Gut microbiome-based secondary metabolite biosynthetic gene clusters detection in Parkinson’s disease

Abstract

The microbiota of individuals with Parkinson’s disease (PD) has been the focus of research in recent years. However, the mechanisms underlying the interactions between the gut microbiome and the brain, as well as its role in PD pathogenesis, remain to be elucidated. In this study, we used a systematic approach to predict putative biosynthetic gene clusters (BGCs) from the raw metagenomic data of the gut microbiome, and identified 43 BGCs that were significantly enriched in the PD patients. Fourteen of these clusters originated from microbes that were not increased in the patients, and the most significantly enriched one encoded a putative efflux protein and a radical SAM protein, indicating a potential role in PD. Based on a random forest classifier, these BGCs can be used to correctly discriminate between PD patients and healthy controls, with a cross-validated AUC of 0.91 from the 31 early stage PD patients and 28 healthy controls. Our study provides an alternative method to analyze the microbiota of PD patients, and further increase our understanding of this disease.