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Harnessing expression data to identify novel candidate genes in polycystic ovary syndrome.

ABSTRACT: Novel pathways in polycystic ovary syndrome (PCOS) are being identified in gene expression studies in PCOS tissues; such pathways may contain key genes in disease etiology. Previous expression studies identified both dickkopf homolog 1 (DKK1) and DnaJ (Hsp40) homolog, subfamily B, member 1 (DNAJB1) as differentially expressed in PCOS tissue, implicating them as candidates for PCOS susceptibility. To test this, we genotyped a discovery cohort of 335 PCOS cases and 198 healthy controls for three DKK1 single nucleotide polymorphisms (SNPs) and four DNAJB1 SNPs and a replication cohort of 396 PCOS cases and 306 healthy controls for 1 DKK1 SNP and 1 DNAJB1 SNP. SNPs and haplotypes were determined and tested for association with PCOS and component phenotypes. We found that no single nucleotide polymorphisms were associated with PCOS risk; however, the major allele of rs1569198 from DKK1 was associated with increased total testosterone (discovery cohort P?=?0.0035) and dehydroepiandrosterone sulfate (replication cohort P?=?0.05). Minor allele carriers at rs3962158 from DNAJB1 had increased fasting insulin (discovery cohort P?=?0.003), increased HOMA-IR (discovery cohort P?=?0.006; replication cohort P?=?0.036), and increased HOMA-%B (discovery cohort P?=?0.004). Carriers of haplotype 2 at DNAJB1 also had increased fasting insulin, HOMA-IR, and HOMA-%B. These findings suggest that genetic variation in DKK1 and DNAJB1 may have a role in the hyperandrogenic and metabolic dysfunction of PCOS, respectively. Our results also demonstrate the utility of gene expression data as a source of novel candidate genes in PCOS, a complex and still incompletely defined disease, for which alternative methods of gene identification are needed.

SUBMITTER: Jones MR

PROVIDER: S-EPMC3096662 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature

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Harnessing expression data to identify novel candidate genes in polycystic ovary syndrome.

Jones Michelle R MR Chua Angela A Chen Yii-Der I YD Li Xiaohui X Krauss Ronald M RM Rotter Jerome I JI Legro Richard S RS Azziz Ricardo R Goodarzi Mark O MO

PloS one 20110517 5

Novel pathways in polycystic ovary syndrome (PCOS) are being identified in gene expression studies in PCOS tissues; such pathways may contain key genes in disease etiology. Previous expression studies identified both dickkopf homolog 1 (DKK1) and DnaJ (Hsp40) homolog, subfamily B, member 1 (DNAJB1) as differentially expressed in PCOS tissue, implicating them as candidates for PCOS susceptibility. To test this, we genotyped a discovery cohort of 335 PCOS cases and 198 healthy controls for three D ...[more]

PMID: 21611153

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Project description:BackgroundIn this study, we aimed to identify novel biomarkers for polycystic ovary syndrome (PCOS) and analyze their potential roles in immune infiltration during PCOS pathogenesis.MethodsFive datasets, namely GSE137684, GSE80432, GSE114419, GSE138518, and GSE155489, were obtained from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were selected from the train datasets. The least absolute shrinkage and selection operator logistic regression model and support vector machine-recursive feature elimination algorithm were combined to screen potential biomarkers. The test datasets validated the expression levels of these biomarkers, and the area under the curve (AUC) was calculated to analyze their diagnostic value. Quantitative real-time PCR was conducted to verify biomarkers' expression in clinical samples. CIBERSORT was used to assess differential immune infiltration, and the correlations of biomarkers with infiltrating immune cells were evaluated.ResultsHerein, 1265 DEGs were identified between PCOS and control groups. The gene sets related to immune response and adaptive immune response were differentially activated in PCOS. The two diagnostic biomarkers of PCOS identified by us were HD domain containing 3 (HDDC3) and syndecan 2 (SDC2; AUC, 0.918 and 0.816, respectively). The validation of hub biomarkers in clinical samples using RT-qPCR was consistent with bioinformatics results. Immune infiltration analysis indicated that decreased activated mast cells (P = 0.033) and increased eosinophils (P = 0.040) may be a part of the pathogenesis of PCOS. HDDC3 was positively correlated with T regulatory cells (P = 0.0064), activated mast cells (P = 0.014), and monocytes (P = 0.024) but negatively correlated with activated memory CD4 T cells (P = 0.016) in PCOS. In addition, SDC2 was positively correlated with activated mast cells (P = 0.0021), plasma cells (P = 0.0051), and M2 macrophages (P = 0.038) but negatively correlated with eosinophils (P = 0.01) and neutrophils (P = 0.031) in PCOS.ConclusionHDDC3 and SDC2 can serve as candidate biomarkers of PCOS and provide new insights into the molecular mechanisms of immune regulation in PCOS.

Project description:Polycystic Ovary Syndrome (PCOS) is a multifactorial syndrome with reproductive, endocrine, and metabolic symptoms, affecting about 10% women of reproductive age. Pathogenesis of the syndrome is poorly understood with genetic and fetal origins being the focus of the conundrum. Genetic predisposition of PCOS has been confirmed by candidate gene studies and Genome-Wide Association Studies (GWAS). Recently, the expression of PCOS candidate genes across gestation has been studied in human and bovine fetal ovaries. The current study sought to identify potential upstream regulators and mechanisms associated with PCOS candidate genes. Using RNA sequencing data of bovine fetal ovaries (62-276 days, n = 19), expression of PCOS candidate genes across gestation was analysed using Partek Flow. A supervised heatmap of the expression data of all 24,889 genes across gestation was generated. Most of the PCOS genes fell into one of four clusters according to their expression patterns. Some genes correlated negatively (early genes; C8H9orf3, TOX3, FBN3, GATA4, HMGA2, and DENND1A) and others positively (late genes; FDFT1, LHCGR, AMH, FSHR, ZBTB16, and PLGRKT) with gestational age. Pathways associated with PCOS candidate genes and genes co-expressed with them were determined using Ingenuity pathway analysis (IPA) software as well as DAVID Bioinformatics Resources for KEGG pathway analysis and Gene Ontology databases. Genes expressed in the early cluster were mainly involved in mitochondrial function and oxidative phosphorylation and their upstream regulators included PTEN, ESRRG/A and MYC. Genes in the late cluster were involved in stromal expansion, cholesterol biosynthesis and steroidogenesis and their upstream regulators included TGFB1/2/3, TNF, ERBB2/3, VEGF, INSIG1, POR, and IL25. These findings provide insight into ovarian development of relevance to the origins of PCOS, and suggest that multiple aetiological pathways might exist for the development of PCOS.

Dataset Information

Harnessing expression data to identify novel candidate genes in polycystic ovary syndrome.

Publications

Harnessing expression data to identify novel candidate genes in polycystic ovary syndrome.

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