ABSTRACT
Precocious puberty (PP) is an important endocrine disorder affecting children globally. Several genes, SNPs and comorbidities are reported to be associated with PP; however, this data is scattered across scientific literature and has not been systematically collated and analysed. In this study, we present PrecocityDB as the first manually curated online database on genes and their ontology terms, SNPs, and pathways associated with PP. A tool for visualizing SNP coordinates and allelic variation on each chromosome, for genes associated with PP is also incorporated in PrecocityDB. Pathway enrichment analysis of PP-associated genes revealed that endocrine and cancer-related pathways are highly enriched. Disease enrichment analysis indicated that individuals with PP seem to be highly likely to suffer from reproductive and metabolic disorders such as PCOS, hypogonadism, and insulin resistance. PrecocityDB is a useful resource for identification of comorbid conditions and disease risks due to shared genes in PP. PrecocityDB is freely accessible at http://www.precocity.bicnirrh.res.in . The database source code and content can be downloaded through GitHub ( https://github.com/bic-nirrh/precocity ).
Subject(s)
Puberty, Precocious/etiology , Puberty, Precocious/genetics , Comorbidity , Data Management , Databases, Factual , Endocrine Cells/physiology , Humans , Polymorphism, Single Nucleotide/genetics , Signal Transduction/physiologyABSTRACT
PolyCystic Ovary Syndrome KnowledgeBase (PCOSKBR2) is a manually curated database with information on 533 genes, 145 SNPs, 29 miRNAs, 1,150 pathways, and 1,237 diseases associated with PCOS. This data has been retrieved based on evidence gleaned by critically reviewing literature and related records available for PCOS in databases such as KEGG, DisGeNET, OMIM, GO, Reactome, STRING, and dbSNP. Since PCOS is associated with multiple genes and comorbidities, data mining algorithms for comorbidity prediction and identification of enriched pathways and hub genes are integrated in PCOSKBR2, making it an ideal research platform for PCOS. PCOSKBR2 is freely accessible at http://www.pcoskb.bicnirrh.res.in/ .
Subject(s)
Disease/genetics , Gene Regulatory Networks/genetics , Polycystic Ovary Syndrome/genetics , Signal Transduction/genetics , Algorithms , Computational Biology/methods , Databases, Genetic , Female , Gene Expression Profiling/methods , Humans , Knowledge Bases , MicroRNAs/genetics , Polymorphism, Single Nucleotide/geneticsABSTRACT
Polycystic ovary syndrome (PCOS) is a complex multigenic disorder and women with PCOS suffer from several comorbidities. Although, obesity is a known risk factor for PCOS, the incidence of lean women with PCOS is on the rise. A systematic and comparative study on lean and obese PCOS with respect to genes, pathways and comorbidity analysis has not been attempted so far. Analysis of differentially expressed genes (DEGs) across tissue types for lean and obese PCOS revealed that the majority of them were downregulated for lean and obese PCOS. Ovarian and endometrial tissues shared several commonly dysregulated genes, suggesting shared PCOS pathophysiology mechanisms exist across tissues. Several pathways for cellular homeostasis, such as inflammation and immune response, insulin signaling, steroidogenesis, hormonal and metabolic signaling, regulation of gonadotrophic hormone secretion, cell structure and signaling that are known to be affected in PCOS were found to be enriched in our gene expression analysis of lean and obese PCOS. The gene-disease network is denser for obese PCOS with a higher comorbidity score as compared to lean PCOS.
