Abstract
Abstract
Parkinson's disease (PD) 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 PD and 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 PD research.
We leveraged transcriptomic data from 4343 participants from four independent datasets to robustly identify PD-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 PD and healthy controls.
We identified 296 differentially expressed transcripts, 28 of which were transcribed from known PD-associated loci. Furthermore, we found a significant overlap between our findings and transcripts dysregulated in brain, as well as proteins that differentially accumulate in CSF. Our results suggested that expression of the identified transcripts was affected by genetic background, including ancestry and PD-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 PD from healthy controls with an area under the receiver operating characteristic curve of 0.727–0.733. The predictive models detected transcriptomic PD signatures even before symptom onset. Overall, two transcripts, DLD and CD55, showed particular promise as early stage, minimally invasive PD biomarkers. DLD was significantly related to PD in the expression quantitative trait loci (eQTL) analyses, and we identified a putative eQTL for CD55. Their protein products differentially accumulated in CSF, and both DLD and CD55 were included in all three predictive models.
Having performed the largest transcriptomic study of PD to date, we demonstrate that the transcriptome can be leveraged to develop minimally invasive biomarkers that may aid in diagnosing the disease.