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Differential Gene Sets Profiling in Gram-Negative and Gram-Positive Sepsis

BACKGROUND: The host response to bacterial sepsis is reported to be nonspecific regardless of the causative pathogen. However, newer paradigms indicated that the host response of Gram-negative sepsis may be different from Gram-positive sepsis, and the difference has not been clearly clarified. The c...

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Autores principales: Wang, Qingliang, Li, Xiaojie, Tang, Wenting, Guan, Xiaoling, Xiong, Zhiyong, Zhu, Yong, Gong, Jiao, Hu, Bo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8863667/
https://www.ncbi.nlm.nih.gov/pubmed/35223539
http://dx.doi.org/10.3389/fcimb.2022.801232
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author Wang, Qingliang
Li, Xiaojie
Tang, Wenting
Guan, Xiaoling
Xiong, Zhiyong
Zhu, Yong
Gong, Jiao
Hu, Bo
author_facet Wang, Qingliang
Li, Xiaojie
Tang, Wenting
Guan, Xiaoling
Xiong, Zhiyong
Zhu, Yong
Gong, Jiao
Hu, Bo
author_sort Wang, Qingliang
collection PubMed
description BACKGROUND: The host response to bacterial sepsis is reported to be nonspecific regardless of the causative pathogen. However, newer paradigms indicated that the host response of Gram-negative sepsis may be different from Gram-positive sepsis, and the difference has not been clearly clarified. The current study aimed to explore the difference by identifying the differential gene sets using the genome-wide technique. METHODS: The training dataset GSE6535 and the validation dataset GSE13015 were used for bioinformatics analysis. The distinct gene sets of sepsis with different infections were screened using gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA). The intersection gene sets based on the two algorithms were confirmed through Venn analysis. Finally, the common gene sets between GSE6535 and GSE13015 were determined by GSEA. RESULTS: Two immunological gene sets in GSE6535 were identified based on GSVA, which could be used to discriminate sepsis caused by Gram-positive, Gram-negative, or mixed infection. A total of 19 gene sets were obtained in GSE6535 through Venn analysis based on GSVA and GSEA, which revealed the heterogeneity of Gram-negative and Gram-positive sepsis at the molecular level. The result was also verified by analysis of the validation set GSE13015, and 40 common differential gene sets were identified between dataset GSE13015 and dataset GSE6535 by GSEA. CONCLUSIONS: The identified differential gene sets indicated that host response may differ dramatically depending on the inciting organism. The findings offer new insight to investigate the pathophysiology of bacterial sepsis.
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spelling pubmed-88636672022-02-24 Differential Gene Sets Profiling in Gram-Negative and Gram-Positive Sepsis Wang, Qingliang Li, Xiaojie Tang, Wenting Guan, Xiaoling Xiong, Zhiyong Zhu, Yong Gong, Jiao Hu, Bo Front Cell Infect Microbiol Cellular and Infection Microbiology BACKGROUND: The host response to bacterial sepsis is reported to be nonspecific regardless of the causative pathogen. However, newer paradigms indicated that the host response of Gram-negative sepsis may be different from Gram-positive sepsis, and the difference has not been clearly clarified. The current study aimed to explore the difference by identifying the differential gene sets using the genome-wide technique. METHODS: The training dataset GSE6535 and the validation dataset GSE13015 were used for bioinformatics analysis. The distinct gene sets of sepsis with different infections were screened using gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA). The intersection gene sets based on the two algorithms were confirmed through Venn analysis. Finally, the common gene sets between GSE6535 and GSE13015 were determined by GSEA. RESULTS: Two immunological gene sets in GSE6535 were identified based on GSVA, which could be used to discriminate sepsis caused by Gram-positive, Gram-negative, or mixed infection. A total of 19 gene sets were obtained in GSE6535 through Venn analysis based on GSVA and GSEA, which revealed the heterogeneity of Gram-negative and Gram-positive sepsis at the molecular level. The result was also verified by analysis of the validation set GSE13015, and 40 common differential gene sets were identified between dataset GSE13015 and dataset GSE6535 by GSEA. CONCLUSIONS: The identified differential gene sets indicated that host response may differ dramatically depending on the inciting organism. The findings offer new insight to investigate the pathophysiology of bacterial sepsis. Frontiers Media S.A. 2022-02-09 /pmc/articles/PMC8863667/ /pubmed/35223539 http://dx.doi.org/10.3389/fcimb.2022.801232 Text en Copyright © 2022 Wang, Li, Tang, Guan, Xiong, Zhu, Gong and Hu https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Cellular and Infection Microbiology
Wang, Qingliang
Li, Xiaojie
Tang, Wenting
Guan, Xiaoling
Xiong, Zhiyong
Zhu, Yong
Gong, Jiao
Hu, Bo
Differential Gene Sets Profiling in Gram-Negative and Gram-Positive Sepsis
title Differential Gene Sets Profiling in Gram-Negative and Gram-Positive Sepsis
title_full Differential Gene Sets Profiling in Gram-Negative and Gram-Positive Sepsis
title_fullStr Differential Gene Sets Profiling in Gram-Negative and Gram-Positive Sepsis
title_full_unstemmed Differential Gene Sets Profiling in Gram-Negative and Gram-Positive Sepsis
title_short Differential Gene Sets Profiling in Gram-Negative and Gram-Positive Sepsis
title_sort differential gene sets profiling in gram-negative and gram-positive sepsis
topic Cellular and Infection Microbiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8863667/
https://www.ncbi.nlm.nih.gov/pubmed/35223539
http://dx.doi.org/10.3389/fcimb.2022.801232
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