- Open Access
Construction of a polycystic ovarian syndrome (PCOS) pathway based on the interactions of PCOS-related proteins retrieved from bibliomic data
© Mohamed-Hussein and Harun; licensee BioMed Central Ltd. 2009
Received: 14 June 2009
Accepted: 1 September 2009
Published: 1 September 2009
Polycystic ovary syndrome (PCOS) is a complex but frequently occurring endocrine abnormality. PCOS has become one of the leading causes of oligo-ovulatory infertility among premenopausal women. The definition of PCOS remains unclear because of the heterogeneity of this abnormality, but it is associated with insulin resistance, hyperandrogenism, obesity and dyslipidaemia. The main purpose of this study was to identify possible candidate genes involved in PCOS. Several genomic approaches, including linkage analysis and microarray analysis, have been used to look for candidate PCOS genes. To obtain a clearer view of the mechanism of PCOS, we have compiled data from microarray analyses. An extensive literature search identified seven published microarray analyses that utilized PCOS samples. These were published between the year of 2003 and 2007 and included analyses of ovary tissues as well as whole ovaries and theca cells. Although somewhat different methods were used, all the studies employed cDNA microarrays to compare the gene expression patterns of PCOS patients with those of healthy controls. These analyses identified more than a thousand genes whose expression was altered in PCOS patients. Most of the genes were found to be involved in gene and protein expression, cell signaling and metabolism. We have classified all of the 1081 identified genes as coding for either known or unknown proteins. Cytoscape 2.6.1 was used to build a network of protein and then to analyze it. This protein network consists of 504 protein nodes and 1408 interactions among those proteins. One hypothetical protein in the PCOS network was postulated to be involved in the cell cycle. BiNGO was used to identify the three main ontologies in the protein network: molecular functions, biological processes and cellular components. This gene ontology analysis identified a number of ontologies and genes likely to be involved in the complex mechanism of PCOS. These include the insulin receptor signaling pathway, steroid biosynthesis, and the regulation of gonadotropin secretion among others.
Stein and Leventhal pioneered the study of Polycystic Ovary Syndrome (PCOS) in 1935 when they identified the abnormality in a small group of women with amenorrhea, hirsutism, obesity and histological evidence of polycystic ovaries . Today, PCOS is a common endocrine disorder affecting 6.5-8.0% of all women of reproductive age . There is no universal definition for this heterogeneous endocrine disorder . However, during the 2003 Rotterdam Consensus workshop, PCOS was defined as a multi-system network of abnormalities that includes obesity, insulin resistance, hyperandrogenism, elevated luteinizing hormone (LH) concentrations, increased risk of type 2 diabetes mellitus, cardiovascular events and menstrual irregularities . Insulin resistance is found in up to 70% of women with PCOS and 80% of the PCOS patients are hyperandrogenemic . Several pathways are thought to be involved in PCOS, and these include steroid hormone synthesis [5, 6], the insulin-signaling pathway  and gonadotrophin hormone action . Mutation analyses, linkage studies and case-control association studies have been used to assess the roles of candidate genes from these pathways in PCOS . CYP11A is a steroid synthesis gene that was found to be associated with PCOS and serum testosterone levels by a genetic polymorphism study . A linkage analysis using PCOS patients revealed the involvement of a 5' region of the insulin gene that contains a variable number of tandem repeats (VNTRs) . However, none of those genes are likely to be the key players in the pathogenesis of PCOS because its complexity and heterogeneity suggest the involvement of many genes as well as environmental factors [4, 9].
Another genomic technique that has been widely used to investigate the mechanism of PCOS and to identify candidate PCOS genes is the microarray-based comparison of ovarian tissues (theca cells, follicular granulose cells, total ovarian tissue, and ovarian connective tissue) from PCOS patients with ovarian tissues from healthy controls . The first PCOS microarray study was published by Wood and colleagues in 2003 . They used theca cells from PCOS women and healthy controls as their samples and identified 244 differentially expressed genes. Their findings on the upregulation of GATA-6, which is involved in the transcription of CYP11A supported earlier linkage analyses . Several other microarray analyses have helped shed light on the pathophysiology of PCOS. These results contributed to the dataset used in this study. The goal of this study was to obtain a clearer view of the mechanism of PCOS, since the definition of the abnormality remains unclear. Therefore we collated information on proteins related to PCOS, constructed a hypothetical network of interactions among PCOS-related proteins, and then inferred the function of a hypothetical protein that may be involved in PCOS.
A number of previous studies, including mutation analyses, linkage studies and case-control association studies have identified 58 candidate PCOS genes . In order to identify more proteins that may be related to PCOS, results from microarray analyses were used as a dataset in this study. These results were gathered from a literature search of various literature databases such as ScienceDirect http://www.sciencedirect.com and PubMed http://www.ncbi.nih.gov/pubmed/ among others. Candidate proteins were then classified manually as either known proteins or hypothetical proteins. The sequences of the hypothetical proteins were analyzed in detail to shed light on their functions. BLAST http://blast.ncbi.nlm.nih.gov/Blast.cgi was used to run similarity searches on the hypothetical proteins to infer functional and evolutionary relationships between protein sequences. To gain further functional information, InterProScan http://www.ebi.ac.uk/InterProScan was used to search the protein sequences for motifs characteristic of previously described domains and protein families. Moreover, PROSCAN http://npsa-pbil.ibcp.fr/cgi-bin/npsa_automat.pl?page=/NPSA/npsa_proscan.html was used to scan the protein sequences for sites and/or signatures contained in the PROSITE database. This tool is used to identify biologically relevant sites, patterns and profiles in a protein sequences.
All of the proteins identified by these methods were combined with the 58 PCOS-related proteins identified from the literature review. These proteins were then loaded into Cytoscape 2.6.1  using the BioNetBuilder plugin. BioNetBuilder 2.0  is an open-source network visualization platform. BioNetBuilder uses a variety of databases that include DIP (Database of Interacting Proteins), BIND (Biomolecular Interaction Network Database), HPRD (Human Protein Reference Database), KEGG (Kyoto Encyclopedia of Genes and Genomes) and MINT (Molecular Interaction Database) among others. However, since our study involves only proteins found in humans, only four databases were used: KEGG, HPRD, BIND and MINT. All of the collated proteins have their own UniProt ID and these were used as input for BioNetBuilder 2.0. Pathway construction with BioNetBuilder 2.0 usually takes several minutes depending on the amount of input loaded as well as the internet server used. BiNGO  was used to analyze the gene ontology in the PCOS network.
Results and Discussion
Microarray analyses of PCOS samples from 2003 to 2007
Number of differentially expressed genes
The molecular phenotype of PCOS theca cells and new candidate genes defined by microarray analysis 
Valproate-induced alteration in human theca cell gene expression 
Abnormal gene expression profiles in human ovaries from polycystic ovary syndrome 
The molecular characteristics of PCOS defined by human ovary cDNA microarray 
Molecular profiling of polycystic ovaries for markers of cell invasion and matrix turnover 
Differential gene expression profile in omental adipose tissue in women with PCOS 
Molecular abnormalities in oocytes from women with PCOS revealed by microarray analysis 
Sequence analyses were conducted to infer the function of each hypothetical protein. These analyses yielded numerous results. However, BLASTP analysis failed to identify any important functional or evolutionary relationship between the hypothetical proteins and known proteins. Moreover, most of the hypothetical proteins did not have any recognizable domains or protein family signatures in their sequences. Only one hypothetical protein (KIAA0247) had domain, family and superfamily associations in its protein sequence. The domain recognized is a sushi domain, also known as a complement control protein (CCP) module or short consensus repeat (SCR). Most of the hypothetical proteins contain casein kinase II phosphorylation sites and protein kinase C phosphorylation sites. Casein kinase II (CK-2) is a serine/threonine protein kinase whose activity is independent of cyclic nucleotides and calcium. CK-2 phosphorylates many different proteins. This pattern is found in most of its known physiological substrates . Protein kinase C preferentially phosphorylates serine and threonine residues that are near C-terminal basic residues. The presence of additional basic residues at the N- or C-terminus of the target amino acid enhances the Vmax and Km of the phosphorylation reaction .
Information from genomic analysis is ideally suited to elucidating the mechanism of complex syndrome such as PCOS. Therefore, we constructed a dataset composed of information from microarray analyses and other genomic studies of PCOS patients. This dataset, which consists of 1081 candidate genes, was used to construct a PCOS network. This network contains 504 protein nodes and 1408 interactions between those proteins. The network predicted that a hypothetical protein whose function was previously unknown interacts with cyclin B1. Thus this hypothetical protein may be involved in the cell cycle. The network also identified a number of molecular functions and biological processes likely to be involved in PCOS. These include steroid dehydrogenase activity, estradiol 17-beta-dehydrogenase activity, lipid metabolism, regulation of apoptosis, the insulin receptor signaling pathway, steroid biosynthesis and the regulation of gonadotropin secretion. The genes involved in these molecular functions and biological processes were then categorized as genes likely to have important roles in the mechanism of PCOS.
The work was supported by Genomics and Molecular Biology Initiative (GMBI) Grant (UKM-MGI-NBD0005/2007) awarded to ZAMH by Malaysia Ministry of Science, Technology and Innovation (MOSTI) and the MSc scholarship for SH is funded by Malaysia National Science Fellowship (NSF).
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