Evidence map›Paper›PMID 42270630›Full record

ArticleNature communications2026

Mutation-biased adaptation is consequential even in large bacterial populations.

Jake N Barber, Alejandro Couce

Abstract read
In one paragraph

Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

2 authors.

Jake N BarberCentro de Biotecnología y Genómica de Plantas (CBGP), Universidad Politécnica de Madrid (UPM), Madrid, Spain.ORCID 0000-0001-9279-7763
Alejandro CouceCentro de Biotecnología y Genómica de Plantas (CBGP), Universidad Politécnica de Madrid (UPM), Madrid, Spain. a.couce@upm.es.ORCID 0000-0002-4153-6018

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Because mutation rates vary widely across genomes and environments, natural selection is typically presented with highly biased variation. Yet, the idea that mutational tendencies can influence adaptation is still controversial. While mutation-driven adaptation has been observed in diverse taxa, critics contend it reflects small populations or weak-effect mutations. Therefore, the importance and generality of this phenomenon remain unclear, largely due to a lack of empirical tests across broad population-size gradients and multiple fitness-relevant traits. Here, we address this gap using a system in which two Escherichia coli mutator lineages evolve antibiotic resistance via two mutationally favoured, yet genetically distinct, routes. Simulations and experiments show that the scaling of mutation-biased adaptation with population size is complex, highly dependent on biological details, and - most critically - on how closely mutation bias aligns with selection. Contrary to the common view, we find that mutation-biased adaptation may not wane in large populations, but instead intensify depending on the bias. Crucially, we demonstrate that distinct mutation biases produce markedly different collateral sensitivity profiles to multiple antibiotics, even at large population sizes. Our findings suggest that mutation-biased adaptation may be widespread, with far-reaching and unpredictable consequences both within and beyond the original selective context.

Indexed as

Adaptation, PhysiologicalEscherichia coliMutationAnti-Bacterial AgentsComputer SimulationDrug Resistance, BacterialEvolution, MolecularModels, GeneticMutation RateSelection, GeneticAnti-Bacterial Agents

Identifiers

PMID42270630
PMCPMC13402312

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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.