Multi-omics machine learning classifier and blood transcriptomic signature of Parkinson's disease.

Abstract

Early diagnosis and biomarker discovery to bolster the therapeutic pipeline for Parkinson's disease (PD) are urgently needed. In this study, we leverage the large-scale, whole-blood total RNA and DNA sequencing data from the Accelerating Medicines Partnership in Parkinson's Disease (AMP PD) program to identify PD-associated RNAs, including both known genes and novel circular RNAs (circRNA) and enhancer RNAs (eRNAs). Initially, 874 known genes, 783 eRNAs, and 35 circRNAs were found differentially expressed in PD blood in the PPMI cohort (FDR < 0.05). Based on these findings, a novel multi-omics machine learning model was built to predict PD diagnosis with high performance (AUC = 0.89), which was superior to previous models. We further replicated this discovery in an independent PDBP/BioFIND cohort and confirmed 1,111 significant marker genes, including 491 known genes, 599 eRNAs, and 21 circRNAs. Functional enrichment analysis showed that the PD-associated genes are involved in neutrophil activation and degranulation, as well as the TNF-α signaling pathway. By comparing the PD-associated genes in blood with those in human brain dopamine neurons in our BRAINcode cohort, we found only 44 genes (9% of the known genes) showing significant changes with the same direction in both PD brain neurons and PD blood, among which are neuroinflammation-associated genes IKBIP, CXCR2, and NFKBIB. Our findings demonstrated consistently lower SNCA mRNA levels and the increased expression levels of VDR gene in the blood of early-stage PD patients. In summary, this study provides a generally useful computational framework for further biomarker development and early disease prediction. We also delineate a wide spectrum of the known and novel RNAs linked to PD that are detectable in circulating blood cells in a harmonized, large-scale dataset.