Molecular Systems Evolution Research Group, Max Planck Institute for Evolutionary Biology, Plön, Schleswig-Holstein, Germany.
Institut des sciences de l'évolution, Montpellier, Languedoc-Roussillon, France.
PLoS Comput Biol. 2023 Apr 20;19(4):e1010982. doi: 10.1371/journal.pcbi.1010982. eCollection 2023 Apr.
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.
表达噪声，即在相同条件下生长的同基因细胞中基因产物数量的可变性，源于转录和翻译过程中涉及的分子元件扩散和结合的固有随机性。已经表明，表达噪声是可进化的特征，并且在基因网络中，中心基因的噪声比外围基因的噪声小。这种模式的一个可能解释是，由于中心基因将其噪声传播到下游靶标，从而导致噪声放大，因此对中心基因的选择压力增加。为了验证这一假设，我们开发了一个具有遗传随机基因表达的新基因调控网络模型，并在网络水平上模拟了在约束条件下基因特异性表达噪声的进化。在网络中的所有基因的表达水平上施加稳定选择，并进行了突变、选择、复制和重组的轮次。我们观察到，局部网络特征既影响对选择的响应概率，又影响作用于单个基因的选择压力强度。特别是，作为对基因表达水平上稳定选择的响应，中心度较高的基因的基因特异性表达噪声的降低幅度更高。此外，网络直径、集中化和平均度数等全局拓扑结构会影响组成基因的平均表达方差和作用于其的平均选择压力。我们的研究结果表明，网络水平上的选择会导致基因水平上的差异选择压力，并且局部和全局网络特征是基因特异性表达噪声进化的重要组成部分。
