A cross-sectional and longitudinal study of gut microbiota in Parkinson's disease

Project Summary/Abstract The majority of the PD patients have a long history of constipation before the onset of the motor symptoms of PD. Abnormal gut microbiota (dysbiosis) and its metabolomics have been proposed to play a crucial role in the formation and migration of pathologies found in Parkinson’s disease (PD) – leading to the notion of a “Gut

Microbiome-Brain Axis” in the pathogenesis of PD. However, it remains unclear whether the PD-associated gut dysbiosis is a cause or consequence. The goal of the proposed studies is to gain a better understanding of the role of gut dysbiosis in the development, progression, and prognosis of PD, which will lay the foundation for the

identification of microbiome-based predictive biomarkers and open opportunities to develop gut microbiome- based targeted interventions to prevent, mitigate and treat different phenotypes or forms of PD at various stages. Our multi-disciplinary team proposes a multi-pronged approach that will include cross-sectional and longitudinal

studies of PD patients with different age at the onset, clinical manifestation, and severity of motor and non-motor symptoms. Clinical metadata will be collected along with fecal microbiota samples that will be subjected to functional multi’omic analyses that includes two discovery tools: a supervised metabolomics panel and a novel

transfer RNA-based (MSRseq) technology developed by our team that can simultaneously track gut microbial membership and function on different time scales. These measures are important in defining states of gut microbiota health and managing patients with gut dysbiosis back to eubiosis. Our studies will also be unique

because of the large Black PD patient population at the University of Chicago (35% here vs < 6% elsewhere) who come from underserved urban communities on the South and West sides of Chicago. So little information is available on Black populations with PD because of disparities in socioeconomic status and health care access.

The clinical study design is unique in having both cross-sectional and longitudinal arms and training and validation cohort components, where fecal microbiota and skin and colonoscopic biopsies for α-synuclein will be collected at years 1, 3 and 5 along with clinical metadata that includes PD phenotypes or forms, age at disease

onset (early vs usual mid vs late onset), clinical manifestations (tremor dominant vs akinetic rigidity or postural instability and gait disorder form vs the mixed), severity of motor and non-motor symptoms, and race (Black vs White), and age- and sex-matched healthy controls. Machine learning (ML) and artificial intelligence (AI) will be

used to identify best performing PD predictive gut microbiota and metabolic biomarkers. To gain additional insights into potential gut microbial drivers and mediators that underpin etiopathogenesis of different PD phenotypes and progression, we will identify PD-promoting microbiota candidates and mediators for which

microbiome-based interventions that potentially prevent, mitigate, or treat PD can be developed. The clinical meta-data and multi’omic gut microbiota data will be deposited with the Coordinating and Data Management Center (CDMC) to be available to the broader research community for hypothesis generation and testing.