Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: EC:3.4.24.11 (CD10)
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The aim of this study was to screen the genes related to the pathogenesis of major depression disorder (MDD) by bioinformatics. Taking GSE98793 chip data from GEO public database of National Biotechnology Information Center (NCBI) website as the research object, 116 differentially expressed genes (DEGs) were screened by R language limma package. Among the 116 DEGs, 66 genes were up-regulated and 50 down-regulated. The results of gene functional annotation analysis of Gene Ontology (GO) showed that the DEGs were mainly distributed in mitochondria intima and mitochondria. They were involved in copper ion binding, cysteine-type endopeptidase activity, the cell response of interleukin-1, protein processing and other biological processes. KEGG pathway enrichment analysis results showed that the DEGs were mainly concentrated in oxidative phosphorylation, Parkinson's disease, non-alcoholic fatty liver disease, Alzheimer's disease and Huntington's disease etc. The results of protein interaction network analysis showed that there were interactions among proteins encoded by 54 DEGs. Combined with the analysis results of the above methods, 11 key genes were screened out, including UQCRC1, GZMB, NDUFB9, NSF, SLC17A5, CTSH, NDUFB10, UQCR10, ATOX1, CST7 and CTSW, which could be used as candidate genes for the diagnosis and treatment of MDD. Taken together, the key genes were obtained by analyzing the microarray and the DEGs of MDD in the present study, which would provide important clues for revealing the molecular mechanism and clinical targeted therapy of depression.
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PMID:[Bioinformatics analysis of genes related to pathogenesis of major depression disorder]. 3011 61

Sepsis is a critical, complex medical condition, and the major causative pathogens of sepsis are both Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli). Genome-wide studies identify differentially expressed genes for sepsis. However, the results for the identification of DEGs are inconsistent or discrepant among different studies because of heterogeneity of specimen sources, various data processing methods, or different backgrounds of the samples. To identify potential transcriptional biomarkers that are differently expressed in S. aureus- and E. coli-induced sepsis, we have analyzed four microarray datasets from GEO database and integrated results with bioinformatics tools. 42 and 54 DEGs were identified in both S. aureus and E. coli samples from any three different arrays, respectively. Hierarchical clustering revealed dramatic differences between control and sepsis samples. GO functional annotations suggested that DEGs in the S. aureus group were mainly involved in the responses of both defense and immune regulation, but DEGs in the E. coli group were mainly related to the regulation of endopeptidase activity involved in the apoptotic signaling pathway. Although KEGG showed inflammatory bowel disease in the E. coli group, the KEGG pathway analysis showed that these DEGs were mainly involved in the tumor necrosis factor signaling pathway, fructose metabolism, and mannose metabolism in both S. aureus- and E. coli-induced sepsis. Eight common genes were identified between sepsis patients with either S. aureus or E. coli infection and controls in this study. All the candidate genes were further validated to be differentially expressed by an ex-vivo human blood model, and the relative expression of these genes was performed by qPCR. The qPCR results suggest that GK and PFKFB3 might contribute to the progression of S. aureus-induced sepsis, and CEACAM1, TNFAIP6, PSTPIP2, SOCS3, and IL18RAP might be closely linked with E. coli-induced sepsis. These results provide new viewpoints for the pathogenesis of both sepsis and pathogen identification.
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PMID:Identification of Potential Transcriptional Biomarkers Differently Expressed in Both S. aureus- and E. coli-Induced Sepsis via Integrated Analysis. 3109 95