Frontiers in Immunology (Apr 2023)

Integrative single-cell RNA sequencing and metabolomics decipher the imbalanced lipid-metabolism in maladaptive immune responses during sepsis

  • Han She,
  • Han She,
  • Lei Tan,
  • Lei Tan,
  • Yi Wang,
  • Yi Wang,
  • Yuanlin Du,
  • Yuanqun Zhou,
  • Jun Zhang,
  • Yunxia Du,
  • Yunxia Du,
  • Ningke Guo,
  • Zhengbin Wu,
  • Qinghui Li,
  • Daiqin Bao,
  • Qingxiang Mao,
  • Yi Hu,
  • Liangming Liu,
  • Tao Li

DOI
https://doi.org/10.3389/fimmu.2023.1181697
Journal volume & issue
Vol. 14

Abstract

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BackgroundTo identify differentially expressed lipid metabolism-related genes (DE-LMRGs) responsible for immune dysfunction in sepsis.MethodsThe lipid metabolism-related hub genes were screened using machine learning 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 landscapes between septic patients (SP) and healthy control (HC). Then, the support vector 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.ResultsA 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.ConclusionThe lipid metabolism-related hub genes may have great potential in prognosis prediction and precise treatment for sepsis patients.

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