Being noisy in a crowd: Differential selective pressure on gene expression noise in model gene regulatory networks - Nataša Puzović; Tanvi Madaan; Julien Y. Dutheil (PLoS Computational Biology, 2023)
Expression noise, the variability of the amount of gene product among isogenic cells grown in identical conditions, originates from the inherent stochasticity of diffusion and binding of the molecular players involved in transcription and translation. It has been shown that expression noise is an evolvable trait and that central genes exhibit less noise than peripheral genes in gene networks. A possible explanation for this pattern is increased selective pressure on central genes since they propagate their noise to downstream targets, leading to noise amplification. To test this hypothesis, we developed a new gene regulatory network model with inheritable stochastic gene expression and simulated the evolution of gene-specific expression noise under constraint at the network level. Stabilizing selection was imposed on the expression level of all genes in the network and rounds of mutation, selection, replication and recombination were performed. We observed that local network features affect both the probability to respond to selection, and the strength of the selective pressure acting on individual genes. In particular, the reduction of gene-specific expression noise as a response to stabilizing selection on the gene expression level is higher in genes with higher centrality metrics. Furthermore, global topological structures such as network diameter, centralization and average degree affect the average expression variance and average selective pressure acting on constituent genes. Our results demonstrate that selection at the network level leads to differential selective pressure at the gene level, and local and global network characteristics are an essential component of gene-specific expression noise evolution. Author summary “No man is an island, entire of itself. Each is a piece of the continent, a part of the main.” declares John Donne in his poem For Whom the Bell Tolls, emphasizing that no individual human is entirely separate from humanity as a whole interconnected system. Organisms are biological systems constituted of many interacting components that also interact with each other and the environment. Understanding the evolution of single components such as individual cells or genes can only be fully achieved by considering the interactions with other components. Here, we study the evolution of the cell-to-cell variability of gene expression, the so-called expression noise. To understand the evolution of gene-specific expression noise, we develop a model of gene network evolution with selection at the gene regulatory network level. We find that selection at the gene network level has different repercussions for individual genes based on their position in the network and that gene expression noise is more constrained in genes that are central in the network. Furthermore, the topological structure of the background network affects the propagation and evolvability of gene expression noise. These findings indicate that selection on a given system results in differential selective pressures at the level of subsystems. Our results further suggest that selection to mitigate inherent noise plays a role in network and gene evolution.