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Identification of key genes in Gram-positive and Gram-negative sepsis using stochastic perturbation
Sepsis is an inflammatory response to pathogens (such as Gram-positive and Gram-negative bacteria), which has high morbidity and mortality in critically ill patients. The present study aimed to identify the key genes in Gram-positive and Gram-negative sepsis. GSE6535 was downloaded from Gene Express...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
D.A. Spandidos
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5548058/ https://www.ncbi.nlm.nih.gov/pubmed/28714002 http://dx.doi.org/10.3892/mmr.2017.7013 |
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author | Li, Zhenliang Zhang, Ying Liu, Yaling Liu, Yanchun Li, Youyi |
author_facet | Li, Zhenliang Zhang, Ying Liu, Yaling Liu, Yanchun Li, Youyi |
author_sort | Li, Zhenliang |
collection | PubMed |
description | Sepsis is an inflammatory response to pathogens (such as Gram-positive and Gram-negative bacteria), which has high morbidity and mortality in critically ill patients. The present study aimed to identify the key genes in Gram-positive and Gram-negative sepsis. GSE6535 was downloaded from Gene Expression Omnibus, containing 17 control samples, 18 Gram-positive samples and 25 Gram-negative samples. Subsequently, the limma package in R was used to screen the differentially expressed genes (DEGs). Hierarchical clustering was conducted for the specific DEGs in Gram-negative and Gram-negative samples using cluster software and the TreeView software. To analyze the correlation of samples at the gene level, a similarity network was constructed using Cytoscape software. Functional and pathway enrichment analyses were conducted for the DEGs using DAVID. Finally, stochastic perturbation was used to determine the significantly differential functions between Gram-positive and Gram-negative samples. A total of 340 and 485 DEGs were obtained in Gram-positive and Gram-negative samples, respectively. Hierarchical clustering revealed that there were significant differences between control and sepsis samples. In Gram-positive and Gram-negative samples, myeloid cell leukemia sequence 1 was associated with apoptosis and programmed cell death. Additionally, NADH:ubiquinone oxidoreductase subunit S4 was associated with mitochondrial respiratory chain complex I assembly. Stochastic perturbation analysis revealed that NADH:ubiquinone oxidoreductase subunit B2 (NDUFB2), NDUFB8 and ubiquinol-cytochrome c reductase hinge protein (UQCRH) were associated with cellular respiration in Gram-negative samples, whereas large tumor suppressor kinase 2 (LATS2) was associated with G1/S transition of the mitotic cell cycle in Gram-positive samples. NDUFB2, NDUFB8 and UQCRH may be biomarkers for Gram-negative sepsis, whereas LATS2 may be a biomarker for Gram-positive sepsis. These findings may promote the therapies of sepsis caused by Gram-positive and Gram-negative bacteria. |
format | Online Article Text |
id | pubmed-5548058 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | D.A. Spandidos |
record_format | MEDLINE/PubMed |
spelling | pubmed-55480582017-10-24 Identification of key genes in Gram-positive and Gram-negative sepsis using stochastic perturbation Li, Zhenliang Zhang, Ying Liu, Yaling Liu, Yanchun Li, Youyi Mol Med Rep Articles Sepsis is an inflammatory response to pathogens (such as Gram-positive and Gram-negative bacteria), which has high morbidity and mortality in critically ill patients. The present study aimed to identify the key genes in Gram-positive and Gram-negative sepsis. GSE6535 was downloaded from Gene Expression Omnibus, containing 17 control samples, 18 Gram-positive samples and 25 Gram-negative samples. Subsequently, the limma package in R was used to screen the differentially expressed genes (DEGs). Hierarchical clustering was conducted for the specific DEGs in Gram-negative and Gram-negative samples using cluster software and the TreeView software. To analyze the correlation of samples at the gene level, a similarity network was constructed using Cytoscape software. Functional and pathway enrichment analyses were conducted for the DEGs using DAVID. Finally, stochastic perturbation was used to determine the significantly differential functions between Gram-positive and Gram-negative samples. A total of 340 and 485 DEGs were obtained in Gram-positive and Gram-negative samples, respectively. Hierarchical clustering revealed that there were significant differences between control and sepsis samples. In Gram-positive and Gram-negative samples, myeloid cell leukemia sequence 1 was associated with apoptosis and programmed cell death. Additionally, NADH:ubiquinone oxidoreductase subunit S4 was associated with mitochondrial respiratory chain complex I assembly. Stochastic perturbation analysis revealed that NADH:ubiquinone oxidoreductase subunit B2 (NDUFB2), NDUFB8 and ubiquinol-cytochrome c reductase hinge protein (UQCRH) were associated with cellular respiration in Gram-negative samples, whereas large tumor suppressor kinase 2 (LATS2) was associated with G1/S transition of the mitotic cell cycle in Gram-positive samples. NDUFB2, NDUFB8 and UQCRH may be biomarkers for Gram-negative sepsis, whereas LATS2 may be a biomarker for Gram-positive sepsis. These findings may promote the therapies of sepsis caused by Gram-positive and Gram-negative bacteria. D.A. Spandidos 2017-09 2017-07-15 /pmc/articles/PMC5548058/ /pubmed/28714002 http://dx.doi.org/10.3892/mmr.2017.7013 Text en Copyright: © Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. |
spellingShingle | Articles Li, Zhenliang Zhang, Ying Liu, Yaling Liu, Yanchun Li, Youyi Identification of key genes in Gram-positive and Gram-negative sepsis using stochastic perturbation |
title | Identification of key genes in Gram-positive and Gram-negative sepsis using stochastic perturbation |
title_full | Identification of key genes in Gram-positive and Gram-negative sepsis using stochastic perturbation |
title_fullStr | Identification of key genes in Gram-positive and Gram-negative sepsis using stochastic perturbation |
title_full_unstemmed | Identification of key genes in Gram-positive and Gram-negative sepsis using stochastic perturbation |
title_short | Identification of key genes in Gram-positive and Gram-negative sepsis using stochastic perturbation |
title_sort | identification of key genes in gram-positive and gram-negative sepsis using stochastic perturbation |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5548058/ https://www.ncbi.nlm.nih.gov/pubmed/28714002 http://dx.doi.org/10.3892/mmr.2017.7013 |
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