AI Article Synopsis
- Scientists wanted to find special genes related to fat processing that cause problems in the immune system during sepsis (a serious infection).
- They used smart computer programs to analyze these genes and studied how they affected immune cells in sick people compared to healthy ones.
- They discovered several important genes and found that changing their activity could help reduce inflammation and improve the health of sepsis patients.
Article Abstract
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 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.
Results: A total of 508 DE-LMRGs were identified between SP and HC, and 5 hub genes relevant to lipid metabolism (, and ) 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 Finally, inhibiting 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.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10172510 | PMC |
| http://dx.doi.org/10.3389/fimmu.2023.1181697 | DOI Listing |
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