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  Being noisy in a crowd: differential selective pressure on gene expression noise in model gene regulatory networks

Puzović, N., Madaan, T., & Dutheil, J. Y. (2023). Being noisy in a crowd: differential selective pressure on gene expression noise in model gene regulatory networks. PLoS Computational Biology, 19(4): e1010982. doi:10.1371/journal.pcbi.1010982.

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Puzović, Nataša1, Author           
Madaan, Tanvi1, Author           
Dutheil, Julien Y.1, Author                 
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1Research Group Molecular Systems Evolution (Dutheil), Department Evolutionary Genetics (Tautz), Max Planck Institute for Evolutionary Biology, Max Planck Society, ou_2068287              

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 Abstract: 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.

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Language(s): eng - English
 Dates: 2022-08-082023-02-272023-04-20
 Publication Status: Published online
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 Rev. Type: Peer
 Identifiers: DOI: 10.1371/journal.pcbi.1010982
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Title: PLoS Computational Biology
Source Genre: Journal
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Publ. Info: San Francisco, CA : Public Library of Science
Pages: - Volume / Issue: 19 (4) Sequence Number: e1010982 Start / End Page: - Identifier: ISSN: 1553-734X
CoNE: https://pure.mpg.de/cone/journals/resource/1000000000017180_1