Identification and prediction of Parkinson's disease subtypes and progression using machine learning in two cohorts.

The clinical manifestations of Parkinson's disease (PD) are characterized by heterogeneity in age at onset, disease duration, rate of progression, and the constellation of motor versus non-motor features. There is an unmet need for the characterization of distinct disease subtypes as well as improved, individualized predictions of the disease course. We used unsupervised and supervised machine learning methods on comprehensive, longitudinal clinical data from the Parkinson's Disease Progression Marker Initiative (n = 294 cases) to identify patient subtypes and to predict disease progression. The resulting models were validated in an independent, clinically well-characterized cohort from the Parkinson's Disease Biomarker Program (n = 263 cases). Our analysis distinguished three distinct disease subtypes with highly predictable progression rates, corresponding to slow, moderate, and fast disease progression. We achieved highly accurate projections of disease progression 5 years after initial diagnosis with an average area under the curve (AUC) of 0.92 (95% CI: 0.95 ± 0.01) for the slower progressing group (PDvec1), 0.87 ± 0.03 for moderate progressors, and 0.95 ± 0.02 for the fast-progressing group (PDvec3). We identified serum neurofilament light as a significant indicator of fast disease progression among other key biomarkers of interest. We replicated these findings in an independent cohort, released the analytical code, and developed models in an open science manner. Our data-driven study provides insights to deconstruct PD heterogeneity. This approach could have immediate implications for clinical trials by improving the detection of significant clinical outcomes. We anticipate that machine learning models will improve patient counseling, clinical trial design, and ultimately individualized patient care

Transcriptome analysis of tetrodotoxin sensing and tetrodotoxin action in the central nervous system of tiger puffer Takifugu rubripes juveniles

To reveal the sensing of tetrodotoxin (TTX) by tiger puffer Takifugu rubripes juveniles and its action in the central nervous system (CNS), we conducted transcriptome analysis using next-generation sequencing for the olfactory system and brain of non-toxic cultured juveniles administered TTX. Sixty-seven million reads from the nasal region (olfactory epithelium and skin) and the brain of each of three individuals of the control, TTX-sensing and TTX-administered juveniles were assembled into 153,958 contigs. Mapping raw reads from each sample onto the nucleotide sequences of predicted transcripts in the T. rubripes genome (FUGU version 4) and the de novo assembled contigs to investigate their frequency of expression revealed that the expression of 21 and 81 known genes significantly changed in TTX-sensing and TTX-administered juveniles in comparison with control juveniles, respectively. These genes included those related to feeding regulation and a reward system, and indicated that TTX ingestion of T. rubripes juveniles is controlled in the feeding center in the brain, that T. rubripes may sense TTX as a reward, and that accumulated TTX directly acts on the central nervous system to adjust TTX ingestion