Biology subjects

Petit, A. J. R.

Publications and source records attributed to Petit, A. J. R..

3 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↗

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↗

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↗