Evidence map›Paper›PMID 38709811›Full record

ArticleMolecular biology and evolution2024

Unpredictability of the Fitness Effects of Antimicrobial Resistance Mutations Across Environments in Escherichia coli.

Aaron Hinz, André Amado, Rees Kassen, Claudia Bank, Alex Wong

Abstract read
In one paragraph

Article in Molecular biology and evolution, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

  1. Review
  2. Article
  3. Pairwise Growth Dynamics of EnvironmentalAntibiotics (Basel, Switzerland) · 2026
    Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Epistasis-mediated compensatory evolution in a fitness landscape with adaptational tradeoffs.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  10. Geo-Temporal Variation in the Antimicrobial Resistance ofAntibiotics (Basel, Switzerland) · 2025
    Article
  11. Review
  12. Article
  13. Article
  14. Article
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

5 authors.

Aaron HinzDepartment of Biology, Carleton University, Ottawa, ON K1S 5B6, Canada.ORCID 0000-0003-4438-7043
André AmadoInstitute of Ecology and Evolution, University of Bern, Bern, Switzerland.ORCID 0000-0002-9027-5037
Rees KassenDepartment of Biology, University of Ottawa, Ottawa, ON K1N 6N5, Canada.ORCID 0000-0002-5617-4259
Claudia BankInstitute of Ecology and Evolution, University of Bern, Bern, Switzerland.ORCID 0000-0003-4730-758X
Alex WongDepartment of Biology, Carleton University, Ottawa, ON K1S 5B6, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The evolution of antimicrobial resistance (AMR) in bacteria is a major public health concern, and antibiotic restriction is often implemented to reduce the spread of resistance. These measures rely on the existence of deleterious fitness effects (i.e. costs) imposed by AMR mutations during growth in the absence of antibiotics. According to this assumption, resistant strains will be outcompeted by susceptible strains that do not pay the cost during the period of restriction. The fitness effects of AMR mutations are generally studied in laboratory reference strains grown in standard growth environments; however, the genetic and environmental context can influence the magnitude and direction of a mutation's fitness effects. In this study, we measure how three sources of variation impact the fitness effects of Escherichia coli AMR mutations: the type of resistance mutation, the genetic background of the host, and the growth environment. We demonstrate that while AMR mutations are generally costly in antibiotic-free environments, their fitness effects vary widely and depend on complex interactions between the mutation, genetic background, and environment. We test the ability of the Rough Mount Fuji fitness landscape model to reproduce the empirical data in simulation. We identify model parameters that reasonably capture the variation in fitness effects due to genetic variation. However, the model fails to accommodate the observed variation when considering multiple growth environments. Overall, this study reveals a wealth of variation in the fitness effects of resistance mutations owing to genetic background and environmental conditions, which will ultimately impact their persistence in natural populations.

Indexed as

Drug Resistance, BacterialEscherichia coliGenetic FitnessMutationAnti-Bacterial AgentsEnvironmentModels, GeneticAnti-Bacterial Agentsantimicrobial resistancecosts of resistanceepistasisfitness landscapesgenotype-by-environment interactionsRough Mount Fuji model

Identifiers

PMID38709811
PMCPMC11110942

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Registered trials

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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.