Table 1.
Information about study participants.
Figure 1.
Design of workflow used to identify splice variant-specific risk markers of PD.
The numbers inside the boxes correspond to the number (N) of samples. Samples from PD patients were randomly chosen to be part of the training or test sets of samples. (A) Identifying the PD risk markers using a training set. In order to identify putative biomarkers, the training set (training set 1) was used in the microarray screen. The data from the microarray analysis was analyzed by three methods in order to identify markers with good specificity, sensitivity and predictive accuracy. PD patients were compared to each control group separately, compared to pooled controls as a single group and using LOOCV. Splice variants that were up- or down-regulated by 2-fold (P<0.05) in PD patients were considered candidate risk markers. The candidate risk markers were manually curated to include those that may play a role in PD based on pathway and disease analysis (Ingenuity Systems software) and to exclude those for which primers could not be designed or could not be detected by qPCR. Thirteen of the risk markers were replicated by qPCR (Training set 1). (B) Experimentally testing the PD risk markers with training and test sets. The 13 risk markers were validated in two independent test sets (training set 2 and test set) using qPCR. Clinical diagnosis of the participants was based on neurological exam. (C) Statistical analysis. LOOCV KNN was used to determine the predictive accuracy of the samples from the training set (training set 1). In addition, linear discriminant analysis was used to test the predictive accuracy on the training and test sets. PD is Parkinson’s disease patients represented in green, HC is healthy controls represented in red, MSA is multiple system atrophy controls represented in blue, PSP is progressive supranuclear palsy controls represented in purple and Control is HC+MSA+PSP controls represented in yellow. TP = true positive; TN = true negative; FP = false positive; FN = false negative; PPV = positive predictive value; NPV = negative predictive value.
Figure 2.
Heat map and PCA plot of the PD risk markers.
(A) Venn diagram of splice events that were differentially expressed in PD patients compared to controls. PD patients were compared to each control group (HC, MSA or PSP) individually or a pooled control group that included HC, MSA and PSP participants. (B) PCA analysis of the data from the microarrays of the risk markers. HC is healthy control represented in red, PD is Parkinson’s disease patient represented in green, MSA is multiple system atrophy control represented in blue and PSP is progressive supranuclear palsy control represented in purple. (C) Heat map of the data from the qPCR assay of the risk markers analyzed using the ΔΔCt method. Each column in the heat map corresponds to a PD patient or a control. Each row represents the relative level of abundance of a single splice variant. Each splice variant is denoted by the name of the mRNA. Color scales representing splice variant expression with red representing high abundance relative to the mean abundance; blue representing low abundance relative to the mean abundance; and gray representing no significant change in abundance level between the sample and control. Gene expression microarray data has been deposited at Gene Expression Omnibus under accession number GSE34287.
Figure 3.
Relative mRNA quantification graphs of the risk markers comparing PD patients with each control group.
A one-way ANOVA and tukey-kramer post-hoc analysis was used to compare PD patients with HC, MSA and PSP controls separately. Fold change values relative to a calibrator are displayed with error bars indicating SEM. Gapdh mRNA was used as a reference gene and HC as a calibrator. PD is Parkinson’s disease, HC is healthy control, MSA is multiple system atrophy control and PSP is progressive supranuclear palsy control. *p<0.01, **p<0.005, ***p<0.001 and ****p<0.0001.
Figure 4.
Linear discriminant analysis of the biomarkers.
(A) Discriminant scores of PD samples (green) are compared to controls (HC, PSP and MSA, yellow) using the 13 PD biomarkers. (B) Discriminant scores of PD samples (green) are compared to APD (PSP and MSA, yellow) using the 8 APD biomarkers.
Figure 5.
Regulatory gene and protein interaction networks.
(A) Network of the PD biomarkers. (B) Network of the APD biomarkers. Computational molecular interaction network prediction based on genes and proteins significantly associated according to the Ingenuity Pathways Knowledge Base. The markers are highlighted in grey and node properties are indicated by shapes. Interactions between the nodes are solid (confirmed interactions) and dashed (predicted interactions).
Table 2.
Convergence of PD gene expression studies.