The clinical diagnosis of RHD-PAH was performed as previously described (3). Specifically, the inclusion criteria were as follows: i) Diagnosis of mitral valve prolapse and scheduled for mitral valve replacement; ii) left ventricular ejection fraction volume >50% and left ventricular end-diastolic diameter <55 mm; iii) pulmonary artery systolic pressure >40 mmHg prior to surgery; and iv) no history of cardiomyopathy, congenital heart disease, liver disease or renal disease. The present study was approved by the Ethics Committee of Lihuili Hospital and informed consent forms were signed by all participants. The clinical and pathological characteristics of the involved individuals are described in Table I. Since acute rheumatic fever occurs more frequently in females (2), a total of 6 female patients with RHD-PAH (the study group; age, 57.00±8.39 years) and 6 normal female donors (the control group; age, 55.00±6.39 years) were recruited for the present study from Lihuili Hospital (Ningbo, China) between March 2014 and September 2015.

Blood samples from the two groups of patients were collected into EDTA tubes. DNA extraction and quantification procedures were subsequently performed as previously described (18). Genomic DNA bisulfite conversion (500 ng) was performed using an EZ DNA Methylation-Gold^™ kit (Zymo Research, Irvine, CA, USA), according to the manufacturer's protocol.

A methylation assay was performed using the Infinium HumanMethylation450 BeadChip kit (Illumina, Inc., San Diego, CA, USA) as described previously (19). In brief, bisulfite-converted DNA (200 ng) was used in the whole-genome amplification reaction, followed by an enzymatic end-point fragmentation, precipitation and resuspension in hybridization buffer (19). Subsequently, all steps were performed following the standard Infinium protocol. Sample labeling, hybridization to chips and image scanning were also carried out according to the Infinium kit's protocol (20). All samples were processed on a single chip to avoid the batch effect. A total of 485,577 methylation loci, covering 21,231 genes, were tested using this kit. Gene methylation datasets were submitted to the Gene Expression Omnibus (accession number GSE84003).

Statistical analyses were conducted using R software version 3.0.1 (www.r-project.org). Methylation levels of CpG sites are expressed as β values ranging from 0–1. Methylation levels were measured using β-values, which are based on the fluorescence intensity of methylated and unmethy-lated probes. The calculation of β values was performed as previously described (21). Prior to further calculations, probes designed for sequences on sex chromosomes, with random single nucleotide polymorphisms in the probe-mapping genomic regions or with detection values of P>0.01 on all arrays were removed. The remaining 463,289 probes were used for subsequent analysis. Methylation levels were then compared with the differential detection procedure in the Limma package version 3.30.13 (www.bioconductor.org/packages/release/bioc/html/limma.html) using R software, as previously described (21). P<0.05 and an absolute value of ∆β>0.2 were considered to indicate a statistically significant difference. The selection process is presented in Fig. 1. IlluminaHumanMethylation450k.db annotation package version 2.0.9 (http://www.bioconductor.org/packages/release/data/annotation/html/IlluminaHumanMethylation450k.db.html) was used to provide detailed information about the 450k chip platform, including mappings between gene symbol identifiers and manufacturer identifiers and genomic positions. All annotations are based on human genome build 19. A heatmap was created via heatmap.2 function in the gplots package. Signaling pathway and gene ontology (GO) enrichment analyses were conducted using the Database for Annotation, Visualization and Integrated Discovery (DAVID; version 6.7; http://david.abcc.ncifcrf.gov) and the Kyoto Encyclopedia of Genes and Genomes (KEGG; www.genome.jp/kegg), respectively.

Genome-wide DNA methylation profiles of the 12 samples were generated using the Infinium HumanMethylation450 BeadChip kit (Fig. 1). The density distribution of the β-values is presented in Fig. 2A. The results revealed a typical bimodal-shape distribution of β-values. Furthermore, boxplots of β-value distributions demonstrated homogenous levels of methylation across all of the samples (Fig. 2B).

The graph in Fig. 3 represents the distribution of CpG sites sorted by mean ∆β values and P-values (Fig. 3). In the RHD-PAH group there were 40 hypomethylated and 64 hypermethylated CpG sites compared with the healthy controls. These 104 identified DMRs were mapped to 60 known genes (data not shown), such as protein kinase C alpha (PRKCA), fibroblast growth factor receptor 2, protamine 1, S-phase kinase associated protein 2 and hyperpolarization activated cyclic nucleotide gated potassium channel 2. A total of 17 (16.35%) of the DMRs were in promoter regions (5′UTR, TSS200, TSS1500 and 1stExon), 41 (39.42%) in gene body regions, 5 (4.81%) in 3′UTR regions and 41 (39.42%) were unknown.

The chromosomal distribution of DMRs was evaluated according to the annotation of genomic positions. It was revealed to affect a large range of genes and chromosomal regions (Fig. 4). Patient samples were also ordered by hierarchical clustering, and a heat map was produced to allow the visualization of the hypermethylated DMRs associated with RHD-PAH (Fig. 5). The DMRs were mapped to a number of genes, including PRKCA, protein kinase AMP-activated non-catalytic subunit γ 2 (PRKAG2), sprouty related EVH1 domain containing 2 (SPRED2) and LIF interleukin 6 family cytokine (LIF).

DAVID GO enrichment analysis of the genes that the DMRs were mapped to revealed several significantly enriched GO terms, including negative regulation of protein kinase activity, positive regulation of protein amino acid phosphorylation, negative regulation of transferase activity and positive regulation of phosphate metabolic process (P<0.05; Table II). PRKCA, PRKAG2, SPRED2 and LIF were each enriched in at least two of these processes. No significantly enriched signaling pathways were identified by KEGG.