Table_1_Multi-Omics Integration in Mice With Parkinson’s Disease and the Intervention Effect of Cyanidin-3-O-Glucoside.DOCX (480.71 kB)
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Table_1_Multi-Omics Integration in Mice With Parkinson’s Disease and the Intervention Effect of Cyanidin-3-O-Glucoside.DOCX

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posted on 29.04.2022, 04:27 authored by Wang Wang, Guoxue Zhu, Yuwen Wang, Wei Li, Shilin Yi, Kai Wang, Lu Fan, Juanjuan Tang, Ruini Chen
Background

Parkinson’s disease (PD) is a multifactorial degenerative disease of the central nervous system, which affects mostly older adults. To date, research has focused on the progression of PD. Simultaneously, it was confirmed that the imbalances in gut microbiota are associated with the onset and progression of PD. Accurate diagnosis and precise treatment of PD are currently deficient due to the absence of effective biomarkers.

Methods

In this study, the pharmacodynamic study of cyanidin-3-O-glucoside in PD mice was used. It intends to use the “imbalance” and “balance” of intestinal microecology as the starting point to investigate the “gut-to-brain” hypothesis using metabolomic-combined 16S rRNA gene sequencing methods. Simultaneously, metabolomic analysis was implemented to acquire differential metabolites, and microbiome analysis was performed to analyze the composition and filter the remarkably altered gut microbiota at the phylum/genera level. Afterward, metabolic pathway and functional prediction analysis of the screened differential metabolites and gut microbiota were applied using the MetaboAnalyst database. In addition, Pearson’s correlation analysis was used for the differential metabolites and gut microbiota. We found that cyanidin-3-O-glucoside could protect 1-methyl-4-phenyl-1,2,3,6− tetrahydropy ridine (MPTP)-induced PD mice.

Results

Metabolomic analysis showed that MPTP-induced dysbiosis of the gut microbiota significantly altered sixty-seven metabolites. The present studies have also shown that MPTP-induced PD is related to lipid metabolism, amino acid metabolism, and so on. The 16S rRNA sequencing analysis indicated that 5 phyla and 22 genera were significantly altered. Furthermore, the differential gut microbiota was interrelated with amino acid metabolism, and so on. The metabolites and gut microbiota network diagram revealed significant correlations between 11 genera and 8 differential metabolites.

Conclusion

In combination, this study offers potential molecular biomarkers that should be validated for future translation into clinical applications for more accurately diagnosing PD. Simultaneously, the results of this study lay a basis for further study of the association between host metabolisms, gut microbiota, and PD.

History

References