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Updated: Jun 12, 2026

Monitoring Intraspecies Competition in a Bacterial Cell Population by Cocultivation of Fluorescently Labelled Strains
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Mutation-biased adaptation is consequential even in large bacterial populations.

Jake N Barber1, Alejandro Couce2,3

  • 1Centro de Biotecnología y Genómica de Plantas (CBGP), Universidad Politécnica de Madrid (UPM), Madrid, Spain.

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|June 10, 2026
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Summary

Mutation bias can drive adaptation, even in large populations, influencing antibiotic resistance evolution. Distinct mutation biases lead to varied collateral sensitivity profiles, with broad implications.

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Area of Science:

  • Evolutionary biology
  • Microbial genetics

Background:

  • Mutation rates vary, creating biased genetic variation for natural selection.
  • The role of mutation bias in adaptation is debated, with critics questioning its significance in large populations.

Purpose of the Study:

  • To investigate the influence of mutation bias on adaptation across population sizes.
  • To determine if mutation-driven adaptation persists or intensifies in large populations.
  • To examine how different mutation biases affect antibiotic resistance and collateral sensitivity.

Main Methods:

  • Utilized Escherichia coli mutator lineages to study antibiotic resistance evolution.
  • Combined experimental evolution with simulations to analyze adaptation dynamics.
  • Assessed the impact of distinct mutation biases on multiple fitness-relevant traits.

Main Results:

  • Mutation-biased adaptation shows complex scaling with population size, dependent on biological specifics.
  • Contrary to expectations, mutation-biased adaptation can intensify in large populations.
  • Different mutation biases result in distinct collateral sensitivity profiles to antibiotics.

Conclusions:

  • Mutation-biased adaptation is a significant evolutionary force, not limited by population size.
  • The alignment between mutation bias and selection is critical for adaptation.
  • Understanding mutation bias is crucial for predicting evolutionary trajectories and antibiotic resistance outcomes.