Biology subjects

Rocabert, C.

Publications and source records attributed to Rocabert, C..

3 recordsLinked to original sources

bioRxiv2024

Gene expression evolution is predicted by selection, genetic covariance and network topology

Changes in gene expression play a fundamental role in the process of adaptation and can provide insight into the genetic basis of adaptation. We utilized transcriptome-wide variation in gene expression as a means to uncover genes under selection for expression changes during adaptation to heat and drought stress, and to understand the nature of selection on gene expression traits of the red flour beetle Tribolium castaneum. We showed that estimates of genetic selection on transcript abundance were predictive of evolutionary changes in gene expression after 20 generations of adaptation in seven independent experimental lines. Having measured the genetic covariance between gene expression and relative fitness and among expression traits, we showed that evolutionary changes were driven more by indirect selection acting on genetically correlated partners rather than by direct selection acting on isolated genes. Consequently, genes with central positions in gene co-expression networks experienced stronger selection and exhibited larger evolutionary changes in expression. Our genomic analysis revealed that selection on expression levels is associated with parallel allele frequency changes (AFCs) in the respective genes. More pleiotropic genes and those carrying expression quantitative trait loci (eQTLs) showed a higher degree of parallel evolution. More generally, the stronger the parallelism of AFCs in a gene, the stronger its genetic selection. Contrary to previous evidence of constrained evolution at more connected genes, adaptation was driven by selection acting disproportionately on genes central to co-expression gene networks. We demonstrated that measures of selection at the transcriptome level can provide accurate evolutionary predictions and critical information on the molecular basis of rapid adaptation.

evolutionary biology↗

bioRxiv2022

Principles of metabolome conservation in animals

Metabolite concentrations shape cellular physiology and disease susceptibility, yet the general principles governing metabolome evolution are largely unknown. Here we introduce a measure of conservation of individual metabolite concentrations among related species. By analysing multispecies metabolome datasets in mammals and fruit flies, we show that conservation varies extensively across metabolites. Three major functional properties, metabolite abundance, essentiality and association with human diseases predict conservation, highlighting a striking parallel between the evolutionary forces driving metabolome and protein sequence conservation. Metabolic network simulations recapitulated these general patterns, and revealed that abundant metabolites are highly conserved due to their strong coupling to key metabolic fluxes in the network. This study uncovers simple rules governing metabolic evolution in animals and implies that most metabolome differences between species are permitted, rather than favored by selection. More broadly, our work paves the way towards using evolutionary information to discover biomarkers, as well as to detect pathogenic metabolome alterations in individual patients.

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↗