Integrative single-cell RNA sequencing and metabolomics decipher the imbalanced lipid-metabolism in maladaptive immune responses during sepsis - She, Han; Tan, Lei; Wang, Yi; Du, Yuanlin; Zhou, Yuanqun; Zhang, Jun; Du, Yunxia; Guo, Ningke; Wu, Zhengbin; Li, Qinghui; Bao, Daiqin; Mao, Qingxiang; et al. (Frontiers in Immunology, 2023)
Background: To identify differentially expressed lipid metabolism-related genes (DE-LMRGs) responsible for immune dysfunction in sepsis. Methods: The lipid metabolism-related hub genes were screened using machine learn-ing algorithms, and the immune cell infiltration of these hub genes were assessed by CIBERSORT and Single-sample GSEA. Next, the immune function of these hub genes at the single-cell level were validated by comparing multiregional immune land-scapes between septic patients (SP) and healthy control (HC). Then, the support vec-tor machine-recursive feature elimination (SVM-RFE) algorithm was conducted to compare the significantly altered metabolites critical to hub genes between SP and HC. Furthermore, the role of the key hub gene was verified in sepsis rats and LPS-induced cardiomyocytes, respectively. Results: A total of 508 DE-LMRGs were identified between SP and HC, and 5 hub genes relevant to lipid metabolism (MAPK14, EPHX2, BMX, FCER1A, and PAFAH2) were screened. Then, we found an immunosuppressive microenvironment in sepsis. The role of hub genes in immune cells was further confirmed by the single-cell RNA landscape. Moreover, significantly altered metabolites were mainly enriched in lipid metabolism-related signaling pathways and were associated with MAPK14. Finally, inhibiting MAPK14 decreased the levels of inflammatory cytokines and improved the survival and myocardial injury of sepsis. Conclusion: The lipid metabolism-related hub genes may have great potential in prognosis prediction and precise treatment for sepsis patients. Keywords: Sepsis; Lipid-Metabolism; Machine learning algorithm; Single-cell RNA sequencing; Metabolomics