Individual health is the outcome of dynamic interactions between the environment and a diversity of organisms, such as human cells, bacteria, fungi, or viruses. We use tools and concepts from ecology and evolution to analyse the functioning of such within-host communities and understand the factors that may lead to or help prevent diseases. From a multi-scale perspective, we focus in particular on the spread of human viruses with the goal of deciphering how public health interventions may impose a selective pressure to prevent evolutionary responses that could jeopardize population health. Our work involves a variety of fields such as microbiology, immunology, and epidemiology. The approaches we develop are highly interdisciplinary and typically combine clinical data (e.g.~immune cell counts or genetic sequence data), with mathematical and statistical modelling. Since March 2020, the team has been involved in the analysis and surveillance of the COVID-19 epidemic, especially through its expertise in phylodynamics, and, more generally, computational biology.
The team's project currently revolve around four major research questions:
Why do some human papillomavirus (HPV) infections clear naturally, while others remain chronic? Although HPVs are the most oncogenic human viruses, we know little about their natural history in the majority of the infections they cause, that is the ones that are benign and clear naturally within 3 years. Using the unique dataset of the PAPCLEAR clinical study conducted by the team (thousands of samples from 149 women followed from 2016 to 2020), we address this question by combining detailed biological analyses (flow cytometry, RNA sequencing, metagenomics) and mathematical & statistical modelling.
Which factors shape vaginal microbiota dynamics? This microbiota is a crucial component of women's health but is currently largely understudied compared to the gut microbiota. Thanks to the unique length and (weekly) resolution of the PAPCLEAR follow-up, we use ecological modelling and metagenomics to understand how `perturbations' such as menses, antibiotic treatments, or sexual intercourse can affect vaginal microbiota diversity from the community level to the gene level.
How can genomic sequence data help us understand population dynamics? Many viruses evolve rapidly and the emerging field of phylodynamics hypothesises that the mutations they accumulate in their genomes can inform us about the way they spread. The team is developing original approaches involving approximate Bayesian computation and machine learning to analyse large datasets and study human virus epidemics, especially HIV in Occitanie or SARS-CoV-2 in France.
How do life history and public health policies interact to shape virus adaptive evolution? The emergence of SARS-CoV-2 variants of concern in 2021 and the strict public health response they triggered have put in the spotlight the importance of virus evolution. Building on classical epidemiological models, we explore the consequence of natural factors (e.g.~different susceptibility to the infection between sexes) or public health interventions (e.g. vaccination, treatment, contact-tracing) on the evolution of infection traits such as lethality or resistance to treatment.
The team is also involved in research projects to better understand how SARS-CoV-2 spreads and evolves in populations an in patients. Additional details on this topic can be found at https://covid-ete.ouvaton.org/
