Study of blood linear RNA nominates CD55 and DLD as early-stage biomarkers for Parkinson's sisease.

Abstract

Parkinson's disease is the second leading neurodegenerative disease, pathologically characterized by the accumulation of alpha-synuclein in the brain and the loss of dopaminergic neurons in the substantia nigra pars compacta. Despite the intensive efforts to identify diagnostic biomarkers for Parkinson's disease nominating prospective candidates such as such as CSF α-synuclein seed amplification assay or α-/β-synuclein ratio, establishment of minimally invasive biomarkers remains the focus of Parkinson's disease research. We leveraged transcriptomic data from 4,343 participants from four independent datasets to robustly identify Parkinson's disease-associated transcripts. Following the differential abundance analyses, we integrated our findings with several brain transcriptomic, CSF proteomic, plasma proteomic and genomic data to add biological context. We further leveraged our findings to develop predictive models that could differentiate between Parkinson's disease and healthy controls. We identified 296 differentially expressed transcripts, 28 of which were transcribed from known Parkinson's disease-associated loci. Further, we found a significant overlap between our findings and transcripts dysregulated in brain, as well as proteins differentially accumulated in CSF. Our results suggest that expression of the identified transcripts was affected by genetic background including ancestry and Parkinson's disease-related mutations, and nearly half of the identified transcripts were dysregulated before symptom onset. The differentially expressed transcripts were utilized to develop three predictive models that distinguished between Parkinson's disease and healthy controls with a ROC AUC of 0.727-0.733. The predictive models were capable of detecting Parkinson's disease transcriptomic signatures even before symptom onset. Overall, two transcripts, DLD and CD55, showed particular promise as early stage, minimally invasive Parkinson's disease biomarkers. DLD significantly related to Parkinson's disease in the eQTL analyses and we identified a suggestive eQTL for CD55, their protein products were differentially accumulated in CSF, and both DLD and CD55 were included in all three predictive models. Thus, we have performed the largest Parkinson's disease transcriptomic study to date and demonstrated that the transcriptome can be leveraged for development of minimally invasive biomarkers that could aid in diagnosing Parkinson's disease.