Evidence map›Paper›PMID 41587990›Full record

ArticleNature communications2026

Repeatability of gene expression evolution in experimental environmental adaptation.

Jiachen Li, Jianzhi Zhang

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. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

2 authors.

Jiachen LiDepartment of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.ORCID 0000-0001-5389-6128
Jianzhi ZhangDepartment of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI, USA. jianzhi@umich.edu.ORCID 0000-0001-6141-1290

Funding

Genomic and Systemic Approaches to Evolutionary MechanismsR35GM139484 · NIGMS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI JIANZHI ZHANG · 2021 to 2026
$3.2M
NIGMS NIH HHS R35 GM139484U.S. Department of Health & Human Services | National Institutes of Health (NIH) R35GM139484
6 · The paper itself

Abstract

Assessing the relative roles of chance and necessity in evolution is of wide interest, but it requires evolving the same population under the same environment multiple times-a virtually impossible task in nature that has been repeatedly accomplished in the laboratory. Capitalizing on the transcriptome data collected in 10 laboratory evolution studies conducted in 22 distinct environments, we investigate the evolutionary repeatability of a total of 182,103 gene expression traits in a prokaryotic and five eukaryotic species. The number of gene expression traits exhibiting concordant changes in direction and magnitude between two replicate populations typically exceeds the chance expectation by 10 to 100 standard deviations. Contrasting replicate evolution in the same environment with that in different environments suggests that the concordance in expression evolution is largely attributable to environment-specific selections, a finding that is further supported by comparing with the outcome of mutation accumulation experiments where the efficacy of selection is minimized. Additionally, genes controlled by more transcription factors tend to show more repeatable expression evolution, likely due to a higher certainty in the occurrence of mutations altering their expressions. In conclusion, contrary to almost unrepeatable genotypic evolution, phenotypic evolution during environmental adaptation is quite repeatable and deterministic, at least for gene expressions.

Indexed as

Adaptation, PhysiologicalEvolution, MolecularAnimalsEnvironmentGene Expression ProfilingMutationPhenotypeSelection, GeneticTranscriptome

Identifiers

PMID41587990
PMCPMC12946262

What Socratic holds

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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 Socratic graph.