Mutational Landscape Screening Through Comprehensive In Silico Analysis for Polycystic Ovarian Syndrome–Related Genes

Dhar, Shrinjana; Mridha, Saptarshi; Bhattacharjee, Pritha

doi:10.1007/s43032-021-00752-7

Cited by 8 publications

(7 citation statements)

References 91 publications

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“…The structural and functional alterations of the proteins encoded by the candidate genes selected in the current study and their impact on disease predisposition have been our speci c focus. In our previous study [32], we considered the missense variants of a few candidate genes in the energy homeostasis pathway including LEP, LEPR, ADIPOQ, and PPARG, etc.The pathogenicity prediction and structural and functional alteration screening identi ed the variant rs757012788 (LEPR) among the others, to be a probable cause of disturbing the domain and resulting in a loss of function of the protein. In the current study, the genes were targeted as an extension of the relevant pathways.…”

Section: Discussionmentioning

confidence: 99%

Predictive Analysis of the Leptin-Melanocortin and Adiponectin Signaling Pathways in Obesity through In Silico Techniques

Ghosh,

Dhar,

Roy

et al. 2024

Preprint

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Genetic and epigenetic alterations have been reported to significantly influence the global burden of obesity. Single nucleotide polymorphisms (SNPs) including both coding and non-coding amino acid changes are the key regulators of the protein structural and functional modifications. The current computational study utilizing in silico techniques focused on the screening and identification of the most pathogenic missense SNPs of the selected candidate genes of the leptin-melanocortin and adiponectin signaling pathways provoking obesity. A total of 2424 SNPs from 9 candidate genes were extracted from the NCBI database followed by pathogenicity prediction using seven servers, SIFT, PANTHER, Meta-SNP, PhD-SNP, PredictSNP, PolyPhen-2, and SNAP2. The shortlisted variants (n = 7) were analyzed for structural stability using DynaMut, iMutant, INPS3D, MuPro, and iStable followed by the functional stability analysis (n = 3) using Mut-Pred2, Project HOPE, and I-TASSER. Gene-network analysis of the finally screened SNPs (n = 3) was created using the STRING database. Two SNPs of ADIPOR1 (rs1419320091 and rs1654109863) and one variant of MC4R (rs1159323398) were predicted in the study to be the most pathogenic resulting in altered protein functionality. Therapeutic approaches designed based on early pathogenicity predictions using in silico analysis techniques would be a new horizon for the effective control of disease prevalence.

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Section: Discussionmentioning

confidence: 99%

Predictive Analysis of the Leptin-Melanocortin and Adiponectin Signaling Pathways in Obesity through In Silico Techniques

Ghosh,

Dhar,

Roy

et al. 2024

Preprint

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“…The SNPs that were predicted to be deleterious by all tools were considered the most deleterious ones. Using six different tools with varying algorithms, an approach that was applied previously ( Hossain et al, 2020 ; Dhar et al, 2022 ; Rajput and Gahlay, 2021 ), could increase the confidence and accuracy of our analysis.…”

Section: Methodsmentioning

confidence: 99%

Computational Analysis of Deleterious SNPs in NRAS to Assess Their Potential Correlation With Carcinogenesis

et al. 2022

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The NRAS gene is a well-known oncogene that acts as a major player in carcinogenesis. Mutations in the NRAS gene have been linked to multiple types of human tumors. Therefore, the identification of the most deleterious single nucleotide polymorphisms (SNPs) in the NRAS gene is necessary to understand the key factors of tumor pathogenesis and therapy. We aimed to retrieve NRAS missense SNPs and analyze them comprehensively using sequence and structure approaches to determine the most deleterious SNPs that could increase the risk of carcinogenesis. We also adopted structural biology methods and docking tools to investigate the behavior of the filtered SNPs. After retrieving missense SNPs and analyzing them using six in silico tools, 17 mutations were found to be the most deleterious mutations in NRAS. All SNPs except S145L were found to decrease NRAS stability, and all SNPs were found on highly conserved residues and important functional domains, except R164C. In addition, all mutations except G60E and S145L showed a higher binding affinity to GTP, implicating an increase in malignancy tendency. As a consequence, all other 14 mutations were expected to increase the risk of carcinogenesis, with 5 mutations (G13R, G13C, G13V, P34R, and V152F) expected to have the highest risk. Thermodynamic stability was ensured for these SNP models through molecular dynamics simulation based on trajectory analysis. Free binding affinity toward the natural substrate, GTP, was higher for these models as compared to the native NRAS protein. The Gly13 SNP proteins depict a differential conformational state that could favor nucleotide exchange and catalytic potentiality. A further application of experimental methods with all these 14 mutations could reveal new insights into the pathogenesis and management of different types of tumors.

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“…Research worldwide has investigated the prevalence of menstrual disorders and patterns across different ethnicities. Menstrual irregularities are heavily influenced by genetic predisposition [ 34 ], lifestyle patterns like dietary habits [ 35 , 36 ], physical activity [ 37 ], sleeping habits [ 38 ], and environmental exposures [ 39 ]. However, in developing countries like India, studying female reproductive health is challenging due to limited awareness and social stigma.…”

Section: Reviewmentioning

confidence: 99%

The Impact of Irregular Menstruation on Health: A Review of the Literature

Attia,

Alharbi,

Aljohani

2023

Cureus

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Women are considered to have an irregular menstrual cycle if their cycle length is less than 21 days or more than 35 days, accompanied by less or very severe blood flow. The prevalence of menstrual cycle irregularities varies across countries. Irregular periods can occur due to changes in the body's levels of estrogen and progesterone hormones, which disrupt the normal pattern of the period. Menstrual irregularity has been found to be associated with various diseases and medical conditions, such as metabolic syndrome, coronary heart disease, type 2 diabetes mellitus, and rheumatoid arthritis. Anemia, osteoporosis, psychological problems, impaired quality of life, and infertility have also been recorded. Moreover, a significant correlation between irregular periods and the risk of developing pregnancy-related hypertensive disorders, as well as an increased risk of adverse obstetric and neonatal outcomes, has been proven. Therefore, irregular menstruation is considered an important health indicator among women. Physical, mental, social, psychological, and reproductive problems are often associated with menstrual irregularities. Thus, evaluating the factors associated with irregular menstruation is necessary to determine appropriate preventive and treatment strategies and to decrease the associated health problems. The aim of this review was to define normal and irregular menstruation, their types, and prevalence, to recognize the risk factors and causes of irregular menstruation, and to understand their impact on women's health.

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Mutational Landscape Screening Through Comprehensive In Silico Analysis for Polycystic Ovarian Syndrome–Related Genes

Cited by 8 publications

References 91 publications

Predictive Analysis of the Leptin-Melanocortin and Adiponectin Signaling Pathways in Obesity through In Silico Techniques

Predictive Analysis of the Leptin-Melanocortin and Adiponectin Signaling Pathways in Obesity through In Silico Techniques

Computational Analysis of Deleterious SNPs in NRAS to Assess Their Potential Correlation With Carcinogenesis

The Impact of Irregular Menstruation on Health: A Review of the Literature

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