The identification of the key genes and pathways in septic shock using an integrated bioinformatics analysis
INTERNATIONAL JOURNAL OF CLINICAL AND EXPERIMENTAL MEDICINE
Authors: Lin, Min; Lin, Wei; Chen, Jianxin
Abstract
Sepsis is a critical illness with a high mortality rate in intensive care units. Septic shock is a severe, dangerous type of sepsis; however, the molecular mechanisms involved in septic shock are largely unclear. The present study integrated three datasets with 210 septic shock and 68 control samples to identify the hub genes involved in septic shock progression. Datasets GSE9692, GSE26378, and GSE26440 were downloaded from the Gene Expression Omnibus (GEO). Using bioinformatics tools such as GEO2R, Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes, and Genomes (KEGG) pathway analysis, and a protein-protein interaction (PPI) network analysis, five clusters and ten hub genes were successfully identified. The hub genes, including ITGAM, TLR4, TLR8, TLR2, MMP9, C3AR1, CCL5, FPR2, MPO, and LCK, were mainly enriched in the immune signaling pathway and in the inflammatory response. In summary, the present study identified ten septic shock-related genes using a bioinformatics analysis. The results indicate that the candidate genes may be involved in the regulation of immunity and the inflammatory response in septic shock and may act as predictors or therapeutic targets for septic shock.
Molecular signatures of chronic periodontitis in gingiva: A genomic and proteomic analysis
JOURNAL OF PERIODONTOLOGY
Authors: Guzeldemir-Akcakanat, Esra; Alkan, Begum; Sunnetci-Akkoyunlu, Deniz; Gurel, Busra; Balta, V. Merve; Kan, Bahadir; Akgun, Emel; Yilmaz, Elif Busra; Baykal, Ahmet Tarik; Cine, Naci; Olgac, Vakur; Gumuslu, Esen; Savli, Hakan
Abstract
Background To elucidate molecular signatures of chronic periodontitis (CP) using gingival tissue samples through omics-based whole-genome transcriptomic and whole protein profiling. Methods Gingival tissues from 18 CP and 25 controls were analyzed using gene expression microarrays to identify gene expression patterns and the proteins isolated from these samples were subjected to comparative proteomic analysis by liquid chromatography-tandem mass spectrometry (LC-MS/MS). The data from transcriptomics and proteomics were integrated to reveal common shared genes and proteins. Results The most upregulated genes in CP compared with controls were found as MZB1, BMS1P20, IGLL1/IGLL5, TNFRSF17, ALDH1A1, KIAA0125, MMP7, PRL, MGC16025, ADAM11, and the most upregulated proteins in CP compared with controls were BPI, ITGAM, CAP37, PCM1, MMP-9, MZB1, UGTT1, PLG, RAB1B, HSP90B1. Functions of the identified genes were involved cell death/survival, DNA replication, recombination/repair, gene expression, organismal development, cell-to-cell signaling/interaction, cellular development, cellular growth/proliferation, cellular assembly/organization, cellular function/maintenance, cellular movement, B-cell development, and identified proteins were involved in protein folding, response to stress, single-organism catabolic process, regulation of peptidase activity, and negative regulation of cell death. The integration and validation analysis of the transcriptomics and proteomics data revealed two common shared genes and proteins, MZB1 and ECH1. Conclusion Integrative data from transcriptomics and proteomics revealed MZB1 as a potent candidate for chronic periodontitis.