Exploring potential therapeutic agents for lipopolysaccharide-induced septic cardiomyopathy based on transcriptomics...

Area of Science:

• Cardiology
• Genomics
• Bioinformatics

Background:

• Septic cardiomyopathy (SCM) is a severe sepsis complication causing heart failure, but its pathogenesis is poorly understood.
• Identifying SCM pathogenesis is crucial for developing effective treatments.

Purpose of the Study:

• To investigate the pathophysiology of SCM using bioinformatics.
• To identify potential therapeutic targets and drugs for SCM.

Main Methods:

• Established an SCM rat model using lipopolysaccharide (LPS) and performed RNA sequencing.
• Constructed a protein-protein interaction (PPI) network of differentially expressed genes (DEGs).
• Identified hub genes, analyzed pathways (GO, KEGG, GSEA), predicted miRNAs, and screened drugs using the Connectivity Map (CMAP) database.

Main Results:

• Identified four hub genes (Itgb1, Il1b, Rac2, Vegfa) and six key miRNAs as potential SCM biomarkers.
• Revealed the roles of cytokine-cytokine receptor interactions, complement and coagulation cascades, chemokine signaling, and MAPK signaling pathways in SCM.
• Identified KU-0063794 and dasatinib as candidate therapeutic compounds for SCM.

Conclusions:

• The identified hub genes and pathways offer potential diagnostic and therapeutic strategies for SCM.
• This research provides novel insights into SCM pathophysiology and potential drug development avenues.