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      Integrated Analyses of Microbiome and Longitudinal Metabolome Data Reveal Microbial-Host Interactions on Sulfur Metabolism in Parkinson’s Disease

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          Summary

          Parkinson’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.

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          Highlights

          • Longitudinal metabolomics reveal disturbed transsulfuration in Parkinson’s disease

          • Metabolic modeling of gut microbiomes show altered microbial sulfur metabolism

          • Changed microbial sulfur metabolism is linked to B. wadsworthia and A. muciniphila

          • Taurine-conjugated bile acids are associated with incident Parkinson’s disease

          Abstract

          Hertel et al. demonstrate complex alterations in human and microbial sulfur metabolism in Parkinson’s disease by integrating longitudinal metabolomics and computational modeling of gut microbiomes. Then, potential clinical importance is revealed as secondary taurine-conjugated bile acids are shown to be associated with disease severity and Parkinson’s disease incidence.

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          Econometric methods for fractional response variables with an application to 401(k) plan participation rates

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            Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota

            A large set of microbial metabolic models (AGORA) could be applied to better understand the functions of the human gut microbiome.
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              Recon3D: A Resource Enabling A Three-Dimensional View of Gene Variation in Human Metabolism

              Genome-scale network reconstructions have helped uncover the molecular basis of metabolism. Here we present Recon3D, a computational resource that includes three-dimensional (3D) metabolite and protein structure data and enables integrated analyses of metabolic functions in humans. We use Recon3D to functionally characterize mutations associated with disease, and identify metabolic response signatures that are caused by exposure to certain drugs. Recon3D represents the most comprehensive human metabolic network model to date, accounting for 3,288 open reading frames (representing 17% of functionally annotated human genes), 13,543 metabolic reactions involving 4,140 unique metabolites, and 12,890 protein structures. These data provide a unique resource for investigating molecular mechanisms of human metabolism. Recon3D is available at http://vmh.life.
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                Author and article information

                Contributors
                Journal
                Cell Rep
                Cell Rep
                Cell Reports
                Cell Press
                2211-1247
                12 November 2019
                12 November 2019
                12 November 2019
                : 29
                : 7
                : 1767-1777.e8
                Affiliations
                [1 ]School of Medicine, National University of Galway, Galway, Ireland
                [2 ]Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-sur-Alzette, Luxembourg
                [3 ]Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
                [4 ]Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research, Faculty of Science, Leiden University, Leiden, the Netherlands
                [5 ]MRC Epidemiology Unit, University of Cambridge, Cambridge CB2 0QQ, UK
                [6 ]Paracelsus-Elena-Klinik, 34128 Kassel, Germany
                [7 ]Department of Neurosurgery, University Medical Center Goettingen, 37075 Goettingen, Germany
                [8 ]Department of Neurology, University Medical Center Goettingen, 37075 Goettingen, Germany
                [9 ]Centre Hospitalier de Luxembourg (CHL), Luxembourg, Luxembourg
                [10 ]Division of Microbiology, National University of Galway, Galway, Ireland
                [11 ]APC Microbiome Ireland, Ireland
                Author notes
                []Corresponding author ines.thiele@ 123456nuigalway.ie
                [12]

                Lead Contact

                Article
                S2211-1247(19)31335-X
                10.1016/j.celrep.2019.10.035
                6856723
                31722195
                1be3fde7-2e2a-45ee-969f-4810af9d22b9
                © 2019 The Authors

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 29 November 2018
                : 17 July 2019
                : 9 October 2019
                Categories
                Article

                Cell biology
                metabolism,parkinson's disease,neurodegenerative disease,microbiome,transsulfuration pathway,metabolomics,metagenomics,metabolic modeling,taurine metabolism,bile acid metabolism

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