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Differential gene expression in granulosa cells from polycystic ovary syndrome patients with and without insulin resistance: identification of susceptibility gene sets through network analysis.

ABSTRACT: Polycystic ovary syndrome (PCOS) is a heterogeneous, genetically complex, endocrine disorder of uncertain etiology in women.Our aim was to compare the gene expression profiles in stimulated granulosa cells of PCOS women with and without insulin resistance vs. matched controls.This study included 12 normal ovulatory women (controls), 12 women with PCOS without evidence for insulin resistance (PCOS non-IR), and 16 women with insulin resistance (PCOS-IR) undergoing in vitro fertilization. Granulosa cell gene expression profiling was accomplished using Affymetrix Human Genome-U133 arrays. Differentially expressed genes were classified according to gene ontology using ingenuity pathway analysis tools. Microarray results for selected genes were confirmed by real-time quantitative PCR.A total of 211 genes were differentially expressed in PCOS non-IR and PCOS-IR granulosa cells (fold change?1.5; P?0.001) vs. matched controls. Diabetes mellitus and inflammation genes were significantly increased in PCOS-IR patients. Real-time quantitative PCR confirmed higher expression of NCF2 (2.13-fold), TCF7L2 (1.92-fold), and SERPINA1 (5.35-fold). Increased expression of inflammation genes ITGAX (3.68-fold) and TAB2 (1.86-fold) was confirmed in PCOS non-IR. Different cardiometabolic disease genes were differentially expressed in the two groups. Decreased expression of CAV1 (-3.58-fold) in PCOS non-IR and SPARC (-1.88-fold) in PCOS-IR was confirmed. Differential expression of genes involved in TGF-? signaling (IGF2R, increased; and HAS2, decreased), and oxidative stress (TXNIP, increased) was confirmed in both groups.Microarray analysis demonstrated differential expression of genes linked to diabetes mellitus, inflammation, cardiovascular diseases, and infertility in the granulosa cells of PCOS women with and without insulin resistance. Because these dysregulated genes are also involved in oxidative stress, lipid metabolism, and insulin signaling, we hypothesize that these genes may be involved in follicular growth arrest and metabolic disorders associated with the different phenotypes of PCOS.

SUBMITTER: Kaur S

PROVIDER: S-EPMC3674289 | biostudies-literature | 2012 Oct

REPOSITORIES: biostudies-literature

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Differential gene expression in granulosa cells from polycystic ovary syndrome patients with and without insulin resistance: identification of susceptibility gene sets through network analysis.

Kaur Surleen S Archer Kellie J KJ Devi M Gouri MG Kriplani Alka A Strauss Jerome F JF Singh Rita R

The Journal of clinical endocrinology and metabolism 20120817 10

<h4>Context</h4>Polycystic ovary syndrome (PCOS) is a heterogeneous, genetically complex, endocrine disorder of uncertain etiology in women.<h4>Objective</h4>Our aim was to compare the gene expression profiles in stimulated granulosa cells of PCOS women with and without insulin resistance vs. matched controls.<h4>Research design and methods</h4>This study included 12 normal ovulatory women (controls), 12 women with PCOS without evidence for insulin resistance (PCOS non-IR), and 16 women with ins ...[more]

PMID: 22904171

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Project description:BackgroundPolycystic ovary syndrome (PCOS) is one of the most common endocrine abnormalities in women of reproductive age. In this study, we set out to construct a molecular long non-coding RNA (lncRNA)-microRNA (miRNA)-messenger RNA (mRNA) network according to the competitive endogenous RNA (ceRNA) theory and obtain insights into the related biological characteristics and pathways.MethodsWe downloaded two gene expression profile datasets of mural granulosa cells (MGCs) of women with PCOS and healthy women without PCOS (GSE84376 and GSE106724) from Gene Expression Omnibus (GEO) DataSets. Using GEO2R, we identified the mRNAs and non-coding RNAs with differential expression. The DIANA-microT-CDS algorithm was applied to predict the genes targeted by the differentially expressed miRNAs. The lncRNA-miRNA interactions were predicted using DIANA-LncBase v2. Then, we constructed the lncRNA-miRNA-mRNA network. The Database for Annotation, Visualization and Integrated Discovery (DAVID) was employed to identify the functions and enriched pathways of the genes. Subsequently, STRING was used to construct the protein-protein interaction (PPI) network. cytoHubba in Cytoscape was used to rank the hub genes, and finally, PPI modules were screened with Cytoscape MCODE.ResultsThere were 462 mRNAs, 2,464 lncRNAs, and 55 miRNAs which showed differential expression between the MGCs of patients with PCOS and those of healthy controls. Based on the PPI analysis, differentially expressed genes (DEGs) were significantly enriched in retinol metabolism, drug metabolism-cytochrome P450, malaria, the Hippo signaling pathway, and glycine, serine, and threonine metabolism. The ceRNA network contained 71 lncRNA nodes, 14 miRNA nodes, and 69 mRNA nodes, as well as 167 edges. We identified some novel genes and non-coding RNAs that might be involved in PCOS, including CD163, MRC1, VSIG4, CCL2, CCR2, SPP1, hsa-miR-3135b, hsa-miR-4649-3p, hsa-miR-1231, hsa-miR-3609, and hsa-miR-4433b-3p.ConclusionsThis study identified a novel lncRNA-miRNA-mRNA network based on the ceRNA mechanism in PCOS. Some novel genes and non-coding RNAs that may be involved in the occurrence and development of PCOS were excavated, including CD163, MRC1, VSIG4, CCL2, CCR2, SPP1, hsa-miR-3135b, hsa-miR-4649-3p, hsa-miR-1231, hsa-miR-3609, and hsa-miR-4433b-3p. However, our findings need to be validated by in vivo and in vitro experiments.

Project description:BackgroundPolycystic ovary syndrome (PCOS) is the most common reproductive endocrine disease in women of childbearing age, and insulin resistance is an important etiological mechanism in PCOS. This study revealed the microRNA (miRNA) expression profile of PCOS with insulin resistance and explored the potential biological functions of differentially expressed miRNA.MethodsA total of 76 patients with PCOS and 30 normal healthy women were recruited in the gynecological clinic of the Second Hospital of Tianjin Medical University. We divided the patients with PCOS into a group with insulin resistance (n=46) and a group without insulin resistance (n=30). Peripheral venous serum samples from each group were used for deep sequencing to identify differentially expressed miRNAs. Hierarchical clustering heat maps were used to show differences in miRNA expression. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and target gene network databases were used to explore the potential target genes of differentially expressed miRNAs and to analyze their specific biological functions.ResultsA case-control analysis found that the levels of body mass index (BMI), prolactin (PRL), total testosterone (T), fasting blood glucose (FBG), and fasting insulin (INS) in patients with PCOS were higher than those in healthy controls. High BMI, high blood sugar, and hyperinsulinemia were more significant in the PCOS with insulin resistance group than without insulin resistance group. Among the patients with PCOS, miR-122-5p was found to have more significant differences in the PCOS with insulin resistance group. GO and KEGG pathway analysis showed that the identified miRNAs were involved in the regulation of different biological processes, such as signal transduction, negative regulation of GTPase activity, chloride channel complex. The predicted target genes were related to the citrate cycle (TCA cycle) and the biosynthesis of mucin-type O-glycans.ConclusionsOur research demonstrated the use of miRNAs as new biomarkers for the diagnosis, treatment and presented a new strategy to lessen the symptoms of PCOS with insulin resistance.

Dataset Information

Differential gene expression in granulosa cells from polycystic ovary syndrome patients with and without insulin resistance: identification of susceptibility gene sets through network analysis.

Publications

Differential gene expression in granulosa cells from polycystic ovary syndrome patients with and without insulin resistance: identification of susceptibility gene sets through network analysis.

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