Evidence map›Paper›PMID 25758009›Full record

ArticleMolecular biology and evolution2015

Adaptive Evolution as a Predictor of Species-Specific Innate Immune Response.

Andrew E Webb, Z Nevin Gerek, Claire C Morgan, Thomas A Walsh, Christine E Loscher, Scott V Edwards, Mary J O'Connell

Open access · bronzeAbstract read
In one paragraph

Article in Molecular biology and evolution, 2015. 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
1.8field-weighted citation impact, top 15% of its field
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, 29 citations in OpenAlex.

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  7. An Analysis of Methylome Evolution in Primates.Molecular biology and evolution · 2021
    Article
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  9. Integrated structural and evolutionary analysis reveals common mechanisms underlying adaptive evolution in mammals.Proceedings of the National Academy of Sciences of the United States of America · 2020
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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

7 authors at 3 institutions in 2 countries.

Andrew E WebbBioinformatics and Molecular Evolution Group, School of Biotechnology, Dublin City University, Dublin 9, Ireland Centre for Scientific Computing & Complex Systems Modeling (SCI-SYM), Dublin City University, Dublin 9, Ireland.
Z Nevin GerekInstitute for Genomics and Evolutionary Medicine, Temple University, Philadelphia.
Claire C MorganBioinformatics and Molecular Evolution Group, School of Biotechnology, Dublin City University, Dublin 9, Ireland Centre for Scientific Computing & Complex Systems Modeling (SCI-SYM), Dublin City University, Dublin 9, Ireland.
Thomas A WalshBioinformatics and Molecular Evolution Group, School of Biotechnology, Dublin City University, Dublin 9, Ireland Centre for Scientific Computing & Complex Systems Modeling (SCI-SYM), Dublin City University, Dublin 9, Ireland.
Christine E LoscherImmunomodulation Research Group, School of Biotechnology, Dublin City University, Glasnevin, Dublin 9, Ireland.
Scott V EdwardsDepartment of Organismic and Evolutionary Biology and Museum of Comparative Zoology, Harvard University.
Mary J O'ConnellBioinformatics and Molecular Evolution Group, School of Biotechnology, Dublin City University, Dublin 9, Ireland Centre for Scientific Computing & Complex Systems Modeling (SCI-SYM), Dublin City University, Dublin 9, Ireland Department of Organismic and Evolutionary Biology and Museum of Comparative Zoology, Harvard University mary.oconnell@dcu.ie.
Dublin City University · IEHarvard University · USTemple University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

It has been proposed that positive selection may be associated with protein functional change. For example, human and macaque have different outcomes to HIV infection and it has been shown that residues under positive selection in the macaque TRIM5α receptor locate to the region known to influence species-specific response to HIV. In general, however, the relationship between sequence and function has proven difficult to fully elucidate, and it is the role of large-scale studies to help bridge this gap in our understanding by revealing major patterns in the data that correlate genotype with function or phenotype. In this study, we investigate the level of species-specific positive selection in innate immune genes from human and mouse. In total, we analyzed 456 innate immune genes using codon-based models of evolution, comparing human, mouse, and 19 other vertebrate species to identify putative species-specific positive selection. Then we used population genomic data from the recently completed Neanderthal genome project, the 1000 human genomes project, and the 17 laboratory mouse genomes project to determine whether the residues that were putatively positively selected are fixed or variable in these populations. We find evidence of species-specific positive selection on both the human and the mouse branches and we show that the classes of genes under positive selection cluster by function and by interaction. Data from this study provide us with targets to test the relationship between positive selection and protein function and ultimately to test the relationship between positive selection and discordant phenotypes.

Indexed as

Biological EvolutionAdaptation, PhysiologicalAmino AcidsAnimalsGenetics, PopulationHumansImmunity, InnateMicePhylogenyProtein Structure, TertiarySelection, GeneticSpecies SpecificityToll-Like Receptor 3Amino AcidsTLR3 protein, humanToll-Like Receptor 3adaptive evolutioninnate immune evolutionpredicting phenotypic responseprotein functional shiftspecies-specific responses

Identifiers

PMID25758009
PMCPMC4476151
OpenAlexW2172028434

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.