Optimal transcriptional regulation of dynamic bacterial responses to sudden drug exposures.
Journal
Biophysical journal
ISSN: 1542-0086
Titre abrégé: Biophys J
Pays: United States
ID NLM: 0370626
Informations de publication
Date de publication:
01 11 2022
01 11 2022
Historique:
received:
11
04
2022
revised:
22
08
2022
accepted:
23
09
2022
pubmed:
29
9
2022
medline:
5
11
2022
entrez:
28
9
2022
Statut:
ppublish
Résumé
Cellular responses to the presence of toxic compounds in their environment require prompt expression of the correct levels of the appropriate enzymes, which are typically regulated by transcription factors that control gene expression for the duration of the response. The characteristics of each response dictate the choice of regulatory parameters such as the affinity of the transcription factor to its binding sites and the strength of the promoters it regulates. Although much is known about the dynamics of cellular responses, we still lack a framework to understand how different regulatory strategies evolved in natural systems relate to the selective pressures acting in each particular case. Here, we analyze a dynamical model of a typical antibiotic response in bacteria, where a transcriptionally repressed enzyme is induced by a sudden exposure to the drug that it processes. We identify strategies of gene regulation that optimize this response for different types of selective pressures, which we define as a set of costs associated with the drug, enzyme, and repressor concentrations during the response. We find that regulation happens in a limited region of the regulatory parameter space. While responses to more costly (toxic) drugs favor the usage of strongly self-regulated repressors, responses where expression of enzyme is more costly favor the usage of constitutively expressed repressors. Only a very narrow range of selective pressures favor weakly self-regulated repressors. We use this framework to determine which costs and benefits are most critical for the evolution of a variety of natural cellular responses that satisfy the approximations in our model and to analyze how regulation is optimized in new environments with different demands.
Identifiants
pubmed: 36168291
pii: S0006-3495(22)00778-0
doi: 10.1016/j.bpj.2022.09.028
pmc: PMC9675034
pii:
doi:
Substances chimiques
Transcription Factors
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
4137-4152Subventions
Organisme : NIGMS NIH HHS
ID : P20 GM130454
Pays : United States
Organisme : NIGMS NIH HHS
ID : R01 GM069811
Pays : United States
Informations de copyright
Copyright © 2022 Biophysical Society. Published by Elsevier Inc. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of interests The authors declare no competing interests.
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