Identification of potential blood biomarkers for Parkinson’s disease by gene expression and DNA methylation data integration analysis

Abstract

BackgroundBlood-based gene expression or epigenetic biomarkers of Parkinson’s disease (PD) are highly desirable. However, accuracy and specificity need to be improved, and methods for the integration of gene expression with epigenetic data need to be developed in order to make this feasible.MethodsWhole blood gene expression data and DNA methylation data were downloaded from Gene Expression Omnibus (GEO) database. A linear model was used to identify significantly differentially expressed genes (DEGs) and differentially methylated genes (DMGs) according to specific gene regions 5′—C—phosphate—G—3′ (CpGs) or all gene regions CpGs in PD. Gene set enrichment analysis was then applied to DEGs and DMGs. Subsequently, data integration analysis was performed to identify robust PD-associated blood biomarkers. Finally, the random forest algorithm and a leave-one-out cross validation method were performed to construct classifiers based on gene expression data integrated with methylation data.ResultsEighty-five (85) significantly hypo-methylated and upregulated genes in PD patients compared to healthy controls were identified. The dominant hypo-methylated regions of these genes were significantly different. Some genes had a single dominant hypo-methylated region, while others had multiple dominant hypo-methylated regions. One gene expression classifier and two gene methylation classifiers based on all or dominant methylation-altered region CpGs were constructed. All have a good prediction power for PD.ConclusionsGene expression and methylation data integration analysis identified a blood-based 53-gene signature, which could be applied as a biomarker for PD.Electronic supplementary materialThe online version of this article (10.1186/s13148-019-0621-5) contains supplementary material, which is available to authorized users.

The conserved DNMT1 dependent methylation regions in human cells are vulnerable to environmental rotenone

“…To further explore the significance of identified human DNMT1 dependent genes, we evaluated the recent literature on potential blood biomarkers in Parkinson's disease (PD) patients. Encouragingly, we observed five differentially methylated genes in these studies within our conserved human regions (Henderson-Smith et al 2018; Wang et al 2019). They are COL9A2, SCNN1A, AMICA1, SLC16A3, and DLK1.…”

“…We evaluated the recent literature on candidate blood biomarkers in Parkinson's disease patients and observed five differentially methylated genes (COL9A2, SCNN1A, AMICA1, SLC16A3, and DLK1) in these studies within our conserved human regions (Henderson-Smith et al 2018; Wang et al 2019). Of these genes, COL9A2 was differentially expressed in our rotenone treated cells and was determined to have high regional expression in the substantia nigra (Table II, Figure 4) (Henderson-Smith et al 2018).…”

A decade of epigenetic change in aging twins: genetic and environmental contributions to longitudinal DNA methylation

“…Thus, the age-related sites showed a significantly higher proportion of variance attributed to heritable and shared environmental influences due to lower nonshared factors in Aging set I and due to higher genetic and common environmental influences in Aging set II. Zhang et al, 2019). Significantly higher heritabilities (A+D, A) and common environmentality (C) were observed for the 1190 unique Clocks sites compared to all remaining CpGs (.02 to .05 higher, p < 8.68E-10, see Table S2 Variance Components).…”

“…Dense regions of CpGs referred to as 'islands' and represent about 5% of CpGs occurring in the genome (about 20,000 total) and often reside in promotor regions (11); in addition, surrounding 'shores' and 'shelves' to these islands are of interest and may be differentially methylated compared to islands (4). The addition of methylation tags to CpG sites alters gene expression, typically by interfering with or silencing gene transcription although upregulation of gene expression has been documented (12), and may differentially occur in cells across multiple tissue types including brain, muscle and leukocytes (13). Methylation tags can be removed as a consequence of exposures as well, leading to dynamics in expression across time (9).…”

“…Jones et al, 2015). The addition of methylation tags to CpG sites is associated with altered gene expression, typically by interfering with or silencing gene transcription although upregulation of gene expression has been documented (C. Wang et al, 2019), and may differentially occur in cells across multiple tissue types including brain, muscle and leukocytes (Fernandez et al, 2012). Methylation tags can be removed as a consequence of exposures as well, leading to dynamics in expression across time (Kanherkar et al, 2014).…”