Background: Neutrophil elastase (NE) is associated with sepsis occurrence and progression. We hypothesized that the NE inhibitor Sivelestat might modulate abnormal gut microbiota and metabolites during sepsis.
Methods: Sixty Sprague-Dawley (SD) rats were randomly divided into sham control (SC), sepsis (CLP), and sepsis+Sivelestat (Sive) groups. The rats' survival status was monitored for 24 hours postoperatively, and feces were collected for microbiome and non-targeted metabolomics analyses.
Results: Sivelestat administration significantly improved the survival of septic rats (80% vs 50%, P = 0.047). Microbiome analysis showed that the microbiota composition of rats in the CLP group was significantly disturbed, as potential pathogens such as Escherichia-Shigella and Gammaproteobacteria became dominant, and the beneficial microbiota represented by Lactobacillus decreased. These changes were reversed in Sive group, and the overall microbial status was restored to a similar composition to SC group. Differential analysis identified 36 differential operational taxonomic units and 11 metabolites between the Sive and CLP groups, such as 6-Aminopenicillanic acid, gamma-Glutamyl-leucine, and cortisone (variable importance in projection>1and P<0.05). These discriminatory metabolites were highly correlated with each other and mainly involved in the phenylalanine, tyrosine, and tryptophan biosynthesis pathways. Integrated microbiome and metabolome analyses found that almost all Sivelestat-modulated microbes were associated with differential metabolites (P < 0.05), such as Lactobacillus and some amino acids, suggesting that the Sivelestat-induced metabolic profile differences were in part due to its influence on the gut microbiome.
Conclusion: Sivelestat administration in septic rats improved survival, gut microbiota composition and associated metabolites, which could provide new options for sepsis treatment.
Keywords: Sivelestat; gut microbiota; metabolomics; neutrophil elastase inhibitor; sepsis.
Copyright © 2022 Sun, Ding, Cui, Li, Wang, Liang, Liu, Zhang, Wang and Sun.