Evidence map›Paper›PMID 40056383›Full record

ArticleMolecular biology and evolution2025

Regulatory Plasticity of the Human Genome.

Jaya Srivastava, Ivan Ovcharenko

Abstract read
In one paragraph

Article in Molecular biology and evolution, 2025. 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.

Jaya SrivastavaDivision of Intramural Research, National Library of Medicine, National Institutes of Health, Bethesda, MD 20892, USA.ORCID 0000-0002-1657-4004
Ivan OvcharenkoDivision of Intramural Research, National Library of Medicine, National Institutes of Health, Bethesda, MD 20892, USA.ORCID 0000-0002-9730-7732

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Evolutionary turnover in noncoding regions has driven phenotypic divergence during past speciation events and continues to facilitate environmental adaptation through variants. We used a deep learning model to identify the substrates of regulatory turnover using genome-wide mutations mimicking three evolutionary pathways: recent history (human-chimp substitutions), modern population (human population variation), and mutational susceptibility (random mutations). We observed enhancer turnover in approximately 6% of the whole genome, with more than 80% of the novel activity arising from repurposing of enhancers between cell types. Frequency of turnover in a cell type is remarkably similar across the three pathways, despite only ∼19% overlap in the source regions. The majority of turnover loci were found to be localized within 100 kb of a gene, with the highest turnover occurring near neurodevelopmental genes including CNTNAP2, NPAS3, and AUTS2. Flanking enhancers of these genes undergo high turnover irrespective of the mutational model pathway, suggesting a high plasticity in neurocognitive evolution. Based on susceptibility to random mutations, these enhancers were identified as vulnerable by nature and feature a higher abundance of cell type-specific transcription factor binding sites. Our findings suggest that enhancer repurposing within vulnerable loci drives regulatory innovation while keeping the core regulatory networks intact.

Indexed as

Enhancer Elements, GeneticEvolution, MolecularGenome, HumanDeep LearningHumansMutationdeep learningenhancer evolutionregulatory elements

Identifiers

PMID40056383
PMCPMC11934273

What OpenQuestion 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 OpenQuestion graph.