Antoine Frénoy - Postdoc in microbial evolution

I am currently a postdoc with Eduardo Rocha (Microbial Evolutionary Genomics) at Institut Pasteur, discovering the wonderful world of bioinformatics.

Before this I was a postdoc with Sebastian Bonhoeffer (Theoretical Biology) at ETH Zurich, working mainly on stress-response, evolvability and robustness in bacteria (read our last preprint here).

And before I was a PhD student with François Taddei and Dusan Misevic (INSERM U1001). My PhD focused on the links between second order selection and evolution of cooperation, using both microbial and in silico systems (some of our work here and here).

Email address

antoine DOT frenoy AT pasteur DOT fr


Increased evolvability under stress: stress-induced mutagenesis and death

Stress-induced mutagenesis (SIM) has been a major paradigm shift in the past decades: it postulates that as an answer to stress, bacteria would increase their genome-wide mutation rate, for example thanks to over-expression of error-prone DNA polymerases, increasing the chances that a descendant is able to face the stress. This has implications for antibiotic treatment: a sub-inhibitory dose of antibiotics has been reported to increase the genome-wide mutation rate, and thus the rate at which resistance mutations appear, increasing the probability of treatment failure.

However currents methods do not allow to estimate mutation rate under a stress that affects population growth. Even a sub-inhibitory dose of antibiotics (or other stress) may trigger a significant death rate, although the population is still growing because division rate is still higher than death rate. We show that death will strongly bias mutation rate estimates, because death events will be compensated by more replications to reach a given population size (usually stationary phase), giving more opportunities to acquire mutations. Not taking into account these extra replications will lead to overestimating mutation rates in stressed populations.

Evolution of cooperation and second-order selection pressures

Artificial life

A part of my PhD relied on in silico simulations of bacterial evolution. To simulate the evolution and maintenance of cooperation in spatially structured environments, we use the Aevol system. Aevol is an individual-based model that has a bacterial-inspired genomic layer and is well suited to study second order selection pressures on genome structures. Aevol individuals have the ability to cooperate with each others by secreteting a diffusible public good molecule, costly to produce but beneficial to other individuals. We investigate the link between genome architecture and robustness of cooperation, going further than “classical” simulations of cooperation that often only consider two distinct behaviours. In Aevol there is a multitude of possibility of encoding a continuous secretion value, and all these possibilities have different robustness and evolvabilities, sometimes changing the fate of cooperation.

Genome architecture and the evolution of cooperation

Because genes coding for cooperation (here public good secretion) face very different selection pressures than more classical genes coding for private traits (affecting only the individual bearing them), we wondered whether they would somehow evolve different genetic properties. We found that genes related to cooperation (coding for secretion of a public good) tend to spontaneously form operons (using the same promoters and terminators) and overlap (using the same base pairs but in different reading frames) with “metabolic” (only contributing to the focal individual's private fitness in our vocabulary) genes. A large part of “cheating” (decreasing secretion) mutations are thus also impacting “private” genes, causing a drop in fitness and the mutation being wiped out by selection [Frénoy et al, 2013, PLoS Computational Biology]. This shows the need of going beyond simple binary models when studying cooperation. Several recent studies show the potential relevance of this kind of second order selection pressures on cooperation in microbial world [Foster et al, 2004, Nature and Dandekar et al, 2012, Science].

We are currently applying this idea of evolvability suppresion by gene overlap to synthetic microbial systems. We designed algorithms allowing us to re-encode a gene, making him overlap with an other gene, and we are currently conducting mutagenesis experiments to show that this kind of evolutionary constraint can partially protect a costly gene from removal by mutations.

Curriculum Vitae





Teaching documents