Alteration of the gut microbiome and correlated metabolism in a rat model of long-term depression
Open Access
- 27 March 2023
- journal article
- research article
- Published by Frontiers Media SA in Frontiers in Cellular and Infection Microbiology
- Vol. 13, 1116277
Abstract
Objective: This study aims to investigate the composition and function of the gut microbiome in long-term depression using an 8-week chronic unpredictable mild stress (CUMS) rat model.Materials and methods: Animals were sacrificed after either 4 weeks or 8 weeks under CUMS to mimic long-term depression in humans. The gut microbiome was analyzed to identify potential depression-related gut microbes, and the fecal metabolome was analyzed to detect their functional metabolites. The correlations between altered gut microbes and metabolites in the long-term depression rats were explored. The crucial metabolic pathways related to long-term depression were uncovered through enrichment analysis based on these gut microbes and metabolites.Results: The microbial composition of long-term depression (8-week CUMS) showed decreased species richness indices and different profiles compared with the control group and the 4-week CUMS group, characterized by disturbance of Alistipes indistinctus, Bacteroides ovatus, and Alistipes senegalensis at the species level. Additionally, long-term depression was associated with disturbances in fecal metabolomics. D-pinitol was the only increased metabolite in the 8-week CUMS group among the top 10 differential metabolites, while the top 3 decreased metabolites in the long-term depression rats included indoxyl sulfate, trimethylaminen-oxide, and 3 alpha,7 alpha-dihydroxy-12-oxocholanoic acid. The disordered fecal metabolomics in the long-term depression rats mainly involved the biosynthesis of pantothenate, CoA, valine, leucine and isoleucine.Conclusion: Our findings suggest that the gut microbiome may participate in the long-term development of depression, and the mechanism may be related to the regulation of gut metabolism.Keywords
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