Deciphering sepsis molecular subtypes using large-scale data to identify subtype-specific drug repurposing

Rationale: Sepsis is a life-threatening syndrome that can quickly cause organ failure and death if untreated. Patient variability complicates therapy development, and its molecular mechanisms remain poorly understood. Advancing knowledge of these mechanisms is essential for effective treatments and clearer phenotype definitions. Objectives: To address this issue, we created a large-scale transcriptomic atlas of publicly available adult sepsis patient data with which we performed molecular phenotyping to evaluate patterns of gene expression and identified potential phenotype-specific drug therapies. Methods: We identified studies of bacterial sepsis in three genomic databases (SRA, GEO, and refine.bio) and reviewed metadata from each study to ensure samples met our predetermined inclusion criteria. We combined this data into one large comprehensive data atlas and harmonized gene expression and associated metadata from each sample. Molecular phenotypes of sepsis were identified via clustering analysis of this sepsis transcriptomic data atlas. We then examined clinical correlates of each phenotype and identified gene signatures associated with each. We performed gene set enrichment analysis on those signatures and identified phenotype-specific potential drug repurposing candidates. We also evaluated the associations between computed phenotypes and mortality. Measurements and Main Results: We harmonized data from 3,713 samples (all are included in this dataset) across 28 data sets, of which 2,251 were sepsis patients. Clustering analysis identified four phenotypes within the data. We identified statistically significant phenotype associations with survival, disease, and age. Pathway analysis revealed that MHC class II functions, DNA damage, homeostatic pathways and coagulation may characterize underlying response phenotypes, and may have the potential to guide drug development of sepsis therapeutics. Conclusions: We created the largest transcriptomic sepsis atlas to date, from which we identified four molecular sepsis phenotypes. We described underlying dysregulated molecular mechanisms of these phenotypes, associated clinical covariates, and several potential candidate therapies specific to each phenotype. Future studies should seek to validate such drug-phenotype links to advance sepsis precision medicine.