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Cattle infection response network and its functional modules

BACKGROUND: Weighted 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,...

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Autores principales: Beiki, Hamid, Pakdel, Abbas, Javaremi, Ardeshir Nejati, Masoudi-Nejad, Ali, Reecy, James M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5755453/
https://www.ncbi.nlm.nih.gov/pubmed/29301495
http://dx.doi.org/10.1186/s12865-017-0238-4
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author Beiki, Hamid
Pakdel, Abbas
Javaremi, Ardeshir Nejati
Masoudi-Nejad, Ali
Reecy, James M.
author_facet Beiki, Hamid
Pakdel, Abbas
Javaremi, Ardeshir Nejati
Masoudi-Nejad, Ali
Reecy, James M.
author_sort Beiki, Hamid
collection PubMed
description BACKGROUND: Weighted 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. RESULTS: A 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. CONCLUSIONS: We 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12865-017-0238-4) contains supplementary material, which is available to authorized users.
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spelling pubmed-57554532018-01-08 Cattle infection response network and its functional modules Beiki, Hamid Pakdel, Abbas Javaremi, Ardeshir Nejati Masoudi-Nejad, Ali Reecy, James M. BMC Immunol Research Article BACKGROUND: Weighted 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. RESULTS: A 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. CONCLUSIONS: We 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12865-017-0238-4) contains supplementary material, which is available to authorized users. BioMed Central 2018-01-05 /pmc/articles/PMC5755453/ /pubmed/29301495 http://dx.doi.org/10.1186/s12865-017-0238-4 Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Beiki, Hamid
Pakdel, Abbas
Javaremi, Ardeshir Nejati
Masoudi-Nejad, Ali
Reecy, James M.
Cattle infection response network and its functional modules
title Cattle infection response network and its functional modules
title_full Cattle infection response network and its functional modules
title_fullStr Cattle infection response network and its functional modules
title_full_unstemmed Cattle infection response network and its functional modules
title_short Cattle infection response network and its functional modules
title_sort cattle infection response network and its functional modules
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5755453/
https://www.ncbi.nlm.nih.gov/pubmed/29301495
http://dx.doi.org/10.1186/s12865-017-0238-4
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