Generic placeholder image

Current Pharmacogenomics and Personalized Medicine

Editor-in-Chief

ISSN (Print): 1875-6921
ISSN (Online): 1875-6913

Research Article

In Silico Analysis of Non-Synonymous Snps in the Human WWOX Gene

Author(s): Nihala Sidhic and Usha Subbiah*

Volume 21, Issue 1, 2024

Published on: 24 May, 2024

Page: [38 - 50] Pages: 13

DOI: 10.2174/0118756921312973240517112757

Price: $65

Abstract

Aim: To analyse the nsSNPs associated with the human WWOX gene using bioinformatics tools.

Background: WW domain-containing oxidoreductase (WWOX) is a protein-coding gene that controls several biological processes, including RNA splicing, transcription, and protein degradation. The modification in the WWOX gene is associated with osteopenia, metabolic syndrome, gestational diabetes, tumour progression, and disruption in lipid metabolism.

Objective: The study focused on understanding the structural and functional distribution of high-risk nsSNPs of the WWOX gene using several bioinformatics tools.

Methods: Retrieval of nsSNPs of WWOX gene from NCBI and Uniprot database. Identification of deleterious missense SNPs using the tools SIFT, Polyphen v2, PROVEAN, FATHMM, PhD-SNP, and SNPs & GO. The gene-gene and protein-protein interactions were investigated using GeneMANIA and STRING, respectively. The structural and functional characterisation of the gene was predicted using I-Mutant, MUPro, SOPMA, Alpha Fold, and NetPhos 3.1.

Results: The study identified 7 out of 646 nsSNPs (rs193001955, rs200371768, rs370792938, rs2303192, rs371364838, rs372362643, rs374343152) as deleterious. The identified nsSNPs were destabilizing the WWOX protein. The secondary structure prediction indicated that the majority of the nsSNPs were random coil and alpha-helix. Meanwhile, phosphorylation was observed in several positions in threonine and serine residues, and the least phosphorylation was observed for tyrosine in the WWOX gene. Phosphorylation of high-risk variants of this gene may lead to alteration in the regulation of posttranslational modification.

Conclusion: Our study predicted 7 functional nsSNPs that had detrimental effects on the structure and function of the WWOX gene. This will aid in the identification of candidate deleterious nsSNPs markers as a potential therapeutic target for disease diagnosis.

« Previous
Graphical Abstract

[1]
Salah ZRIA, Alian A. The Lautenberg Center for General and Tumor Immunology, Department of Immunology and Cancer Research-IMRIC, Hebrew University-Hadassah Medical School, Jerusalem 91120, Israel. Front Biosci 2012; 1: 331-48.
[2]
Baryła I, Kośla K, Bednarek AK. WWOX and metabolic regulation in normal and pathological conditions. J Mol Med 2022; 100(12): 1691-702.
[http://dx.doi.org/10.1007/s00109-022-02265-5] [PMID: 36271927]
[3]
Huang SS, Hsu LJ, Chang NS. Functional role of WW domain‐containing proteins in tumor biology and diseases: Insight into the role in ubiquitin‐proteasome system. FASEB Bioadv 2020; 2(4): 234-53.
[http://dx.doi.org/10.1096/fba.2019-00060] [PMID: 32259050]
[4]
Chang NS, Hsu LJ, Lin YS, Lai FJ, Sheu HM. WW domain-containing oxidoreductase: A candidate tumor suppressor. Trends Mol Med 2007; 13(1): 12-22.
[http://dx.doi.org/10.1016/j.molmed.2006.11.006] [PMID: 17142102]
[5]
Hsu CY, Lee KT, Sun TY, et al. Wwox and its binding proteins in neurodegeneration. Cells 2021; 10(7): 1781.
[http://dx.doi.org/10.3390/cells10071781] [PMID: 34359949]
[6]
Jomard A, Osto E. High density lipoproteins: Metabolism, function, and therapeutic potential. Front Cardiovasc Med 2020; 7: 39.
[http://dx.doi.org/10.3389/fcvm.2020.00039] [PMID: 32296714]
[7]
Gao G, Smith DI. WWOX, large common fragile site genes, and cancer. Exp Biol Med 2015; 240(3): 285-95.
[http://dx.doi.org/10.1177/1535370214565992] [PMID: 25595185]
[8]
Baryła I, Styczeń-Binkowska E, Bednarek AK. Alteration of WWOX in human cancer, a clinical view. Exp Biol Med 2015; 240(3): 305-14.
[http://dx.doi.org/10.1177/1535370214561953] [PMID: 25681467]
[9]
Ng IYH, Shen X, Sim H, Sarri RC, Stoffregen E, Shook JJ. Genetic changes NIH Public Access. J Neurochem 2015; 4(1): 1-15.
[http://dx.doi.org/10.1161/CIRCGENETICS.113.000248.The]
[10]
Mahmud MAA, Noguchi M, Domon A, Tochigi Y, Katayama K, Suzuki H. Cellular expression and subcellular localization of Wwox protein during testicular development and spermatogenesis in rats. J Histochem Cytochem 2021; 69(4): 257-70.
[http://dx.doi.org/10.1369/0022155421991629] [PMID: 33565365]
[11]
Sim NL, Kumar P, Hu J, Henikoff S, Schneider G, Ng PC. SIFT web server: Predicting effects of amino acid substitutions on proteins. Nucleic Acids Res 2012; 40(W1): W452-7.
[http://dx.doi.org/10.1093/nar/gks539] [PMID: 22689647]
[12]
Zamenhof S. Mutations. Am J Med 1963; 34(5): 609-26.
[http://dx.doi.org/10.1016/0002-9343(63)90102-5] [PMID: 14003161]
[13]
Choi Y, Chan AP. PROVEAN web server: A tool to predict the functional effect of amino acid substitutions and indels. Bioinformatics 2015; 31(16): 2745-7.
[http://dx.doi.org/10.1093/bioinformatics/btv195] [PMID: 25851949]
[14]
Kashan HS, Albakrye AM, Elnasri HA, Khaier MAM. In silico analysis of single nucleotide polymorphisms in human GCH1 gene. Informat Med Unlocked 2021; 27: 100808.
[http://dx.doi.org/10.1016/j.imu.2021.100808]
[15]
Capriotti E, Calabrese R, Casadio R. Predicting the insurgence of human genetic diseases associated to single point protein mutations with support vector machines and evolutionary information. Bioinformatics 2006; 22(22): 2729-34.
[http://dx.doi.org/10.1093/bioinformatics/btl423] [PMID: 16895930]
[16]
Calabrese R, Capriotti E, Fariselli P, Martelli PL, Casadio R. Functional annotations improve the predictive score of human disease-related mutations in proteins. Hum Mutat 2009; 30(8): 1237-44.
[http://dx.doi.org/10.1002/humu.21047] [PMID: 19514061]
[17]
Shihab HA, Gough J, Cooper DN, et al. Predicting the functional, molecular, and phenotypic consequences of amino acid substitutions using hidden Markov models. Hum Mutat 2013; 34(1): 57-65.
[http://dx.doi.org/10.1002/humu.22225] [PMID: 23033316]
[18]
Capriotti E, Fariselli P, Casadio R. I-Mutant2.0: Predicting stability changes upon mutation from the protein sequence or structure. Nucleic Acids Res 2005; 33(S2): 306-10.
[http://dx.doi.org/10.1093/nar/gki375]
[19]
Cheng J, Randall A, Baldi P. Prediction of protein stability changes for single‐site mutations using support vector machines. Proteins 2006; 62(4): 1125-32.
[http://dx.doi.org/10.1002/prot.20810] [PMID: 16372356]
[20]
Farley WD. The GeneMANIA prediction server: Biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res 2010; 38(2): 214-20.
[http://dx.doi.org/10.1093/nar/gkq537]
[21]
Subbiah VH, Babu RP, Subbiah U. In silico analysis of non-synonymous single nucleotide polymorphisms of human DEFB1 gene. Egypt J Med Hum Genet 2020; 21(1): 66.
[http://dx.doi.org/10.1186/s43042-020-00110-3]
[22]
Szklarczyk D, Gable AL, Lyon D, et al. STRING v11: Protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res 2019; 47(D1): D607-13.
[http://dx.doi.org/10.1093/nar/gky1131] [PMID: 30476243]
[23]
Geourjon C, Deléage G. SOPMA: significant improvements in protein secondary structure prediction by consensus prediction from multiple alignments. Bioinformatics 1995; 11(6): 681-4.
[http://dx.doi.org/10.1093/bioinformatics/11.6.681] [PMID: 8808585]
[24]
Jumper J, Evans R, Pritzel A, et al. Highly accurate protein structure prediction with AlphaFold. Nature 2021; 596(7873): 583-9.
[http://dx.doi.org/10.1038/s41586-021-03819-2] [PMID: 34265844]
[25]
Blom N, Gammeltoft S, Brunak S. Sequence and structure-based prediction of eukaryotic protein phosphorylation sites. J Mol Biol 1999; 294(5): 1351-62.
[http://dx.doi.org/10.1006/jmbi.1999.3310] [PMID: 10600390]
[26]
Iacomino M, Baldassari S, Tochigi Y, et al. Loss of Wwox perturbs neuronal migration and impairs early cortical development. Front Neurosci 2020; 14(June): 644.
[http://dx.doi.org/10.3389/fnins.2020.00644] [PMID: 32581702]
[27]
Shahid M, Azfaralariff A, Tufail M, et al. Screening of high-risk deleterious missense variations in the CYP1B1 gene implicated in the pathogenesis of primary congenital glaucoma: A comprehensive In silico approach. PeerJ 2022; 10: e14132.
[http://dx.doi.org/10.7717/peerj.14132] [PMID: 36518267]
[28]
Raman K. Construction and analysis of protein-protein interaction networks. Autom Exp 2010; 2(1): 2.
[http://dx.doi.org/10.1186/1759-4499-2-2]
[29]
Chen S, Wang H, Huang YF, et al. WW domain-binding protein 2: An adaptor protein closely linked to the development of breast cancer. Mol Cancer 2017; 16(1): 128.
[http://dx.doi.org/10.1186/s12943-017-0693-9] [PMID: 28724435]
[30]
Tahir R, Madugundu AK, Udainiya S, et al. Proximity-dependent biotinylation to elucidate the interactome of TNK2 nonreceptor tyrosine kinase. J Proteome Res 2021; 20(9): 4566-77.
[http://dx.doi.org/10.1021/acs.jproteome.1c00551] [PMID: 34428048]
[31]
Peng CYNDR, Khavari R. Myocardial extraction from suckling rats HHS public access. Physiol Behav 2017; 176(3): 139-48.
[http://dx.doi.org/10.1159/000444169.Carotid]
[32]
Yaradoddi J. MERLY DP, Keti MR, Kumar A. Structural analysis of pkna protein in mycobacterium tuberculosis. IJBPAS 2013; 2(7): 1513-25.
[33]
Ajith A, Subbiah U. In silico prediction of deleterious non-synonymous SNPs in STAT3. Asian Biomed 2023; 17(4): 185-99.
[http://dx.doi.org/10.2478/abm-2023-0059] [PMID: 37860678]

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy