Abstract
SummaryParkinson’s disease (PD) exhibits systemic effects on the human metabolism, with emerging roles for the gut microbiome. Here, we integrate longitudinal metabolome data from 30 drug-naive, de novo PD patients and 30 matched controls with constraint-based modeling of gut microbial communities derived from an independent, drug-naive PD cohort, and prospective data from the general population. Our key results are (1) longitudinal trajectory of metabolites associated with the interconversion of methionine and cysteine via cystathionine differed between PD patients and controls; (2) dopaminergic medication showed strong lipidomic signatures; (3) taurine-conjugated bile acids correlated with the severity of motor symptoms, while low levels of sulfated taurolithocholate were associated with PD incidence in the general population; and (4) computational modeling predicted changes in sulfur metabolism, driven by A. muciniphila and B. wadsworthia, which is consistent with the changed metabolome. The multi-omics integration reveals PD-specific patterns in microbial-host sulfur co-metabolism that may contribute to PD severity.
Highlights
Parkinson’s disease (PD) is a complex neurodegenerative disease with diverse underlying etiological paths and systemic consequences for patients’ physiology and metabolism (Kalia andLang, 2015)
We obtained EDTA-plasma samples taken from the well-defined longitudinal DeNoPa cohort of initially drug-naive PD patients (n = 30) and matched healthy controls (n = 30; Table S1), each followed for 4 years, with samples taken every 2 years (Mollenhauer et al, 2013, 2016)
Using principal-component analysis (PCA) of the type 3 metabolites, we found that the first PC displayed high loadings for all of the amines, while the second PC was primarily composed of the 3 long-chain fatty acids (FA) (Figure S1B)
Summary
Parkinson’s disease (PD) is a complex neurodegenerative disease with diverse underlying etiological paths and systemic consequences for patients’ physiology and metabolism (Kalia andLang, 2015). Cumulative evidence suggests a contribution of peripheral metabolic factors, such as gut microbiome changes (Bedarf et al, 2017; Heintz-Buschart et al, 2018; Scheperjans et al, 2015), metabolic alterations (Havelund et al, 2017), and peripheral inflammation (Qin et al, 2016) to disease risk and progression (Mule and Singh, 2018; Sampson et al, 2016). Their causal role in the progression of the disease remains largely unknown, partly due to a lack of longitudinal human omics data. Condition-specific metabolic models can be derived through the application of condition-specific constraints, such as omics data (Yizhak et al, 2010) Capitalizing on metabolic reconstructions of hundreds of gut microbes (Magnusdottir et al, 2017), metagenomics data have been used to predict metabolic outputs of microbial community (Baldini et al, 2019; Heinken et al, 2017), which can be integrated with metabolomic data
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