Dataset Information

Identification of key genes in Gram?positive and Gram?negative sepsis using stochastic perturbation.

ABSTRACT: 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.

SUBMITTER: Li Z

PROVIDER: S-EPMC5548058 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

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Identification of key genes in Gram‑positive and Gram‑negative sepsis using stochastic perturbation.

Li Zhenliang Z Zhang Ying Y Liu Yaling Y Liu Yanchun Y Li Youyi Y

Molecular medicine reports 20170715 3

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). Hi ...[more]

PMID: 28714002

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Project description:Objective: It is unclear whether the host response of gram-positive sepsis differs from gram-negative sepsis at a transcriptome level. Using microarray technology, we compared the gene-expression profiles of gram-positive sepsis and gram-negative sepsis in critically ill patients. Design: A prospective cross-sectional study. Setting: A 20-bed general intensive care unit of a tertiary referral hospital. Patients: Seventy-two patients admitted to the intensive care unit. Interventions: Intravenous blood was collected for leukocyte separation and RNA extraction. Microarray experiements were then performed examing the expression level of 19,232 genes in each sample. Measurements and Main Results: There was no difference in the expression profile between gram-positive and gram-negative sepsis. The finding remained unchanged even when genes with lower expression level were included or after statistical stringency was lowered. There were, however, ninety-four genes differentially expressed between sepsis and control patients. These genes included those involved in immune regulation, inflammation and mitochondrial function. Hierarchical cluster analysis confirmed that the difference in gene expression profile existed between sepsis and control patients, but not between gram-positive and gram-negative patients. Conclusion: Gram-positive and gram-negative sepsis share a common host response at a transcriptome level. These findings support the hypothesis that the septic response is non-specific and is designed to provide a more general response that can be elicited by a wide range of different micro-organisms. The study included seventy-two critically ill patients admitted to the intensive care unit (ICU) of Nepean Hospital, Sydney, Australia. Of these, fifty-five patients were diagnosed to have sepsis, as confirmed by microbiological culture. The remaining seventeen patients did not have sepsis and were therefore used as controls. The study was approved by the hospital ethics committee and informed consent was obtained from all patients or their relatives. Patient Samples. Whole blood was taken from each patient on admission to ICU. Neutrophils were separated from whole blood using density-gradient separation with Ficoll-PaqueP P(Amersham). Subsequent neutrophil RNA extraction was performed using guanidinium thiocyanate (Ambion). Microarray Experiment. The neutrophil RNA was converted to cDNA, fluorescently labeled and hybridized to its complimentary sequences on the microarray (Invitrogen). The fluorescent signals on each micrroarray were captured using the GenePix 4000B laser scanner (Axon Instruments). Expression level of each gene was represented by the intensity of its fluorescent signal. Data Extraction. All signal intensity values were processed using background-subtraction method. Prior to analysis, all values were log-transformed and normalized by fitting a print-tip group Lowess curve. Normalization minimizes bias due to dye chemistry, signal intensity or location of a gene on the array. It ensures the detection of genes that are truly differentially expressed, instead of those caused by experimental artifacts or variation in the hybridization process. After normalization, genes that had more than 50% of data missing were removed. We then selected genes that had at least 80% of the data showing two-fold changes from the geneâs median values. After filtering, 1617 genes were available for further analysis.

Project description:BackgroundGram-negative sepsis is closely related to the immune response, involving collaborative efforts of different immune cells. However, the mechanisms underlying immune cell regulation in gram-negative sepsis remain unclear. Therefore, this study investigated the potential regulatory mechanisms and identified the key genes related to immune cells in gram-negative sepsis.MethodsThe RNA-sequencing data for gram-negative sepsis samples and normal samples were collected from the Gene Expression Omnibus (GEO) dataset GSE9960. CIBERSORT was performed to analyze the proportion of 22 types of immune cells in gram-negative sepsis and normal samples. Weighted gene co-expression network analysis (WGCNA) was used to determine the networks that are associated with the differentially distributed immune cells in the two groups. Differentially expressed genes were identified using the limma package. The least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) algorithms were applied to ascertain hub gene signatures. The gene interaction network of hub gene signatures was determined by ingenuity pathway analysis. Furthermore, the expression levels of the key genes were verified using quantitative real-time polymerase chain reaction (qRT-PCR).ResultsCIBERSORT analysis showed that the proportions of plasma cells, resting CD4+ memory T cells, M1 macrophages, and eosinophils were significantly different between gram-negative sepsis and normal samples. WGCNA identified 1,100 genes in the most relevant modules associated with these immune cells. In addition, 87 differentially expressed genes were identified. By overlapping the genes found in the WGCNA and the differentially expressed genes, a total of 46 genes related to immune cells were identified. Integrative analysis of LASSO and SVM-RFE identified NLR family CARD domain-containing 4 (NLRC4) and ral guanine nucleotide dissociation stimulator like 4 (RGL4) as key gene signatures related to immune cells in gram-negative sepsis. The qRT-PCR results demonstrated that both NLRC4 and RGL4 were upregulated in peripheral blood mononuclear cells (PBMCs) from patients with sepsis.ConclusionsThis investigation provides novel insights into the molecular mechanisms of immune cells involved in the pathogenesis of gram-negative sepsis. NLRC4 and RGL4 were identified as key gene signatures related to immune cells and may act as potential diagnostic biomarkers for gram-negative sepsis.

Project description:BackgroundTreatment strategies for various subtypes of breast cancer (BC) are different based on their distinct molecular characteristics. Therefore, it is very important to identify key differentially expressed genes (DEGs) between ER-positive/HER2-negative BC and ER-negative/HER2-negative BC.MethodsGene expression profiles of GSE22093 and GSE23988 were obtained from the Gene Expression Omnibus database. There were 74 ER-positive/HER2-negative BC tissues and 85 ER-negative/HER2-negative BC tissues in the two profile datasets. DEGs between ER-positive/HER2-negative tissues and ER-negative/HER2-negative BC tissues were identified by the GEO2R tool. The common DEGs among the two datasets were detected with Venn software online. Next, we made use of the Database for Annotation, Visualization and Integrated Discovery to analyze enriched Kyoto Encyclopedia of Gene and Genome (KEGG) pathways and gene ontology terms. Then, the protein-protein interactions (PPIs) of these DEGs were visualized by Cytoscape with the Search Tool for the Retrieval of Interacting Genes. Of the proteins in the PPI network, Molecular Complex Detection plug-in analysis identified nine core upregulated genes and one core downregulated gene. UALCAN and Gene Expression Profiling Interactive Analysis were applied to determine the expression of these 10 genes in BC. Furthermore, for the analysis of overall survival among those genes, the Kaplan-Meier method was implemented.ResultsNinety-three common DEGs (63 upregulated and 30 downregulated) were identified. KEGG pathway enrichment analysis showed that upregulated DEGs were particularly enriched in the progesterone-mediated oocyte maturation pathway. In addition, PGR might be a prognostic biomarker for ER-positive/HER2-negative BC. CCND1 is a poor prognostic biomarker for ER-positive/HER2-negative BC and ER-negative/HER2-negative BC. Moreover, TFF1, AGR2 and EGFR might be predictive biomarkers of node metastasis in ER-positive/HER2-negative BC and ER-negative/HER2-negative BC.ConclusionsCCND1, AGR2, PGR, TFF1 and EGFR are the key DEGs between ER-positive/HER2-negative BC and ER-negative/HER2-negative BC. Further studies are required to confirm the functions of the identified genes.

Dataset Information

Identification of key genes in Gram?positive and Gram?negative sepsis using stochastic perturbation.

Publications

Identification of key genes in Gram‑positive and Gram‑negative sepsis using stochastic perturbation.

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