Evidence map›Paper›PMID 24554585›Full record

ArticleGenome biology and evolution2014

Global footprints of purifying selection on Toll-like receptor genes primarily associated with response to bacterial infections in humans.

Souvik Mukherjee, Debdutta Ganguli, Partha P Majumder

Open access · goldAbstract read
In one paragraph

Article in Genome biology and evolution, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
1.2field-weighted citation impact, top 22% 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

11 citing papers in PubMed, 26 citations in OpenAlex.

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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

3 authors at 1 institution in 1 country.

Souvik MukherjeeNational Institute of Biomedical Genomics, Kalyani, West Bengal, India.
Debdutta Ganguli
Partha P Majumder
National Institute of Biomedical Genomics · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Toll-like receptors (TLRs) are directly involved in host-pathogen interactions. Polymorphisms in these genes are associated with susceptibility to infectious diseases. To understand the influence of environment and pathogen diversity on the evolution of TLR genes, we have undertaken a large-scale population-genetic study. Our study included two hunter-gatherer tribal populations and one urbanized nontribal population from India with distinct ethnicities (n = 266) and 14 populations inhabiting four different continents (n = 1,092). From the data on DNA sequences of cell-surface TLR genes, we observed an excess of rare variants and a large number of low frequency haplotypes in each gene. Nonsynonymous changes were few in every population and the commonly used statistical tests for detecting natural selection provided evidence of purifying selection. The evidence of purifying selection acting on the cell-surface TLRs of the innate immune system is not consistent with Haldane's theory of coevolution of immunity genes, at least of innate immunity genes, with pathogens. Our study provides evidence that genes of the cell-surface TLRs, that is, TLR2 and TLR4, have been so optimized to defend the host against microbial infections that new mutations in these genes are quickly eliminated.

Indexed as

Selection, GeneticAllelesBacterial InfectionsEvolution, MolecularGenetic LociGenetics, PopulationHaplotypesHost-Pathogen InteractionsHumansImmunity, InnatePolymorphism, GeneticProtein FootprintingSequence Analysis, DNAToll-Like ReceptorsToll-Like ReceptorsevolutionhaplotypeIndian populationsinnate immune systempurifying selectionToll-like receptors

Identifiers

PMID24554585
PMCPMC3971583
OpenAlexW2101653542

What OpenQuestion holds

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LicenceCC BY-NC
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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.