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. 2022 Jul 26:14:927625.
doi: 10.3389/fnagi.2022.927625. eCollection 2022.

Identification of gut metabolites associated with Parkinson's disease using bioinformatic analyses

Affiliations

Identification of gut metabolites associated with Parkinson's disease using bioinformatic analyses

Jun Yan et al. Front Aging Neurosci. .

Abstract

Background: Parkinson's disease (PD) is a common neurodegenerative disease affecting the movement of elderly patients. Environmental exposures are the risk factors for PD; however, gut environmental risk factors for PD are critically understudied. The proof-of-concept study is to identify gut metabolites in feces, as environmental exposure risk factors, that are associated with PD and potentially increase the risk for PD by using leverage of known toxicology results.

Materials and methods: We collected the data regarding the gut metabolites whose levels were significantly changed in the feces of patients with PD from the original clinical studies after searching the following databases: EBM Reviews, PubMed, Embase, MEDLINE, and Elsevier ClinicalKey. We further searched each candidate metabolite-interacting PD gene set by using the public Comparative Toxicogenomics Database (CTD), identified and validated gut metabolites associated with PD, and determined gut metabolites affecting specific biological functions and cellular pathways involved in PD by using PANTHER tools.

Results: Sixteen metabolites were identified and divided into the following main categories according to their structures and biological functions: alcohols (ethanol), amino acids (leucine, phenylalanine, pyroglutamic acid, glutamate, and tyrosine), short-chain fatty acids (propionate and butyrate), unsaturated fatty acids (linoleic acid and oleic acid), energy metabolism (lactate, pyruvate, and fumarate), vitamins (nicotinic acid and pantothenic acid), and choline metabolism (choline). Finally, a total of three identified metabolites, including butyrate, tyrosine, and phenylalanine, were validated that were associated with PD.

Conclusion: Our findings identified the gut metabolites that were highly enriched for PD genes and potentially increase the risk of developing PD. The identification of gut metabolite exposures can provide biomarkers for disease identification, facilitate an understanding of the relationship between gut metabolite exposures and response, and present an opportunity for PD prevention and therapies.

Keywords: Comparative Toxicogenomics Database; Parkinson’s disease; environmental exposures; gut metabolites; visual analyses.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
The flow diagram showing metabolite identification and validation processes.
FIGURE 2
FIGURE 2
Validation of intersection metabolites in the selected metabolite set and the targeted metabolite set through Venn diagram software. A total of three intersection metabolites, including butyrate, tyrosine, and phenylalanine, were obtained. Then, the three metabolites (butyrate, tyrosine, and phenylalanine) were validated that they were highly associated with Parkinson’s disease.
FIGURE 3
FIGURE 3
Biological processes enriched for validated metabolite gene sets. (A) The ancestor chart for enriched GO terms represented by the 106 genes contained in the CTD term “Parkinson’s Disease”. (B) The validated metabolites influenced five main biological process GO terms associated with Parkinson’s disease.
FIGURE 4
FIGURE 4
Signaling pathways enriched for validated metabolite gene sets. Phenylalanine impacting gene set was enriched for the four main pathways. Butyrate and tyrosine impacting gene sets were enriched for three of the four main pathways.
FIGURE 5
FIGURE 5
Identification of the hub genes among the targeting PD gene sets of the validated metabolites. Network analyses were conducted by using a Cytoscape plug-in CytoHubba. The top 10 genes in the networks were identified by Maximal Clique Centrality (MCC) scores and visualized.

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