Multi-omic studies on the pathogenesis of Sepsis - Hongjie Tong; Yuhang Zhao; Ying Cui; Jiali Yao; Tianlong Zhang (Journal of Translational Medicine)
Abstract Background Sepsis is a life-threatening inflammatory condition, and its underlying genetic mechanisms are not yet fully elucidated. We applied methods such as Mendelian randomization (MR), genetic correlation analysis, and colocalization analysis to integrate multi-omics data and explore the relationship between genetically associated genes and sepsis, as well as sepsis-related mortality, with the goal of identifying key genetic factors and their potential mechanistic pathways. Methods To identify therapeutic targets for sepsis and sepsis-related mortality, we conducted an MR analysis on 11,643 sepsis cases and 1,896 cases of 28-day sepsis mortality from the UK Biobank cohort. The exposure data consisted of 15,944 potential druggable genes (expression quantitative trait loci, eQTL) and 4,907 plasma proteins (protein quantitative trait loci, pQTL). We then performed sensitivity analysis, SMR analysis, reverse MR analysis, genetic correlation analysis, colocalization analysis, enrichment analysis, and protein-protein interaction network analysis on the overlapping genes. Validation was conducted using 17,133 sepsis cases from FinnGen R12. Drug prediction and molecular docking were subsequently used to further assess the therapeutic potential of the identified drug targets, while PheWAS was used to evaluate potential side effects. Finally, mediation analysis was conducted to identify the mediating role of related metabolites. Results The MR analysis results identified a significant causal relationship between 24 genes and sepsis. The robustness of these causal associations was further strengthened by SMR analysis, sensitivity analysis, and reverse MR analysis. Genetic correlation analysis revealed that only two of these genes were genetically correlated with sepsis. Colocalization analysis showed that only one gene was closely associated with sepsis, while validation using the FinnGen dataset identified three genes. In the MR analysis of 28-day sepsis mortality, seven genes were found to have significant associations, with reverse MR analysis excluding one gene. The remaining genes passed sensitivity analysis, with no significant genes identified in genetic correlation and colocalization analyses. Molecular docking demonstrated excellent binding affinity between drugs and proteins with available structural data. PheWAS at the gene level did not reveal any potential side effects of the related drugs. Conclusions The identified drug targets, associated pathways, and metabolites have enhanced our understanding of the complex relationships between genes and sepsis. These genes and metabolites can serve as effective targets for sepsis treatment, paving new pathways in this field and laying a foundation for future research.