signaling repressed trans-infection of autologous primary T cells mediated by both immature, LPS-matured MDDCs (Figure 4A), and PAM3CSK4-matured MDDCs (Figure 4B), suggesting that the repression is independent of MDDC maturation. To confirm NR-dependent maturation-independent repression of DC-mediated HIV-1 trans-infection, we matured MDDCs with LPS for two days prior to treatment with NR ligands and then assayed for HIV-1 transfer. As seen in figure 4C, the ability of mature MDDCs to transfer virus was impaired when treated with PPARγ and LXR ligands. In addition, we found that PPARγ and LXR ligand treatment of MDDCs prevented trans-infection over a wide range of input virus (Figure 4D). Of note, NR-ligand treatment inhibited immature and mature MDDC-mediated trans-infection of both R5- and X4-tropic envelope glycoprotein-pseudotyped single-round replication competent reporter viruses and replication-competent R5- and X4- tropic wild-type HIV-1 (data not shown). Together these data suggest that, unlike PPARγ- and LXR-mediated inhibition of migration, the inhibition of trans-infection is independent of the maturation state of the DC. Importantly, MDDC-mediated trans-infection is also inhibited by rosiglitazone (Figure 4E), a PPARγ agonist that is currently licensed for the systemic treatment of type II diabetes.
Next, we wanted to examine the mechanism accounting for the inhibition of trans-infection. We began by examining the effects of PPARγ or LXR ligand treatment on HIV-1 binding to MDDCs. Ciglitazone and TO-901317 treatment led to a 2 to 5-fold decrease in the amount of HIV-1 associated with MDDCs as measured by an ELISA for the HIV-1 p24 capsid protein (Figure 5A). Another PPARγ ligand, rosiglitazone, was also tested and had a comparable effect on HIV-1 capture (Figure 5B). Treatment with these NR ligands also inhibited the capture of HIV-1 by DCs at 4°C, suggesting that NR ligand treatment prevents DC binding of HIV-1 (Figure S2). In addition, we found that PPARγ and LXR ligand treatment of MDDCs prevented capture over a wide range of input virus (Figure 5C). Although NR signaling can repress inflammatory gene expression by a trans-repression mechanism [30], [31], [32], [33], [34], [36], [37], [38], [52], it likely decreases HIV-1 capture through a different mechanism. MDDCs must be treated with PPARγ and LXR ligands for at least 12 hours in order to observe inhibition of virus capture (Figure 5D), suggesting that changes in cellular gene expression are required for the observed effect. Though the amount of virus captured by MDDCs upon NR ligand treatment was reduced, the relative amount of virus particles internalized was similar (Figure 5E) suggesting that reduced ability of MDDCs to capture HIV-1 particles upon NR ligand treatment was not due to gross reduction in cellular endocytic function. To confirm that NR ligand treatment does not alter the ability of MDDCs to internalize particles, we examined their effects on the ability of MDDCs to macropinocytose FITC-labeled dextran. NR ligand treatment had no effect on FITC-dextran internalization by immature or mature MDDCs (Figure S3 and data not shown).
Our data suggest that changes in cellular gene expression are necessary for the observed decrease in HIV-1 capture by MDDCs. We therefore considered the possibility that PPARγ and LXR ligand treatment altered the expression of known HIV-1 attachment factors expressed on the surface of immature MDDCs. However, we found that NR ligand treatment did not alter the expression of CD4, CCR5, or DC-SIGN (Figure 5F), which have been implicated in DC capture of HIV-1 [5], [58]. Despite these findings, we cannot rule out whether NR signaling alters the expression of other factors implicated in HIV-1 attachment such as other C-type lectins [59], [60], [61], [62], heparan sulfate proteoglycans [63], [64], [65], or GSLs [66], [67], [68], [69], [70].
Although MDDCs are a faithful representation of myeloid or conventional DCs (mDCs) with respect to their interactions with HIV-1 [25], we decided to utilize mDCs freshly isolated from the peripheral blood of healthy volunteers. We found that PPARγ and LXR signaling inhibited the ability of immature and LPS-matured mDCs to capture HIV-1ADA and transfer it to autologous T cells (Figure 6) in a manner consistent with results obtained using MDDCs.
Because direct DC-T cell contact is required for efficient virus transfer [24], [57] (and Figure 3), we wanted to determine whether NR ligand treatment interfered with the ability of MDDCs to form conjugates with T cells. Using a FACS-based conjugate formation assay [71], we determined that NR ligand-treated MDDCs were able to form conjugates with primary autologous T cells in a manner similar to untreated MDDCs (Figure 7A). Because NR ligand treatment did not alter the ability of DCs to form conjugates with T cells, we next wanted to examine whether such treatment prevented the formation of functional virological synapses between DCs and T cells. Confocal microscopy data suggest that PPARγ and LXR ligand-treated DCs are capable of forming virological synapses, as indicated by co-localization of virus and the tetraspanin CD81 at the site of DC-T cell contact (Figure 7B). However, number of virus particles localized at the virological synapse is decreased in NR ligand-treated cells. Taken together, our data suggest that NR signaling impairs the ability of MDDCs to transfer virus to T cells by inhibiting the capture of HIV-1 by MDDCs.
Recent studies have demonstrated that DCs can bind to infectious HIV-1 and envelope-deficient virus-like particles (VLPs) in a GSL-dependent, viral envelope glycoprotein-independent manner [72], [73]. We therefore wanted to assess whether triggering PPARγ and LXR signaling could alter the ability of MDDCs to bind virus independently of the envelope glycoprotein gp120. We found that PPARγ and LXR ligand treatment led to a 2 to 5-fold decrease in the amount of envelope glycoprotein (Env)-deficient HIV-1 particles captured by both immature and mature MDDCs (Figure 8A), suggesting that GSL-based virus-DC interactions may be targeted by NR signaling. To demonstrate that this interaction is truly envelope glycoprotein-independent, we also examined the effects of PPARγ and LXR signaling on the ability of DCs to capture HIV-1 particles pseudotyped with the glycoproteins of vesicular stomatitis virus (VSV), Ebola virus (EboV), and Marburg virus (MarV). As shown in figure 8B, treatment with PPARγ and LXR ligands inhibited the ability of DCs to capture EboV or MarV glycoprotein-pseudotyped HIV-1 particles, whereas the treatment had no effect on the ability of DCs to capture VSV-G-pseudotyped particles. Since, like HIV-1, both EboV and MarV glycoproteins are known to require cholesterol for infection [74], [75], whereas VSV-G does not [43], [75], [76], this suggested that PPARγ and LXR might be exerting their effects through the regulation of cellular cholesterol.
Previous studies have shown that DC capture of HIV-1 is dependent upon the cholesterol content of the cell membrane [51]. Since both PPARγ and LXR are known to modulate genes involved in cholesterol metabolism and transport [29], [39], [40], [77], we were interested in determining whether ciglitazone or TO-901317 affected the cholesterol content of MDDCs. Treatment with PPARγ and LXR ligands increased cholesterol efflux from immature MDDCs approximately 2 to 3-fold (Figure 9A) and led to a concomitant 2-fold decrease in the amount of cholesterol in immature MDDCs (Figure 9B). We next wanted to see if cholesterol depletion resulting from PPARγ and LXR ligand treatment was responsible for the decreased ability of MDDCs to capture and transfer HIV-1. In order to do this, we replenished membrane cholesterol in PPARγ and LXR ligand-treated MDDCs using cholesterol-saturated methyl-β-cyclodextrin and assayed for HIV-1 capture and transfer to T cells. Cholesterol repletion of NR ligand-treated MDDCs with cholesterol-saturated methyl-β-cyclodextrin restored cholesterol content (Figure 9B) and, importantly, fully restored the ability of both immature and mature MDDCs to capture HIV-1 (Figure 9C) and transfer it to CD4+ T cells (Figure 9D).
PPARγ and LXR signaling upregulate expression of ATP-binding cassette protein A1 (ABCA1) that facilitates the apoA1-dependent efflux of cholesterol from cells [39], [41], [77]. We therefore examined whether treatment of DCs with ciglitazone or TO-901317 affected ABCA1 expression. We found by western blot analysis that both NR ligands increased ABCA1 expression (Figure 9E). Importantly, targeted knockdown of ABCA1 using shRNA abrogated the effect of PPARγ and LXR ligand treatment on cholesterol efflux (data not shown), HIV-1 capture by DCs (Figure 9F), and HIV-1 transfer to T cells (Figure 9G). These findings suggest that ligand-activated PPARγ and LXR mediate their effects through the depletion of cholesterol from the DC plasma membrane via the up-regulation of the ABCA1 cholesterol transport protein. It will be interesting to determine whether HIV-1 particles interact directly with cholesterol in the plasma membrane of DCs or with factors that localize to cholesterol-rich lipid rafts.
Sexual transmission of HIV-1 is enhanced by inflammatory and ulcerative co-infections with STI pathogens that cause diseases such as genital herpes, gonorrhea, syphilis, Chlamydia, bacterial vaginosis, and fungal infections [78], [79], [80], [81], [82], [83]. This enhanced susceptibility to infection may be due to a number of factors, including disruption of epithelial integrity [6], [11], [14], [84], [85], [86], [87], recruitment of HIV-1 target cells such as Langerhans cells, DCs, macrophages, and T lymphocytes to sites of inflammation [8], and activation of HIV-1 expression by pro-inflammatory cytokines or microbial components [21], [22], [88], [89], [90], [91], [92]. It is likely that STI pathogens enhance these latter two processes, at least in part, through engagement of the TLR family of innate immune receptors. Clearly, prophylactic methods that inhibit infection of the genital or rectal mucosa would significantly limit the global spread of HIV. To this end, considerable efforts have been directed toward the development of microbicides that interfere with virus integrity or with key steps in virus replication. However, to date, little attention has been paid to targeting cellular pathways involved in active suppression of inflammation and its effects on mucosal HIV-1 infection and virus dissemination. With this in mind, we have focused our efforts on the nuclear receptor family of transcription factors that have recently been shown to be potent inhibitors of TLR-induced inflammation [30], [31], [32], [33], [34], [36], [37], [38]. Here, we demonstrate that PPARγ and LXR signaling inhibit several aspects of DC biology that are important for HIV-1 mucosal transmission. These include TLR-induced pro-inflammatory cytokine expression, DC migration in response to the chemokine, CCL21, and, importantly, DC-mediated capture of infectious virus particles and trans-infection of CD4+ T cells. Our findings highlight the therapeutic potential of PPARγ and LXR ligands as topical treatments that could be used in conjunction with conventional microbicides to limit mucosal transmission of HIV-1.
DC-mediated trans-infection of T cells is thought to play a critical role in the mucosal transmission of HIV-1. Studies suggest that DCs can mediate trans-infection either by internalizing infectious virions into a protected tetraspanin-rich intracellular compartment, or deep membrane invaginations contiguous with the cell surface, and releasing them for the subsequent infection of T cells [5], [56], [73], [93], [94], [95] or by retaining virions at the cell surface and transferring them to T cells [57], [95]. Regardless of the mechanism, maturation of DCs with ligands for TLRs such as TLR4 and TLR2/TLR1 increases DC-mediated HIV-1 capture and trans-infection of T cells. DC maturation also contributes to HIV-1 mucosal transmission in a number of other ways. Mature DCs create a pro-inflammatory environment that favors virus replication [20], [88], [96], [97], [98] and leads to disruption of the mucosal integrity [83], [99]. Mature DCs may also contribute to virus dissemination by virtue of their enhanced ability to traffic to regional lymph nodes in response to chemokine gradients and, once there, transfer virus to resident CD4+ T cells. Here we show that PPARγ or LXR ligand treatment can prevent DC maturation as measured by the expression of cell surface markers such as HLA-DR, CD80, and CD86 (Figure 1A). Importantly, treatment with PPARγ or LXR ligands also potently inhibit expression of maturation-associated pro-inflammatory cytokines (Figure 1B), such as TNF-α and IL-6 and the pro-inflammatory chemokine, IL-8, that have been shown to augment HIV-1 replication in infected cells and to increase HIV-1 transmission to T cells [21], [22], [91], [100]. Moreover, we demonstrate that PPARγ and LXR signaling can interfere with the migration of DCs in response to a CCL21 chemokine gradient (Figure 2A). This appears to be due to the effects of PPARγ and LXR signaling on the expression of CCR7 (Figure 2B), one of the receptors for CCL21. CCR7 is up-regulated upon DC maturation and has been shown to be important for the migration of DCs from the mucosa to regional lymph nodes in vivo [54]. By preventing DC migration in response to CCL21, PPARγ and LXR ligands may help to block the dissemination of DC-associated virus from mucosal sites of infection to regional lymph nodes.
Recent studies demonstrated that activation/maturation of DCs through TLR4 or TLR2/TLR1 enhances HIV-1 transmission to target cells via increased HIV-1 capture [23], [24], [25], [92] and Figure 4 and 5). Here, we demonstrate that activating PPARγ or LXR signaling pathways in DCs decreases the ability of both immature and TLR-matured DCs to capture and transfer HIV-1 to T cells (Figure 3, 4A and 5A). Furthermore, NR signaling can inhibit HIV-1 transfer by previously matured DCs (Figure 4C) These results suggest that PPARγ and LXR signaling alter other pathways involved with HIV-1 trans-infection that are independent of the maturation state of the DC (Figure 4C), however we cannot rule out the possibility that the prevention of DC maturation may contribute to the NR-mediated decrease in HIV-1 capture and transfer. Many studies have demonstrated a role for PPARγ and LXR signaling in cholesterol metabolism and transport [29], [39], [40]. For example, both signaling pathways stimulate the expression of ABCA1 and ABCG1, which have been implicated in apolipoprotein A1 (ApoA1)- and high density lipoprotein (HDL)-mediated cholesterol efflux, respectively [39]. Given the importance of cholesterol for a number of aspects of HIV-1 biology, including virus binding and infection [42], [43], [44], [45], [47], [48], [49], [50], [51], [76], we hypothesized that PPARγ and LXR signaling was altering the cholesterol content of DC membranes, thereby rendering them incapable of efficiently binding HIV-1 particles. Previous studies have demonstrated that treatment with cholesterol depleting drugs, such as methyl-β-cyclodextrin, or with cholesterol synthesis inhibitors, such as HMGCoA-reductase inhibitors (statins), alters the ability of cells, including DCs, to bind HIV-1 and renders them refractory to HIV-1 infection [42], [43], [45], [49], [50], [51], [101]. Here, we show that cholesterol repletion of PPARγ and LXR ligand-treated DCs reverses the effects of the NR ligands on virus capture and transfer (Figure 9C and 9D), confirming that PPARγ and LXR are mediating their effects through membrane cholesterol. In addition, targeted shRNA knock-down of ABCA1 abrogates the effects of PPARγ and LXR signaling on HIV-1 capture and transfer (Figure9F and 9G). A recent study suggests that LXR-dependent cholesterol efflux in macrophages is mediated entirely through ABCA1, with little to no contribution from ABCG1 [102]. We cannot, however, formally exclude a contribution from ABCG1-dependent cholesterol efflux to the effects we report here. Our data show that PPARγ and LXR signaling decrease cellular cholesterol content, which may in turn deplete cholesterol from membrane lipid rafts. It will be interesting to determine whether treatment of DCs with PPARγ and LXR ligands disrupts lipid rafts and whether this accounts for the decreased ability or NR-treated DCs to capture and transfer HIV-1.
We found that PPARγ and LXR ligand treatments do not alter the levels of a number of known virus attachment factors expressed on DCs including CD4, CCR5, and DC-SIGN (Figure 5F). Moreover, PPARγ and LXR signaling prevents the capture of Env-deficient HIV-1 virus like particles (Figure 8A), suggesting that virus envelope glycoprotein/receptor interactions are not involved in the observed effect. That the effects of PPARγ and LXR signaling on HIV-1 capture are virus envelope glycoprotein-independent is supported by our finding that treatment of DCs with ciglitazone or TO-901317 prevents the capture of viral particles pseudotyped with the envelope glycoproteins of Ebola virus and Marburg virus (Figure 8B). Interestingly, these two viruses are known to require cholesterol for infection [74], [75]. In contrast, treatment with the NR ligands had no effect on the ability of DCs to capture virus particles pseudotyped with the envelope glycoprotein of VSV. Previous studies have demonstrated that VSV-G-pseudotyped HIV-1 particles are efficiently captured by cells depleted of cholesterol using methyl-β-cyclodextrin [43], [75], [76]. These data further support the hypothesis that PPARγ and LXR signaling alter the membrane cholesterol content of DCs, rendering them incapable of efficiently capturing HIV-1 particles.
Although NR ligand treatment limits the expression of immune-activating cytokines and co-stimulatory molecules that are up-regulated as DCs mature, we found that it does not alter the ability of DCs to form conjugates with T cells. The number of DC-T cell conjugates formed with PPARγ and LXR ligand-treated DCs was comparable to that of control untreated DCs (Figure 7A). It will be interesting to determine whether these conjugates represent functional immunological synapses between DCs and T cells. It is worth noting that DC-to-T cell transfer of HIV-1 most likely occurs through the formation of virological synapses [103], [104], [105], [106], [107], [108]. We found that NR ligand treatment does not prevent the formation of virological synapses between DCs and T cells as assessed by confocal microscopy, although ligand treatment does seem to decrease the amount of virus concentrated at the virological synapse (Figure 7B).
Beyond demonstrating the ability of PPARγ and LXR signaling pathways to prevent DC capture and transfer of virus, our results provide support for a number of observations regarding the interactions between DCs and HIV-1. First, we demonstrate that immature DCs can transfer single round replication competent virus to T cells through a Transwell insert that prevents direct contact between the two cell types (Figure 3). Although direct cell-cell contact is required for efficient virus transfer, our data suggest that approximately 20% of infectious virus can be transferred by immature DCs via exosomes or shedding from the cell surface. In contrast, although mature DCs bind approximately 10-fold more virus, less than 10% of transfer is mediated through cell-surface bound viral particles (Figure 4A). Second, our data suggest that a large percentage of virions captured by DCs is internalized or otherwise protected from proteases (Figure 5E). Previous studies have demonstrated that DCs internalize HIV-1, resulting in either degradation of virus particles [56], [65], [109], establishment of productive infection [110], [111], [112], or sequestration into protected intracellular compartments [56], [73], [94], [95]. Although PPARγ and LXR signaling alters the amount of virus captured by DCs, it does not seem to alter the percentage of captured virus that is internalized by DCs (Figure 5E). This is not surprising, since PPARγ and LXR ligand treatment does not alter the endocytic capacity of DCs, as measured by the internalization of FITC-dextran (Figure S2). Finally, our data confirm that DCs can bind virus particles in a gp120-independent manner (Figure 8A). Recent reports demonstrate that host cell-derived GSLs incorporated into the budding virus particle play a critical role in mediating HIV-1 capture by immature and mature DCs in a gp120-independent manner [72], [73]. Taken together with current and previous findings that cholesterol depletion from DC membranes prevents HIV-1 binding [51] (and Figure 9C), these data argue for the presence of a yet-to-be-identified GSL-recognizing attachment factor(s) within lipid raft-like membrane microdomains at the surface of DCs whose function is compromised upon NR ligand treatment.
NR signaling may have beneficial effects on the prevention of HIV-1 transmission beyond the effects on pro-inflammatory cytokine production, migration, and virus capture and transfer. STIs, through engagement of TLRs, and STI/TLR-induced inflammation, can directly activate HIV-1 replication in infected cells. Our data suggest that both PPARγ and LXR ligands repress HIV-1 replication in DCs (Figure 3), although the levels of replication in this cell type are quite low. This finding is consistent with previous studies that have shown that PPARγ ligands repress HIV-1 expression in infected monocytes and macrophages [113], [114], [115]. Recent findings from our laboratory suggest that NR-mediated repression of HIV-1 replication is due to trans-repression (T. Hanley and G. Viglianti, manuscript in preparation), as is thought to be the case for NR-mediated repression of pro-inflammatory cytokine production [30], [31], [32], [33], [34], [35], [36], [37], [38]. Although our data suggest that the majority of virus transferred to T cells is due to virus captured by DCs, and not due to virus newly synthesized in infected DCs, NR-mediated inhibition of HIV-1 replication may contribute to the inhibition of trans-infection that we report here. By preventing HIV-1 replication, in addition to DC migration, pro-inflammatory cytokine and chemokine production, and trans-infection, PPARγ and LXR ligands may block the dissemination of DC-associated virus from the local site of infection to regional lymph nodes.
In the absence of an effective vaccine for HIV-1, the development of topical microbicides that block the early steps of HIV-1 infection and transmission may represent the best option for containing the spread of this global pandemic. To date, there has been limited success with antiviral microbicides. In order to ensure success with future microbicide development, a much greater understanding of the mechanisms involved in the very early stages of mucosal infection and transmission of HIV-1, and the role of DCs in HIV-1 pathogenesis, in particular, are required. Our results contribute to a better delineation of the mechanisms underlying the HIV-1 trans-infection activity of DCs, while having implications for the development of new anti-HIV microbicide strategies. PPARγ and LXR ligands are small lipophilic molecules that readily diffuse across cell membranes and might be amenable to topical formulations. Two PPARγ agonists, rosiglitazone and pioglitazone, are currently approved for the systemic treatment of type II diabetes. A limitation of the present study is that we have not yet examined the effects of NR signaling on HIV-1 transmission in the context of a complex tissue model or an animal model. Despite this limitation, the anti-inflammatory and anti-HIV-1 activities of PPARγ and LXR provide a solid rationale for considering them as drug targets that can act synergistically with conventional anti-viral microbicides that target other aspects of mucosal transmission including virion structure, virus binding/entry, or reverse transcription.
This research has been determined to be exempt by the Institutional Review Board of the Boston University Medical Center since it does not meet the definition of human subjects research.
Primary human CD14+ monocytes were isolated from the peripheral blood mononuclear cells (PBMCs) of healthy donors using anti-CD14 magnetic beads (Miltenyi Biotec) per the manufacturer's instructions. CD14+ monocytes (1.5×106 cells/ml) were cultured in RPMI 1640 supplemented with 10% FBS, 100 U/ml penicillin, 100 µg/ml streptomycin, 0.29 mg/ml L-glutamine, 1000 U/ml IL-4 (PeproTech), and 1400 U/ml GM-CSF (PeproTech) for 6-8 days at the end of which the cells acquired an immature dendritic cell phenotype as assessed by flow cytometry (CD11c+, DC-SIGN+, HLA-DRlo, CD80−, CD86−). Cells were given fresh medium supplemented with IL-4 and GM-CSF every 2 days. Mature dendritic cells were obtained following 48 hour exposure to 100 ng/ml ultra-pure E. coli K12 LPS or 100 ng/ml PAM3CSK4. Primary human myeloid DCs (mDCs) and plasmacytoid DCs (pDCs) were isolated from monocyte- and B cell-depleted PBMCs using anti-CD11c and anti-BDCA4 magnetic beads (Miltenyi Biotec) per the manufacturer's instructions. mDCs were cultured in RPMI 1640 with 10% FBS, 100 U/ml penicillin, 100 µg/ml streptomycin, 0.29 mg/ml L-glutamine, 1000 U/ml IL-4, and 1400 U/ml GM-CSF. pDCs were cultured in RPMI 1640 supplemented with 10% FBS, 100 U/ml penicillin, 100 µg/ml streptomycin, 0.29 mg/ml L-glutamine, and 10 ng/ml IL-3 (PeproTech). Primary human CD4+ T cells were isolated from CD14-depleted peripheral blood mononuclear cells using anti-CD4 magnetic beads (Miltenyi Biotec) per the manufacturer's instructions. CD4+ T cells (2×106 cells/ml) were cultured in RPMI 1640 supplemented with 10% FBS, 100 U/ml penicillin, 100 µg/ml streptomycin, 0.29 mg/ml L-glutamine, 50 U/ml IL-2 (R&D Systems), and 5 µg/ml PHA-P (Sigma) for 6-8 days at the end of which the cells acquired a memory T cell phenotype as assessed by flow cytometry (CD3+, CD4+, CD45RO+, CD45RA–). 293T cells were cultured in DMEM supplemented with 10% FBS, 100 U/ml penicillin, 100 µg/ml streptomycin, and 0.29 mg/ml L-glutamine. MAGI-CCR5 cells were cultured in DMEM supplemented with 10% FBS, 100 U/ml penicillin, 100 µg/ml streptomycin, 0.29 mg/ml L-glutamine, 500 µg/ml G418, 1 µg/ml puromycin, and 0.1 µg/ml hygromycin B. PM1 cells were cultured in RPMI 1640 supplemented with 10% FBS, 100 U/ml penicillin, 100 µg/ml streptomycin, and 0.29 mg/ml L-glutamine.
The LXR ligand TO-901317 was purchased from Calbiochem. The PPARγ ligands ciglitazone and rosiglitazone were purchased from Cayman Chemicals. The ligands were reconstituted in DMSO. The TLR2 ligand PAM3CSK4, the TLR4 ligand E. coli K12 LPS, the TLR7 ligand CLO97, and the TLR9 ligand CpG ODN 2006 were purchased from Invivogen. Unless otherwise noted, DCs were treated with PPARγ and LXR ligands for 24–48 hours, beginning one hour prior to treatment with TLR ligands.
Replication competent R5-tropic HIV-1ADA and X4-tropic HIV-1NL4-3 were generated by infection of PM1 cells. Single-round replication-competent HIV-1-based reporter viruses were generated by packaging a luciferase expressing reporter virus, BruΔEnvLuc2, with the envelope glycoproteins from CCR5-tropic HIV-1(Ada-M), CXCR4-tropic HIV-1(HXB2), VSV (VSV-G), Ebola virus Zaire (EboV-Z), Ebola virus Sudan (EboV-S), or Marburg virus (MarV). EGFP-labeled virus particles were generated by co-transfection of the pro-viral clone HIV-1NL4-3 with an expression vector encoding a Vpr-EGFP fusion protein. Virus stocks were generated by transfecting HEK293T cells using the calcium phosphate method. All viruses were titered on MAGI-CCR5 cells and p24gag content was determined by ELISA.
To assess DC-mediated transfer of HIV-1 to T cells, DCs were incubated with Ada-M- or HXB2-pseudotyped HIV-luciferase reporter virus at an MOI = 0.1 (37.8–40.4 ng p24gag) for four hours at 37°C. Cells were washed five times with PBS to remove unbound virus, seeded in 96-well plates (2.5×105 cells/well), and then cultured with either PM1 T cells (5×105 cells/well) or autologous primary CD4+ T cells (5×105 cells/well) for 48 hours. In some instances, the DCs were seeded in 24-well plates separated from the T cells by a Transwell insert (Corning) with a 0.4 µm pore size. As controls, virus-exposed DCs and virus-exposed T cells were cultured alone for 48 hours. After 48 hours, the cells were harvested, washed two times with PBS, and lysed in PBS/0.02% Triton X-100. Protein levels in cell lysates were determined using a modified Lowry protein assay (BioRad) and luciferase activity was measured using luciferase reagent (Promega) and a MSII luminometer (Molecular Devices). In some experiments, replication competent R5-tropic HIV-1ADA or X4-tropic HIV-1NL4-3 (5 ng p24gag) were used in place of packaged reporter virus and transfer was measured by p24gag ELISA.
DCs (2.5×105 cells/well) were incubated with replication competent R5-tropic HIV-1ADA or X4-tropic HIV-1NL4-3 (5 ng p24gag) for three to four hours at 37°C. Cells were washed four to five times with PBS to remove unbound virus, and lysed in PBS/10%FBS/0.5% Triton X-100. In some experiments, virus-exposed DCs were incubated with 0.5% trypsin for 5 minutes at 37°C to degrade surface-bound virus particles, washed twice in culture medium, and then lysed as above. An ELISA was used to determine the amount of p24gag protein associated with the cells. In some experiments, Ada-M-, HXB2-, VSV-G, EboV-, or MarV-packaged HIV-luciferase reporter virus (5 ng p24gag) or an equal amount of reporter virus lacking envelope glycoproteins (ΔEnv) was used.
Primary CD4+ T cells were labeled with the cytoplasmic dye CMTMR (CellTracker Orange, Molecular Probes) for 30 minutes at 37°C and then washed three times with PBS to remove excess dye. Cells were then incubated for 16 hours at 37°C and washed twice with PBS prior to use in conjugate formation assays. Following labeling, 5×105 T cells were incubated with 2.5×105 unlabeled iMDDCs for four hours at 37°C in a total volume of 200 µl. The conjugates were then fixed by gently adding an equal volume of 4% paraformaldehyde. Samples were run immediately through a flow cytometer. Conjugate formation was assessed by fluorescence associated with the MDDC population.
Primary CD4+ T cells were labeled with the cytoplasmic dye CMCA (CellTracker Blue, Molecular Probes) for 30 minutes at 37°C and then washed three times with PBS to remove excess dye. T cells were then incubated for 16 hours at 37°C and washed twice with PBS prior to use in virological synapse formation assays. 2.5×105 unlabeled mMDDCs were incubated with 100 ng HIV-1NL4-3 virions packaged with Vpr-EGFP for four hours at 37°C, washed four times with PBS, and incubated with 5×105 CMCA-labeled autologous T cells for four additional hours. The cells were fixed in 1% paraformaldehyde, stained with anti-CD81-PE (BD Pharmingen). Z-stacks were captured on the Nikon deconvolution wide-field Epifluorescence Scope at 100×. Using ImageJ software, the images were deconvolved and the fluorescence was summed.
Cholesterol-saturated methyl-β-cyclodextrin was prepared as previously described [116]. Briefly, cholesterol powder was added to 240 mM methyl-β-cyclodextrin solution at 1.16 mg/ml, agitated overnight, and filter sterilized using a 0.22-µm filter. To replete cholesterol, MDDCs were incubated with cholesterol-saturated methyl-β-cyclodextrin at a concentration of 300 µM cholesterol for 30 minutes at 37°C and then washed five times with PBS before being used in virus capture and transfer studies.
MDDCs (2.5×105 cells) were seeded above a Transwell insert with a 5 µm pore size and allowed to migrate through the insert in response to medium or CCL21 (PeproTech). Cells above and below the Transwell insert were fixed in 2% paraformaldehyde and counted in a hemocytometer to determine the relative migratory capacity of the MDDCs. Migration index was calculated by dividing the number of experimental cells that migrated in response to CCL21 by the number of untreated cells that migrated in response to media alone.
Cholesterol efflux into cell-free culture supernatants and cholesterol content of lysed MDDCs were measured using the AmplexRed cholesterol assay kit per the manufacturer's instructions (Invitrogen).
MDDCs were transfected with plasmids that encoded either a mixture of three to five shRNAs directed against ABCA1 or a mixture of control shRNAs (Santa Cruz Biotechnology) and a puromycin-resistance gene using Oligofectamine (Invitrogen) per the manufacturer's instructions. Transfected cells were selected by culture in the presence of puromycin for 48 hours and then used for cholesterol efflux assays, used for HIV-1 capture assays, or lysed for immunoblot analysis to measure ABCA1 expression.
MDDC phenotypes were assessed using antibodies against HLA-DR, CD80, CD86, DC-SIGN, CD11c, CD4, CCR5, CXCR4, and CCR7. Primary CD4+ T cell phenotypes were assessed using antibodies to CD3, CD4, CD8, CD45RO, CD45RA, CCR5, and CXCR4. Flow cytometric data was acquired using a Becton-Dickenson FACScan II and data was analyzed using FlowJo software.
MDDCs (2.5×105 cells/well) or pDCs (1×105 cells/well) were treated with PAM3CSK4 (100 ng/ml), LPS (100 ng/ml), CLO97 (1 µg/ml), or CpG ODN 2006 (5 µM) for 24 hours in the presence or absence of nuclear receptor ligands as described in the legend to figure 1. Cell-free culture supernatants were collected and analyzed for TNF-α (eBioscience), IL-6 (eBioscience), IL-8 (BioLegend), MIP-1α (PeproTech), and RANTES (PeproTech) release by commercially-available ELISA following the manufacturer's instructions.
MDDC cell viability was assessed by trypan blue dye exclusion, MTT cytotoxicity assay, and LDH release using a commercial kit (Promega) per the manufacturer's instructions.
Untreated control and ligand-treated experimental samples were compared using a two-tailed t-test. Experiments were performed in duplicate (mDCs and pDCs) or triplicate (MDDCs) using cells from a minimum of three different donors as indicated in the figure legends (n). Data are presented as the mean ± standard deviation of pooled data from at least three donors.
PPARγ Swiss-Prot # P37231; LXRα Swiss-Prot # Q13133; LXRβ Swiss-Prot # P55055; CCR7 Swiss-Prot # P32248; CCL21 Swiss-Prot # O00585;TLR1 Swiss-Prot # Q15399; TLR2 Swiss-Prot # O60603; TLR4 Swiss-Prot # O00206; TLR7 Swiss-Prot # Q9NYK1; TLR9 Swiss-Prot # C3W5P5; ABCA1 Swiss-Prot # O95477; ABCG1 Swiss-Prot # P45844.
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10.1371/journal.pgen.1007910 | Mycoplasmas under experimental antimicrobial selection: The unpredicted contribution of horizontal chromosomal transfer | Horizontal Gene Transfer was long thought to be marginal in Mycoplasma a large group of wall-less bacteria often portrayed as minimal cells because of their reduced genomes (ca. 0.5 to 2.0 Mb) and their limited metabolic pathways. This view was recently challenged by the discovery of conjugative exchanges of large chromosomal fragments that equally affected all parts of the chromosome via an unconventional mechanism, so that the whole mycoplasma genome is potentially mobile. By combining next generation sequencing to classical mating and evolutionary experiments, the current study further explored the contribution and impact of this phenomenon on mycoplasma evolution and adaptation using the fluoroquinolone enrofloxacin (Enro), for selective pressure and the ruminant pathogen Mycoplasma agalactiae, as a model organism. For this purpose, we generated isogenic lineages that displayed different combination of spontaneous mutations in Enro target genes (gyrA, gyrB, parC and parE) in association to gradual level of resistance to Enro. We then tested whether these mutations can be acquired by a susceptible population via conjugative chromosomal transfer knowing that, in our model organism, the 4 target genes are scattered in three distinct and distant loci. Our data show that under antibiotic selective pressure, the time scale of the mutational pathway leading to high-level of Enro resistance can be readily compressed into a single conjugative step, in which several EnroR alleles were transferred from resistant to susceptible mycoplasma cells. In addition to acting as an accelerator for antimicrobial dissemination, mycoplasma chromosomal transfer reshuffled genomes beyond expectations and created a mosaic of resistant sub-populations with unpredicted and unrelated features. Our findings provide insights into the process that may drive evolution and adaptability of several pathogenic Mycoplasma spp. via an unconventional conjugative mechanism.
| Genome downsizing is often viewed as a degenerative process of evolution. Such erosion has left current mycoplasmas with a minimal genome: for some species its size barely exceeds the amount of information needed for sustaining autonomous life. Despite such limitations, these simple bacteria showcase a baffling capacity for adaptation to complex environments such as that provided by the animal host. By using the enrofloxacin antibiotic as selective pressure, we performed a genome scale analysis of macro- and micro-events leading to antimicrobial resistance in mycoplasmas. Sexually competent cells were found to shortcut this process by using an unconventional mechanism of chromosomal transfer driving massive exchanges of DNA materials. Remarkably, this powerful mechanism was associated with a profound genomic reorganization that reshuffled parental features and created mosaicism. This finding emphasizes the extraordinary adaptability of some pathogenic Mycoplasma spp. and provides major insights into the processes that contribute to shaping the evolution of their minimal genome. While unconventional conjugative mechanisms are being documented in more complex bacteria, the reduced mycoplasma genome may provide a simplified model to study mosaicism and its role in bacterial evolution.
| Over the past decade, advances in metagenomics have uncovered the fascinating richness and diversity of bacterial taxa. As free-living cells or as parasites, bacteria colonize an impressive array of ecosystems, from those offering ideal conditions to those too extreme to support most life forms. To better understand the forces that have shaped bacterial evolution, tremendous efforts have been invested in decrypting their genomes. One main outcome is that our traditional view of bacterial clonality and species boundaries is currently being challenged by the many facets of horizontal gene transfer (HGT), a key player of microbial diversification [1,2]. In this phenomenon, the role of mobile genetic elements (MGE) is central [3–6] and an increasing number of reports suggests that the transfer of these might only represent the tip of the iceberg [7–11]. Indeed, the conjugative transfer of large chromosomal fragments across genomes and their subsequent recombination might be more prominent and complex than first envisaged, with several new emerging mechanisms [7–11] that differ from the canonical Hfr- (or oriT-based) transfers. These latter ones were initially described in Hfr strains of Escherichia coli [12] and are initiated from an origin of transfer (oriT) integrated in the donor chromosome. oriT-based transfers are characterized by a gradient, with genes closer to the oriT being more reliably and more frequently transferred [13], mainly because of physical constraints applying on large molecules during transfer. Usually, in oriT-based transfers, a single region of the chromosome is transferred and incorporated.
HGT was long thought to be marginal in Mycoplasma (class Mollicutes), a large group of wall-less bacteria often portrayed as minimal cells because of their reduced genomes (ca. 0.5 to 2.0 Mb) and their limited metabolic pathways [14,15]. Despite this simplicity, several mycoplasma species are important pathogens of human and a wide range of animals [15,16]. This situation reflects our failure in providing efficient preventive and therapeutic strategies and is due to the mycoplasma astonishing capacity to face the challenging host-environment, escape the immune response and develop antimicrobial resistance (AMR) [14,17,18]. Mycoplasmas live in close contact with their immunocompetent hosts on which they rely for nutrients and, in these wall-less bacteria, several loci have been selected over the course of their evolution that generate surface diversity [17,19]. These encode for a broad range of molecules that are key in host-interactions [17,20] and in escaping the host humoral response. The variation in expression and structure of these products relies on sophisticated genetic systems that combined large gene repertoires with high frequency, stochastic mutations or specific-recombination. In mycoplasmas, these systems account for extensive intra-clonal and inter-strains variability [17]. More recently, comparative genomic studies have uncovered the occurrence of massive HGT in between phylogenetically distant mycoplasma species, a phenomenon that may counteract erosion of the reduced mycoplasma genome and account for genome plasticity [21,22]. This finding was further supported by experimental data showing the conjugative exchange of large chromosomal fragments in Mycoplasma agalactiae, an important ruminant pathogen and a model organism [9]. Congruent in silico and in vitro data further demonstrated that these transfers equally affected all part of the chromosome via an unconventional mechanism, so that the whole mycoplasma genome is potentially mobile. While this has been formally demonstrated for M. agalactiae and M. bovis, increasing evidences point towards HGT also occurring in other species such as in M. pulmonis [23], M. genitalium [24] and in other genera of the class Mollicutes such as in Ureaplasma or Spiroplasma [25–28]. HGT may have tremendous impact on the long and short-term evolution and adaptability of these minimal bacteria but has yet to be explored. Using antibiotics as selective pressure would offer a powerful approach for testing this question in vitro; at the same time understanding the emergence of antibiotic resistance in pathogenic mycoplasmas is of primary importance for public health [18,29].
The horizontal dissemination of MGE carrying AMR genes, within and across bacterial species is one main determinant of the antibiotic crisis [30,31]. In mycoplasmas, the role of HGT in acquiring AMR has long been ignored mainly because of (i) the paucity in MGEs and the total lack of known conjugative plasmids that could disseminate AMR genes and (ii) the scarcity of appropriate genetic tools which, combined to the mycoplasma fastidious culture hampered testing the hypothesis under laboratory conditions. The main genetic pathway described so far for the emergence of AMR in these organisms is the occurrence, selection and fixation of chromosomal mutations in target genes [18,29]. For instance, mutations conferring quinolone resistance have been reported in pathogenic mycoplasma species [32–34] as well as in several other bacterial taxa [35,36]. Quinolones, an important class of antibiotics effective on the wall-less mycoplasma cell, exert their antibacterial effect by preventing the DNA gyrase and the topoisomerase IV from unwinding and duplicating DNA [37,38]. Mutations occurring in genes encoding DNA gyrase subunits, gyrA and gyrB, and/or topoisomerase IV subunits, parC and parE, result in structural changes in the respective enzyme that limit antibiotic fixation, with the QRDR (Quinolone Resistance Determining Region) of GyrA and ParC being most often affected at key positions (amino acids 83 for GyrA and 80, 84 for ParC according to E. coli numbering) [36].
In this study, we experimentally explored the impact of conjugative chromosomal transfer on mycoplasma evolution and adaptation using a fluoroquinolone, the enrofloxacin (Enro), as selective pressure and M. agalactiae, as a model organism. For this purpose, we first generated spontaneous isogenic mutants displaying different combination of mutations in gyrA, gyrB, parC and parE together with various level of resistance to Enro. We then tested whether these mutations can be acquired by a susceptible population via conjugative chromosomal transfer knowing that in several Mycoplasma spp., including our model organism, the 4 target genes are located in distinct chromosomal loci; in M. agalactiae these are separated by at least 250 kb, with parE and parC being part of a same operon. Under antibiotic selective pressure, spontaneous mutants emerged stepwise following a similar pathway with HGT acting as an evolutionary accelerator that reshuffled genomes and created a mosaic of resistant sub-populations with unpredicted and unrelated features. Our findings provide insights into the process that may drive evolution and adaptability of several pathogenic mycoplasma species and bring into the light an unconventional conjugative mechanism.
Prior to testing the impact of Mycoplasma Chromosomal Transfer (MCT) on AMR acquisition (see below), we analysed the evolutionary pathway leading to high resistance to enrofloxacin (Enro) in a set of spontaneous mutants derived from PG2 55–5. For this purpose, six lineages namely MF26 and MF29 to MF33 (Fig 1), were generated by rounds of single-colony bottleneck selection on solid medium containing stepwise concentrations of Enro (0.5 to 32 μg·ml-1, with a two-fold step interval). At each step, single colonies were picked and analysed as described in Materials and Methods. Overall, 22 individual isogenic clones with MIC (Minimal Inhibitory Concentration) values ranging from 1 to 64 μg·ml-1 were selected and their parE-, parC-, gyrA- and gyrB-QRDR (Quinolone Resistance Determining Region) [39,40] were sequenced, directly from the chromosome.
Sequence data revealed the occurrence of 1 to 3 single-point mutations in each of the 22 EnroR clones (Fig 1), with a total of 11 SNPs (Single Nucleotide Polymorphisms) detected in distinct positions, none being silent. All had at least one mutation within parC which always corresponded to a transition, C>T or G>A, and resulted in amino-acid changes at codon 80 and/or 84 of the QRDR (further designated parC80 and parC84, respectively). In 68% of the mutants, an additional point mutation was present in gyrA that also resulted in codon change within the QRDR, most often at codon 83 and in some cases, at codon 87 or 81 (further designated gyrA83, gyrA87 and gyrA81, respectively). Finally, additional mutations were occasionally found in parE and gyrB, affecting the canonical QRDR only in mutants MF29-1-3-6 and MF31-1, in parE codon 420 (parE420) and gyrB codon 426 (gyrB426), respectively (Fig 1).
Within each lineage, the MIC value was shown to increase over the stepwise selection process together with the number of accumulated mutations. This is illustrated in Fig 1, with for instance the MF26 lineage acquiring a new mutation at each round of selection, in parC, gyrA and then parE, concomitantly to a MIC increase from 1 to 32 μg·mL-1. This pattern was observed for all lineages, with a few cases of one-step MIC increase that were not linked to an additional mutation in the sequenced regions (Fig 1, MF30-1>MF30-1-4 and MF29-1>MF29-1-3).
Overall, EnroR mutations accumulate following a common pathway, emerging first in parC, then in gyrA (or for MF31 in gyrB) and last in parE. From these data, the contribution of parE mutations did not seem as critical towards resistance as those occurring in parC and gyrA, some mutants having a high MIC value and no parE mutation (see for instance MF30-1-4-1).
Passaging of PG2 55–5 in broth medium containing increasing concentration of Enro, without intermediate rounds of sub-cloning onto solid medium, resulted in a selected PG2E10 population having a MIC of 64 μg·ml-1 and 3 SNPs located in parC80, parC84 and gyrA83, as in MF30-1-4-1, MF30-1-4-8 and MF29-1-3-1 (Fig 1).
Some discrepancies between the number of mutations and the MIC values (see above) raised the question of whether the level of resistance may be modulated by mutations occurring outside the QRDR regions. To address this issue, the genome of 13 clones belonging to 3 independent and representative lineages (MF26, MF33, and MF30) was fully sequenced by Illumina with a mean coverage of 3100X. SNPs and indels were identified by variant calling analyses using the PG2 55–5 parent clone as reference (Fig 2). For 6 mutants (MF33, MF30, MF33-1, MF30-1-4, MF30-1-4-1, and MF30-1-4-8), WGS (Whole Genome Sequencing) data revealed the occurrence of the SNPs parE86, parE112, parC291, parC547, gyrB29 and gyrB278 (S2 Table) in the 3 target genes; these SNPs are located outside the region previously sequenced with the Sanger method and thus are outside the QRDR. Data also suggested that gyrB mutations found in MF30 and MF33 lineages may have a negative impact on the further selection of highly resistant mutants. Indeed, the gyrB29 and gyrB218 mutations were only detected in the founders, MF33 and MF30. Reversion of these mutations in progenies coincided with the emergence of mutations in gyrA which ones were further transmitted under increasing antibiotic pressure, a series of events that may reflect an epistatic phenomenon. As well, the reversion of parC291 and parC547 mutations in MF30 lineage was accompanied by the appearance in more resistant progenies of new mutations in parC and parE. Overall, these abrupt changes may be due to the constraints imposed by the interdependence of gyrA and gyrB or parE and parC subunits in forming a functional DNA gyrase or topoisomerase IV, respectively, that will best withstand the antibiotic pressure. A few mutations (SNPs and indels) were also detected outside of the classical quinolone target genes (Fig 2 and S2 Table), with most occurring in homopolymeric tracts that are known as being prone to high frequency insertion-deletion [17]. For instance, nt-707306 and/or nt-711627 both underwent a C deletion within a polyC of the so-called spma locus which encode phase variable membrane proteins [41]. As well, the MF33-1-1 and -1-2 siblings both contained sub-populations displaying a large number of SNPs (14 and 11) within the highly variable vpma locus [42]. Overall, sequenced genomes contained from 3 to 23 mutations, with a mean of 10.2 ± 5.8 mutations, with approximately half being fixed in the population (present in ≥95% of the reads).
In parallel, the genome of the EnroR PG2E10 was analysed and a total of 8 mutations were detected, 3 fixed and 5 non-fixed (present in <95% of the reads and further refer as polymorphic sites), when compared to the parental strain (Fig 2 and S2 Table). As expected, 3 SNPs were found in the Enro target genes, parC80, parC84, gyrA83, and an additional one was detected in parE112, outside of the QRDR initially sequenced by Sanger (see above). Among the 3 studied lineages, this combination of 4 SNPs was only found in MF30-1-4-1 and MF30-1-4-8, which MIC of 64 μg·ml-1 is identical to that of PG2E10. The other 4 mutations occurred outside the target genes and correspond to either highly variable loci (see above) or to mutations occurring only in minor subpopulations (polymorphic sites). Based on competition fitness assays, these mutations did not appear to impose a cost on the PG2E10 fitness (w = 1.03 ± 0.10) when compared to PG2 55–5 parent (see Materials and methods).
Our hypothesis is that horizontal conjugative chromosomal transfer may act as a driving force of mycoplasma short-term evolution. With this in mind, we tested whether multiple, distant chromosomal EnroR point-mutations can be simultaneously transferred by HGT from a resistant to a susceptible strain and further selected in presence of the antimicrobial. For this purpose, two independent mating experiments (T5 and T6) were performed using as donor the EnroR PG2E10 mutant (see above) which displayed the highest MIC (64 μg.ml-1) but the smallest number of fixed mutations (see Fig 2 and S2 Table). As recipient, we choose the 5632G3 clone previously derived from the EnroS 5632 strain (MIC = 0.125 μg.mL-1) and in which the gentamicin-resistance marker (Gm) is stably inserted as a proxy [9] (see Materials and methods) (Fig 3A). Transconjugants were selected on solid media containing 50 μg·mL-1 of gentamicin in addition to Enro at concentrations ranging from 0.25 to 8 μg·mL-1 (equal to 2 to 64 fold the MIC 5632G3). Repeated attempts consistently yielded transconjugants colonies on solid medium containing 0.25 μg·mL-1 of Enro (except for T5-5 obtained at 0.5 μg·mL-1) with a low frequency ranging from 2.7.10−11 to 7.2.10−8 transconjugants per donor-CFU, depending on the 5632G3:PG2E10 initial ratio (1:10 or 10:1, respectively, see Materials and methods).
A total of 18 individual transconjugants were then picked and subjected to a series of PCR assays. These targeted the Gm marker and 11 distant loci that are distributed around the genome and discriminate 5632 from PG2 (S1 Fig, S1 Table). Of these, 8 were previously described [9] and 3 were specifically designed in this study to distinguish PG2-parC, -gyrA and -gyrB from their 5632 counterparts. PCR data indicated that the transconjugant genotypes were a composite of PG2 and 5632 genomes (S1 Fig) except for T6-7 which was further shown by sequencing to have a chimeric gyrA (see below). They further designated 5632G3 as the recipient chromosome, with a majority of the PCR products being 5632-specific and the Gm marker constantly detected at the same position, as in the parent. This finding was in agreement with our previous data showing that chromosomal transfers always occurred from PG2 (donor) to 5632 (recipient) [9]. Overall, 10 distinct PCR profiles were observed, with several transconjugants sharing identical profiles (S1 Fig).
Whole Genome Sequencing (WGS) by Illumina was performed with a subset of 13 transconjugants that were selected (i) to represent each of the 10 PCR profiles identified above and (ii) to include transconjugants with identical PCR profiles that were generated during independent (T5-4 and T6-1) or during the same (T6-4, -8, -9) mating experiments. Sequence data confirmed that all displayed the 5632G3 chromosome as genetic background designating the corresponding strain as the recipient (Fig 3A). Further analyses demonstrated the systematic transfer of PG2E10 donor remote Enro target-genes containing (i) the mutated parE/parC operon (10/13 transconjugants) together with either the mutated-gyrA or the wild-type gyrB (wt) or (ii) the mutated gyrA alone (3/13) (Fig 3A). A close-up image of these regions is depicted in Fig 3A and shows that two fixed mutations, corresponding to parC80 and/or gyrA83, were always associated to the transfer. Of note, the chimeric structure of T6-7 gyrA explains the PCR result obtained above (S1 Fig).
Of the 13 transconjugants analysed, 10 had received parE sequences from the donor. One, T5-2, had acquired two mutations that were pre-existing in PG2E10 sub-populations (92% and 60% of the reads, respectively), parE112 and parC84. The remaining 9 transconjugants displayed one mutation in parE, not previously detected in the donor, that was either (i) an insertion of 3 nt resulting in adding an Ala residue at codon 390 or (ii) a non-synonymous SNP corresponding to codon 423 or 625. At least, mutations corresponding to codons 390 and 423 were independently confirmed by direct genome sequencing. Whether the occurrence of these mutations in some transconjugants reflects the heterogeneity of the PG2E10 donor population, with sub-populations being selected here, or whether they have arisen independently after transfer is not known. Of note, T5-2 parC and T6-5 gyrA were more chimeric than in other transconjugants as if multiple recombination events have occurred to produce mosaic genes composed of 5632 and PG2 intermingled sequences (Fig 4A).
Interestingly, none of the transconjugants accumulated all 4 SNPs described for the PG2E10 in the Enro target genes. As well, none reached the 64 μg·mL-1 MIC of the PG2E10 parental strain but their individual MIC value that ranged from 0.5 to 32 μg·mL-1 (Fig 4A) was always higher than the concentration used for selection (0.25 μg·mL-1). More specifically, transconjugants having concomitantly acquired the two distant PG2E10 loci containing the mutated parE-parC and the mutated gyrA had the highest MIC (16 μg·mL-1 to 32 μg·mL-1). In T5-1 and T5-2 that displayed the lowest MIC value (0.5 μg·mL-1), the mutated parE-parC were co-transferred with the wild-type PG2E10-gyrB instead of the mutated PG2E10-gyrA. Since donor and recipient gyrB allelic sequences differ slightly, this event introduced amino acid changes in GyrB when compared to the parental 5632-background (S5 Table). PCR genotyping indicates that this same combination was also observed in T6-2, a transconjugant derived from the same partner but in an independent mating experiment (see S1 Fig). Finally, it is interesting to note that the co-transfer of the PG2E10 mutated-gyrA and its wt-gyrB was never observed. In agreement with data obtained with the spontaneous mutants, the transfer of mutated-donor gyrA only was not sufficient to confer the recipient strain with the EnroR phenotype.
Overall, mutations conferring resistance to Enro with MIC values ranging from 16–32 μg.mL-1 could be acquired by a susceptible population within one mating experiment via HGT, while reaching the same levels of EnroR MIC through spontaneous mutations would have required approximately 100 generations and multiple passages under selective pressure.
The co-transfer of multiple loci and the possible occurrence of additional macro- and micro-heterogeneities were addressed in the 13 sequenced transconjugants. Reconstruction of the composite-genomes was performed as previously described with some minor modifications [9] (see Materials and methods). Briefly, reads generated by NGS (Next Generation Sequencing) were mapped onto the 5632 and PG2 reference genomes and reads perfectly matching to one or the other genome were retained. Analyses of the reconstructed genomes and more specifically of the PG2 inherited sequences confirmed that other fragments, unrelated to topoisomerase genes carrying EnroR mutations, were also exchanged (Fig 3A and S2 Fig). This resulted in complex mosaic genomes, containing an average of 18 ± 7 PG2E10 fragments (Fig 3B) which size varied from 77 bp to 53429 bp. All transconjugants display distinct patterns of transferred fragments, except for 3, which had strictly identical genome sequences (S2 Fig). These clones, namely T6-4, -8 and -9, were all selected from the same mating experiment and are most likely the result of the expansion of a single transconjugant as all were shown to be fitter than the parent or than other transconjugants produced during the same mating (i. e. T6-5 and T6-1) (Fig 3B). Competitive culture assays also indicated that there was no correlation between the number of fragments or the overall DNA amount that was exchanged and the fitness level (Fig 3B), with some combinations imposing a fitness cost while other conferring a fitness benefit.
Overall, the most frequently transferred regions were clustered within 20 kb around the selective EnroR determinants, but distant loci were also exchanged in all transconjugants. This suggested that multiple events of genomic replacements by recombination have occurred simultaneously. Although the PG2 and 5632 genomes are highly syntenic, some genes or regions are only present in one strain. Thus, in some cases, replacement of 5632 recipient genome by a PG2 fragment resulted in the loss or in the gain of strain-specific genes. On average, 13 ± 11 5632-specific genes were lost for 6 ± 4 PG2-specific genes that were gained (S3 Table). One extreme case of replacement resulted in the deletion of a large region (ca. 22 genes, 27 Kb) which contained an integrated conjugative element (ICE) specific to 5632 and not present in PG2 [43]. This was observed in T5-2, T5-5, T6-1 and T6-7 transconjugants (Fig 3A, S3 Table) where the loss of the 22 genes was not due to the ICE excision but to recombination events occurring at homologous sites on each side of the ICE.
In addition, micro-complexity events were observed, with transconjugants displaying short PG2-inherited fragments (180 ± 29 nt) that were defined by only one or two PG2-specific variations (SNPs or indels), and/or the occurrence within PG2-inherited fragments of short 5632 fragments defined by one or two 5632-specific variations.
Overall, chromosomal exchanges by recombination of large or small fragments were shown to often occur within a coding sequence, resulting in chimeric PG2/5632 genes as illustrated above for parC and gyrA. On average 20 ± 7 genes were mosaic to various degrees, for each transconjugant.
To evaluate the stability of the EnroR spontaneous mutants and transconjugants in absence of selective pressure, two different spontaneous mutants, namely, MF33-1-1 (MIC = 32 μg·mL-1) and PG2E10 (MIC = 64 μg·mL-1), and two different transconjugants T5-1 (MIC = 0.5 μg·ml-1) and T5-5 (MIC = 16 μg·mL-1), were submitted to serial passages in broth medium 40 times (P0 to P40). WGS was performed with DNA extracted at P10 and P40 that correspond to approximately 165 and 605 generations, respectively (see Materials and methods). Based on comparative analysis, the genomes of the 2 mutants and the 2 transconjugants were remarkably stable over this period, in agreement with the overall stability of their MIC over passages (Fig 4B and S4 Table).
Interestingly, the two non-fixed mutations pre-existing in PG2E10 parE and parC, respectively, gradually faded in favour of the wildtype (Fig 4B): one was detected in parE112 in 92%, 30% and 0% of the reads and the other in parC84 in 60%, 27% and 0% of the reads, at P0, P10 and P40 respectively. Concomitantly, an indel emerged in parE390 at P10 (66%) and P40 (93%) that resulted in the insertion of an Ala residue. Interestingly, this same insertion occurred in 3 transconjugants: T5-1, T5-3 and T6-5, in which it was fixed. As shown in Fig 4A, the absence of mutation in parC84 in all but one transconjugant coincides with the presence of mutations in parE. Altogether, these data suggested that mutations in parC might have a slight fitness cost that tended to be compensated over passages by the introduction of mutations in parE, the functional partner of parC. Of note, other polymorphic sites were observed elsewhere in the genome during passages. In particular, MF33-1-1 with 9 polymorphic sites at P10, displayed the highest number of non-fixed mutations (excluding those in vpma locus) most of which (6/9) being lost at P40 (S4 Table).
We then investigated the fitness of the two mutants and two transconjugants over passages in broth medium. Data presented in Fig 5 indicated that mutants MF33-1-1 and PG2E10 displayed a fitness similar to that of the PG2 55–5 ancestor (P0) that remained stable over passages (P10 and P40). In contrast, the fitness of transconjugants T5-1 and T5-5 at P0 was reduced by 30 to 20%, respectively and increased over successive passaging to reach 120 and 100% when compared to the recipient strain, 5632G3. WGS showed that a few different, polymorphic sites accumulate over passages in both the mutants and the transconjugants, without any obvious link to fitness (see S4 Table).
Over the past decade, HGT has increasingly attracted attention and is now recognized as a main driver of microbial innovation, with conjugation as one prominent mechanism [2,44,45]. Yet, knowledge regarding the mechanisms and impacts of HGT in mycoplasmas is very limited, with only a few publications dedicated to this topic [9,21–23]. By combining next generation sequencing to classical mating and evolutionary experiments, this study uncovered the role of an unconventional mechanism of HGT in generating mosaic genomes in M. agalactiae. Under evolutionary experimental conditions, this phenomenon acted as an accelerator of AMR dissemination by providing susceptible mycoplasma cells with the ability to rapidly acquire, from pre-existing resistant populations, multiple chromosomal loci carrying AMR mutations.
In M. agalactiae, high-level of Enro resistance can be reached via the emergence of spontaneous chromosomal mutations during propagation with increasing concentrations of the antimicrobial, as shown for other Mycoplasma species [32,34,46]. The comparison of several, independent lineages indicates that these mutations accumulate following a similar trajectory: first in parC resulting in a 8 to 16-fold increase in resistance, followed by additional mutations in gyrA to reach up to 128-fold increase. Higher resistance levels (up to 500-fold) were further achieved by combining either two mutations in parC with one in gyrA or, one mutation in each with one or more mutations in parE. WGS data further showed that only very few other mutations were selected and fixed outside of these genes, none that could account for the resistance phenotypes. Whether these played a role in counterbalancing a potential fitness cost during the selection process is not known but mutations in type II topoisomerase genes had no effect on PG2E10 fitness in vitro when compared to the ancestor strain (w = 1.03 ± 0.10). Overall, accumulation of fixed mutations in type II topoisomerase genes and high levels of resistance were reached over several weeks of propagation, after approximately 200 or 100 generations depending on whether selection was performed with or without bottleneck selection, respectively. A limited number of reports addressed evolutionary trajectories of fluoroquinolones resistance in bacteria and each identified species-specific mutational trajectories with identical target-site mutations emerging in different order [47–49]. In M. agalactiae, the convergent outcome of parallel independent experiments strongly suggested intermolecular epistatic interactions between DNA topoisomerases in the mechanism of fluoroquinolone resistance.
The time scale of the mutational pathway leading to high-level of Enro resistance could be compressed into a single mating experiment, in which EnroR alleles were co-transferred from resistant to susceptible mycoplasma cells. Such event required the physical contact and a form of sexual competence of the pair [5,9], as well as one partner being already highly resistant. Independent mating experiments generated progenies with chimeric genomes made of the 5632-recipient chromosome in which sequences of the resistant PG2E10-donor were transferred and recombined at homologous loci. Under the antimicrobial selective pressure, all transconjugants displayed the mutated parE-parC operon or/and the mutated gyrA of the donor, but resistance per se (from 4 up to 250-fold-increase in resistance) was reached only when both mutated loci were co-transferred. These data are in agreement with conclusions drawn from the analyses of co-evolved lineages (see above): alteration of both the topoisomerase IV and the DNA gyrase subunit A is critical for mycoplasmas’ quinolones resistance.
Mutations not previously detected in neither of the parents even as a minor population, were observed in the transconjugants having acquired parE-parC donor sequences (corresponding to parE390, parE423 and parE625) (Fig 4A). The mutation affecting parE390 is also emerging in the donor strain after 40 serial passages in medium without Enro (Fig 4B), raising the question of whether parE mutations (i) were pre-existing in the parent population at undetectable levels and were preferentially selected after mating or (ii) occurred de novo during or just after mating. It is interesting to note that in the donor strain, while the parE390 mutation emerged over passages, the mutations parE112 and parC84 were conversely being replaced by wild type (wt) sequences. Whether parE390 is being beneficial to the transconjugants in the context of the experiment, either towards resistance or fitness, is not known.
Surprisingly, none of the selected transconjugants reached the MIC of the donor, most likely because none displayed the exact combination of parE-parC and gyrA mutations found in the predominant PG2E10 population. Whether such transconjugants did arise during mating but were outcompeted by others is one possible explanation. An interesting observation is that all transconjugants carried the mutated gyrA or the wt-gyrB of the donor, none having inherited both genes from a single parent. While the two strains, 5632 and PG2, encode very similar GyrA and GyrB products these are not strictly identical, with 99.2 and 98.6% identity respectively (S5 Table). Since the DNA gyrase is composed of two GyrA and two GyrB subunits, all transconjugants expressed a modified version when compared to that of the recipient cell prior to mating. Whether this provided an advantage in the context of our experiment, or whether it reflects an epistatic phenomenon [50,51] remains to be addressed.
The most unexpected outcome of this study was the extent of combinatorial variation obtained after mating. Mycoplasma Chromosomal Transfer (MCT) was initially shown to differ from classical Hfr- or oriT-mediated transfer in that it affects nearly every position of the genome with equal efficiency [9]. Because NGS data had been obtained using pools of transconjugants, MCT was then thought to be limited to the transfer of one or two proximate loci in between two cells. Here, analyses of individual transconjugants revealed a much complex picture with the simultaneous transfer of small and large fragments distributed around the genome (Fig 3A). Indeed, this phenomenon created within a single step a set of totally new genomes that were a combinatorial blend of the two parents. Thanks to the significant differences in genome sequence existing between the two parental strains (average 1 variation every 26 nt), transconjugant genomes could be reconstructed with a high level of precision, revealing that besides the gain and loss of entire genes, MCT also generated chimeric genes. Overall, MCT affected from 6 to 17% of the genome regardless of whether these encoded housekeeping or accessory gene functions.
An average of 18 donor-fragments co-exists in the new transconjugant genomes, of which only 2 carried the selectable EnroR mutations. This implies (i) that a large amount of unrelated fragments silently co-transferred along with the selectable marker, some of which may confer the cell with new, yet unpredictable phenotypes and (ii) that a large proportion of mosaic genomes have not been selected and that most likely, the combinatorial possibilities of conjugative MCT are endless. Because MCT introduces variation instantly, one limitation of this phenomenon is the viability and adaptability rate of the resulting chimeric cells. Although both parents are of the same species, the overall success of the transconjugants depends on how well the donor and new chimeric genes interact with the remaining recipient’s genes in a particular environment [52]. For a few generations, the cell may have to cope with multimeric enzymes or products which sub-units are not of a perfect match depending of the protein turn-over of the recipient cell. To a lower extent, this situation resembles genome transplantation used to engineer mycoplasmas and thus faced a number of similar issues [53]. Although several transconjugants turned out to be highly resistant to Enro, their initial selection could only be achieved in low concentration of Enro (0.25 μg.mL-1). This raised the question of whether growth on selective media was impaired because of the low turnover of recipient wt GyrA/ParC or because of a synergistic effect of the Enro with the gentamicin used for selecting transconjugants.
Incorporating large amount of incoming donor DNA had a fitness cost for most transconjugants but not all. Surprisingly, this was counterbalanced after a few passages in media with even one transconjugant ending with a higher fitness than the recipient or the donor cell. In contrast, passaging had no effect on the fitness of EnroR spontaneous mutants derived from the donor (Fig 5) suggesting that new genome configurations may require a certain period of time for fine-tuning. In search for compensatory mutations, comparative analyses of WGS before and after passages were conducted that indicated the emergence of subpopulations with an overall low number of mutations, none of which could explain the improved fitness. Quantifying pathogen fitness in its entire life cycle is not trivial [54] and whether transconjugants selected in this study perform better than their parents in the animal host remains to be addressed.
Clues on the impacts of MCT were provided by our earlier in silico work that revealed massive HGT in between phylogenetically distant ruminant Mycoplasma spp. [55]. Loci that were exchanged in M. agalactiae accounted for 18% of its genome and often encompassed gene cluster with highly conserved organisation that were distributed around the genome [55]. Rather than successive independent HGT events, this picture might reflect the concomitant transfers of multiple unrelated fragments during mating. Within the ruminant host, M. agalactiae and some members of the M. mycoides cluster are often re-isolated from a same organ where they co-habit [14], a prerequisite to conjugative transfers. Throughout the process of infection, these populations have to face a series of bottlenecks applied by the host-response and the host-hostile environment. The mycoplasma minimal cell may be particularly vulnerable to the deleterious effect of Muller’s ratchet due to its limited genetic content and lack in DNA repair components. MCT may provide these organisms with a means to rescue their injured genomes by restoring deleted or inactivated genes. Yet, the repertoire of mosaic genomes produced in the host is likely to be limited by a low MCT frequency, although some parameters such as stress may trigger the phenomenon, and the viability of the chimeric genomes within the hostile host-environment.
While sharing the same ecological niche is an obvious facilitator of HGT, MCT was shown in M. agalactiae to rely on ICE, most likely because these conjugative transfers being dependent on the ICE-encoded conjugal pore [3,5,9]. Although ICE occurrence varies among strains of a same species [25,56], conserved ICE-elements have been detected in about 50% of the mycoplasma species with sequenced genome [25] suggesting that MCT might not be restricted to ruminant mycoplasmas but may occur in species that colonize man and swine. Horizontal chromosomal transfers that do not conform to the canonical Hfr- (or oriT-based) model are increasingly being reported [7,8,57,58] and mosaic genomes were recently described in Mycobacterium smegmatis as the result of Distributive Conjugative Transfer (DCT) [52]. As for MCT, the exact molecular mechanism driving these events remains to be fully elucidated.
Overall, our study unravelled the astonishing capacity of MCT to generate unlimited genome diversity. While this process may contribute to counteract the erosion of the small mycoplasma genome [59], it can also rapidly promote the mycoplasma short-term adaptability to changing environment. Our findings reinforce the central role played by HGT in promoting evolutionary adaptation but also challenge our view on the boundaries of bacterial species and on our capacity in predicting the emergence of new phenotypes.
The PG2 clone 55–5 [57] and 5632 clone C1 [41], further referred as PG2 and 5632 for simplicity, were previously derived from the PG2 and the 5632 strains of Mycoplasma agalactiae respectively. The 5632 gentamicin-resistant clone (5632G3), designated as 5632G-3 in previous publication [9], was obtained by stable insertion at nucleotide 919899 of the gentamicin-resistance gene, aacA-aphD [9]. All strains were propagated at 37°C in SP4 medium [60] supplemented with 5 mM pyruvic acid (Sigma-Aldrich) and 45 μg.mL-1 cefquinome (cobactan 4.5%, MSD Animal Health) and, when needed, with enrofloxacin (Sigma-Aldrich) and/or gentamicin (Sigma-Aldrich) at specified concentrations.
Based on CFU counts taken at different times of the exponential growth phase, the doubling time (or generation time, G) of M. agalactiae PG2 55–5 was calculated to be equal to 3.3 ± 0.14 hours per generation (ca. 7.2 generations per 24h). The number of generations needed to generate the mutants and the transconjugants was estimated using this value as reference multiplied by their time of growth in broth medium only (the number of generations needed for a single cell to form a colony was not taken into account).
Mycoplasma PG2 55–5 cells from a 1 mL of mid-exponential culture were centrifuged for 15 min at 8000 g at room temperature and re-suspended in SP4 medium containing 0.5 μg.mL-1 enrofloxacin. After 48h, 10 μL cultures of 108 to 109 CFU.mL-1 were plated on SP4 agar plates with increasing enrofloxacin concentrations (0.5 to 2 μg.mL-1). Colonies were only obtained on plates containing 0.5 μg.mL-1 enrofloxacin. They were picked and propagated in SP4 broth medium with the same antimicrobial concentration. This represented the first step of selection used in this study and constituted the basis of the lineages. One to 3 additional rounds of selection were similarly performed with increasing concentration of enrofloxacin (ranging from 0.5 to 32 μg.mL-1), with colonies growing on the highest concentration being picked and subjected to the next round. A total of 108 clones were obtained, among which 22 clones corresponding to 6 lineages were analysed. In parallel, PG2 55–5 cultures (108 to 109 CFU.mL-1) were propagated by serial passaging (dilution 1/50 or 1/100), in broth medium containing increasing enrofloxacin concentration (0.25, 0.5, 1 and 10 μg.mL-1) to generate the resistant PG2E10 population.
Mutations in Quinolone Resistance Determining Region (QRDR) sequences of the target genes were identified by direct sequencing using the BigDye Terminator chemistry [61,62] and by whole genome sequencing. Direct sequencing of genomic DNA was performed at the genomic platform of Get-Purpan (Toulouse, France) using primers listed in S1 Table and genomic DNA extracted with chloroform, as previously described [63]. Of note, amino-acid positions of type II topoisomerases were numbered according to the Escherichia coli K-12 strain nomenclature, GyrA (AAC75291.1), GyrB (AAT48201.1), ParC (AAC76055.1) and ParE (AAA69198.1).
Mating experiments were performed as previously described [5]. Briefly, the donor strain (PG2E10) and the recipient strain (5632G3) were grown individually in SP4 medium, during 24 h. The two cultures were mixed at a 5632G3:PG2E10 cell ratio of 1:10 for one experiment (T5) and 10:1 for the second (T6) and then centrifuged for 5 min at 8000 g at room temperature. Cells were re-suspended in SP4 medium, incubated during 16 h at 37°C and an aliquot of 300μl was plated in SP4 agar containing gentamicin (50 μg.mL-1) and different enrofloxacin concentrations (from 0.25 to 8 μg.mL-1). After several days of incubation at 37°C, single colonies were picked from plates with the highest antibiotic concentration before being propagated in SP4 liquid medium with the same concentration of enrofloxacin. Of note, colonies were only observed on solid media containing 0.25 μg.mL-1 of enrofloxacin, with the exception of one transconjugant, T5-5, which grew at 0.5 μg.mL-1. Mating experiments using PG2 55–5 (EnroS) and 5632G3 were used as negative control, to test the absence of enrofloxacin spontaneous resistant clones. The frequency of transconjugants was determined as the number of transconjugants, divided by the number of PG2E10 donor parental cells.
PG2- or 5632-specific PCR assays were used to determine the parental origin of 11 genomic loci across the transconjugant genomes (S1 Table). Of these, 8 were previously described [9] and 3 were specifically designed for this study that targeted parC, gyrA and gyrB. PCR assays were conducted using genomic DNA extracted with the chloroform method [63] and primers listed in S1 Table. The presence and position of the 5632G3-specific gentamicin resistance marker (Gm) was confirmed by a specific PCR using one primer inside the marker and the other in the flanking chromosomal sequence (S1 Table). All PCR amplifications were performed according to the recommendations of the Taq DNA polymerase suppliers (M0267S, New England Biolabs).
The enrofloxacin MICs were determined according to the recommendation of Hannan 2000 [64] using the agar dilution method as previously described [32]. Briefly, 1 μL of each clone diluted to 104−105 CFU.mL-1 was spotted on agar plates containing serial two-fold dilution of enrofloxacin (from 0.0625 to 64 μg.mL-1). MIC assays were performed in triplicates for each clone, and the median value was retained. The MIC was defined as the lowest concentration of enrofloxacin that prevented visible growth after 5 days at 37°C while, in parallel 30 to 300 CFU were observed on the antimicrobial-free control plate. Based on Hannan 2000 [64], we considered in this study isolates with MIC of ≤0.5 μg.mL-1 as susceptible (EnroS) while MIC ≤1 μg.mL-1 and ≥2 μg.mL-1 corresponded to intermediate and resistant isolates (EnroR). Here, isolates with MIC ≥16 μg.mL-1 were further referred as being highly resistant.
A pairwise competition assay was performed to estimate the relative fitness of evolved strains versus their ancestor (i. e. transconjugants versus 5632G3 or spontaneous mutants versus PG2 55–5). For each pair, the evolved and the ancestor clones were mixed in SP4 medium at a 1:1 cell ratio (104 CFU.mL-1). Serial dilutions of the starting (0h, T0) and final (18h, T18) co-cultures were plated on SP4 plates containing none or 4 μg.mL-1 of enrofloxacin. After 5 days at 37°C, the number of CFU was determined for the ancestor and the evolved clones. Fitness of each clone relative to its ancestor was calculated according to the equation: w = Fitness evolved/ancestor = ln(evolved at T18/evolved at T0)/ln(ancestor at T18/ancestor at T0) [65,66]. Concerning the fitness of EnroS GentaR transconjugants with MIC ≤0.5 μg.mL-1, selection onto enrofloxacin solid media was obviously not feasible. These were then performed using 5632 as the ancestor and the gentamicin as selective antimicrobial (50 μg.mL-1). Their fitness ratio was then corrected by multiplying by 1.17, a value equal to the fitness ratio of 5632 versus 5632G3. At least three replicates were performed for each assay and the mean value and the standard deviation (SD) were calculated. A value of 1 indicated a fitness of the evolved strain similar to the ancestor (5632G3 or PG2 55–5), a ratio lesser than 1 or greater than 1 indicated a fitness-cost or -benefit for the evolved strain, respectively.
Genomic DNA was extracted from mycoplasma cells using the phenol-chloroform method [67]. Whole genome sequencing was performed at the GATC Biotech facility (Konstanz, Germany) using Illumina technology HiSeq (paired-end, 2x150 bp). An average of 2x107 reads by mutants or transconjugants was obtained, corresponding to an average of 3100X for coverage depth. One exception is the PG2E10 population that was sequenced by the Genome-Transcriptome facility of Bordeaux (France) using HiSeq (paired-end, 2x100 bp, 1.6x107 reads, coverage 1700X). All bioinformatics analyses were performed using the galaxy platform hosted by Genotoul, Toulouse, France (bioinfo.genotoul.fr) and default parameters unless specified (see workflow S6 Table). The reads of each clone (fastq file) were mapped on the reference genome M. agalactiae PG2 (NC_009497.1) or 5632 (NC_013948.1), using Burrows-Wheeler Aligner (BWA, MEM algorithms, Galaxy version 0.8.0) [68]. The quality of the alignments was controlled with Qualimap 2.2.1 [69]. Calling variant analyses were performed using successively RealignerTargetCreator, IndelRealigner, PrintReads and HaplotypeCaller of GATK3 (Galaxy version 3.5.0) for SNPs and indels detection [70]. Variations with a quality lower than 10000 were excluded (Filter VCF file tool, Galaxy Version 1.0.0). Variations were considered as (i) fixed when present in ≥95% of the reads or (ii) non-fixed when present in <95% of the reads, as a result of coexisting sub-populations [71]. The percentage of each variation was calculated using the ratio AD/DP (AD: Allelic depths for the reference and alternative alleles; DP: Approximate read depth) provided by GATK. Alignments (bam file) and variations (vcf file) were visualized using the Integrative Genome Viewer (IGV 2.3.93) [72], Artemis 16.0.0 [73] and ACT 13.0.0 [74]. Reconstruction of the composite genome of transconjugants (PG2/5632) was possible because of the frequent polymorphisms existing between PG2 and 5632, on average 1 variation every 26 nt calculated using Nucmer [75] (S2 Fig). This was performed by PG2 specific reads detection as follows: transconjugants reads were aligned on the 5632 genome, reads with mismatch were recovered (select lines tool, Galaxy version 1.0.1) and these reads were then aligned on the PG2 genome. Only reads with no mismatch and regions with a coverage higher than 15 reads were conserved. These mapped reads, corresponding to PG2 transferred regions, were manually curated using Artemis (S2 Fig, S6 Table). This consisted in removing (i) false-positive fragments (also present in the negative control 5632G3), (ii) the vpma and hsd gene families which ones spontaneously undergo high-frequency, intraclonal recombination in propagating population [41] and (iii) fragments having no SNPs based on Bam files. Of note, the absence of contaminations between DNA libraries was ensured by (i) treating separate DNA batches, (ii) by matching PCR genotyping with sequence data (S1 Fig) and (iii) by independent Sanger sequencing of regions containing mutations detected by WGS in quinolone target genes.
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10.1371/journal.ppat.1000820 | MicroRNA Antagonism of the Picornaviral Life Cycle: Alternative Mechanisms of Interference | In addition to modulating the function and stability of cellular mRNAs, microRNAs can profoundly affect the life cycles of viruses bearing sequence complementary targets, a finding recently exploited to ameliorate toxicities of vaccines and oncolytic viruses. To elucidate the mechanisms underlying microRNA-mediated antiviral activity, we modified the 3′ untranslated region (3′UTR) of Coxsackievirus A21 to incorporate targets with varying degrees of homology to endogenous microRNAs. We show that microRNAs can interrupt the picornavirus life-cycle at multiple levels, including catalytic degradation of the viral RNA genome, suppression of cap-independent mRNA translation, and interference with genome encapsidation. In addition, we have examined the extent to which endogenous microRNAs can suppress viral replication in vivo and how viruses can overcome this inhibition by microRNA saturation in mouse cancer models.
| Virus host range is shaped by cellular determinants such as transcription factors and receptor expression. In addition, we have previously shown that tissue-specific microRNAs can be utilized to direct the specificity of a replication competent picornavirus, Coxsackievirus A21. In this report, we demonstrate the mechanism by which microRNAs are able to directly influence oncolytic viruses, an important class of anticancer agents. We show that microRNA expression is an important determinant of permissivity to oncolytic virus replication, but the actual abundance of that expression is far more important. In addition, we show that there are actually multiple different stages in the life cycle of a replication competent picornavirus that are amenable to regulation by cellular microRNAs. We proceed to illustrate that microRNAs can regulate virus tropism in vivo, but demonstrate that circulating high viral titers in the blood can overcome this mechanism of conferring tissue specificity. MicroRNAs are well known to have both oncogenic or oncosuppressive activities in human cancers. Here, we show that tissue-specific microRNA expression can also be used to modulate the efficacy of viral anticancer therapeutics, and the mechanism by which they are able to do so.
| MicroRNAs (miRNAs) are a class of small, ∼22 nt regulatory RNAs that modulate a diverse array of cellular activities. Through recognition of sequence complementary target elements found most often in the 3′UTR of cellular mRNAs, miRNAs post-transcriptionally regulate numerous cellular processes by way of mRNA translation inhibition or, less commonly, by catalytic mRNA degradation. It is thought that upwards of one-third of all human mRNAs are regulated by the over 700 human miRNAs that are currently known [1],[2]. Many miRNAs can have tissue-specific localizations and, in addition, some are now known to have cancer-specific signatures. Cancer-specific miRNAs can be both oncogenic and oncosuppressive, and growing evidence now indicates that certain miRNAs are also involved in disease progression, through the promotion of metastasis [3].
The mechanisms by which a miRNA regulates a given mRNA are influenced by parameters such as the degree of sequence homology [4] and target site multiplicity [5] as well as by features of the mRNA itself, including target site secondary structure [6] and location [7]. In addition, the cellular machinery used to translate mRNAs is thought to profoundly affect miRNA regulation. While capped mRNAs are known to be amenable to both catalytic miRNA-induced cleavage and miRNA-mediated translational repression, it has been suggested that uncapped mRNAs that rely on an IRES (Internal Ribosome Entry Site) for translation initiation are not susceptible to translational repression [8][9].
In addition to their roles in the pathogenesis of human disease, endogenous cellular miRNAs can also play a role in viral infection, acting to suppress [10][11][12][13] or enhance [14] viral replication. Recently, miRNAs have been exploited to influence the tissue tropism and pathogenicity of viruses used as vaccines [10], anticancer therapeutics [13], and gene transfer vehicles [15]. MiRNAs of both cellular and viral origin are thought to be involved in regulating the host response to viral infection [16] and miRNAs have been shown to regulate viral antigen presentation [17], the antiviral interferon response [18], viral tissue tropism and antiviral immunity [19].
Engineering miRNA-responsiveness in viruses used as cancer therapeutics has been shown to be an effective way to generate tumor selectivity [19]. And although miRNAs clearly play a role in multiple aspects of both the viral replication cycle and the host response to viral pathogens, little is known about the mechanisms by which these regulatory molecules act to directly influence the level of virus replication. To this end, we engineered the oncolytic picornavirus Coxsackievirus A21 (CVA21), to encode artificial miRNA targets complementary to cellular miRNAs. Here, we look at the ability of these miRNA targets to restrict viral replication in cells expressing cognate miRNAs and the mechanisms by which they are able to do so. By utilizing CVA21 derivatives bearing different miRNA target elements (miRTs), including targets with varying degrees of homology to endogenous cellular miRNAs and targets in orientations designed to target either the positive-strand RNA genome or the negative-strand antigenome, we were able to identify multiple, distinct steps in the picornaviral life cycle that are amenable to miRNA-mediated regulation.
CVA21 is a picornavirus known to cause upper respiratory and inflammatory muscle infections in humans [20][21]. It replicates quickly and efficiently in both cell culture and in mouse models, and is a typical positive-strand RNA virus. The genome of CVA21 is a single-stranded, uncapped, positive-strand RNA, which is translated to yield a polyprotein that is then cleaved by viral proteases to form the viral capsid and nonstructural proteins. We have previously reported that CVA21 has potent oncolytic activity against a variety of human cancers, and can mediate complete tumor regression in mice bearing human melanoma or myeloma xenografts [13]. However, this potent and curative oncolytic activity is accompanied by a high-level viremia and rapid-onset lethal myositis that hampers the feasibility of employing this virus as an anticancer therapeutic in the clinic.
Incorporation of miRNA target elements (miRTs) corresponding to two muscle-specific cellular miRNAs (miR-133 and miR-206) was shown to mediate silencing of CVA21 gene expression in cells expressing muscle-specific miRNA mimics, in muscle culture lines and, most importantly, in vivo in mice. Cells expressing exogenous or endogenous miRNAs complementary to miRTs are intrinsically immune to infection by the engineered CVA21 and are protected from the cytolytic effects of this virus. Thus, tumor-bearing mice infected with a recombinant muscle-restricted virus (CVA21 miRT) were cured of established tumors whilst being protected from productive muscle infection and the resultant myositis. Our ability to engineer the oncolytic picornavirus CVA21 to contain diverse miRTs provided an opportunity to look at the mechanisms by which cellular miRNAs can act directly on a virus to silence gene expression and mediate tumor selectivity.
To investigate at what stage(s) of the viral life cycle cellular miRNAs are able to perturb viral replication, we utilized our previous recombinant miRT design whereby four tandem copies of a given target element corresponding to a cellular miRNA are incorporated into the 3′UTR of the viral genome (Figure 1A). In order to identify a cellular miRNA that could effectively inhibit CVA21, we engineered the CVA21 genome to encode tandem miRTs complementary to four different tissue-specific miRNAs. Target elements corresponding to muscle-specific (miR-133 and miR-206), hematopoetic-specific (miR-142-3p) or tumor-suppressor (miR-145) miRNAs were incorporated in the 3′UTR of CVA21 and protection of HeLa cells transfected with sequence-complementary miRNA mimics (synthetic dsRNAs corresponding to cellular miRNA duplex intermediates) was analyzed (Figure 1B). While miRNA-mimics were able to protect cells infected with viruses containing sequence complementarity from ∼60% to levels that were not significantly different from control, the hematopoetic-specific miR-142-3p target provided the most complete and most consistent protection, and hence provided the best candidate to study the different steps of virus replication at which cellular miRNAs could act to perturb viral replication.
To identify miRNA-mediated antiviral activity acting at distinct steps in the viral life cycle, we designed a panel of recombinant viruses expressing variations of the miR-142-3p target. Four tandem fully complementary sites were designed to provide the best opportunity for direct catalytic cleavage of the viral RNA genome and mRNA (CVA21 142T); four copies in reverse orientation were designed to address the existence of miRNA-mediated antiviral activity acting on the negative-sense antigenome (CVA21 142rT). In addition, two viruses were constructed to look at the potential for translational silencing of the viral mRNA: a virus containing 7 base pairs of mismatch between the target and miR-142-3p (CVA21 mm7T) and a virus that contained the entire miR-142-3p target site with a 6 base pair (bp) stuffer sequence inserted between bp 10/11 of the target site (CVA21 b6T), each with four replicate copies (Figure 1C). The latter, or ‘bulge’, virus contained extra sequence at the catalytic cleavage site to provide a strong candidate for miRNA-mediated translational suppression: four perfect copies of the miRT to promote miRNA recognition, and a “stuffer sequence,” at the site at which the RNA induced silencing complex (RISC) ‘slicer’ activity cleaves (to block catalytic degradation of the viral RNA). These recombinant viruses were rescued in the absence of the corresponding miRNAs and found to replicate to high titer with growth kinetics analogous to the wild-type (WT) virus in cells lacking miR-142-3p (Figure 1D).
Cellular microRNAs are known to mediate post-transcriptional silencing of sequence-complementary mRNAs through translational repression and/or transcript degradation. However, in the absence of a 5′ cap, and in the presence of the IRESes utilized by many viruses, it has been hypothesized that miRNAs are unable to induce translational repression [22]. These studies have been performed primarily with reporter mRNAs containing one of several viral IRESes and generally in the presence of short interfering RNAs (siRNAs), cell free extracts, or miRNA mimics supplemented in trans [8],[23] [24].
Translation of the CVA21 mRNA into the viral polyprotein is mediated by a type I IRES in a cap-independent manner. We reasoned that the analysis of recombinant CVA21 variants targeted by miRNAs might be particularly useful in studying the translational silencing of cap-independent mRNAs because cells can be infected at very low multiplicities of infection (MOI), thus circumventing the potentially confounding issue of miRNA saturation [25], and enabling the quantitative analysis of virus expansion whilst simultaneously allowing precise measurement of cell death at specific times post-infection.
Similar to experiments conducted by other groups [9] [23], we found that in the presence of miRNA mimics added in trans, viruses containing multiple imperfectly complementary target sites were unresponsive to miRNA-mediated repression and cells were not significantly protected from viral cytopathic effects (Figure 2A). Increasing miRNA mimic copy numbers did not cause any increase in protection from cytolysis by these viruses (data not shown). However, hematopoetic cell lines and primary hematopoetic cells expressing high levels of endogenous miR-142-3p were significantly less susceptible to viruses containing mismatch (mm7T) or bulge (b6T) targets (Figure 2B; p<0.01 and p<0.004, respectively). In both cases, viral titers were reduced by up to 5 orders of magnitude (Figure 2C). Although targeting the viral antigenome had no significant effect on cell viability, this did reduce viral titers by up to 100 fold (Figures 2B and 2C).
To determine whether the observed increase in cell viability was indeed as a result of translational silencing rather than transcript cleavage, we looked at CVA21 RNA copy numbers. A panel of hematopoetic cells, as well as H1-HeLa cells (serving as a control cell line) were supplemented with control or miR-142 mimics, and all cells were infected with the panel of recombinant CVA21 viruses. Total cellular RNA was harvested and viral RNA was quantified and normalized to cellular GAPDH mRNA levels. While inserting perfectly complementary miR-142-3p targets (in CVA21 142T) reduced CVA21 RNA levels by up to a million-fold, the mismatched CVA21 mm7T and bulged CVA21 b6T targets induced only a modest decrease in viral RNA (Figure 2D). Translational silencing would be expected to indirectly cause a small drop in viral RNA levels through production of less viral polymerase, presumably accounting for the up to 40 fold decrease in CVA21 RNA in the presence of bulged targets (Figure 2D).
To test whether CVA21 142T RNA was reduced in the presence of cells bearing sequence-complementary miRNAs due to direct cleavage of the viral RNA, or rather because translational silencing had reduced the amount of viral polymerase to amplify the viral RNA, we performed a trans-complementation experiment. Hematopoetic MEC-1 cells were infected with two different CVA21s: miRT CVA21 (containing muscle-targets), and CVA21 142T (containing hematopoetic targets). In MEC-1 hematopoetic cells, CVA21 142T should be miRNA-targeted, while miRT CVA21 should replicate unencumbered. qRT PCR was then performed to ascertain the total amount of viral RNA in the cells, and what portion of it was miRT CVA21 (the difference between total CVA21 RNA and miRT CVA21 RNA would therefore be RNA corresponding to CVA 142T in cells doubly infected with miRT CVA21 and CVA21 142T). Complementation by miRT CVA21 polymerase should be capable of synthesizing CVA21 142T if any remained that had not been catalytically destroyed, (however the efficiency of trans-complementation in picornaviruses still remains under debate [26][27][28]).
Quantitative PCR analysis revealed that all virus present in singly infected miRT CVA21 analysis was indeed from miRT CVA21 (Figure 2E). MEC-1 cells infected with only CVA21 142T had a larger than 5 log decrease in total CVA21 RNA (and, as expected did not contain any miRT CVA21). In cells doubly infected with WT+ miRT virus, less than 50% of progeny genomes were found to be miRT genomes (the remainder are therefore WT progeny genomes). However, in cells infected with CVA21 142T + miRT CVA21, 100% of progeny genomes were found to be miRT genomes (none were therefore 142T genomes). Hence, 142T genomes are destroyed and not amplified in cells containing the 142T miRNA, even when the cells are infected with another virus that provides all viral proteins in trans.
While we observed that the CVA21 142T RNA genome was profoundly decreased in the presence of perfectly matched miR-142 mimics, there was significant variability in the degree of inhibition seen in different hematopoetic cell lines (Figure 2D). To determine whether this variability correlated with the level of miR-142-3p expression in these cells, we analyzed the abundance of endogenous miR-142-3p in our panel of hematopoetic cell lines by qRT-PCR. The chronic lymphocytic leukemia (CLL) line MEC-1 and the multiple myeloma cell line Kas 6/1 expressed the highest levels of endogenous miR-142-3p (Figure 2F), and showed the greatest inhibition of CVA21 142T RNA expression (Figure 2D), followed by the CLL lines MEC-2, MEC-1, and WAC-3. We observed an inverse correlation between miR-142-3p expression levels and the CVA21 142T RNA copy numbers detected, suggesting that the abundance of this particular miRNA contributes substantially to the efficiency with which it acts to silence complementary mRNAs.
Overall these results show that perfectly matched miR-142 targets are recognized and catalytically degraded. In addition, while clearly not as efficient as mRNA cleavage, translational repression was seen to occur with cap-independent type I IRES-containing mRNAs and provided protection from cytopathic effects mediated by the virus, and the reduced the infectious virus released into the cellular supernatant. This translational silencing was only detected in the presence of high levels of endogenous miRNAs, however, and at a low multiplicity of viral infection.
Having determined that viral replication was amenable to miRNA-mediated regulation at two of the earliest steps in viral infection (genome cleavage after uncoating and initial mRNA translation), we next sought to look at later steps in the viral life cycle. While perturbing the virus life cycle at early steps after infection (attacking genome after uncoating, initial mRNA expression) would be most likely to slow or inhibit a productive infection, later steps such as mRNA accumulation, the bulk of mRNA and genome accumulation, and encapsidation of the viral genome could be equally amenable to miRNA-mediated regulation as exposed targets are still present on viral RNAs. In order to examine the ability of microRNAs to act on late steps of viral replication, we conducted a time-course experiment where miRNA mimics were added prior to, concomitant with, or at specific times after infection.
Picornaviruses are known to inhibit host cell translation beginning at about 1 hour after infection [29], with viral mRNA accumulation peaking by 5 hours post infection [30], followed by encapsidation of the positive-strand viral RNA into the provirion and cytolytic release thereafter [31]. In order to present blocks at different stages of the picornaviral life cycle, we therefore infected cells either pre-treated with miRNA mimic, or mimics were added at 0, 3, or 6 hours post infection (pi).
Analysis of cell viability in the presence of miRNA mimics added at successive times after infection demonstrated that, while mimic presence prior to infection provides the greatest protection from viral cytolysis, miRNAs can negatively impact viral replication such that cell viability is protected by nearly 50% even when added 6 hours after infection (Figure 3A). Moreover, production of viral progeny is greatly diminished in the presence of miRNAs added at varying time points up to 6 hours post-infection (Figure 3B). To confirm that this was not an artifact of virus expansion (ie cells merely being protected by virtue of not being infected until one round of viral replication had occurred), this experiment was repeated at an MOI = 3 or 10 and similar results observed (Figure 3C–3F). In addition, we analyzed the quantity of CVA21 142T by qRT-PCR in the presence of control miRNA mimics as compared to CVA21 142T infection in the presence of miR-142-3p mimic at different MOI (Figure 4A) and found that virus abundance was similar to that observed when analyzed through infectious titer (Figure 3B, 3D, 3F).
Since the picornavirus life cycle is particularly rapid and the bulk of viral RNA amplification, protein expression and provirion production has already taken place by 6 hours after infection, we wondered whether miRNA mimics might be interfering with genome encapsidation. We therefore sought to look at the particle: infectivity ratio of the released progeny viruses in the presence of miRNA mimics added after viral infection. Infectivity was determined by titration of cell supernatant on H1-HeLa cells while capsid protein content was assayed after concentration through sucrose cushions of the supernatant of infected cells by both protein assay and Western analysis. Capsid proteins were not detected in sucrose-purified samples of cellular supernatant to which sequence-complementary mimics (miR-142-3p) were added before or simultaneously with CVA21-142T infection (Figure 4B). However, when miR-142-3p mimic was added 3 or 6 hours after virus infection, total capsid protein was not significantly diminished as compared to cells transfected with a control miR (p = 0.3, p = 0.14, Figure 4B). Infectious units, however, were decreased by 4 and 3 logs, respectively (Figure 3B). Similarly, when these sucrose-purified fractions were subjected to Western analysis with antibodies raised against CVA21, CVA21 protein was roughly equivalent when CVA 142T infection was performed with in the presence of control miRNA mimics (Figure 4C). However, miR-142-3p mimic present 4 hours prior to infection or simultaneously with CVA 142T infection inhibited detectable expression of CVA21 capsid in the immunoblot. In contrast, when miR-142-3p mimic was added at either 3 or 6 hours after expression, accumulation of viral capsid was detected in Western blots of the sucrose-purified portion and corresponded with capsid quantified by protein assay.
Together these results reveal that alternative steps in the picornaviral life cycle are amenable to miRNA-mediated inhibition: including (but perhaps not limited to) RNA cleavage after uncoating, reduced translation of the viral mRNAs, reduced viral mRNA accumulation, and diminished encapsidation of the viral genome into the procapsid (Figure 5).
While the in vitro determination of the mechanisms by which miRNAs are able to inhibit virus replication can facilitate the design of better and more elegant ways to exploit cellular miRNAs for gene transfer and vaccine development for multiple virus families, in vivo evaluation is necessary to validate this targeting paradigm for translational purposes, and to demonstrate that it will indeed occur in model organisms.
Therefore, we next sought to confirm that tumor cells expressing endogenous miRNAs were capable of targeting the CVA21 genome and would indeed protect from viral replication in vivo in the mouse model. To this end, 5×106 Mel-624 (melanoma) or MEC-1 (chronic lymphocytic leukemia, CLL) cells were implanted subcutaneously in immunodeficient mice. When tumors reached an average of 250 mm3 in size, the animals were treated with a single intratumoral dose of 106 TCID50 of a recombinant CVA21, after which time tumor growth and survival were monitored (Figure 6).
As expected, all CVA21 variants (WT, miRT and142T) replicated in the melanoma-xenografts (which express no sequence-complementary miRNAs) such that complete (or near complete) tumor regression took place (Figure 6A). Animals receiving the WT or 142T viruses rapidly succumbed to myositis, necessitating euthanasia (Figure 6E). Also as expected, animals treated with our previously described muscle-restricted virus (miRT) had complete tumor regressions and were protected from the development of myositis, while Opti-MEM injected control tumors grew unencumbered and mice had to be sacrificed when tumors reached ∼2.0 cm3. In contrast to the melanoma xenografts, MEC-1 CLL xenografts (which express abundant miR-142) were susceptible to the WT and muscle-restricted viruses but were highly resistant to oncolysis by CVA21 142T (Figure 6B).
Since all recombinant viruses behaved as expected in mice bearing one susceptible or one restricted tumor type, we next decided to examine their behavior in animals with a different tumor on each flank after intravenous virus administration. By using contralateral tumors we were able to create a more representative in vivo scenario, with multiple CVA21-permissive tissues having distinct (permissive or restrictive) miRNA expression profiles. Unexpectedly, the virus bearing tandem repeats of miR-142 (CVA21 142T) replicated in both the permissive melanoma xenograft (Figure 6C) and in the “nonpermissive” CLL xenograft (Figure 6D) in these mice, such that both tumors regressed just as rapidly as when they were infected with the nontargeted WT or miRT viruses. Once again, only the muscle restricted virus (miRT CVA21) prolonged the survivals of tumor-bearing mice, (Figure 6E p = 0.0018, Figure 6F p = 0.0004, Figure 6G p = 0.0062).
To confirm that CVA 142T was indeed replicating in both the melanoma and myeloma xenografts, both tumors were explanted from two separate mice at time of euthanasia. Tissue was homogenized and overlaid on H1-HeLa cells to look for viral recovery. In each tumor of both animals, virus was recovered and found to contain no alteration in the 142T sequence, indicating that there was indeed productive infection in both tumors.
In order to determine why regression of mir-142-expressing hematopoetic tumors was observed in animals bearing contralateral melanoma xenografts when treated with the miR-142 restricted virus, but not in animals with a single hematopoetic tumor, we compared the viral titers in serum from animals treated with CVA21 142T versus WT CVA21 (Figure 6H). Serum titers were typically high in mice treated with WT CVA21, irrespective of whether they were implanted with melanoma, CLL or bilateral xenografts. In mice treated with CVA-142T, the serum titers were very high (>108) in animals bearing melanoma xenografts, very low (<102) in those with hematopoetic tumor xenografts, and were again very high in animals with the combination of melanoma and hematopoetic xenografts on either flank.
Since serum titers were very high in mice with bilateral tumors (>108 TCID50/ml), we investigated the possibility that endogenous miR-142 expressed in MEC-1 xenografts could have been saturated by this high-level viremia. To investigate this possibility, we conducted an in vitro assay looking at saturation of endogenous miR-142 in MEC-1 cells. While cells were completely protected against cytotoxicity at MOIs of up to 30, increasing virus titer thereafter resulted in decreased cell viability such that by an MOI = 1,000 we saw near complete cytopathic effects in CVA21 142T infected cells (Figure 6I), thus suggesting that the endogenous miR-142 may have been effectively saturated, a phenomenon which has been observed by a number of groups previously [32] [25]. While our in vitro data suggest that a sequence-specific saturation of endogenous miRNAs is possible, other possibilities include a non-specific shutdown of all RNAi machinery in infected cells, or possibly the destruction of tumor vasculature that serves as a necessary scaffolding for xenografts.
Together these data show that miRNAs act in vivo to restrict virus tropism in cells permissive to viral replication. Perhaps not surprisingly, we found that persistent high-level viremia was able to overcome this miRNA-mediated restriction, possibly through allowing the saturation of endogenous miRNAs for which our virus bore tandem cognate targets. We speculate that in animals bearing bilateral tumors treated with intravenous CVA21 142T, virus trafficked to the susceptible melanoma tumor, which created a reservoir of viral amplification. This then resulted in a high-level viremia, which was able to overwhelm the miRNA-restricted CLL tumor by saturating the sequence-complementary endogenous miRNA. We detected no sequence alteration in the virally encoded miRT insert in animals treated with miRT or CVA21 142T, and therefore can dismiss the possibility that the viruses overcame miRNA restriction in this manner. However, we did identify significant sequence insert changes in viruses bearing the tumor suppressor miR-145T inserts when administered to this same panel of animals (data not shown). We therefore surmise that miRNA targets are differentially effective and susceptible to different selective pressures to mutationally inactivate these targets, and that this may correspond to the specific tissues from which they are being excluded.
Increasing insights into the interactions between miRNAs and viruses have suggested new therapeutic targets for antiviral drugs and new techniques for the creation of vaccines, as well as for regulating the tissue specificity of gene transfer vehicles and oncolytic viruses. However, the ways in which cellular miRNAs can act to inhibit viral replication have remained ill-defined. In this report, we identify alternative steps in the viral life cycle that are amenable to miRNA-mediated inhibition in the context of a replication-competent oncolytic virus that acts as a fully curative therapy in xenograft-bearing mice.
Here, we show that picornaviruses are most susceptible to miRNA-mediated attack immediately after infection, and that the viral RNA genome can be recognized and cleaved by fully sequence complementary miRNAs. However, contrary to the currently accepted paradigm, we also present evidence to suggest that miRNAs can translationally suppress uncapped, IRES-dependent mRNAs bearing imperfectly matched target sequences at low multiplicity of infection. Several previous studies have used the tumor-suppressor miRNA let-7a to look at miRNA regulation in cap dependent vs. IRES dependent mRNAs. However, despite very high let-7a abundance in a number of cell lines, target elements corresponding to let-7 do not functionally suppress sequence complementary targets as efficiently as does miR-142 (or a number of other well-characterized miRNAs) [5]. In addition, the interpretation of data generated using cell-free extracts and synthetic miRNAs supplemented in trans, as employed previously by several groups, may be made more difficult by miRNA saturation. We believe the use of a highly efficient miRNA target (miR-142T), cell lines expressing high levels of endogenous, sequence-complementary miRNAs, and the ability to infect cells at low MOI, have enabled us to see the subtle translational suppression of a viral mRNA in a cap-independent context.
The observation that the picornaviral life cycle can be efficiently interrupted by miRNAs even at very late stages after infection, by which time the viral genome copy number has amplified to a very high level, is truly remarkable. This late interruption appears to require perfect complementarity between the viral genome and the miRNA, and probably in large part reflects the rapid catalytic destruction of viral genomes. An interesting consequence of adding the miRNA late after infection to interrupt the viral life cycle is that the cell still releases abundant virus particles, but that they lack infectivity (Figures 3, 4). The possibility exists that miRNAs could also block encapsidation of viral RNA genomes by steric hindrance (RISC bound-RNAs being too large to be packaged), although our data do not address this possibility.
While our data show that miRNAs can recognize and antagonize a virus, our observation that endogenous cellular miRNAs can actually overwhelm this regulation is also of potential importance. The phenomenon of miRNA saturation, whereby miRNA targets can act as ‘sponges’ to bind and sequester endogenous cellular miRNAs, has been reported previously, but has been generally thought to occur in the context of translational silencing [25] [32]. Here, we show that miRNA saturation can also occur when miRNA regulation is occurring primarily by catalytic degradation of a perfectly complementary RNA target (Figure 6I), and that it can happen in the context of viral infection in vivo.
We have shown that miRNAs can suppress virus propagation in vivo and can provide post-entry blocks to virus replication in otherwise permissive cells. However, we also show that miRNA defenses can be overcome at very high multiplicities of viral infection in vivo, allowing unencumbered virus replication. Therefore, clinicians considering therapeutic intervention utilizing miRNA targeted-viruses (ie vaccines and cancer therapeutics) must be particularly wary of the possibility that unencumbered virus replication in permissive cells, eg a tumor, may eventually lead to viral infection and pathogenesis in normal cells that express a restricting miRNA (if that miRNA becomes saturated).
MiRNAs are known for their ability to regulate numerous cellular functions, often at multiple steps within a single signal cascade. In this report, we have shown that miRNAs can also act to antagonize sequence complementary viruses at multiple, alternative steps in the virus life cycle such that tumor selectivity is generated, but also a mechanism by which this can be overcome. Although we show that picornaviruses are particularly susceptible to miRNA interference, the different replication steps affected by miRNAs are common to many viruses and it is therefore likely that multiple virus families will be amenable to miRNA-mediated regulation.
Mayo Clinic Institutional Care and Use Committee reviewed and approved protocol A7007 with investigators SJR, EJK, and EMH, entitled “MicroRNA-mediated targeting of an oncolytic enterovirus, Coxsackievirus A21”. All mice were housed in the BSL-2 facility at Mayo Clinic and experiments were performed in compliance with outlined and approved institutional guidelines.
H1-HeLa, cells were obtained from American Type Culture Collection and were maintained in DMEM supplemented with 10% FBS in 5% CO2. MEC-1, MEC-2, WAC-3, and Kas 6/1 cells were obtained from Diane Jelenik, Dept. of Immunology, Mayo Clinic. MEC-1 and MEC-2 cells were maintained in IMDM 10% FBS in 5% CO2. WAC-3 and Kas 6/1 cells were cultured in RPMI with 10% FBS in 5% CO2.
pGEM-CVA21 clone was kindly provided by Matthias Gromeier. miRNA sequences were obtained from the Sanger Institute miRBase (http://microrna.sanger.ac.uk/sequences/). The following sequences were cloned into the 3′UTR of pGEM-CVA21 in between bp 7344/7345 by overlap extension PCR.
miR-142 3pT
TCCATAAAGTAGGAAACACTACACGATTCCATAAAGTAGGAAACACTACAACCGGTTCCATAAAGTAGGAAACACTACATCACTCCATAAAGTAGGAAACACTACA
miR-142revT
TGTAGTGTTTCCTACTTTATGGAATCGTGTAGTGTTTCCTACTTTATGGAACCGGTTGTAGTGTTTCCTACTTTATGGAATCGTGTAGTGTTTCCTACTTTATGGA
miR-142 mm7T
TTAATGCAGTCATAAACACTACACGATTTAATGCAGTCATAAACACTACAACCGGTTTAATGCAGTCATAAACACTACATCACTTAATGCAGTCATAAACACTACA
miR-142 b6T
TCCATAAAGTAGGTCGATTAAACACTACACGATTCCATAAAGTAGGTCGATTAAACACTACAACCGGTTCCATAAAGTAGGTCGATTAAACACTACATCACTCCATAAAGTAGGTCGATTAAACACTACA
miR-145T
AGGGATTCCTGGGAAAACTGGACCGATAGGGATTCCTGGGAAAACTGGACACCGGTAGGGATTCCTGGGAAAACTGGACTCACAGGGATTCCTGGGAAAACTGGAC
Viral RNA was produced using Ambion Megascript and Megaclear T7 polymerase kit according to manufacturers instructions. For rCVA21 rescue 1 ug RNA/well was transfected into H1-HeLa cells in 12 well plates using the Mirus RNA transfection reagent and at 24 hours post infection wells were scraped and cell pellets harvested. Cell pellets were subjected to 3 freeze/thaw cycles in liquid N2, cell debris was cleared by centrifugation and cleared lysate was added to H1-HeLa cells in a T-75 flask. Titration of CVA21 was performed on H1-HeLa cells. Cells were plated in 96 well plates at 50 percent confluence. After 24 hours, serial ten-fold dilutions (−2 to −10) were made of the virus; 100 uL of each dilution was added to each of eight duplicate wells. Following incubation at 37°C for 72 hours, wells were then assessed for CPE and TCID50 values were determined using the Spearman and Kärber equation.
H1-HeLa cells were incubated with rCVA21 at a multiplicity of infection (MOI, determined by TCID50 per cell) of 3.0 for 2 hours at 37°C. Following this incubation, cells were washed and resuspended in fresh growth media at predetermined time-points (2, 4, 6, 18, 12, 24, hours), cells pellets were harvested and frozen at −80°C. At the completion of all time-points, they were thawed, and cell pellets were cleared from the samples by centrifugation providing a cleared cell lysate fraction.
miRNA mimics were purchased from Dharmacon, Inc. Control miRNA mimic corresponded to a C. elegans miRNA with no predicted miRTs in mammalian cells according to manufacturer. miRNA mimics were transfected with Mirus ™ RNA transfection reagent at a 200 nM concentration. 4 hours post mimic transfection, cells were infected with recombinant CVA21 at MOI = 1.0, 3, or 10, unless other time noted. After 24 hrs. post infection, cells were harvested for MTT viability assay and supernatant was harvested for titration.
Total cellular RNA was harvested using the Qiagen RNeasy Kit, according to manufacturer instructions. Primers and probes corresponding to the 2A region or miRT insert region of CVA21 were used to quantitate CVA21 RNA and GAPDH primers and probes were used to normalize total cellular RNA. For each sample analyzed, 50 ng of total RNA was subjected to qRT-PCR in triplicate on Stratagene Mx4000 qPCR system using the Taqman One Step RT PCR master mix.
Small RNAs were harvested from all cell lines with the Ambion miRVana microRNA isolation kit, according to manufacturer instructions. RNA was resuspended in 50 ul nuclease free water, and quantified by spectrophotometer. For analysis of miRNA expression, the Applied Biosystems Taqman microRNA Assay system was used. For each miRNA analyzed, 5 ng of small RNA was subjected to qPCR in triplicate on Stratagene Mx4000 qPCR system.
Ten-centimeter dishes of H1-HeLa cells were transfected with miRNA mimics and infected with CVA21 142T at MOI = 1.0 at indicated time points. Cell supernatant was collected centrifuged for 5 mins at 10,000 g to clear cell debris. Infectious titer was calculated on cell supernatant, and thereafter .5% SDS and 2 mM EDTA was added to supernatant, and then overlaid on a 5 ml sucrose cushion. Total virus particles were subjected to ultracentrifugation for 4 h at 28,000 rpm using an SW28 swinging bucket rotor. Supernatants were discarded and centrifuge tubes were rinsed with PBS and re-spun. After PBS wash, virus pellets were resuspended in PBS containing 0.2% SDS and 5 mM EDTA. Virus capsid protein was then quantified using Biorad protein assay kit and normalized to mock infected and concentrated samples.
Twenty female Balb/C mice were inoculated intraperitoneally with 1e6 CVA21 three times over a period of six weeks. Two weeks following the final inoculation, mice were terminally bled, serum was collected and pooled and frozen at −20°C for use in immunoblotting.
Supernatant from miRNA-mimic timecourse experiments were sucrose-purified as above and run on a 12.5% SDS page gel and transferred to a PVDF membrane using the Trans-Blot SD semi-dry transfer apparatus (BioRad) for 45 minutes at 15 Volts. Blots were blocked in TBS-10% milk for 1 hour at room temperature. Primary CVA21 antibody generated as described above was diluted 1∶500 in TBS-5% milk +.05% Tween and blots were incubated overnight at 4°C. Membranes were washed in TBS +.1% Tween and incubated in secondary Goat-Anti Mouse IgG (Dako) at a 1∶5000 dilution in TBS-5% milk+.05%Tween at room temperature for 1 hour. Membranes were washed in TBS +.1% Tween and bound antibodies were detected using SuperSignal West Pico Chemluminescent reagent (Pierce).
All animal protocols were reviewed and approved by Mayo Clinic Institutional Care and Use Committee. CB17 ICR-SCID mice were obtained from Harlan. Mice were irradiated and implanted with 5e6 Kas 6/1 or Mel 624 cells in the right flank. When tumors reached an average of .5×.5 cm, tumors were treated with 1e6 CVA21. Tumor volume was measured using a hand held caliper and blood was collected by retro-orbital bleeds.
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10.1371/journal.pmed.1002802 | Predicting seizures in pregnant women with epilepsy: Development and external validation of a prognostic model | Seizures are the main cause of maternal death in women with epilepsy, but there are no tools for predicting seizures in pregnancy. We set out to develop and validate a prognostic model, using information collected during the antenatal booking visit, to predict seizure risk at any time in pregnancy and until 6 weeks postpartum in women with epilepsy on antiepileptic drugs.
We used datasets of a prospective cohort study (EMPiRE) of 527 pregnant women with epilepsy on medication recruited from 50 hospitals in the UK (4 November 2011–17 August 2014). The model development cohort comprised 399 women whose antiepileptic drug doses were adjusted based on clinical features only; the validation cohort comprised 128 women whose drug dose adjustments were informed by serum drug levels. The outcome was epileptic (non-eclamptic) seizure captured using diary records. We fitted the model using LASSO (least absolute shrinkage and selection operator) regression, and reported the performance using C-statistic (scale 0–1, values > 0.5 show discrimination) and calibration slope (scale 0–1, values near 1 show accuracy) with 95% confidence intervals (CIs). We determined the net benefit (a weighted sum of true positive and false positive classifications) of using the model, with various probability thresholds, to aid clinicians in making individualised decisions regarding, for example, referral to tertiary care, frequency and intensity of monitoring, and changes in antiepileptic medication. Seizures occurred in 183 women (46%, 183/399) in the model development cohort and in 57 women (45%, 57/128) in the validation cohort. The model included age at first seizure, baseline seizure classification, history of mental health disorder or learning difficulty, occurrence of tonic-clonic and non-tonic-clonic seizures in the 3 months before pregnancy, previous admission to hospital for seizures during pregnancy, and baseline dose of lamotrigine and levetiracetam. The C-statistic was 0.79 (95% CI 0.75, 0.84). On external validation, the model showed good performance (C-statistic 0.76, 95% CI 0.66, 0.85; calibration slope 0.93, 95% CI 0.44, 1.41) but with imprecise estimates. The EMPiRE model showed the highest net proportional benefit for predicted probability thresholds between 12% and 99%. Limitations of this study include the varied gestational ages of women at recruitment, retrospective patient recall of seizure history, potential variations in seizure classification, the small number of events in the validation cohort, and the clinical utility restricted to decision-making thresholds above 12%. The model findings may not be generalisable to low- and middle-income countries, or when information on all predictors is not available.
The EMPiRE model showed good performance in predicting the risk of seizures in pregnant women with epilepsy who are prescribed antiepileptic drugs. Integration of the tool within the antenatal booking visit, deployed as a simple nomogram, can help to optimise care in women with epilepsy.
| Pregnant women with epilepsy are at increased risk of death and complications from seizures; their high-risk status during pregnancy and after childbirth is often not recognised.
Knowledge of an individual’s risk of seizures could help healthcare professionals and pregnant women make decisions regarding management.
To our knowledge, there are currently no models to predict risk of seizures in pregnant women with epilepsy on medication.
We developed the EMPiRE model to predict the risk of seizures in pregnancy and up to 6 weeks after delivery in women with epilepsy on medication whose drug doses were managed based on clinical findings; we validated the model in a separate group of women whose dose management was based on drug levels in the blood.
The model discriminated well between those with and without seizures, with good agreement between predicted and observed risks across both low- and high-risk women.
The model is clinically useful for decision-making where the threshold of choice for seizure risk is between 12% and 99%.
The model showed promising transportability to the validation cohort.
The EMPiRE prediction model can be used by healthcare professionals to identify pregnant women at high risk of seizures and to plan early referral for specialist input; determine the need for close monitoring in pregnancy, labour, and after childbirth; and assess antiepileptic drug management.
The performance of the model is unlikely to vary with the antiepileptic drug dose management strategy.
| Women with epilepsy are 10 times more likely to die in pregnancy than those without the condition [1]—seizures are a common cause of death [2]. Despite warnings from consecutive reports of the Confidential Enquiry into Maternal Deaths (UK) on the failings in antenatal, intrapartum, and postnatal management of women with epilepsy, care of these women remains fragmented [3,4]. A lack of recognition of the women’s high-risk status by professionals in primary and in secondary care has been highlighted consistently as the main factor behind epilepsy-related maternal deaths [2,3,5]. Furthermore, up to 4 in 10 women discontinue their antiepileptic medication in pregnancy due to concerns about the effects of drugs on the fetus, thereby increasing their risk of seizures [6,7]. Many maternal deaths in women with epilepsy could be averted with timely specialist input [5]. Seizures in pregnancy also have a negative impact on daily living. For example, the loss of driving license following seizures affects employment, relationships, and quality of life [8–10].
Pregnant women with epilepsy at risk of seizures need a personalised management plan for antenatal, intrapartum, and postnatal care, which requires multidisciplinary input through joint obstetric neurology clinics; however, these clinics are not available in all healthcare centres [11]. Furthermore, women at high risk of seizures need close monitoring in labour, with adequate pain relief measures such as epidural analgesia, and use of long-acting benzodiazepines such as clobazam [11]. Current guidelines recommend the use of these measures in high-risk women [11]. But a lack of guidance on what constitutes high-risk pregnancy is one factor that has contributed to variations in the care of pregnant women with epilepsy [11].
Prediction of seizures based on a woman’s individual characteristics not only provides an accurate picture of the risks to inform decision-making, but also promotes effective communication between the multi-specialty teams caring for women with epilepsy. A tool for predicting seizure risk can empower women to make informed decisions on their antenatal and intrapartum care. Furthermore, awareness of one’s risk status may lower any anxiety arising from the unpredictable nature of seizures [12], and promote adherence to medication through risk-informed counselling [13].
To our knowledge, there are currently no models to predict seizure risk in pregnant women with epilepsy. Existing, small retrospective studies provide imprecise estimates of the performance of individual predictors, such as type of seizures and seizure status in pre-pregnancy [14–16]. We aimed to develop and externally validate a prognostic model to predict the risk of seizures in pregnant women with epilepsy on medication, until 6 weeks postpartum. We also planned to determine the net benefit of using the model at various threshold probabilities using decision curve analysis.
We developed and validated the prognostic model for seizures in the prospective multicentre EMPiRE (AntiEpileptic drug Monitoring in PREgnancy) study, which recruited pregnant women with epilepsy on antiepileptic drugs at first antenatal visit from 50 maternity units in the UK between 4 November 2011 and 17 August 2014 [17]. The UK National Research Ethics Committee approved the EMPiRE study (11/WM/0164), written consent was obtained from participants, and the protocol can be accessed at https://www.journalslibrary.nihr.ac.uk/programmes/hta/095538#/. The research reported here did not require further review by an ethics committee. We reported our prognostic study in line with the TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) recommendations, and present our findings as a nomogram, a graphical representation of the model to calculate an individual’s risk of seizure [18–20] (S1 TRIPOD Checklist).
EMPiRE was a prospective study, and recruited pregnant women with epilepsy on lamotrigine, carbamazepine, phenytoin, or levetiracetam before 24 weeks’ gestation. Serum antiepileptic drug levels were assessed every month, but the women and clinicians were blinded to these levels. For women for whom drug levels remained stable, the blinding was maintained (non-randomised cohort) until delivery, and drug doses were adjusted based on clinical features, in line with national recommendations [17,21]. Women whose serum drug levels fell were randomly allocated either to a strategy of adjusting antiepileptic doses based on serum drug levels (after unblinding) or to a strategy of changing the drug doses based on only clinical features (blinding maintained). All participants were followed up until 6 weeks after delivery (S1 Appendix). The study is described in detail elsewhere [17]. For model development, we used the cohort of women who were managed without routine serum drug level monitoring (non-randomised and randomised women), which is in line with standard epilepsy care in the UK [17,21]. We validated the model in the separate cohort of women managed differently, with routine therapeutic drug monitoring, as practised in some countries such as the US, to determine if the model was transportable across varied healthcare practices [22].
A multidisciplinary team of neurologists, obstetricians, and researchers selected the candidate predictors for further evaluation in the prognostic model, based on existing evidence and their relevance to clinical care [14,16,23–26]. From an initial list of 65 baseline variables, we selected the following candidate predictors: age at first seizure, history of learning difficulty or mental health disorder, baseline seizure classification (tonic-clonic, non-tonic-clonic, unspecified), history of seizure in the 3 months before pregnancy (tonic-clonic, non-tonic-clonic), number of seizures between start of pregnancy and baseline visit, type of antiepileptic drug taken at baseline, dose of antiepileptic drug taken at baseline, gestational age at baseline, and hospital admission for seizures in a previous pregnancy. All continuous predictors were assumed to be linearly associated with the outcome.
Our main outcome was the occurrence of tonic-clonic (convulsive) or non-tonic-clonic (non-convulsive) seizure [27]. Participants prospectively recorded their epileptic seizures, if any, in purpose-built seizure diaries. To avoid overfitting of multivariable models, the rule of thumb is to ensure that there are 10 events for each predictor variable that was considered for inclusion in the model [28]; we worked within this rule limitation.
We assessed the predictive performance of the model using measures of discrimination (C-statistic) and accuracy (calibration slope). The C-statistic represents the ability of the model to discriminate between those who do and do not experience seizures; a value of 1 indicates perfect discrimination, and a value of 0.5 indicates no discrimination beyond chance [29]. Models are considered to have a good performance when the C-statistic exceeds 0.7 [30]. Calibration refers to agreement between the predicted and observed risk of seizure for all groups of predicted probabilities. A well-calibrated model will have a calibration slope of 1, and all groups will fit close to this line.
The EMPiRE study recruited 560 pregnant women. The model development cohort included 399 women; the validation cohort included 128 women (S1 Appendix).
The average gestational age at baseline was 16.6 weeks (SD 4.0) in the development cohort and 14.9 weeks (SD 4.4) in the validation cohort (Table 1). The mean age at first seizure was similar in both cohorts, at 16 years, and 10%–15% of women had a learning difficulty or mental health disorder. A similar proportion of women in the development and validation cohorts were previously diagnosed to have tonic-clonic seizures (development, 39%; validation, 36%). Overall, 46% (182/399) of women in the development cohort and 39% (50/128) in the validation cohort had experienced seizures in the 3 months prior to pregnancy. Lamotrigine was the commonest antiepileptic drug prescribed in both cohorts; more than half of the women took lamotrigine in the development (226/399, 57%) and validation cohorts (80/128, 63%).
Overall, 46% (183/399) of women in the development cohort experienced 1 or more seizures at any time from baseline until 6 weeks after delivery. Tonic-clonic seizures accounted for half of all seizures (90/183, 49%). Eight predictors were significantly associated with seizures and were included in the final multivariable model: age at first seizure, history of mental health disorder or learning difficulty, baseline seizure classification (tonic-clonic, non-tonic-clonic, unspecified), hospital admission for seizures in a previous pregnancy, tonic-clonic seizure in the 3 months before pregnancy, non-tonic-clonic seizure in the 3 months before pregnancy, baseline dose of lamotrigine, and baseline dose of levetiracetam (Table 2). The model is presented as a graphical calculator (nomogram) in Fig 1.
The equation of the EMPiRE prediction model for risk of seizures during pregnancy and until 6 weeks after delivery in women with epilepsy on antiepileptic drugs was as follows:
probability(seizure)=exp(Y)/(1+exp(Y))
where Y = −1.39 + (−0.02 * age at first seizure) + 0.61 [unspecified seizures] + 0.75 [non-tonic-clonic seizures] + (0.02 * dose of levetiracetam/100) + (0.29 * dose of lamotrigine/100) + 0.66 [non-tonic-clonic seizures in the 3 months before pregnancy] + 1.97 [tonic-clonic seizures in the 3 months before pregnancy] + 0.67 [learning difficulty or mental health disorder] + 0.17 [admitted to hospital for seizures during previous pregnancy].
All variables were coded as binary (1 when present and 0 when absent) except for age at first seizure (years), dose of lamotrigine (mg/day), and dose of levetiracetam (mg/day).
The apparent C-statistic for the model was 0.80 (95% CI 0.76, 0.85). After bootstrap adjustment for optimism, the final prediction model had a C-statistic of 0.79 (95% CI 0.75, 0.84) to discriminate between women with and without seizures (Table 3). The optimism-adjusted calibration plot (Fig 2) showed mostly good agreement between the predicted and observed risks, and the calibration slope was 1.26 (95% CI 0.98, 1.54).
Our sensitivity analysis, which combined all available data (n = 527), resulted in a model with the same predictors and similar coefficients as the EMPiRE model developed using only the development cohort. The antiepileptic drug monitoring strategy was not found to be a significant predictor of seizures and was therefore not selected in the combined model. The C-statistic and calibration slope of the combined model were 0.78 (95% CI 0.74, 0.82) and 1.22 (95% CI 0.44, 1.46), respectively (S2 Appendix).
In the external validation cohort, 45% (57/128) of women experienced seizures at any time from baseline until 6 weeks after delivery; tonic-clonic seizures were reported in 39% (22/57) of women who had seizures. The final model showed good discrimination when externally validated, with a C-statistic of 0.76 (95% CI 0.66, 0.85). The model showed mostly good agreement between the predicted and observed risks, with a calibration slope of 0.93 (95% CI 0.44, 1.41) (Fig 2).
In our decision curve analysis (Fig 3), the curve for the EMPiRE model showed positive net benefit for predicted probability thresholds between 12% and 99% compared to managing pregnant women with epilepsy as if they will all have seizures or managing them as if none of them will have seizures (i.e., treat-all or treat-none strategies). Table 4 provides estimates of the net benefit of using the model for various probability thresholds. For low thresholds, below 12%, there was no difference between using the EMPiRE model and treating women as if they will all have seizures.
The EMPiRE model performs well in predicting the risk of seizures at the time of antenatal booking in pregnant women with epilepsy who are prescribed antiepileptic medication. The model incorporates routinely available characteristics that are easy to measure, such as age at first seizure, type of seizures, seizures in the 3 months before pregnancy, mental health, admission to hospital for seizures during a previous pregnancy, and dose of antiepileptic drugs. The model is clinically useful over a range of threshold probabilities, and is relevant to general practitioners, epilepsy specialists, obstetricians, and midwives in identifying high-risk women. The model shows potential for transportability across risk groups to settings where routine therapeutic drug monitoring is undertaken, but the findings should be interpreted with caution due to the small number of events in the validation sample. Our simple nomogram is designed to facilitate the model’s use in clinical practice.
To our knowledge, ours is the only clinical prognostic model to predict seizures in pregnant women with epilepsy. We developed the model using data from a prospective, high-quality, multicentre study. We evaluated predictors that were clinically relevant and routinely available to healthcare professionals, so that the model can be easily applied in clinical practice. Missing values of predictors were dealt with by multiple imputation, thereby avoiding loss of useful information [39,40]. We developed the model to predict seizures not only in pregnancy, but up to 6 weeks after delivery, a period with increased risks to the mother and baby [11,41]. We adjusted for optimism and addressed issues around overfitting in the model. In addition to providing the model as an easy-to-use nomogram, we provided information on its clinical use at various threshold probabilities for decision-making [35]. The model includes clinical variables that are easily accessible at the time of booking for incorporation into an app or integration into computer systems within healthcare services. A convenient and easy-to-use nomogram of the EMPiRE model allows for immediate use of the model to predict the risk of seizures without the need to remember the formulae behind it.
Our model development and validation approach took into account the significant variations in the management of antiepileptic drug dosages in pregnancy to prevent seizures [42]. While the American Academy of Neurology recommends routine serum therapeutic drug monitoring, with dosage increased if the serum drug level falls [22], the UK National Institute for Health and Care Excellence, Royal College of Obstetricians and Gynaecologists, and Scottish Intercollegiate Guidelines Network guidelines do not recommend routine drug monitoring but drug dose adjustments based mainly on clinical features [11,21,43]. In this paper, we developed and internally validated the model in women whose drug dose was managed based on clinical features to determine the accuracy and reproducibility of the model. Through our external validation, we assessed the transportability of the model to women managed using a different management strategy (therapeutic drug monitoring), which appears promising [44]. Furthermore, when we developed the combined model by using all available data (including women routinely and not routinely monitored for drug levels) in our sensitivity analysis, we did not observe any differences either in the number and type of predictors or in the model’s performance compared to the model developed using only women in the development cohort. The antiepileptic drug dose monitoring strategy was not identified to be a significant predictor in the combined model, implying that the model is generalisable irrespective of the strategy.
There are some limitations to this study. The cohorts consisted of women recruited with pre-specified criteria, which may limit the use of the model in all women [19,45]. We did not include women on sodium valproate; this is consistent with current recommendations against valproate use in pregnancy, due to the increased risk of birth defects and neurodevelopmental disorders [46]. The model can only be used in women managed on phenytoin, lamotrigine, levetiracetam, or carbamazepine and when information is available on all predictors in the context of care in a high-income setting. This limits its transportability to low- and middle-income countries with resource constraints and non-availability of these drugs [47]. Women were recruited at varied gestational ages. However, we evaluated gestational age as a predictor, and it was not selected by the modelling strategy in the final model. The model included history of seizures in the 3 months before pregnancy obtained through retrospective recall, with resultant bias. We consider this to reflect the real life scenario, where women who do not receive pre-pregnancy specialist epilepsy care often do not maintain a prospective seizure diary prior to seeing an epilepsy specialist in pregnancy. It is possible that a different predictor such as history of seizures in the 9 months before pregnancy instead of the 3-month history in our model may have improved its performance [48,49]. We only included clinical predictors routinely available at the time of antenatal booking, and did not evaluate other tests such as electroencephalogram (EEG) or MRI, or risk factors such as history of nocturnal or prolonged seizures, which may be available in specialist epilepsy care. We could not assess any changes in antiepileptic medication before conception because this information was not routinely recorded at antenatal booking, and was not collected in the EMPiRE trial. These additional variables may have improved the performance of the model. Due to the small sample size and the small number of events in the validation cohort (<100), we were limited in our interpretation of the transportability of the model [50].
To our knowledge, 2 other prediction models exist for seizures, both involving non-pregnant individuals: seizure prediction in children and adults who have recently stopped their antiepileptic drugs, reported using individual participant data (IPD) meta-analysis, and prediction of subsequent seizures after a single seizure in individuals without clear indication to commence treatment (MESS study) [51,52]. Some predictors in the IPD meta-analysis model such as the age at onset of epilepsy were also present in our model. It is not appropriate or feasible to apply other variables, such as seizure-free interval before antiepileptic drug withdrawal and epileptiform abnormality on EEG, to the pregnant population [51]. The performance of our EMPiRE model was better than that of the IPD meta-analysis model (C-statistic 0.65) [51]. The MESS study, which used split sample validation, did not report the performance of the model with currently recommended measures such as C-statistic and calibration slope, and hence we are unable to compare that model with the EMPiRE model.
Other individual studies such as the EURAP (European and International Registry of Antiepileptic Drugs and Pregnancy) have reported on the association between maternal risk factors and seizures in pregnancy—none provided multivariable prognostic models [14,16]. Compared to a third of pregnant women with epilepsy on medication developing seizures in the EURAP study, 46% of women in the EMPiRE cohorts experienced seizures in pregnancy or until 6 weeks after delivery [14,23]. This difference could be attributable to the known increase in seizures that occurs after delivery in new mothers, as EURAP did not include postnatal mothers or prospective seizure diaries [41]. Inclusion of a selective group of women in the EMPiRE study may also have contributed to the difference. Similarly to the EURAP study, our final model identified lamotrigine dose to be a predictor of seizures [14]. Another small retrospective study identified pre-pregnancy seizure status to be the main predictor of seizures in pregnancy [16], which was also the strongest predictor of seizures in our model.
Currently pregnant women with epilepsy are managed by varied healthcare professionals such as general practitioners, obstetricians and midwives, and epilepsy specialists. There is no clear pathway for multidisciplinary communication. Joint obstetric neurology clinics are not available in half of the maternity units in UK, a major hindrance for integration of epilepsy care within antenatal care [53]. The first step towards achieving integrated care is effective risk communication of the mother’s seizure status. Such a risk-based approach using quantified risk estimates can help to avoid maternal deaths such as those reported in the MBRRACE-UK (Mothers and Babies: Reducing Risk through Audits and Confidential Enquiries across the UK) report [5], where women with epilepsy were never treated by an epilepsy specialist in pregnancy, and were left unmonitored during their hospital admissions without specialist input [5].
We refrained from recommending specific decision thresholds for various interventions as these are likely to vary with the potential adverse effects and costs of the planned intervention. For example, primary care clinicians may consider a 20% cutoff, a level of risk associated with driving restrictions, to be appropriate to make decisions on early referral to tertiary units with joint obstetric neurology clinics. However, secondary care clinicians may choose a higher threshold when the intervention involves frequent antenatal monitoring (weekly or fortnightly), intrapartum use of invasive interventions for pain relief such as epidural and other medications (such as clobazam, which carries a risk of neonatal respiratory depression), or close monitoring in the postnatal period. The choice of threshold in a clinical setting is also likely to vary depending on the epilepsy syndrome and seizure types. For example, the intervention threshold may be lower for patients who tend to experience convulsive seizures than for those who experience absence seizures.
Women’s choice of thresholds may depend on the additional time and resources required (for example, long-distance travel to access tertiary care) and the perceived risks to themselves and their babies from the various interventions. If the ability to drive is crucial to the mother for her job and other responsibilities, after discussion with clinicians, she may opt for a lower threshold for interventions in secondary care. But if minimising the risk of long-term adverse offspring neurodevelopmental outcomes is valued more by the mother than minimising the risk of seizures, she may choose a higher threshold for increasing the dose and number of antiepileptic drugs. Our decision curve analysis shows that the model is useful across a wide range of threshold probabilities.
Use of the model in clinical practice should be complementary to individualised advice on safety, risk assessment, drug adherence, and triggers for seizures. Awareness of seizure risk can minimise non-adherence to medication in pregnancy, one of the major factors behind seizure deterioration in pregnancy [6,7,13]. Women predicted to have a low risk of seizure by the model should be informed that their risk status is subject to adherence to their antiepileptic medication. The EMPiRE model does not identify women below 12% risk. Women and clinicians should be aware of this limitation if the probability threshold to make decisions on eligibility for home or water birth is below this threshold.
The effect of the addition of other markers, such as EEG findings or historical MRI brain imaging reports, on the performance of the model needs further evaluation. There is a need for multiple external validations across different settings and populations to fully appreciate the transportability of the model [54]. The impact of using the EMPiRE model in clinical practice needs to be evaluated through cluster-randomised trials, to assess whether it helps improve outcomes such as the seizure-free period or quality of life of these women. While the tool is expected to improve women’s knowledge of their risk status for seizures in pregnancy, the effect of the EMPiRE model on women’s anxiety levels is not known and needs to be assessed. Further studies are needed to assess the acceptability of the tool to women with epilepsy and to healthcare providers, their preferred thresholds of choice, and the cost utilities of consequences of decisions for various false positive and false negative cases.
The EMPiRE nomogram is a simple 8-item prediction tool to calculate the individualised risk of seizures at antenatal booking in pregnant women with epilepsy on antiepileptic drugs. The estimates can help guide individually tailored choices made by patients and clinicians, which may influence the intensity of monitoring in pregnancy and after delivery, place of care, and antiepileptic drug dose adjustment strategy. The model is not clinically useful for decision-making at very low thresholds.
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10.1371/journal.pbio.0060119 | Circadian Transcription Contributes to Core Period Determination in Drosophila | The Clock–Cycle (CLK–CYC) heterodimer constitutes a key circadian transcription complex in Drosophila. CYC has a DNA-binding domain but lacks an activation domain. Previous experiments also indicate that most of the transcriptional activity of CLK–CYC derives from the glutamine-rich region of its partner CLK. To address the role of transcription in core circadian timekeeping, we have analyzed the effects of a CYC–viral protein 16 (VP16) fusion protein in the Drosophila system. The addition of this potent and well-studied viral transcriptional activator (VP16) to CYC imparts to the CLK–CYC-VP16 complex strongly enhanced transcriptional activity relative to that of CLK–CYC. This increase is manifested in flies expressing CYC-VP16 as well as in S2 cells. These flies also have increased levels of CLK–CYC direct target gene mRNAs as well as a short period, implicating circadian transcription in period determination. A more detailed examination of reporter gene expression in CYC-VP16–expressing flies suggests that the short period is due at least in part to a more rapid transcriptional phase. Importantly, the behavioral effects require a period (per) promoter and are therefore unlikely to be merely a consequence of generally higher PER levels. This indicates that the CLK–CYC-VP16 behavioral effects are a consequence of increased per transcription. All of this also suggests that the timing of transcriptional activation and not the activation itself is the key event responsible for the behavioral effects observed in CYC-VP16-expressing flies. The results taken together indicate that circadian transcription contributes to core circadian function in Drosophila.
| The existence of circadian clocks, which allow organisms to predict daily changes in their environments, have been recognized for centuries, yet only recently has the molecular machinery responsible for their generation been uncovered. The current model in animals posits that interlocked feedback loops of transcription-translation produce these 24-hour rhythms. In fruit flies, the transcription loop contains a key activator complex, composed of the transcription factors Clock and Cycle. This CLK-CYC complex stimulates the synthesis of repressor proteins like Period and Timeless, which repress the activator complex. The synthesis–repression cycle takes precisely 24 hours under environmental conditions that influence the circadian period. An almost identical process relies on the ortholog proteins CLK-BMAL in mammals. Recent findings have challenged the transcription-translation feedback model and suggest that circadian transcription is an output process and that the post-translational modification of clock proteins is the real central pacemaker mechanism. In the present study, we have manipulated the levels and strength of the CLK-CYC complex. The results demonstrate that its activity is vital for proper period determination and thus indicate that the transcriptional feedback loop is part of the core circadian mechanism.
| Circadian rhythms are widespread in nature and help to maintain internal temporal order as well as anticipate daily environmental changes [1]. They use self-sustained biochemical oscillators that generate oscillations at the molecular, physiological, and behavioral levels [2,3].
Results over the past 15 years have highlighted the importance of transcription to circadian biology [4]. In eukaryotic systems, a large fraction of mRNAs, perhaps 10% or more, undergoes circadian transcription (e.g., [5,6]). Circadian transcriptional oscillations contribute to myriad physiological and behavioral outputs in diverse tissues of eukaryotic organisms (e.g., [5,7–10]). Recent data from humans, mice, and flies indicate that numerous syndromes and even pathologies result from a disruption of these daily oscillations [11–16].
A conserved heterodimeric transcription factor, constituted by the proteins Clock and BMAL1 (CLK–BMAL) in mammals and Clock and Cycle (CLK–CYC) in flies, sits at the top of the system that generates circadian transcriptional oscillations [17–22]. These complexes direct the transcription of direct target genes, some of which encode repressors of the activity that leads to their transcription. These repressor proteins, chiefly Timeless and Period in flies or Cryptochrome and Period in mammals, accumulate over the course of many hours and ultimately result in the repression of CLK–CYC or CLK–BMAL activity, respectively [23–28]. A complete cycle takes approximately 24 h and is entrained or reset to exactly 24 h by the daily light–dark (LD) cycle.
These transcriptional cycles constitute the core circadian transcriptional feedback loop of flies and mammals. There are also subsidiary loops involving additional repressors and activators, but genetic evidence indicates that they are less important to circadian timekeeping [29–31]. The circadian transcriptional feedback loop was originally proposed in flies and based on the circadian oscillation of per transcription as well as the role of PER in the parallel timing of behavioral and transcriptional oscillations [23,25]. Subsequent evidence made a direct role of PER in transcriptional repression more likely [18,24,32–34].
There is also an important contribution of post-transcriptional and post-translational regulation to circadian timekeeping in both the fly and the mammalian systems. In Drosophila, genetic evidence indicates that major alterations in circadian period result from mutations of key kinase genes, and there is similar evidence in mammals. For example, the key Drosophila clock gene doubletime (dbt) encodes CKIε, and its mammalian relative is also a clock gene [35–37]. This importance of phosphorylation to circadian timekeeping even derives from studies of humans with advanced sleep phase syndrome [11–13,38]. Manipulation of phosphatase activities within Drosophila clock cells also affects circadian period [39,40].
Major targets of these post-translational modifications appear to be the transcriptional repressors PER and TIM. Their modification status as well as the rates with which these modifications take place have a major influence on their degradation rate [35,38,41–50]. Modification of PER may additionally influence its transcriptional repressor activity or the timing of this activity [34,38,51–53]. It is also likely that the repression of CLK–CYC activity occurs at least in part via CLK phosphorylation, which may be mediated by a PER–DBT complex and/or a PER–TIM–DBT complex [42–44,54].
The importance of post-translational modification to period determination has been strengthened by recent results from cyanobacteria [55]. The three key clock proteins—KaiA, KaiB, and KaiC—are transcription factors. However, recombinant versions of these proteins undergo circadian oscillations of association and modification state in vitro (KaiC has autokinase and autophosphatase activity) in the absence of transcription and without nucleic acids [56,57]. These results make it very likely that the core circadian system in cyanobacteria is predominantly if not exclusively post-translational and suggest that circadian transcriptional regulation is a downstream output feature, unnecessary for core circadian timekeeping. This raises the possibility that a similar situation occurs in flies and mammals: the core circadian system may be primarily post-translational (e.g., based on the temporal modification of PER and TIM). Consistent with this notion, Yang and Sehgal have shown that circadian locomotor activity rhythms can occur with per- and tim-expressing transgenes missing their natural promoters [58]. This work extended previous indications that behavioral rhythms require PER activity but do not require circadian transcription of the per gene [59].
To pursue the contribution of transcription to core circadian timekeeping in Drosophila, we have analyzed the in vivo effects of a CYC–viral protein 16 (VP16) fusion gene. VP16 is a potent transcriptional activator derived from Herpes virus [60] and imparts to the CLK–CYC-VP16 complex enhanced transcriptional activity relative to the normal CLK–CYC heterodimeric complex. This is based on activity in S2 cells as well as flies expressing CYC-VP16. These flies also have increased levels of CLK–CYC direct target gene mRNAs, including those from per and tim. Moreover, the CYC-VP16-expressing flies have short periods, implicating circadian transcription in period determination. Taken together with more detailed molecular analyses of these flies as well as behavioral assays of strains missing the normal per promoter, we suggest that CLK–CYC-mediated transcription of the per gene is important for period determination.
To manipulate the transcriptional activation potential of the CLK–CYC heterodimer, we generated a fusion protein between the CYC protein and the strong and well-characterized viral transcriptional activator VP16 (Figure 1A) [60]. Current indications are that all activator activity of the CLK–CYC heterodimer normally comes from the polyglutamine region of CLK (Figure 1A) [18], so we considered that VP16 might increase the activity of a CLK–CYC-VP16 heterodimer. As an initial assay, DNA encoding the fusion protein was transfected into S2 cells along with a standard timeless promoter-luciferase (tim-luc) reporter gene [18,61], which responds well to CLK–CYC activity.
Transfection of the fusion protein gene has little or no activity (Figure 1B). This is expected and reflects the absence of its partner CLK from S2 cells [18]. In contrast, transfection of a CLK gene alone partners with endogenous CYC and potently increases reporter gene activity (Figure 1B), identically to what has been reported previously [18]. Cotransfection of CLK with CYC-VP16 increases activity a further 5-fold (Figure 1B), which presumably reflects the transcriptional activation potential of VP16. Importantly, cotransfection of CLK with CYC or with another VP16 fusion protein (GAL4-VP16) has no effect over transfection with CLK alone (Figure S1A and unpublished data). An assay of endogenous tim mRNA expression by real-time PCR and TIM protein by western blotting gives rise to similar results: CYC-VP16 alone has no activity, whereas CLK plus CYC-VP16 cotransfection has considerably more activity than CLK alone (Figure 1C and Figure S1B). Moreover, coexpression of CYC-VP16 rescues activity of the truncated CLKJrk protein in this tissue culture assay system (Figure S1C); CLKJrk is missing most of its activation domain [17].
CLK-driven transcription is inhibited by double-stranded RNAs (dsRNAs) against the 5′ and 3′ untranslated regions (UTRs) of the endogenous cyc mRNA present in S2 cells (Figure 1B and 1C). In contrast, activity due to cotransfection of CLK and CYC-VP16 is insensitive to incubation with the same dsRNAs (Figure 1B and 1C). This is because the CYC-VP16 expression plasmid does not carry the cyc UTRs. The result indicates that most CLK activity is derived from the CLK–CYC-VP16 heterodimer. Neither CLK–CYC nor CLK–CYC-VP16 has activity on a tim-luc reporter with mutant E-boxes [61], indicating that the CLK–CYC-VP16 fusion has DNA-binding properties similar to wild-type CLK–CYC (Figure 1B). Because there is no detectable endogenous per expression in S2 cells, even after clk expression [62], the higher target gene mRNA levels are likely the consequence of a stronger transcriptional activation independent of any possible weaker PER-mediated repression on CYC-VP16.
Cotransfection with per cDNA inhibits CLK–CYC-VP16 activity, similar to what is observed for CLK–CYC activity (Figure 1D) [18,34] Given the entirely different nature of the VP16 activator compared to the polyglutamine region of CLK and the 5-fold increase in activity, this suggests that per repression involves a similar inhibition of CLK–CYC and CLK–CYC-VP16, probably an inhibition of DNA binding [54]. The similar properties of the two heterodimers are despite the much more potent activity of the former.
To generate flies with cyc-vp16 expression in circadian cells, we created uas-cyc-vp16 transgenic flies and crossed them to tim-gal4 driver lines. We then assayed circadian locomotor behavior in these tim-cyc-vp16 flies (Figure 2A and 2B, top). They were robustly rhythmic with ∼22-h periods, approximately 2 h shorter than those of wild-type flies. Figure 2C summarizes comparable period shortening by uas-cyc-vp16 combined with a highly spatially restricted circadian driver (pdf-gal4) and with two broader expression drivers (actin-gal4 and the pan-neuronal elav-gal4). This indicates that the ∼22-h period is not an idiosyncrasy of the tim-gal4 driver. Moreover, the short period was not simply caused by cyc overexpression. This is because elav-cyc flies (uas-cyc rather than uas-cyc-vp16 in combination with the same elav-gal4 driver) have a wild-type–like period (Figure 2C, bottom). We thus attribute the period-shortening effect to increased transcriptional activity from the CLK–CYC-VP16 heterodimer within circadian cells.
Consistent with this interpretation is the period of tim-cyc-vp16 in combination with the classic pers allele; these flies have ∼17-h periods, 2 h shorter than the canonical pers 19–20 h phenotype (Figure 2B, bottom). The additive nature of tim-cyc-vp16 and pers suggests that they shorten period in independent ways, the former by increasing transcription of CLK–CYC direct target genes and the latter by causing more rapid PER turnover [50].
To further study the period-shortening effect of tim-cyc-vp16, we characterized the molecular clock of these flies. To this end, we added a tim-luc or a per-luc reporter gene to the tim-cyc-vp16 strain (generating tim-luc-cyc-vp16 flies or per-luc-cyc-vp16 flies).
The expression of luciferase is robustly rhythmic in tim-luc-cyc-vp16 flies and isolated wings. The patterns are similar to those of wild-type tim-luc flies, but luciferase levels were about 2–3 times higher (Figure 3A for isolated wings and Figure S2A for intact flies). This is a comparable activity difference to what was observed above between CLK–CYC and CLK–CYC-VP16 in S2 cells (Figure 1B and 1C). Robust cycling and an even greater activity difference are observed with the per-luc reporter gene (Figure 3B).
Normalization to the first peak of the oscillations in Figure 3B and 3A allowed a useful comparison between the controls and the per-luc-cyc-vp16 and the tim-luc-cyc-vp16 profiles (Figure 3C and Figure S2B). The normalized pairs are very similar, but the CYC-VP16 curves are phase-advanced as they decrease more rapidly and then increase more rapidly during the next cycle (Figure 3C and Figure S2B). The peaks remain coincident, almost certainly reflecting entrainment to the superimposed 24-h LD cycle. Careful observation of the tim-luc reporter in constant darkness (DD) conditions reveals shorter circadian period in CYC-VP16 flies, in parallel with the behavior (Figure S2B). The damping oscillations of the wing transcriptional reporters in DD (always true in our hands) precluded a precise period determination.
To compare these reporter effects with those on bona-fide circadian mRNAs, microarray assays were performed on tim-cyc-vp16 head RNA from Zeitgeber time 15 (ZT15) and ZT3 (the timepoints when the CLK target genes have the peak and trough mRNA amounts in wild-type flies) and compared to the same timepoints from wild-type flies (Figure 4A and 4B). CLK–CYC direct target gene (tim, per, vrille (vri), and par domaine protein 1 (pdp1)) mRNA peak levels increase 2–3-fold, and an increase is also observed in trough levels (Figure 4A). The increase in trough levels suggests that they normally result from residual CLK–CYC activity that resists repression and/or that there is a minority of CLK–CYC-expressing cells that lack a robust circadian repression system. The microarray results are qualitatively similar although quantitatively less striking than the reporter gene assays shown above (Figure 3A and 3B). This may reflect the longer half-lives of the CLK–CYC direct target gene mRNAs relative to the luciferase reporter mRNAs or another level of post-transcriptional regulation. It is also possible that the reporter genes have a larger transcriptional response to CYC-VP16 than the CLK–CYC direct target genes.
In contrast to these direct target genes, maximal values for cycling mRNAs that peak at the opposite time of day are not increased in tim-cyc-vp16 flies (Figure 4B). Trough levels are decreased, however, suggesting that this might reflect an increase in the level of a transcriptional repressor protein, itself the product of a CLK–CYC direct target gene (e.g., VRI [29,30]).
We also tested whether the increase in CLK-mediated transcription was predominantly due to impaired per repression. To this end, we measured the effect of the CYC-VP16 protein in a per null mutant (per01) background [63]. The tim and vri mRNA levels are increased in per01 flies, comparable to the increase in the S2 cell (also without PER) experiments (Figure 1B). This indicates that transcription is increased independent of any more subtle effects on per repression.
Although we attribute the shorter period of the cyc-vp16 flies to a direct enhancement of transcription, it is still possible that the VP16 activation domain has a subtle effect on some other aspect of repression, which then only indirectly enhances transcription. Therefore we decided to assay the periods of transgenic flies carrying increasing numbers of copies of the Clk genomic region. Introduction of additional copies of the Clk transgene shortens circadian period and increases CLK–CYC-mediated transcription similar to the effects of the cyc-vp16 transgene (Figure 4A and 4B).
Homozygous ClkAR flies have significantly diminished levels of functional CLK and very low amplitude transcriptional oscillations of core clock genes [64]. As a consequence, these mutant flies do not have circadian activity patterns in DD or even in standard LD conditions. In addition they do not show the typical burst of activity at the beginning of the light cycle present in wild-type flies (lights-on startle response). Because CYC-VP16 increases CLK-driven transcription, we tested it for rescue of circadian activity in the ClkAR mutant background. Although introduction of CYC-VP16 into the ClkAR background failed to rescue circadian locomotor activity rhythms in DD conditions, most of the abnormal features of LD behavior conditions were restored: this included the presence of behavioral cycles (higher diurnal than night activity) as well as the lights-on startle response (Figure 6).
The effect of CYC-VP16 on the transcriptional profiles of the reporters and CLK–CYC direct target mRNAs suggested that the period-shortening effect might be simply due to a CYC-VP16-mediated change in the timing or level of per transcription. To test this possibility, we assayed the period of tim-cyc-vp16 flies in the context of uas-per (i.e., a period gene that can be driven constitutively by GAL4 but not by CLK–CYC or by CLK–CYC-VP16). Importantly, Sehgal and co-workers [58] have shown previously that uas-per can rescue the arrhythmic per01 genotype (per01; elav-gal4; uas-per), and we verified this finding (Figure 7A). Importantly, the elav-gal4 driver in combination with uas-cyc-vp16 (and a wild-type per gene) also manifests the ∼2-h period shortening as shown above (Figure 2C). However, these two transgenes in combination with the uas-per and per01 only shorten circadian period by 20 min (Figure 7A–7C, and Figure S3B). This indicates that an increase in the levels and/or timing of per transcription is a major contributor to CLK–CYC-VP16 period shortening. We also note the broad distribution of individual fly periods from genotypes containing the uas-per; per01 combination compared to the much tighter distribution in genotypes containing a proper per promoter (Figure 7C and Figure S3C); this is an additional indication that per transcription contributes to period determination (see Discussion section).
This role of per transcription is consistent with previous reports showing a relationship between per gene dose and behavioral period: more per genes cause shorter periods [65–67]. To determine if other ways of increasing per transcription also give rise to period shortening, we compared behavioral period between genotypes with one or two doses of uas-per (Figure 7A and Figure S3C). Rather than shortening period, however, the extra copy of uas-per slightly lengthens it. This is consistent with previous reports showing that overexpression of a uas-per transgene does not shorten period [44,58,68]. Taken together with other data shown above, we conclude that the short period of tim-cyc-vp16 requires not just increased levels of per mRNA but proper timing of the per transcriptional increase.
To address the role of transcription in core circadian timekeeping in the Drosophila system, we have analyzed the effects of a cyc-vp16 fusion gene in S2 cells as well as in flies. VP16 is a potent and well-studied transcriptional activator, which imparts to the CLK–CYC-VP16 heterodimer enhanced activity relative to that of the normal CLK–CYC complex. This increased activity is manifested with reporter genes, and transgenic flies also have increased levels of CLK–CYC direct target gene mRNAs, including those from per and tim. Importantly, the cyc-vp16-expressing flies have a short period, implicating circadian transcription in period determination. As this short period and proper period control more generally require a per promoter, we suggest that CLK–CYC-VP16 drives increased per transcription, which leads to more rapid accumulation of PER and a consequent advanced phase of per repression. This is also consistent with reporter gene profiles in cyc-vp16-expressing flies. The results indicate that circadian transcription contributes to core period determination in Drosophila.
This conclusion fits with several other pieces of data from the Drosophila system. First, recent studies have identified the transcriptional repressor–encoding gene clockwork orange (cwo) as a clock gene [62,69,70]. The protein product synergizes with PER and aids the repression of CLK–CYC direct target genes. Importantly, mutations in cwo or changes in cwo expression cause substantial period changes. Second, an increase in per gene dose leads to flies with short periods. There is a decrease of approximately 0.5 h for each additional gene copy up to about four copies, which have a ∼22-h period (e.g., [65]). Third, a hemizygous deletion that includes clock lengthens circadian period by about 0.5 h [17]. Although this deletion removes more DNA than just clk (including the adjacent clock gene pdp1), our results indicate that additional copies of the clk locus indeed shorten the circadian period of otherwise wild-type flies (Figure 5). All of these observations are qualitatively similar to the increase in transcription and period shortening caused by expression of cyc-vp16 in flies.
Because of the molecular analyses (Figure 3 and Figure S2), we suspect that it is the timing of per transcription rather than a simple increase in per mRNA levels that causes the period shortening by expression of cyc-vp16. As the reporter genes contain proper per and tim promoters, their profiles indicate that per and tim transcription decreases more steeply and then increases more steeply in the cyc-vp16 flies (Figure 3C and Figure S2B). The steeper decrease presumably reflects a faster accumulation of active PER repressor, and the steeper increase reflects the enhanced potency of CLK–CYC-VP16. In addition, we note that an increase in per dose with a uas-per transgene slightly increases rather than decreases period (Figure 7A and Figure S3C) [44,58]. This genetic requirement for the per promoter also emphasizes the contribution of proper transcriptional regulation to period determination.
The increased transcriptional potency of CLK–CYC-VP16 is unlikely to be a consequence of impaired PER-mediated repression, due in turn to some structurally anomalous feature of the artificial fusion protein. This is because the stronger activation of CLK direct targets by the CLK–CYC-VP16 dimer is apparent even in the absence of PER (Figures 1C and 4C). Shorter periods due to more potent transcription is also the conclusion of Figure 5, which shows that increasing clk gene dose (an independent and “more natural” way to increase CLK-mediated transcription) leads to molecular and behavioral changes that resemble those observed in tim-cyc-vp16 flies. Finally, cyc-vp16 expression rescues several aspects of the ClkAR phenotype (Figure 6). This suggests that these features are due to low direct target mRNA levels, which are increased by the more potent CLK–CYC-VP16 complex. The failure to rescue the behavioral arrhythmicity of homozygous ClkAR flies may reflect a requirement for minimal CLK levels, which would not be expected to increase by the addition of CYC-VP16.
The robust behavioral and molecular rhythms of cyc-vp16 flies (Figure 2 and 3) more generally indicate that CLK–CYC-VP16 circadian function, including the mechanism(s) that temporally activate or repress transcription of this hyperactive complex, must be similar to those that regulate the activity of the wild-type CLK–CYC complex. This is also because the increased transcription as well as RNA levels in tim-cyc-vp16 flies suggests that most CLK–CYC direct target gene transcription is carried out by CYC-VP16 rather than endogenous CYC. Because the VP16 activation domain almost certainly functions differently from the CLK polyglutamine region, this indicates that the recruitment of specific activator and/or repressor proteins is unlikely to play a prominent, mechanistic role in the circadian regulation of transcription. A more likely mechanism involves the cyclical inhibition of CLK–CYC DNA binding. Importantly, this notion is consistent with recent chromatin immunoprecipitation results from the mammalian as well as the fly system [54,71]. Nonetheless, we suggest that per transcription as well as DNA binding of the CLK–CYC dimer to per E-boxes is the actual timekeeper of the circadian cycle during the mid-late day, when they are both increasing. This predicts that the additional activation power of VP16 indirectly shortens the DNA binding time of the CLK–CYC-VP16 dimer by accelerating the rate of PER accumulation and function. This hypothesis also fits well with the behavioral and molecular defects observed in cwo mutant flies [62,69,70].
The emphasis on the per promoter is seemingly contradicted by the rhythmicity of flies missing not only this promoter but also the tim promoter [58]. In our hands as well, per01; elav-gal4; uas-per flies are largely rhythmic despite weak rhythms, and their average period is near-normal. However, the period distribution of individual flies is unusually broad (Figure 7C and Figure S3C), indicating a contribution of the per promoter to the proper control of period within individual flies—even without CYC-VP16. Moreover, luciferase recordings from these transgenic flies show poor or no transcriptional oscillations (unpublished data). These observations suggest that individual neurons from this per01; elav-gal4; uas-per strain might be impaired even more than indicated by the behavioral rhythms of this strain (i.e., circadian brain circuitry might help to compensate for poor core circadian function within individual cells). This is analogous to the superior circadian performance of behavioral rhythmicity and the suprachiasmatic nucleus (SCN) from mutant mouse strains compared to that of individual tissue culture cells (mouse embryonic fibroblasts) derived from the same strains [72].
The role of circadian transcription described in this study complements the well-documented role of PER, TIM, and CLK post-translational regulation in period determination [34,35,43,44,48,49,54,73–75]. Given the parallel role of mammalian CLK and BMAL1 to CLK and CYC, it would be surprising were there not a similar contribution of circadian transcription to mammals. This suggests that there is a division of labor in animals between transcriptional and post-translational regulation of circadian timekeeping, which may even be temporally segregated. In contrast and as mentioned above, recent indications are that post-translational regulation is the pre-eminent mechanism in cyanobacteria. It is also the case that individual bacterial cells keep excellent circadian time, essentially indistinguishable from the culture [76]. This contrasts with individual eukaryotic cells, for example, separated SCN cells, which show substantially more variation in period than the intact SCN or organism [72,77]. All of these considerations suggest that the intracellular timekeeping mechanism of animals is different from that of cyanobacteria. We suggest that this important difference between systems reflects their separate origins, a view that is supported by the lack of sequence conservation between cyanobacterial and animal clock proteins.
pAc-clk, pAc-per, Copia Renilla luciferase, and tim-luc have been described previously [61]. pAc-cyc-vp16 was constructed by amplifying the cyc coding region and the vp16 activation domain by PCR and ligating in-frame into pAcA V5/His6 (Invitrogen). pAc-cyc was constructed by amplifying the cyc coding region and ligating in-frame into pAcA V5/His6.
S2 cells were maintained in 10% fetal bovine serum (Invitrogen) insect tissue culture medium (HyClone). Cells were seeded in a six-well plate. Transfection was performed at 70–90% confluence according to company recommendations (12 μl of Cellfectin (Invitrogen) and 2 μg of total DNA). In all experiments 50 ng of pCopia Renilla luciferase plus 50 ng of the luciferase firefly reporter were used. pBS-KS+ (Stratagene) was used to bring the total amount of DNA to 2 μg.
For both procedures we follow the RNAi protocol in S2 cells previously described [34]. Two dsRNAs were synthesized against cyc: one containing its 5′ UTR and another containing the 3′ UTR.
Total RNA was prepared from S2 cells or adult fly heads using Trizol reagent (Invitrogen) according to the manufacturer's protocol. cDNA derived from this RNA (using Invitrogen Superscript II) was utilized as a template for quantitative real-time PCR performed with the Corbett Research Rotor-Gene 3000 real-time cycler. The PCR mixture contained Platinum Taq polymerase (Invitrogen), optimized concentrations of Sybr-green, and the corresponding primers. tim: 5′-CCTTTTCGTACACAGATGCC-3′, 5′ –GGTCCGTCTGGTGATCCCAG-3′; vri: 5′-GCGCTCGCGATAAGTCTCTA-3′, 5′-CTTTGTTGTGGCTGTTGGTG-3′; rp49: 5′-ATCCGCCCAGCATACAG-3′, 5′-TCCGACCAGGTTACAAGAA-3′; and cyc: 5′-GGACGAGCGAGATTGACTATA-3′, 5′-TTTGGAGTGTATACAAATGTCG-3′.
Cycling parameters were 95 °C for 3 min, followed by 40 cycles of 95 °C for 30 s, 55 °C for 45 s, and 72 °C for 45 s. Fluorescence intensities were plotted versus the number of cycles by using an algorithm provided by the manufacturer. mRNA levels were quantified using a calibration curve based upon dilution of concentrated cDNA. mRNA values from heads were normalized to that from ribosomal protein 49 (rp49).
Forty-eight hours after transfection cells were assayed using the Dual Luciferase Assay Kit (Promega) following the manufacturer's instructions.
Lysate for the luciferase activity assay was electrophoresed in 6% SDS-PAGE. The protein was transferred to a membrane. The membrane was blocked and probed with primary and secondary antibodies according to standard techniques. Rat anti-TIM antibody [78] and horseradish-peroxidase-conjugated anti-rat antibody (Sigma) were used.
The following drivers were utilized: tim-gal4 [79], pdf-gal4 [80], and elav-gal4 [58]. per-luc, tim-luc, pers, uas-cycHA, and per-rescued flies (per01 elav-gal4; uas-per) were previously described [58,63,64,81,82].
The uas-cyc-vp16 plasmids were generated by cloning a PCR fragment from pAc-cyc-vp16 into pUAST [83]. This construct was used to generate germ-line transformants by injecting yw; Ki pp P[ry+Δ2–3]/+.
D. melanogaster RP98-5K6 bacterial artificial chromosome, which contains the complete dClk gene, was used as a template (BACPAC Resources Center at Children's Hospital Oakland Research Institute). Four different fragments covering the entire gene were first PCR amplified and cloned into pBS vector (first fragment from 7751817 to 7747254 with KpnI and SacI; second fragment from 7747617 to 7745531 with KpnI and SacII; third fragment from 7745570 to 7741748 with KpnI and SacI; fourth fragment from 7741779 to 7736982 with XhoI and NotI; the position of nucleotides refer to D. melanogaster 3L chromosome sequence). A V5 tag was inserted in the fourth fragment by quick change PCR (Stratagene) in the C terminus just before the stop codon at 7738162. The four fragments were then cut and ligated together in the pBS vector using three endogenous restriction sites, BglII at 7747320, NheI at 7745537, and NcoI at 7741772, resulting in a final dClk transgene of 14878 bp (14836 bp of dClk and 42 bp of V5 tag) with KpnI on the 5′ and NotI on the 3′ ends. The dClk-V5 transgene was then cut and ligated in the pCaSpeR 4.0 vector, sequenced, and injected into yw embryo (CBRC Transgenic Drosophila Fly Core).
Male flies were monitored for 4 d in LD conditions, followed by 4–5 d in DD conditions using Trikinetics Drosophila Activity Monitors. Analyses were performed with a signal-processing toolbox [84]. We utilized autocorrelation and spectral analysis to estimate behavioral cycle durations (periods) and the Rhythm Index to assess rhythm strength [84].
Adult male flies and dissected wings were cultured in 12:12 LD conditions, and luciferase was measured as described previously [85]. In the case of the experiments described in Figure 3A and Figure S2B, the assay was performed for three days in LD (12:12 LD) and then in DD conditions.
Probe preparation. Total RNA was extracted from fly heads, using Trizol reagent (Invitrogen) according to the manufacturer's protocol. cDNA synthesis was carried out as described in the Expression Analysis Technical Manual (Affymetrix). The cRNA reactions were carried out using the IVT Transcript Labeling Kit (Affymetrix). Affymetrix high-density arrays for D. melanogaster Genome 2.0 were probed, hybridized, stained, and washed according to the manufacturer's protocol.
Data analysis. GeneChip.CEL files were analyzed using R (http://www.r-project.org/) and the bioconductor package (gcrma algorithm; http://www.bioconductor.org/). An anti-logarithm (base 2) was applied to the data to obtain the expression values.
Accession numbers for genetic sequences mentioned in this paper from the National Center for Biotechnology Information (http://www.ncbi.nlm.nih.gov) are the D. melanogaster RP98-5K6 bacterial artificial chromosome, which contains the complete dClk gene, (AC010042) and the D. melanogaster 3L chromosome sequence (NT_037436.2).
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10.1371/journal.pntd.0001609 | Incrimination of Phlebotomus kandelakii and Phlebotomus balcanicus as Vectors of Leishmania infantum in Tbilisi, Georgia | A survey of potential vector sand flies was conducted in the neighboring suburban communities of Vake and Mtatsminda districts in an active focus of visceral Leishmaniasis (VL) in Tbilisi, Georgia. Using light and sticky-paper traps, 1,266 male and 1,179 female sand flies were collected during 2006–2008. Five Phlebotomus species of three subgenera were collected: Phlebotomus balcanicus Theodor and Phlebotomus halepensis Theodor of the subgenus Adlerius; Phlebotomus kandelakii Shchurenkova and Phlebotomus wenyoni Adler and Theodor of the subgenus Larroussius; Phlebotomus sergenti Perfil'ev of the subgenus Paraphlebotomus. Phlebotomus sergenti (35.1%) predominated in Vake, followed by P. kandelakii (33.5%), P. balcanicus (18.9%), P. halepensis (12.2%), and P. wenyoni (0.3%). In Mtatsminda, P. kandelakii (76.8%) comprised over three fourths of collected sand flies, followed by P. sergenti (12.6%), P. balcanicus (5.8%), P. halepensis (3.7%), and P. wenyoni (1.1%). The sand fly season in Georgia is exceptionally short beginning in early June, peaking in July and August, then declining to zero in early September. Of 659 female sand flies examined for Leishmania, 12 (1.8%) specimens without traces of blood were infected including 10 of 535 P. kandelakii (1.9%) and two of 40 P. balcanicus (5.0%). Six isolates were successfully cultured and characterized as Leishmania by PCR. Three isolates from P. kandelakii (2) and P. balcanicus (1) were further identified as L. infantum using sequence alignment of the 70 kDa heat-shock protein gene. Importantly, the sand fly isolates showed a high percent identity (99.8%–99.9%) to human and dog isolates from the same focus, incriminating the two sand fly species as vectors. Blood meal analysis showed that P. kandelakii preferentially feeds on dogs (76%) but also feeds on humans. The abundance, infection rate and feeding behavior of P. kandelakii and the infection rate in P. balcanicus establish these species as vectors in the Tbilisi VL focus.
| Visceral Leishmaniasis (VL) is a public health problem in Tbilisi, capital of the Republic of Georgia. VL is caused by Leishmania parasites and dogs represent the main infection reservoirs. VL is transmitted among humans and dogs by sand fly bites. Here, we carried out a three-year survey to assess the sand fly species in two communities within the VL focus of Tbilisi in the districts of Vake and Mtatsminda. We collected five sand fly species, and the most abundant was Phlebotomus kandelakii. We found live parasites in the midgut of P. kandelakii and another species, P. balcanicus. Using molecular techniques we identified the parasites as Leishmania infantum. We also found that these sand fly isolates shared a high identity to parasites isolated from dogs and humans in the same focus. This incriminates P. kandelakii and P. balcanicus as vectors of VL in this focus. The source of the blood meals in fed flies revealed that P. kandelakii preferentially feeds on dogs but also feeds on humans. The abundance, infection rate and feeding behavior of P. kandelakii and the consistently higher infection rate of P. balcanicus compared to P. kandelakii incriminate these species as primary vectors in the Tbilisi VL focus.
| Historically, visceral leishmaniasis (VL) in Georgia has been characterized by sporadic cases chiefly in eastern mountainous districts indicative of an endemic situation [1], [2]. It wasn't until the 1990s that VL due to Leishmania infantum was recognized as a significant public health problem in the Republic of Georgia [3]. From 1990–2007 there was a resurgence of the disease, with 1,414 cases reported involving an 18-fold increase from 10–12 cases per year in the 1990's to 182 cases in 2007 [Official statistical records, National Centers for Disease Control and Public Health (NCDCPH), Tbilisi, Georgia]. Sixty percent of these cases occurred within the capital city of Tbilisi, a modern city of 1.2 million inhabitants. In response to this alarming increase, a three-phase program was initiated to gain a better understanding of the epidemiologic cycle of the disease in this focus, including active surveillance in children, dogs and potential vector sand flies. Surveillance of children showed that 7.3% of 4,250 children aged 1–14 years were seropositive for Leishmania at the baseline survey, and 6.0% became seropositive over one year [4]. Risk of infection was associated with living in areas where clustered flying insects and stray dogs were observed. For dogs, the major reservoir of L. infantum infection, 18.2% of 588 domestic and 15.3% of 718 stray dogs surveyed were seropositive [4].
Among about 20 sand fly species that play a significant role in transmission of Leishmania parasites in the Mediterranean basin, members of the subgenus Larroussius represent the most important vectors of L. infantum [5], [6]. In countries of the former Soviet Union, members of the subgenera Larroussius and Adlerius, such as Phlebotomus brevis Theodor & Mesghali and Phlebotomus perfiliewi transcaucasicus Perfil'ev in Azerbaijan, Phlebotomus longiductus Parrot in Uzbekistan and Kazakhstan, and Phlebotomus kandelakii Shchurenkova in Georgia, were suspected as vectors of L. infantum but none were incriminated [5], [7]. Recent investigations on vectors of VL in northwestern Iran (Ardebil and Fars provinces) and East Azerbaijan found P. perfiliewi transcaucasicus, P. kandelakii, and Phlebotomus (Adlerius) sp., naturally infected with parasites belonging to the Leishmania donovani complex by PCR [8]–[10].
Studies on the distribution, seasonality and behavior of sand flies in disease foci in Georgia have not been carried out for the past 20 years. Lemer [1] conducted an investigation of potential vector sand flies in eastern Georgia from 1942–1952. He reported on the specific composition of sand fly populations in four localities and on the seasonal occurrence and epidemiological importance of Phlebotomus kandelakii Shchurenkova and P. chinensis balcanicus Newstead (Phlebtomus balcanicus, Theodor), which were the predominant species and the only ones common to all four localities. In one of the localities, it was observed that both species were infected with leptomonads (promastigotes), the infection rate being 3.7 percent [1]. Although such evidence casts suspicion on these species as vectors of L. infantum it does not prove the role of a vector [5]. A more recent review listed fourteen species of Phlebotomus sand flies identified in previous entomological surveys, all from eastern Georgia: Phlebotomus papatasi Scopoli of the subgenus Phlebotomus; Phlebotomus caucasicus Marzinowsky, Phlebotomus mongolensis Sinton, Phlebotomus sergenti Perfil'ev and Phlebotomus jacusieli Theodor of the subgenus Paraphlebotomus; P. kandelakii, Phlebotomus tobbi Adler & Theodor, Phlebotomus syriacus Adler & Theodor, P. transcaucasicus and Phlebotomus wenyoni Adler & Theodor of the subgenus Larroussius; and Phlebotomus simici Nitzulescu, Phlebotomus halepensis Theodor, Phlebotomus chinensis Newstead, and Phlebotomus balcanicus Theodor of the subgenus Adlerius [11]. P. kandelakii, P. balcanicus and P. sergenti were the predominant species, with P. kandelakii being the most abundant [11]. The involvement of these sand flies in the transmission of VL was not addressed in any of these surveys.
Here, we report on the species diversity, relative abundance and spatial and temporal distribution of phlebotomine sand flies within an active VL focus in Tibilisi, Georgia. We also provide compelling evidence incriminating P. kandelakii and P. balcanicus as vectors of L. infantum in this focus, report their natural Leishmania infection rates and demonstrate that isolates obtained from these wild-caught specimens are identical to L. infantum isolates obtained from humans and canines in the same focus. Additionally, this is the first study in which live parasites were isolated from the sand fly species P. kandelakii and P. balcanicus and characterized as L. infantum.
Oral consent was obtained from heads of compounds chosen for collection of sand flies in Vake and Mtatsminda. Light traps and sticky traps were only placed outside houses, in courtyards, animal pens and shelters. Flies fed on human volunteers were obtained from activities related to a project addressing human immune responses to sand fly saliva. This project was approved by the Walter Reed Army Medical Center Human Use committee (protocol # 355023). All the subjects provided written informed consent. Acquisition of dog blood was done under animal protocol LMVR 7E approved by the NIAID DIR ACUC committee that adheres to the U. S. Government Principles for the Utilization and Care of Vertebrate Animals Used in Testing, Research, and Training and maintains animals in accordance with the PHS Policy on Humane Care and Use of Laboratory Animals, the Guide for the Care and Use of Laboratory Animals, and the Animal Welfare Act and Animal Welfare Regulations user guidelines.
The city of Tbilisi is situated in a narrow valley on the banks of the Mt'k'vari (Kura) River, flanked on the east and west by steep hills. The neighboring communities of Vake and Mtatsminda, from whence cases of VL are reported, are located on the western flank of the valley overlooking the city (Fig. 1). Homes in these communities are modest, mostly of brick or stone construction. Most are within fenced or walled compounds containing courtyards, trees and orchards, gardens, grape arbors and pens for animals including dogs, chickens and rabbits, thus offering a diversity of blood meal sources as well as protected microhabitats suitable as resting and breeding sites for sand flies. Windows are unscreened, permitting sand flies free access to residents.
During the 2010 field trip, 39 blood-fed wild-caught female sand flies were collected for host blood meal analysis by PCR. The head and tip of the abdomen of each fly were removed and used to identify the fly to species. The midguts, with blood meal intact, were removed and squashed onto a Whatman Clone Saver card (Whatman plc Springfield Mill, Kent ME14 2LE, UK) to preserve them for later analysis. Each preserved blood meal was labeled with the collection date, collection method (light trap or sticky trap), collection site and species identity. PCR-based identification of the mammalian blood was carried out according to the method of Kent and Norris [20]. DNA was isolated from punches of the Clone Saver card using the QIAmp DNA Micro kit (Qiagen Inc., Valencia, CA) according to the manufacturer's protocol and the DNA was eluted in 20 µL of ultrapure H2O. Due to the variability in the size of the bloodmeal of field-collected samples, the integrity of the DNA was verified using universal vertebrate-specific primers. Separate PCR reactions were prepared for the amplification of dog, human or universal vertebrate-specific cytochrome b. Each 25 µL reaction mixture consisted of 12.5 µL of PCR Master (Roche Applied Science, Indianapolis, IN), 200 pmol of Human741F, Dog 368F, or UNFOR403 and UNREV1025 primers, 2.5 µL RediLoad (Invitrogen) and approximately 15 ng DNA template. The reaction was initiated with a 5-minute denaturation at 95°C followed by 35 cycles at 95°C for 1 minute, 58°C for 1 minute and 72°C for 1 minute. The extension step was performed at 72°C for 7 minutes. Amplicons were produced in a GeneAmp PCR System 9700 (Applied Biosystems, Carlsbad, CA) thermocycler and the products visualized on cyanine-stained 1.5% agarose gel alongside a TrackIt 100 bp DNA ladder (Invitrogen, Carlsbad, CA). Positive control DNA templates were extracted from Phlebotomus perniciosus fed artificially through a membrane on dog blood and Phlebotomus duboscqi fed on a human and were processed in a manner identical to field-caught specimens.
Sand flies were collected in abundance in and around case sites, especially near chicken coops and animal pens, from early June through mid August during each of the survey years (Figure 3). Analysis of collection data over three summers clearly reveals a short sand fly season and a unimodal annual distribution pattern, with sand fly emergence beginning around the first week of June and population densities increasing to a peak in July and then declining through late August to zero in early September (Figure 3).
In July–August of 2008, three of 283 (1.1%) female sand flies dissected and examined for Leishmania, were found infected. These included two of 223 P. kandelakii (0.9%) and one of 14 P. balcanicus (7.1%) (Table 2). All three flies harbored massive, mature infections with attachment to the microvillar lining of the midgut wall and to the cuticular intima of the stomodeal valve. Nectomonads and haptomonads were packed tightly in the thoracic midgut, behind the stomodeal valve, forming a “plug” with a high proportion of free-swimming metacyclic parasites that escaped into the dissection fluid and were distinguished by a small cell body, long flagella and rapid movement. These infectious forms were clearly visible under high magnification (40×). In some of the infections, the entire thoracic midgut was occluded with a plug of parasites that were mostly trapped in a gel matrix [21]. Similarly, in July of 2010, nine of 376 (2.4%) females dissected and examined were found infected, including eight of 320 P. kandelakii (2.5%) and one of 24 P. balcanicus (4.2%) (Table 2). PCR analysis of six successful isolates identified the parasites as Leishmania (Figure 4A). Further sequencing of the Leishmania HSP70 from two P. kandelakii and one P. balcanicus isolates followed by sequence alignment against sequences from a human and two dog isolates from the same focus and other Leishmania species confirmed the parasite identity as L. infantum (Figure 4B). Of note, the sand fly isolates were identical to the human and dog isolates from the same focus (Figure 4B).
Of 39 blood-fed Phlebotomus sand flies collected from the Mtatsminda district of Tbilisi, Georgia, 27 were suitable for analysis by PCR amplification of the cytochrome b gene. As L. infantum infection is propagated between dogs, the main infection reservoirs, and humans by sand flies, it was important to identify which of the blood-fed flies fed on either dog (730 bp) or human (360 bp) blood (Table 3, Figure 5A). A universal cytochrome b target was used as a DNA quality control (Figure 5B). Phlebotomus kandelakii was the most abundant sand fly species collected, comprising 25 of the 27 blood-fed females available for analysis (Table 3). The remaining two blood-fed specimens were identified as P. halepensis and P. sergenti. There were no blood-fed P. balcanicus captured. The host source was identified in 80% of the 25 P. kandelakii blood meals. Dogs were identified as the preferred host (76%) of P. kandelakii and were the source of blood for each of the fed P. halepensis and P. sergenti specimens (Table 3). A human blood meal was identified in a single P. kandelakii sand fly (Figure 5A). Five P. kandelakii sand flies contained blood meals that were not identified as dog or human but were confirmed as vertebrate hosts by universal amplification of cytochrome b. Mixed dog/human blood meals were not detected.
Fourteen Phlebotomus species have been reported from the eastern part of Georgia, most in mountainous rural or periurban areas [11]. The rather low species diversity observed in the current study may be a reflection of the peri-urban environment in which they were collected and emphasizes the urban nature of the Tbilisi focus where sand flies are likely breeding within or close to human habitation.
Of the species collected in this study, those of the subgenera Larroussius and Adlerius were of particular interest as potential vectors. Species of these subgenera have been incriminated or implicated elsewhere as vectors of L. infantum [5], [7]–[10], [22]. P. (Larroussius) kandelakii is a proven vector of L. infantum in Iran [22] and P. (Adlerius) balcanicus is a suspected vector of L. infantum in Greece and Serbia [23].
In the Old world there is a noticeable association between Leishmania species or complexes and particular subgenera of Phlebotomus [24]. Thus, it is reasonable to suspect putative vectors in previously unexplored foci based on their close taxonomic relationship to known vectors. Killick-Kendrick [5] noted that most incriminated vectors of L. infantum belong to the subgenus Larroussius. Phylogentically, the subgenus Adlerius is closely related to this subgenus [25] but most studies have not fully incriminated members of the subgenus Adlerius as vectors.
Routine vector surveillance in the suburban communities of Vake and Mtatsminda revealed an abundance of sand flies during a remarkably short season spanning early June through early September, suggesting a univoltine population that undergoes an obligatory or facultative diapause that carries them through the fall, winter and spring months. This is consistent with the findings of earlier workers who studied the biology of sand flies in foci of VL in mountainous areas of eastern Georgian USSR from 1945–1952 [1]. Diapause appears to be a common strategy in temperate sand fly species for surviving unfavorably cold conditions. For example, Phlebotomus ariasi in the Cevennes region of southern France undergoes a facultative diapause triggered by lower temperatures and a shorter photoperiod at the end of a short summer season [26], [27]. Other species undergo obligatory diapause triggered by diminishing photo period length [14]. In light of the shortness of the sand fly season, the high seroprevalence in children and dogs living in this focus [4] is indicative of a highly efficient transmission cycle. This is perhaps sustained by the presence, as established in this study, of more than one vector species transmitting and spreading the infection. The expansion and emergence of new foci of L. infantum in Georgia may also be accounted for by the breakdown of surveillance and vector control efforts in areas where the infection is prevalent.
The overall infection rate reported in this study (1.8%) is about half that reported by Lemer [1] over six decades ago. However, the promastigotes (referred to as leptomonads) in the earlier study were not identified as Leishmania nor was the feeding status of the infected flies determined. Killick-Kendrick and Ward [5], [28] outlined five criteria that should be fulfilled before a sand fly species is incriminated as a vector of human leishmaniasis with reasonable certainty: 1) Overlap of the geographic distribution of the suspected vector and human cases; 2) Sufficient abundance of the suspected vector necessary to maintain transmission; 3) Mature infections in naturally or experimentally infected flies; 4) Experimental transmission by bite; and 5) Isolation of Leishmania from wild-caught sand flies indistinguishable from the parasite causing disease in humans in the same place. These are highly stringent criteria and in the case of P. kandelakii, all have been satisfied apart from experimental transmission. This includes the first isolation and characterization of live L. infantum parasites from this species. With the advent of PCR technologies there has been a tendency by some to incriminate vectors based solely on the presence of Leishmania DNA. However, because a non-vector may imbibe blood from an infected host and thereby ingest Leishmania parasites, such findings, particularly in the absence of careful assessment of the feeding status, must be interpreted with caution. For P. balcanicus, the three most relevant incrimination criteria (the above-mentioned 1, 3, and 5) were also met, and while P. kandelakii was more prevalent in collections throughout the study period, the higher infection rates observed in P. balcanicus in 2008 and 2010 indicate that both are competent vectors of L. infantum in the Tbilisi focus. The finding in this study of massive, mature infections with a high proportion of metacyclic Leishmania parasites in both P. kandelakii and P. balcanicus is a demonstration of their natural ability to harbor the parasites through their complete extrinsic life cycle. More extensive trapping is needed to determine whether P. halepensis and P. wenyoni are also involved in parasite transmission in Tbilisi.
VL due to L. infantum is a zoonosis where the parasites are circulated between canines, the reservoirs of the infection. Humans become infected as accidental or incidental hosts with dogs being the most relevant source of infection. Blood meal source identification clearly implicates dogs as the favored host for P. kandelakii and the best reservoir of L. infantum infection [29]. Additionally, finding one P. kandelakii specimen with human blood is epidemiologically relevant providing evidence that this species feeds on both dogs and humans and thus can potentially spread infections from dogs to humans. Unfortunately, the absence of P. balcanicus blood-fed flies from our collection did not allow us to determine its feeding behavior and whether its role is primarily to propagate the infection among dogs or whether, similar to P. kandelakii, it feeds on humans and thus is likely to also transmit the parasite from dogs to humans. In addition to dogs and humans, chickens, cats, rodents, rabbits and bovines were present in the compounds where sand flies were collected. These animals, though epidemiologically irrelevant, represent potential sources of blood for the sand flies and likely account for the five unidentified P. kandelakii blood meals.
In conclusion, finding mature infections containing a high proportion of metacyclic promastigotes in both P. kandelakii and P. balcanicus provides strong evidence that they are capable of harboring L. infantum through its complete life cycle. These findings enable us to declare with confidence that P. kandelakii and P. balcanicus are vectors of Leishmania infantum in the VL focus in Tbilisi, Georgia. Based on its high relative abundance, P. kandelakii is probably the primary vector. However, the higher percentage of natural infections observed in P. balcanicus indicates that it is the more efficient vector and therefore plays a significant role in the epidemiology of visceral Leishmaniasis in this focus.
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10.1371/journal.pgen.1005165 | The Power of Gene-Based Rare Variant Methods to Detect Disease-Associated Variation and Test Hypotheses About Complex Disease | Genome and exome sequencing in large cohorts enables characterization of the role of rare variation in complex diseases. Success in this endeavor, however, requires investigators to test a diverse array of genetic hypotheses which differ in the number, frequency and effect sizes of underlying causal variants. In this study, we evaluated the power of gene-based association methods to interrogate such hypotheses, and examined the implications for study design. We developed a flexible simulation approach, using 1000 Genomes data, to (a) generate sequence variation at human genes in up to 10K case-control samples, and (b) quantify the statistical power of a panel of widely used gene-based association tests under a variety of allelic architectures, locus effect sizes, and significance thresholds. For loci explaining ~1% of phenotypic variance underlying a common dichotomous trait, we find that all methods have low absolute power to achieve exome-wide significance (~5-20% power at α=2.5×10-6) in 3K individuals; even in 10K samples, power is modest (~60%). The combined application of multiple methods increases sensitivity, but does so at the expense of a higher false positive rate. MiST, SKAT-O, and KBAC have the highest individual mean power across simulated datasets, but we observe wide architecture-dependent variability in the individual loci detected by each test, suggesting that inferences about disease architecture from analysis of sequencing studies can differ depending on which methods are used. Our results imply that tens of thousands of individuals, extensive functional annotation, or highly targeted hypothesis testing will be required to confidently detect or exclude rare variant signals at complex disease loci.
| Re-sequencing technologies allow for a more complete interrogation of the role of human variation in complex disease. The inadequate power of single variant methods to assess the role of less common variation has led to the development of numerous statistical methods for testing aggregate groups of variants for association with disease. Such endeavors pose substantial analytical challenges, however, due to the diverse array of genetic hypotheses that need to be considered. In this work, we systematically quantify and compare the performance of a panel of commonly used gene-based association methods under a range of allelic architectures, significance thresholds, locus effect sizes, sample sizes, and filters for neutral variation. We find that MiST, SKAT-O, and KBAC have the highest mean power across simulated datasets. Across all methods, however, the power to detect even loci of relatively large effect is very low at exome-wide significance thresholds for sample sizes comparable with those of ongoing sequencing studies; as such, the absence of signal in studies of a few thousand individuals does not exclude a role for rare variation in complex traits. Finally, we directly compare the results reported by different gene-based methods in order to identify their comparative advantages and disadvantages under distinct locus architectures. Our findings have implications for meaningful interpretation of both positive and negative findings in ongoing and future sequencing studies.
| To assess whether a single variant at a locus contributes to disease risk, the statistical analysis framework is relatively straightforward: compare the frequencies of alleles or genotypes at the site in relation to phenotype. To assess whether multiple variants in the same gene contribute to disease, a much larger array of potential genetic models must be considered. If the causal alleles are rare (defined here as MAF<1%), then power to detect each variant’s effect individually is limited. For example, power to detect a variant with MAF = 0.5% and relative risk (RR) = 3 in 3K case-control samples (1.5K cases and 1.5K controls) at α = 5×10-8 is ~5% [1]. Variants that are private to individuals, as some deleterious mutations are hypothesized to be, present greater challenges yet. As a result, numerous statistical methods have been developed in recent years to test aggregate groups of rare variants for association to disease [2–4].
Re-sequencing experiments have identified a handful of rare variants which modulate risk for common, complex diseases. Examples include variants in NOD2 for Crohn’s disease (4 variants with MAF 0.1–0.8%, ORs 1.4–4.0, detected by single variant association)[5], PCSK9 for coronary heart disease (2 variants with MAF 0.8 and 1.8%, OR ~0.1, detected by single variant association)[6], LPL for hypertriglyceridemia (154 missense variants with MAF<1%, present in cases, detected using the T1 gene-based association method)[7], and MTNR1B for type 2 diabetes (13 functionally-screened variants with MAF<0.1%, collective OR ~5.5, detected using the KBAC gene-based method)[8]. Each of these disease loci is characterized by different numbers, frequencies, and effect sizes of rare variants, but in each of these examples, the estimated proportion of phenotypic variance explained per locus is ~0.5–1.5%.
As large-scale (e.g. genome-wide or exome-wide) studies are now being conducted in hundreds and thousands of individuals, several questions emerge. If loci similar to LPL or MTNR1B exist undiscovered across the genome, what is the power of different gene-based methods to detect them? What effect sizes are studies of a given sample size well-powered to detect? To what extent does power depend on the underlying architecture of causal allelic variation, and how should researchers navigate through the ensemble of available gene-based tests? To interpret the results of gene-based association methods in sequencing studies, it is critical to quantify the power of each method to detect signals under a range of hypothesized locus architectures.
Although the introduction of each novel gene-based association test has typically been accompanied by evaluation of the test’s performance alongside alternatives, each such analysis has compared different subsets of tests, made different assumptions about locus architecture and study design, and employed different simulation approaches. Comparative studies on the relative power of different methods [9–11], while informative, have used small sample sizes, simulated limited locus architectures (e.g., with fixed numbers of causal variants) that may not be representative of complex diseases, and considered only nominal levels of significance (α>0.01). Thus, further work is required to determine how different gene-based tests perform under different genetic models of complex disease.
In this study, we systematically explore the power of eleven currently available and widely-used gene-based association methods to detect rare variant signals drawn from a range of principled genetic architectures of disease, in sample sizes consistent with those of ongoing re-sequencing studies. We assess the impact of locus architecture, effect size, and functional variant filters on the power of each method at stringent levels of significance. By evaluating all tests together at loci simulated under a range of continuous frequency-effect size distributions, we characterize each method’s success and failure modes, and describe genetic hypotheses for which particular methods may be better powered than others.
We first developed a simulation approach to evaluate the power of each gene-based method. We assumed two key requirements for simulations to be informative: 1) simulated genetic variation must approximate the site frequency spectrum (SFS) and haplotype structure of empirical data, and 2) the distribution of relative risks by frequency class should correspond to hypotheses about the genetic architecture of disease that are compatible with observation.
To achieve these objectives, we employed the program HAPGEN2 [12] to simulate variation across the full SFS in thousands of individuals and build a phased reference panel with more individuals than are publicly available at present for a single ethnic group. We started with phased haplotypes from 379 European individuals (1000G Project Phase 1, release 3) [13]. To expand this reference panel to a larger number of individuals, we applied a staged, iterative approach which preserves linkage disequilibrium structure between relatively common variants while introducing new low-frequency variants upon the original haplotypes to match the empirical SFS observed at exonic regions of 202 genes in a study of >12K individuals of European ancestry [14] (S1 Text and Figs 1B, 1C, 1D and S1 and S2). All simulations were performed on 24 human genes of average coding length on chromosome 10 (Fig 1A and S1 Table). While gene coding length does likely contribute to the power to detect association signals, the selection of genes with average length in this study enabled us to conduct controlled characterization of the effects of locus architecture on power.
We modeled the complex disease type 2 diabetes (T2D, assuming prevalence 8%), and introduced phenotypic effects (relative risk per variant, assuming additive effects) by sampling up to 35 exonic causal variants per locus (variant cap imposed due to software limitations, see Methods) from six different joint distributions of causal variant frequencies and effect sizes (S3 and S4 Figs and Tables 1 and S2). These distributions were obtained from forward simulations of global genetic architecture under different disease models that are consistent with properties of empirical sequence variation and the observed prevalence and heritability of T2D [15]. The three main architectures assume strong (AR1), moderate (AR2), or weak (AR3) purifying selection against causal alleles. Broadly, AR1 results in a sharp inverse correlation between variant frequency and effect size, AR2 produces modest correlation, and AR3 is characterized by rare and common alleles that have similar additive effects on phenotype. AR4 and AR5 are variations of AR1 and AR2, respectively, in which only rare (MAF<1%) variants at a locus contribute to disease. AR6 assumes a frequency-effect size map identical to AR2, but assigns a 50%-50% mixture of risk and protective effects; this represents the hypothesis that some variants in a gene increase disease risk, while other variants in the same gene have protective effects.
We evaluated a set of eleven gene-based association methods (CMC [16], VT [17], FRQWGT [18], WILCOX-WSS [19], KBAC [20], BURDEN [18], UNIQ [18], C-ALPHA [21], SKAT [22], SKAT-O [23], and MiST [24]; see Table 2) on these simulated datasets. The tests we applied can be broadly categorized as unidirectional ‘burden’ tests, bidirectional variance-component tests (SKAT, C-ALPHA), and linear combinations of these two classes (SKAT-O, MiST). The unidirectional tests can be further sub-divided into collapsing regression methods (CMC), weighted sum methods (FRQWGT, KBAC, WILCOX-WSS, VT), and permutation-based summary count methods (BURDEN, UNIQ). We selected this set of tests because they represent a broad range of analytical approaches, most of which are readily available in the widely-used software packages PLINK/Seq [18] and EPACTS [25]. Before further evaluation, we confirmed that all tests were well-calibrated, at α = 0.05 and 10-4, in (null) datasets where no variants were assigned any causal effects (S5 Fig).
A key question for re-sequencing studies is: what is the power of gene-based association methods to detect causal loci at stringent levels of significance? To address this, we ran each gene-based test at simulated loci explaining 1% of the variance in T2D liability [26, 27] (see Methods) in 1500 cases and 1500 controls (sample size comparable to several recent or ongoing complex trait sequencing studies [28, 29]). Each gene-based test was run on all exonic variants (causal and non-causal) with MAF<1%, unless otherwise stated. The power of each test is shown as a function of significance threshold (α) and architecture in Figs 2, S6, and S7.
In the context of an exome-wide sequencing study, where an appropriate threshold may be α = 2.5×10-6 (α = 0.05, after Bonferroni correction for ~20K genes), we found that power is very low (<20%) across all architectures and tests considered. At a less stringent threshold of α = 10-4, which might be used to nominate loci for follow-up (under the null, only ~2 such genes would be expected exome-wide), power of the best performing tests across AR1-AR5 remained low (10–50%). This was true irrespective of the allele frequency threshold used for variant inclusion; results for a MAF threshold of 0.5% and 5% are shown in S8 Fig.
We noted that at a nominal level of significance (α = 0.05), many methods had high power (~75%-95%) to detect loci at which deleterious variants (AR1-AR5) explain ~1% of phenotypic variance (Figs 2 and S7). KBAC was consistently the most sensitive method to detect deleterious effects at less stringent levels of significance (up to 95% power at α = 0.05, under AR4). This high sensitivity could be useful in identifying putative signals when only a small number of hypotheses are being tested (e.g. sequencing across only a few targeted loci), or to exclude rare variant models at candidate loci.
Next, we asked whether any of the gene-based methods appear to be uniformly more powerful than others, across the various locus architectures we considered. Under simulated architectures where causal variants all have unidirectional (deleterious) effects (Fig 2A, 2B, 2C, 2D, and 2E), we found that MiST, SKAT-O, and KBAC consistently achieve highest power, while UNIQ is least-powered. However, we did observe differential behavior of these tests depending on the significance threshold: MiST and SKAT-O retained greater power than unidirectional alternatives at stringent thresholds (α<10-5), while at less conservative thresholds (α>10-3), KBAC was more sensitive (Figs 2A, 2B, 2C, 2D, 2E, 2F and S7).
We next sought to understand how power is influenced by locus architecture. Unsurprisingly, we found that power is higher when the majority of the locus’ total phenotypic effect is due to rare variants included in the association test (e.g. those with MAF<1%). This is evidenced by the gain in power under models with a greater contribution of rare variants: the power of MiST, for example, increased from AR3 (10% at α = 10-4 in 3K individuals) to AR2 (23%) to AR1 (36%). Power was higher still under architectures where variants with MAF<1% (i.e. those variants tested) contributed all of the locus’ effect (AR4 and AR5): here, the power of MiST was ~50% at α = 10-4. Power also depends on the direction of causal effects at a locus: under AR6 (where both risk and protective effects are present), the variance-component tests (SKAT and C-ALPHA) and combined tests (MiST and SKAT-O) were least affected (by design) [21–24] and outperformed all the other methods, retaining ~10% power at α = 10-4, while that of unidirectional tests was reduced to <5% (Figs 2F and S7). Finally, we find that power is inversely related to the degree of linkage disequilibrium between causal variants at a locus (S9 Fig).
We next queried the overlap between signals detected by gene-based methods versus those detected by single variant association. In direct contrast to gene-based methods, the power of single variant association decreased as the contribution of rare variants increased: power at a genome-wide threshold of α = 5×10-8 for single variants was ~20%, ~10%, and ~7% under AR3, AR2, and AR1, respectively (blue bars in Fig 3A, 3C, and 3E). However, in all cases, the combined application of gene-based and single variant methods yielded greater sensitivity than single variant association alone (yellow bars in Figs 3A, 3C, 3E, and S10). This occurred because the association tests detect distinct subsets of loci: gene-based methods uniquely identified loci where the signal was driven by groups of rare variants for which single variant association test statistics were not individually significant (pink loci in Fig 3B, 3D, and 3F).
As expected, the comparative advantage of gene-based tests was most evident under architectures where there is strong purifying selection against causal alleles (under AR4, for example, the power of single-variant tests at α = 5×10-8 was <5%, while gene-based tests achieved ~50% power at α = 10-4, and ~20% power even at α = 2.5×10-6; S10A and S10B Fig). Under both AR2 and AR3 (where limited purifying selection made causal alleles more common), the power of single variant association (~20% at α = 5×10-8 under AR3) exceeded that of the best gene-based test (<5% at α = 2.5×10-6 under AR3), though each method detected unique loci. These results confirm that single variant and gene-based association methods should be jointly employed for maximal power across divergent locus architectures.
To characterize the impact of locus effect size on the power of gene-based tests, we simulated loci where the phenotypic variance explained (VE) by genetic variants is 0.5%, 1% (as in Figs 2 and 3), and 2% (all under AR2). At loci where VE = 2%, power increased to nearly 40% (at α = 10-4), as compared to ~23% when VE = 1% (Figs 4A, S11A, and S11B). When VE = 0.5%, power was extremely low (<8% at α = 10-4 in 3K individuals), indicating that exome-wide sequencing studies of this size are substantially under-powered to interrogate genes for weaker effects (S11A Fig).
The relatively modest power of gene-based tests at stringent levels of significance across the architectures considered here presents challenges to investigators seeking to discover novel disease-associated loci in studies of this size. Thus, we next investigated the extent to which power could be improved by a) increasing sample size, or b) excluding neutral variation at a locus.
We found that gene-based methods exhibit differential gains in power as sample size increases from 3K to 10K individuals (Fig 4B). The median power of MiST, for example, increased from ~23% to ~60% (at α = 10-4, under AR2) in 10K samples and was largely retained (~50%) even at α = 2.5×10-6 (S11C Fig). However, the increase in power was not uniform across methods. This occurred, in part, because (unlike for single variant tests) the relationship between sample size and power is not straightforward for gene-based tests: as sample size increases, causal alleles are observed more times, but the number of (rare) non-causal alleles also grows sharply. Thus, methods that up-weight all rare alleles regardless of their observed effect (e.g., FRQWGT) may benefit least from increases in sample size (S11–S13 Fig).
As the number of observations of rare alleles increases with sample size, the performance of single variant association tests will certainly improve, but our analysis suggests that gene-based tests will still uniquely identify loci at which the aggregate signal is driven by variants too rare to be individually detected. When the top single variant in our simulated datasets had MAF < = 0.4%, the locus was rarely detected by single variant association in a sample of 3K individuals (Fig 3B, 3D, and 3F). Single variant tests would have <80% power to detect an effect at a variant of that frequency (at α = 5×10-8) even in 10K samples, unless the RR of that variant was over 3. Moreover, as sample sizes increase, the threshold required to assess significance for gene-based methods will remain the same (as the number of independent tests performed will not change), while that for single variant association tests will need to become more stringent as more novel variants are discovered. Hence, we expect the joint application of single variant and gene-based methods to remain beneficial even as sample sizes increase.
Our study also confirmed that gene-based tests are highly sensitive to the fraction of neutral variation at a locus (Figs 4C and S13), as has been previously described [10, 11, 23]. We additionally found that unidirectional burden tests exhibit the sharpest increases in power as the fraction of neutral variation decreases. Under AR2 in 3K individuals, KBAC power at α = 10-4 exceeded 50% when only disease-causing variants were included (increasing from ~22% prior to variant filtering). These tests may therefore be most powerful for testing targeted hypotheses at loci where rich functional annotation enables exclusion of a subset of neutral variants. Conversely, variance-component tests (C-ALPHA, SKAT) as well as combined methods (MiST, SKAT-O) are characterized by a relative immunity to neutral variation. This latter group of methods, then, are attractive options for jointly testing large numbers of less strictly filtered variants (e.g. in a pathway-based analysis).
We next investigated the degree of overlap between signals detected by each gene-based method. For each pair of association methods, we computed Pearson’s correlation coefficients between their reported p-values on a logarithmic scale (Figs 5A, 5B, and S14). We found that tests with similar design characteristics (e.g., SKAT and C-ALPHA, R2 = 0.99) exhibit very high correlation, as expected (Fig 5C). Some methods were highly correlated, but there was variability in the p-values reported (e.g., MiST and SKAT-O, R2 = 0.92), while others were much less related or even uncorrelated (e.g., SKAT-O and UNIQ, R2 = 0.02). While in this latter case low correlation was driven by the lower mean power of UNIQ relative to SKAT-O, it is worth noting that there did exist a set of true causal loci (where many case-private singletons segregate) at which UNIQ reported p<10-4, but SKAT-O reported p>0.01 (Fig 5C).
Other methods, such as SKAT and SKAT-O, showed asymmetric concordance (R2 = 0.78): SKAT-O detected a set of causal loci entirely undetected by SKAT, but was more conservative on the whole, reporting p-values up to an order of magnitude higher than those reported by SKAT at the majority of loci tested. These correlations were also architecture-dependent: under AR2 (where there are only deleterious effects), for example, SKAT-O exhibited high concordance with KBAC (R2 = 0.86), while under AR6 (where bidirectional effects are present), SKAT-O was most concordant with C-ALPHA and SKAT (R2 = 0.93). MiST shared this behavior, reflecting the ‘unified’ design of these tests as combinations of a unidirectional burden test and a bidirectional variance-based method [23, 24].
To understand the drivers of such differences and identify scenarios where certain tests may be more powerful than others, we conducted pairwise comparisons between KBAC (one of the highest performing methods at α = 10-4 across AR1-AR5) and the other gene-based methods. We focused here on loci where VE = 1%, simulated under AR2. For each comparison, we characterized the properties of loci at which KBAC (but not the other method) reports p<0.01, and vice-versa. In the comparison between KBAC and C-ALPHA (Fig 6A), we found that loci at which only KBAC detected signal were characterized by a higher aggregate skew in case to control counts (often driven by singletons, which do not contribute to the variance component tests’ dispersion statistic). Loci at which only C-ALPHA detected signal, on the other hand, were characterized by a relatively common single variant of large effect (in the background of many variants with balanced case to control counts).
For loci where the ratio of aggregate case to control counts is high, but no individual variants/genotypes show any substantial skew, the BURDEN test may be more powerful than KBAC (Fig 6B). This makes sense: KBAC adaptively weights multi-site genotype counts by their observed case-bias, and if all variants have low weights, the maximum achievable KBAC statistic is low, whereas BURDEN quantifies the significance of the observed signal in aggregate. Finally, UNIQ (unsurprisingly) more readily detected loci at which signal is driven by either many rare variants private to cases, or by a single relatively frequent case-unique (or control-unique) variant (Fig 6C). Taken together, these data indicate that although a given method may exhibit high mean power across divergent architectures, it may not be optimal for testing specific genetic hypotheses.
Given the observation that different methods capture different signals, we wondered whether a strategy in which subsets of methods are collectively applied to a locus might be informative in an exome-wide setting (e.g., to test multiple hypotheses about locus architecture at once). To test this, we employed a stepwise forward selection approach, starting with each of the three best-performing gene-based methods across architectures (MiST, SKAT-O and KBAC) and using the degree of difference (in orders of magnitude) between additional methods’ reported p-values as the inclusion criterion (see Methods, S1 Text).
In 3K individuals, under AR2 (where MiST power is ~23% at α = 10-4), we found that particular combinations of tests (e.g., KBAC+MiST+VT+UNIQ+FRQWGT) could jointly achieve ~31% sensitivity at α = 10-4 (using the single minimum p-value reported across all three tests). However, this gain came at the cost of a higher false positive rate (FPR): after adjusting the p-value significance threshold to correct for the increase in FPR, we found negligible gains in power compared to the application of a single test (S3 Table). Joint application of gene-based tests may still be useful, however, in settings where a higher FPR is tolerable, e.g., to increase sensitivity in a ‘discovery’ exome-wide sequencing scan which precedes large-scale targeted follow-up.
Given the wide array of aggregate rare variant association methods now available for application in re-sequencing or genotyping studies of complex traits [30], it is critical to characterize and quantify the statistical power of each method to test heterogeneous genetic hypotheses. In this study, we conducted a comparative analysis of a panel of commonly used gene-based rare variant association tests under a broad range of realistic allelic architectures, significance thresholds, locus effect sizes, sample sizes, and filters for neutral variation.
In sample sizes comparable to those of many contemporary sequencing studies (3K case-control individuals), we find that while gene-based association methods augment the power of single variant tests by preferentially detecting loci at which rare variants drive the causal architecture, their absolute power is low. All gene-based methods evaluated in this study have limited power, even to detect loci explaining as much as 1% of the variance in phenotypic liability underlying a common trait such as type 2 diabetes (mean power across architectures is ~5–20% at α = 2.5×10-6). Even in 10K case-control samples, power remains modest (~60% at α = 2.5×10-6). Based on estimates of variance explained by known rare and common variant signals (the strongest single common variant association for T2D, mapping near TCF7L2, explains ~1% of phenotypic variance), it seems probable that for any given complex trait, at best a handful of loci will have effects on this scale. The full potential of exome sequencing to provide biological insights into disease, then, will depend largely on the detection of loci of smaller aggregate effects, and will require far larger sample sizes than these.
The low mean power to detect disease-associated loci prompted the question of whether some methods are better powered than others to discover novel signals under specific hypothesized locus architectures. We find that at more stringent significance thresholds (α<10-4), MiST and SKAT-O have the highest power across architectures simulated here, especially when rare variants have bidirectional effects on disease. Thus, for investigators looking to discover signals across thousands of loci (e.g., in exome-wide scans), these tests are likely to maximize sensitivity.
Weighted sum methods (and KBAC in particular), on the other hand, are consistently best-powered to detect rare variants of deleterious effect at less stringent levels of significance, and also show the greatest gains in power when neutral variation can be filtered out. These attributes may be useful in various scenarios: to test a small number of biological hypotheses (e.g. at only a few loci, especially if functional annotations are available), to prioritize signals for further follow-up from a discovery scan, or to place bounds (e.g., after an exome-wide sequencing study) on the total number of genes harboring rare variants of a given effect size that are likely to exist.
In addition to MiST, SKAT-O and KBAC, we find that other methods may have individual strengths under particular scenarios (e.g., UNIQ to test whether a gene harbors an excess of highly penetrant rare variants, or BURDEN to detect a collection of variants each of very weak effect); these methods may be optimal for testing such specific genetic hypotheses. Finally, in larger sample sizes (n = 10K case-control individuals), our simulations demonstrate that the increasing number of neutral (non-causal) rare variants may limit gains in the power of some methods (e.g. FRQWGT). Here, MiST is best-powered at stringent significance thresholds. Taken together, these results suggest that the interpretation of novel signal discovery (or the lack thereof) in sequencing studies may vary based on the specific gene-based methods that are used.
This study has a number of limitations. It is based on simulated data (albeit data consistent with available empirical information on genetic variation and disease epidemiology [15]). It does not explore the effects of properties such as demographic history, gene size, mutation rate, haplotype length, or degree of linkage disequilibrium between causal variants on the power of gene-based association methods. Moreover, it does not characterize the performance of these methods at non-coding regions, where causal variant frequencies and effect sizes may be different, and where there is likely a higher proportion of neutral variation. This simulation approach, however, enabled us to undertake a controlled, quantitative characterization of the performance of gene-based association methods under a range of scenarios. Future work should characterize these methods in study populations of different ethnicities, where different site frequency spectra and linkage disequilibrium patterns between causal variants may alter power (S9 Fig). Architectures we simulated assumed a common binary trait; power to detect loci explaining phenotypic variance for less prevalent traits is likely higher, but we did not study this relationship. The tools available on our website (http://mccarthy.well.ox.ac.uk/publications/2014/moutsianas_simulations/) allow the investigation of this question for any complex trait by generating simulated data using a custom, user-specified RR-by-allele frequency heat-map and disease prevalence.
In summary, we find that specific gene-based association methods are best deployed in the setting of particular experimental study designs, and when testing for particular genetic models of disease. Such an approach will likely enable meaningful interpretation of both positive and negative findings in ongoing sequencing studies, and is bound to remain important even as sample sizes increase and new statistical methods for aggregate testing of rare variants are developed.
Simulated datasets were generated using HAPGEN2 [12]. HAPGEN2 generates case-control data using a haplotype reshuffling approach based on the Li & Stephens model [31]. Under this model, simulated (unobserved) haplotypes are assumed to be an imperfect mosaic of actual (observed) haplotypes and are simulated using a Hidden Markov Model with recombination and mutation rates as parameters. Case and control samples are generated by over-sampling haplotype segments which contain alleles at which phenotypic effects are introduced (based on the relative risks assigned to them). A phased reference panel of haplotypes from 379 European (98 TSI, 89 GBR, 85 CEU, 14 IBS, and 93 FIN) individuals from the 1000 Genomes Project (1000G Project Phase 1, release 3) [13] was augmented to 12,514 individuals by iteratively simulating haplotypes (with no phenotypic effects) and adding them to the original reference panel, in increments of 300 individuals per iteration.
An excess of rare variation was introduced to the data using an empirically selected value of θ = 0.08 for the mutation parameter in HAPGEN2, so as to match the singleton count observed in empirical re-sequencing data in a sample of this size. We used the SFS reported by Nelson et al [14], which was based on sequencing 351kb of coding sequence in 12,514 samples of European descent. The resulting dataset was subsequently thinned using a rejection sampling approach, to match the full site frequency spectrum observed in real data. This two-step approach (matching for singletons, and then thinning the dataset) was necessary to model the excess in rarer variation observed in whole exome sequencing datasets while preserving the LD structure of the reference panel. In order to validate that this approach led to a realistic SFS when sub-sampled to smaller sizes, we compared the SFS observed in the simulated, thinned panel, in subsets of 2,738 individuals, to that of empirical exome-wide sequencing data on the same number of individuals, from the GoT2D project (dark and light blue lines, Fig 1).
Forward population genetic simulations of global complex disease architecture (specifically, for type 2 diabetes, a disease of prevalence 8% and heritability ~45%) were conducted across a range of disease models varying in mutational target size and coupling to purifying selection [15]. By varying only these two parameters, a wide range of continuous joint frequency and effect size distributions were generated; under models with strong coupling to selection, rare variants explain the bulk of heritability and have large effects, while under models with weak coupling to selection, common variants explain the bulk of heritability and rare variants have weaker effects. For the HAPGEN2 simulations conducted here, we sampled variant effect sizes from the distributions observed in the forward simulated datasets at loci explaining ~1% of phenotypic variance underlying T2D (S3 Fig).
Variant effects were selected from the frequency-effect size distributions described above. We simulated these effects at randomly selected exonic variants across each gene. We used variant frequencies measured in the augmented reference panel of 12,514 individuals. In unidirectional architectures, all rare variants were assumed to increase risk of disease (RR>1). In bidirectional architectures, protective effects were sampled in the same way, but the relative risks were inverted. Variant effects were sampled until the cumulative variance explained (VE) on the liability scale by each locus reached the desired threshold (e.g. VE = 0.5%, 1%, or 2%). The following procedure was followed for introducing variation at each locus:
Pick an exonic variant at random
Introduce an effect by sampling from the frequency-RR distribution of the respective architecture
If the cumulative variance explained (on the liability scale, %VE) by variants at the locus is below of the specified threshold, go to step (i) and repeat
If the variance is above the specified threshold, remove one of the introduced effects (at random) and go to step (i)
If the cumulative variance explained is close enough to the specified threshold (0.95*VE,1.05*VE), then
If the number of introduced variants is over 35, quit and restart, else:
Accept the sampling and simulate data using the variants and effect sizes chosen, using HAPGEN2.
The upper bound of 35 on the total number of causal variants introduced per locus was imposed due to instability in HAPGEN2 behavior above this threshold; this limit was rarely reached in 3K samples, but it did restrict architectures simulated in 10K samples (S11C and S12 Figs).
The calculation of variance explained at each locus was conducted using the method described by So et al., which is available online as an R script [26]. This calculation requires three parameters as input (per variant): the prevalence of the trait (in this case assumed to be 8%, to model type 2 diabetes), the population frequency of the risk allele, and the genotype relative risk. We assumed independence between risk variants at a given locus, and thus estimated the total percentage of variance explained as the sum of the variance explained by each individual variant.
The latest releases of the PLINK/SEQ (v0.09) [18] and EPACTS (v3.2.3) [25] software packages were used to run ten of the gene-based methods evaluated in this study. MiST was run using a publicly available R package (http://cran.r-project.org/web/packages/MiST/index.html) [24]. All exonic variants (causal and non-causal) below varying minor allele frequency thresholds (1% for all analyses discussed in the main text, unless otherwise stated) were included in the tests, except when the fraction of neutral variation was varied. In this case, the proportion of causal variants included in the test was fixed to 0.25, 0.50, 0.75, or 1 (Fig 4C).
The subsets of tests chosen for inclusion into composite tests were selected using a stepwise forward selection approach. Starting with a single test (three runs per architecture, each starting with one of the top three performing tests across architectures, MiST, SKAT-O and KBAC), the next test to be included at each step was the one which reported the greatest number of novel signals, i.e. not previously detected by the tests already included. Novel signals were defined as loci for which the p-value reported by the candidate test for inclusion was lower by a specified multiplicative “margin” (factor) than the lowest p-value reported by tests already included in the composite test. Three margins were used (100, 10, and 1); a margin of 100, for example, implies that for signals to be considered novel, they p-value of the candidate test needs to be two orders of magnitude lower than the lowest of the ones already included in the composite test.
All datasets discussed in this study, together with the scripts used to generate them and results of both single variant association and gene-based methods across all architectures, are available on the website http://mccarthy.well.ox.ac.uk/publications/2014/moutsianas_simulations/. The website also contains the software used for the script generation (a wrapper for HAPGEN2 [12]), which can be used to generate analogous simulated data for the genes we included in the manuscript under alternative scenarios/architectures.
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10.1371/journal.ppat.1000559 | Malarial Hemozoin Activates the NLRP3 Inflammasome through Lyn and Syk Kinases | The intraerythrocytic parasite Plasmodium—the causative agent of malaria—produces an inorganic crystal called hemozoin (Hz) during the heme detoxification process, which is released into the circulation during erythrocyte lysis. Hz is rapidly ingested by phagocytes and induces the production of several pro-inflammatory mediators such as interleukin-1β (IL-1β). However, the mechanism regulating Hz recognition and IL-1β maturation has not been identified. Here, we show that Hz induces IL-1β production. Using knockout mice, we showed that Hz-induced IL-1β and inflammation are dependent on NOD-like receptor containing pyrin domain 3 (NLRP3), ASC and caspase-1, but not NLRC4 (NLR containing CARD domain). Furthermore, the absence of NLRP3 or IL-1β augmented survival to malaria caused by P. chabaudi adami DS. Although much has been discovered regarding the NLRP3 inflammasome induction, the mechanism whereby this intracellular multimolecular complex is activated remains unclear. We further demonstrate, using pharmacological and genetic intervention, that the tyrosine kinases Syk and Lyn play a critical role in activation of this inflammasome. These findings not only identify one way by which the immune system is alerted to malarial infection but also are one of the first to suggest a role for tyrosine kinase signaling pathways in regulation of the NLRP3 inflammasome.
| Malaria is widespread in the tropical and sub-tropical regions of the world, and is responsible for 2–3 million deaths annually. This disease is caused by parasites of the Plasmodium genus. The parasite feeds on the hemoglobin of red blood cells and generates a metabolic waste called hemozoin (Hz). Hz is released into the blood circulation during the rupture of red blood cells, which coincides with the production of many cytokines such as interleukin-1β (IL-1β), responsible in part for the periodic fever that is characteristic of the malaria disease. Here, we investigated how Hz activates macrophages (cells that engulf foreign material) to produce IL-1β. We found that Hz is taken up by macrophages initiating signals such as the tyrosine kinases Syk and Lyn that communicate to intracellular receptors. We also showed that Hz-induced IL-1β production is dependent on activation of the intracellular receptor NLRP3, the adaptor protein ASC and a protease called caspase-1 that cleaves IL-1β, therefore allowing it to be released from the cells. These findings not only identify one way in which the immune system is alerted to malarial infection but also dissect some of the signaling events triggered by Hz in the NLRP3 inflammasome pathway.
| Malaria is a widespread infectious disease that affect up to 300 million individuals in the tropical and sub-tropical regions of the world, and is responsible for 2–3 million deaths annually [1]. Malaria is caused by parasites of the Plasmodium genus and is characterized by episodic fevers, anemia, headache and organ failure. Plasmodium parasites feed on erythrocyte hemoglobin and uses a heme detoxification mechanism that results in the formation of an insoluble, inert, dark-brown crystalline metabolic waste called hemozoin (Hz) [1],[2]. Hz is involved in the fever observed during the malaria process as intravenous injection of Hz caused thermal deregulation and was associated with the induction of pyrogenic cytokines [3]. In addition, the release of both Plasmodium-derived Hz and merozoites during the erythrocyte burst phase of the disease coincides with the massive induction of pro-inflammatory cytokines, such as IL-1β and TNF, and with the periodic fevers characteristic of malaria [3],[4].
IL-1β secretion is controlled by the recently described inflammasome, a signaling platform scaffold composed of NLR family members such as NLRC4 (NOD-like receptor containing CARD domain or IPAF) and members of the NLRP (NOD-like receptor containing pyrin domain) family including NLRP1 and NLRP3 (also known as NALP3 and cryopyrin). In addition, the NLRP3 inflammasome is composed of the adaptor molecule ASC (Apoptosis-Associated Speck-Like Protein) and the effector molecule caspase-1, the latter which is responsible for the cleavage of pro-IL-1β into its active form [5],[6]. TNF is induced by a wide variety of innate receptors but in particular by many members of the Toll-like receptors (TLR). It was previously reported that Hz can induce IL-1β secretion in vitro and in vivo [7],[8], however, TLRs are not required for the Hz-induced inflammatory response [9]. Given the clear association of IL-1β with the induction of fever and recent studies demonstrating that the NLRP3 inflammasome senses inorganic materials, such as monosodium urate (MSU, a gout-associated uric-acid crystals), silica, asbestos and aluminum hydroxide by producing IL-1β [6], we tested whether Hz can activate the NLRP3 inflammasome.
In addition, while NLRP3 ligands have been well identified, little is known about the upstream mechanisms that regulate its activation. Some mechanisms that have been proposed include efflux of potassium, increased intracellular calcium, reactive oxygen species (ROS) generation and lysosome disruption [6],[10]. However, having previously reported that both MSU and Hz can trigger production of inflammatory mediators via the activation of signaling cascades involving MAP kinase family members and various transcription factors, we have herein addressed the role of upstream signaling in the activation of the inflammasome that results in IL-1β production in response to the malarial pigment Hz.
In these studies we utilized a chemically synthesized Hz to prevent contamination that could result from native Hz purification; the synthetic Hz is morphologically and chemically similar to native Plasmodium-isolated Hz (Fig. S1). Previously, we reported that both synthetic and native Hz induce similar expression profiles of chemokines and pro-inflammatory cytokines [7]. In addition, the synthetic Hz was subjected to elemental analysis to assess its purity. Theoretical calculated values of the molecular formula of Hz (C68H62N8O8Fe2) give 66.35% of carbon (C), 5.08% of hydrogen (H) and 9.10% of nitrogen (N). We have obtained elemental values from our synthetic Hz preparation very close with the theoretical one (C: 66.5%; H: 5.3%; N: 8.9%). To further show the purity of Hz, we performed an agarose gel with 200 µg of Hz and we did not detect any trace DNA or RNA contamination (Fig. S2A) and treatment with DNase or RNase did not interfere with Hz-induced IL-1β production (Fig. S2B). These data indicate that our synthetic Hz preparation is high purity and free of contaminant.
To evaluate whether Hz activates the inflammasome, we measured IL-1β secretion by PMA-differentiated human monocytic cell line (THP-1) stimulated with increasing concentrations of Hz or MSU. Hz- and MSU-induced IL-1β production was found to be comparable (Fig. 1A). In accordance with previous studies showing that HSP-90 stability [11] modulates inflammasome assembly, we found that Hz-induced IL-1β secretion was reduced in the presence of the HSP-90 inhibitor geldanamycin D (Fig. 1B). Inhibition of caspase-1 activity using a specific competitor (Y-VAD-FMK) [12] or a broad caspase inhibitor (Z-VAD-CHO) also blocked Hz-induced IL-1β (Fig. 1C). To confirm the activation of caspase-1 we used the bone-marrow-derived macrophages (BMDM), since detection of the active form of caspase-1 in THP-1 cells is difficult as reported by others [13],[14]. Here, we show that Hz induced cleavage of caspase-1 to its enzymatically active (p10 subunit) form. BMDM were pre-stimulated with LPS in order to prime the induction of pro-IL-1β. As shown in Figure 1D, Hz and MSU, but not the pre-treatment with LPS, induced cleavage of caspase-1 and mature IL-1β production, which was completely abolished in BMDM from caspase-1 deficient mice.
These results suggest a role for the inflammasome in Hz-induced IL-1β production. To further establish which intracellular receptors and/or adaptor proteins are activated by Hz, we used BMDM from mice deficient in NLRP3, ASC or another NLR, NLRC4 (NLR containing CARD domain, also known as IPAF). We found that Hz- and MSU-induced caspase-1 activation and IL-1β maturation were dependent on NLRP3 and ASC but not NLRC4 (Fig. 2A). On the other hand, macrophages from NLRC4 mice failed to respond to Salmonella typhimurium infection (Fig. S3). To evaluate whether activation of the NLRP3 inflammasome is involved in Hz-induced inflammatory responses in vivo, mice were injected intraperitoneally with Hz and then neutrophil recruitment to the site of injection was examined. Hz induced significant recruitment of neutrophils to the peritoneal cavity in wild type, but not in ASC-deficient (Fig. 2B) or in NLRP3-deficient mice (Fig. 2C). As expected, NLRC4 was not involved in the inflammatory response induced by Hz (Fig. 2C). We further investigated whether IL-1β directly contributed to the recruitment of neutrophils. As expected, IL-1β deficient mice showed a significant decrease in the number of neutrophils elicited by Hz stimulation (Fig. 2D). However, we did not observe a complete abrogation of neutrophil influx as previously seen with IL-1 receptor-deficient mice stimulated with other inflammasome ligands [15]. These results suggest that a portion of the Hz-induced inflammatory response in vivo may results from other ligands of the IL-1 receptors and/or other cytokines and chemokines known to be induced by Hz [3],[7],[8].
Thus far, we have shown that Hz-induced IL-1β production is dependent on the NLRP3 inflammasome, in addition, it is known that IL-1β is involved in malarial fever [4]. To evaluate the role of IL-1β and the NLRP3 inflammasome during malarial disease we infected IL-1β- and NLRP3-deficient mice with Plasmodium chabaudi adami DS, which is a mouse virulent strain. Of interest, both IL-1β- and NLRP3 mice presented a slight but significant lower body temperature (Fig. 3A and 3B) and parasitemia (Fig. 3C and 3D) in the early phase of infection. These knockout mice also showed a significantly prolonged survival compared with wild type mice, but ultimately succumbed to the infection (Fig. 3E and 3F). Finally, in the late phase of infection, the level of IL-1β was significantly lower in NLRP3-deficient mouse in comparison with wild type mice (Fig. 3G) and was not detectable in IL-1β-deficient mouse (data not shown). These results indicate that IL-1β is an important factor in the pathophysiology during malaria infection.
Hz is rapidly engulfed by phagocytes, both in infectious and experimental conditions [2]. Therefore, to test the importance of phagocytosis on Hz-induced IL-1β production, cells were treated with cytochalasin D - a powerful actin polymerization inhibitor - prior to the addition of the crystals. Consistent with other crystals that induce inflammasome activation [15],[16],[17], we found that Hz-induced IL-1β seems to be dependent on its internalization (Fig. 4A). Furthermore, under certain conditions phagocytosis requires cholesterol-rich lipid domains [18] and as expected, cholesterol depletion by MβCD inhibited HZ-induced IL-1β (Fig. S5A), which was due to the disruption of lipid rafts (Fig. S5C). Further characterization of Hz phagocytosis by confocal immunofluorescence microscopy revealed that Hz was internalized in a vacuole that acquired lysosomal features, as shown by the presence of Lamp-1 surrounding the engulfed Hz phagosomes (Fig. 4B). Phagocytosis is generally accompanied by the generation of reactive oxygen species (ROS), which modulates inflammasome activation by crystals such as silica [19], MSU [15] and asbestos [20]. Since Hz induces ROS production [7] its requirement in Hz-induced IL-1β production was evaluated. The ROS scavenger, N-acetylcysteine (NAC) inhibited both Hz- and MSU-induced IL-1β production (Fig. 4C), which suggests a potential upstream role for ROS in inflammasome activation by Hz. Cellular potassium efflux is another critical step in inflammasome activation induced by all known NLRP3 activators [21],[22]. As shown in the Figure 4D, inhibition of potassium efflux by high concentrations of extracellular potassium decreased IL-1β production induced by Hz. The above results suggest that Hz shares a common mechanistic pathway in the activation of the NRLP3 inflammasome with classical triggers such as ATP and others insoluble crystals [21],[23].
Recently, lysosomal destabilization has been proposed as one mechanism whereby inorganic materials such as silica and aluminum hydroxide activate the inflammasome [17]. To assess lysosomal morphology in the context of Hz stimulation, we performed a confocal analysis of PMA-matured THP-1 cells loaded with a self-quenched conjugate of ovalbumin (DQ-OVA) that fluoresces only upon proteolytic degradation. We found that Hz did not affect the shape of lysosomes in comparison to untreated cells. In contrast, silica-treated cells contained swollen lysosomes (Fig. 4E), suggesting that Hz may activate the inflammasome through distinct, but related pathway. Indeed, inhibition of the lysosomal cysteine protease (cathepsin B) by the specific inhibitor CA-074 abrogated IL-1β induced by Hz and silica (Fig. 4F) [17]. However, it is still unclear how this enzyme is involved in inflammasome activation and indeed, many of the proximal signaling events in NLRP3 and NLR activation remain unknown.
Whereas we obtained clear evidence that Hz can induce IL-1β production in an inflammasome-dependent manner that required active cathepsin B, we did not find evidence of Hz-induced lysosomal rupture as previously reported with silica [17]. Release of cathepsin B without lysosomal rupture has been observed in monocytes treated with the potassium ionophore nigericin [24]. In addition, the widely expressed Spleen Tyrosine Kinase (Syk) was shown to be required for cathepsin B release into the cytosol in a model of B cell receptor-mediated apoptosis [25]. We therefore screened Hz-activated macrophages for changes in their tyrosine phosphorylation profiles. Consistent with the possible involvement of Syk, we observed a band with an apparent molecular weight of 72 kDa that was phosphorylated in response to Hz, but not MSU (Fig. 5A). We then carried out anti-Syk immunoprecipitation, followed by anti phospho-tyrosine analysis and found that Syk was phosphorylated in response to Hz, but not MSU stimulation (Fig. 5B). Even by extending the time-course of stimulation, MSU did not induce Syk phosphorylation (Fig S4A).
Syk is typically activated via receptors or adaptor proteins containing immunoreceptor tyrosine-based activation motifs (ITAMs) or ITAM-like domains phosphorylated by Scr family kinases following receptor clustering [26],[27]. The Src kinase inhibitor PP2 decreased the Hz-induced Syk phosphorylation in a dose dependent manner (Fig. 5C). Syk activation can be mediated by the Scr family kinase member Lyn [28]. Lyn is typically found in lipid raft signaling platforms and disruption of these rafts by MβCD (Fig. S5C) indeed blocked, in dose-dependent manner, Syk phosphorylation in Hz-stimulated monocytes (Fig. S5B). Using BMDM from Lyn-deficient mice, we found that Hz-induced Syk phosphorylation required Lyn, and further confirmed that MSU does not utilize this signaling pathway in either murine or human macrophages (Fig. 5).
Next we evaluated the role of Lyn and Syk in Hz-induced IL-1β production. IL-1β secretion stimulated by Hz was inhibited in macrophages treated with the Syk inhibitor piceatannol (Fig. 6A), the Scr kinase inhibitor PP2 (Fig. 6B), and more specifically using Lyn-deficient BMDM (Fig. 6C). Importantly, in this last experiment, Hz-induced IL-1β production was only partially inhibited, which suggest that another member of the Src kinase family could play the same role of Lyn, since these kinases are known to be functionally redundant [28]. Of note, MSU-induced IL-1β production was not affected in Lyn-deficient BMDM pre-treated with LPS. To evaluate the relative roles of LPS and Hz in the induction of this signaling pathway, we treated BMDM with LPS and we observed that LPS by itself did not induce phospho-Syk, and indeed pre-treatment with LPS reduced Hz-induced Syk phosphorylation (Fig. S4B). Furthermore, Hz-induced Syk activation is not affected by the absence of the MyD88 adaptor protein (Fig. S4C). However, MyD88-deficient cells show a delay in the phosphorylation of c-jun N-terminal kinase (JNK) stimulated by LPS (Fig. S4C), similar as previously reported [29]. These results rule out a possible effect of LPS on Syk phosphorylation. Consistent with the involvement of this kinase in a pathway upstream of the inflammasome, NLRP3-, ASC- and NLRC4-deficient macrophages exhibited normal Syk phosphorylation upon Hz stimulation (Fig. 6D).
Syk activates various downstream signaling pathways, including phosphoinositide 3-kinase (PI3K) [30] and extracellular signal-regulated kinase (ERK). To test whether the PI3K pathway is required for propagation of the Syk signaling pathway following Hz exposure, the PI3K inhibitor wortmannin was used prior to Hz stimulation. Inhibition of PI3K indeed abrogated IL-1β maturation (Fig. 7A). We have previously identified MAPK activation upon Hz stimulation of macrophages [31]. We therefore attempted to isolate which pathways might be required for Hz-induced IL-1β production using known p38 and ERK kinase inhibitors. Whereas p38 phosphorylation can be observed following Hz stimulation, inhibition of p38 with SB203580 failed to block Hz-induced IL-1β production (Fig. 7B–D). On the other hand, inhibition of ERK with Apigenin abrogated Hz-induced IL-1β secretion (Fig. 7E). Altogether, these results reveal that Lyn/Syk activation following Hz exposure initiates the PI3K and ERK signaling pathways and these pathways appear to regulate the production of mature IL-1β.
While a number of stimuli are known to activate the NLRP3 inflammasome, there is no evidence that NLRP3 directly recognizes these ligands. Therefore an indirect pathway of NLRP3 activation is likely, however the identity of the direct molecular switch of NLRP3 has not been identified. Our studies provide the first evidence for a role of tyrosine kinase signaling molecules in NLRP3 activation. To examine whether Syk can modulate the inflammasome by directly interacting with its components, we immunoprecipitated Syk and then immunoblotted for potential partners associated with Syk by silver staining and western blotting (Fig. 8A). Selected differential bands were analyzed by LC-tandem mass spectrometry. Interestingly, two to three different peptides covering 11–23% of the Pyrin domain (Pyd) [32] were identified. Pyrin domains are known to mediate protein-protein interactions and are crucial in many of the NLR inflammasome complexes, and in particular, mediate the NLRP3 and ASC interaction [6]. We therefore confirmed by western blotting whether NLRP3 or ASC can be co-immunoprecipitated (co-IP) with Syk. Whereas NLRP3 was shown to weakly interact with Syk, ASC was found to strongly associate with this kinase upon Hz stimulation (Fig. 8B). These findings suggest that Syk, and possibly other unidentified signaling kinases, can associated with the ASC/NRLP3 inflammasome.
Another possible mechanism is that Syk could be controlling the NLRP3 inflammasome by regulating cathepsin B activation. First, we tested if Hz can induce release of the active form of cathepsin B in the supernatant and as showed in the Figure 9A, Hz did not induce cathepsin B release into supernatant as has been observed with MSU and silica. However, using a cathepsin B substrate that emits red fluorescence upon cleavage we demonstrated that Hz induces rapid (30 min) and transient (maximum 1.5 h) intra-compartmental cathepsin B activation that was dependent on Syk activation (Fig. 9B). These results indicate that Syk not only can associate with the inflammasome component but it can also modulate cathepsin B activation.
It has been described that NLRP3 senses many crystalline materials that are involved in inflammatory diseases, such as MSU [15], silica [19], and asbestos [20]. Here we provide the first demonstration that the malaria pigment hemozoin (Hz) can also activate the NLRP3 inflammasome. Importantly, the Hz concentration shown to activate the NLRP3 inflammasome in vitro is similar in range to the concentration of Hz in the blood of patients with moderate parasitemia [8],[33]. Moreover, it was never shown in the previous studies that direct contact between a crystal and NLRP3 is necessary to induce activation. Similarly, we found that Hz does not translocate from the phagosome/lysosome compartment to the cytoplasm, as it is located within LAMP-1-positive compartments, suggesting that Hz activated the NLRP3 inflammasome in an indirect manner.
It has been proposed that the NLRP3 inflammasome senses not only pathogen-associated molecular patterns but also danger signals such as stress-related molecules [5]. In agreement, here we show that Hz-induced IL-1β production was dependent on ROS generation and potassium efflux into the cytoplasm. In addition to previous studies on the inflammasome, we further identified an upstream signaling pathway involving the Src kinase Lyn, the tyrosine kinase Syk and Syk-downstream kinases such as PI3K and ERK that collectively appear to be involved in the regulation of Hz-induced IL-1β production. Simultaneously to us, it has been recently reported that Syk kinase is involved in upstream signaling of NLR inflammasome triggered by fungi [34]. Whether these findings represent a general regulatory mechanism of this intracellular innate immune response will need further investigation.
The Lyn/Syk pathway appears to be uniquely activated in the innate response to Hz crystals, as opposed to other NLRP3-activating crystals such as MSU. In our hands, MSU did not induce Syk or Lyn phosphorylation in PMA-differentiated THP-1 cells nor in BMDM. However, MSU was previously reported to trigger Syk phosphorylation in dendritic cells [35] and human neutrophils [36], as well as Lyn phosphorylation in neutrophils [37].
An intriguing question is how this signaling cascade may modulate the inflammasome/IL-1β production. For instance, we found some indication that Syk can interact with ASC, but not NLRP3. ASC, as it is well known, interacts with NLRP3. These results suggest that Syk may modify ASC. In support of this finding, there is evidence that the ASC pyrin domain can be phosphorylated [38]. Moreover, hyperphosphorylated PSTPIP1 (proline serine threonine phosphatase-interacting protein) was shown to interact with the pyrin protein [39], resulting in its conformational change and further its interaction with ASC [40]. Another possible mechanism whereby kinases can modulate IL-1β production is by modulating intracellular calcium concentration or cathepsin B activation. Syk is involved in the activation of intracellular calcium mobilization in other models [41]. In fact, increased calcium concentrations have been found to modulate inflammasome activation by different stimuli such as MSU and UV radiation [22],[42]. Finally, Syk was found to control the activation of cathepsin B and Hz-induced IL-1β production was dependent on cathepsin B activation, similar to other inflammasome activators such as silica, MSU [17] or nigericin [24]. We showed that specific inhibition of Syk blocked the Hz-induced cathepsin B activation. Collectively, it is clear that different steps in the Hz-induced IL-1β production can be regulated by intracellular signaling. However, further study will be necessary to better characterize these regulatory events in regards to the different inorganic crystals that can trigger NLRP3 inflammasome activation.
Another interesting observation is that Hz-activated cathepsin B occurred in the intracellular compartment and is rapidly quenched (1–3 hours), suggesting either a transient activation or cathepsin B release into the cytosol. The idea of transient activation of cathepsin B by Hz is supported by the absence of cathepsin B in the supernatant of cells stimulated with Hz and the absence of lysosomal damage upon Hz treatment. The mechanism utilized by Hz-activated cathepsin B to modulate the inflammasome remains unclear. However, a possible mechanism is that cathepsin B can activate directly caspase-1 as it has been shown in previous works [17],[24]. Of interest, both caspase-1 and cathepsin B, in addition to inflammasome components and IL-1β are found in multivesicular bodies surrounded by LAMP-1 [43]. It is known that Syk and Syk-activated downstream kinases such as PI3K regulate the trafficking of intracellular vesicles [44]. In this way, Hz-induced Syk might be controlling not only the inflammasome cascade but also the trafficking of multivesicles.
The Lyn/Syk activation finding raises the intriguing possibility that an as yet unidentified receptor or adaptor protein containing an ITAM or ITAM-like domain, such as Dectin-1, TREM family members, Siglec or DAP12 [26],[27], might be activated upon Hz stimulation to trigger the signaling cascade involved in inflammasome activation. However, a recent work with dendritic cells demonstrated that MSU did not require a surface receptor - instead the crystals interact with surface lipid rafts and this was enough to trigger Syk/PI3K pathway [35]. In our study, we have demonstrated that lipid rafts are involved in the Hz-induced signaling pathway and IL-1β production. Other potential receptors that could mediate Hz-triggered signaling are the Toll-like receptors (TLR). However, we have recently demonstrated in collaboration with Parroche and colleagues [9] that Hz alone fails to activate TLRs except when Hz is coated with parasitic DNA and consequently activating TLR9. Similarly, we also observed that HEK293 cells transfected with different TLRs were not activated by Hz although these cells were able to induce NF-κB activation following specific ligand stimulations (Jaramillo and Olivier, unpublished data). We also showed that the MyD88 signaling pathway is not involved in the Hz-induced Syk phosphorylation. Experiments to identify surface receptors or lipids that recognize Hz are currently underway.
In the present work we further supported the role of NLRP3-mediated IL-1β production in Hz-mediated inflammatory cell recruitment using IL-1β deficient mice. Apart from its inflammatory role, IL-1β is a pyrogenic cytokine that in small concentrations induces the production of other cytokines such as IL-6 and can cause hypertension and fever [45]. In fact, we showed that NLRP3- and IL-1β-deficient mice exhibited lower body temperature during the early phase of P. chabaudi Adami infection. Hz-induced IL-1β can be the mediator of the up-regulation of chemokines and cytokines during malaria infection, which is independent of TLRs but dependent on MyD88 [46]. This suggests that another MyD88 dependent receptor such as IL-1R is involved and supports a role for IL-1β in malaria-related pathology. Corroborating this hypothesis, we showed that IL-1β- and NLRP3- deficient mice showed a better survival than wild type mice in murine experimental model of malaria. Not surprisingly, it was not sufficient to provide full protection likely due to the complexity of malarial disease, which is under the regulation of many different receptors, cytokines, signaling events and physiological features.
Collectively, our study provides the first demonstration that a malarial-derived metabolic product, namely hemozoin, can induce NLRP3 inflammasome activation and IL-1β production though the involvement of the Src kinase Lyn and the tyrosine kinase Syk. However, excessive IL-1β secretion can be deleterious to the host; in fact, we observed that higher production of IL-1β correlates with early death in murine experimental malaria. Therefore these findings strongly support the fact that Hz is critical in malaria pathology. A better understanding of the molecular and cellular events regulating malaria inflammatory-related pathologies may provide new insights into the design of treatments aimed at reducing the exaggerated inflammatory disorders and debilitating sequelae.
With the subheading Ethics Statement, all protocols used in this study were approved by the Institutional Animal Care and Use Committees at the McGill University or Yale University. IL-1β- and Lyn-deficient mice were provided by Dr. G.Sébire and Dr. K. W. Harder (University of Sherbrooke, Quebec and University of British Columbia, Vancouver, Canada), respectively. The generation of IL-1β-, Lyn-, NLRP3-, ASC-, caspase-1-, and NLRC4-deficient mice has been described previously [47],[48],[49],[50],[51]. Caspase-1-, ASC-, and NLRP3-deficient mice were backcrossed onto the C57BL/6 genetic background for at least nine generations. NLRC4-deficient mice were backcrossed onto the C57BL/6 genetic background for at least six generations. Age- and sex-matched C57BL/6 mice purchased from the National Cancer Institute or Charles River were used as WT controls.
Hemin (>99% of purity) was purchased from Fluka; RPMI-1640 medium, Penicillin-Streptomycin-Glutamine (PSG) from Wisent, fetal bovine serum (FBS), Alpha MEM medium from Gibco; CV-Cathepsin B detection kit, PP2, piceatannol, geldanamycin, cytochalasin D, Y-VAD-FMK and Z-VAD-CHO from Biomol; MSU, anti-human NLRP3 and ASC from Alexis Biochemical; inhibitor protease cocktail from Roche; CHAPs from Fisher; A/G-coupled agarose beads, anti-human pro-IL-1β, anti-human or murine caspase-1 and anti-Syk from Santa Cruz; True Blot anti-rabbit Ig, anti-phosphoY/HRP from eBioscience; PVDF from Bio-rad; anti-LAMP-1 Ab from Developmental Studies Hybridoma Bank at the University of Iowa; anti-human mature IL-1β, anti-pp38 and anti-p38 from Cell signal; anti-pSyk and anti-pY (4G10) from Upstate; rat or goat anti-murine IL-1β and recombinant IL-1β from R&D system; DQ-OVA from Invitrogen; anti-rat AlexaFluor 568, cholera toxin B-AlexaFluor 568 from Molecular Probes; DRAQ5 from Biostatus; Fluoromount-G from Southern Biotechnology; all others unlisted or not indicated reagents were purchased from Sigma. L929 and THP-1 cell line from ATCC. MyD88 KO BMDM was generated from MyD88-deficient mice and kindly supplied by Dr. Danuta Radzioch (McGill University, Montreal, Canada).
Native and Synthetic Hz have been obtained as previously described [8],[31]. We have modified synthetic Hz preparation, using high purity chemical reagents (>99% of purity), as follows: 0.8 mmol Hemin was dissolved in degassed NaOH (0.1 M) for 30 minutes with mild stirring. pH 4.0 was adjusted adding drop-wise propionic acid. The mixture was allowed to anneal at 70°C for 18 hours. Then washed three times with NaHCO3 (0.1 M) for three hours and the last wash with MeOH. All washes were alternated with distilled H2O. Finally, the sample was then dried in a vacuum oven overnight over phosphorous pentoxyde. All synthetic hemozoin samples were analyzed by X-ray powder diffraction, field emission gun scanning electron microscopy, and infra-red spectroscopy to characterize the crystalline state of Hz. Hz purity was assessed by elemental analysis [52].
THP-1 cells (ATCC) were cultured with RPMI-1640 medium supplemented with 10% FBS, 1% PSG, 50 µM of 2-β-mercaptoetanol, Glucose 4.5 g/L and 1 mM sodium pyruvate. THP-1 differentiation: (1.5×106 cells/mL) were incubated with 0.5 µM of PMA, after three hours cells were washed and plated at 0.75×106 cells/mL or 0.2×106 cell/0.5 mL in 12 well plates (IL-1β) or 24 well plates containing coverslips (confocal) and incubated for 20–24 hours. This treatment increases the phagocytic properties of the cells and induces a constitutive production of pro-IL-1β. Prior to stimulation, cells were washed and 500 µL of Alpha MEM medium without FBS was replaced. Cells were pre-treated with different drugs for 1 hour and stimulate with Hz, MSU or silica as indicated in figure legends.
Gender and age matched wild type (WT), NLRP3- or IL-1β-deficient mice were injected i.p. with 5×104 Plasmodium chabaudi adami DS infected red blood cells obtained from syngeneic infected mice. Parasitemia was assessed at day 5, 7 and then every day by examination of Giemsa stained blood smears and was expressed as mean parasitemia. Body temperature was measured using an infrared thermometer (La Crosse Technology). Survival of mice was monitored and blood serum was collected when the temperature dropped down to 26°C. IL-1β was measured by ELISA with rat monoclonal and goat anti-mouse IL-1β. The detection limit was 6.25 pg/mL of IL-1β.
Bone marrow cells were obtained by flushing the femurs and tibias from mice. Cells were used from fresh or from frozen marrows. Erythrocytes were lysed with 2 mL of NH4Cl (155 mM) in Tris/HCl (10 mM), pH 7.2 (9∶1 solution)/mouse. Bone marrow cells were adjusted to 7×106 cells/10 mL and plated in 100 mm dishes with RPMI-1640 medium supplemented with 1% of PSG, 10% FBS and 30% (v/v) L929 cell culture supernatant. The supernatants of bone marrow cells were changed every two days in order to renew the cytokines and nutrients. After 7 days, the culture dishes were washed with PBS and replaced by ice cold PBS, incubated on ice for 15 min and cells were vigorously detached. BMDM were adjusted to 1.5×106/2 mL or 0.2×106 cells/0.5 mL in RPMI medium supplemented with 5% FBS (Gibco) and 1% of PSG and plated in 6 well plates (IL-1β) or 24 wells plate (confocal). The next day, cells were washed with warm PBS (37°C) and replaced by 500 µL of Alpha MEM medium without FBS. Cells were, as indicated in figure legends, stimulated with Hz, MSU or infected with Salmonella typhimurium as described by Franchi et al. [53].
Supernatant and cell extract analysis: After designated incubation time, supernatants were collected and protein was precipitated with trichloroacetic acid at 10% final concentration. Precipitates were then dissolved in Tris/HCl 0.1 mM pH 8.0 and Laemmli sample load buffer. Cell extracts were obtained by lysing cells with Igepal 1% (for signaling, in 1× PBS, 20% Glycerol, 1× inhibitor protease cocktail, 2 mM Na3VO4 and 1 mM NaF) or triton 1% (for caspase-1, in TNE buffer: 10 mM Tris/HCl pH 7.5, 150 mM NaCl, 5 mM EDTA and 1.5× inhibitor protease cocktail). Whole supernatant protein and equal amount of protein or cell lysate were subjected to SDS-PAGE and immunoblot analysis.
IP: Cells lysates were extracted with lysis buffer (1% CHAPs detergent in TNE buffer, 1× inhibitor cocktail, 2 mM Na3VO4 and 1 mM NaF). Cells lysates were pre-incubated for two hours at 4°C with protein A/G-coupled agarose beads and 1 µg of unspecific matched isotype control antibody (Ab). Equal amount of protein were immunoprecipitated with protein A/G-coupled agarose beads or True Blot anti-rabbit Ig and 2 µg of specific or unspecific matched isotype control Ab overnight. Beads were spun down 3 times with lysis buffer and proteins were denatured in Laemmli load buffer.
SDS-PAGE/Immunoblot: Samples from supernatants, cell extracts or IP were subjected to 10% (signaling) or 15% (IL-1β and caspase-1) acrylamide gel (all reagents from Laboratoire Mat. Inc., Montreal, Qc, Canada) or 4–12% NuPAGE® gel (for p10 caspase-1 and IP, Invitrogen). After transfer onto PVDF membranes, they were subjected to immunoblot analysis with the indicated Ab and matched secondary HRP-conjugated Ab. In some experiments, optical density was determined using AlphaDigiDoc 1000 v3.2 software (Alpha Innotech corporation).
OVA uptake: THP-1 cells (0.2×106 cells/coverslip 12 mm from Fisher) were treated with 10 µg of DQ-OVA in the absence or presence of Hz (200 µg/mL) or Silica (400 µg/mL) for 30 min, washed and incubated up to three hours. Laser settings were adjusted on DQ-OVA fluorescence emission that is stronger than hemozoin or silica. Phagosome: BMDM were fixed, permeabilized using 0.1% Triton X-100, and non-specific surface Fcγ-receptor binding were blocked as described [54]. For immunofluorescence experiments, cells were labelled with the rat anti-LAMP-1 Ab and an anti-rat AlexaFluor 568. DRAQ5 was used to visualize DNA. Cathepsin B activity: THP-1 cells (0.2×106 cells/coverslip 12 mm from Fisher) were pre-treated for 30 min with 5 µM of piceatannol and stimulated or not with Hz (200 µg/mL). A cathepsin B substract (Arg-Arg)2 linked with cresyl violet were given 30 min before the end of incubation time and cleaved substract generated a red fluorescence. All coverslips (THP-1/OVA or BMDM) were mounted on slides with Fluoromount-G. Detailed analysis of protein localization on the phagosome was performed by using an oil immersion Nikon Plan Apo 100 (N.A. 1.4) objective mounted on a Nikon Eclipse E800 microscope equipped with a Bio-Rad Radiance 2000 confocal imaging system (Bio-Rad Laboratories, Hercules, CA).
WT, IL-1β-, NLRP3-, ASC-, caspase-1- and NLRC4-deficient mice were injected intraperitoneally with 800 µg of hemozoin in 1 ml of endotoxin-free PBS. Control groups were injected with 1 mL of PBS. After six hours, the mice were euthanized and the peritoneal cavity was washed with 10 mL of PBS. Cells recovered from the peritoneum were counted and the percentage of neutrophils was determined from an H&E stain (DiffQuick; Dade Behring, Inc.) of a cytospun sample.
Unpaired Student's t-test was used when comparing two groups and ANOVA/Bonferroni test when comparing more than two groups. The differences were considered significant when p<0.05. Survival curves for infected and control mice were compared using the Mantel-Haenszel test. Statistical analysis was performed using Prism 5.00 software (GraphPad, San Diego, Calif.).
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10.1371/journal.pgen.1003689 | Depletion of Retinoic Acid Receptors Initiates a Novel Positive Feedback Mechanism that Promotes Teratogenic Increases in Retinoic Acid | Normal embryonic development and tissue homeostasis require precise levels of retinoic acid (RA) signaling. Despite the importance of appropriate embryonic RA signaling levels, the mechanisms underlying congenital defects due to perturbations of RA signaling are not completely understood. Here, we report that zebrafish embryos deficient for RA receptor αb1 (RARαb1), a conserved RAR splice variant, have enlarged hearts with increased cardiomyocyte (CM) specification, which are surprisingly the consequence of increased RA signaling. Importantly, depletion of RARαb2 or concurrent depletion of RARαb1 and RARαb2 also results in increased RA signaling, suggesting this effect is a broader consequence of RAR depletion. Concurrent depletion of RARαb1 and Cyp26a1, an enzyme that facilitates degradation of RA, and employment of a novel transgenic RA sensor line support the hypothesis that the increases in RA signaling in RAR deficient embryos are the result of increased embryonic RA coupled with compensatory RAR expression. Our results support an intriguing novel mechanism by which depletion of RARs elicits a previously unrecognized positive feedback loop that can result in developmental defects due to teratogenic increases in embryonic RA.
| Retinoic acid (RA) is the most active metabolic product of Vitamin A. Appropriate levels of RA are required for proper embryonic development and tissue maintenance in all vertebrates. Inappropriate levels of RA in human embryos can cause congenital defects that affect many organs, including the heart and limbs, and lead to numerous types of cancers. Understanding how animals maintain appropriate RA levels and the consequences of inappropriate RA signaling will therefore provide insight into human congenital defects and diseases. RA signaling is mediated by RA receptors (RARs), which are transcription factors that are activated when binding RA. We have found that depletion of RARs in zebrafish results in defects that are surprisingly due to increases in embryonic RA and not a deficiency of RA signaling. Our results are the first to demonstrate that RAR depletion elicits a positive feedback mechanism that promotes RA signaling through complementary increases in both embryonic RA and RAR expression. Therefore, our analysis provides novel insight into the molecular mechanisms that are required to maintain appropriate RA signaling and will positively impact our understanding of the mechanisms underlying congenital defects.
| Improper retinoic acid (RA) signaling during development can cause congenital malformations that affect the forelimbs, ocular, cardiovascular, respiratory, urogenital and nervous systems [1]–[4]. Despite almost a century of investigation, the mechanisms underlying many congenital defects due to fluctuations in RA signaling are still not understood. RA acts as a ligand for RA receptors (RARs), members of the nuclear hormone family of transcription factors [5]. Work using disparate embryonic models has provided critical insight into the molecular mechanisms and developmental requirements of RAR function in vertebrate embryos [6]–[12]. In addition, RAR deficiency and inappropriate RA signaling are associated with numerous types of cancers [13]. In the majority of cases, the mechanism by which loss of RARs promote tumorigenesis is not understood. Therefore, understanding the roles of RARs during development will help elucidate the mechanisms underlying congenital defects, and possibly cancers, caused by inappropriate RA signaling [3], [4].
RA signaling employs a number of feedback mechanisms in order to maintain appropriate levels in the embryo and tissues. The best characterized feedback mechanism is through regulation of the RA producing [retinol dehydrogenases (RDHs) and retinaldehyde dehydrogenases (Aldh1a)] and degrading (Cyp26) enzymes. Specifically, increased RA signaling inhibits the expression of the RA producing enzymes, while promoting Cyp26a1 expression. Conversely, decreased RA signaling promotes expression of the RA producing enzymes, while inhibiting Cyp26a1 expression [14]–[18]. While modulation of RA signaling also affects the expression of other factors that control RA signaling [5], [19], less well understood are feedback mechanisms that may influence RAR expression. RA response elements (RAREs) have been found in murine RARα2 and RARβ2 promoters and RARβ2 has been shown to be RA responsive 20–22. However, if decreases in RA signaling, in particular due to loss of RAR expression, lead to compensatory expression of other RARs is less clear. While initial studies of mouse RAR KO mice suggested that there was not compensatory RAR expression in RAR deficient mice [11], [12], more recent studies using siRNA to deplete RARα have challenged this model and suggested that there may be compensatory RAR expression in RARα deficient embryos [23]. Therefore, if there are RA feedback mechanisms that influence RAR expression and how the employment of these feedback mechanisms impact embryonic development are not well understood.
Here, we find that depletion of RARαb1, a previously unrecognized yet conserved zebrafish RARα splice variant, causes an increase in CM specification and heart size, which is due to the triggering of a feedback mechanism that surprisingly promotes increased RA signaling from surplus embryonic RA and compensatory RAR expression. Our results provide insight into a newly recognized positive feedback mechanism that we posit resists fluctuations in RA signaling due to perturbation in RAR expression. However, if improperly maintained, the positive feedback can result in RA induced congenital defects. Altogether, the results from this study significantly enhance our understanding of the feedback mechanisms that are used to maintain appropriate RA signaling levels and previously unexplored mechanisms that potentially underlie congenital defects.
In contrast to the studies of RARs in mice [9]–[12], depletion of RARs has not been able to recapitulate all of the consequences of loss of RA signaling in zebrafish [8], which prompted us to determine if additional conserved RAR variants exist in zebrafish beyond what has already been reported [24]. We cloned a previously unrecognized RARα splice variant that is orthologous to human, mouse and Xenopus RARα1 termed RARαb1 (Figure 1A–1C). The previously cloned zebrafish RARα homologs RARαa and RARαb are teleost specific paralogs and both are orthologous to the splice variant 2 found in tetrapods (Figure 1B, 1D) [24]. Both rarαb1 and rarαb2 are expressed maternally and zygotically (Figure 1E), with ubiquitous expression until the tailbud stage (Figure S1A–S1I). After the tailbud stage, their expression patterns deviate (Figure 1F–1H and Figure S1J–S1O).
We used a translation blocking morpholino (MO) to examine the function of RARαb1 (Figure 1B). By 48 hours post-fertilization (hpf), RARαb1 deficient embryos had enlarged hearts with increased CM number and expression of CM marker genes myl7, vmhc and amhc (Figure 2A, 2B, 2M, 2N and Figure S2A–S2D). Similar increases in CM number were also found at 55 hpf (Figure S3A–S3C), suggesting the major addition of surplus CMs occurs during earlier stages of development. Consistent with this idea, we observed an expansion of CM differentiation (myl7, vmhc, and amhc) and progenitor (nkx2.5 and hand2) marker expression in RARαb1 deficient embryos at earlier stages via in situ hybridization (ISH) and quantitative real-time PCR (qPCR; Figure 2C–2L, 2O–2Q). Injecting the RARαb1 MO along with rarαb1 mRNA that lacks the 5′UTR MO binding sequence is able to rescue the increased heart size, supporting the specificity of the phenotype (Figure S4A–S4D). Together, these results suggest that RARαb1 deficient embryos have increased CM specification, number and heart size.
The increased atrial and ventricular CM number in RARαb1 deficient embryos are reminiscent of RA signaling deficient embryos [25], [26]. Therefore, we examined hoxb5b expression, which functions downstream of RA signaling to restrict atrial CM number [26] and is likely a direct target of RARs (Figure S5A–S5D). Unexpectedly, we found that hoxb5b expression was increased in RARαb1 deficient embryos (Figure 3A–3C). While this was initially perplexing, our recent studies showed that Hoxb5b overexpression is able to mimic many of the teratogenic effects of RA treatment [27]. Therefore, we asked if the increases in hoxb5b expression in RARαb1 deficient embryos could be a cause of the enlarged hearts. While depletion of hoxb5b alone using a low concentration of hoxb5b MO does not affect CM number (Figure S6A–S6C), we found that concurrent depletion of RARαb1 and Hoxb5b largely restored heart morphology, CM differentiation marker expression, and CM number relative to the RARαb1 deficient embryos (Figure 3F–3N), suggesting that the increased CM number in RARαb1 deficient embryos is in part a consequence of the increased hoxb5b expression.
We next examined the expression of additional RA signaling responsive genes. Similar to hoxb5b, we found that the expression of additional RA signaling responsive genes, including cyp26a1, dhrs3a, hoxb6b and hoxb5a, was increased in RARαb1 deficient embryos (Figure 3A). Comparing RA responsive gene expression in RA treated and RARαb1 deficient embryos, we found that the trends were similar, but that RA treatment typically induced a greater increase in expression (Figure 3A). Conversely, treatment with DEAB, an antagonist of the RA producing enzyme Aldh1a, inhibited RA responsive gene expression (Figure 3A). These findings indicate that RARαb1 depletion paradoxically results in increased expression of RA signaling responsive genes.
We next wanted to determine if increases in RA signaling responsive genes were specific to RARαb1 depletion, so we examined RA responsive gene expression in RARαb2 deficient embryos. Previous studies found that RARαb2 deficient embryos lack forelimbs (pectoral fins) and tbx5a expression [8], [28], which we confirmed (Figure S7A, S7C, S7D, S7F, S7H, S7I). However, similar to RARαb1 depletion (Figure 3A and Figure 4A), RARαb2 deficient embryos had increased expression of RA signaling responsive genes (Figure 4A). While the previous studies found a loss of forelimbs, defects in heart development were not reported. Despite the loss of forelimbs and increase in RA signaling responsive genes, we did not observe an increase in heart size, CM number or CM gene expression (Figure S8A–S8D). Therefore, although eliciting similar increases in RA signaling responsive gene expression, individual depletion of RARαb1 and RARαb2 results in distinct defects.
To determine the functional consequences of concurrent RARαb1 and RARαb2 depletion, we co-injected a suboptimal dose of each MO. Unfortunately, co-injection of an optimal dose of each MO resulted in significant non-specific toxicity even when injected along with p53 MO. However, concurrent depletion of the RARαbs using suboptimal MO doses resulted in a dramatic increase in RA signaling responsive genes, above what was seen with depletion of RARαb1 and RARαb2 alone using the optimal MO doses (Figure 4A). Additionally, there was an anterior shift of hoxb5a expression in the spinal cord of RARαb1+2 deficient embryos, suggesting the spinal cords are posteriorized (Figure S9A–S9E). Increased RA signaling inhibits aldh1a2 expression through a negative feedback mechanism 16–18. Although aldh1a2 expression in individual RARαb1 and RARαb2 deficient embryos was not suppressed (Figure 4B), aldh1a2 expression was decreased in embryos depleted for both RARαb variants (Figure 4B). To corroborate the increases in endogenous RA signaling responsive genes, we used the RA signaling reporter line Tg(12XRARE-ef1a:EGFP)sk72 29. Again, co-depletion of both RARαbs resulted in a greater expansion of egfp expression, compared to the individual depletion of each RARαb (Figure 4D–4H). Therefore, these experiments support the hypothesis that the RARαb1+2 deficient embryos are sensing more significant increases in RA signaling than embryos deficient for either RARαb variant alone.
We next examined the consequences of this functional interaction on heart development. We found that the hearts of RARαb1+2 deficient embryos had increased atrial size, CM number, and a dramatic increase in amhc expression (Figure 4I, 4L–4N and Figure S10A–S10D). Significant effects on CM number or heart size were not found when using a suboptimal dose of either RARαb1 or RARαb2 MO alone (Figure 4I–4K, 4M), though we did find a modest increase in CM marker gene expression in the RARαb1 deficient embryos (Figure 4N). Interestingly, in RARαb1+2 deficient embryos we found more significant increases in atrial CM number and amhc expression (Figure 4M, 4N), which were remarkably similar to the consequences of modest increases in RA signaling due to RA treatment 27. Increased RA signaling can also inhibit forelimb development 17 and RARαb1 deficient embryos also have smaller forelimbs and a modest reduction of tbx5a expression (Figure S7A, S7B, S7D, S7F, S7G, S7I). A functional interaction with the RARαb variants that resulted in loss of forelimbs was also observed (Figure S7D, S7E). Therefore, concurrent depletion of RARαb variants elicits increases in RA signaling with heart and forelimb phenotypes that are strikingly similar to increases in RA signaling caused from RA treatment.
We sought to understand the mechanism underlying the increase in RA signaling in RARαb deficient embryos. In the absence of RA, RARs are thought to interact with transcriptional co-repressors, while binding of RA converts the RARs to transcriptional activators 1,5. A previous study in Xenopus suggested that RARs are required as transcriptional repressors in some developmental contexts 6. However, our gain-of-function analysis did not support that these zebrafish RARs function as transcriptional repressors (Figure S11A–S11L), consistent with what we have reported previously 29. However, Manshouri et al. 23 found a compensatory increase in the expression of other RARs when using siRNA to deplete RARα in mice. Similarly, we found that the expression of other zebrafish RARs 24 was increased in RARαb deficient embryos (Figure 4C and Figure S12A–S12L), suggesting that compensatory RAR expression is a conserved response to depletion of RARα homologs in vertebrates. Although Manshouri et al. 23 proposed the compensatory RAR expression was RA signaling dependent, our results suggest that the expression of most RARs is potentially regulated independent of RA signaling (Figure 4C), because the effects on RAR expression did not parallel modulation of RA signaling using RA and DEAB. While we observed compensatory expression of other RARs in RARαb deficient embryos, it is difficult to conclude that increased RAR expression is the sole cause of the increase in RA signaling since overexpression of RARs in zebrafish embryos does not produce significant positive or negative effects on RA responsive gene expression (Figure S11A–S11J) 29. Nevertheless, our results suggest that when depleting RARαbs in zebrafish embryos compensatory RARs are present that can mediate RA signaling.
Because we did not have evidence that RARs act as transcriptional repressors or that the increased expression of RARs alone contributes to the increases in RA signaling in RARαb deficient embryos, we hypothesized that the depletion of RARs may trigger an increase in embryonic RA. Although aldh1a2 expression was suppressed in RARαb1+2 deficient embryos similar to when embryos sense increases in RA signaling (Figure 4B) 16–18, the expression of rdh10a and rdh10b, which control a limiting step in RA production in vertebrates by generating retinal from retinol 14,15, was increased in RARαb1 and RARαb1+2 depleted embryos (Figure 4B and Fig. S13A–S13C). Interestingly, rdh10b expression, which was not sensitive to modulation of RA signaling, was increased in RARαb deficient embryos (Figure 4B). Therefore, our results suggest that depletion of RARαbs triggers an increase in RA through promoting rdh10 expression.
In addition to inhibiting aldh1a2 expression, increased RA signaling promotes a negative feedback mechanism that limits RA levels by positively regulating Cyp26a1 expression 16–18. Since we observe an increase in cyp26a1 expression in RARαb1 deficient embryos (Figure 3A, 3D, 3E and Figure 4A), which was also consistent with the hypothesis that there is increased embryonic RA, we postulated that the increased Cyp26a1 may be protecting the RARαb1 deficient embryos from teratogenic increases in embryonic RA. Therefore, we concurrently depleted RARαb1 and Cyp26a1 to determine if there was a functional interaction indicative of increased embryonic RA. For these experiments, a suboptimal dose of cyp26a1 MOs (Figure S14A–S14E) was used to more easily discern a functional interaction. In either the RARαb1 or Cyp26a1 deficient embryos alone, we never observed absence of the MHB or defects in tail elongation (Figure 5A–5C, 5E–5G). However, co-depletion of RARαb1 and Cyp26a1 resulted in a loss of the MHB and truncated tails (Figure 5D, 5H), similar to increases in RA signaling 17,19,29,30. Furthermore, we found that RARαb1+Cyp26a1 deficient embryos had dismorphic hearts with a specific reduction in ventricular CM number compared to controls embryos hearts (Figure 5I–5L, 5Q), which interestingly resembles the trend we previously found in embryos with intermediate increases in RA signaling 27.
Although one interpretation of the functional interaction of RARαb1 and Cyp26a1 depletion is that there is increased embryonic RA levels in these embryos, we wanted to further test this hypothesis using additional assays. First, we sought to use a distinct readout of embryonic RA, so we made a novel stable transgenic RA sensor line which incorporated the RARαb ligand binding domain (RLBD) fused to the Gal4 DNA binding domain (GDBD) expressed under the β-actin promoter (Figure S15A–S15G) 31. Previous studies have found that similar GDBD fusions with nuclear hormone receptor LBDs create an effective reporter of nuclear hormone activity 6,32,33. We observed a dramatic increase in reporter expression when RARαb1 and Cyp26a1 were depleted together in Tg(β-actin:GDBD-RLBD); Tg(UAS:EGFP) embryos (Figure 5M–5P, 5R) 34. Second, our hypothesis predicted that reducing embryonic RA levels should be able to rescue teratogenic phenotypes found in RARαb1+Cyp26a1 and RARαb1 deficient embryos. Consistent with this hypothesis, DEAB treatment of RARαb1+Cyp26a1 deficient embryos was able to rescue the loss of MHB (Figure 6A–6J). Additionally, treatment of RARαb1 deficient embryos with DEAB partially rescue the enlarged heart phenotype and restored atrial CM number (Figure 6K–6O). Lastly, our hypothesis predicts that exogenous treatment with a concentration of RA that causes a minor increase in RA signaling should result in aberrant heart phenotypes that are similar to RARαb1 deficient embryos. Indeed, embryos treated with low concentrations of exogenous RA (lower than we had reported using previously 27) had enlarged hearts with an increase in both atrial and ventricular CM number at 48 hpf (Figure 6P–6R). Altogether, our results suggest that increases in embryonic RA, coupled with compensatory RAR expression, contribute to the developmental defects found in RARαb1 deficient embryos.
Together, our study supports a novel paradigm whereby RARαb depletion elicits a positive feedback mechanism that can result in teratogenic increases in RA signaling. Importantly, our work highlights that loss and gain of RA signaling can cause similar developmental defects. RA signaling is required to restrict CM specification 25,26, while high increases in RA signaling can eliminate CM specification (Figure 7A) 27. However, our present findings suggest that low increases in RA signaling, achieved when treating embryos with µM concentrations of RA or through RARαb depletion, can also promote increases in both atrial and ventricular CM specification (Figure 7A). As we found previously, modest, but slightly higher increases of RA signaling can promote atrial CM specification without significantly affecting ventricular CM specification 27, which is strikingly similar to what we found with concurrent depletion of the RARαb variants here (Figure 7A). Moreover, intermediate increases in RA signaling can inhibit ventricular CM specification, which is similar what we observed when concurrently depleting RARαb1 and Cyp26a1 (Figure 7A). It also appears that modulation of Hox activity downstream of both gain and loss RA signaling is at least partially responsible for the increases in CM specification, suggesting the hypothesis that the similar effects on CM number are actually due to opposite perturbations of anterior-posterior patterning within the ALPM. Therefore, our analysis corroborates and extends previous observations that there are differential effects on atrial and ventricular CM populations as there is a progressive increase from low to intermediate levels of RA signaling in the early embryo.
It is interesting that depletion of RARα homologs using MOs in zebrafish, presented in this study, and Xenopus 6 elicit similar phenotypic responses. In Xenopus embryos, RARα depletion alone results in loss of the MHB 6. While depletion of RARαb1 alone does not result in MHB defects in zebrafish embryos, we have found that RARαb1+Cyp26a1 deficient embryos completely lack the MHB. Taken together, these results suggest that the underlying consequences of increased RA signaling due to depletion of RARα homologs are likely conserved at least in Xenopus and zebrafish embryos, but that in Xenopus perhaps the role of Cyp26 enzymes in protecting the brain has been lost. Despite similarities in the phenotypes that both point to an increase in RA signaling in RARα and RARαb deficient Xenopus and zebrafish embryos, our results contrast with the model proposed by Koide et al. 6, which concluded that RARs are required to function as transcriptional repressors. Importantly, the tools used in the previous study, including dominant-negative RARs, transcriptional co-repressors, and inverse agonists, would not have allowed them to distinguish between a transcriptional de-repressive model and the positive feedback mechanism involving the production of excess RA supported here.
In addition to the phenotypic similarities when depleting RARα homologs in Xenopus and zebrafish, depletion of zebrafish RARαbs results in compensatory RAR expression similar to RARα depletion in mice 23, supporting the hypothesis that this feedback response to RARα deficiency is conserved in vertebrates. Importantly, the response to RAR depletion is likely different than complete ablation of RARs. RAR KO mice have not been reported to have compensatory increases in other RARs 11,12, suggesting that a complete loss of RAR expression may cause a breakdown of this feedback loop. However, when considering the probability that RAR expression would be completely lost vs. depleted, we postulate that insults resulting in depletion of RAR expression would be much more likely. Consistent with this idea, variable levels of RAR expression deficiency, which in the case of RARβ can be due to epigenetic silencing, is commonly observed in a variety of cancers 13.
Given the conserved feedback mechanisms already recognized that limit fluctuations in RA signaling in vertebrates 16,17,19,23, it seems logical that a conserved mechanism that senses RAR deficiency would also exist to prevent loss of RA signaling. We propose that this newly recognized positive feedback mechanism would be more suitable to prevent transient deficiency in RARs. As demonstrated here, persistent RARαb depletion can result in a hypervigilant response of RA signaling and RA-induced teratogenic defects. Overall, these data provide insight into a previously unappreciated RAR-dependent positive feedback mechanism (Figure 7B), which is active during development. Further elucidation of this RA signaling feedback mechanism may illuminate the etiology of poorly understood RA-insensitive cancers 13,23 and congenital defects 1,3.
All zebrafish husbandry and experiments were performed in accordance with protocols approved by the Institutional Animal Care and Use Committee (IACUC) of Cincinnati Children's Hospital Medical Center.
Zebrafish (Danio rerio) were raised and maintained as previously described 35. The following transgenic lines were used: Tg(-5.1myl7:DsRed-NLS) 36, Tg(-5.1myl7:EGFP)twu26 37, Tg(12XRARE-ef1a:EGFP)sk72 29,Tg(β-actin:GDBD-RLBD)cch1 (was created using the Gateway/Tol2 system 38 and additional characterization is reported in 31), Tg(UAS:EGFP) 34, and Tg(UAS:nfsB-mcherry) 39.
Whole-mount ISH was carried out using standard procedures 40. All probes except rarαb1 (accession number: KF030797) and rarαb2 were reported previously. myl7 (formerly called cmlc2; ZDB-GENE-991019-3), amhc (ZDB-GENE-031112-1), vmhc (ZDB-GENE-991123-5), nkx2.5 (ZDB-GENE-980526-321), hand2 (ZDB-GENE-000511-1), hoxb5a (ZDB-GENE-980526-70), hoxb5b (ZDB-GENE-000823-6), dhrs3a (ZDB-GENE-040801-217), cyp26a1 (ZDB-GENE-990415-44), rarαb1/2 (which recognizes both isoforms and was formerly called rarαb 24; ZDB-GENE-980526-72), rarαa (ZDB-GENE-980526-284), rarγa (ZDB-GENE-980526-531), rarγb (ZDB-GENE-070314-1), rdh10a (ZDB-GENE-070112-2242), tbx5a (ZDB-GENE-030909-7), eng2a (ZDB-GENE-980526-167), egr2b (formerly called krox20; ZDB-GENE-980526-283), egfp (accession number: JQ064510.1), and mcherry (accession number: JN795134.1).
The rarαb1 MO (5′-TGCAGGTCATCCGTAATGCCCGATC) was designed to the 5′ UTR of rarαb1. Additional MOs targeting another region of the 5′ UTR and the donor splice junction, which saturated the available MO target sites, were also tried. However, injection of these MOs resulted in significant toxicity and were not able to be used for analysis. Sequences to the rarαb2 and hoxb5b MOs were reported previously 8,26. The total amount of rarαb1 MO injected was 16 ng. The total amount of rarαb2 MO injected was 7 ng. The suboptimal doses used to test genetic interactions were half these concentrations. The amount of hoxb5b MO used was 0.25 ng. A cocktail of 4 ng cyp26a1 MO1 (5′-TCTTATCATCCTTACCTTTTTCTTG) and 2 ng cyp26a1 MO2 (5′-TAAAAATAATACACTACCTGCAAAC) produced a phenotype similar to gir mutants 17. Suboptimal doses used in experiments were 0.9 ng (cyp26a1 MO1) and 0.45 ng of (cyp26a1 MO2). For all injection experiments, 3 ng of p53 MO were used to help suppress non-specific MO-induced cell death 41. For experiments, the total amount of MO injected was always kept constant by equilibrating the concentrations with Standard Control MO (Gene Tools).
Capped mRNA was made using a Message Machine Kit (Ambion). 150 pg of mRNA was used for over-expression of all mRNAs in all experiments.
Luciferase reporter assays were performed in HEK 293 cells as previously described 29.
Western blots were performed as previously described 29. Mouse monoclonal anti-myc antibody (Covance) was used for both Western blot analysis and ChIP experiments. The dynabeads (Invitrogen) ChIP protocol was adapted from the Dorsky Lab (University of Utah) ZFIN Protocol. qPCR was used to quantify the enrichment of the fragment containing the RARE (DR5) in embryos injected with the myc-rarαb1 mRNA with respect to control uninjected embryos.
The genomic sequence flanking zebrafish hoxb5b (−8 to +8 kb) was compared with the corresponding region for Hoxb5 in mouse using mVista. NHR SCAN was used to identify binding sites for nuclear receptor.
Rarαb1 was identified by using BLAST against the zebrafish genome (Ensemble_V7) with the human and mouse RARα1 A domains. MacVector was used for sequences alignments. For RT-PCR, primer pairs were designed so that they specifically recognized rarαb1 and rarαb2 (Figure 1B). Primer sequences are available upon request.
The full-length coding sequence for rarαb1 was cloned into pCS2p+. The rarαb2-pCS2p+ construct used for overexpression was reported previously 29. The myc tagged RARαb1 was made using the pCS2+MT vector. For rarαb1 and rarαb2 probes, 536 base pairs (bps) of rarαb1 and 443 bps of rarαb2, which include the 5′ untranslated region (UTR) and the specific A domains with no overlap, were cloned (Figure 1B). These fragments were cloned into pGEM-T easy (Promega).
Total RNA was isolated from 25 embryos, homogenized in TRIzol (Ambion) and collected using Pure link RNA Micro Kit (In Vitrogen). 1 µg or 0.5 µg RNA was used for cDNA synthesis using the ThermoScript Reverse Transcriptase kit (Invitrogen). Quantitative real time PCR (qPCR) for myl7, amhc, vmhc, nkx2.5, hand2, hoxb5b, hoxb5a, hoxb6b, dhrs3a, cyp26a1, aldhh1a2, rdh10a, rdh10b, rarαa, rarαb1, rarαb2, rarγa and rarγb, egfp and mcherry was performed using standard PCR conditions in a Bio-Rad CFX PCR machine with Power SYBR Green PCR Master Mix (Applied Biosystems). Expression levels were standardized to ef1α expression and all the data were analyzed using the 2−ΔΔCT Livak Method. All experiments were performed in a biological triplicate. Primer sequences are available upon request.
Areas of myl7, vmhc and amhc expressing cells were measured using ImageJ and statistical analysis was performed as previously described 26. Length of egfp expression and distance between hoxb5b and egr2b were measured also using ImageJ and statistical analysis was performed as previously described.
Immunohistochemistry, cell counting and statistical analysis were done as previously described 26.
RA and DEAB, treatment of embryos was done as previously described 26,27. Embryos that have been used for gene expression analysis at 8 somites were treated with 1 µM DEAB, an Aldh1a2 inhibitor, beginning at 40% epiboly or with 1 µM RA for 1 hr beginning at 40% epiboly. For analysis of the effects of low concentrations of RA on heart development, embryos were treated with 0.05 µM RA for 1 hr beginning at 40% epiboly and harvested at 48 hpf. For rescue experiments related to the heart phenotype of RARαb1 deficient embryos, embryos were treated with 0.025 µM DEAB beginning at 40% epiboly until 24 hpf. For rescue experiments related to the MHB in RARαb1+Cyp26a1 deficient embryos, embryos were treated with 0.25 µM DEAB.
To assess whether the means of two groups are statistically different from each other, we applied the Student's t-test. A p value of <0.05 was considered statistically significant.
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10.1371/journal.pntd.0006759 | Typhoid fever in Santiago, Chile: Insights from a mathematical model utilizing venerable archived data from a successful disease control program | Typhoid fever is endemic in many developing countries. In the early 20th century, newly industrializing countries including the United States successfully controlled typhoid as water treatment (chlorination/sand filtration) and improved sanitation became widespread. Enigmatically, typhoid remained endemic through the 1980s in Santiago, Chile, despite potable municipal water and widespread household sanitation. Data were collected across multiple stages of endemicity and control in Santiago, offering a unique resource for gaining insight into drivers of transmission in modern settings. We developed an individual-based mathematical model of typhoid transmission, with model components including distinctions between long-cycle and short-cycle transmission routes. Data used to fit the model included the prevalence of chronic carriers, seasonality, longitudinal incidence, and age-specific distributions of typhoid infection and disease. Our model captured the dynamics seen in Santiago across endemicity, vaccination, and environmental control. Both vaccination and diminished exposure to seasonal amplified long-cycle transmission contributed to the observed declines in typhoid incidence, with the vaccine estimated to elicit herd effects. Vaccines are important tools for controlling endemic typhoid, with even limited coverage eliciting herd effects in this setting. Removing the vehicles responsible for amplified long-cycle transmission and assessing the role of chronic carriers in endemic settings are additional key elements in designing programs to achieve accelerated control of endemic typhoid.
| Typhoid fever was successfully controlled in Santiago, Chile, after a series of interventions including vaccination with a live oral vaccine (Ty21a), and an environmental sanitation improvement, when a ban was put on the irrigation of salad vegetable crops with untreated sewage. Data collected during this period inform seasonality, age distribution and longitudinal trends of disease. We developed an individual-based, mathematical model to both simulate the dynamics of typhoid seen in Santiago, as well as to investigate relative impacts of the vaccine and sanitation interventions. We found that herd immunity resulted from field trials of the Ty21a vaccine and that chronic carriers were a likely driver of sustained transmission at low incidence levels. Modeling typhoid fever in areas that have demonstrated successful control provides insight for control strategies in modern settings.
| Typhoid fever caused by Salmonella Typhi was controlled in developed countries after widespread water and sanitation improvements were introduced, but remains a pressing public health problem in many developing countries [1–3], with estimates of global burden ranging from approximately 10 to 20 million cases per year [4–7]. Effective control of typhoid is often impeded by lack of knowledge of the local dominant transmission routes, age-specific incidence of disease and role played by chronic carriers (individuals with S. Typhi-colonized gallbladders who can transmit the pathogen for decades). Modeling endemic typhoid in specific epidemiologic niches can guide investments and prioritization of control strategies such as identifying cost-effective targets for immunization with new and existing typhoid vaccines and improving water/sanitation/hygiene (WASH) infrastructure [8–11]. The usefulness of mathematical models is enhanced when comprehensive and precise input data are available. Regrettably, such data are often unavailable where typhoid is currently endemic. Thus, modeling data from sites where typhoid has already been successfully controlled offers an opportunity to dissect the mechanisms of transmission in those data-rich settings and also allows those models to be adapted to study transmission and the impact of potential interventions in modern endemic settings.
Enigmatically, typhoid was hyper-endemic in Santiago, Chile from the mid-1970s through early 1990s [12], even though 96% of Santiago households had access to treated, bacteriologically-monitored water and ~80% had toilets connected to the municipal sewerage system [12–14]. Typhoid fever incidence in Santiago was stable through the early 1970s but doubled in 1977 and 1978 without an obvious explanation [12,13]. This prompted the Head of the Epidemiology Unit of the Ministry of Health of Chile (MoHC), Dr. José-Manuel Borgoño, and the Pan American Health Organization in 1978 to invite two external advisers to Chile, Drs. Branco Cvjetanovic and Myron M. Levine, to provide independent unbiased assessments of the endemicity of typhoid and offer recommendations. One recommendation was to establish a Typhoid Fever Control Program (TFCP), which was instituted in 1979 by Dr. Borgoño with Dr. Levine as external adviser.
During the ensuing 13 years, the Chilean TFCP: strengthened clinical, epidemiologic and bacteriological surveillance within the Metropolitan Region (Santiago and environs) [13]; quantified the reservoir of chronic gallbladder typhoid carriers (prevalence, 694/105 adults) [15,16]; identified risk and protective factors for transmission [14]; hypothesized that an unusual mechanism of amplified transmission was maintaining hyper-endemic typhoid disease across all socioeconomic levels and neighborhoods in an urban population with widespread access to potable water and flush toilet sanitation [17,18]; and undertook environmental bacteriology investigations to confirm the hypothesis [17,18]. The environmental bacteriology studies identified irrigation of crops with untreated raw sewage wastewater during the rainless summer months as the predominant mechanism that was sustaining amplified transmission [17,18]; 90% of the crops were vegetables (lettuce, cabbage, celery) eaten uncooked. A computer-based model of endemic typhoid in Santiago was developed based on pre-intervention incidence data (1968–1976) to explore the impact of future vaccination and sanitation interventions to control typhoid [8].
Two major preventive interventions were instituted in Santiago during the period of the TFCP, with each followed by marked drops in typhoid incidence. First, from 1982 through 1991, were four large-scale field trials of Ty21a live oral vaccine among 514,150 schoolchildren, the age group that accounted for >60% of cases of typhoid [19–24]. Second was a sanitation intervention. In April 1991, following an outbreak of 41 confirmed cholera cases that occurred in Santiago [25], the practice of irrigating crops with raw sewage-containing wastewater was prohibited [13,25]. Thenceforth, this strictly-enforced intervention abruptly interrupted the long-standing amplified transmission of typhoid in Santiago [13,18].
The availability of data from an extended period of endemic transmission and well-documented non-coincident vaccine and sanitation interventions with surveillance across both time periods offered unique data to examine the drivers of endemicity in Santiago, and to estimate the impacts of multiple interventions across a single population. By examining assumptions and constraints through mathematical modeling of data from a historical site, one can better understand typhoid transmission in current hyper-endemic loci.
The typhoid model was built upon the EMOD 2.11 framework [26]. The structure was created by modifying a previous individual-based typhoid model [27]. Modifications include adding multiple transmission routes and simplifying immunity; sterilizing and clinical immunities were combined into a single immunity structure, absent data to inform individual durations. Santiago’s population was simulated using age-specific fertility and mortality rates estimated with Instituto Nacional de Estadisticas census data [28]. The model was initialized with the earliest reported age distribution, with individuals entering and exiting the model through age-specific fertility and mortality.
Typhoid transmission occurs through either the “short-cycle” or “long-cycle”. Short-cycle denotes infections transmitted from person-to-person through proximate contaminated food vehicles [29]. Long-cycle signifies infections transmitted through environmental mediators such as contaminated water [30], crops irrigated with untreated sewage [18] or widely-distributed contaminated commercial food products [31]. Our model captures both transmission routes; individuals can both contaminate and become exposed to the short-cycle and long-cycle “composite of contaminated vehicles of transmission,” or CCVT.
Infectious individuals shed into both short-cycle and long-cycle CCVTs at a daily rate per their infectiousness, in colony-forming units (CFU). In the absence of quantitative shedding data, this value is an estimated free parameter for acute infectiousness (AI, Table 1), with multipliers for non-acute disease states, explained in detail below. The die-off of S. Typhi over time outside the human body varies in different environmental niches [32,33]. Our estimate of the long-cycle CCVT daily decay at rate LD (Table 2) was based on the S. Typhi die-off results reported by Cho et al [32]. Assuming short-cycle transmission ensues primarily via proximate food vehicles that are prepared and consumed daily, short-cycle CCVT decays at 100% each day.
The number of times a susceptible individual is exposed to each CCVT is determined by a daily Poisson process, with rates for both short-cycle and long-cycle (EL, ES, Table 1) left free for model fitting. In the model, the probability of clinical response post-exposure differs by transmission route. Short-cycle transmission is assumed to always involve a food vehicle that protects S. Typhi against gastric acid or contains a large inoculum (or both). Thus, we assume that small inocula that may not transmit through the long-cycle are successfully transmitted by short-cycle vehicles. Thus, short-cycle is modeled in a direct transmission framework, where probability of infection is the population-scaled short-cycle CCVT divided by the total potential short-cycle CCVT.
We undertook to address the heterogeneity inherent in long-cycle transmission, which leads to potential variation in inoculum size (i.e., water-borne vs. food-borne long-cycle exposure). Therefore, the probability of infection through the long-cycle is determined by a dose-response function where population-scaled CCVT for the long-cycle is the infecting inoculum size. The dose-response function is a beta-Poisson curve fitted to Maryland experimental challenge dose-response data where S. Typhi inocula were administered without buffer (Table 2) [34,35]. We assume Maryland challenge data represent Santiago as a whole, absent additional data.
Infected individuals in the model transition through disease states beginning with the incubation period (Fig 1), an asymptomatic infectious period monitored via stool cultures in typhoid challenges [36,37]. The incubation period is informed by the Maryland challenge model [38], which demonstrated shorter incubations for those challenged with high (108−109 CFU) versus lower (105 CFU) inocula. Incubation period in the model can be drawn from one of two estimated lognormal distributions [38], with the cutoff for high vs. low dose being the mid-point between 105 and 108 CFU.
We are unaware of direct comparisons of S. Typhi counts in stools of incubating, sub-clinical and clinical infections. To approximate, we used isolation rates from stool cultures to inform the relative infectiousness of the incubation period versus acute clinical infections. In the Oxford human challenge model, 26% of stools cultured within 72 hours post-challenge were positive for S. Typhi among those eventually diagnosed with typhoid disease [37]. Similarly, the maximum percentage of positive stools in the first three days post-challenge was ~30% in the Maryland model, assuming equal stool isolation rates each day [36]. In the following week, the maximum daily percentage of positive stool cultures was approximately 60%. The pre-antibiotic era report of Ames and Robins described stool isolation rates to be in the range of 60–70%, during the three weeks following diagnosis of clinical illness [39]. Using these isolation rates as a proxy for relative infectiousness, we assume a baseline relative infectiousness, rI, of 0.5 for incubating individuals compared to acute cases (Table 2).
Individuals who excrete virulent S. Typhi following ingestion of an inoculum may or may not develop clinical typhoid [34], and clinical severity does not correlate with infectious dose [34,37,38,41]. Individuals progress to either clinical or subclinical infection, according to probability of clinical infection pA. During the primary bacteremia of acute typhoid infection, be it clinical or subclinical, S. Typhi always reaches the gallbladder [42]. Further, older adults have abnormal gallbladder mucosa more often than younger adults or children. Therefore, even though all adults with gallbladder disease who have acute typhoid fever or acute sub-clinical infection do not become chronic carriers, they may nevertheless have delayed clearance of S. Typhi from their gallbladder. This feature was observed by Ames and Robins in the pre-antibiotic era [39]. Duration of clinical and subclinical shedding is sampled from a lognormal distribution, stratified by <30 and ≥30 years of age, derived from non-chronic carrier shedding durations of acute infections [39].
Following the infectious period, individuals can revert to susceptible class or become chronic carriers. Both clinical and subclinical infections may lead to chronic carriers in this model, and the probability of becoming a carrier after each infection is age- and gender-specific. The propensity for S. Typhi (or S. Paratyphi A or B) to reside long-term in the gallbladder is related to whether a patient has chronic gallbladder disease due to gallstones [43,44], though long-term carriage may occasionally occur in persons without gallstones. Regardless, the prevalence of chronic typhoid carriers parallels the prevalence of cholelithiasis and chronic gallbladder disease. Both are much greater in females than males and the prevalence increases with age [15]. Our model utilizes the sex- and age-specific prevalence of gallstones in Santiago [15], and multiplies this value by an additional free parameter, pC, to inform the probability of chronic carriage per infection when an individual has gallstones.
When examining the age-specific incidence of typhoid fever in Area Norte, Santiago, from 1971–1981, there appear to be abrupt increases in incidence at 3 and 6 years of age (Fig 2A). As these are the common entry ages of preschool and elementary school, this finding suggests that differential age-specific exposures may influence the occurrence of pediatric typhoid. Our model assumes all individuals are born into an unexposed class and move to the susceptible class at probabilities for each age. Specifically, at each month of age a fitted curve determines the probability of an individual entering the susceptible class. The curve is anchored at 0% exposure at birth, and 100% exposure at age 20 years, with a free slope parameter (S) determining the concavity/shape of the function (Fig 2B).
We include a mechanism for reinfection with immunity in our model. All individuals enter the model with no prior infections (Ni = 0). When an individual returns to the susceptible class after a subclinical or clinical infection, the number of previous infections, Ni, increases by 1. Upon exposure to either short-cycle or long-cycle CCVT, the probability of becoming infected is multiplied by the value resulting from the equation: (1-P)Ni. Currently, there is no decay of Ni over an individual’s life because we assume that in the hyper-endemic Santiago setting, repetitive exposures to S. Typhi achieve immunologic boosting. This assumption is reasonable for endemic settings with frequent exposures to S. Typhi but may not hold where typhoid endemicity is unstable or when individuals leave the hyper-endemic setting. P, the reduction in susceptibility after clinical or sub-clinical infection, is left as a free parameter to be fitted to Santiago dynamics.
An individual’s dose through long-cycle transmission in the model is attenuated by a mechanism for seasonality, chosen to represent the likely constant shedding of individuals into the long-cycle vehicle but with differential exposure to it influenced by seasonal crop selection and need for irrigation. In the high (warm, rainless) season, we assume individuals are exposed to 100% of long-cycle CCVT. At low season, we assume no exposure to long-cycle CCVT. Degrees of exposure in the intermediate stages are mediated by a trapezoidal function, where ramp-up and ramp-down durations (RUD and RDD), in combination with high-season duration and timing (PD, EPS), determine a linear function connecting high and low seasons outlined in Fig 1. These parameters are left free for model fitting.
Four large-scale field trials of Ty21a vaccine encompassing 514,150 schoolchildren were performed in Santiago, beginning in 1982 [19–24]; three trials included placebo control groups. The timing and number of vaccinees for each of the trial years are summarized in Table 3, with age ranges restricted between 6 and 17 years of age in the absence of exact age distributions. Formulation, number of doses and inter-dose intervals varied in the trials, affecting vaccine efficacy and duration of protection (Table 3).
Vaccination is implemented as a multiplier on an individual’s probability of infection, equal to one minus the vaccine efficacy corresponding to the year and dose listed in Table 3. The multiplier exists for the duration listed for partially protective formulations (Table 3), or the estimated parameter, D, for trials equivalent to full efficacy doses or better (Table 1). After the defined duration of protection, there is no residual immunity or protection assumed in the model, and individuals return to a fully susceptible state. This choice was made according to the observations of cohorts that received sub-optimal regimens (too few doses) of otherwise protective vaccines during the field trials. Whereas significant efficacy was observed during years 1 and 2 of a Ty21a vaccine efficacy trial in Area Norte, Santiago, little or no residual immunity was observed during the 3–5 year follow-up [20].
Shifts in age-specific incidence during the vaccine trials offer insight into the true duration of protection from Ty21a vaccine, as maximum follow-up times were 7 years, 5 years and 3 years, with most trials still indicating measurable protection at the last follow-up point. Thus, duration (D) of efficacy for full-dose vaccinees was left as a free parameter and included in model fitting.
Longitudinal trends in the pre-vaccine era indicate a relatively stable incidence from 1970 to 1976, with notable increases in 1977–1978 and 1982–1983 (Fig 3). Apparent shifts in GDP growth and copper prices during these years may be loosely correlated with changes in irrigation practices or food purchase habits (Fig 3) [45], and may therefore drive exposure to the long-cycle CCVT. Our model included multipliers on the long-cycle CCVT exposure frequency (mEL), for these pre-defined time periods plotted in Fig 3. We also explored whether the increase in cases from 1982 to 1983, beginning not long after initiation of the TFCP, could be attributed to increased diagnostics, by fitting a higher pA value for the years after 1982.
The multiplier mEL was assumed to be zero from 1992 to 2000, when exposure through the long-cycle was interrupted due to a prohibition of irrigation with sewage. Thus, the model was constrained exclusively to short-cycle transmission during this time (Table 2). Longitudinal changes in exposure to long-cycle CCVT may also have occurred during the vaccine period. After fitting vaccine duration parameters, a linear multiplier on long-cycle CCVT exposure frequency (mEL) was fitted between 1984 and 1992 to capture additional incidence reduction seen during the vaccine period that wasn’t captured by the vaccine.
Parameters that were not identified from the literature were estimated through model fitting. The estimated parameter values were identified from pre-defined ranges (Table 1) using a gradient ascent algorithm, which iteratively maximizes a combined log-likelihood to approach a local optimum, calculated from the fit of the model to identified data components (S1 Appendix). The TFCP and CMoH collected and reported data from many independent sources, leading to heterogeneity in years of reporting, age tranches and timeframes of each data component. Data were collated from the authors’ personal files (S1 dataset), with additional components and age tranches shared from recent exploratory initiatives [46]. Data from simulated years were extracted to match to report years for calculation of each component of the likelihood.
Data related to typhoid incidence was informed by passive surveillance, meaning the patient or the parent/caretaker of a sick child had to make the decision to actively seek health care. The surveillance activities and vaccine field trials were carried out in parts of Santiago and in an era when the vast majority of the population (except the very wealthy) sought acute care at health centers (consultorios) run by the government. Typhoid fever was a notifiable disease, meaning all healthcare providers were required to report diagnosed cases. Infections reported to the TFCP and CMoH were assumed to be represented by the acute infection disease state in the model (Fig 1).
Four distinct components of the data were used in the first stage of model fitting (S1 Table), which estimated all free parameters in the model with the exception of mEL_C (Table 1). Age distribution before, during and after the vaccination period (1971–1992) and pre-vaccine period seasonality (1970–1979) were derived from Ministry of Health reports. Age distribution of incidence for the year 1984 was excluded due to missing age-specific demographic data from that year. Chronic carrier prevalence (1980) was obtained from literature estimates, which multiply population cholelithiasis prevalence by gallbladder carriage of S. Typhi. Annual incidence in the pre-vaccine period (1970–1983) and post environmental intervention (1993–1996) were derived from Ministry of Health reports, and used for the first stage of model fitting.
Annual incidence during the vaccination period was withheld in the first stage of model fitting, due to the potential for the estimate of the free parameter for vaccine duration (D) to be influenced by the changing incidence rates during the vaccination period. Due to our assumptions of long-lasting immunity after repetitive exposures and infections in endemic locations, we similarly assume that the age distribution of typhoid in the model is robust to incidence rate changes in the short term, and therefore attribute changes to age distribution during the vaccine period to be a result of the vaccine.
The second stage of model fitting involved fixing the parameters estimated in the first stage, and utilizing annual incidence data from the vaccine period (1983–1992) to fit mEL_C. This allowed us to independently estimate exposure-related changes during the vaccine period, with vaccine duration and other individual-level parameters fixed.
Fit of the model to Santiago dynamics of age distribution, typhoid seasonality, and prevalence of chronic carriers is shown in Fig 4. Fits of the model to longitudinal trends in reported typhoid fever are plotted in Fig 5A, with estimated parameter values summarized in Table 1. Increases in exposure to the long cycle CCVT in simulated years 1978 and 1983 resulted in an increased estimated population immunity (measured by percentage of the simulated population exposed at age 25) prior to the vaccination period (Fig 5B), which is a likely contributor to the decline in incidence in the following years. We also modeled a scenario where the increase in cases in 1983 was due to improved diagnostics, which led to a poor fit of the model to data for years after 1983 (S1 Fig).
The model estimates that the majority of typhoid infections result from long-cycle transmission in the endemic period but the ratio to short-cycle infections varies seasonally (Fig 4B). Parameters driving seasonality estimate an asymmetrical exposure, with a short ramp-up beginning on day 279 (October 6th), a duration of peak long-cycle exposure of 108 days ending on January 22nd, and a gradual ramp down of 227 days. Predictions from Chile in 1979 based on temperature estimate the growing season to begin August 30th [47], somewhat preceding our estimated exposure start.
Population immunity drives the adult age distribution of simulated typhoid fever in Santiago, which is created by both immunity after clinical typhoid and a high incidence of immunity-boosting repetitive sub-clinical infections. The best-fit model estimates a protection per-infection parameter (P) of 99.8%, indicating that each initial acute clinical or subclinical S. Typhi infection causes a substantial reduction in susceptibility to subsequent clinical typhoid in the model. Repeat episodes of typhoid fever have been observed in individuals who participated in experimental human challenge/re-challenge studies [48], or who were members of circumscribed populations that experienced successive typhoid epidemics [49]: these data indicate only modest protection against subsequent typhoid conferred by the initial clinical infection. It is presumed that recurring subsequent exposures in hyper-endemic areas repetitively boost immunity and maintain long-lived protection [50]. No recent studies have addressed clinical reinfections in endemic settings. The best-fit model estimates a large proportion of sub-clinical infections, with the symptomatic fraction of typhoid infection (pA) at 5.3%.
When fitting the model to the estimated chronic carrier prevalence in Santiago, the best-fit probability of carriage following acute or subclinical infection of persons with gallstones (pC) was 10.8%. When multiplied by the gallstone carriage prevalence rates [15], we independently estimate the probability of carriage due to infection at age-specific rates that are lower than pre-antibiotic era estimates from New York State, with an age-adjusted rate of 1.5 vs. 2.9% (Table 4, S1 Table) [39].
During the period of hyper-endemic transmission in the Santiago model, one could offset low levels of short-cycle transmission (ES) with high levels of chronic carrier infectiousness (rC) to capture longitudinal trends, based on likelihood values (Fig 5C). The availability of incidence data after the interruption of long-cycle transmission in 1991 allowed us to constrain these two parameters that would otherwise be unidentifiable. This is additionally aided by our prevalence estimate of chronic carriers, which is unknown in most endemic locations. The model estimated the relative infectiousness of chronic carriers to be 24% of the infectiousness of acute cases and a short-cycle transmission rate of 0.0093.
Other parameters were less identifiable. Specifically, a trade-off exists between acute infectiousness (AI) and exposure to the long-cycle (EL), the dominant route of transmission in this context. As acute infectiousness is the primary driver of infectious dose in the model, we can simulate high or low-dose scenarios by adjusting values for acute infectiousness. A higher value of long-cycle exposure frequency (EL) can offset a lower value of dose, and vice-versa, resulting in a stable probability of infection despite fluctuating values. Fitted values of 13,436 CFU and 0.54 were estimated for AI and EL, respectively (Table 1), but further studies to help identify one or both of these parameters would be valuable.
The best-fit model estimates duration of the efficacy of Ty21a vaccine to be 8.4 years, as determined by the model’s fit to age distributions during and after the vaccination period (Fig 4C). The model estimates that in addition to the vaccination-related decline beginning in 1983, there was an estimated 23–53% reduction in exposure to the long-cycle over this period, increasing linearly until after 1991 (Fig 5A).
We estimated vaccine impact by comparing simulations of WASH-only and WASH+vaccine scenarios, using best-fit parameters. We see a maximum estimate of 11.7% reduction of cases across all age groups in the year 1985. With 5.3% of the overall population receiving full dose vaccines by the end of the trial, and an additional 4.6% receiving partially protective formulations, this indicates indirect protection of non-vaccinated age groups is likely occurring. The high coverage and direct protection within vaccinated age groups is reflected in the shifts in age distribution of incidence between 1982 and 1991, with peak age-specific incidence shifting from 15–19 years of age in 1982, to 5–9 years of age by 1991 (Fig 4C).
Between 1979 and 1993, through a multi-faceted applied public health research agenda, the Chilean TFCP generated data on the magnitude of the human chronic carrier reservoir, modes of transmission and impact of vaccine and sanitation interventions in Santiago [13]. We utilized these data in a mathematical model to understand the mechanisms of transmission in this setting, and to estimate the impact of both vaccine and environmental control measures.
Similar to observations in many current typhoid-endemic locations [1], the age distribution of typhoid in Santiago has a paucity of adult cases relative to children. Two primary mechanisms can create this pattern in the mathematical model: i) the degree of immunity after infection; ii) and the incidence rate of clinical and sub-clinical infections. Both mechanisms appear to play a role in Santiago. Parameter estimates when fitting the mathematical model to the age distribution of typhoid in Santiago suggest robust immunity after clinical and sub-clinical infection, with a very low probability of repeated infection after an initial infection. Our model-estimated immunity after infection is much higher than what has been demonstrated in challenge studies [48], but studies of repeat infections in endemic settings are lacking. Additionally, the model estimates the occurrence of approximately 19 sub-clinical infections for each clinical case, leading to a large amount of circulating S. Typhi infection that remains ‘unreported’ in the model.
Population-based seroprevalence surveys that detect long-lived anti-flagella H:d responses from both prior sub-clinical and clinical typhoid infections cumulatively over time offer insight into the levels of circulating disease that may go undetected by clinical surveillance [13,50]. A cross-sectional prevalence survey of S. Typhi H antibody in Santiago in 1978 found that approximately 50% of 25 year-olds had a reciprocal titer ≥ 40. Estimates from our model (Fig 5B) corroborate the estimate derived by seroepidemiology and predict that 60–70% of the population has been infected by the age of 25 during the pre-vaccination period. Absent data informing decay rates of the H antibody over time, the higher percentage of estimated individuals ever having been exposed compared to antibody prevalence may be explained by the decay of H antibody over time, and at a minimum supports our finding that many subclinical or mild infections occur for each reported clinical case [13].
The proportion of incident typhoid cases reported to public health authorities is notoriously variable among modern typhoid-endemic healthcare locations and can be attributed to differences in treatment-seeking, volumes of blood drawn for culture, and microbiological methods. If we assume the estimated parameter specifying immunity after initial infection is consistent across diverse locations, differences in the adult age distribution of typhoid should only be driven by the rate of disease transmission. The shape of the adult age-specific case distribution may better indicate the force of infection than incidence rate based on an unknown case reporting fraction [13,50]. Serological surveillance is needed to confirm these observations in modern typhoid-endemic locations.
We estimated a probability of carriage after infection that is lower than the age-specific rates estimated in the pre-antibiotic era (Table 4) [39]. This is expected due to the ability of certain antibiotics (fluoroquinolones, e.g., ciprofloxacin) to diminish chronic carriage after treatment of acute infection. Indeed, a longer (4-week) course of these antibiotics can even eliminate established chronic gallbladder carriage without cholecystectomy [51,52]. Our estimate was dependent on the accepted dogma that sub-clinical cases can lead to chronic carriage, while Ames and Robins only followed-up clinically detected cases [39].
We utilized two pieces of data, the prevalence of chronic carriers and the incidence after the environmental intervention, to estimate parameters that typically are unidentifiable: the infectiousness of chronic carriers and the short-cycle transmission rate. When investigating persistence after extreme WASH interventions in modern endemic locations, one should consider that the Santiago results are likely a lower-bound for short-cycle transmission rates, due to the widespread availability of potable water in Santiago households and other improved WASH indicators. Because we see sustained but progressively diminishing transmission in Santiago after interruption of long-cycle transmission in 1991, we posit that chronic carriers transmitting through the short-cycle are largely responsible. The contribution of carriers should be studied intensively in future projects aimed to achieve accelerated control (and eventually local elimination) of typhoid, once amplified long-cycle endemic transmission has been curtailed, including after widespread vaccination with effective vaccines that alter the susceptibility of the population.
The impact of large-scale use of Ty21a vaccine was assessed within the context of other potential changes occurring during the 1980s in Santiago. Pre-vaccination incidence increases during 1977 and 1982 led to a subsequent decrease of naïve individuals in the model, leading to an estimated decline in incidence over time independent of vaccination (Fig 5A). Shifts in the age distribution of typhoid fever incidence during the vaccination period were valuable for understanding the duration of efficacy of Ty21a, which the model estimates to be ~1.5 years longer than the maximum duration of protection documented in field trials (8.4 versus 7 years). Data additionally support longer durations of efficacy for some less protective formulations and immunization regimens of Ty21a vaccine, past the follow-up times published in literature [19]. For example, over six years of follow-up, three doses of enteric coated capsule and gelatin capsule formulations given in long intervals (21 days) between doses exhibited 55.1% efficacy (95% CI, 38.2–67.4) and 35.0% efficacy (95% CI, 13.7–51.0), respectively, while the enteric-coated capsule formulation administered at short interval (2 days) conferred 62.7% (95% CI, 47.7–73.5) efficacy (S2 Table). When evaluating vaccination impacts on a population level, it will be important to consider potential shifts in population immunity and age distribution of infection, in the case of an age-targeted vaccine.
The model shows that with Ty21a use typhoid incidence at the population level falls more than expected based on direct protection only, indicating indirect effects of the vaccine, which have been described using other methods of analysis [24]. Our finding depends on modeling reduced shedding in protected vaccinees, an assumption documented in Ty21a challenge studies [53]. Additionally, this finding is influenced by the model structure, which assumes a well-mixed environmental reservoir, consistent with the known transmission route. In locations without known transmission routes, the assumption of a well-mixed pool of infection may not be valid and may over-estimate indirect effects. Investigations into spatial scales of transmission are needed when modeling modern endemic locations.
Utilizing the model’s trade-offs between dose-response and exposure frequency, driven by parameters acute infectiousness (AI) and long-cycle exposure frequency (EL), typhoid dynamics can be simulated at both high-dose and low-dose scenarios. The unidentifiability of these parameters is a limitation of model structure, at present, and studies quantifying estimated exposure levels and frequency would greatly improve our understanding of transmission dynamics and vaccine efficacy in relation to infectious dose.
The vaccine efficacy estimates we used in the model were derived from field trial data, which do not account for potential differences in vaccine efficacy in relation to variations in the size of the inoculum ingested. Thus, our impact estimates did not account for potential variation in infectious dose. Experimental challenge studies that assessed the efficacy of parenteral killed whole-cell typhoid vaccines in volunteers showed that high inocula could overwhelm the protective effect of vaccines efficacious against lower doses [34]. Since a similar concern was raised by investigators who assessed the efficacy of a Vi conjugate vaccine in a challenge model [54], this should be considered when projecting the impact of new conjugate vaccines.
In summary, this study utilized unique datasets collected during multiple stages of endemicity and control in Santiago, Chile. Paired with mathematical modeling, we aimed to better understand both the complex dynamics contributing to sustained transmission in this setting. Modeling also allowed us to estimate the contributions of mass use of vaccine, in a time when other water and sanitation measures were underway. Our findings support the use of typhoid vaccines to reduce transmission, but also highlight the importance of identifying and intervening upon the critical long-cycle transmission pathways, allowing for targeted and sustained control.
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