Evidence map›Paper›PMID 37336901›Full record

ArticleNature communications2023

Evolvability-enhancing mutations in the fitness landscapes of an RNA and a protein.

Andreas Wagner

Abstract read
In one paragraph

Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

  1. Article
  2. Article
  3. Deconstructing empirical fitness seascapes across scales of granularity.bioRxiv : the preprint server for biology · 2026
    Article
  4. Article
  5. Article
  6. Favorable epistasis in ancestral diterpene synthases promoted convergent evolution of a resin acid precursor in conifers.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  7. Article
  8. Article
  9. Bioengineering hybrid artificial life.Frontiers in bioinformatics · 2025
    Review
  10. Article
  11. Effects of selection stringency on the outcomes of directed evolution.bioRxiv : the preprint server for biology · 2024
    Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. 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.

Andreas WagnerDepartment of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland. andreas.wagner@ieu.uzh.ch.ORCID http://orcid.org/0000-0003-4299-3840

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Can evolvability-the ability to produce adaptive heritable variation-itself evolve through adaptive Darwinian evolution? If so, then Darwinian evolution may help create the conditions that enable Darwinian evolution. Here I propose a framework that is suitable to address this question with available experimental data on adaptive landscapes. I introduce the notion of an evolvability-enhancing mutation, which increases the likelihood that subsequent mutations in an evolving organism, protein, or RNA molecule are adaptive. I search for such mutations in the experimentally characterized and combinatorially complete fitness landscapes of a protein and an RNA molecule. I find that such evolvability-enhancing mutations indeed exist. They constitute a small fraction of all mutations, which shift the distribution of fitness effects of subsequent mutations towards less deleterious mutations, and increase the incidence of beneficial mutations. Evolving populations which experience such mutations can evolve significantly higher fitness. The study of evolvability-enhancing mutations opens many avenues of investigation into the evolution of evolvability.

Indexed as

Evolution, MolecularRNABiological EvolutionGenetic FitnessModels, GeneticMutationRNA

Identifiers

PMID37336901
PMCPMC10279741

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.