Systems biology and in silico-based analysis of PCOS revealed the risk of metabolic disorders. Issue 12 (December 2022)

• Record Type: Journal Article
• Authors: Hossain, Md. Arju; Al Ashik, Sheikh Abdullah; Mahin, Moshiur Rahman; Al Amin, Md.; Rahman, Md Habibur; Khan, Md. Arif; Emran, Abdullah Al
• Is Part Of: Heliyon. Volume 8:Issue 12(2022)
• Publication Date: 2022-12
• Subjects: Polycystic ovary syndrome -- Type 2 diabetes -- Cardiovascular disease -- Obesity -- Metabolic comorbidities -- Phytochemicals -- ADMET; Research -- Periodicals; Medical sciences -- Periodicals; Natural history -- Periodicals; Social sciences -- Periodicals; Earth sciences -- Periodicals; Physical sciences -- Periodicals; 507.2
• DOI: 10.1016/j.heliyon.2022.e12480
• Languages: English
• ISSNs: 2405-8440

Abstract

Abstract: Background: Polycystic ovarian syndrome (PCOS) is a common condition of hyperandrogenism, chronic ovulation, and polycystic ovaries in females during the reproduction and maturation of the ovum. Although PCOS has been associated with metabolic disorders, including type 2 diabetes (T2D), obesity (OBE), and cardiovascular disease (CVD), Causal connection and molecular features are still unknown. Purpose: Therefore, we investigated the shared common differentially expressed genes (DEGs), pathways, and networks of associated proteins in PCOS and metabolic diseases with therapeutic intervention. Methods: We have used a bioinformatics pipeline to analyze transcriptome data for the polycystic ovarian syndrome (PCOS), type 2 diabetes (T2D), obesity (OBE), and cardiovascular diseases (CVD) in female patients. Then we employed gene-disease association network, gene ontology (GO) and signaling pathway analysis, selection of hub genes from protein-protein interaction (PPI) network, molecular docking, and gold benchmarking approach to screen potential hub proteins. Result: We discovered 2225 DEGs in PCOS patients relative to healthy controls and 34, 91, and 205 significant DEGs with T2D, Obesity, and CVD, respectively. Gene Ontology analysis revealed several significant shared and metabolic pathways from signaling pathway analysis. Furthermore, we identified ten potential hub proteins from PPI analysis that may serve as a therapeutic intervention in the future. Finally, we targeted one significant hub protein, IGF2R (PDB ID: 2V5O ), out of ten hub proteins based on the Maximal clique centrality (MCC) algorithm and literature review for molecular docking study. Enzastaurin (−12.5), Kaempferol (−9.1), Quercetin (−9.0), and Coumestrol (−8.9) kcal/mol showed higher binding affinity in the molecular docking approach than 19 drug compounds. We have also found that the selected four compounds displayed favorable ADMET properties compared to the native ligand. Conclusion: Our in-silico research findings identified a shared molecular etiology between PCOS and metabolic diseases that may suggest new therapeutic targets and warrants future experimental validation of the key targets. Highlights: The aim of study lies in exploring metabolic risk factors that drive the progression of PCOS based on computational approaches. Using global transcriptomic data to identify their genetic profile, pathways, and PPI network analysis for therapeutic intervention. Hub protein IGF2R (PDB ID: 2V5O ) was employed as a molecular target with certain phytoestrogenic compounds to manage PCOS. Integrating molecular docking and the ADMET analysis employed in computational drug discovery as the most inexpensive approach. Finally, we have used gold benchmark databases like OMIM and dbGAP to validate the DEGs and molecular pathways. Abstract : Polycystic ovary syndrome; Type 2 diabetes; Cardiovascular disease; Obesity; Metabolic comorbidities; Phytochemicals; ADMET.