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.
Graphical Abstract
[http://dx.doi.org/10.1007/s00109-022-02265-5] [PMID: 36271927]
[http://dx.doi.org/10.1096/fba.2019-00060] [PMID: 32259050]
[http://dx.doi.org/10.1016/j.molmed.2006.11.006] [PMID: 17142102]
[http://dx.doi.org/10.3390/cells10071781] [PMID: 34359949]
[http://dx.doi.org/10.3389/fcvm.2020.00039] [PMID: 32296714]
[http://dx.doi.org/10.1177/1535370214565992] [PMID: 25595185]
[http://dx.doi.org/10.1177/1535370214561953] [PMID: 25681467]
[http://dx.doi.org/10.1161/CIRCGENETICS.113.000248.The]
[http://dx.doi.org/10.1369/0022155421991629] [PMID: 33565365]
[http://dx.doi.org/10.1093/nar/gks539] [PMID: 22689647]
[http://dx.doi.org/10.1016/0002-9343(63)90102-5] [PMID: 14003161]
[http://dx.doi.org/10.1093/bioinformatics/btv195] [PMID: 25851949]
[http://dx.doi.org/10.1016/j.imu.2021.100808]
[http://dx.doi.org/10.1093/bioinformatics/btl423] [PMID: 16895930]
[http://dx.doi.org/10.1002/humu.21047] [PMID: 19514061]
[http://dx.doi.org/10.1002/humu.22225] [PMID: 23033316]
[http://dx.doi.org/10.1093/nar/gki375]
[http://dx.doi.org/10.1002/prot.20810] [PMID: 16372356]
[http://dx.doi.org/10.1093/nar/gkq537]
[http://dx.doi.org/10.1186/s43042-020-00110-3]
[http://dx.doi.org/10.1093/nar/gky1131] [PMID: 30476243]
[http://dx.doi.org/10.1093/bioinformatics/11.6.681] [PMID: 8808585]
[http://dx.doi.org/10.1038/s41586-021-03819-2] [PMID: 34265844]
[http://dx.doi.org/10.1006/jmbi.1999.3310] [PMID: 10600390]
[http://dx.doi.org/10.3389/fnins.2020.00644] [PMID: 32581702]
[http://dx.doi.org/10.7717/peerj.14132] [PMID: 36518267]
[http://dx.doi.org/10.1186/1759-4499-2-2]
[http://dx.doi.org/10.1186/s12943-017-0693-9] [PMID: 28724435]
[http://dx.doi.org/10.1021/acs.jproteome.1c00551] [PMID: 34428048]
[http://dx.doi.org/10.1159/000444169.Carotid]
[http://dx.doi.org/10.2478/abm-2023-0059] [PMID: 37860678]