Biology subjects

Le Rouzic, A.

Publications and source records attributed to Le Rouzic, A..

12 recordsLinked to original sources

bioRxiv2026

Adaptive evolution of transcriptome and transcriptomic plasticity in stable and fluctuating environments

Adaptation to new environments often involves multiple genes and can induce substantial changes in biological systems, but the overall consequences of the genetic response to specific environmental pressures remain poorly understood. Here, we investigate the large-scale effects of adaptation to new temperature regimes on the whole transcriptome from experimental evolution carried on the fungus Zymoseptoria tritici. Two distinct strains from the wild were grown in constant cool (17{degrees}C), constant warm (23{degrees}C), and fluctuating temperature environments, for about 250 clonal generations. The expression of > 10,000 genes was estimated by RNA-seq before and after evolution at several temperatures. We observed massive convergent change in both gene expression and gene expression plasticity. Fluctuating environments did not favor plastic gene expression in general, although fluctuating populations did evolve towards the optimal reaction norms. Some of our observations were at odds with common expectations; adaptive mutations were highly pleiotropic, and adaptation to stable temperature conditions did not match gene expression plasticity. We thus showed that the evolution of complex biological systems follow some general patterns which challenge gene-centered or quantitative genetics predictions.

evolutionary biology↗

bioRxiv2025

The Baldwin effect reloaded: Intermediate levels of phenotypic plasticity favor evolutionary rescue

Since the late 1890s and until today, how phenotypic plasticity interacts with genetic adaptation is a debated issue. Proponents of a positive causal role of phenotypic plasticity -James M. Bald-win in the first place- supported the view that, in altered environmental conditions, phenotypic plasticity is a key factor allowing a population to avoid extinction and then genetic evolution to catch up ("Original Baldwin Effect", thereafter OBE). Opponents, like for instance Ernst Mayr, regularly pointed out that phenotypic plasticity, by masking genetic variation, slows gene-level evolution ("Mayr Effect", thereafter ME). For decades this opposition remained only verbal and qualitative. To resolve it, we propose here a stochastic model that, following Baldwins intuitive take, combines the minimal number of ingredients to account for extinction, selection, mutation and plasticity. We study evolutionary rescue of the population (arrival and invasion of an adaptive genetic mutant) in the altered environment for different values of phenotypic plasticity, here quantified as the probability p that the maladapted genotype develops into the adapted phenotype. Our claim is that OBE can be a genuine evolutionary mechanism, depending on the level of phenotypic plasticity with respect to a threshold value p[*]: when p < p[*], increasing p promotes evolutionary rescue by delaying extinction ("Strong" OBE); when p > p[*], plasticity sustains population survival and increasing p has two antagonistic effects: to accelerate adaptation by increasing the supply of adaptive mutants ("Weak" OBE, intermediate values of p), and to slow down adaptation by decreasing their fitness advantage (ME, high values of p).

evolutionary biology↗

bioRxiv2024

Gene network topology drives the mutational landscape of gene expression

Regulatory mutations, coding sequence alterations, and gene deletions and duplications are generally expected to have qualitatively different effects on fitness. We aim to ground this expectation within a theoretical framework using evolutionary simulations of gene regulatory networks (GRNs) controlling the expression of fitness-related genes. We examined the distribution of fitness effects as a function of the type of mutation and the topology of the gene network. Contrary to our expectation, the GRN topology had more influence on the effect of mutations than the type of mutation itself. In particular, the topology conditioned (i) the speed of adaptation, (ii) the distribution of fitness effects, and (iii) the degree of pleiotropy, acting as explanatory factor for all mutation types. All mutations had the potential to participate in adaptation, although their propensity to generate beneficial variants differed according to the network topology. In scalefree networks, the most common topology for biological networks, coding mutations were more pleiotropic and overrepresented in both beneficial and deleterious mutations, while regulatory mutations were more often neutral. However, this observation was not general, as this pattern was reversed in alternative networks. These results highlight the critical role of network topology in defining mutations contributions to adaptation.

evolutionary biology↗

bioRxiv2024

Association between gene expression plasticity and regulatory network topology

Over the past two decades, a large body of theoretical and empirical work has been conducted with the aim of identifying the gene regulatory network topologies responsible for gene expression dynamics. Some studies have linked gene expression plasticity to specific network motifs such as feedforward loops, diamond motifs, or feedback loops. However, both theoretical and empirical work have produced equivocal results, as the same topologies have also been associated with expression robustness. As a step toward understanding the regulatory basis of gene expression plasticity, our goal is to understand how local regulatory topologies may contribute to it. To this end we compared theoretical predictions from a simulated network evolution model with empirical data based on Escherichia coli regulatory network. We investigated the link between network topology and gene expression at three levels: the number of regulators, the number and the proportion of loops (feedback loops, feedforward loops and diamond loops), and the proportion of unique motifs (here characterized by the position of up- or down-regulations within loops). Consistent results from our empirical and theoretical approaches revealed that plastic genes are, on average, regulated by a greater number of genes. In addition, our theoretical predictions showed that selection, as opposed to genetic drift, strongly biases the distribution of network motifs. However, similar results were observed when comparing plastic and non-plastic genes. Overall, this work illustrates that our current understanding of network topology may be insufficient to fully explain or predict gene expression plasticity.

systems biology↗

bioRxiv2023

Detecting directional epistasis and dominance from cross-line analyses in Alpine populations of Arabidopsis thaliana

The contribution of non-additive genetic effects to the genetic architecture of fitness, and to the evolutionary potential of populations, has been a topic of theoretical and empirical interest for a long time. Yet, the empirical study of these effects in natural populations remains scarce, perhaps because measuring dominance and epistasis relies heavily on experimental line crosses. In this study, we explored the contribution of dominance and epistasis in natural alpine populations of Arabidopsis thaliana, for two fitness traits, the dry biomass and the estimated number of siliques, measured in a greenhouse. We found that, on average, crosses between inbred lines of A. thaliana led to mid-parent heterosis for dry biomass, but outbreeding depression for estimated number of siliques. While heterosis for dry biomass was due to dominance, we found that outbreeding depression for estimated number of siliques could be attributed to the breakdown of beneficial epistatic interactions. We simulated and discussed the implication of these results for the adaptive potential of the studied populations, as well as the use of line-cross analyses to detect non-additive genetic effects.

evolutionary biology↗

bioRxiv2022

Correlated stabilizing selection shapes the topology of gene regulatory networks

The evolution of gene expression is constrained by the topology of gene regulatory networks, as co-expressed genes are likely to have their expressions affected together by mutations. Conversely, co-expression can also be an advantage when genes are under joint selection. Here, we assessed theoretically whether correlated selection (selection for a combination of traits) was able to affect the pattern of correlated gene expressions and the underlying gene regulatory networks. We ran individual-based simulations, applying a stabilizing correlated fitness function to three genetic architectures: a quantitative genetics (multilinear) model featuring epistasis and pleiotropy, a quantitative genetics model where each genes has an independent mutational structure, and a gene regulatory model, mimicking the mechanisms of gene expression regulation. Simulations showed that correlated mutational effects evolved in the three genetic architectures as a response to correlated selection, but the response in gene networks was specific. The intensity of gene co-expression was mostly explained by the regulatory distance between genes (largest correlations being associated to genes directly interacting with each other), and the sign of co-expression was associated with the nature of the regulation (transcription activation or inhibition). These results concur to the idea that gene network topologies could partly reflects past correlated selection patterns on gene expression.

evolutionary biology↗

bioRxiv2022

A Population Genetics Theory for piRNA-regulatedTransposable Elements

Transposable elements (TEs) are self-reproducing selfish DNA sequences that can invade the genome of virtually all living species. Population genetics models have shown that TE copy numbers generally reach a limit, either because the transposition rate decreases with the number of copies (transposition regulation) or because TE copies are deleterious, and thus purged by natural selection. Yet, recent empirical discoveries suggest that TE regulation may mostly rely on piRNAs, which require a specific mutational event (the insertion of a TE copy in a piRNA cluster) to be activated -- the so-called TE regulation "trap model". We derived new population genetics models accounting for this trap mechanism, and showed that the resulting equilibria differ substantially from previous expectations based on a transposition-selection equilibrium. We proposed three sub-models, depending on whether or not genomic TE copies and piRNA cluster TE copies are selectively neutral or deleterious, and we provide analytical expressions for maximum and equilibrium copy numbers, as well as cluster frequencies for all of them. In the full neutral model, the equilibrium is achieved when transposition is completely silenced, and this equilibrium does not depend on the transposition rate. When genomic TE copies are deleterious but not cluster TE copies, no long-term equilibrium is possible, and active TEs are eventually eliminated after an active incomplete invasion stage. When all TE copies are deleterious, a transposition-selection equilibrium exists, but the invasion dynamics is not monotonic, and the copy number peaks before decreasing. Mathematical predictions were in good agreement with numerical simulations, except when genetic drift and/or linkage disequilibrium dominates. Overall, the trap-model dynamics appeared to be substantially more stochastic and less repeatable than traditional regulation models.

evolutionary biology↗

bioRxiv2021

Gene network simulations provide testable predictions for the molecular domestication syndrome

The domestication of plant species lead to repeatable morphological evolution, often referred to as the phenotypic domestication syndrome. Domestication is also associated with important genomic changes, such as the loss of genetic diversity compared to adequately large wild populations, and modifications of gene expression patterns. Here, we explored theoretically the effect of a domestication-like scenario on the evolution of gene regulatory networks. We ran population genetics simulations in which individuals were featured by their genotype (an interaction matrix encoding a gene regulatory network) and their gene expressions, representing the phenotypic level. Our domestication scenario included a population bottleneck and a selection switch mimicking human-mediated directional and canalizing selection, i.e., change in the optimal gene expression level and selection towards more stable expression across environments. We showed that domestication profoundly alters genetic architectures. Based on four examples of plant domestication scenarios, our simulations predict (i) a drop in neutral allelic diversity, (ii) a change in gene expression variance that depends upon the domestication scenario, (iii) transient maladaptive plasticity, (iv) a deep rewiring of the gene regulatory networks, with a trend towards gain of regulatory interactions, and (v) a global increase in the genetic correlations among gene expressions, with a loss of modularity in the resulting coexpression patterns and in the underlying networks. We provide empirically testable predictions on the differences of genetic architectures between wild and domesticated forms. The characterization of such systematic evolutionary changes in the genetic architecture of traits contributes to define a molecular domestication syndrome.

evolutionary biology↗

bioRxiv2020

Interplay between high drift-high selection limits the genetic load in small selfing maize populations

Population and quantitative genetic models provide useful approximations to predict long-term selection responses sustaining phenotypic shifts, and underlying multilocus adaptive dynamics. Valid across a broad range of parameters, their use for understanding the adaptive dynamics of small selfing populations undergoing strong selection intensity (thereafter High Drift-High selection regime, HDHS) remains to be explored. Saclay Divergent Selection Experiments (DSEs) on maize flowering time provide an interesting example of populations evolving under HDHS, with significant selection responses over 20 generations in two directions. We combined experimental data from Saclay DSEs, forward individual-based simulations, and theoretical predictions to dissect the evolutionary mechanisms at play in the observed selection responses. We asked two main questions: How do mutations arise, spread, and reach fixation in populations evolving under HDHS ? How does the interplay between drift and selection influence observed phenotypic shifts ? We showed that the long-lasting response to selection in small populations is due to the rapid fixation of mutations occurring during the generations of selection. Among fixed mutations, we also found a clear signal of enrichment for beneficial mutations revealing a limited cost of selection. Both environmental stochasticity and variation in selection coefficients likely contributed to exacerbate mutational effects, thereby facilitating selection grasp and fixation of small-effect mutations. Together our results highlight that despite a small number of polymorphic loci expected under HDHS, adaptive variation is continuously fueled by a vast mutational target. We discuss our results in the context of breeding and long-term survival of small selfing populations.

evolutionary biology↗

bioRxiv2020

Unidirectional response to bidirectional selection on body size. II Quantitative genetics.

Anticipating the genetic and phenotypic changes induced by natural or artificial selection requires reliable estimates of trait evolvabilities (genetic variances and covariances). However, whether or not multivariate quantitative genetics models are able to predict precisely the evolution of traits of interest, especially fitness-related, life-history traits, remains an open empirical question. Here, we assessed to what extent the response to bivariate artificial selection on both body size and maturity in the medaka Oryzias latipes, a model fish species, fits the theoretical predictions. Three lines (Large, Small, and Control lines) were differentially selected for body length at 75 days of age, conditional on maturity. As maturity and body size were phenotypically correlated, this selection procedure generated a bi-dimensional selection pattern on two life history traits. After removal of non-heritable trends and noise with a random effect (animal) model, the observed selection response did not match the expected bidirectional response. For body size, Large and Control lines responded along selection gradients (larger body size and stasis, respectively), but, surprisingly, the Small did not evolve a smaller body length, and remained identical to the Control line throughout the experiment. The magnitude of the empirical response was smaller than the theoretical prediction in both selected directions. For maturity, the response was opposite to the expectation (the Large line evolved late maturity compared to the Control line, while the Small line evolved early maturity, while the opposite pattern was predicted due to the strong positive genetic correlation between both traits). The mismatch between predicted and observed response was substantial and could not be explained by usual sources of uncertainties (including sampling effects, genetic drift, and error in G matrix estimates).

evolutionary biology↗

bioRxiv2019

Differential Metabolic Sensitivity of mTORC1- and mTORC2-Dependent Overgrowth

The glycolytic/lipogenic axis promotes the synthesis of energetic molecules and building blocks necessary to support cell growth, although the absolute requirement of this metabolic axis must be deeply investigated. Here, we used Drosophila genetics and focus on the mTOR signaling network that controls cell growth and homeostasis. mTOR is present in two distinct complexes, mTORC1 and mTORC2. The former directly responds to amino acids and energetic levels, whereas the latter is required to sustain the signaling response downstream of insulin-like-peptide (Ilp) stimulation. Either signaling branch can be independently modulated in most Drosophila tissues. We confirm this independency in the fat tissue. We show that ubiquitous over-activation of mTORC1 or Ilp signaling affects carbohydrate and lipid metabolism, supporting the use of Drosophila as a powerful model to study the link between growth and metabolism. We show that cell-autonomous restriction of glycolysis or lipogenesis in fat cells impedes overgrowth dependent on Ilp-but not mTORC1-signaling. Additionally, ubiquitous deficiency of lipogenesis (FASN mutants) results in a drop in mTORC1 but not Ilp signaling, whereas, at the cell-autonomous level, lipogenesis deficiency affects none of these signals in fat cells. These findings thus, reveal differential metabolic sensitivity of mTORC1- and Ilp-dependent overgrowth. Furthermore, they suggest that local metabolic defects may elicit compensatory pathways between neighboring cells, whereas enzyme knockdown in the whole organism results in animal death. Importantly, our study weakens the use of single inhibitors to fight mTOR-related diseases and strengthens the use of drug combination and selective tissue-targeting.

cell biology↗

bioRxiv2018

Anthropogenic selection along directions of most evolutionary resistance

Anthropogenic perturbations such as harvesting often select against a large body size and are predicted to induce rapid evolution towards smaller body sizes and earlier maturation. However, body-size evolvability and, hence, adaptability to anthropogenic perturbations remain seldom evaluated in wild populations. Here, we use a laboratory experiment over 6 generations to measure the ability of wild-caught medaka fish (Oryzias latipes) to evolve in response to bidirectional size-dependent selection mimicking opposite harvest regimes. Specifically, we imposed selection against a small body size (Large line), against a large body size (Small line) or random selection (Control line), and measured correlated responses across multiple phenotypic, life-history and endocrine traits. As expected, the Large line evolved faster somatic growth and delayed maturation, but also evolved smaller body sizes at hatch, with no change in average levels of pituitary gene expressions of luteinizing, folliclestimulating or growth (GH) hormones. In contrast, the Small medaka line was unable to evolve smaller body sizes or earlier maturation, but evolved smaller body sizes at hatch and showed marginally-significant signs of increased reproductive investment, including larger egg sizes and elevated pituitary GH production. Natural selection on medaka body size was too weak to significantly hinder the effect of artificial selection, indicating that the asymmetric body-size response to size-dependent selection reflected an asymmetry in body-size evolvability. Our results show that trait evolvability may be contingent upon the direction of selection, and that a detailed knowledge of trait evolutionary potential is needed to forecast population response to anthropogenic change.

evolutionary biology↗