Biology subjects

Alizon, S.

Publications and source records attributed to Alizon, S..

15 recordsLinked to original sources

bioRxiv2026

Teddy: neural inference of epidemiological parameters from viral sequences

Estimating how fast infections spread or how long they last is essential to control outbreaks. Phylodynamics methods enable the inference of key epidemiological pa-rameters from viral genomic data but remain limited in terms of biological realism and speed because they need to derive and compute likelihoods. We address this is-sue using simulation-based inference and introduce a deep learning-based framework directly trained on alignments of viral genetic sequences. Our neural posterior estima-tion matches the accuracy of leading Bayesian likelihood-based methods while running a thousand times faster and avoiding a phylogeny reconstruction step. This perfor-mance can be harnessed to analyze large datasets and opens new perspectives to tackle biologically realistic models in terms of pathogen life histories or genomic evolution.

genomics↗

bioRxiv2024

Resource landscape shapes the composition and stability of the human vaginal microbiota

The vaginal microbiota is associated with the health of women and newborns alike. Despite its comparatively simple composition relative to other human microbiota systems, the mechanisms underpinning the dynamics and stability of vaginal microbial communities remain elusive. A crucial, yet so far underexplored, aspect of vaginal microbiota ecology is the role played by nutritional resources. Glycogen and its derivatives, produced by vaginal epithelia, are accessible to all bacterial constituents of the microbiota. Concurrently, free sialic acid and fucose offer supplementary nutritional resources to bacterial strains capable of cleaving them from glycans, which are structurally integral to mucus. Notably, bacteria adept at sialic acid exploitation are often correlated with adverse clinical outcomes and are frequently implicated in bacterial vaginosis (BV). In this study, we introduce a novel mathematical model tailored to human vaginal microbiota dynamics to explore the interactions between bacteria and their respective nutritional landscape. Our resource-based model examines the impact of the relative availability of glycogen derivatives (accessible to all bacterial species) and sialic acid (exclusive to some BV-associated bacteria) on the composition of the vaginal microbiota. Our findings elucidate that the success of BV-associated bacteria is intricately linked to their exclusive access to specific nutritional resources. This private access fortifies communities dominated by BV-associated bacteria, rendering them resilient to compositional transitions. We empirically corroborate our model prediction with longitudinal clinical data on microbiota composition and previously unpublished metabolomic profiles obtained from a North American cohort. The insights gleaned from this study shed light on potential pathways for BV prevention and treatment. Significance statementThe vaginal microbiota has a notable impact on womens health at various stages of life, namely puberty, infection protection, sexual health, fertility, pregnancy, and menopausal changes. At present, most non-anti-microbial products developed to mitigate adverse vaginal symptoms emphasise competitive interactions through acids (boric or lactic acid) or probiotics as a means to "rebalance" microbiota communities. Despite recent advances in profiling the composition of vaginal microbiota communities, there remains a major gap in our mechanistic understanding of how to maintain or reinstate a resilient Lactobacillus-dominated microbiota that improves vaginal health and outcomes. This study explores the role of nutritional resources in the vaginal microbiota by introducing a mathematical model that analyses how access to specific nutrients like glycogen derivatives and sialic acid affects the balance of bacterial vaginosis (BV) and non-BV-associated bacteria. Our findings, supported by original cohort-derived microbiological and metabolomics data, demonstrate that exclusive access to these nutrients is linked to the dominance and resilience of BV-associated bacteria, providing new insights for BV prevention and treatment.

microbiology↗

bioRxiv2022

COVFlow: virus phylodynamics analyses from selected SARS-CoV-2 sequences

Phylodynamic analyses can generate important and timely data to optimise public health response to SARS-CoV-2 outbreaks and epidemics. However, their implementation is hampered by the massive amount of sequence data and the difficulty to parameterise dedicated software packages. We introduce the COVFlow pipeline, accessible at https://gitlab.in2p3.fr/ete/CoV-flow, which allows a user to select sequences from the Global Initiative on Sharing Avian Influenza Data (GISAID) database according to user-specified criteria, to perform basic phylogenetic analyses, and to produce an XML file to be run in the Beast2 software package. We illustrate the potential of this tool by studying two sets of sequences from the Delta variant in two French regions. This pipeline can facilitate the use of virus sequence data at the local level, for instance, to track the dynamics of a particular lineage or variant in a region of interest.

bioinformatics↗

bioRxiv2020

Simulating trajectories and phylogenies from population dynamics models with TiPS

We introduce TiPS, an R package to generate trajectories and phylogenetic trees associated with a compartmental model. Trajectories are simulated using Gillespies exact or approximate stochastic simulation algorithm, or a newly-proposed mixed version of the two. Phylogenetic trees are simulated from a trajectory under a backwards-in-time approach (i.e. coalescent). TiPS is based on the Rcpp package, allowing to combine the flexibility of R for model definition and the speed of C++ for simulations execution. The model is defined in R with a set of reactions, which allow capturing heterogeneity in life cycles or any sort of population structure. TiPS converts the model into C++ code and compiles it into a simulator that is interfaced in R via a function. Furthermore, the package allows defining time periods in which the models parameters can take different values. This package is particularly well suited for population genetics and phylodynamics studies that need to generate a large number of phylogenies used for population dynamics studies. This package is available on the CRAN at https://cran.r-project.org/package=TiPS.

bioinformatics↗

bioRxiv2020

Treating symptomatic infections and the co-evolution of virulence and drug resistance

AO_SCPLOWBSTRACTC_SCPLOWAntimicrobial therapeutic treatments are by definition applied after the onset of symptoms, which tend to correlate with infection severity. Using mathematical epidemiology models, I explore how this link affects the coevolutionary dynamics between the virulence of an infection, measured via host mortality rate, and its susceptibility to chemotherapy. I show that unless resistance pre-exists in the population, drug-resistant infections are initially more virulent than drug-sensitive ones. As the epidemic unfolds, virulence is more counter-selected in drug-sensitive than in drug-resistant infections. This difference decreases over time and, eventually, the exact shape of genetic trade-offs govern long-term evolutionary dynamics. Using adaptive dynamics, I show that two types of evolutionary stable strategies (ESS) may be reached in the context of this simple model and that, depending on the parameter values, an ESS may only be locally stable. In general, the more the treatment rate increases with virulence, the lower the ESS value. Overall, both on the short-term and long-term, having treatment rate depend on infection virulence tend to favour less virulent strains in drug-sensitive infections. These results highlight the importance of the feedbacks between epidemiology, public health policies and parasite evolution, and have implications for the monitoring of virulence evolution.

evolutionary biology↗

bioRxiv2019

Using multiple infection history to infer contact network structure

Interactions within a population shape the spread of infectious diseases but contact patterns between individuals are difficult to access. We hypothesised that key properties of these patterns can be inferred from multiple infection data in longitudinal follow-ups. We developed a simulator for epidemics with multiple infections on networks and analysed the resulting individual infection time series by introducing the concept of infection barcodes. We find that, depending on infection multiplicity and network sampling, infection barcode summary statistics can recover network properties such as degree distribution. Furthermore, we show that by mining infection barcodes for multiple infection patterns, one can detect immunological interference between pathogens (i.e. the fact that past infections in a host condition future probability of infection). The combination of individual-based simulations and barcode analysis of infection histories opens promising perspectives to infer and validate transmission networks and immunological interference for infectious diseases from longitudinal cohort data. Author summaryInfectious disease dynamics are constrained both by between-host contacts and pathogen interactions within a host. Furthermore, multiple parasites circulate such that hosts are infected (sometimes simultaneously) by a variety of strains or species. We hypothesise that multiple infection history can inform us about the networks on which parasites are transmitted, but also on within-host interactions such as immunological interference. We developed a simulator for multiple infections on networks. By combining intuitive novel metrics for multiple infection events and established tools from computational data analysis, we show that similarity in infection history between two hosts correlates with their proximity in the contact network. By analysing pathogens co-occurrence patterns within hosts, we also recover immunological interference at the population level. The demonstrated robustness of our results in terms of observability, network clustering, and pathogen diversity opens new perspectives to extract host contact and between-pathogen immunity information from longitudinal cohort data.

ecology↗

bioRxiv2019

Quantifying transmission dynamics of acute hepatitis C virus infections in men having sex with men using phylodynamics

Opioid substitution and syringes exchange programs have drastically reduced hepatitis C virus (HCV) spread in France but HCV sexual transmission in men having sex with men (MSM) has recently arisen as a significant public health concern. The fact that the virus is transmitting in a heterogeneous population, with new and classical hosts, makes prevalence and incidence rates poorly informative. However, additional insights can be gained by analyzing virus phylogenies inferred from dated genetic sequence data. By combining a phylodynamics approach based on Approximate Bayesian Computation (ABC) and an original transmission model, we estimate key epidemiological parameters of an ongoing HCV epidemic among MSMs in Lyon (France). We show that this new epidemic is largely independent of the classical HCV epidemics and that its doubling time is ten times lower (0.44 years versus 4.37 years). These results have practical implications for HCV control and illustrate the additional information provided by virus genomics in public health.

evolutionary biology↗

bioRxiv2019

COUSIN (COdon Usage Similarity INdex): a normalized index of Codon Usage Preferences.

Codon Usage Preferences (CUPrefs) describe the unequal usage of synonymous codons at the gene, genomic region or genome scale. Numerous indices have been developed to measure the CUPrefs of a sequence. We introduce a normalized index to calculate CUPrefs called COUSIN for COdon Usage Similarity INdex. This index compares the CUPrefs of a query against those of a reference dataset and normalizes the output over a Null Hypothesis of random codon usage. COUSIN results can be easily interpreted, quantitatively and qualitatively. We exemplify the use of COUSIN and highlight its advantages with an analysis on the complete coding sequences of eight divergent genomes, two of them with extreme nucleotide composition. Strikingly, COUSIN captures a hitherto unreported bimodal distribution in CUPrefs in genes in the human and in the chicken genomes. We show that this bimodality can be explained by the global nucleotide composition bias of the chromosome in which the gene resides, and by the precise location within the chromosome. Our results highlight the power of the COUSIN index and uncover unexpected characteristics of the CUPrefs in human and chicken. An eponymous tool written in python3 to calculate COUSIN is available for online or local use.

bioinformatics↗

bioRxiv2019

Modelling the evolution of viral oncogenesis

Most human oncogenic viruses share several characteristics, such as being DNA viruses, having long (co)evolutionary histories with their hosts and causing either latent or chronic infections. They can reach high prevalences while causing relatively low case mortality, which makes them quite fit according to virulence evolution theory. After analysing the life-histories of DNA oncoviruses, we use a mathematical modelling approach to investigate how the virus life cycle may generate selective pressures favouring or acting against oncogenesis at the within-host or at the between-host level. In particular, we focus on two oncoprotein activities, namely extending cell life expectancy and increasing cell proliferation rate. These have immediate benefits (increasing viral population size) but can be associated with fitness costs at the epidemiological level (increasing recovery rate or risk of cancer) thus creating evolutionary trade-offs. We interpret the results of our nested model in the light of the biological features and identify future perspectives for modelling oncovirus dynamics and evolution.

evolutionary biology↗

bioRxiv2018

Modelling coinfections to detect within-host interactions from genotype combination prevalences

Parasite genetic diversity can provide information on disease transmission dynamics but most methods ignore the exact combinations of genotypes in infections. We introduce and validate a new method that combines explicit epidemiological modelling of coinfections and regression Approximate Bayesian Computing (ABC) to detect within-host interactions. Using genital infections by different types of Human Papillomaviruses (HPVs) as a test case, we show that, if sufficiently strong, within-host parasite interactions can be detected from epidemiological data and that this detection is robust even in the face of host heterogeneity in behaviour. These results suggest that the combination of mathematical modelling and sophisticated inference techniques is promising to extract additional epidemiological information from existing datasets.

epidemiology↗

bioRxiv2018

Why multiple infections favour virulent parasites

It is now a fact that several strains/species (hereafter types) of parasites circulate in natural host populations. Parasite polymorphism can even occur within the same host, where distinct parasite types can interact in various ways. This can affect their transmission and, therefore, their evolution. We still lack general predictions regarding the evolution, in such multiple infection contexts, of virulence - the infection-induced host mortality, essentially because its emanation from within-host growth was often ignored so far. Here, we explicitly investigate within-host interactions, within-host competition outcomes, epidemiological dynamics and evolutionary invasibility using a formalism as general as possible. Focusing on chronic dimorphic infections caused by horizontally-transmitted microparasites, we apply both dynamical systems and probabilistic approaches to this bottom-up sequence of dynamics to explore the evolutionary outcomes. We show that within-host growth traits are under strong selective pressure and when small mutations affect them, most of the surviving mutants are more virulent than their resident. We thus identify a robust and unavoidable selection bias towards higher virulence.

evolutionary biology↗

bioRxiv2017

Epithelial stratification shapes infection dynamics

Infections of stratified epithelia collectively represent a large burden on global health. Experimental models provide a means to understand how the cell dynamics themselves influence the outcomes of these infections. Mathematical approaches are needed to improve quantification and theoretical advancement of these complex systems. Here, we develop a general ecology-inspired model for stratified epithelial dynamics, which allows us to simulate infections and to estimate parameters that are difficult to measure with organotypic cell cultures. To explore how epithelial cell dynamics affect infection dynamics, we focus on two contrasting pathogens of the cervicovaginal epithelium: Chlamydia trachomatis and Human papillomaviruses. We find that key infection symptoms stem from differential interactions with the layers, while clearance and pathogen burden are bottom-up processes. Cell protective responses to infections (e.g. increased cell proliferation) generally lowered pathogen load but there were specific effects based on infection strategies. These generic responses by the epithelium, then, will have varying results depending on the pathogens infection strategy. Our modeling approach opens new perspectives for 3D tissue culture experimental systems of infections and, more generally, for developing and testing hypotheses related to infections of stratified epithelia.

systems biology↗

bioRxiv2017

Co-evolution of virulence and immunosuppression through multiple infections

Many components of host-parasite interactions have been shown to affect the way virulence (i.e., parasite-induced harm to the host) evolves. However, coevolution of multiple parasite traits is often neglected. We explore how an immunosuppressive mechanism of parasites affects and co-evolves with virulence through multiple infections. Applying the adaptive dynamics framework to epidemiological models with coinfection, we show that immunosuppression is a double-edged-sword for the evolution of virulence. On one hand, it amplifies the adaptive benefit of virulence by increasing the abundance of coinfections through epidemiological feedbacks. On the other hand, immunosuppression hinders host recovery, prolonging the duration of infection and elevating the cost of killing the host. The balance between the cost and benefit of immunosuppression varies across different background mortality rates of hosts. In addition, we find that immunosuppression evolution is influenced considerably by the precise trade-off shape determining the effect of immunosuppression on host recovery and susceptibility to further infection. These results demonstrate that the evolution of virulence is shaped by immunosuppression while highlighting that the evolution of immune evasion mechanisms deserves further research attention.

evolutionary biology↗

bioRxiv2017

The underestimated importance of acute infections by human papillomaviruses

Most infections by human papillomaviruses (HPVs) are acute, that is non-persistent. Yet, for HPVs, as for many other oncoviruses, there is a striking gap between our detailed understanding of chronic infections and our limited data on the early stages of infection. Here we argue that studying HPV acute infections is necessary and timely. Focusing on early interactions will help explain why certain infections are cleared while others become chronic or latent. From a molecular perspective, descriptions of immune effectors and pro-inflammatory pathways during the initial stages of infections have the potential to lead to novel treatments or to improved handling algorithms. From a dynamical perspective, adopting concepts from spatial ecology, such as meta-populations or meta-communities, can help explain why HPV acute infections sometimes last for years. Furthermore, cervical cancer screening and vaccines impose novel iatrogenic pressures on HPVs, implying that anticipating any viral evolutionary response remain essential. Finally, hints at the associations between HPV acute infections and fertility deserve further investigation given their high worldwide prevalence. Overall, understanding asymptomatic and benign infections may be instrumental in reducing HPV virulence.

epidemiology↗

bioRxiv2017

Can Ebola Virus evolve to be less virulent in humans?

This preprint has been reviewed and recommended by Peer Community In Evolutionary Biology ( ). Understanding Ebola Virus (EBOV) virulence evolution is not only timely but also raises specific questions because it causes one of the most virulent human infections and it is capable of transmission after the death of its host. Using a compartmental epidemiological model that captures three transmission routes (by regular contact, via dead bodies and by sexual contact), we infer the evolutionary dynamics of case fatality ratio (CFR) on the scale of an outbreak and on the long term. Our major finding is that the viruss specific life cycle imposes selection for high levels of virulence and that this pattern is robust to parameter variations in biological ranges. In addition to shedding a new light on the ultimate causes of EBOVs high virulence, these results generate testable predictions and contribute to informing public health policies. In particular, burial management stands out as the most appropriate intervention since it decreases the R0 of the epidemics, while imposing selection for less virulent strains.\n\nImpact SummaryThe severe haemorrhagic fever caused by Ebola Virus (EBOV) usually kills more than one infected individual out of two in the absence of treatment, which makes this pathogen one of the most virulent known to humans. The recent outbreak in West Africa (2013-2016) revealed that the virus is able to spread and persist for months across countries. It is often thought that virulence could be due to the fact that the virus is adapted to its reservoir host. Given that microbes evolve rapidly, it is important to determine whether EBOV virulence is likely to decrease as the virus adapts to its human host. To address this problem, we developed a novel mathematical model tailored to EBOVs life cycle, notably by capturing its three main transmission routes (by regular contact, sexual contact and via dead bodies). We investigated the evolutionary trends of EBOVs virulence on different time scales (outbreak initiation, short term and long term). Our results reveal that the virulence of EBOV might not be due to the maladaptation of the virus, but could rather originate from its unique life cycle. These results are robust to the parameter values chosen. From a public health perspective, burial management stands out as the main leverage to fight the virulence of EBOV, both on the short and long terms.

evolutionary biology↗