Integrative of Single-Cell RNA Sequencing and Metabolomics Decipher the Imbalanced Lipid-Metabolism in Maladaptive Immune Responses during Sepsis

To identify differentially expressed lipid metabolism-related genes (DE-LMRGs) responsible for immune dysfunction in sepsis. Machine learning algorithms were applied for screening hubgenes related to lipid metabolism. CIBERSORT and Single-sample GSEA were employed for assessing the immune cell infiltration of hubgenes. Next, the immune function of these hubgenes at the single cell level were validated by comparing multiregional immune landscapes between septic patients (SP) and healthy control (HC). Then, support vector machine - recursive feature elimination (SVM-RFE) algorithm was conducted to explore significantly altered metabolites critical to hubgenes using non-targeted liquid chromatography–high resolution mass spectrometry metabolomics from SP and HC. Furthermore, the role of most significant hubgene was verified in sepsis rats and LPS-induced cardiomyoctes, respectively. A total of 508 DE-LMRGs were identified between SP and HC and 5 hubgenes relevant to lipid metabolism-MAPK14, EPHX2, BMX, FCER1A and PAFAH2- were screened via multiple machine learning algorithms. Then, the correlation between hub genes and immune-cell infiltrations was analyzed and suggested an immunosuppressive microenvironment in sepsis. The role of hubgenes in immune cells was further confirmed by single-cell RNA landscape. Moreover, significantly altered metabolites were mainly enriched in lipid metabolism-related signaling pathways and was associated with MAPK14. Finally, inhibiting MAPK14 could decrease the levels of inflammatory cytokines, and improve the survival and myocardial injury of sepsis. This study indicates that these lipid-metabolic hubgenes may has great potential in prognosis prediction and precise treatment of sepsis.