RESEARCH INTERESTS

I am a CNRS researcher ("Chargé de Recherche") based in two teams: the Stochastic Models for the Inference of Life Evolution team at Collège de France, and the Quantitative Evolutionary Microbiology team in the Infection Antimicrobial Modelling Evolution unit in hôpital Bichat-Claude Bernard.

I work in evolutionary biology with a focus on the evolution of human pathogens:

Evolutionary epidemiology

Decline in virulence in HIV in the Rakai cohort, Uganda
 
Temporal fluctuations in resistance
I work on the evolutionary epidemiology of HIV and particularly the evolution of virulence. We showed that virulence, as measured by the set-point viral load (the stable value of viraemia in asymptomatic infection) is significantly determined by viral genetic factors. We also showed that the average set-point viral load in a well-studied cohort in Uganda declined over the last 20 years, a finding that can be explained by viral adaptation to a low fitness optimum. We now study global patterns of virulence evolution across many cohorts, and develop methods to find the genomic changes underlying the trends in virulence.


I am also interested in the evolution of antibiotic resistance in bacterial species such as Streptococcus pneumoniae and Escherichia coli. Surveillance data shows that average levels of resistance are often stable over decades. Resistances that have emerged more recently, such as third-generation cephalosporin resistance in E. coli, also tend to stabilise (around 10-15% in European countries). The reasons why resistance genes or alleles stabilise at an intermediate level, instead of going to fixation, are unclear: this has been called the "coexistence problem". Using very finely resolved temporal data on levels of antibiotic consumption and resistance in Israel, we show that  rapid seasonal changes in resistance, at timescales of a few months to years, can be predicted, and that indeed a strong force stabilises resistance to an intermediate level. We are now developing new mathematical models to  better describe resistance evolution in a structured host population.

Fitness landscape models and the dynamics of adaptation

The dynamics of adaptation crucially depends on the relationship between genotypes and fitness: the fitness landscape. The fitness landscape determines the genetic variation that is available for selection. We studied theoretically Fisher's Geometric model, a fitness landscape model. We developed theoretical predictions for the distribution of fitness effects, the epistasis coefficients, and higher order interactions between mutations, under Fisher’s Geometric Model. We found Fisher’s model is sufficiently flexible to generate a great diversity of genotypic landscapes, from smooth to rugged, and in particular reproduces patterns of epistasis and higher order interactions observed in several experimental landscapes. By analysing a large number of published experimental fitness landscapes, we found Fisher’s Geometric model was able to explain several statistical properties of the landscapes, but it was often unable to explain the full structure of fitness landscapes and the fitness of specific combinations of mutations.  
Fisher's Geometric Model of adaptation

Adaptation to heterogeneous environment

I study how spatio-temporal variability in the environment impacts the demography and adaptation of populations. We showed that spatio-temporal variability in the environment can select for migration, and interacts in complex ways with kin competition. We developed models describing adaptation in spatially heterogeneous environment, generating predictions on levels of local adaptation as a function of migration, genetic drift, and the heterogeneity in the environment, and best practice to measure local adaptation.

Contact:

francois [dot] blanquart [at] normalesup [dot] org