Study on the Mechanism of UMI-77 in the Treatment of Sepsis-Induced Acute Lung Injury Based on Transcriptomics and Metabolomics

Abstract

Introduction: Sepsis-induced acute lung injury (ALI), a critical sequela of systemic inflammation, often progresses to acute respiratory distress syndrome, conferring high mortality. Although UMI-77 has demonstrated efficacy in mitigating lung injury in sepsis, the molecular mechanisms underlying its action have not yet been fully elucidated.

Methods: This study aimed to delineate the mechanism by which UMI-77 counteracts sepsis-induced ALI using comprehensive transcriptomic and metabolomic analyses.

Results: UMI-77 significantly ameliorated histopathological changes in the lungs of mice with sepsis-induced ALI Transcriptomic analysis revealed that 124 differentially expressed genes were modulated by UMI-77 and were predominantly implicated in chemokine-mediated signaling pathways, apoptosis regulation, and inflammatory responses. Integrated metabolomic analysis identified Atp4a, Ido1, Ctla4, and Cxcl10 as key genes, and inosine 5'-monophosphate (IMP), thiamine monophosphate, thymidine 3',5'-cyclic monophosphate (dTMP) as key differential metabolites. UMI-77 may regulate key genes (Atp4a, Ido1, Ctla4, and Cxcl10) to affect key metabolites (IMP, thiamine monophosphate, and dTMP) and their target genes (Entpd2, Entpd1, Nt5e, and Hprt) involved in cytokine-cytokine receptor interaction, gastric acid secretion, pyrimidine, and purine metabolism in the treatment of sepsis-induced ALI.

Conclusion: UMI-77 exerts its therapeutic effect in sepsis-induced ALI through intricate modulation of pivotal genes and metabolites, thereby influencing critical biological pathways. This study lays the groundwork for further development and clinical translation of UMI-77 as a potential therapeutic agent for sepsis-associated lung injuries.

Keywords: cytokine signaling; genes; inflammation; metabolites; sepsis-induced ALI.

Figure 1

The histomorphological changes of lung tissue in mice. Magnification of ×400, scale bar = 100 µm.

Figure 2

Transcriptomics landscape of lung tissue from septic mice treated by UMI-77. (a) Volcano plot of differentially expressed genes (LPS group vs Control group), red: up-regulated, blue: down-regulated. (b) Volcano plot of differentially expressed genes (UMI-77 group vs LPS group), red: up-regulated, blue: down-regulated. (c and d) Venn diagram of differentially expressed genes among three groups. (e) Biological process GO terms. (f) Cellular component GO terms. (g) Molecular function GO terms.

Figure 3

Metabolomics landscape of lung tissue from septic mice treated by UMI-77. (a) OPLS-DA analysis (LPS group vs Control group). (b) OPLS-DA analysis (UMI-77 group vs LPS group). (c) Permutation test (LPS group vs Control group). (d) Permutation test (UMI-77 group vs LPS group). (e and f) Venn diagram of differentially metabolites among three groups.

Figure 4

The correlation heatmaps of 42 genes and 15 metabolites.

Figure 5

Integrated analysis of lung transcriptomics and metabolomics. (a) The integrated pathway analysis of 42 genes and 15 metabolites. (b) The metabolites–genes network constructed by MetScape. (c) PPI network of genes and metabolites targets. (d) Network of genes, metabolites and metabolites targets. “↑” represented up-regulated, while “↓” represented down-regulated, the left mean LPS vs Control and the right mean UMI-77 vs LPS.

Figure 6

Validation of key genes. (a) ELISA-based validation of key genes. **p<0.01. (b) Binding mode of UMI-77 to key genes by molecular docking. (I) Binding mode of UMI-77 to Entpd2. (II) Binding mode of UMI-77 to Nt5e. (III) Binding mode of UMI-77 to Hprt. (IV) Binding mode of UMI-77 to Ido1. (V) Binding mode of UMI-77 to Ctla4. (VI) Binding mode of UMI-77 to Atp4a. (VII) Binding mode of UMI-77 to Cxcl10. (VIII) Binding mode of UMI-77 to Entpd1.