Shaini Joseph , Krutika Patil , Niharika Rahate , Jatin Shah , Srabani Mukherjee , Smita D. Mahale
{"title":"综合数据驱动分析确定了与多囊卵巢综合症相关的潜在候选基因。","authors":"Shaini Joseph , Krutika Patil , Niharika Rahate , Jatin Shah , Srabani Mukherjee , Smita D. Mahale","doi":"10.1016/j.compbiolchem.2024.108191","DOIUrl":null,"url":null,"abstract":"<div><p>Polycystic ovary syndrome (PCOS) is one of the most common anovulatory disorder observed in women presenting with infertility. Several high and low throughput studies on PCOS have led to accumulation of vast amount of information on PCOS. Despite the availability of several resources which index the advances in PCOS, information on its etiology still remains inadequate. Analysis of the existing information using an integrated evidence based approach may aid identification of novel potential candidate genes with a role in PCOS pathophysiology. This work focuses on integrating existing information on PCOS from literature and gene expression studies and evaluating the application of gene prioritization and network analysis to predict missing novel candidates. Further, it assesses the utility of evidence-based scoring to rank genes for their association with PCOS. The results of this study led to identification of ∼2000 plausible candidate genes associated with PCOS. Insilico validation of these identified candidates confirmed the role of 938 genes in PCOS. Further, experimental validation was carried out for four of the potential candidate genes, a high-scoring (PROS1), two mid-scoring (C1QA and KNG1), and a low-scoring gene (VTN) involved in the complement and coagulation pathway by comparing protein levels in follicular fluid in women with PCOS and healthy controls. While the expression of PROS1, C1QA, and KNG1 was found to be significantly downregulated in women with PCOS, the expression of VTN was found to be unchanged in PCOS. The findings of this study reiterate the utility of employing insilico approaches to identify and prioritize the most promising candidate genes in diseases with a complex pathophysiology like PCOS. Further, the study also helps in gaining clearer insights into the molecular mechanisms associated with the manifestation of the PCOS phenotype by contributing to the existing repertoire of genes associated with PCOS.</p></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"113 ","pages":"Article 108191"},"PeriodicalIF":2.6000,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Integrated data driven analysis identifies potential candidate genes associated with PCOS\",\"authors\":\"Shaini Joseph , Krutika Patil , Niharika Rahate , Jatin Shah , Srabani Mukherjee , Smita D. Mahale\",\"doi\":\"10.1016/j.compbiolchem.2024.108191\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Polycystic ovary syndrome (PCOS) is one of the most common anovulatory disorder observed in women presenting with infertility. Several high and low throughput studies on PCOS have led to accumulation of vast amount of information on PCOS. Despite the availability of several resources which index the advances in PCOS, information on its etiology still remains inadequate. Analysis of the existing information using an integrated evidence based approach may aid identification of novel potential candidate genes with a role in PCOS pathophysiology. This work focuses on integrating existing information on PCOS from literature and gene expression studies and evaluating the application of gene prioritization and network analysis to predict missing novel candidates. Further, it assesses the utility of evidence-based scoring to rank genes for their association with PCOS. The results of this study led to identification of ∼2000 plausible candidate genes associated with PCOS. Insilico validation of these identified candidates confirmed the role of 938 genes in PCOS. Further, experimental validation was carried out for four of the potential candidate genes, a high-scoring (PROS1), two mid-scoring (C1QA and KNG1), and a low-scoring gene (VTN) involved in the complement and coagulation pathway by comparing protein levels in follicular fluid in women with PCOS and healthy controls. While the expression of PROS1, C1QA, and KNG1 was found to be significantly downregulated in women with PCOS, the expression of VTN was found to be unchanged in PCOS. The findings of this study reiterate the utility of employing insilico approaches to identify and prioritize the most promising candidate genes in diseases with a complex pathophysiology like PCOS. Further, the study also helps in gaining clearer insights into the molecular mechanisms associated with the manifestation of the PCOS phenotype by contributing to the existing repertoire of genes associated with PCOS.</p></div>\",\"PeriodicalId\":10616,\"journal\":{\"name\":\"Computational Biology and Chemistry\",\"volume\":\"113 \",\"pages\":\"Article 108191\"},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2024-08-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computational Biology and Chemistry\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1476927124001798\",\"RegionNum\":4,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational Biology and Chemistry","FirstCategoryId":"99","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1476927124001798","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOLOGY","Score":null,"Total":0}
Integrated data driven analysis identifies potential candidate genes associated with PCOS
Polycystic ovary syndrome (PCOS) is one of the most common anovulatory disorder observed in women presenting with infertility. Several high and low throughput studies on PCOS have led to accumulation of vast amount of information on PCOS. Despite the availability of several resources which index the advances in PCOS, information on its etiology still remains inadequate. Analysis of the existing information using an integrated evidence based approach may aid identification of novel potential candidate genes with a role in PCOS pathophysiology. This work focuses on integrating existing information on PCOS from literature and gene expression studies and evaluating the application of gene prioritization and network analysis to predict missing novel candidates. Further, it assesses the utility of evidence-based scoring to rank genes for their association with PCOS. The results of this study led to identification of ∼2000 plausible candidate genes associated with PCOS. Insilico validation of these identified candidates confirmed the role of 938 genes in PCOS. Further, experimental validation was carried out for four of the potential candidate genes, a high-scoring (PROS1), two mid-scoring (C1QA and KNG1), and a low-scoring gene (VTN) involved in the complement and coagulation pathway by comparing protein levels in follicular fluid in women with PCOS and healthy controls. While the expression of PROS1, C1QA, and KNG1 was found to be significantly downregulated in women with PCOS, the expression of VTN was found to be unchanged in PCOS. The findings of this study reiterate the utility of employing insilico approaches to identify and prioritize the most promising candidate genes in diseases with a complex pathophysiology like PCOS. Further, the study also helps in gaining clearer insights into the molecular mechanisms associated with the manifestation of the PCOS phenotype by contributing to the existing repertoire of genes associated with PCOS.
期刊介绍：
Computational Biology and Chemistry publishes original research papers and review articles in all areas of computational life sciences. High quality research contributions with a major computational component in the areas of nucleic acid and protein sequence research, molecular evolution, molecular genetics (functional genomics and proteomics), theory and practice of either biology-specific or chemical-biology-specific modeling, and structural biology of nucleic acids and proteins are particularly welcome. Exceptionally high quality research work in bioinformatics, systems biology, ecology, computational pharmacology, metabolism, biomedical engineering, epidemiology, and statistical genetics will also be considered.
Given their inherent uncertainty, protein modeling and molecular docking studies should be thoroughly validated. In the absence of experimental results for validation, the use of molecular dynamics simulations along with detailed free energy calculations, for example, should be used as complementary techniques to support the major conclusions. Submissions of premature modeling exercises without additional biological insights will not be considered.
Review articles will generally be commissioned by the editors and should not be submitted to the journal without explicit invitation. However prospective authors are welcome to send a brief (one to three pages) synopsis, which will be evaluated by the editors.
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