Evidence map›Paper›PMID 29301495›Full record

ArticleBMC immunology2018

Cattle infection response network and its functional modules.

Hamid Beiki, Abbas Pakdel, Ardeshir Nejati Javaremi, Ali Masoudi-Nejad, James M Reecy

Open access · goldAbstract read
In one paragraph

Article in BMC immunology, 2018. 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
1.0field-weighted citation impact, top 24% 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

3 citing papers in PubMed, 7 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

5 authors at 3 institutions in 2 countries.

Hamid BeikiDepartment of Animal Science, Iowa State University, Ames, IA, 50011, USA.
Abbas PakdelDepartment of Animal Science, College of Agriculture, Isfahan University of Technology, Isfahan, 84156-83111, Iran. pakdel@cc.iut.ac.ir.
Ardeshir Nejati JavaremiDepartment of Animal Science, University College of Agriculture and Natural Resources, University of Tehran, Karaj, 31587-11167, Iran.
Ali Masoudi-NejadLaboratory of Systems Biology and Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, 31587-11167, Iran.
James M ReecyDepartment of Animal Science, Iowa State University, Ames, IA, 50011, USA.
Iowa State University · USUniversity of Tehran · IRIsfahan University of Technology · IR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWeighted Gene Co-expression Network analysis, a powerful technique used to extract co-expressed gene pattern from mRNA expression data, was constructed to infer common immune strategies used by cattle in response to five different bacterial species (Escherichia coli, Mycobacterium avium, Mycobacterium bovis, Salmonella and Staphylococcus aureus) and a protozoa (Trypanosoma Congolense) using 604 publicly available gene expression microarrays from 12 cattle infection experiments.

resultsA total of 14,999 transcripts that were differentially expressed (DE) in at least three different infection experiments were consolidated into 15 modules that contained between 43 and 4441 transcripts. The high number of shared DE transcripts between the different types of infections indicated that there were potentially common immune strategies used in response to these infections. The number of transcripts in the identified modules varied in response to different infections. Fourteen modules showed a strong functional enrichment for specific GO/pathway terms related to "immune system process" (71%), "metabolic process" (71%), "growth and developmental process" (64%) and "signaling pathways" (50%), which demonstrated the close interconnection between these biological pathways in response to different infections. The largest module in the network had several over-represented GO/pathway terms related to different aspects of lipid metabolism and genes in this module were down-regulated for the most part during various infections. Significant negative correlations between this module's eigengene values, three immune related modules in the network, and close interconnection between their hub genes, might indicate the potential co-regulation of these modules during different infections in bovine. In addition, the potential function of 93 genes with no functional annotation was inferred based on neighbor analysis and functional uniformity among associated genes. Several hypothetical genes were differentially expressed during experimental infections, which might indicate their important role in cattle response to different infections.

conclusionsWe identified several biological pathways involved in immune response to different infections in cattle. These findings provide rich information for experimental biologists to design experiments, interpret experimental results, and develop novel hypothesis on immune response to different infections in cattle.

Indexed as

Gene Expression ProfilingGene Regulatory NetworksAnimalsCattleCattle DiseasesEscherichia coliGene OntologyHost-Pathogen InteractionsMycobacterium aviumMycobacterium bovisSalmonellaSignal TransductionSpecies SpecificityStaphylococcus aureusTrypanosoma congolenseBioinformaticsCattleData integrationImmune responseNetwork analysis

Identifiers

PMID29301495
PMCPMC5755453
OpenAlexW2781815607

What OpenQuestion holds

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LicenceCC BY
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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.