Multiomics implicate gut microbiota in altered lipid and energy metabolism in Parkinson’s disease

Pereira, Pedro; Trivedi, Drupad K.; Silverman, Justin D.; Duru, Ilhan Cem; Paulín, Lars; Auvinen, Petri; Scheperjans, Filip

doi:10.1038/s41531-022-00300-3

Cited by 18 publications

(13 citation statements)

References 75 publications

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“…S14). Previous analyses based on multiomics approaches of the same cohort (but using samples taken two years later than the ones used in this study) indicated that metabolites involved in the squalene and cholesterol pathways could be used as predictive molecules [ 8 ], and the BGCs detected in this study could be potentially involved in the synthesis of these metabolites.…”

Section: Discussionmentioning

confidence: 90%

“…Following the Braak hypothesis, we have also investigated the microbiome and PD connections in samples from nasal and oral locations [ 6 ]. These original microbiome analyses have been followed by studies on SCFA and immune responses [ 7 ], and recently on the links between metabolomics, metabolic enzymes, and epigenetic regulation of the host [ [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

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Metagenome-assembled microbial genomes from Parkinson’s disease fecal samples

Duru

Lecomte

Shishido

et al. 2023

Preprint

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The human gut microbiome composition has been linked to Parkinson’s disease (PD). However, knowledge of the gut microbiota on the genome level is still limited. Here we performed deep metagenomic sequencing and binning to build metagenome-assembled genomes (MAG) from 136 human fecal microbiomes (68 PD samples and 68 control samples). We constructed 952 non-redundant high-quality MAGs and compared them between PD and control groups. Among these MAGs, there were 22 different versions ofCollinsellaandPrevotellaMAGs, indicating high variability of those genera in the human gut environment. Microdiversity analysis indicated thatRuminococcus bromiiwas statistically significantly (p < 0.002) more diverse on the strain level in the control samples compared to the PD samples. In addition, by clustering all genes and performing presence-absence analysis between groups, we identified several control-specific (p < 0.05) related genes, such asspeFand Fe-S oxidoreductase. We also report detailed annotation of MAGs, including Clusters of Orthologous Genes (COG), Cas operon type, antiviral gene, prophage, and secondary metabolites biosynthetic gene clusters, which can be useful for providing a reference for future studies.

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Section: Discussionmentioning

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Metagenome-assembled microbial genomes from Parkinson’s disease fecal samples

Duru

Lecomte

Shishido

et al. 2023

Preprint

Self Cite

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“…A recent multi-omics study revealed a strong link between the composition of the gut bacterial community, serum metabolic profile, and disease severity in patients with PD [ 40 ]. Therefore, we speculated that L. plantarum CCFM405 might affect serum metabolite composition in mice by modifying the intestinal microbiota composition and microbial metabolites.…”

Section: Resultsmentioning

confidence: 99%

Lactobacillus plantarum CCFM405 against Rotenone-Induced Parkinson’s Disease Mice via Regulating Gut Microbiota and Branched-Chain Amino Acids Biosynthesis

Chu

et al. 2023

Nutrients

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Recent studies have demonstrated that disturbances in the gut microbiota and microbiota -derived metabolites contribute to the pathogenesis of Parkinson’s disease (PD), suggesting that probiotic treatments that restore them may delay disease progression. This study aimed to examine the attenuating efficacy of L. plantarum CCFM405 and the potential mechanisms in mice with rotenone-induced PD. Our results indicate that L. plantarum CCFM405 ameliorated rotenone-induced motor deficits and constipation, decreased dopaminergic neuronal death, reduced intestinal inflammation and neuroinflammation, and raised dopamine levels, 5-HT, and associated metabolites in the striatal region of the brain in mice with PD. Sequencing of 16S rRNA from fecal microbiota revealed that L. plantarum CCFM405 normalized the gut bacterial composition in mice with PD, as evidenced by the increased relative abundance of the following genus, Bifidobacterium, Turicibacter, and Faecalibaculum, and decreased relative abundance of Alistipes, Bilophila, Akkermansia, and Escherichia-Shigella. The PICRUSt-predicted gut microbiota function revealed that L. plantarum CCFM405 enhanced the biosynthesis of amino acid pathways, particularly valine, leucine, and isoleucine (branched-chain amino acids, BCAAs). A non-metabolomic analysis of the serum and feces showed that L. plantarum CCFM405 markedly increased the levels of BCAAs. Pathway enrichment analysis based on the KEGG database further suggested that L. plantarum CCFM405 supplementation can promote BCAAs biosynthesis. Collectively, L. plantarum CCFM405 can help to prevent rotenone-induced PD by modulating the gut microbiota–metabolite axis. BCAAs may play a dominant role in L. plantarum CCFM405-associated neuroprotection in PD mice. This probiotic could be utilized as a potential food supplement in the management of PD.

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“…The diagnostic performance of NN was at least 10% higher than previously reported by Gonzalez-Riano et al when a SVM model was used. 13 In addition, the diagnostic performance obtained using NN is the highest reported to date for any PD diagnosis regardless of the sample matrix including blood plasma, 25,26 blood serum, 27 and skin sebum. 10,11,16,28,29 Diagnostic Performance Is Uncompromised by Including the Whole Metabolomics Dataset.…”

Section: T H I S C O N T Ementioning

confidence: 87%

Interpretable Machine Learning on Metabolomics Data Reveals Biomarkers for Parkinson’s Disease

et al. 2023

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The use of machine learning (ML) with metabolomics provides opportunities for the early diagnosis of disease. However, the accuracy of ML and extent of information obtained from metabolomics can be limited owing to challenges associated with interpreting disease prediction models and analyzing many chemical features with abundances that are correlated and “noisy”. Here, we report an interpretable neural network (NN) framework to accurately predict disease and identify significant biomarkers using whole metabolomics data sets without a priori feature selection. The performance of the NN approach for predicting Parkinson’s disease (PD) from blood plasma metabolomics data is significantly higher than other ML methods with a mean area under the curve of >0.995. PD-specific markers that predate clinical PD diagnosis and contribute significantly to early disease prediction were identified including an exogenous polyfluoroalkyl substance. It is anticipated that this accurate and interpretable NN-based approach can improve diagnostic performance for many diseases using metabolomics and other untargeted ‘omics methods.

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Multiomics implicate gut microbiota in altered lipid and energy metabolism in Parkinson’s disease

Cited by 18 publications

References 75 publications

Metagenome-assembled microbial genomes from Parkinson’s disease fecal samples

Metagenome-assembled microbial genomes from Parkinson’s disease fecal samples

Lactobacillus plantarum CCFM405 against Rotenone-Induced Parkinson’s Disease Mice via Regulating Gut Microbiota and Branched-Chain Amino Acids Biosynthesis

Interpretable Machine Learning on Metabolomics Data Reveals Biomarkers for Parkinson’s Disease

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