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      Gut metagenomics-derived genes as potential biomarkers of Parkinson’s disease

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          Abstract

          Identification of the gut microbiome compositions associated with disease has become a research focus worldwide. Emerging evidence has revealed the presence of gut microbiota dysbiosis in Parkinson’s disease. In this study, we aimed to identify the gut microbiome associated with Parkinson’s disease and subsequently to screen and to validate potential diagnostic biomarkers of Parkinson’s disease. This case-control study investigated gut microbial genes in faeces from 40 volunteer Chinese patients with Parkinson’s disease and their healthy spouses using shotgun metagenomic sequencing. Furthermore, the identified specific gut microbial gene markers were validated with real-time PCR in an independent Chinese cohort of 78 Parkinson’s disease patients, 75 control subjects, 40 patients with multiple system atrophy and 25 patients with Alzheimer’s disease. We developed the first gut microbial gene catalogue associated with Parkinson’s disease. Twenty-five gene markers were identified that distinguished Parkinson’s disease patients from healthy control subjects, achieving an area under the receiver operating characteristic curve (AUC) of 0.896 (95% confidence interval: 83.1–96.1%). A highly accurate Parkinson’s disease index, which was not influenced by disease severity or Parkinson’s disease medications, was created. Testing these gene markers using quantitative PCR distinguished Parkinson’s disease patients from healthy controls not only in the 40 couples (AUC = 0.922, 95% confidence interval: 86.4–98.0%), but also in an independent group of 78 patients with Parkinson’s disease and 75 healthy control subjects (AUC = 0.905, 95% confidence interval: 86.0–95.1%). This classifier also performed a differential diagnosis power in discriminating these 78 patients with Parkinson’s disease from a cohort of 40 patients with multiple system atrophy and 25 patients with Alzheimer’s disease based on the panel of 25 biomarkers. Based on our results, the identified Parkinson’s disease index based on the gene set from the gut microbiome may be a potential diagnostic biomarker of Parkinson’s disease.

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          Enterotypes in the landscape of gut microbial community composition

          Population stratification is a useful approach towards a better understanding of complex biological problems in human health and well-being. The proposal that such stratification applies to the human gut microbiome, in the form of distinct community composition types, termed “enterotypes”, was met with both excitement and controversy. In view of accumulated data and re-analyses since the original work, we revisit the enterotype concept, discuss different methods of dividing up the landscape of possible microbiome configurations, and put these concepts into a functional, ecological and medical context. As enterotypes are of use in describing the gut microbial community landscape and may become relevant in clinical practice, we aim to reconcile differing views and encourage a balanced application of the concept.
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            MINIMUM REDUNDANCY FEATURE SELECTION FROM MICROARRAY GENE EXPRESSION DATA

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              Is Open Access

              Functional implications of microbial and viral gut metagenome changes in early stage L-DOPA-naïve Parkinson’s disease patients

              Background Parkinson’s disease (PD) presently is conceptualized as a protein aggregation disease in which pathology involves both the enteric and the central nervous system, possibly spreading from one to another via the vagus nerves. As gastrointestinal dysfunction often precedes or parallels motor symptoms, the enteric system with its vast diversity of microorganisms may be involved in PD pathogenesis. Alterations in the enteric microbial taxonomic level of L-DOPA-naïve PD patients might also serve as a biomarker. Methods We performed metagenomic shotgun analyses and compared the fecal microbiomes of 31 early stage, L-DOPA-naïve PD patients to 28 age-matched controls. Results We found increased Verrucomicrobiaceae (Akkermansia muciniphila) and unclassified Firmicutes, whereas Prevotellaceae (Prevotella copri) and Erysipelotrichaceae (Eubacterium biforme) were markedly lowered in PD samples. The observed differences could reliably separate PD from control with a ROC-AUC of 0.84. Functional analyses of the metagenomes revealed differences in microbiota metabolism in PD involving the ẞ-glucuronate and tryptophan metabolism. While the abundances of prophages and plasmids did not differ between PD and controls, total virus abundance was decreased in PD participants. Based on our analyses, the intake of either a MAO inhibitor, amantadine, or a dopamine agonist (which in summary relates to 90% of PD patients) had no overall influence on taxa abundance or microbial functions. Conclusions Our data revealed differences of colonic microbiota and of microbiota metabolism between PD patients and controls at an unprecedented detail not achievable through 16S sequencing. The findings point to a yet unappreciated aspect of PD, possibly involving the intestinal barrier function and immune function in PD patients. The influence of the parkinsonian medication should be further investigated in the future in larger cohorts. Electronic supplementary material The online version of this article (doi:10.1186/s13073-017-0428-y) contains supplementary material, which is available to authorized users.
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                Author and article information

                Journal
                Brain
                Oxford University Press (OUP)
                0006-8950
                1460-2156
                August 2020
                August 01 2020
                August 25 2020
                August 2020
                August 01 2020
                August 25 2020
                : 143
                : 8
                : 2474-2489
                Affiliations
                [1 ]Department of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, P.R. China
                [2 ]Department of Biostatistics, Institute of Medical Sciences, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, P.R. China
                [3 ]Department of Bioinformatics, Shanghai Biotechnology Corporation, Shanghai, 200025, P.R. China
                Article
                10.1093/brain/awaa201
                09416d35-ad42-4aa2-8c6b-5d36ab7917d5
                © 2020

                https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model

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