Evidence map›Paper›PMID 26391223›Full record

ArticleBMC evolutionary biology2015

The effects of natural selection across molecular pathways in Drosophila melanogaster.

Jeffrey P Vedanayagam, Daniel Garrigan

Abstract read
In one paragraph

Article in BMC evolutionary biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Gene age shapes functional and evolutionary properties of theProceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  2. Article
  3. 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.

Jeffrey P VedanayagamDepartment of Biology, University of Rochester, Rochester, New York, 14627, USA.
Daniel GarriganDepartment of Biology, University of Rochester, Rochester, New York, 14627, USA. dgarriga@ur.rochester.edu.

Funding

PHS HHS R01-ODO1054801
6 · The paper itself

Abstract

backgroundWhole-genome RNA interference post-transcriptional silencing (RNAi) is a widely used method for studying the phenotypic effects of knocking down individual genes. In this study, we use a population genomic approach to characterize the rate of evolution for proteins affecting 26 RNAi knockdown phenotypes in Drosophila melanogaster.

resultsWe find that only two of the 26 RNAi knockdown phenotypes are enriched for rapidly evolving proteins: innate immunity and regulation of Hedgehog signaling. Among all genes associated with an RNAi knockdown phenotype, we note examples in which the adaptively evolving proteins play a well-defined role in a given molecular pathway. However, most adaptively evolving proteins are found to perform more general cellular functions. When RNAi phenotypes are grouped into categories according to cellular function, we find that genes involved in the greatest number of phenotypic categories are also significantly more likely to have a history of rapid protein evolution.

conclusionsWe show that genes that have been demonstrated to have a measurable effect on multiple molecular phenotypes show higher rates of protein evolution than genes having an effect on a single category of phenotype. Defining pleiotropy in this way yields very different results than previous studies that define pleiotropy by the number of physical interactions, which show highly connected proteins tend to evolve more slowly than lowly connected proteins. We suggest that a high degree of pleiotropy may increase the likelihood of compensatory substitution, consistent with modern theoretical work on adaptation.

Indexed as

Selection, GeneticAnimalsBiological EvolutionDrosophila melanogasterGene Knockdown TechniquesGenetic PleiotropyGenome, InsectGenomicsRNA Interference

Identifiers

PMID26391223
PMCPMC4578789

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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.