Abstract Polycystic ovary syndrome (PCOS) is a complex, heterogeneous disorder of uncertain etiology. Recent studies suggested that insulin resistance (IR) plays an important role in the development of PCOS. In the current study, we aimed to investigate the molecular mechanism of IR in PCOS. We employed genome-wide methylated DNA immunoprecipitation (MeDIP) analysis to characterize genes that are differentially methylated in PCOS patients vs. healthy controls. Besides, we also identified the differentially methylated genes between patients with PCOS-non-insulin resistance (PCOS-NIR) and PCOS-insulin resistance (PCOS-IR). A total of 79 genes were differentially methylated between PCOS-NIR vs. PCOS-IR patients, and 40 genes were differentially methylated in PCOS patients vs. healthy controls. We analyzed these differentially methylated genes by constructing regulatory networks and protein-protein interaction (PPI) networks. Further, Gene Ontology (GO) and pathway enrichment analysis were also performed to investigate the biological functions of networks. We identified multiple categories of genes that were differentially methylated between PCOS-NIR and PCOS-IR patients, or between PCOS patients and healthy controls. Significantly, GO categories of immune response were differentially methylated in PCOS-IR vs. PCOS-NIR. Further, genes in cancer pathways were also differentially methylated in PCOS-NIR vs. PCOS-IR patients or in PCOS patients vs. healthy controls. The results of this current study will help to further understand the mechanism of PCOS. Introduction Polycystic ovary syndrome (PCOS) is a complex, heterogeneous disorder of uncertain etiology. Strong evidence suggest that it can be classified as a genetic [33][1], [34][2], [35][3] and epigenetic disorders [36][4]. Such evidence include the familial clustering of cases, greater concordance in monozygotic compared with dizygotic twins and heritability of endocrine and metabolic features of PCOS [37][5]. PCOS is one of the leading causes of female subfertility and is seen in approximately 5%–10% of women of 12–45 years old [38][6], [39][7], [40][8]. The features of PCOS include chronic anovulation or few ovulations, polycystic ovaries enlargement and hyperandrogenism. In addition, PCOS patients are often accompanied with insulin resistance (IR) and β-cell dysfunction [41][9]. Further, patients with PCOS have decreased conception rate, and increased prevalence rates of spontaneous abortion and gestational diabetes [42][10], [43][11]. Besides, PCOS patients are at higher risk of suffering from endometrial carcinoma. Recent studies show that PCOS patient's incidence of metabolic syndrome (MS) is also higher [44][12], which is associated with cardiovascular diseases and IR. At present, many scholars have been focusing on the relationship between IR and PCOS, and results show that PCOS patients' endocrine condition and their reproduction can be relieved by ameliorating their IR. Life style adjustment can be an efficient way to achieve this goal. Besides, oral hypoglycemic agents are also subscribed to treat the IR in PCOS patients [45][13]. Recent studies have elaborated that inappropriate epigenetic reprogramming is an important contributing factor for PCOS [46][14], [47][15], [48][16]. However, the concrete mechanisms of epigenetic alterations and downstream signal cross-talk responsible for PCOS are remaining largely unknown. We employed genome-wide methylated DNA immunoprecipitation (MeDIP) analysis to characterize methylated genes in patients with PCOS vs. healthy controls. Besides, we also identified the differentially methylated genes between patients with PCOS-non-insulin resistance (PCOS-NIR) and PCOS-insulin resistance (PCOS-IR). Materials and Methods Sample collection The study was approved by the institutional review board of Renji Hospital, Shanghai Jiao Tong University School of Medicine, and written informed consent was obtained from all patients. All clinical investigations were conducted according to the principles expressed in the Declaration of Helsinki. Our subjects included 10 unrelated female patients with PCOS (5 PCOS-NIR patients and 5 PCOS-IR patients) and 5 unrelated female healthy controls. These subjects were selected from an existing cohort of 86 cases and 44 controls, which were recruited at Renji Hospital affiliated to Shanghai Jiao Tong University School of Medicine. PCOS was defined according to criteria of the Androgen Excess Society (AES) at 2006 [49][17]. All cases and controls in this study did not take hormone therapy for at least 3 months. Serum total testosterone (TT) and fasting insulin (FINS) were assayed by radioimmunoassay (RIA) (Beckman Coulter, Inc. Shanghai, China). Serum free testosterone (FT) and sex hormone binding globulin (SHBG) were determined by RIA kit (Beckman Coulter, Inc. California, USA) according to the manufacturer's instructions. Serum fasting blood-glucose (FBG) was determined by the glucose oxidase method (Sysmex Corporation, Shanghai, China). Typical values for the free androgen index (FAI; calculated by the equation FAI = TT×100/SHBG) in women were 7–10 [50][18]. Homeostatic model assessment IR (HOMA-IR; calculated by the equation HOMA-IR = FBG×FINS/22.5)≥2.5 indicates IR [51][19]. Peripheral blood samples were extracted from all subjects for MeDIP analysis. Genome-wide methylated DNA immunoprecipitation (MeDIP) analysis PCOS-associated and PCOS-IR-associated methylation profiles were gained from the MeDIP-chip platform (Shanghai Biochip, Shanghai, China) based on Nimblegen Human Meth 3×720K CpGRfSq Prom Arr Del (Roche NimbleGen, Wisconsin, USA). Each subject's sample was analyzed with one MeDIP-chip separately. Genomic DNA extracted from peripheral blood sample of the 5 controls and 10 PCOS patients (5 PCOS-NIR patients and 5 PCOS-IR patients) was prepared using the DNeasy Blood & Tissue kits (Qiagen, USA). About 2 µg of DNA was bisulfite-treated with the EpiTect Bisufite kit (Qiagen, USA) following the manufacturer's protocol. Amplification across the entire bisulfate converted genome was performed by the EpiTect Whole Bisufitome kit (Qiagen, USA) according to the manufacture's protocol. To verify the specificity of DNA methylation, we performed methylation-specific PCR (MSP). According to the principle of methylation, we designed the methylation-specific PCR primers for estrogen receptor beta (ER-β) by using MethPrimer ([52]http://www.urogene.org/methprimer/), which were shown in [53]Table 1. Genetic DNA extracted from peripheral blood sample of normal control, PCOS-NIR patients and PCOS-IR patients was amplified using methylated-specific primer (M) and unmethylated-specific primer (U). Positive control of methylation was achieved by using the EpiTect MSP kit (Qiagen, USA). Negative control of methylation was achieved by using distilled water. Table 1. The primers for ER-β in this experiment. Primer Direction Sequences (5′-3′) Product size Methylated-specific primer (M) Forward CGAGGGTGTTTTTATTTAGAGGTTAC 256 bp Reverse ATTTCAAAAAACAATTATTTCTCGC Unmethylated-specific primer (U) Forward TGAGGGTGTTTTTATTTAGAGGTTAT 255 bp Reverse TTTCAAAAAACAATTATTTCTCACA [54]Open in a new tab Before carrying out MeDIP, we sonicated genomic DNA to produce random fragments ranging in size from 300 bp to 1000 bp. MeDIP assay was carried out as described previously [55][20]. Briefly, the samples were independently labeled with Cy5 (IP) and Cy3 (INPUT) using a NimbleGen Dual Color DNA labeling kit (Roche NimbleGen, Wisconsin, USA). Co-hybridizations in dye-swap were performed using a NimbleGen Human Meth 3× 720K CpG RfSq Prom Arr Del array. After heat denaturation at 95°C for 10 min, DNA was incubated with antibody against 5- methylcytidine (Diagnode, Belgium) in 1× IP buffer (10 nM sodium phosphate, pH 7.0, 140 mM NaCl, 0.05% (w/v) Triton X-100) at 4°C overnight. Immune complex were collected with Dynabeads Protein A (Invitrogen, USA), washed with 1× IP buffer for seven times, treated with Proteinase K for 4 hours at 42°C, and purified by phenol and chloroform extraction and isopropanol precipitation. Then they were scanned using an AXON GenePix 4000B Microarray Scanner (AXON, California, USA). Signals were localized and expression ratio between experimental and reference (Cy5/Cy3 ratio) was determined using by Nimblescan software V2.5 (Roche NimbleGen, Wisconsin, USA). The ratio was then log 2 transformed. Then the probability of genes (p value) being differentially methylated among groups was computed using ACME (Algorithm for Capturing Microarray Enrichment). The lower p value, the higher probability of probes being differentially methylated. Finally, peak score was calculated according to the p value of each probes (peak score = −lg P). The peak score indicates the reliability of peak. The probes with peak score >2 and p value<0.0005 may be the methylated regions. Transcription regulatory data A total of 774 regulatory relations between 219 transcriptional factors and 265 target genes were collected from TRANSFAC ([56]http://www.gene-regulation.com/pub/databases.html) and 5,722 regulatory relations between 102 transcriptional factors and 2, 920 target genes were collected from TRED ([57]http://rulai.cshl.edu/TRED/). We integrated both groups and obtained 6,328 regulatory relations between 276 transcriptional factors and 3, 002 target genes. Based on these data, we constructed the regulatory network of PCOS-NIR/PCOS-IR, PCOS/healthy controls. Protein-protein interaction (PPI) network data We collected 39, 240 PPIs from HPRD database [58][21] and 379, 426 protein-protein relations from BIOGRID database [59][22]. After integration for both databases, a total of 326,119 PPIs were obtained. Then, we mapped all the differentially methylated genes to the PPIs, and only kept the interactive differentially methylated genes and their nearest neighbor genes. Based on them, we constructed the PPIs network for PCOS-NIR/PCOS-IR, PCOS/healthy controls. Gene Ontology (GO) function and pathways analysis The Database for Annotation, Visualization and Integrated Discovery [60][23] (DAVID) version 6.7 provides a comprehensive set of functional annotation tools to understand biological meaning behind large lists of genes. In our study, we used DAVID software ([61]http://david.abcc.ncifcrf.gov/) to perform GO and PATHWAY analysis for regulatory network and PPI network. Statistical analyses Data were analyzed with the IBM SPSS Statistics software V19.0 (IBM, New York, USA). Independent t tests were performed to evaluate the significance of any differences between test and control groups. All p-values were 2-sided, and p<0.05 was considered to be significant. Results Clinical data were summarized in [62]Table 2. The three groups were comparable in terms of age, height, weight, BMI, hormone and glucose levels. Serum total testosterone (TT), free testosterone (FT) and follicle count of PCOS patients were higher than healthy controls (p<0.05). The sex hormone binding globulin (SHBG) level in PCOS patients was lower compared with healthy controls (p<0.05). The levels of fasting insulin (FINS) and homeostatic model assessment insulin resistance (HOMA-IR) were higher in PCOS-IR patients than PCOS-NIR patients or healthy controls (p<0.05). Table 2. baseline patients' characteristics. PCOS-NIR (n = 5) PCOS-IR (n = 5) Control (n = 5) Age (year) 24.8±2.17 27.4±3.44 25.4±2.51 Height (cm) 162.6±3.44 158.4±3.21 161.4±3.13 Weight (kg) 56.2±3.90 66.6±1.52 51.2±6.30 BMI (kg/m^2) 21.3±1.11 26.6±0.77 19.7±2.41 TT (nmol/l) 3.59±0.58 3.25±0.51 1.67±0.52 SHBG (nmol/l) 30.3±6.02 18.7±7.99 63.1±19.8 FT (pg/ml) 9.43±1.08 11.2±1.72 5.16±0.42 FBG (mmol/l) 5.07±0.32 5.42±0.36 5.24±0.27 FINS (µIU/ml) 8.42±3.08 13.4±3.51 6.54±0.79 FAI 12.0±1.56 19.8±7.55 2.87±1.34 HOMA-IR 1.87±0.55 3.18±0.67 1.53±0.22 Follicle count 15.6±1.34 14.4±2.30 6.2±1.30 [63]Open in a new tab Data are presented as means±standard deviations. PCOS, Polycystic ovary syndrome; IR, insulin resistance; NIR, non-insulin resistance; BMI, body mass index; TT, total testosterone; SHBG, sex hormone binding globulin; FT, free testosterone; FINS, fasting insulin; FBG, fasting blood glucose; HOMA-IR, homeostatic model assessment-insulin resistance = (FBG×FINS)/22.5; FAI, free androgen index = (TT×100)/SHBG. Specificity analysis of methylated DNA To investigate the specificity of methylated DNA, MSP was performed. Genetic DNA was extracted from peripheral blood sample of normal control, PCOS-NIR and PCOS-IR patients, respectively. As shown in [64]Figure 1, the fragment of approximate 250 bp was specifically appeared in samples amplified by M primer. Figure 1. MS-PCR electrophoretogram showing the specificity of methylated DNA. Figure 1 [65]Open in a new tab Bands in the M lanes represent the methylated PCR products of ER-β, whereas bands in the U lanes represent the unmethylated ER-β. The presence of a 250 bp band indicates hypermethylated DNA fragment and the fuzzy band in the U lane indicates partially methylated DNA fragment. Genome-wide methylated DNA immunoprecipitation (MeDIP) analysis identification of differentially methylated genes We applied PCOS related and PCOS-IR related methylation profiles from MeDIP-chip platform. Fold-change and t-test methods were used to identify differentially methylated genes. Of the genes examined, 79 genes of them were identified as differentially methylated in PCOS-NIR patients vs. PCOS-IR patients (p<0.0005; [66]Table 3). A total of 40 genes were identified as differentially methylated in PCOS vs. healthy controls (p<0.0005; [67]Table 4). Table 3. The 79 differentially methylated genes in PCOS-NIR patients vs. PCOS-IR patients (p<0.0005). Gene ID Official Symbol P value Gene ID Official Symbol P value 398 ARHGDIG 0.00036 84307 ZNF397 0.00035 83864 TTTY9A 0.00019 4606 MYBPC2 0.00004 55013 CCDC109B 0.00019 90427 BMF 0.00028 283464 GXYLT1 0.00006 1636 ACE 0.00037 494514 C18orf56 0.00039 341457 PPIAP8 0.00027 60673 C12orf44 0.00011 8536 CAMK1 0.00038 127829 ARL8A 0.00003 84896 ATAD1 0.00015 51608 GET4 0.00031 3978 LIG1 0.00002 8493 PPM1D 0.00027 91662 NLRP12 0.00015 358 AQP1 0.00030 5028 P2RY1 0.00030 1120 CHKB 0.00043 1119 CHKA 0.00040 433 ASGR2 0.00047 440107 PLEKHG7 0.00037 3266 ERAS 0.00016 284996 RNF149 0.00024 340120 ANKRD34B 0.00023 2618 GART 0.00048 6013 RLN1 0.00006 5480 PPIC 0.00021 153328 SLC25A48 0.00044 7087 ICAM5 0.00040 8320 EOMES 0.00005 285679 C5orf60 0.00022 3241 HPCAL1 0.00006 51465 UBE2J1 0.00031 51529 ANAPC11 0.00048 145942 TMCO5A 0.00032 6340 SCNN1G 0.00037 9240 PNMA1 0.00020 389941 C1QL3 0.00030 25894 PLEKHG4 0.00047 2805 GOT1 0.00030 164091 PAQR7 0.00030 128322 LOC128322 0.00036 7296 TXNRD1 0.00040 53820 DSCR6 0.00025 9659 PDE4DIP 0.00049 6531 SLC6A3 0.00045 79854 LINC00115 0.00007 84698 CAPS2 0.00018 165530 CLEC4F 0.00025 85027 SMIM3 0.00023 644613 LOC644613 0.00012 9525 VPS4B 0.00003 8439 NSMAF 0.00027 645369 TMEM200C 0.00017 4255 MGMT 0.00026 64417 C5orf28 0.00001 1051 CEBPB 0.00017 55333 SYNJ2BP 0.00045 115548 FCHO2 0.00028 4953 ODC1 0.00020 55020 TTC38 0.00011 644765 LOC644765 0.00041 51646 YPEL5 0.00050 10799 RPP40 0.00047 57684 ZBTB26 0.00038 389384 C6orf222 0.00048 111 ADCY5 0.00028 80724 ACAD10 0.00038 4065 LY75 0.00018 83858 ATAD3B 0.00031 2837 UTS2R 0.00010 195828 ZNF367 0.00013 254312 LINC00710 0.00004 56957 OTUD7B 0.00033 163049 ZNF791 0.00021 1746 DLX2 0.00024 [68]Open in a new tab Table 4. The 40 differentially methylated genes in PCOS vs. healthy controls (p<0.0005). Gene ID Official Symbol P value Gene ID Official Symbol P value 120425 AMICA1 0.00001 164781 WDR69 0.00022 166348 KBTBD12 0.00002 245929 DEFB115 0.00023 2030 SLC29A1 0.00003 54826 GIN1 0.00026 11245 GPR176 0.00005 158038 LINGO2 0.00027 51778 MYOZ2 0.00005 390084 OR56A5 0.00028 51604 PIGT 0.00005 158405 KIAA1958 0.00028 388125 C2CD4B 0.00005 643812 KRTAP27-1 0.00030 56141 PCDHA7 0.00006 59269 HIVEP3 0.00031 3159 HMGA1 0.00008 9532 BAG2 0.00033 54510 PCDH18 0.00010 56674 TMEM9B 0.00034 6738 TROVE2 0.00010 221914 GPC2 0.00037 374928 ZNF773 0.00010 2324 FLT4 0.00039 6336 SCN10A 0.00011 644624 LOC644624 0.00039 220766 CEP170L 0.00011 64928 MRPL14 0.00040 375287 RBM43 0.00012 445582 POTEE 0.00040 53838 C11orf24 0.00015 1984 EIF5A 0.00044 6319 SCD 0.00015 1047 CLGN 0.00045 128488 WFDC12 0.00020 4062 LY6H 0.00047 3676 ITGA4 0.00021 441268 LOC441268 0.00048 2931 GSK3A 0.00022 10156 RASA4 0.00048 [69]Open in a new tab Construction of regulatory network To get the regulatory relationships between PCOS-NIR and PCOS-IR patients as well as between PCOS patients and healthy control, we mapped their differentially methylated genes into regulation data collected from TRANSFAC and TRED, and built regulatory networks by Cytoscape software [70][24] ([71]Figure 2A and B). In the regulatory network between PCOS-NIR and PCOS-IR, significant difference in CEBPB gene methylation was observed (p = 0.00017). CEBPB formed a local network by regulating a number of genes, suggesting it may play an important role in PCOS-IR. Besides, CEBPB indirectly regulated the methylated gene ODC1 through regulating the normal gene (unmethylated gene) CREB1. In our network, we observed that methylated gene GART regulated the methylated gene GOT1 directly, and regulated another methylated gene PDE4DIP indirectly ([72]Figure 2A). The regulatory network of differentially methylated genes between PCOS patients and healthy controls was much simpler. In this network, the methylated gene EPM2A regulated two normal genes, MYC and E2F2. The methylated genes ITGA4 and HMGA1 regulated the normal genes ETS1 and IGFBP1, respectively ([73]Figure 2B). Figure 2. Regulatory network analysis of differentially methylated genes. [74]Figure 2 [75]Open in a new tab A. regulatory network of differentially methylated genes between PCOS-NIR and PCOS-IR patients; B. regulatory network of differentially methylated genes between PCOS patients and healthy controls. The yellow nodes represent methylated genes and pink nodes represent normal genes. Gene Ontology (GO) function analysis of regulatory network To explore the biological function of genes in the regulatory network of PCOS-NIR vs. PCOS-IR, we applied the online biological classification tool DAVID and observed significant enrichments of these differentially methylated genes in multiple GO categories ([76]Table 5). The most significant enrichment was GO category of defense response with FDR = 3.16E-18. The other significant GO categories included inflammatory response (FDR = 1.73E-17), response to wounding (FDR = 9.91E-17) and regulation of cytokine production (FDR = 2.09E-12). In fact, all significant GO category clusters were associated with immune response ([77]Table 5). Table 5. GO function analysis of regulatory network of PCOS-NIR vs. PCOS-IR. Term Description Count FDR GO:0006952 defense response 33 3.16E-18 GO:0006954 inflammatory response 26 1.73E-17 GO:0009611 response to wounding 30 9.91E-17 GO:0001817 regulation of cytokine production 18 2.09E-12 GO:0006953 acute-phase response 11 7.78E-11 GO:0031328 positive regulation of cellular biosynthetic process 27 9.00E-11 GO:0009891 positive regulation of biosynthetic process 27 1.26E-10 GO:0010557 positive regulation of macromolecule biosynthetic process 26 2.65E-10 GO:0051240 positive regulation of multicellular organismal process 18 2.91E-10 GO:0010033 response to organic substance 27 2.97E-10 [78]Open in a new tab Pathway analysis of regulatory network To identify the deregulated pathways in patients with PCOS-IR vs. PCOS-NIR, we performed pathway enrichment analysis on the differentially methylated genes using the online tool of DAVID ([79]Table 6). At a FDR of 0.01, four pathways were enriched, including cytokine-cytokine receptor interaction (FDR = 8.39E-06), hematopoietic cell lineage (FDR = 2.76E-04), asthma (FDR = 5.24E-04) and Jak-STAT signaling pathway (FDR = 6.15E-04) ([80]Table 6). Table 6. Pathway analysis of regulatory network of PCOS-NIR vs. PCOS-IR. Term Description Count FDR hsa04060 Cytokine-cytokine receptor interaction 17 8.39E-06 hsa04640 Hematopoietic cell lineage 10 2.76E-04 hsa05310 Asthma 7 5.24E-04 hsa04630 Jak-STAT signaling pathway 12 6.15E-04 [81]Open in a new tab Construction of protein-protein interaction (PPI) networks Transcriptional changes are not always strictly correlated with protein expressions and functions. To investigate the differentially methylated genes in protein level, we constructed PPI networks between PCOS-NIR and PCOS-IR as well as PCOS and healthy controls through analyzing the data collected from HPRD and BIOGRID ([82]Figure 3). The importance of a gene is often dependent on how well it associates with other genes in a network. Studies suggest that more centralized genes in the network are more likely to be key drivers to proper cellular function than peripheral genes (nodes) [83][25]. From the PPI network of PCOS-NIR vs. PCOS-IR, we observed that the methylated genes CEBPB, GOT1, GET4, ODC1 and C12orf44 formed local networks ([84]Figure 3A). In the PPI network of PCOS vs. healthy controls, the methylated genes GSK3A, HMGA1, ITGA4, EPM2A and BAG2 were hub nodes ([85]Figure 3B). Figure 3. Protein-protein interaction (PPI) network analysis. [86]Figure 3 [87]Open in a new tab A. Protein-protein interaction (PPI) network of PCOS-NIR and PCOS-IR; B. PPI network of PCOS and controls. Yellow nodes represent methylated genes and pink nodes represent normal genes. GO function analysis of PPI network To investigate the biological function of genes in PPI networks, we performed GO function analysis for these genes in each PPI network, respectively. [88]Table 7 shows the top 10 enriched GO gene categories in the PPI network of PCOS-NIR vs. PCOS-IR. The most significant GO gene category was regulation of transcription of RNA polymerase II promoter (FDR = 8.38E-22). The other significant GO categories included positive regulation of nitrogen compound metabolic process (FDR = 1.21E-19), positive regulation of macromolecule metabolic process (FDR = 4.40E-18) and positive regulation of macromolecule metabolic process (FDR = 6.68E-18) ([89]Table 7). [90]Table 8 shows the top 10 GO categories in the PPI network of PCOS vs. healthy controls. The most significant GO category was positive regulation of macromolecule metabolic process (FDR = 5.47E-11). The other significant GO categories included positive regulation of cellular biosynthetic process (FDR = 2.43E-06), positive regulation of biosynthetic process (FDR = 3.15E-06) and positive regulation of nitrogen compound metabolic process (FDR = 5.17E-06) ([91]Table 8). Table 7. GO function analysis of PPI network of PCOS-NIR vs. PCOS-IR. Term Description Count FDR GO:0006357 regulation of transcription from RNA polymerase II promoter 62 8.38E-22 GO:0051173 positive regulation of nitrogen compound metabolic process 56 1.21E-19 GO:0010604 positive regulation of macromolecule metabolic process 62 4.40E-18 GO:0045935 positive regulation of nucleobase, nucleoside, nucleotide and nucleic acid metabolic process 53 6.68E-18 GO:0010628 positive regulation of gene expression 49 4.02E-16 GO:0045941 positive regulation of transcription 48 7.12E-16 GO:0045893 positive regulation of transcription, DNA-dependent 44 1.50E-15 GO:0051254 positive regulation of RNA metabolic process 44 2.06E-15 GO:0031328 positive regulation of cellular biosynthetic process 52 2.26E-15 GO:0045944 positive regulation of transcription from RNA polymerase II promoter 39 2.68E-15 [92]Open in a new tab Table 8. GO function analysis of PPI network of PCOS vs. healthy controls. Term Description Count FDR GO:0010604 positive regulation of macromolecule metabolic process 30 5.47E-11 GO:0031328 positive regulation of cellular biosynthetic process 22 2.43E-06 GO:0009891 positive regulation of biosynthetic process 22 3.15E-06 GO:0051173 positive regulation of nitrogen compound metabolic process 21 5.17E-06 GO:0010628 positive regulation of gene expression 20 5.93E-06 GO:0010557 positive regulation of macromolecule biosynthetic process 21 6.73E-06 GO:0006468 protein amino acid phosphorylation 21 9.42E-06 GO:0045893 positive regulation of transcription, DNA-dependent 18 1.26E-05 GO:0051254 positive regulation of RNA metabolic process 18 1.43E-05 GO:0045935 positive regulation of nucleobase, nucleoside, nucleotide and nucleic acid metabolic process 20 1.90E-05 [93]Open in a new tab Pathway analysis of PPI network Furthermore, we performed the pathway enrichment analysis for these genes in PPI network. [94]Table 9 shows pathways enriched in PPI network of PCOS-NIR vs. PCOS-IR. At a FDR of 0.01, 6 pathways were enriched, including pathways in cancer (FDR = 4.78E-08), chronic myeloid leukemia (FDR = 3.17E-05), and prostate cancer (FDR = 2.62E-04) ([95]Table 9). [96]Table 10 showed the enriched pathways in PPI network of PCOS vs. healthy controls. At a FDR of 0.01, 5 pathways were enriched, including pathways in cancer (FDR = 0.00112), ErbB signaling pathway (FDR = 0.001209), and focal adhesion (FDR = 0.001848) ([97]Table 10). Table 9. Pathway analysis of PPI network of PCOS-NIR vs. PCOS-IR. Term Description Count FDR hsa05200 Pathways in cancer 32 4.78E-08 hsa05220 Chronic myeloid leukemia 14 3.17E-05 hsa05215 Prostate cancer 14 2.62E-04 hsa05221 Acute myeloid leukemia 11 0.001466 hsa05212 Pancreatic cancer 12 0.001485 hsa05219 Bladder cancer 9 0.008231 [98]Open in a new tab Table 10. Pathway analysis of PPI network of PCOS vs. healthy controls. Term Description Count FDR hsa05200 Pathways in cancer 15 0.00112 hsa04012 ErbB signaling pathway 9 0.001209 hsa04510 Focal adhesion 12 0.001848 hsa04010 MAPK signaling pathway 13 0.00462 hsa04310 Wnt signaling pathway 10 0.009299 [99]Open in a new tab Discussion PCOS affects 6–10% of women of childbearing age, many groups suggested that insulin resistance plays a critical role in PCOS development [100][26], [101][27]. Despite significant research advances have been achieved over the past decade [102][28], many questions remain uncertain. In the current study, we employed genome-wide methylated DNA immunoprecipitation (MeDIP) analysis to characterize genes that are differently methylated between PCOS patients and healthy controls, or between PCOS-NIR vs. PCOS-IR patients. Besides, we constructed the regulatory networks and PPI networks after analyzing these differentially methylated genes in PCOS-NIR vs. PCOS-IR, or in PCOS vs. control. Furthermore, the GO function and pathway analysis were performed for regulatory networks and PPI networks. We found various GO categories were enriched including cytokine-cytokine receptor interaction, hematopoietic cell lineage, and asthma. Bio-pathway analysis for these genes in PPI network showed that cancer pathways were enriched after comparing PCOS-NIR with PCOS-IR patients, as well as comparing PCOS patients with healthy controls. DNA methylation is an epigenetic modification associated with gene transcription regulation, X-chromosome inactivation, development and cell differentiation regulation. Aberrant DNA methylation is closely associated with cancer development and progression. The advent of microarray technology has provided new opportunities for high-throughput study on DNA methylation. Microarray-based methods include immunoprecipitation and restriction digestion. Each technique has its own advantages. Immunoprecipitation uses the specificity of antibodies to isolate target proteins (antigens) out of complex sample mixtures [103][29]. Restriction enzyme digestion using methylcytosine-sensitive enzymes, followed by ligation-mediated PCR amplification of the targets [104][30]. Therefore, the immunoprecipitation method is more specific while the restriction digestion method is more sensitive. Together, they provide many choices for the study of genome-wide DNA methylation profile in disease. In order to further confirm the specificity of methylation, we performed a MSP using estrogen receptor beta (ER-βER-β) gene. ER-βER-β is expressed by many tissues and its expression can be regulated by DNA methylation of the promoter region. Previous study suggested that the methylation of ER-βER-β is related to genesis of tumor and endocrine disease [105][31]. Besides, the ER-βER-β gene polymorphism was reported to be associated with pathophysiologic aberrancies involved in PCOS [106][32]. From [107]Figure 1, we could find that the fragment of 250 bp appeared in samples amplified by M primer. The fuzzy band in the U lane indicated partially methylated DNA fragment. From [108]Table 2, we could find that significant difference in CEBPB gene methylation was observed between PCOS-NIR patients and PCOS-IR patients (p = 0.000170). Besides, CEBPB formed local networks in both regulatory network ([109]Figure 2A) and PPI network ([110]Figure 3A). These results all suggested CEBPB plays an important role in insulin resistance in PCOS patients. CEBPB is a bZIP transcription factor which can bind as a homodimer to certain DNA regulatory regions. CEBPB is important in the regulation of genes involved in immune and inflammatory responses and has been shown to bind to the interleukin (IL) −1 response element in the IL- 6 gene, as well as to regulatory regions of several acute- phase and cytokine genes [111][33]. Expression of CEBPB in blood leukocytes has been shown to be positively associated with muscle strength in humans, emphasizing the importance of the immune system [112][34]. In particular, CEBPB is a downstream effector of the luteinizing hormone signaling pathway and thus plays key roles in the luteinizing hormone response of the follicle [113][35]. CEBPB is involved in the acquisition of insulin receptor substrate (IRS) −2 and glucose transporter 4 (GLUT4) expression as well as in insulin - sensitive glucose uptake during adipocyte differentiation [114][36]. We could find a significant difference of CEBPB gene methylation between PCOS-NIR and PCOS-IR patients (p = 0.00017), suggesting CEBPB involving in insulin resistance in PCOS patients. Besides, CEBPB indirectly regulated the methylated gene ODC1 through regulating the normal gene CREB1, as shown in [115]Figure 2A. ODC1 (ornithine decarboxylase 1) is a rate-limiting enzyme of the polyamine biosynthesis pathway which catalyzes ornithine to putrescine. A previous study suggested that exposure to ethanol results in insulin resistance and thereby disrupts the molecular path by which induces the expression of ODC enzymatic activity [116][37], indicating the role of ODC1 in insulin resistance. As shown in [117]Table 5, genes of defense response, inflammatory response, and the response to wounding belong to the cellular immunity term were differentially methylated in PCOS vs healthy controls, suggesting that PCOS may be associated with the immune response. The immune response is how your body recognizes and defends itself against bacteria, viruses, and substances that appear foreign and harmful [118][38]. An efficient immune response protects against many diseases and disorders. The gene categories of regulation of transcription from RNA polymerase II promoter, positive regulation of macromolecule metabolic process, positive regulation of transcription, DNA-dependent and positive regulation of cellular biosynthetic process appeared in both GO function and pathway analysis. These genes are all necessary in biological growth and differentiation, proliferation and development [119][39]. The biosynthesis process often consists of several enzymatic steps in which the product of one step is used as substrate in the following step. Examples for such multi-step biosynthetic pathways are those for the production of amino acids, fatty acids, and natural products [120][40]. Biosynthesis plays a major role in all cells, and many dedicated metabolic routes combined constitute general metabolism. Both PCOS-NIR and PCOS-IR were related to biosynthesis. [121]Table 9 and [122]Table 10 showed that the category of genes related to pathways in cancer were differently methylated PCOS-NIR and PCOS-IR. The abnormal activation of signaling pathways is a critical event in cancer pathogenesis [123][41]. In particular, activation of these pathways can lead to inappropriate cellular survival, proliferation, pluripotency, invasion, metastasis, and angiogenesis [124][41]. Funding Statement This work was supported by grants from the International Collaborative Items of Ministry of Science and Technology of China (No. 2006DFA32780). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. References