Documentation

Data Sources

PCOS-Related Proteins Data

PCOSBase integrates all the proteins that are related to PCOS from various sources. The two main sources are databases and expression studies (transcriptomics and proteomics).

Databases
Users can view this information at a browse page, datasets tab or at database tab of a protein description. There will be a list of databases if that protein were retrieved from the database. There are nine databases that were used to retrieve the PCOS-related proteins.

  1. DGA: The Disease and Gene Annotations (DGA) database that provides the relationship between the genes and the diseases in the network format (Peng et al., 2013).
  2. DISEASES: The DISEASES database integrates all disease-gene associations from various sources, which are automatic text-mining, manually curated literature, cancer mutation data and genome-wide association studies (GWAS) (Pletscher-Frankild et al., 2015).
  3. DisGeNET: The DisGeNET database compiles gene-disease associations data from expert curated databases, animal models and scientific literature (Piñero et al., 2016).
  4. GWAS Catalog: GWAS Catalog provides by National Human Genome Research Institute (NHGRI)and the European Bioinformatics Institute (EMBL-EBI), which deposits all published GWAS data (Welter et al., 2014).
  5. GWASdb: GWASdb is a database that manually curates all the published GWAS data (Li et al., 2016)
  6. HGMD: The Human Gene Mutation Database (HGMD) collects published genes that are mutated in human inherited disease (Stenson et al., 2003). HGMD consists of professional and public versions and we used public version of HGMD.
  7. MalaCards: MalaCards provides the human diseases and their annotations including related genes, related diseases, GO terms, pathways and others (Rappaport et al., 2017).
  8. OMIM: The Online Mendelian Inheritance in Man (OMIM) is a database of human genes and genetic phenotypes (Hamosh et al., 2005). PCOSBase collects PCOS-related proteins from all the genes/proteins provide in the text of PCOS page in OMIM.
  9. PhenomicDB: PhenomicDB supplies gene-disease associations across variety of species (Kahraman et al., 2005).

Expression studies
Users can view these publications at the browser page, datasets tab or resource tab of a protein description. There will be a list of references if that protein were retrieved from the expression studies. Expression studies consist of gene (transcriptomics) and protein (proteomics) expression studies. PCOS-related proteins are genes/proteins that were significantly up- or down-regulated in PCOS women. 

  1. Transcriptomics: There are 19 microarray studies, where PCOS-related proteins were retrieved from  (Wood et al., 2003; Diao et al., 2004; Jansen et al., 2004; Wood et al., 2005; Oksjoki et al., 2005; Cortón et al., 2007; Wood et al., 2007; Skov et al., 2007; Qiao et al., 2008; Kenigsberg et al., 2009; Kim et al., 2009; Savaris et al., 2011; Chazenbalk et al., 2012; Ouandaogo et al., 2012; Yan et al., 2012; Kaur et al., 2012; Haouzi et al., 2012; Piltonen et al., 2013; Lansdown and Rees, 2012; Lan et al., 2015).
  2. Proteomics: There are 11 protein expression studies, where PCOS-related proteins were retrieved from (Ma et al., 2007; Matharoo-Ball et al., 2007; Borro et al., 2007; Cortón et al., 2008; Misiti et al., 2010; Choi et al., 2010; Insenser et al., 2010; Dai and Lu, 2012; Kim et al., 2013; Montes-Nieto et al., 2013; Ambekar et al., 2015).

Relevant Information Data

Relevant information data are the data that are related to PCOS-related proteins. This section provides all of the sources, where all these data were taken from.

  1. BioCarta: BioCarta database provides pathways for human and mouse
  2. DisGeNET: The DisGeNET database compiles gene-disease associations data from expert curated databases, animal models and scientific literature. All diseases that are associated with PCOS-related proteins were only retrieved from curated databases of DisGeNET (Piñero et al., 2016). Curated databases consist of UniProt, Comparative Toxicogenomics DatabaseTM (CTD), ClinVar, Orphanet, GWAS Catalog, PsyGeNET and Human Phenotype Ontology (HPO).
  3. Gene Ontology Consortium: This database provides biological knowledge of genes or proteins functions by using branches of ontology (biological process, cellular component and molecular function) across various species (Gene Ontology Consortium, 2015).
  4. HIPPIE: The Human Integrated Protein-Protein Interaction rEference (HIPPIE) database is a database of human protein-protein interactions (PPI) that were compiled from various PPI databases and publications (Schaefer et al., 2012).
  5. The Human Protein Atlas: This database supplies information of gene or protein expression profiles (Navani, 2011).
  6. InterPro: InterPro combines protein signatures from several databases, categorises protein sequences into families and predicts the domains and important sites to supply functional analysis of protein sequence (Mitchell et al., 2015).
  7. KEGG: Kyoto Encyclopaedia of Genes and Genomes (KEGG) contains human pathway information (Ogata et al., 1999).
  8. MeSH: Medical Subject Headings (MeSH) controls vocabulary thesaurus from the National Library of Medicine. MeSH contains hierarchical tree that categorises the disease into different class.
  9. NCBI Gene: It integrates information from a wide range of species. A record may include nomenclature, Reference Sequences (RefSeqs), maps, pathways, variations, phenotypes, and links to genome-, phenotype-, and locus-specific resources worldwide.
  10. PubMed: PubMed contains more than 27 million publications from MEDLINE, life sciences journals and online books.
  11. UMLS: Unified Medical Language Systems (UMLS) integrates and distributes key terminology, classification and coding standards, and associated resources to promote creation of more effective and interoperable biomedical information systems and services, including electronic health records.
  12. UniProtKB/ Swiss-Prot: This database supplies a comprehensive, high-quality and freely accessible resource of protein sequence and functional information (The UniProt Consortium, 2015).
  13. WikiPathways: WikiPatyways provides biological pathways (Pico et al., 2008).

References:

Ambekar,A.S. et al. (2015) Proteomics of follicular fluid from women with polycystic ovary syndrome suggests molecular defects in follicular development. J. Clin. Endocrinol. Metab., 100, 744–753.

Borro,M. et al. (2007) Proteomic analysis of peripheral T lymphocytes, suitable circulating biosensors of strictly related diseases. Clin. Exp. Immunol., 150, 494–501.

Chazenbalk,G. et al. (2012) Abnormal expression of genes involved in inflammation, lipid metabolism, and Wnt signaling in the adipose tissue of polycystic ovary syndrome. J. Clin. Endocrinol. Metab., 97, E765-70.

Choi,D.-H. et al. (2010) The apolipoprotein A-I level is downregulated in the granulosa cells of patients with polycystic ovary syndrome and affects steroidogenesis. J. Proteome Res., 9, 4329–36.

Cortón,M. et al. (2007) Differential Gene Expression Profile in Omental Adipose Tissue in Women with Polycystic Ovary Syndrome. J. Clin. Endocrinol. Metab., 92, 328–337.

Cortón,M. et al. (2008) Proteomic analysis of human omental adipose tissue in the polycystic ovary syndrome using two-dimensional difference gel electrophoresis and mass spectrometry. Hum. Reprod., 23, 651–661.

Dai,G. and Lu,G. (2012) Different protein expression patterns associated with polycystic ovary syndrome in human follicular fluid during controlled ovarian hyperstimulation. 893–904.

Diao,F.-Y. et al. (2004) The molecular characteristics of polycystic ovary syndrome (PCOS) ovary defined by human ovary cDNA microarray. J. Mol. Endocrinol., 33, 59–72.

Gene Ontology Consortium (2015) Gene Ontology Consortium: going forward. Nucleic Acids Res., 43, D1049-56.

Hamosh,A. et al. (2005) Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res., 33.

Haouzi,D. et al. (2012) Altered gene expression profile in cumulus cells of mature MII oocytes from patients with polycystic ovary syndrome. Hum. Reprod., 27, 3523–3530.

Insenser,M. et al. (2010) Proteomic analysis of plasma in the polycystic ovary syndrome identifies novel markers involved in iron metabolism, acute-phase response, and inflammation. J. Clin. Endocrinol. Metab., 95, 3863–3870.

Jansen,E. et al. (2004) Abnormal Gene Expression Profiles in Human Ovaries from Polycystic Ovary Syndrome Patients. Mol. Endocrinol., 18, 3050–3063.

Kahraman,A. et al. (2005) PhenomicDB: A multi-species genotype/phenotype database for comparative phenomics. Bioinformatics, 21, 418–420.

Kaur,S. et al. (2012) Differential gene expression in granulosa cells from polycystic ovary syndrome patients with and without insulin resistance: identification of susceptibility gene sets through network analysis. J. Clin. Endocrinol. Metab., 97, E2016-21.

Kenigsberg,S. et al. (2009) Gene expression microarray profiles of cumulus cells in lean and overweight-obese polycystic ovary syndrome patients. Mol. Hum. Reprod., 15, 89–103.

Kim,J.Y. et al. (2009) Transcriptional profiling with a pathway-oriented analysis identifies dysregulated molecular phenotypes in the endometrium of patients with polycystic ovary syndrome. J. Clin. Endocrinol. Metab., 94, 1416–1426.

Kim,Y.-S. et al. (2013) Apolipoprotein A-IV as a novel gene associated with polycystic ovary syndrome. Int. J. Mol. Med., 707–716.

Lan,C.-W. et al. (2015) Functional microarray analysis of differentially expressed genes in granulosa cells from women with polycystic ovary syndrome related to MAPK/ERK signaling. Sci. Rep., 5, 14994.

Lansdown,A. and Rees,D.A. (2012) The sympathetic nervous system in polycystic ovary syndrome: A novel therapeutic target? Clin. Endocrinol. (Oxf)., 77, 791–801.

Li,M.J. et al. (2016) GWASdb v2: An update database for human genetic variants identified by genome-wide association studies. Nucleic Acids Res., 44, D869–D876.

Ma,X. et al. (2007) Proteomic analysis of human ovaries from normal and polycystic ovarian syndrome. Mol. Hum. Reprod., 13, 527–535.

Matharoo-Ball,B. et al. (2007) Characterization of biomarkers in Polycystic Ovary Syndrome (PCOS) using multiple distinct proteomic platforms. J. Proteome Res., 6, 3321–3328.

Misiti,S. et al. (2010) Proteomic profiles in hyperandrogenic syndromes. J. Endocrinol. Invest., 33, 156–164.

Mitchell,A. et al. (2015) The InterPro protein families database: The classification resource after 15 years. Nucleic Acids Res., 43, D213–D221.

Montes-Nieto,R. et al. (2013) A nontargeted proteomic study of the influence of androgen excess on human visceral and subcutaneous adipose tissue proteomes. J. Clin. Endocrinol. Metab., 98, 576–585.

Navani,S. (2011) The human protein atlas. J. Obstet. Gynecol. India, 61, 27–31.

Ogata,H. et al. (1999) KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res., 27, 29–34.

Oksjoki,S. et al. (2005) Molecular profiling of polycystic ovaries for markers of cell invasion and matrix turnover. Fertil. Steril., 83, 937–944.

Ouandaogo,Z.G. et al. (2012) Differences in transcriptomic profiles of human cumulus cells isolated from oocytes at GV, MI and MII stages after in vivo and in vitro oocyte maturation. Hum. Reprod., 27, 2438–2447.

Peng,K. et al. (2013) The disease and gene annotations (DGA): An annotation resource for human disease. Nucleic Acids Res., 41.

Pico,A.R. et al. (2008) WikiPathways: Pathway Editing for the People. PLoS Biol., 6, 1403–1407.

Piltonen,T.T. et al. (2013) Mesenchymal stem/progenitors and other endometrial cell types from women with polycystic ovary syndrome (PCOS) display inflammatory and oncogenic potential. J. Clin. Endocrinol. Metab., 98, 3765–3775.

Piñero,J. et al. (2016) DisGeNET: A comprehensive platform integrating information on human disease-associated genes and variants. NAR, 45, D833-D839.

Pletscher-Frankild,S. et al. (2015) DISEASES: Text mining and data integration of disease-gene associations. Methods, 74, 83–89.

Qiao,J. et al. (2008) Microarray evaluation of endometrial receptivity in Chinese women with polycystic ovary syndrome. Reprod. Biomed. Online, 17, 425–435.

Rappaport,N. et al. (2017) MalaCards: An amalgamated human disease compendium with diverse clinical and genetic annotation and structured search. Nucleic Acids Res., 45, D877–D887.

Savaris,R.F. et al. (2011) Progesterone resistance in PCOS endometrium: A microarray analysis in clomiphene citrate-treated and artificial menstrual cycles. J. Clin. Endocrinol. Metab., 96, 1737–1746.

Schaefer,M.H. et al. (2012) Hippie: Integrating protein interaction networks with experiment based quality scores. PLoS One, 7.

Skov,V. et al. (2007) Expression of Nuclear-Encoded Genes Involved in Mitochondrial Oxidative Metabolism in Skeletal Muscle of Insulin-Resistant Women With Polycystic Ovary Syndrome. Diabetes, 56, 2349–2355.

Stenson,P.D. et al. (2003) Human Gene Mutation Database (HGMD??): 2003 Update. Hum. Mutat., 21, 577–581.

The UniProt Consortium (2015) UniProt: a hub for protein information. Nucleic Acids Res., 43, D204-12.

Welter,D. et al. (2014) The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res., 42.

Wood,J.R. et al. (2007) Molecular abnormalities in oocytes from women with polycystic ovary syndrome revealed by microarray analysis. J. Clin. Endocrinol. Metab., 92, 705–713.

Wood,J.R. et al. (2003) The molecular phenotype of polycystic ovary syndrome (PCOS) theca cells and new candidate PCOS genes defined by microarray analysis. J. Biol. Chem., 278, 26380–26390.

Wood,J.R. et al. (2005) Valproate-induced alterations in human theca cell gene expression: clues to the association between valproate use and metabolic side effects. Physiol. Genomics, 20, 233–243.

Yan,L. et al. (2012) Expression of apoptosis-related genes in the endometrium of polycystic ovary syndrome patients during the window of implantation. Gene, 506, 350–354.