Evidence map›Paper›PMID 29743200›Full record

ArticlemSphere

Experimental Evolution as a High-Throughput Screen for Genetic Adaptations.

Vaughn S Cooper

Abstract read
In one paragraph

Article in mSphere. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers.

0numbers the graph read from it
0cells of the map it votes in
59citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

59 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Journal of bacteriology · 2026
    Article
  5. Article
  6. Macrolide resistance trajectories across threeFrontiers in microbiology · 2026
    Article
  7. Article
  8. bioRxiv : the preprint server for biology · 2025
    Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
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  15. Article
  16. Article
  17. Article
  18. Article
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  20. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Vaughn S CooperDepartment of Microbiology and Molecular Genetics, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA vaughn.cooper@pitt.edu.ORCID 0000-0001-7726-0765

Funding

Predicting the emergence of antibiotic resistance through multi-omics approaches and Immune System-surveillanU01AI124302 · NIAID · BOSTON COLLEGE · PI BENTO, JOSE, COOPER, VAUGHN · 2016 to 2020
$10.2M
Molecular mechanisms of adaptive diversity in Burkholderia biofilmsR01GM110444 · NIGMS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI COOPER, VAUGHN · 2015 to 2019
$1.5M
NIAID NIH HHS U01 AI124302NIGMS NIH HHS R01 GM110444
6 · The paper itself

Abstract

Experimental evolution is a method in which populations of organisms, often microbes, are founded by one or more ancestors of known genotype and then propagated under controlled conditions to study the evolutionary process. These evolving populations are influenced by all population genetic forces, including selection, mutation, drift, and recombination, and the relative contributions of these forces may be seen as mysterious. Here, I describe why the outcomes of experimental evolution should be viewed with greater certainty because the force of selection typically dominates. Importantly, any mutant rising rapidly to high frequency in large populations must have acquired adaptive traits in the selective environment. Sequencing the genomes of these mutants can identify genes or pathways that contribute to an adaptation. I review the logic and simple mathematics why this evolve-and-resequence approach is a powerful way to find the mutations or mutation combinations that best increase fitness in any new environment.

Indexed as

Adaptation, BiologicalEvolution, MolecularModels, BiologicalGenetics, MicrobialGenetics, PopulationSelection, Geneticevolutionary biologygenomicspopulation genetics

Identifiers

PMID29743200
PMCPMC5956144

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.