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Measuring selection coefficients below 10(-3): method, questions, and prospects.

Romain Gallet1, Tim F Cooper, Santiago F Elena

  • 1CEFE-UMR 5175 1919 route de Mende, F-34293 Montpellier, CEDEX 5, France.

Genetics
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PubMed
Summary
This summary is machine-generated.

Researchers precisely measured small fitness effects of mutations in E. coli using advanced methods. This overcomes limitations of sampling error and drift, revealing distributed selection coefficients even in controlled experiments.

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Area of Science:

  • Evolutionary Biology
  • Microbiology
  • Genetics

Background:

  • Precise measurement of fitness, especially for mutations with small effects, is crucial in evolutionary biology.
  • Sampling error and genetic drift traditionally limit the accurate quantification of very small fitness effects.

Purpose of the Study:

  • To develop and apply a novel combined approach for measuring and analyzing fitness with high precision.
  • To accurately estimate the mutational fitness effect (MFE) of mini-Tn10 transposon insertion mutations in Escherichia coli.

Main Methods:

  • Utilized competition experiments in large Escherichia coli populations under controlled laboratory conditions.
  • Employed flow cytometry for precise genotype frequency assessment, minimizing sampling error.
  • Implemented massive replication and large populations to control for genetic drift.
  • Decomposed fitness measures into four parameters (marker effect, mutation effect, epistasis, transitivity) using four competition experiments.

Main Results:

  • Achieved a precision of 2 × 10(-4) in estimating mean selection coefficients.
  • Detected small but significant epistatic interactions between mutation and marker effects.
  • Confirmed transitivity of fitness effects in most cases.
  • Observed unexpected variation in selection coefficients, indicating cryptic genetic variation.

Conclusions:

  • The developed method allows for highly precise estimation of mutational fitness effects.
  • Epistatic interactions and cryptic variation play roles in fitness landscapes.
  • Selection coefficients are inherently distributed, posing a fundamental limit on measurement precision even under ideal conditions.