Finding a needle in the haystack: leveraging bioinformatics to identify a functional genetic risk factor for sepsis death

Therapeutic Effects of Retinoic Acid in Lipopolysaccharide-Induced Cardiac Dysfunction: Network Pharmacology and Experimental Validation

Purpose

Sepsis, which is deemed as a systemic inflammation reaction syndrome in the face of infectious stimuli, is the primary cause of death in ICUs. Sepsis-induced cardiomyopathy (SIC) may derive from systemic inflammation reaction and oxidative stress. Retinoic acid (RA) is recognized by its beneficial roles in terms of the immunoresponse to infections and antioxygen actions. However, the treatment efficacy and potential causal links of RA in SIC are still elusive.

Methods

By virtue of the STITCH database, we identified the targets of RA. Differentially expressed genes in SIC were acquired from the GEO database. The PPI network of intersected targets was established. GO and KEGG pathway enrichment analysis was completed. Hub genes were analyzed by cytoHubba plug-in. In the process of experimental validation, a mouse sepsis model was established by lipopolysaccharide (LPS), and the treated mice were intraperitoneally injected with RA or Dexamethasone (DEX) 60 min prior to LPS injections. Survival conditions, cardiac functions and antioxidant levels of the mice were assessed. Cardiac inflammation and injury were detected by HE and TUNEL. The levels of key genes and signal pathway expression were analyzed by RT-PCR and Western blot.

Results

PPARA, ITGAM, VCAM-1, IGF-1 and IL-6 were identified as key therapeutic targets of RA by network pharmacology. PI3K-Akt signaling pathway is the main regulatory pathway of RA. In vivo researches unraveled that RA can improve the survival rate and cardiac function of LPS-treated mice, inhibit inflammatory factors and myocardial injury, and regulate the expression of key therapeutic targets and key pathways, which is PI3K-Akt signaling pathway.

Conclusion

Network pharmacological method offers a predicative strategy to explore the treatment efficacy and causal links of RA in endotoxemic myocarditis. Through experimental verification, we discover that RA can reduce lipopolysaccharide-induced cardiac dysfunction by regulating the PI3K-Akt signaling pathway and key genes.

Bioinformatics Analysis of Gene Expression Profiles for Risk Prediction in Patients with Septic Shock

Background

Septic shock occurs when sepsis is associated with critically low blood pressure, and has a high mortality rate. This study aimed to undertake a bioinformatics analysis of gene expression profiles for risk prediction in septic shock.

Material/Methods

Two good quality datasets associated with septic shock were downloaded from the Gene Expression Omnibus (GEO) database, GSE64457 and GSE57065. Patients with septic shock had both sepsis and hypotension, and a normal control group was included. The differentially expressed genes (DEGs) were identified using OmicShare tools based on R. Functional enrichment of DEGs was analyzed using DAVID. The protein-protein interaction (PPI) network was established using STRING. Survival curves of key genes were constructed using GraphPad Prism version 7.0. Each putative central gene was analyzed by receiver operating characteristic (ROC) curves using MedCalc statistical software.

Results

GSE64457 and GSE57065 included 130 RNA samples derived from whole blood from 97 patients with septic shock and 33 healthy volunteers to obtain 975 DEGs, 455 of which were significantly down-regulated and 520 were significantly upregulated (P<0.05). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis identified significantly enriched DEGs in four signaling pathways, MAPK, TNF, HIF-1, and insulin. Six genes, WDR82, ASH1L, NCOA1, TPR, SF1, and CREBBP in the center of the PPI network were associated with septic shock, according to survival curve and ROC analysis.

Conclusions

Bioinformatics analysis of gene expression profiles identified four signaling pathways and six genes, potentially representing molecular mechanisms for the occurrence, progression, and risk prediction in septic shock.