Elucidating the dysbiotic features of gut microbiome, interaction with human genome, and utility as biomarker for treatment of Parkinson's disease

Abstract: Parkinson’s disease (PD) is the fastest growing neurologic disease and a leading cause of disability world-wide. PD affects body and mind, is progressive, and there is no prevention or cure. Gut microbiome, a recently recognized contributing factor in PD, offers new leads for understanding the underlying pathobiology and devising new treatments. Here, we present the most comprehensive study of PD gut microbiome to date, comprised of three large datasets with a sample size of 1,006 PD and 544 neurologically healthy controls, generated with uniform methodology from subject recruitment to data analysis, and characterized with deep shotgun metagenome sequences, genome-wide genotypes, and metadata. We begin with describing the gut dysbiosis at species, gene, pathway, and functional level. Next, we find that PD-associated variants at SNCA gene region are associated with increased abundance of opportunistic pathogens and depletion of fiber degraders in the PD gut. We show that presence of opportunistic pathogens at high levels in the gut increases the penetrance of SNCA variants for PD risk, raising GWAS-derived odds ratio from less than 1.5 to over 8. The SNPs identified here control splicing of the SNCA transcripts into alpha-synuclein isoforms with varying affinity for pathologic aggregation. These data suggest pathogens are triggers for disease in the setting of genetic susceptibility, and the link to the genome implicates the microbes in causation of PD. Finally, pivoting to translation, we show that not all PD patients have the same dysbiotic features, and propose a conceptual framework to identify microbiome-based biomarkers to select appropriate patients for targeted microbiome-based clinical trials and personalized treatment.Zenodo contents: In this Zenodo archive we provide (1) “Source Data” which contains Meta-Data and post sequence QC and post taxonomic and functional profiling used to generate tables and figures in the manuscript and (2) "Code" that was used to perform bioinformatic statistical analyses. Individual level raw shotgun sequences are available on NCBI Sequence Read Archive (SRA) under BioProject ID [to be supplied].