Showing papers by "Arnaud Le Rouzic published in 2021"

Using phenotypic plasticity to understand the structure and evolution of the genotype-phenotype map.

Abstract: Deciphering the genotype-phenotype map necessitates relating variation at the genetic level to variation at the phenotypic level. This endeavour is inherently limited by the availability of standing genetic variation, the rate of spontaneous mutation to novo genetic variants, and possible biases associated with induced mutagenesis. An interesting alternative is to instead rely on the environment as a source of variation. Many phenotypic traits change plastically in response to the environment, and these changes are generally underlain by changes in gene expression. Relating gene expression plasticity to the phenotypic plasticity of more integrated organismal traits thus provides useful information about which genes influence the development and expression of which traits, even in the absence of genetic variation. We here appraise the prospects and limits of such an environment-for-gene substitution for investigating the genotype-phenotype map. We review models of gene regulatory networks, and discuss the different ways in which they can incorporate the environment to mechanistically model phenotypic plasticity and its evolution. We suggest that substantial progress can be made in deciphering this genotype-environment-phenotype map, by connecting theory on gene regulatory network to empirical patterns of gene co-expression, and by more explicitly relating gene expression to the expression and development of phenotypes, both theoretically and empirically.

Quantitative assessment of observed versus predicted responses to selection.

Abstract: Although artificial-selection experiments seem well suited to testing our ability to predict evolution, the correspondence between predicted and observed responses is often ambiguous due to the lack of uncertainty estimates. We present equations for assessing prediction error in direct and indirect responses to selection that integrate uncertainty in genetic parameters used for prediction and sampling effects during selection. Using these, we analyzed a selection experiment on floral traits replicated in two taxa of the Dalechampia scandens (Euphorbiaceae) species complex for which G-matrices were obtained from a diallel breeding design. After four episodes of bidirectional selection, direct and indirect responses remained within wide prediction intervals, but appeared different from the predictions. Combined analyses with structural-equation models confirmed that responses were asymmetrical and lower than predicted in both species. We show that genetic drift is likely to be a dominant source of uncertainty in typically-dimensioned selection experiments in plants and a major obstacle to predict short-term evolutionary trajectories. This article is protected by copyright. All rights reserved.

Interplay between extreme drift and selection intensities favors the fixation of beneficial mutations in selfing maize populations.

Abstract: 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.

Gene network robustness as a multivariate character

Abstract: Robustness to genetic or environmental disturbances is often considered as a key property of living systems. Yet, in spite of being discussed since the 1950s, how robustness emerges from the complexity of genetic architectures and how it evolves still remains unclear. In particular, whether or not robustness to various sources of perturbations is independent conditions the range of adaptive scenarios that can be considered. For instance, selection for robustness to heritable mutations is likely to be modest and indirect, and its evolution might result from indirect selection on a pleiotropically-related character (e.g., homeostasis) rather than adaptation. Here, I propose to treat various robustness measurements as quantitative characters, and study theoretically, by individual-based simulations, their propensity to evolve independently. Based on a simple evolutionary model of a gene regulatory network, I showed that different ways to measure the robustness of gene expression to genetic or non-genetic disturbances were substantially correlated. Yet, robustness was evolvable in several dimensions, and robustness components could evolve differentially under direct selection pressure. Therefore, the fact that the sensitivity of gene expression to e.g. mutations and environmental factors rely on the same gene networks does not preclude that robustness components may have distinct evolutionary histories.

Differential metabolic sensitivity of insulin-like-response- and TORC1-dependent overgrowth in Drosophila fat cells

Abstract: Glycolysis and fatty acid (FA) synthesis directs the production of energy-carrying molecules and building blocks necessary to support cell growth, although the absolute requirement of these metabolic pathways must be deeply investigated. Here, we used Drosophila genetics and focus on the TOR (Target of Rapamycin) signaling network that controls cell growth and homeostasis. In mammals, mTOR (mechanistic-TOR) is present in two distinct complexes, mTORC1 and mTORC2; the former directly responds to amino acids and energy levels, whereas the latter sustains insulin-like-peptide (Ilp) response. The TORC1 and Ilp signaling branches can be independently modulated in most Drosophila tissues. We show that TORC1 and Ilp-dependent overgrowth can operate independently in fat cells and that ubiquitous over-activation of TORC1 or Ilp signaling affects basal 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 FA synthesis in fat cells retrains overgrowth dependent on Ilp signaling but not TORC1 signaling. Additionally, the mutation of FASN (Fatty acid synthase) results in a drop in TORC1 but not Ilp signaling, whereas, at the cell-autonomous level, this mutation affects none of these signals in fat cells. These findings thus reveal differential metabolic sensitivity of TORC1- and Ilp-dependent growth and suggest that cell-autonomous metabolic defects might elicit local compensatory pathways. Conversely, 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.

Optimization of sampling designs for pedigrees and association studies

Abstract: In many studies, related individuals are phenotyped in order to infer how their genotype contributes to their phenotype, through the estimation of parameters such as breeding values or locus effects. When it is not possible to phenotype all the individuals, it is important to properly sample the population to improve the precision of the statistical analysis. This article studies how to optimize such sampling designs for pedigrees and association studies. Two sampling methods are developed, stratified sampling and D optimality. It is found that it is important to take account of mutation when sampling pedigrees with many generations: as the size of mutation effects increases, optimized designs sample more individuals in late generations. Optimized designs for association studies tend to improve the joint estimation of breeding values and locus effects, all the more as sample size is low and the genetic architecture of the trait is simple. When the trait is determined by few loci, they are reminiscent of classical experimental designs for regression models and tend to select homozygous individuals. When the trait is determined by many loci, locus effects may be difficult to estimate, even if an optimized design is used.

Gene network simulations provide testable predictions for the molecular domestication syndrome

Abstract: The domestication of plant and animal 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 and modifications of gene expression patterns. Here, we explored theoretically the effect of domestication at the genomic level by characterizing the impact of a domestication-like scenario on 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 (change in the optimal gene expression level) mimicking canalizing selection, i.e. evolution towards more stable expression to parallel enhanced environmental stability in man-made habitat. We showed that domestication profoundly alters genetic architectures. Based on the well-documented example of the maize (Zea mays ssp. mays) domestication, our simulations predicted (i) a drop in neutral allelic diversity, (ii) a change in gene expression variance that depended 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 between genes, 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. Extending the range of parameters, we provide empirically testable predictions on the differences of genetic architectures between wild and domesticated and forms. The characterization of such systematic evolutionary changes in the genetic architecture of traits contributes to define a molecular domestication syndrome.