Biology subjects

Frenoy, A.

Publications and source records attributed to Frenoy, A..

6 recordsLinked to original sources

bioRxiv2026

Evolution of mutation rates in digital genomes: the roles of genetic drift, mutational supply, and genome size

Mutation is the ultimate mechanism that produces genetic novelty, and thus a central ingredient of evolution. Mutation rates are therefore thought to be tuned by natural selection, for example to optimize a delicate balance between the generation of adaptive diversity and the accumulation of deleterious mutations. As this selection occurs over very long time scales, models and simulations have been powerful tools to understand how mutation rate evolves and which factors influence it. Most simulation methods are nevertheless limited by the over-simplicity of the genotype-to-phenotype map they feature, especially regarding the encoding of mutation rate. We modified Aevol, an evolutionary simulator inspired by bacterial genomics with a realistic genome structure and a complex genotype-to-phenotype layer, to allow organisms to evolve genes coding for higher replication fidelity. This setup permits several degrees of realism absent in other models: mutation-rate modifier genes themselves experience a realistic distribution of effects of mutations and diminishing-returns epistasis, similarly to fitness modifiers. Moreover, a lower mutation rate comes with the trade-off of a larger genome to encode the genes improving replication fidelity. We use this setup to test hypotheses regarding the evolution of prokaryotic mutation rate, and its link with genome size and genetic drift. We found that evolution systematically increases replication fidelity, even when this results in lower fitness. We highlight two factors which limit the mutation rate decrease: genetic drift and the supply of gain-of-fidelity mutations. Significance StatementMutation rate is a central parameter governing the evolution of living systems, but it is also itself the product of evolution, as it is determined by enzymatic processes which are subject to hereditary variations and natural selection. Several hypotheses exist to explain how the mutation rate evolves, and which factors govern mutation rate variation between and within species. We propose a "digital genomics" simulation model which permits testing and refining some of these hypotheses, in a setup capturing key constraints such as a realistic supply of mutations and selection pressure for genome space. We found that selection almost always decreases the mutation rate. We highlight the role of two factors in determining the amount of mutation rate reduction, genetic drift and the supply of gain-of-fidelity mutations, as well as a strong relationship with genome size.

evolutionary biology↗

bioRxiv2025

Emergence of mutants in bacterial populations: a simulation-based approach for parameter inference in extended Luria & Delbru&x0308;ck scenarios

0Bacterial mutation rates are traditionally inferred from phenotypic data using fluctuation assays. Mutation rate computation from these assays relies on a mathematical model describing the emergence of mutants during population growth. However, this standard model relies on restrictive assumptions that are often transgressed in real biological setups. Key assumptions include equal growth rate of the wild-type and the mutant population, no cell death during growth phase, and no post-growth sampling. Although several refined mathematical models have been proposed over the last decades to circumvent some of these assumptions, none can fully account for the complex ecological scenarios frequently observed in the lab or in the wild, such as combination of cell death and unknown fitness effect of the mutation, or non-constant death rates. In this work, we propose to replace these standard mathematical models by a stochastic simulation framework, which can circumvent all these assumptions. We provide a fast and accurate implementation of this stochastic model, and use it to perform simulation-based, likelihood-free parameter inference through the Metropolis-Hastings algorithm. Beyond mutation rate estimation, our approach can simultaneously infer additional biological parameters, provided enough experimental replicates are available. We benchmarked our proposed method against the most recent tools (rSalvador, Flan, and mlemur) in a broad range of biological scenarios with randomly sampled parameter sets. We found that that our method has similar accuracy than existing tools in simple conditions where these tools work, but also reliably estimates mutation rate and some other parameters in more complex conditions where other tools do not work. Overall, our proposed method can be extended to arbitrary biological complications, as long as these can be efficiently simulated.

evolutionary biology↗

bioRxiv2025

Beyond binding motif: genomic context predicts transcription factor dependent regulation in Arabidopsis

Transcription Factors (TFs) play a crucial role in the spatiotemporal control of gene expression. Despite the presence of many potential TF binding sites (TFBS) across the genome, TFs do not interact with all of them, and, among those, only a subset leads to actual regulation. In this work, we investigate how the genomic context of a putative TFBS, including the occurrence of binding motifs for other TFs, can be used to predict effective transcriptional regulation by a TF of interest. We focus on LEAFY (LFY), a plant-specific TF and master regulator of flower development. Using available transcriptomes and TF-DNA binding experiments, we identify 1164 LFY binding sites associated with a regulatory response in Arabidopsis thaliana. We then apply a machine learning approach based on properties of the surrounding genomic region, to discriminate these regulatory LFY binding sites from non-regulatory ones. Detailed analysis of the models components reveals that, for LFY, the density and quality of binding sites constitute the most important features but were not sufficient on their own to predict regulatory activity. The presence of binding sites for other transcription factors and the overall richness in TFBS were also essential. These results clarify the nature of the regulatory code by which LFY operates and could serve as a basis for the study of the regulatory elements of other transcription factors. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=134 SRC="FIGDIR/small/655699v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@1a1a80org.highwire.dtl.DTLVardef@1c1fee5org.highwire.dtl.DTLVardef@1457164org.highwire.dtl.DTLVardef@176c46_HPS_FORMAT_FIGEXP M_FIG C_FIG

plant biology↗

bioRxiv2020

Ecological effects of stress drive bacterial evolvability under sub-inhibitory antibiotic treatments

1Stress is thought to increase mutation rate and thus to accelerate evolution. In the context of antibiotic resistance, sub-inhibitory treatments could then lead to enhanced evolvability, thereby fueling the adaptation of pathogens. Conducting a meta-analysis of published experimental data as well as our own experiments, we found that the increase in mutation rates triggered by antibiotic treatments is often canceled out by reduced population size, resulting in no overall increase in genetic diversity. A careful analysis of the effect of ecological factors on genetic diversity revealed that the potential for regrowth during recovery phase after treatment plays a crucial role in evolvability and is the main predictor of increased genetic diversity in experimental data.

evolutionary biology↗

bioRxiv2019

Engineering gene overlaps to sustain genetic constructs in vivo

Evolution is often an obstacle to the engineering of stable biological systems due to the selection of mutations inactivating costly gene circuits. Gene overlaps induce important constraints on sequences and their evolution. We show that these constraints can be harnessed to increase the stability of synthetic circuits by purging loss-of-function mutations. We combine computational and synthetic biology approaches to rationally design an overlapping reading frame expressing an essential gene within an existing gene to protect. Our algorithm succeeded in creating overlapping reading frames in 80% of E. coli genes. Experimentally, scoring mutations in both genes of such overlapping construct, we found that a significant fraction of mutations impacting the gene to protect have a deleterious effect on the essential gene. Such an overlap thus protects a costly gene from removal by natural selection by associating the benefit of this removal with a larger or even lethal cost. In our synthetic constructs, the overlap converts many of the possible mutants into evolutionary dead-ends, effectively changing the fitness landscape and reducing the evolutionary potential of the system.

synthetic biology↗

bioRxiv2017

Death and population dynamics affect mutation rate estimates and evolvability under stress in bacteria

The stress-induced mutagenesis paradigm postulates that in response to stress, bacteria increase their genome-wide mutation rate, in turn increasing the chances that a descendant is able to withstand the stress. This has implications for antibiotic treatment: exposure to sub-inhibitory doses of antibiotics has been reported to increase bacterial mutation rates, and thus probably the rate at which resistance mutations appear and lead to treatment failure.\n\nMeasuring mutation rates under stress, however, is problematic, because existing methods assume there is no death. Yet sub-inhibitory stress levels may induce a substantial death rate. Death events need to be compensated by extra replication to reach a given population size, thus giving more opportunities to acquire mutations. We show that ignoring death leads to a systematic overestimation of mutation rates under stress.\n\nWe developed a system using plasmid segregation to measure death and growth rates simultaneously in bacterial populations. We use it to replicate classical experiments reporting antibiotic-induced mutagenesis. We found that a substantial death rate occurs at the tested sub-inhibitory concentrations, and taking this death into account lowers and sometimes removes the signal for stress-induced mutagenesis. Moreover even when antibiotics increase mutation rate, sub-inhibitory treatments do not increase genetic diversity and evolvability, again because of effects of the antibiotics on population dynamics.\n\nBeside showing that population dynamic is a crucial but neglected parameter affecting evolvability, we provide better experimental and computational tools to study evolvability under stress, leading to a re-assessment of the magnitude and significance of the stress-induced mutagenesis paradigm.

evolutionary biology↗