Identification of Potential Blood Biomarkers for Parkinson's Disease

Wang et al. Clinical Epigenetics (2019) 11:24 https://doi.org/10.1186/s13148-019-0621-5 RESEARCH Open Access Identification of potential blood biomarkers for Parkinson’s disease by gene expression and DNA methylation data integration analysis Changliang Wang†, Liang Chen†, Yang Yang, Menglei Zhang and Garry Wong* Abstract Background: Blood-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. Methods: Whole 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. Results: Eighty-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. Conclusions: Gene expression and methylation data integration analysis identified a blood-based 53-gene signature, which could be applied as a biomarker for PD. Keywords: Parkinson’s disease, Data integration, DNA methylation, Gene expression Background to PD [3]. But knowledge concerning the detailed pro- Parkinson’s disease (PD) is the second most common cesses governing the initiation and progression of PD is neurodegenerative disease, following Alzheimer’s disease. still unknown and remains a key obstacle on the road of PD mainly affects the patient’s motor system, the symp- PD treatment. Development of robust and accurate bio- toms of which include tremor, rigidity, and slowness of markers would greatly facilitate the early detection and movement [1]. PD was first mentioned in 1817 by Doc- identification of biological features of PD. Therefore, it tor James Parkinson; however, there still remains no cure is urgent to identify potential biomarkers for PD. for PD today [2]. A wide body of literature currently Currently, brain imaging of the nigrostriatal dopamine suggests that genetic or environmental factors can lead system has been used as a biomarker for early disease along with cerebrospinal fluid analysis of α-synuclein. However, these methods remain costly or are invasive * Correspondence: [email protected] †Changliang Wang and Liang Chen contributed equally to this work. [4]. Blood biomarkers are easier to obtain, much Cancer Centre, Centre of Reproduction, Development and Aging, Faculty of cheaper, and less invasive [5], and some researchers have Health Sciences, University of Macau, Taipa, Macau S.A.R., Macau, China © The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Wang et al. Clinical Epigenetics (2019) 11:24 Page 2 of 15 demonstrated that some biomarkers for PD exist in altered methylation in PD patients compared to healthy blood [6–8]. controls. Notably, over 90% of the genes with these over- In past years, the abnormal expression or altered epi- laps are both hypo-methylated and upregulated genes. genetic modification of PD-associated genes including Then, we used the hypo-methylated and upregulated PARK1-15, LRRK2, SNCA, MAPT, and GBA were sug- genes to construct classifiers based on gene expression gested to be associated with PD pathology [9, 10]. For data and methylation data, which can distinguish PD pa- example, α-synuclein, encoded by the SNCA gene, is a tients from healthy controls. major component of Lewy bodies, which are an already known neuropathological feature of PD [11]. Overex- Methods pressed α-synuclein was verified in association with Our methods include the following steps: data collection, pathogenesis of PD [12]. Abnormal accumulation of differential expression analysis, differential methylation α-synuclein and the formation of Lewy bodies could analysis, dominant hypo-methylated region identification , trigger the body’s inflammatory response, which was enrichment analysis, and classifier construction. The previously thought to be the result of PD but recently workflow is shown in Fig. 1. has been verified as one of the causes [13]. In addition, hypo-methylated α-synuclein DNA was observed in PD Data collection patients [14]. Methylation of cytosines, the key epigen- Gene expression data (GSE99039) [21] from 233 healthy etic modification of DNA in eukaryotes, is associated controls and 205 PD patients were downloaded from with inhibition of gene expression [15]. Therefore, Gene Expression Omnibus (GEO) database [22]. The hypo-methylation might be one of the causes of data were measured by the Affymetrix Human Genome over-expression of PD-associated genes. Previous studies U133 Plus 2.0 Array, were preprocessed using RMA, mainly focused on studying methylation of the promoter and the data were log2 transformed and quantile nor- region [16–18]. However, some PD-associated 5′—C— malized. In addition, one Alzheimer’s disease (AD) asso- phosphate—G—3′ (CpGs) are located at other gene re- ciated blood gene expression dataset (GSE85426) gions [19, 20]. including 90 AD samples and 90 healthy controls, and In our study, we measured the gene methylation level one Huntington’s disease (HD) associated blood gene ex- according to CpGs of different regions. In addition, we pression dataset (GSE51799) [23] including 91 HD pa- integrated DNA methylation data and gene expression tients and 33 healthy controls were used to validate the data to identify molecules and their epigenetic modifica- PD specificity of our classifier. We downloaded the ex- tions underlying PD. We found that there are some pression matrix and platform information using R pack- genes that are both abnormally expressed and have age “GEOquery” [24]. Fig. 1 Flowchart of the analysis process Wang et al. Clinical Epigenetics (2019) 11:24 Page 3 of 15 Genome wide DNA methylation data (GSE111629) model to measure the methylation difference between [19], containing data from 335 PD patients and 237 PD patients and healthy controls. In addition, as beta healthy controls in whole blood samples, were down- value has a more intuitive biological interpretation than loaded from GEO database [22]. The data were mea- M value [27], we also calculated the delta of beta value sured by the Illumina Infinium 450 k Human DNA between PD patients and healthy controls for each gene. methylation Beadchip, and the raw methylation data In our study, we used both M value and beta value to (beta values) were preprocessed using the background determine the differentially methylated genes or inter- normalization method from the Genome Studio soft- genic CpG sites. We calculated the 10 quantile of delta ware. We downloaded the normalized methylation data beta value of all genes and all intergenic CpG sites, re- from GEO database (https://www.ncbi.nlm.nih.gov/geo/ spectively, then we used the genes and intergenic CpG download/?acc=GSE111629&format=file&file=G- sites with delta beta value < 1/10 quantile or > 8/10 SE111629%5FPEGblood%5F450kMethylationDataBack- quantile and BH adjusted p value < 0.05 as the signifi- groundNormalized%2Etxt%2Egz) and downloaded the cantly differentially methylated genes or intergenic CpG platform information using R package “GEOquery” [24]. sites between PD patients and healthy controls. The con- version between beta value and M value was fulfilled by Differential expression analysis R package named “lumi” [28]. Differential analysis was A linear model was used to assess differential expression implemented by R package “limma”. The Circos plot between PD patients and healthy controls using R pack- was implemented by R package “RCircos” [29]. The age named “limma” [25]. Benjamini and Hochberg’s chromosome distribution plot was implemented by R method (BH) was used to control the false discovery rate package “chromoMap” [30]. across all the genes. We identified the significantly differentially expressed gene using threshold BH adjusted p Identification of dominant hypo-methylated regions value < 0.05 and absolute log2FC > 0.1. Firstly, we found the gene region with the smallest delta of beta value (PD compared to control). Then, if the dif- Differential methylation analysis

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