Circulating blood circular RNA in Parkinson's Disease; from involvement in pathology to diagnostic tools in at-risk individuals

Abstract

To identify circRNAs associated with Parkinson's disease (PD) we leveraged two of the largest publicly available studies with longitudinal clinical and blood transcriptomic data. We performed a cross-sectional study utilizing the last visit of each participant (N = 1848), and a longitudinal analysis that included 1166 participants with at least two time points. We identified 192 differentially expressed circRNAs, with effects that were sustained during disease, in mutation carriers, and diverse ancestry. The 192 circRNAs were leveraged to distinguish between PD and healthy participants with a ROC AUC of 0.797. Further, 71 circRNAs were sufficient to distinguish between genetic PD (AUC₇₁ = 0.954) and, at-risk participants (AUC₇₁ = 0.929) and healthy controls, supporting that circRNAs have the potential to aid the diagnosis of PD. Finally, we identified five circRNAs highly correlated with symptom severity. Overall, we demonstrated that circRNAs play an important role in PD and can be clinically relevant to improve diagnostic and monitoring.

Fig. 1. Study design summary.

We have followed a two-stage cross-sectional analysis with discovery (PDBP) and replication (PPMI) phases. Then we have performed sensitivity analysis stratifying by mutation, exploring individuals from African Ancestry, and those at risk due to being a carrier of a PD-causing mutation, or suffering from RBD or hyposmia. We also repeated the same analyses in a longitudinal manner using mixed models and leveraged the repeated measures available for each individual. Finally, we have functionally annotated the findings via circRNA-miRNA integration and leveraged circRNAs to build predictive models. The sample size (n) for each subgroup correspond to number of unique individuals except for the longitudinal comparison that correspond to total number of samples per group.

Fig. 2. Volcano plot showing the cross-sectional differential expression analysis (PDBP).

Highlighted in dark gray are those circRNA that replicated in the PPMI cohort, in orange and blue those already described to be differentially accumulated in previous publications in blood and brain tissue respectively.

Fig. 3. circRNAs change over the course of the disease and can be leveraged diagnostic biomarkers.

A Line plots showing trajectories of circRNA abundances for the top three significant circRNAs in longitudinal analysis, as well as the nine high-confidence circRNAs from cross-sectional analysis; B Whisker plot showing ROC AUC values with 90% confidence interval evaluating the ability of three combination of circRNAs to distinguish between PD participants and healthy controls or at-risk participants and healthy controls.