Dataset Information

Multi-omics data reveals the important role of glycerophospholipid metabolism in the crosstalk between gut and brain in depression.

ABSTRACT:

Background

Gut microbiota plays a critical role in the onset and development of depression, but the underlying molecular mechanisms are unclear. This study was conducted to observe the characteristics of gut microbiota, lipid metabolism and neurotransmitters in Gut-Liver-Brain axis in depressed mice (DM), and identify some novel perceptions on relationships between gut microbiota and depression.

Methods

A mouse model of depression was built used chronic unpredictable mild stress (CUMS). Fecal samples (measuring gut microbiota compositions, microbial genes and lipid metabolites), liver samples (measuring lipid metabolites), and hippocampus (measuring neurotransmitters) were collected. Both univariate and multivariate statistical analyses were used to identify the differential gut microbiota, metabolic signatures and neurotransmitters in DM.

Results

There were significant differences on both microbial and metabolic signatures between DM and control mice (CM): 71 significantly changed operational taxonomic units (OTUs) (60.56% belonged to phylum Firmicutes) and 405 differential lipid metabolites (51.11% belonged to Glycerophospholipid (GP) metabolism) were identified. Functional analysis showed that depressive-like behaviors (DLB)-related differential microbial genes were mainly enriched in GP metabolism. Weighted correlation network analysis (WGCNA) showed that DLB-related differential metabolites mainly belonged to GPs. Meanwhile, seven differential neurotransmitters were identified. Comprehensive analysis found that Lachnospiraceae and gamma-aminobutyric acid (GABA) were significantly correlated with 94.20% and 53.14% differential GPs, respectively, and GABA was significantly correlated with three main DLB phenotypes.

Conclusion

Our results provided novel perceptions on the role of Gut-Liver-Brain axis in the onset of depression, and showed that GP metabolism might be the bridge between gut microbiota and depression. "Lachnospiraceae-GP metabolism-GABA" held the promise as a potential way between gut microbiota and brain functions in DM.

SUBMITTER: Xie J

PROVIDER: S-EPMC9903503 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature

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Publications

Multi-omics data reveals the important role of glycerophospholipid metabolism in the crosstalk between gut and brain in depression.

Xie Jing J Zhong Qi Q Wu Wen-Tao WT Chen Jian-Jun JJ

Journal of translational medicine 20230207 1

<h4>Background</h4>Gut microbiota plays a critical role in the onset and development of depression, but the underlying molecular mechanisms are unclear. This study was conducted to observe the characteristics of gut microbiota, lipid metabolism and neurotransmitters in Gut-Liver-Brain axis in depressed mice (DM), and identify some novel perceptions on relationships between gut microbiota and depression.<h4>Methods</h4>A mouse model of depression was built used chronic unpredictable mild stress ( ...[more]

PMID: 36750892

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Project description:IntroductionAlthough researchers have done intensive research on depression, its pathogenesis is still not fully explained. More and more evidence suggests that gut microbiota is closely related to the onset of depression; but its specific functional ways are not clearly identified.ObjectivesThe purpose of our work was to find out how the gut microbiota was involved in the onset of depression, and to identify the potential ways to link the gut and brain in mice with depressive-like behaviors (DLB).MethodsWe used the chronic restraint stress (CRS)-induced depression model here. Gut microbiota compositions in fecal samples, lipid metabolism (in fecal, serum and hippocampus samples) and neurotransmitters in hippocampus samples were detected.ResultsWe found that the 7 of 13 differential genera that significantly correlated with DLB belonged to phylum Firmicutes. The differential lipid metabolites in fecal samples mainly belonged to glycerophospholipids (GP) and fatty acids (FA) metabolism, and three important "metabolite type-bacterial taxa" correlated pairs were identified: "FA/GP-Firmicutes", "FA/GP-Akkermansia", and "FA/GP-Bifidobacterium". The key differential lipid metabolites significantly correlated with DLB mainly belonged to FA and GP, and the DLB-related metagenomic genes were consistently enriched in GP metabolism and FA metabolism. Three significantly changed short-chain fatty acids (SCFAs) were significantly correlated with the majority of differential genera. Meanwhile, we found that the differential lipid metabolites in serum and hippocampus samples were mainly mapped into the GP metabolism, and there were four differential neurotransmitters from the tryptophan pathway in hippocampus samples.ConclusionTogether, our findings could provide novel insights into the role of "microbiota-gut-brain" (MGB) axis in depression, and indicate that the gut microbiota might have a vital role in the onset of DLB by affecting the peripheral/central GP metabolism and tryptophan pathway. The "Firmicutes-SCFAs-GP metabolism-Tryptophan pathway" might be a possible way to link the gut and brain in depressed mice.

Project description:ObjectivesRheumatoid arthritis (RA) is a chronic autoimmune disease with complex causes and recurrent attacks that can easily develop into chronic arthritis and eventually lead to joint deformity. Our study aims to elucidate potential mechanism among control, new-onset RA (NORA) and chronic RA (CRA) with multi-omics analysis.MethodsA total of 113 RA patients and 75 controls were included in our study. Plasma and stool samples were obtained for 16S rRNA sequencing, internally transcribed spacer (ITS) sequencing and metabolomics analysis. And PBMCs were obtained for RNA sequencing. We used three models, logistic regression, least absolute shrinkage and selection operator (LASSO), and random forest, respectively, to distinguish NORA from CRA, and finally we validated model performance using an external cohort of 26 subjects.ResultsOur results demonstrated intestinal flora disturbance in RA development, with significantly increased abundance of Escherichia-Shigella and Proteobacteria in NORA. We also found that the diversity was significantly reduced in CRA compared to NORA through fungi analysis. Moreover, we identified 29 differential metabolites between NORA and CRA. Pathway enrichment analysis revealed significant dysregulation of glycerophospholipid metabolism and phenylalanine metabolism pathways in RA patients. Next, we identified 40 differentially expressed genes between NORA and CRA, which acetylcholinesterase (ACHE) was the core gene and significantly enriched in glycerophospholipid metabolism pathway. Correlation analysis showed a strong negatively correlation between glycerophosphocholine and inflammatory characteristics. Additionally, we applied three approaches to develop disease classifier models that were based on plasma metabolites and gut microbiota, which effectively distinguished between new-onset and chronic RA patients in both discovery cohort and external validation cohort.ConclusionsThese findings revealed that glycerophospholipid metabolism plays a crucial role in the development and progression of RA, providing new ideas for early clinical diagnosis and optimizing treatment strategies.

Project description:Intracerebral hemorrhage (ICH) is the most devastating subtype of stroke, but effective prevention and treatment strategies are lacking. Recently, gut microbiome and its metabolitesis are considered to be an influencing factor of stroke. However, little is known about the effects of the gut microbiome on ICH and host metabolic activity. Therefore, we used 16S sequencing, macrogenomics sequencing and untargeted metabolomics to explore the differences in gut microbial-metabolome interactions between patients with intracerebral hemorrhage and healthy control populations. We found a significant decrease in the phylum of Firmicutes and a significant increase of Bacteroidetes in ICH patients. At the genus level, Streptococcus, Bifidobacterium, Akkermansia, and Lactobacillus were more abundant in ICH patients. Macrogenomic analysis revealed active glycosaminoglycan degradation, heme synthesis, galactose degradation, lipopolysaccharide core region synthesis, and beta-Lactam resistance in ICH patients. Serum untargeted metabolomic analysis combined with ROC curves showed that octanoylcarnitine, decanoylcarnitine, dodecanoylcarnitine, glyceric acid, pyruvic acid, aspartic acid, methylcysteine, pyroglutamic acid, 9E-tetradecenoic acid, N-Acetylneuraminic acid, and aconitic acid were the best markers for the diagnosis of ICH. Correlation analysis showed that microbiome enriched in the gut of ICH patients were significantly correlated with serum metabolites, revealing a close correlation between the gut microbiome of ICH patients and the host metabolome, and significant differences from the healthy population. microbiota-host co-metabolites including pyruvic acid and 9E-tetradecenoic acid is associated with the the National Institutes of Health Stroke Scale (NIHSS) scores. In conclusion, microbiome-related metabolites in ICH patients was associated with the severity of ICH, the microbiota-host co-metabolites may be a potential may be potential therapeutic targets.

Dataset Information

Multi-omics data reveals the important role of glycerophospholipid metabolism in the crosstalk between gut and brain in depression.

Background

Methods

Results

Conclusion

Publications

Multi-omics data reveals the important role of glycerophospholipid metabolism in the crosstalk between gut and brain in depression.

Similar Datasets

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