Biology subjects

Beslon, G.

Publications and source records attributed to Beslon, G..

7 recordsLinked to original sources

bioRxiv2026

Who are my Neighbours? Selection Targets Robustness to Full Replication Events, Not to Individual Mutations

Theory predicts that populations should evolve mutational robustness the ability to maintain a constant phenotype despite mutations. However, selection does not operate on individual mutations but on phenotypes resulting from entire replication events, which may involve a variable number of mutations. We therefore distinguish mutational robustness from replicative robustness, defined as the probability that a replication event produces similarly fit offspring. Mutational and replicative robustness may seem closely aligned at first sight, but we show with an evolutionary simulation model that they can be selected in opposite directions. Our results show that while genomes with high redundancy and/or large amounts of non-coding DNA are mutationally robust, they are replicatively fragile because their size increases the number of mutations per replication. This is especially relevant when genomes are at risk of severe disruption from chromosomal rearrangements. Our results expose limits of the fitness landscape metaphor in the context of robustness evolution: neutral plateaus may be mutationally robust but replicatively unstable and therefore disfavoured by selection, whereas narrow peaks arising from densely encoded genomes can replicate more reliably. Our results identify replicative robustness as a distinct target of selection that depends not only on tolerance to individual mutations but also on their overall likelihood of occurring.

evolutionary biology↗

bioRxiv2025

Pleiotropy accelerates tooth phenotypic and genomic evolution - An in silico study under the lens of development

Pleiotropy, which can occur when a gene affects multiple traits, is a central property of living organisms, influencing their response to mutations and their evolutionary trajectories. Despite many studies and discussions, it remains very difficult to reconcile molecular, developmental and quantitative evolutionary genetics viewpoints on pleiotropy and appreciate how much it puts constraints on genetic evolution, phenotypic evolution and adaptation. Here, we revisit this question by simulating evolution in silico. Our model captures multiple levels of integration observed in complex organisms: from genes to development to phenotype to fitness, additionally allowing to remove pleiotropy to directly test its effect. We focus on the pleiotropic interactions between two organs, specifically teeth. Fitness is determined from the functional interaction between these two teeth, which are produced by an in silico model of tooth morphogenesis. The developmental parameters are produced by a genome consisting of pleiotropic genes which may be influenced by one tooth-specific transcriptional regulator per tooth. Our simulations with and without pleiotropy confirm several acknowledged consequences of pleiotropy. It reduced genetic and phenotypic exploration, and facilitated the rapid accumulation of adaptive mutations in the modular cis-regulatory regions of pleiotropic genes, which are specific to each tooth. Unexpectedly, pleiotropy promoted fitness improvements, morphological complexity, and the accumulation of genetic divergence. Mutations in pleiotropic genes contributed significantly to adaptation, and removing pleiotropy did not increase the proportion of adaptive mutations. Thus pleiotropy does not act as a conservative force, but a channeling force promoting genetic divergence. Significance StatementThe notion of "Gene pleiotropy", just as the notion of "gene", crosses different fields of biology, but appreciations vary on how frequent it is and how much it favors conservation or divergence at the molecular, developmental or phenotypic scale. One reason is the discrepancies of viewpoints between these three scales, another the difficulty to manipulate pleiotropy experimentally. Here, we performed in silico evolution experiments with a computational toy model focusing on the pleiotropy between two organs while bridging the molecular, developmental and phenotypic scales. Moreover, we compared evolutionary scenarios where the two organs shared or not pleiotropic genes. Our results reconcile observations and show that sharing pleiotropic genes accelerates genetic divergence, even though non-pleiotropic genes diverge faster than pleiotropic genes.

evolutionary biology↗

bioRxiv2025

Aevol-9: A simulation platform to decipher the evolution of genome architecture

Aevol is a forward-in-time simulator of genome architecture. It simulates a population of individuals, each with an explicit genome whose sequence and architecture can be modified by various mutational operators, including substitutions, indels, and large-scale chromosomal rearrangements. This enables performing in silico experiments to decipher the effects of evolutionary conditions (e.g. population size, selection strength, mutation rates, and biases) on genome organization.

evolutionary biology↗

bioRxiv2025

Structural mutations set an equilibrium non-coding genome fraction

Non-coding genome size evolution is poorly understood. While some fraction of non-coding DNA has arguably a regulatory function, a large part does not seem to have a detectable impact on any phenotypic trait. The abundance of non-functional DNA in genomes, observed across the Tree of Life, challenges a purely adaptationist explanation. Several non-adaptive theories have been proposed to explain its presence and identify its determinants, emphasizing either the mutational processes or the mutational hazard entailed by non-coding and non-functional DNA. However, those theories have not yet been integrated into a single framework, and the exact nature of the mutational hazard is not yet fully understood. In this work, we propose a simple mathematical model of genome size evolution. The model shows how the non-coding fraction of the genome is shaped by two factors: unavoidable biases in the neutrality of the different mutation types (adding base pairs is more likely to be neutral than removing some), and the robustness selection imposed by the mere existence of structural mutations (larger genomes are more prone to double-strand breaks that can initiate structural mutations, imposing a second-order selection on robustness). Together, these two factors ensure the existence of an equilibrium non-coding fraction. We show that this equilibrium depends solely on mutation biases and the product of population size and mutation rate.

evolutionary biology↗

bioRxiv2024

Genome streamlining: effect of mutation rate and population size on genome size reduction

Genome streamlining, i.e. genome size reduction, is observed in bacteria with very different life traits, including endosymbiotic bacteria and several marine bacteria, raising the question of its evolutionary origin. None of the hypotheses proposed in the literature is firmly established, mainly due to the many confounding factors related to the diverse habitats of species with streamlined genomes. Computational models may help overcome these difficulties and rigorously test hypotheses. In this work, we used Aevol, a platform designed to study the evolution of genome architecture, to test two main hypotheses: that an increase in population size (N) or mutation rate ({micro}) could cause genome reduction. In our experiments, both conditions lead to streamlining, but with very different resulting genome structures. Under increased population sizes, genomes loose a significant fraction of non-coding sequences, but maintain their coding size, resulting in densely packed genomes (akin to streamlined marine bacteria genomes). By contrast, under increased mutation rate, genomes loose both coding and non-coding sequences (akin to endosymbiotic bacteria genomes). Hence, both factors lead to an overall reduction in genome size, but the coding density of the genome appears to be determined by N x {micro}. Thus, a broad range of genome size and density can be achieved by different combinations of N and {micro}. Our results suggest that genome size and coding density are determined by the interplay between selection for phenotypic adaptation and selection for robustness. Significance statementMany bacterial species show reduced genomes. However, the diversity of these species and of their life traits makes it difficult to identify the mechanisms that led to this reduction. Indeed, no unifying hypothesis accounts for the whole diversity of genome size reduction. Here, we used simulations to systematically explore the effect of population size and mutation rate on genome size. We show that the interaction between these two factors tightly determine the size, but also the density of genomes, making it possible to account for the whole diversity of reduced genomes by acting on these two parameters only. Our results suggest a theoretical model in which genome reduction is driven by a robustness/fitness trade off.

evolutionary biology↗

bioRxiv2022

Emergence of Supercoiling-Mediated Regulatory Networks through Bacterial Chromosome Rearrangements

DNA supercoiling - the level of twisting and writhing of the DNA molecule around itself - plays a major role in the regulation of gene expression in bacteria by modulating promoter activity. The level of DNA supercoiling is a dynamic property of the chromosome, which varies both at local and global scales, in response to external factors such as environmental perturbations and to internal factors including gene transcription. As such, local variations in supercoiling could in theory couple the expression levels of neighboring genes by creating feedback loops between their transcription rates. However, the impact of such supercoiling-mediated interactions on the regulation of gene expression remains uncertain. In this work, we study how this coupling between transcription and supercoiling could shape genome organization and help regulate gene transcription. We present a model of genome evolution in which individuals whose gene transcription rates are coupled to supercoiling must adapt to two environments that induce different global supercoiling levels. In this model, we observe the evolution of whole-genome regulatory networks that provide control over gene expression by leveraging the transcription-supercoiling coupling, and show that the structure of these networks is underpinned by the organization of genes along the chromosome at several scales. Local variations in DNA supercoiling could therefore help jointly shape both gene regulation and genome organization during evolution.

systems biology↗

bioRxiv2020

Phenotypic Noise and the Cost of Complexity

Experimental studies demonstrate the existence of phenotypic diversity despite constant genotype and environment. Theoretical models based on a single phenotypic character predict that during an adaptation event, phenotypic noise should be positively selected far from the fitness optimum because it increases the fitness of the genotype, and then be selected against when the population reaches the optimum. It is suggested that because of this fitness gain, phenotypic noise should promote adaptive evolution. However, it is unclear how the selective advantage of phenotypic noise is linked to the rate of evolution, and whether any advantage would hold for more realistic, multi-dimensional phenotypes. Indeed, complex organisms suffer a cost of complexity, where beneficial mutations become rarer as the number of phenotypic characters increases. By using a quantitative genetics approach, we first show that for a one-dimensional phenotype, phenotypic noise promotes adaptive evolution on plateaus of positive fitness, independently from the direct selective advantage on fitness. Second, we show that for multi-dimensional phenotypes, phenotypic noise evolves to a low-dimensional configuration, with elevated noise in the direction of the fitness optimum. Such a dimensionality reduction of the phenotypic noise promotes adaptive evolution and numerical simulations show that it reduces the cost of complexity.

evolutionary biology↗