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Immune gene expression networks in sepsis: A network biology approach

To study the dysregulated host immune response to infection in sepsis, gene expression profiles from the Gene Expression Omnibus (GEO) datasets GSE54514, GSE57065, GSE64456, GSE95233, GSE66099 and GSE72829 were selected. From the Kyoto Encyclopedia of Genes and Genomes (KEGG) immune system pathways,...

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Autores principales: Kim, Kyung Soo, Jekarl, Dong Wook, Yoo, Jaeeun, Lee, Seungok, Kim, Myungshin, Kim, Yonggoo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7935325/
https://www.ncbi.nlm.nih.gov/pubmed/33667236
http://dx.doi.org/10.1371/journal.pone.0247669
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author Kim, Kyung Soo
Jekarl, Dong Wook
Yoo, Jaeeun
Lee, Seungok
Kim, Myungshin
Kim, Yonggoo
author_facet Kim, Kyung Soo
Jekarl, Dong Wook
Yoo, Jaeeun
Lee, Seungok
Kim, Myungshin
Kim, Yonggoo
author_sort Kim, Kyung Soo
collection PubMed
description To study the dysregulated host immune response to infection in sepsis, gene expression profiles from the Gene Expression Omnibus (GEO) datasets GSE54514, GSE57065, GSE64456, GSE95233, GSE66099 and GSE72829 were selected. From the Kyoto Encyclopedia of Genes and Genomes (KEGG) immune system pathways, 998 unique genes were selected, and genes were classified as follows based on gene annotation from KEGG, Gene Ontology, and Reactome: adaptive immunity, antigen presentation, cytokines and chemokines, complement, hematopoiesis, innate immunity, leukocyte migration, NK cell activity, platelet activity, and signaling. After correlation matrix formation, correlation coefficient of 0.8 was selected for network generation and network analysis. Total transcriptome was analyzed for differentially expressed genes (DEG), followed by gene set enrichment analysis. The network topological structure revealed that adaptive immunity tended to form a prominent and isolated cluster in sepsis. Common genes within the cluster from the 6 datasets included CD247, CD8A, ITK, LAT, and LCK. The clustering coefficient and modularity parameters were increased in 5/6 and 4/6 datasets in the sepsis group that seemed to be associated with functional aspect of the network. GSE95233 revealed that the nonsurvivor group showed a prominent and isolated adaptive immunity cluster, whereas the survivor group had isolated complement-coagulation and platelet-related clusters. T cell receptor signaling (TCR) pathway and antigen processing and presentation pathway were down-regulated in 5/6 and 4/6 datasets, respectively. Complement and coagulation, Fc gamma, epsilon related signaling pathways were up-regulated in 5/6 datasets. Altogether, network and gene set enrichment analysis showed that adaptive-immunity-related genes along with TCR pathway were down-regulated and isolated from immune the network that seemed to be associated with unfavorable prognosis. Prominence of platelet and complement-coagulation-related genes in the immune network was associated with survival in sepsis. Complement-coagulation pathway was up-regulated in the sepsis group that was associated with favorable prognosis. Network and gene set enrichment analysis supported elucidation of sepsis pathogenesis.
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spelling pubmed-79353252021-03-15 Immune gene expression networks in sepsis: A network biology approach Kim, Kyung Soo Jekarl, Dong Wook Yoo, Jaeeun Lee, Seungok Kim, Myungshin Kim, Yonggoo PLoS One Research Article To study the dysregulated host immune response to infection in sepsis, gene expression profiles from the Gene Expression Omnibus (GEO) datasets GSE54514, GSE57065, GSE64456, GSE95233, GSE66099 and GSE72829 were selected. From the Kyoto Encyclopedia of Genes and Genomes (KEGG) immune system pathways, 998 unique genes were selected, and genes were classified as follows based on gene annotation from KEGG, Gene Ontology, and Reactome: adaptive immunity, antigen presentation, cytokines and chemokines, complement, hematopoiesis, innate immunity, leukocyte migration, NK cell activity, platelet activity, and signaling. After correlation matrix formation, correlation coefficient of 0.8 was selected for network generation and network analysis. Total transcriptome was analyzed for differentially expressed genes (DEG), followed by gene set enrichment analysis. The network topological structure revealed that adaptive immunity tended to form a prominent and isolated cluster in sepsis. Common genes within the cluster from the 6 datasets included CD247, CD8A, ITK, LAT, and LCK. The clustering coefficient and modularity parameters were increased in 5/6 and 4/6 datasets in the sepsis group that seemed to be associated with functional aspect of the network. GSE95233 revealed that the nonsurvivor group showed a prominent and isolated adaptive immunity cluster, whereas the survivor group had isolated complement-coagulation and platelet-related clusters. T cell receptor signaling (TCR) pathway and antigen processing and presentation pathway were down-regulated in 5/6 and 4/6 datasets, respectively. Complement and coagulation, Fc gamma, epsilon related signaling pathways were up-regulated in 5/6 datasets. Altogether, network and gene set enrichment analysis showed that adaptive-immunity-related genes along with TCR pathway were down-regulated and isolated from immune the network that seemed to be associated with unfavorable prognosis. Prominence of platelet and complement-coagulation-related genes in the immune network was associated with survival in sepsis. Complement-coagulation pathway was up-regulated in the sepsis group that was associated with favorable prognosis. Network and gene set enrichment analysis supported elucidation of sepsis pathogenesis. Public Library of Science 2021-03-05 /pmc/articles/PMC7935325/ /pubmed/33667236 http://dx.doi.org/10.1371/journal.pone.0247669 Text en © 2021 Kim et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Kim, Kyung Soo
Jekarl, Dong Wook
Yoo, Jaeeun
Lee, Seungok
Kim, Myungshin
Kim, Yonggoo
Immune gene expression networks in sepsis: A network biology approach
title Immune gene expression networks in sepsis: A network biology approach
title_full Immune gene expression networks in sepsis: A network biology approach
title_fullStr Immune gene expression networks in sepsis: A network biology approach
title_full_unstemmed Immune gene expression networks in sepsis: A network biology approach
title_short Immune gene expression networks in sepsis: A network biology approach
title_sort immune gene expression networks in sepsis: a network biology approach
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7935325/
https://www.ncbi.nlm.nih.gov/pubmed/33667236
http://dx.doi.org/10.1371/journal.pone.0247669
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