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Related Experiment Video

Updated: Jun 25, 2025

Measuring Microbial Mutation Rates with the Fluctuation Assay

Measuring Microbial Mutation Rates with the Fluctuation Assay

Published on: November 28, 2019

Estimating mutation rates under heterogeneous stress responses.

Lucy Lansch-Justen¹, Meriem El Karoui^2,3,4, Helen K Alexander^1,3

¹Institute of Ecology and Evolution, School of Biological Sciences, University of Edinburgh, Edinburgh, Scotland, United Kingdom.

Plos Computational Biology

|May 28, 2024

Summary

Environmental stressors like antibiotics can increase bacterial mutation rates, accelerating resistance. This study introduces a new model to accurately estimate stress-induced mutation increases, even with heterogeneous responses in bacterial populations.

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Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

Area of Science:

Microbiology
Evolutionary Biology
Genetics

Background:

Environmental stressors, including antibiotics, trigger bacterial stress responses.
Some stress responses elevate mutation rates, potentially accelerating bacterial resistance evolution.
Existing methods for estimating mutation rates under stress do not account for heterogeneous stress response expression in bacterial populations.

Purpose of the Study:

To develop a population dynamic model addressing heterogeneous stress responses and their impact on mutation and division rates.
To derive a method for estimating mutation-rate increases specifically linked to stress response expression.
To differentiate between mutation rate heterogeneity and mutant fitness costs in fluctuation assay data.

Main Methods:

Developed a population dynamic model incorporating heterogeneous stress responses (on/off subpopulations).
Modeled impacts of stress responses on both mutation and cell division rates, inspired by E. coli's SOS response.
Derived mutant count distribution for fluctuation assays and implemented maximum likelihood estimation.

Main Results:

The new inference method accurately estimates mutation-rate increases under stress, especially when the increase is substantial and division rate is reduced.
The study highlights that both heterogeneous stress responses and mutant fitness costs can yield similar fluctuation assay patterns, necessitating further investigation.
Current methods accurately estimate the population mean mutation rate increase, but the novel method can infer distinct stress-induced mutation rates.

Conclusions:

A novel population dynamic model and inference method accurately quantify stress-induced mutation rate increases in bacteria, even with heterogeneous responses.
Distinguishing between heterogeneous stress responses and mutant fitness costs is crucial for understanding bacterial evolution.
The developed method offers a way to parameterize evolutionary models with distinct stress-induced mutation rates.