Estimating mutation rates under heterogeneous stress responses.

Autor: Lansch-Justen L; Institute of Ecology and Evolution, School of Biological Sciences, University of Edinburgh, Edinburgh, Scotland, United Kingdom., El Karoui M; Institute of Cell Biology, School of Biological Sciences, University of Edinburgh, Edinburgh, Scotland, United Kingdom.; Centre for Engineering Biology, University of Edinburgh, Edinburgh, Scotland, United Kingdom.; Bacterial Systems Biology and Anti Microbial Resistance, Laboratoire de Biologie et Pharmacologie Appliquée, École Normale Supérieure Paris-Saclay, Gif-sur-Yvette, France., Alexander HK; Institute of Ecology and Evolution, School of Biological Sciences, University of Edinburgh, Edinburgh, Scotland, United Kingdom.; Centre for Engineering Biology, University of Edinburgh, Edinburgh, Scotland, United Kingdom.
Jazyk: angličtina
Zdroj: PLoS computational biology [PLoS Comput Biol] 2024 May 28; Vol. 20 (5), pp. e1012146. Date of Electronic Publication: 2024 May 28 (Print Publication: 2024).
DOI: 10.1371/journal.pcbi.1012146
Abstrakt: Exposure to environmental stressors, including certain antibiotics, induces stress responses in bacteria. Some of these responses increase mutagenesis and thus potentially accelerate resistance evolution. Many studies report increased mutation rates under stress, often using the standard experimental approach of fluctuation assays. However, single-cell studies have revealed that many stress responses are heterogeneously expressed in bacterial populations, which existing estimation methods have not yet addressed. We develop a population dynamic model that considers heterogeneous stress responses (subpopulations of cells with the response off or on) that impact both mutation rate and cell division rate, inspired by the DNA-damage response in Escherichia coli (SOS response). We derive the mutant count distribution arising in fluctuation assays under this model and then implement maximum likelihood estimation of the mutation-rate increase specifically associated with the expression of the stress response. Using simulated mutant count data, we show that our inference method allows for accurate and precise estimation of the mutation-rate increase, provided that this increase is sufficiently large and the induction of the response also reduces the division rate. Moreover, we find that in many cases, either heterogeneity in stress responses or mutant fitness costs could explain similar patterns in fluctuation assay data, suggesting that separate experiments would be required to identify the true underlying process. In cases where stress responses and mutation rates are heterogeneous, current methods still correctly infer the effective increase in population mean mutation rate, but we provide a novel method to infer distinct stress-induced mutation rates, which could be important for parameterising evolutionary models.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2024 Lansch-Justen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze: MEDLINE
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