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International audience; A recommendation of:Herbeck JT, Mittler JE, Gottlieb GS, Goodreau SM, Murphy JT, Cori A, Pickles M, Fraser C. 2016. Evolution of HIV virulence in response to widespread scale up of antiretroviral therapy: a modeling study. Virus Evolution 2: vew028. doi: 10.1093/ve/vew028

Phylodynamics typically rely on likelihood-based methods to infer epidemiological parameters from dated phylogenies. These methods are essentially based on simple epidemiological models because of the difficulty in expressing the likelihood function analytically. Computing this function numerically raises additional challenges, especially for large phylogenies. Here, we use Approximate Bayesian Computation (ABC) to circumvent these problems. ABC is a likelihood-free method of parameter inference, based on simulation and comparison between target data and simulated data, using summary statistics. We simulated target trees under several epidemiological scenarios in order to assess the accuracy of ABC methods for inferring epidemiological parameter such as the basic reproduction number (R0), the mean duration of infection, and the effective host population size. We designed many summary statistics to capture the information in a phylogeny and its corresponding lineage-through-time plot. We then used the simplest ABC method, called rejection, and its modern derivative complemented with adjustment of the posterior distribution by regression. The availability of machine learning techniques including variable selection, motivated us to compute many summary statistics on the phylogeny. We found that ABC-based inference reaches an accuracy comparable to that of likelihood-based methods for birth-death models and can even outperform existing methods for more refined models and large trees. By re-analysing data from the early stages of the recent Ebola epidemic in Sierra Leone, we also found that ABC provides more realistic estimates than the likelihood-based methods, for some parameters. This work shows that the combination of ABC-based inference using many summary statistics and sophisticated machine learning methods able to perform variable selection is a promising approach to analyse large phylogenies and non-trivial models.

A combination of ecology and evolutionary biology has been one of the hallmarks of The American Naturalist since the introductory comments in its first issue, in which the editors placed the new “magazine” in the footsteps of Darwin (only 8 years after the publication of On the Origin of Species). Singling out a single article to celebrate the 150th anniversary of the journal is no easy task. However, for several reasons, I think that “Parasitic Bacteria and their Relation to Saprophytes,” by Theobald Smith (1887), nicely captures its spirit. First, this article is an illustration of how interwoven ecology and evolution are and why both need to be studied simultaneously. Second is the personality of the author, who was among the first to import evolutionary thinking into human and veterinary medicine. Third, more than a century later, this article, while rarely read, is still inspiring. Theobald Smith (1859–1934) may not be familiar to today’s audience of The American Naturalist (he surely was not when the article was published). Along with Pasteur, Koch, Lister, and their disciples in Europe, Smith was one of the founding fathers of microbiology (P. F. Clark, 1959, “Theobald Smith, Student of Disease (1859–1934),” Journal of the History of Medicine and Allied Sciences 14:490–514). Smith’s broad knowledge of the bacterial world was acquired from reading and from repeating Pasteur’s and Koch’s experiments. Smith’s professional trajectory is also unusual because, as a gifted student in the Albany, New York, public schools, he was primarily attracted to mathematics and teaching. According to Clark, Smith would say, not too seriously, that “his first choice of occupation would have been a tramp, second a musician or mathematician” (p. 510). After gradu

1 Mathematical models are frequently used to assess the impact of control in2 terventions for Chlamydia trachomatis and other sexually transmitted infections 3 (STIs). Modeling approaches that stratify the population by the number of sex 4 partners often assume the transmission risk per partner to be constant. How5 ever, sexual behavior data suggests that people with many partners share less 6 sex acts per partner than people with fewer partners. This should lower the risk 7 of transmission per partner for highly sexually active individuals and could have 8 important epidemiological consequences for STI transmission. We devise a new 9 epidemiological model that we fit to chlamydia prevalence data from Natsal-2 10 and CSF, two population-based probability sample surveys of sexual behavior in 11 Britain and France. Compared to a standard model where the transmission risk 12 per partner is constant, a model with realistic numbers of sex acts per partner 13 provides a better fit to the data. Furthermore, the improved model provides ev14 idence for strong assortative mixing among individuals with different numbers of 15 sex partners. Our results suggest that all chlamydia infected individuals with one 16 or more new heterosexual partners per year contribute significantly to ongoing 17 transmission, underlining that control interventions should be aimed towards all 18 sexually active young adults. 19 2 PeerJ PrePrints | https://dx.doi.org/10.7287/peerj.preprints.940v2 | CC-BY 4.0 Open Access | rec: 30 Oct 2015, publ: 30 Oct 2015

A recent study by smith (2011) challenges the transmissionvirulence trade-off hypothesis of virulence evolution. Although we think that the experimental results are of good quality and informative, we disagree with their interpretation; we do not think they can be used to support or refute this hypothesis. In this commentary, we express two conceptual and one methodological reservations. We also propose a mathematical analysis, which we hope can help in clarifying the problems and in appropriately formalizing empirical tests. The transmission-virulence trade-off hypothesis is one of the few adaptive explanations of virulence evolution. It states that virulence evolution is driven by an overall positive correlation between parasite transmission and virulence. Virulence may evolve to an intermediate optimal level if transmission is a saturating function of virulence, meaning that beyond the optimal level further increases of virulence will be selected against because they decrease parasite fitness (Anderson and May, 1982; Ewald, 1983; Alizon et al., 2009). If the trade-off shape is different, then virulence will evolve either to become maximal or minimal. Most mathematical models involving the trade-off hypothesis have been developed for horizontally and directly transmitted parasites that decrease host survival. Adding other routes of transmission or adding more detailed processes in the model that interact with host survival (e.g., immunopathology, Day et al., 2007) are predicted to affect the exact value of the optimal level of virulence. It has become increasingly popular to empirically challenge the trade-off hypothesis, which is obviously desirable. There are two ways in which one may wish to question this hypothesis. The first is to ask whether it has any relevance in explaining the level of virulence of a given parasite. This can be done by evaluating the empirical support for a relationship between transmission and virulence across different genotypes of the same parasite. If, for example, there is no relationship between transmission and virulence in a biological system then the trade-off hypothesis is irrelevant in explaining the levels of virulence of that system. This approach, which we refer to as the “assumptions challenge,” led to further empirical support for the trade-off hypothesis (Fraser et al., 2007; de Roode et al., 2008). A second, more subtle, kind of challenge is to ask what is the importance of the trade-off hypothesis among a number of other hypotheses in explaining levels of virulence of a given or across several host–parasite systems. The trade-off hypothesis may be relevant but still relatively unimportant. Other processes may under most biologically relevant conditions have a larger influence on virulence levels. Obviously for such tests the experimental procedure will depend on the alternative mechanisms. We refer to this as the “predictions challenge” because it is performed by evolving populations under different conditions that should lead to different evolutionary trajectories of virulence if there is a transmission-virulence trade-off.

A significant goal of recent theoretical research on pathogen evolution has been to develop theory that bridges within- and between-host dynamics. The main approach used to date is one that nests within-host models of pathogen replication in models for the between-host spread of infectious diseases. Although this provides an elegant approach, it nevertheless suffers from some practical difficulties. In particular, the information required to satisfactorily model the mechanistic details of the within-host dynamics is not often available. Here, we present a theoretical approach that circumvents these difficulties by quantifying the relevant within-host factors in an empirically tractable way. The approach is closely related to quantitative genetic models for function-valued traits, and it also allows for the prediction of general characteristics of disease life history, including the timing of virulence, transmission, and host recovery. In a companion paper, we illustrate the approach by applying it to data from a model system of malaria.

Infections by multiple genotypes are common in nature and are known to select for higher levels of virulence for some parasites. When parasites produce public goods (PGs) within the host, such co-infections have been predicted to select for lower levels of virulence. However, this prediction is based on simplifying assumptions regarding epidemiological feedbacks on the multiplicity of infections (MOI). Here, we analyse the case of parasites producing a PG (for example, siderophore-producing bacteria) using a nested model that ties together within-host and epidemiological processes. We find that the prediction that co-infection should select for less virulent strains for PG-producing parasites is only valid if both parasite transmission and virulence are linear functions of parasite density. If there is a trade-off relationship such that virulence increases more rapidly than transmission, or if virulence also depends on the total amount of PGs produced, then more complex relationships between virulence and the MOI are predicted. Our results reveal that explicitly taking into account the distribution of parasite strains among hosts could help better understand the selective pressures faced by parasites at the population level.

Host resistance to parasites can come in two main forms: hosts may either reduce the probability of parasite infection (anti-infection resistance) or reduce parasite growth after infection has occurred (anti-growth resistance). Both resistance mechanisms are often imperfect, meaning that they do not fully prevent or clear infections. Theoretical work has suggested that imperfect anti-growth resistance can select for higher parasite virulence by favouring faster-growing and more virulent parasites that overcome this resistance. In contrast, imperfect anti-infection resistance is thought not to select for increased parasite virulence, because it is assumed that it reduces the number of hosts that become infected, but not the fitness of parasites in successfully infected hosts. Here, we develop a theoretical model to show that anti-infection resistance can in fact select for higher virulence when such resistance reduces the effective parasite dose that enters a host. Our model is based on a monarch butterfly-parasite system in which larval food plants confer resistance to the monarch host. We carried out an experiment and showed that this environmental resistance is most likely a form of anti-infection resistance, through which toxic food plants reduce the effective dose of parasites that initiates an infection. We used these results to build a mathematical model to investigate the evolutionary consequences of food plant-induced resistance. Our model shows that when the effective infectious dose is reduced, parasites can compensate by evolving a higher per-parasite growth rate, and consequently a higher intrinsic virulence. Our results are relevant to many insect host-parasite systems, in which larval food plants often confer imperfect anti-infection resistance. Our results also suggest that - for parasites where the infectious dose affects the within-host dynamics - vaccines that reduce the effective infectious dose can select for increased parasite virulence.

A virus infecting a host faces a heterogeneous and a spatially structured environment. Using a mathematical model that incorporates two types of target cells and spatial structuring, we investigate conditions for viral withinhost diversification. We show that branching occurs for a wide range of parameters but that it always requires some spatial structure. Applying our model to the case of HIV, we show that it captures three main properties of the ‘co-receptor switch’ observed in many HIV infections: the initial dominance of virus strains that infect CCR5 + cells, the late switch in some (but, importantly, not all) HIV infections and the associated drop in the number of uninfected T-cells. This suggests that the co-receptor switch could result from gradual adaptation of the virus population to target cell heterogeneity. More generally, we argue that evolutionary ecology can help us better understand the course of some infections.

It has been more than two decades since the formulation of the so-called ‘trade-off’ hypothesis as an alternative to the then commonly accepted idea that parasites should always evolve towards avirulence (the ‘avirulence hypothesis’). The trade-off hypothesis states that virulence is an unavoidable consequence of parasite transmission; however, since the 1990s, this hypothesis has been increasingly challenged. We discuss the history of the study of virulence evolution and the development of theories towards the trade-off hypothesis in order to illustrate the context of the debate. We investigate the arguments raised against the trade-off hypothesis and argue that trade-offs exist, but may not be of the simple form that is usually assumed, involving other mechanisms (and life-history traits) than those originally considered. Many processes such as pathogen adaptation to within-host competition, interactions with the immune system and shifting transmission routes, will all be interrelated making sweeping evolutionary predictions harder to obtain. We argue that this is the heart of the current debate in the field and while species-specific models may be better predictive tools, the trade-off hypothesis and its basic extensions are necessary to assess the qualitative impacts of virulence management strategies.

Understanding the effect of multiple infections is essential for the prediction (and eventual control) of virulence evolution. Some theoretical studies have considered the possibility that several strains coexist in the same host (coinfection), but few have taken their within-host dynamics explicitly into account. Here, we develop a nested approach based on a simple model for the interaction of parasite strains with their host's immune system. We study virulence evolution by linking the within-host dynamics to an epidemiological framework that incorporates multiple infections. Our model suggests that antigenically similar parasite strains cannot coexist in the long term inside a host. We also find that the optimal level of virulence increases with the efficiency of multiple infections. Finally, we notice that coinfections create heterogeneity in the host population (with susceptible hosts and infected hosts), which can lead to evolutionary branching in the parasite population and the emergence of a hypervirulent parasite strategy. We interpret this result as a parasite specialization to the infectious state of the hosts. Our study has experimental and theoretical implications in a virulence management perspective.

Many pathogens of humans are blood borne, including HIV, Malaria, Hepatitis B and C, West Nile virus, Dengue, and other viral hemorrhagic fevers. Although several of these pathogens are transmitted by blood-feeding arthropods, HIV is not. A number of properties of HIV and its life cycle have been identified as proximate explanations for the absence of arthropod transmission, but little consideration has been given to why HIV has not evolved this form of transmission. We consider the empirical evidence for arthropod transmission, and suggest that mechanical transmission has not evolved in HIV because such strains would induce a faster onset of AIDS during infection, which would thereby limit their ability to spread. On the other hand, it is not as clear why biological transmission has not occurred. Available data suggests that a lack of appropriate genetic variation in HIV is one explanation, but it is also possible that a conflict between natural selection occurring within and between infected individuals has prevented its evolution instead. We discuss the potential significance of these ideas, and argue that taking such an evolutionary perspective broadens our understanding of infectious diseases and the potential consequences of public health interventions.

International audience; Many hosts are infected by several parasite genotypes at a time. In these co-infected hosts, parasites can interact in various ways thus creating diverse within-host dynamics, making it difficult to predict the expression and the evolution of virulence. Moreover, multiple infections generate a combinatorial diversity of cotransmission routes at the host population level, which complicates the epidemiology and may lead to non-trivial outcomes. We introduce a new model for multiple infections, which allows any number of parasite genotypes to infect hosts and potentially coexist in the population. In our model, parasites affect one another's within-host growth through density-dependent interactions and by means of public goods and spite. These within-host interactions determine virulence, recovery and transmission rates, which are then integrated in a transmission network. We use analytical solutions and numerical simulations to investigate epidemiological feedbacks in host populations infected by several parasite genotypes. Finally, we discuss general perspectives on multiple infections.

Predicting the emergence of new pathogenic strains is a key goal of evolutionary epidemiology. However, the majority of existing studies have focussed on emergence at the population level, and not within a host. In particular, the coexistence of pre-existing and mutated strains triggers a heightened immune response due to the larger total pathogen population; this feedback can smother mutated strains before they reach an ample size and establish. Here, we extend previous work for measuring emergence probabilities in non-equilibrium populations, to within-host models of acute infections. We create a mathematical model to investigate the emergence probability of a fitter strain if it mutates from a self-limiting strain that is guaranteed to go extinct in the long-term. We show that ongoing immune cell proliferation during the initial stages of infection causes a drastic reduction in the probability of emergence of mutated strains; we further outline how this effect can be accurately measured. Further analysis of the model shows that, in the short-term, mutant strains that enlarge their replication rate due to evolving an increased growth rate are more favoured than strains that suffer a lower immune-mediated death rate (‘immune tolerance’), as the latter does not completely evade ongoing immune proliferation due to inter-parasitic competition. We end by discussing the model in relation to within-host evolution of human pathogens (including HIV, hepatitis C virus, and cancer), and how ongoing immune growth can affect their evolutionary dynamics., Author Summary The ongoing evolution of infectious diseases provides a constant health threat. This evolution can either result in the production of new pathogens, or new strains of existing pathogens that escape prevailing drug treatments or immune responses. The latter process, also known as immune escape, is a predominant reason for the persistence of several viruses, including HIV and hepatitis C virus (HCV), in their human host. As a consequence, the within-host emergence of new strains has been the intense focus of modelling studies. However, existing models have neglected important feedbacks that affects this emergence probability. Specifically, once a mutated pathogen arises that spreads more quickly than the initial (resident) strain, it potentially triggers a heightened immune response that can eliminate the mutated strain before it spreads. Our study outlines novel mathematical modelling techniques that accurately quantify how ongoing immune growth reduces the emergence probability of mutated pathogenic strains over the course of an infection. Analysis of this model suggests that, in order to enlarge its emergence probability, it is evolutionary beneficial for a mutated strain to increase its growth rate rather than tolerate immunity by having a lower immune-mediated death-rate. Our model can be readily applied to existing within-host data, as demonstrated with application to HIV, HCV, and cancer dynamics.

Most models of virulence evolution assume that a parasite cannot raise its transmission rate without causing more harm to its host. However, the existence of such trade‐off relationships has recently been challenged. Here, we study how a trade‐off can emerge from a model that explicitly incorporates within‐host dynamics. We find that the existence and the convexity of the trade‐off are robust, which implies a definite level of evolutionarily stable virulence (ESV) for the parasite. However, we also show that the dependence of the ESV on parameter values may be very strong. One possible consequence of this sensitivity is that relationships between transmission and virulence observed across populations need not conform to the patterns expected on the basis of a common (fixed) trade‐off. We discuss possible experiments and implications of our results for the development of virulence management strategies.

“Why do parasites harm their host?” is a recurrent question in evolutionary biology and ecology, and has several implications for the biomedical sciences, particularly public health and epidemiology. Contrasting the meaning(s) of the concept of “virulence” in molecular pathology and evolutionary ecology, we review different explanations proposed as to why, and under what conditions, parasites cause harm to their host: whereas the former uses molecular techniques and concepts to explain changes and the nature of virulence seen as a categorical trait, the latter conceptualizes virulence as a phenotypic quantitative trait (usually related to a reduction in the host’s fitness). After describing the biology of emerging influenza viruses we illustrate how the ecological and the molecular approaches provide distinct (but incomplete) explanations of the 1918–19 influenza pandemic. We suggest that an evolutionary approach is necessary to understand the dynamics of disease transmission but that a broader understanding of virulence will ultimately benefit from articulating and integrating the ecological dynamics with cellular mechanisms of virulence. Both ecological and functional perspectives on host-pathogens’ interactions are required to answer the opening question but also to devise appropriate health-care measures in order to prevent (and predict?) future influenza pandemics and other emerging threats. Finally, the difficult co-existence of distinct explanatory frameworks reflects the fact that scientists can work on a same problem using various methodologies but it also highlights the enduring tension between two scientific styles of practice in biomedicine.

The evolutionary emergence of new pathogens via mutation poses a considerable risk to human and animal populations. Most previous studies have investigated cases where a potentially pandemic strain emerges though mutation from an initial maladapted strain (i.e., its basic reproductive ratio R01). However, an alternative (and arguably more likely) cause of novel pathogen emergence is where a "weakly adapted" strain (with R0 ≈ 1) mutates into a strongly adapted strain (with R0 ≫ 1). In this case, a proportion of the host susceptible population is removed as the first strain spreads, but the impact this feedback has on emergence of mutated strains has yet to be quantified. We produce a model of pathogen emergence that takes into account changes in the susceptible population over time and find that the ongoing depletion of susceptible individuals by the first strain has a drastic effect on the emergence probability of the mutated strain, above that assumed by just scaling the reproductive ratio. Finally, we apply our model to the documented emergence of Chikungunya virus on La Réunion Island and demonstrate that the emergence probability of the mutated strain was reduced approximately 10-fold, compared to models assuming that susceptible depletion would not affect outbreak probability. These results highlight the importance of taking population feedbacks into account when predicting disease emergence.

International audience; Until quite recently and since the late 19(th) century, medical microbiology has been based on the assumption that some micro-organisms are pathogens and others are not. This binary view is now strongly criticized and is even becoming untenable. We first provide a historical overview of the changing nature of host-parasite interactions, in which we argue that large-scale sequencing not only shows that identifying the roots of pathogenesis is much more complicated than previously thought, but also forces us to reconsider what a pathogen is. To address the challenge of defining a pathogen in post-genomic science, we present and discuss recent results that embrace the microbial genetic diversity (both within- and between-host) and underline the relevance of microbial ecology and evolution. By analyzing and extending earlier work on the concept of pathogen, we propose pathogenicity (or virulence) should be viewed as a dynamical feature of an interaction between a host and microbes.

International audience; Innovative sequencing techniques now allow the routine access of whole genomes of pathogens, generating vast amounts of data. Phylogenetic trees are a common method for synthesizing this information. Unfortunately, these trees are often seen only as a visual support to guide researchers, thus neglecting the value of employing phylogenetic techniques to perform hypothesis testing on clinical questions. These include investigating how a pathogen spreads within a patient, or whether the infection severity (often measured by virus load) is controlled by viral genetics. Advances in methodology mean the time is ripe for combining phylogenies with clinical data to better understand and fight infectious diseases.

International audience; Measuring epidemic parameters early in an outbreak is essential to inform control efforts. Using the viral genome sequence and collection date from 74 infections in the 2014 Ebola virus outbreak in Sierra Leone, we estimate key epidemiological parameters such as infectious period duration (approximately 71 hours) and date of the first case in Sierra Leone (approximately April 25(th)). We also estimate the effective reproduction number, Re, (approximately 1.26), which is the number of secondary infections effectively caused by an infected individual and accounts for public health control measures. This study illustrates that phylodynamics methods, applied during the initial phase of an outbreak on fewer and more easily attainable data, can yield similar estimates to count-based epidemiological studies.

Hosts are often co-infected by several parasite genotypes of the same species or even by different species and this is known to affect virulence evolution. However, epidemiological models typically assume that only one of the co-infecting strains can be transmitted at the same time, which is often at odds with the observed biology. Here, I study the effect of co-transmission on virulence evolution in a case where parasites compete for host resources. For co-infections by strains of the same species, increased co-transmission selects for less virulent strains. This is because co-transmission aligns the interests of co-infecting strains, thus decreasing the selective pressure for increased within-host competitiveness. For co-infection caused by different parasite species, the evolutionary outcome depends on the respective virulence of the two parasite species. Finally, I investigate asymmetric scenarios, for example that of plant viruses that require "helper" molecules produced by viruses from another species to be transmitted. These results show that even if parasite strains compete for host resources, the prevalence of co-infections can be a poor predictor of virulence evolution.

Multiple infections are intensively studied because of their consequences for the health of the host but also because they can radically alter the selective pressures acting on parasites. I discuss how multiple infections have been modelled in evolutionary epidemiology. First, I briefly mention within-host models, which are at the root of these epidemiological models. Then, I present the super-infection framework, with an original focus on how the definition of the super-infection function can lead to evolutionary branching. There are several co-infection models and, for each of them, I briefly go through the underlying mathematics (especially the invasion fitness of a mutant strain) and I discuss the biological assumptions they make and the questions they consequently may ask. In particular, I show that a widely used co-infection model should not be invoked for invasion analyses because it confers a frequency-dependent advantage to rare neutral mutants. Finally, I present more recent frameworks, such as the Price equation framework in epidemiology, that can account for increased parasite diversity. To conclude, I discuss some perspectives for the study of multiple infections in evolutionary epidemiology.

International audience; Micro-organisms are known to exhibit phenotypic plasticity in response to changes in their environment. Recent studies have shown that a parasite strain can adjust its host exploitation strategies to the presence of unrelated strains, e.g. for Plasmodium chabaudi by adjusting its sex-ratio. J Evol Biol 2013; 26: 1370-1378 claims to report a similar plastic response to the presence of unrelated strains in the case of siderophore-producing bacteria. I argue that she does not provide sufficient evidence to support the interpretation of the plastic response she observes (increasing siderophore production in the presence of cheaters) through a cooperator/cheater framework. I show that known plastic responses to physicochemical factors, such as siderophore or iron concentration, seem to offer a clearer and more parsimonious explanation. Finally, I also challenge the parallel she makes between the process she observes in siderophore-producing bacteria and compensation in bi-parental care models.

International audience; BACKGROUND AND OBJECTIVES:The severity of HIV-1 infection, measured by set-point viral load (SPVL), is highly variable between individuals. Its heritability between infections quantifies the control the pathogen genotype has over disease severity. Heritability estimates vary widely between studies, but differences in methods make comparison difficult. Phylogenetic comparative analysis offers measures of phylogenetic signal, but it is unclear how to interpret them in terms of the fraction of variance in SPVL controlled by the virus genotype.METHODOLOGY:We present computational methods which link statistics summarizing phylogenetic signal to heritability, h(2) in order to test for and quantify it. We re-analyse data from Switzerland and Uganda, and apply it to new data from the Netherlands. We systematically compare established and new (e.g. phylogenetic pairs, PP) phylogenetic signal statistics.RESULTS:Heritability estimates varied by method and dataset. Several methods were consistently able to detect simulated heritability above , but none below. Pagel's λ was the most robust and sensitive. The PP method found no heritability in the Netherlands data, whereas Pagel's λ found significant heritability only in a narrow subdivision (P = 0.038). Heritability was estimated at h(2) = 0.52 (95% confidence interval 0.00-0.63).CONCLUSIONS AND IMPLICATIONS:This standardized measure, h(2), allows comparability of heritability between cohorts. We confirm high heritability in Swiss data, but neither in Ugandan data nor in the Netherlands, where it is barely significant or undetectable. Existing phylogenetic methods are ill-suited for detecting heritability below , which may nonetheless be biologically important.

International audience; BACKGROUND: HIV evolves rapidly at the epidemiological level but also at the within-host level. The virus' within-host evolutionary rates have been argued to be much higher than its between-host evolutionary rates. However, this conclusion relies on analyses of a short portion of the virus envelope gene. Here, we study in detail these evolutionary rates across the HIV genome. RESULTS: We build phylogenies using a relaxed molecular clock assumption to estimate evolutionary rates in different regions of the HIV genome. We find that these rates vary strongly across the genome, with higher rates in the envelope gene (env). Within-host evolutionary rates are consistently higher than between-host rates throughout the HIV genome. This difference is significantly more pronounced in env. Finally, we find weak differences between overlapping and non-overlapping regions. CONCLUSIONS: We provide a genome-wide overview of the differences in the HIV rates of molecular evolution at the within- and between-host levels. Contrary to hepatitis C virus, where differences are only located in the envelope gene, within-host evolutionary rates are higher than between-host evolutionary rates across the whole HIV genome. This supports the hypothesis that HIV strains that are less adapted to the host have an advantage during transmission. The most likely mechanism for this is storage and then preferential transmission of viruses in latent T-cells. These results shed a new light on the role of the transmission bottleneck in the evolutionary dynamics of HIV.

Infections that consist of multiple parasite strains or species are common in the wild and are a major public health concern. Theory suggests that these infections have a key influence on the evolution of infectious diseases and, more specifically, on virulence evolution. However, we still lack an overall vision of the empirical support for these predictions. We argue that within-host interactions between parasites largely determine how virulence evolves and that experimental data support model predictions. Then, we explore the main limitation of the experimental study of such 'mixed infections', which is that it draws conclusions on evolutionary outcomes from studies conducted at the individual level. We also discuss differences between coinfections caused by different strains of the same species or by different species. Overall, we argue that it is possible to make sense out of the complexity inherent to multiple infections and that experimental evolution settings may provide the best opportunity to further our understanding of virulence evolution.

Human sexual networks exhibit a heterogeneous structure where few individuals have many partners and many individuals have few partners. Network theory predicts that the spread of sexually transmitted infections (STI) on such networks should exhibit striking properties (e.g. rapid spread). However, these properties cannot be found in epidemiological data. Current network models typically assume a constant STI transmission risk per partnership, which is unrealistic because it implies that sexual activity is proportional to the number of partners and that individuals have the same activity with each partner. We develop a framework that allows us to weight any sexual network based on biological assumptions. Our results indicate that STI spreading on the resulting weighted networks do not have heterogeneous-related properties, which is consistent with data and earlier studies., Journal of Theoretical Biology, 311, ISSN:0022-5193, ISSN:1095-8541

International audience; Arising from M. A. Nowak, C. E. Tarnita & E. O. Wilson 466, 1057-1062 (2010); Nowak et al. reply. Nowak et al. argue that inclusive fitness theory has been of little value in explaining the natural world, and that it has led to negligible progress in explaining the evolution of eusociality. However, we believe that their arguments are based upon a misunderstanding of evolutionary theory and a misrepresentation of the empirical literature. We will focus our comments on three general issues.

Many viruses and bacteria are known to evolve rapidly over the course of an infection. However, epidemiological studies generally assume that within-host evolution is an instantaneous process. We argue that the dynamics of within-host evolution has implications at the within-host and at the between-host levels. We first show that epidemiologists should consider within-host evolution, notably because it affects the genotype of the pathogen that is transmitted. We then present studies that investigate evolution at the within-host level and examine the extent to which these studies can help to understand infection traits involved in the epidemiology (e.g. transmission rate, virulence, recovery rate). Finally, we discuss how new techniques for data acquisition can open new perspectives for empirical and theoretical research.

UMR BGPI Equipe 2; International audience; The adaptive hypothesis invoked to explain why parasites harm their hosts is known as the trade-off hypothesis, which states that increased parasite transmission comes at the cost of shorter infection duration. This correlation arises because both transmission and disease-induced mortality (i.e. virulence) are increasing functions of parasite within-host density. There is, however, a glaring lack of empirical data to support this hypothesis. Here, we review empirical investigations reporting to what extent within-host viral accumulation determines the transmission rate and the virulence of vector-borne plant viruses. Studies suggest that the correlation between within-plant viral accumulation and transmission rate of natural isolates is positive. Unfortunately, results on the correlation between viral accumulation and virulence are very scarce. We found only very few appropriate studies testing such a correlation, themselves limited by the fact that they use symptoms as a proxy for virulence and are based on very few viral genotypes. Overall, the available evidence does not allow us to confirm or refute the existence of a transmission-virulence trade-off for vector-borne plant viruses. We discuss the type of data that should be collected and how theoretical models can help us refine testable predictions of virulence evolution.

The evolution of infectious diseases is known to affect epidemiological dynamics, but, for some viruses and bacteria, this evolution also takes place inside a host during the course of an infection. I develop an original approach to study intrahost evolutionary dynamics of quantitative disease traits. This approach can be expressed mathematically using the ‘Price equation’ framework recently developed in evolutionary epidemiology. This framework combines population genetics and within-host population dynamics models to identify trade-offs that affect disease intrahost evolution and to predict short-term evolutionary dynamics of life-history traits. I show that this can be applied to study the evolution of viruses competing for host cells or to study the coevolution between parasites and the immune system of the host. This framework can also easily incorporate experimental data. Studying intrahost evolutionary dynamics provides insight at the within-host level, because it allows us to better understand the course of chronic infections, and at the epidemiological level, because it helps to study multi-scale evolutionary processes. This framework can be used to address important biological issues, from immune escape to disease evolutionary response to treatments.

Nested models (also called embedded models) explicitly link dynamical processes that occur at different scales. Recently there has been considerable interest in linking within- and between-host levels of disease dynamics in the study of pathogen evolution. Here we review the extent to which these nested models have increased our understanding of pathogen evolution. We suggest that, although such models have been useful for determining the nature of tradeoffs between epidemiological parameters and for evaluating the consequences of conflicting selection pressures at different scales, the vast majority of previous results could likely have been obtained without the use of nested models per se. Nevertheless, these models have proven very useful through their highlighting of the importance of within-host disease dynamics on pathogen evolution.

There is ample theoretical and experimental evidence that virulence evolution depends on the immune response of the host. In this article, we review a number of recent studies that attempt to explicitly incorporate the dynamics of the immune system (instead of merely representing it by a single black box parameter) in models for the evolution of parasite virulence. A striking observation is that the type of infection (acute or chronic) is invariably considered to be a constraint that model assumptions have to satisfy rather than as a potential outcome of the interaction of the parasite with its host's immune system. We argue that avoiding making assumptions about the type of infection will lead to a better understanding of infectious diseases, even though a number of fundamental and technical problems remain. Dynamical modeling of the immune system opens a wide range of perspectives: for understanding how the immune system eradicates a parasite (which it does for most pathogens but not for all, HIV being a notorious example of a virus that is not completely eliminated), for studying multiple infections through concomitant immunity, for understanding the emergence and evolution of the immune system in animals, and for evolutionary epidemiology in general (e.g., predicting evolutionary consequences of new therapies and public health policies). We conclude by discussing new approaches based on embedded (or nested) models and identify future perspectives for the modeling of infectious diseases.

Parasite evolution is mainly studied through a trade‐off involving host death (i.e., virulence) and transmission. In addition to the lack of evidence, this trade‐off largely fails to understand the evolution of sublethal parasite effects. Here, I argue that considering host recovery as a main selection pressure faced by the parasite helps to address these problems and opens new perspectives for the study of parasite evolution. Using an embedded model, I show how a trade‐off between transmission and recovery may emerge from within‐host dynamics if immune activation is assumed to depend on the parasite's overall growth rate. I also show that the value of the parasite’s optimal growth rate strongly depends on the immunological state of the host. Transmission‐recovery trade‐offs are of particular interest to the study of the evolution of human pathogens because of the use of antipathogen treatments, which strengthens the recovery constraint.

International audience; Spatial structure has been shown to promote altruistic behaviour, however, it also increases the intensity of competition among relatives. Our purpose here is to develop a model in which this competition is minimized, more precisely a local increase in fecundity has a minimal competitive effect on the fitness of nearby individuals. We work with an island model in which sites are allowed to be empty, choosing our demographic rules so that in patches with higher fecundity, empty sites are filled at a higher rate. We also allow dispersal rates to evolve in response to the proportion of empty sites in the patch. Patches with different numbers of empty sites differ in frequency, in within-patch consanguinity and in reproductive value. Using an inclusive fitness argument, we show that our model does promote altruism; indeed Hamilton's Rule is shown to hold. The only negative effect on an actor of a gift of fecundity to a patchmate turns out to be a slight decrease in reproductive value due to an increased probability of an empty site being occupied. We show that altruists are most favoured in islands with an intermediate number of empty sites.

Gradual evolution is a common phenomenon in the fossil record of marine microplankton, yet no theoretical model has so far been presented to explain the observed pattern of unidirectionality in trait evolution lasting over tens of millions of generations. Recent molecular genetic data show that the majority of microfossil-producing plankton groups harbors substantial cryptic diversity. Here, we examine the effect of cryptic diversity on apparent rates of lineage evolution. By using a theoretical approach, we show that under resource competition, an increasing number of sibling species within a hypothetical lineage leads to an exponential slowdown of the apparent rate of evolution. This mechanism explains both the remarkable variation in apparent rates of evolution observed in marine plankton, as well as the presence of long gradual evolutionary trends.

Though it is commonly supposed that there is a trade-off between virulence and transmission, there is little data and little insight into what it should look like. Here, we consider the specific case of vector-borne parasites (inspired by human malaria) and analyse an embedded model to understand how specific life-cycle aspects may affect this trade-off. First, we find that, for such parasites, the transmission function may have an S-shape. Second, we find that the trade-off obtained for vector-borne parasites is less sensitive to parameter variations than the trade-off obtained for directly transmitted parasites. Third, we find that other parasite traits, such as the conversion from replicative to infective stages, could have important epidemiological implications. Finally, we compare the effect of treatments targeting either the asexual or the sexual parasite life-stage.

Parasite evolution is a major public health issue. The aim of this thesis is to understand micro-parasite virulence evolution through a theoretical approach based on within-host models linked to an epidemiological framework (also called embedded models). The host’s immune response exerts a major selection pressure on parasite virulence. In parasitology, most within-host models incorporate the immune system as a dynamical system, but in these models, the outcome of the infection generally is an a priori assumption. Nevertheless, these models offer new perspectives to study virulence evolution. Most studies assume that a parasite cannot increase its transmission rate without causing more harm to its host. However, the existence of such a trade-off relationship between transmission and virulence has been challenged. Using an embedded model, we study how a trade-off can emerge and what its characteristics are. Applying this framework to the case of vector-borne parasites, we work out how specific aspects of the parasites and interaction with its host affect the trade-off. Finally, we investigate the consequences of multiple infections on virulence evolution. The results of our study have public health implications as our models allow to predict possible consequences of anti-parasite treatments on virulence evolution. We suggest it would be reasonable to take these effects into account when elaborating public health policies to avoid an “arms race” between hosts and parasites.; L’évolution des parasites est un enjeu sanitaire majeur, qui a souvent été négligé. L’objet de cette thèse est de comprendre l’évolution de la virulence des micro-parasites grâce à une approche théorique basée sur des modèles intra-hôtes intégrés dans une dimension épidémiologique, aussi appelés modèles emboîtés. La réponse immunitaire de l’hôte exerce une pression de sélection majeure sur la virulence des parasites. En parasitologie, la plupart des modèles incluent le système immunitaire en tant que système dynamique mais dans ces modèles le résultat de l’infection est souvent une hypothèse a priori. Ces modèles ouvrent toutefois de nouvelles perspectives pour l’étude de l’évolution de la virulence.La plupart des études supposent qu’un parasite ne peut augmenter son taux de transmission sans nuire davantage à son hôte. Pourtant, l’existence d’une telle relation de compromis évolutif entre transmission et virulence est toujours remise en cause. Grâce à un modèle emboîté, nous étudions à quelles conditions un compromis évolutif peut émerger et quelles sont alors ses caractéristiques. En appliquant cette approche aux parasites à vecteur, nous montrons comment les aspects spécifiques du parasite et de son interaction avec l’hôte affectent le compromis. Finalement, nous étudions les conséquences des infections multiples sur l’évolution de la virulence.Les résultats de notre travail ont des implications sanitaires car nos modèles permettent de prévoir l’effet potentiel de traitements anti-parasitaires sur l’évolution de la virulence. Il serait judicieux de prendre en compte ces effets dans l’élaboration de politiques de santé afin d’éviter une « course aux armements » entre hôtes et parasites.

Though predation, productivity (nutrient richness), spatial heterogeneity, and disturbance regimes are known to influence species diversity, interactions between these factors remain largely unknown. Predation has been shown to interact with productivity and with spatial heterogeneity, but few experimental studies have focused on how predation and disturbance interact to influence prey diversity. We used theory and experiments to investigate how these factors influence diversification of Pseudomonas fluorescens by manipulating both predation (presence or absence of Bdellovibrio bacteriovorus) and disturbance (frequency and intensity of disturbance). Our results show that in a homogeneous environment, predation is essential to promote prey species diversity. However, in most but not all treatments, elevated diversity was transitory, implying that the effect of predation on diversity was strongly influenced by disturbance. Both our experimental and theoretical results suggest that disturbance interacts with predation by modifying the interplay of resource and apparent competition among prey.

There is currently little evidence to close the question of whether the type-specific HPV vaccines will cause increased prevalence of non-targeted, oncogenic types, a scenario known as ‘type replacement’. The abundance of hypotheses in the literature about how HPV types interact (facilitation, immune-mediated competition and independence) illustrates the perplexing nature of within-host HPV type interactions. In this talk, I will present analyses of within-host mathematical models that represent multiple HPV type co-infections and that are based on ecological theory. The results suggest that co-existence found at the patient level can arise even when types are interacting. I will also show how some common assumptions being made about HPV types are in conflict with current ecological theory. Finally, I will discuss some work I am currently conducting in Montpellier where we are fitting epidemiological models to multiple infection prevalence data. These kinds of collaborations between ecologists and HPV specialists will be essential to resolving why the vaccines may or may not cause type replacement for the benefit of future vaccination programs.

International audience; The study of biological invasions of ecological systems has much to offer research on within–host (WH) systems, particularly for understanding infections and developing therapies using biological agents. Thanks to the ground-work established in other fields, such as community ecology and evolutionary biology, and to modern methods of measurement and quantification, the study of microbiomes has quickly become a field at the forefront of modern systems biology. Investigations of host-associated microbiomes (e.g. for studying human health) are often centred on measuring and explaining the structure, functions and stability of these communities. This momentum promises to rapidly advance our understanding of ecological networks and their stability, resilience and resistance to invasions. However, intrinsic properties of host-associated microbiomes that differ from those of free-living systems present challenges to the development of a WH invasion ecology framework. The elucidation of principles underlying the invasibility of WH networks will ultimately help in the development of medical applications and help shape our understanding of human health and disease.

Prod 2017-108 EA BIOmE CT ? INRA; National audience; Quand l'écologie pose son diagnostic. L'actualité nous le rappelle sans cesse : les maladies comme le paludisme, le Sida, ébola ou zika sont difficiles à combattre, et les maladies chroniques de type cancer, diabète ou maladies cardio-vasculaires toujours plus meurtrières. Pour la première fois de son histoire, l'humanité voit même son espérance de vie diminuer. Pour comprendre les raisons de cette crise sanitaire, l'écologie scientifique pose un autre regard sur la santé qui prend en considération les transformations de l'environnement et l'évolution de nos modes de vie. Cette nouvelle approche évolutive et écologique des problèmes de santé tente d'élucider les mécanismes en jeu dans la transmission et l'émergence de nouveaux pathogènes, mais aussi dans la résistance toujours croissante aux antibiotiques. Elle permet de mesurer l'effet des facteurs environnementaux sur la santé : concentration urbaine et industrielle et nouveaux comportements induits en matière d'alimentation et d'activités physiques. Cet ouvrage rédigé par des spécialistes issus des laboratoires du CNRS ou associés fait le point sur les recherches en écologie de la santé. Il permet à chacun de comprendre pourquoi il est essentiel de remonter aux sources environnementales et comportementales de nos maux, et de ne pas se limiter aux symptômes.

A preprint peer-reviewed and recommended by PCI Mathematical & Computational Biology (https://mcb.peercommunityin.org/); International audience; Antimicrobial efficacy is traditionally described by a single value, the minimal inhibitory concentration (MIC), which is the lowest concentration that prevents visible growth of the bacterial population. As a consequence, bacteria are classically qualitatively categorized as resistant if therapeutic concentrations are below MIC and susceptible otherwise. However, there is a continuity in the space of the bacterial resistance levels. Here, we introduce a model of within-host evolution of resistance under treatment that considers resistance as a continuous quantitative trait, describing the level of resistance of the bacterial population. The use of integro-differential equations allows to simultaneously track the dynamics of the bacterial population size and the evolution of its level of resistance. We analyze this model to characterize the conditions; in terms of (a) the efficiency of the drug measured by the antimicrobial activity relatively to the host immune response, and (b) the cost-benefit of resistance; that (i) prevents bacterial growth to make the patient healthy, and (ii) ensures the emergence of a bacterial population with a minimal level of resistance in case of treatment failure. We investigate how chemotherapy (i.e., drug treatment) impacts bacterial population structure at equilibrium, focusing on the level of evolved resistance by the bacterial population in presence of antimicrobial pressure. We show that this level is explained by the reproduction number R0. We also explore the impact of the initial bacterial population size and their average resistance level on the minimal duration of drug administration in preventing bacterial growth and the emergence of resistant bacterial population.

Controlling infectious diseases is a major issue for human, animal, and plant public health. In addition to traditional epidemiological data such as incidence or prevalence, genetic data resulting from advances in sequencing techniques are now available. The abundance of these data associated with the rapid evolution of pathogens, in particular RNA viruses, has led to the development of a disciplinary field called phylodynamics. One of the underlying hypotheses of this field is that the way viruses spread leaves footprints in their genomes. Phylodynamic methods take advantage of this genomic information to estimate epidemiological parameters such as the growth rates of an epidemic, the number of secondary infections caused, or the average infection duration.Bayesian phylodynamic inference methods are the most commonly used and are generally based on likelihood functions. However, they can be unsuitable for models with many parameters, e.g. those that capture transmission heterogeneity. Other inference methods are based on Approximate Bayesian Computation (ABC) and do not require a likelihood function. These approaches involve simulations from epidemiological models, summary statistics capturing epidemiological information from phylogenies, and regression techniques. So far, phylodynamic ABC approaches have focused on simple epidemiological models.This thesis work comprises two main parts. The first part consisted in developing a tool for rapid simulations of time series and phylogenies, which can be used in ABC approaches. This simulator, TiPS, has the advantage to include a large diversity of epidemiological models while being faster than many existing tools. The second part of the thesis consisted of studying epidemics in different contexts using ABC or likelihood-based approaches.The first application consisted of a phylodynamic analysis of the spread of hepatitis C virus (HCV) in Lyon. The epidemiological context led us to extend the application of the ABC method to a structured model with two types of hosts. For this purpose, we developed new summary statistics adapted to labeled phylogenies, where leaves are associated with a risk group. This ABC approach was then also used in another context to study the transmission advantage that the presence of a certain mutation would confer to HIV-1/O. The last study is devoted to epidemiological and phylodynamic analyses of SARS-CoV-2 from French data using Bayesian likelihood-based inference methods.Although in full expansion, the field of phylodynamics is poorly known and studied in France. In addition to its methodological developments and its analyses on current epidemics, this work illustrates, at a national scale, the potential of genomic sequence data analysis to help public health organisms to establish more efficient control measures, especially in times of crisis.; Le contrôle des maladies infectieuses est un enjeu majeur en santé publique humaine, animale et végétale. Aux données épidémiologiques classiques telles que l'incidence ou la prévalence, se sont ajoutées des données génétiques issues des progrès des techniques de séquençage. L’abondance de ces données associée à l’évolution rapide des pathogènes, en particulier des virus à ARN, a entraîné l’essor d’un champ disciplinaire appelé phylodynamique dont une des hypothèses sous-jacentes est que la manière dont les virus se propagent laisse des traces dans leurs génomes. Les méthodes phylodynamiques tirent parti des ces informations génomiques pour estimer des paramètres épidémiologiques tels que les taux de croissance d’une population virale, le nombre d’infections ou leur durée moyenne.Les méthodes d’inférence phylodynamique bayésienne sont les plus couramment utilisées et reposent en général sur des fonctions de vraisemblance. Cependant, elles peuvent rapidement se révéler inadaptées pour des modèles composés de beaucoup de paramètres, par exemple pour refléter des hétérogénéités de transmission.D’autres méthodes d’inférence sont basées sur le calcul bayésien approché (ABC) et ne nécessitent aucune fonction de vraisemblance. Ces approches reposent sur des simulations à partir de modèles épidémiologiques, des statistiques de résumé capturant l’information épidémiologique des phylogénies et des techniques de régression. Jusqu'ici, les approches de phylodynamique avec ABC ont étudié des modèles épidémiologiques relativement simples.Cette thèse se compose en deux principales parties.La première partie a consisté à développer un outil de simulations rapides de séries temporelles et de phylogénies, qui soit utilisable dans les approches ABC. Ce simulateur, TiPS, présente l’avantage de pouvoir inclure une grande diversité de modèles épidémiologiques tout en étant plus rapide que beaucoup d'outils existants.La seconde partie de la thèse a consisté à étudier des épidémies dans des contextes différents en utilisant des approches ABC ou de vraisemblance.La première étude a concerné l’analyse phylodynamique de la propagation du virus de l’hépatite C (VHC) à Lyon. Le conexte épidémiologique nous a conduit à étendre l’application de la méthode ABC à un modèle structuré avec deux types d'hôtes. Pour cela, nous avons développé de nouvelles statistiques de résumé adaptées aux phylogénies dites labellisées dont les feuilles sont associées à un groupe à risque. Cette approche ABC a ensuite été utilisée pour étudier l'avantage de transmission que conférerait la présence d’une certaine mutation aux VIH-1/O. La dernière étude est consacrée aux analyses épidémiologiques et phylodynamiques du SARS-CoV-2 à partir de données françaises en utilisant des méthodes d’inférence bayésienne basées sur la vraisemblance.Bien qu’en plein essor, le domaine de la phylodynamique est encore peu connu et peu étudié en France. En plus de ses développements méthodologiques et de ses analyses d'épidémies en cours, ce travail de thèse illustre, à l’échelle nationale, le potentiel d’analyses de données de séquences génomiques pour aider à la prise de décision en santé publique surtout en temps de crise.

During the cellular translation process, the ribosomal machinery synthesizes a protein through the successive reading of codons along the messenger RNA. With each codon read, the ribosomes call upon the transfer RNAs, which are loaded with an amino acid (the basic unit of proteins). The complementarity between the codon on the mRNA and the anticodon on the tRNA is evaluated, eventually leading to the polymerization of the amino acid onto the nascent protein. There are 64 codons classically associated with 20 amino acids. Several codons, qualified as synonyms, can thus be associated with the same amino acid. Codon use bias (CUB) refers to the differential use of synonymous codons at the gene, genomic region or genome scale. CUB can be associated with mutational processes, at the origin of local peculiarities of nucleotide composition, but also with selection processes improving the protein synthesis dynamics. The influence of these two processes on CUB has been demonstrated in prokaryotes and in some eukaryotes. However, there is no strong evidence of selection acting at the Vertebrate gene level, and more specifically in mammals. Do we consider the CUB in these species from a correct angle? Do we have the necessary tools to draw such conclusions? To answer these questions, we propose a mathematical, computational and analytical approach to CUB through the analysis of vertebrate paralogs and human viruses.We have designed a new CUB index called COUSIN (COdon Usage Similarity INdex), which quantifies the distance between the CUB of a sequence and that of a reference, and which stands out from other existing indices by its clarity in the interpretation of results. This index is implemented within an eponymous tool (http://cousin.ird.fr). In a second step, we performed a study of the evolutionary history and CUB of the Vertebrates paralogous genes Polypyrimidine Tract-Binding Protein (PTBP) whose tissue-specific expression could be associated with differences in their CUB. We show that PTBP1 paralogs appear to be mutagenically biased towards GC enrichment, while the CUB of PTBP2 paralogs may reflect a translational selection towards the use of rare codons in the genome. We interpret that the evolution of the CUB of PTBPs is compatible with a scenario of paralog sub-functionalization by differential expression during vertebrate development. Finally, we studied CUB in human viruses through human polyomaviruses (PyVs). Due to their obligate parasitism on the cellular protein synthesis machinery, the CUB of viruses could impact the clinical presentation of the infection. Our choice of human PyVs comes precisely from their genotypic diversity as well as their multiplicity of clinical manifestations. Infections with human PyVs are highly prevalent and asymptomatic but, in a context of immunosuppression, they can cause heavy and sometimes fatal tissue symptoms. Polyomavirus BK (BKPyV) is known to cause nephropathy in kidney transplant recipients. To prepare the analysis of longitudinal viremia, viruria and genetic data on kidney transplant patients, we have built two pipelines performing an analysis of the PyV genomes, and in particular of their genotype. In order to better understand the evolutionary dynamics of BKPyV in kidney disease, we have analyzed the evolution and CUB of PyVs in the context of the host-parasite relationship.The results proposed in this thesis enrich the basis for the study of CUB in vertebrates, and foster the debate on the pertinence of tissue-specific analysis through differential gene expression and virus tropism.; Au cours du processus cellulaire de traduction, la machinerie ribosomale synthétise une protéine au travers de la lecture successive des codons le long de l'ARN messager. À chaque codon lu, les ribosomes font appel aux ARN de transfert, chargés d'un acide aminé (l'unité de base des protéines). La complémentarité entre le codon de l'ARNm et l'anticodon de l'ARNt est évaluée, conduisant éventuellement à la polymérisation de l’acide aminé sur la protéine naissante. Il existe 64 codons associés classiquement à 20 acides aminés. Plusieurs codons, qualifiés de synonymes, peuvent donc être associés à un même acide aminé. Le biais d’usage des codons (CUB) désigne l'usage différentiel des codons synonymes à l’échelle d'un gène, d'une région génomique ou d'un génome. Le CUB peut être associé à des processus mutationnels, à l'origine de particularités locales de composition nucléotidique, mais aussi à des processus de sélection pour améliorer la dynamique de synthèse de protéines. L’influence de ces deux processus sur le CUB a été démontrée chez les procaryotes et chez certains eucaryotes. Cependant, il n’existe pas d’évidence forte d’une sélection agissant à l’échelle des gènes des Vertébrés, et plus précisément des mammifères. Considérons-nous donc le CUB dans ces espèces sous un angle correct ? Possédons-nous les outils nécessaires pour tirer de telles conclusions ? Pour répondre à ces questions, nous proposons ici une approche mathématique, informatique et analytique du CUB par le biais de l'analyse de paralogues et de virus humains.Nous avons conçu un nouvel indice de mesure du CUB appelé COUSIN (COdon Usage Similarity INdex), qui quantifie la distance entre le CUB d’une séquence et celui d’une référence, et qui se démarque des autres indices existants par sa clarté dans l’interprétation des résultats. Cet indice est implémenté au sein d’un un outil éponyme (http://cousin.ird.fr). Dans un deuxième temps, nous avons effectué une étude de l’histoire évolutive et du CUB des gènes paralogues de Vertébrés Polypyrimidine Tract-Binding Protein (PTBP) dont l’expression tissu-spécifique pourrait être associée aux différences dans leur CUB. Nous montrons que les paralogues PTBP1 semblent soumis à un biais mutationnel vers un enrichissement en GC, alors que le CUB des PTBP2 pourrait refléter une sélection traductionnelle vers l’utilisation de codons rares dans le génome. Nous interprétons que l’évolution du CUB des PTBPs est compatible avec un scénario de sous-fonctionnalisation des paralogues par expression différentielle pendant le développement des Vertébrés. Finalement, nous avons étudié le CUB chez des virus humains au travers des polyomavirus humains (PyVs). Du fait de leur mode de vie obligatoirement parasite de la machinerie de traduction, le CUB des virus pourrait impacter la présentation clinique de l’infection. Notre choix des PyVs humains vient précisément de leur diversité génotypique ainsi que de leur multiplicité de manifestations cliniques. Les infections par PyVs humains sont fortement prévalentes et asymptomatiques mais, dans un contexte d’immunosuppression, elles peuvent provoquer des symptômes tissulaires importants et parfois mortels. Le Polyomavirus BK (BKPyV) est notamment connu pour provoquer des néphropathies chez des patients récipients d’une greffe de reins. Pour préparer l'analyse de données longitudinales de virémie, de virurie et génétiques sur des patients récipients d'une greffe de reins, nous avons construit deux pipelines permettant une analyse du génome des PyVs et de leur génotype. Afin de mieux comprendre la dynamique évolutive de BKPyV dans le cadre des néphropathies, nous avons analysé l’évolution et le CUB des PyVs dans le contexte de la relation hôte-parasite.Les résultats proposés au sein de cette thèse enrichissent les bases pour l’étude du CUB chez les Vertébrés, et alimentent le débat sur les approches tissu-spécifiques au travers de l'expression différentielle des gènes et du tropisme des virus.

Natural populations of living beings are exposed to a diversity of parasites, be they several species, strains or more generally types. While some evolutionary epidemiology models already incorporate parasite polymorphism, few make the connection between between-host dynamics and within-host parasite growth. As parasite polymorphism can even occur within the same host, distinct parasite types can interact in various ways and thus interfere with their transmission and therefore their evolution. The combinatorial and dynamical complexity explains why we still lack general predictions regarding the evolution of virulence in such multiple infection contexts. Seeking for a general trend in virulence evolution, we model each dynamical level on which parasite evolution relies. In particular, we explicitly investigate the within-host interactions and competition outcomes, the epidemiological and adaptive dynamics. Focusing on chronic infections caused by horizontally-transmitted microparasites, we apply both dynamical systems and probabilistic approaches to this nested sequence of dynamics to explore the evolutionary outcomes. We notably define the concept of infection pattern, that is the set of within-host outcomes of all inoculation challenges and identify five yet undescribed patterns that escape from the classical super/coinfection dichotomy. This typology then allows us to address virulence evolution under a general framework. We in particular observe an unavoidable but bounded evolutionary increase in virulence.; Au sein des populations naturelles d'êtres vivants circulent une diversité de parasites, qu'il s'agisse de plusieurs espèces, souches ou plus généralement types. Si certains modèles d'épidémiologie évolutive intègrent déjà le polymorphisme des parasites, rares sont ceux pour lesquels la dynamique épidémiologique dépend de la croissance intra-hôte et des interactions que les parasites entretiennent lorsqu’ils infectent le même hôte. Les complexité combinatoire et dynamique explique pourquoi il n'y a pour l'heure pas de prédiction générale de l'évolution de la virulence dans de tels contextes d'infections multiples. À la recherche d'une tendance générale d'évolution de la virulence, nous modélisons chaque niveau de dynamique sur lequel l'évolution des parasites repose. En particulier, nous étudions explicitement les interactions et l'issue de la compétition au sein des hôtes, les dynamiques épidémiologique et enfin adaptative. Sur l'exemple des infections chroniques causées par des micro-parasites transmis horizontalement, nous employons les approches propres aux systèmes dynamiques et aux probabilités pour emboîter cette suite de dynamiques afin d'en explorer les conséquences évolutives. Nous définissions notamment le concept de patron d'infection, à savoir l'ensemble des issues intra-hôte associées à chaque configuration d'inoculation et décrivons cinq patrons jusqu'ici non décrits, lesquels échappent à la dichotomie classique entre super- et coinfection. Cette typologie nous permet par la suite d'envisager l'évolution de la virulence dans un cadre général. Nous observons en particulier une inéluctable mais bornée croissance évolutive de la virulence.