Evidence map›Paper›PMID 40830465›Full record

ArticleBMC biology2025

Adaptive genetics reveals constraints on protein structure/function by evolving E. coli under constant nutrient limitation.

Katja Schwartz, Margie Kinnersley, Charles Ross Lindsey, Gavin Sherlock, Frank Rosenzweig

Abstract read
In one paragraph

Article in BMC biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Katja Schwartz *Department of Genetics, Stanford University School of Medicine, Stanford, CA, 94305-5120, USA.
Margie Kinnersley *Division of Biological Sciences, The University of Montana, Missoula, MT, 59812, USA.
Charles Ross LindseySchool of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
Gavin SherlockDepartment of Genetics, Stanford University School of Medicine, Stanford, CA, 94305-5120, USA. gsherloc@stanford.edu.
Frank RosenzweigDivision of Biological Sciences, The University of Montana, Missoula, MT, 59812, USA. frank.rosenzweig@biology.gatech.edu.

Funding

Multisizer 4e - equipment for "Fitness Effects of Beneficial Mutations"R35GM131824 · NIGMS · STANFORD UNIVERSITY · PI Gavin J Sherlock · 2019 to 2026
$3.6M
NIGMS NIH HHS R35 GM131824NIH HHS R35 GM131824Planetary Science Division 80NSSC20K0621
6 · The paper itself

Abstract

backgroundEvolution of microbes under laboratory selection produces genetically diverse populations, owing to the continuous input of mutations and to competition among lineages. Whole-genome whole-population sequencing makes it possible to identify mutations arising in such populations, to use them to discern functional modules where adaptation occurs, and then map gene structure-function relationships. Here, we report on the use of this approach, adaptive genetics, to discover targets of selection and the mutational consequences thereof in E. coli evolving under chronic nutrient limitation.

resultsReplicate bacterial populations were cultured for ≥ 300 generations in glucose limited chemostats and sequenced every 50 generations at 1000X-coverage, enabling identification of mutations that rose to ≥ 1% frequency. Thirty-nine genes qualified as high value targets of selection, being mutated far more often than would be expected by chance. A majority of these encode regulatory proteins that control gene expression at the transcriptional (e.g., RpoS and OmpR), post-transcriptional (e.g., Hfq and ProQ), and post-translational (e.g., GatZ) levels. The downstream effects of these regulatory mutations likely impact not only acquisition and processing of limiting glucose, but also assembly of structural elements such as lipopolysaccharide, periplasmic glucans, and cell surface appendages such as flagella and fimbriae. Whether regulatory or structural in nature, recurrent mutations at high value targets tend to cluster at sites either known or predicted to be involved in RNA-protein or protein-protein interactions.

conclusionsOur observations highlight the value of experimental evolution as a proving ground for inferences gathered from traditional molecular genetics. By coupling experimental evolution to whole-genome, whole-population sequencing, adaptive genetics makes it possible not only the genes whose mutation confers a selective advantage, but also to discover which residues in which genes are most likely to confer a particular type of selective advantage and why.

Indexed as

Adaptation, BiologicalEscherichia coliEscherichia coli ProteinsEvolution, MolecularAmino Acid SequenceMutationProtein Structure, TertiarySelection, GeneticStructure-Activity RelationshipEscherichia coli ProteinsAdaptive geneticsE. coliExperimental evolutionFunctional genomicsMetabolic networksParallelismWhole genome sequencing

Identifiers

PMID40830465
PMCPMC12366229

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.