PREDICTION OF HIGH-RISK NSSNPS ASSOCIATED WITH WISP3 GENE EXPRESSION: AN IN SILICO STUDY

SAUNDARYA M. S.; SUSHA DINESH; SAMEER SHARMA

doi:10.22159/ijap.2023v15i5.48269

Authors

SAUNDARYA M. S. Manipal School of Life Sciences, Manipal Academy of Higher Education (MAHE), Manipal-576104, Karnataka, India https://orcid.org/0009-0007-3827-8621
SUSHA DINESH Department of Bioinformatics, BioNome, Bengaluru-560043, Karnataka, India https://orcid.org/0000-0001-6593-3803
SAMEER SHARMA Department of Bioinformatics, BioNome, Bengaluru-560043, Karnataka, India https://orcid.org/0000-0002-3456-0263

DOI:

https://doi.org/10.22159/ijap.2023v15i5.48269

Keywords:

SNPs, WISP3, CCN6, In silico analysis, Gene expression, Cancer

Abstract

Objective: The primary aim of this investigation is to comprehensively examine the detrimental effects of non-synonymous single nucleotide polymorphisms (nsSNPs) on the WISP3 gene. This objective will be accomplished through intricate evaluations encompassing protein stability prediction, amino acid conservation analysis, investigation of protein-protein interactions (PPI), scrutiny of post-translational modifications (PTM), and the utilization of bioinformatics tools to forecast the potential association between nsSNPs and various diseases. By implementing these sophisticated methodologies, we aim to unveil the intricate mechanisms by which harmful nsSNPs influence the functionality and pathological implications of the WISP3 gene.

Methods: Retrieved rsIDs of SNPs from the dbSNP database and filtered using 5 in silico programs. Selected nsSNPs were subjected to further analysis i.e., protein stability and conservation analysis, solvent accessibility analysis, PPI and PTM analysis, prediction and evaluation of both native and mutant protein, and identification of cancer association and gene expression analysis.

Results: The study found that seven (C122Y, C145Y, C52Y, C78R, C75G, N233K, and R245I) of the nsSNPs are potentially vulnerable due to their higher conservancy and ability to reduce protein stability. Two (D271N and Q56H) of the nsSNPs from the initial screening were found to be associated with colon adenocarcinoma.

Conclusion: The study's findings could help researchers design experiments to validate the predictions and develop potential treatments for diseases associated with the WISP3 gene.

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References

Kutz WE, Gong Y, Warman ML. WISP3, the gene responsible for the human skeletal disease progressive pseudo rheumatoid dysplasia, is not essential for skeletal function in mice. Mol Cell Biol. 2005;25(1):414-21. doi: 10.1128/MCB.25.1.414-421.2005, PMID 15601861.

Yu Y, Hu M, Xing X, Li F, Song Y, Luo Y. Identification of a mutation in the WISP3 gene in three unrelated families with progressive pseudo rheumatoid dysplasia. Mol Med Rep. 2015;12(1):419-25. doi: 10.3892/mmr.2015.3430, PMID 25738435.

Sailani MR, Chappell J, Jingga I, Narasimha A, Zia A, Lynch JL. WISP3 mutation associated with pseudo rheumatoid dysplasia. Cold Spring Harb Mol Case Stud. 2018;4(1):a001990. doi: 10.1101/mcs.a001990. PMID 29092958.

Mansuri MF, Sindhu MA, Hameed M, Javed MN, Laique K, Ullah SS. Progressive pseudo rheumatoid skeletal dysplasia presenting with proportionate short stature and positive WISP3 mutation: a case report. PJR. 2022;32(1).

Lu Y, Wang X, Sun X, Feng W, Guo H, Tang C. WISP3 is highly expressed in a subset of colorectal carcinomas with a better prognosis. Onco Targets Ther. 2016;9:287-93. doi: 10.2147/OTT.S97025. PMID 26834488.

Kleer CG, Zhang Y, Pan Q, van Golen KL, Wu ZF, Livant D. WISP3 is a novel tumor suppressor gene of inflammatory breast cancer. Oncogene. 2002;21(20):3172-80. doi: 10.1038/sj.onc.1205462, PMID 12082632.

Madsen BE, Villesen P, Wiuf C. A periodic pattern of SNPs in the human genome. Genome Res. 2007;17(10):1414-9. doi: 10.1101/gr.6223207, PMID 17673700.

Wang B, Tian W, Lei X, Perez Rathke A, Yuan Tseng Y, Liang J. Structure-based method for predicting deleterious missense SNPs. IEEE EMBS Int Conf Biomed Health Inform. 2019. doi: 10.1109/bhi.2019.8834504, PMID 34136829.

Wanarase SR, Chavan SV, Sharma S. Evaluation of SNPs from human IGFBP6 associated with gene expression: an in-silico study. J Biomol Struct Dyn. 2023:1-13. doi: 10.1080/07391102.2023.2192793.

Yazar M, Ozbek P. In silico tools and approaches for the prediction of functional and structural effects of single-nucleotide polymorphisms on proteins: an expert review. Omics. 2021;25(1):23-37. doi: 10.1089/omi.2020.0141, PMID 33058752.

Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM. DbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 2001;29(1):308-11. doi: 10.1093/nar/29.1.308, PMID 11125122.

UniProt Consortium. UniProt: the universal protein Knowledge Base in 2021. Nucleic Acids Res. 2021;49(D1):D480-9. doi: 10.1093/nar/gkaa1100, PMID 33237286.

Ng PC, Henikoff S. SIFT: Predicting amino acid changes that affect protein function. Nucleic Acids Res. 2003;31(13):3812-4. doi: 10.1093/nar/gkg509, PMID 12824425.

Bromberg Y, Yachdav G, Rost B. SNAP predicts the effect of mutations on protein function. Bioinformatics. 2008;24(20):2397-8. doi: 10.1093/bioinformatics/btn435, PMID 18757876.

Tavtigian SV, Byrnes GB, Goldgar DE, Thomas A. Classification of rare missense substitutions, using risk surfaces, with genetic-and molecular-epidemiology applications. Hum Mutat. 2008;29(11):1342-54. doi: 10.1002/humu.20896, PMID 18951461.

Tang H, Thomas PD. Panther-Psep: predicting disease-causing genetic variants using position-specific evolutionary preservation. Bioinformatics. 2016;32(14):2230-2. doi: 10.1093/bioinformatics/btw222, PMID 27193693.

Adzhubei I, Jordan DM, Sunyaev SR. Predicting functional effect of human missense mutations using PolyPhen-2. Curr Protoc Hum Genet. 2013;Chapter(7):Unit7.20. doi: 10.1002/0471142905.hg0720s76. PMID 23315928.

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. doi: 10.1002/prot.20810, PMID 16372356.

Ashkenazy H, Abadi S, Martz E, Chay O, Mayrose I, Pupko T. ConSurf 2016: an improved methodology to estimate and visualize evolutionary conservation in macromolecules. Nucleic Acids Res. 2016;44(W1):W344-50. doi: 10.1093/nar/gkw408, PMID 27166375.

Klausen MS, Jespersen MC, Nielsen H, Jensen KK, Jurtz VI, Sønderby CK. NetSurfP-2.0: improved prediction of protein structural features by integrated deep learning. Proteins. 2019;87(6):520-7. doi: 10.1002/prot.25674, PMID 30785653.

Wang D, Liu D, Yuchi J, He F, Jiang Y, Cai S. MusiteDeep: a deep-learning based webserver for protein post-translational modification site prediction and visualization. Nucleic Acids Res. 2020;48(W1):W140-6. doi: 10.1093/nar/gkaa275, PMID 32324217.

Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta Cepas J. 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. doi: 10.1093/nar/gky1131, PMID 30476243.

Yang J, Zhang Y. Protein structure and function prediction using I-TASSER. Curr Protoc Bioinformatics. 2015;52(1):5.8.1-5.8.15. doi: 10.1002/0471250953.bi0508s52. PMID 26678386.

Schwede T, Kopp J, Guex N, Peitsch MC. Swiss-model: an automated protein homology-modeling server. Nucleic Acids Res. 2003;31(13):3381-5. doi: 10.1093/nar/gkg520, PMID 12824332.

Colovos C, Yeates TO. Verification of protein structures: patterns of nonbonded atomic interactions. Protein Sci. 1993;2(9):1511-9. doi: 10.1002/pro.5560020916, PMID 8401235.

Wiederstein M, Sippl MJ. ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Res. 2007;35:W407-10. doi: 10.1093/nar/gkm290, PMID 17517781.

Zhang Y, Skolnick J. TM-align: a protein structure alignment algorithm based on the TM-score. Nucleic Acids Res. 2005;33(7):2302-9. doi: 10.1093/nar/gki524, PMID 15849316.

Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2012;2(5):401-4. doi: 10.1158/2159-8290.CD-12-0095. PMID 22588877.

di Micco P, Antolin AA, Mitsopoulos C, Villasclaras Fernandez E, Sanfelice D, Dolciami D. canSAR: update to the cancer translational research and drug discovery knowledge base. Nucleic Acids Res. 2023;51(D1):D1212-9. doi: 10.1093/nar/gkac1004, PMID 36624665.

Tang Z, Li C, Kang B, Gao G, Li C, Zhang Z. GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res. 2017;45(W1):W98-W102. doi: 10.1093/nar/gkx247, PMID 28407145.

Shaji D. The relationship between relative solvent accessible surface area (rASA) and irregular structures in protean segments (ProSs). Bioinformation. 2016;12(9):381-7. doi: 10.6026/97320630012381, PMID 28250616.

Ramensky V, Bork P, Sunyaev S. Human non-synonymous SNPs: server and survey. Nucleic Acids Res. 2002;30(17):3894-900. doi: 10.1093/nar/gkf493, PMID 12202775.

Kamaraj B, Rajendran V, Sethumadhavan R, Purohit R. In silico screening of cancer-associated mutation on PLK1 protein and its structural consequences. J Mol Model. 2013;19(12):5587-99. doi: 10.1007/s00894-013-2044-0, PMID 24271645.

Deller MC, Kong L, Rupp B. Protein stability: a crystallographer’s perspective. Acta Crystallogr F Struct Biol Commun. 2016;72(2):72-95. doi: 10.1107/S2053230X15024619, PMID 26841758.

Miller MP, Kumar S. Understanding human disease mutations through the use of interspecific genetic variation. Hum Mol Genet. 2001;10(21):2319-28. doi: 10.1093/hmg/10.21.2319, PMID 11689479.

Jha NK, Kumar P. Molecular docking studies for the comparative analysis of different biomolecules to target hypoxia-inducible factor-1α. Int J App Pharm. 2017;9(4):83, doi: 10.22159/ijap.2017v9i4.19505.

Tran MN, Kleer CG. Matricellular CCN6 (WISP3) protein: a tumor suppressor for mammary metaplastic carcinomas. J Cell Commun Signal. 2018;12(1):13-9. doi: 10.1007/s12079-018-0451-9, PMID 29357008.

Lim EC, Lim SW, Tan KJ, Sathiya M, Cheng WH, Lai KS. In silico analysis of deleterious SNPs of FGF4 gene and their impacts on protein structure, function and bladder cancer prognosis. Life (Basel). 2022;12(7):1018. doi: 10.3390/life12071018, PMID 35888106.

Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H. The protein data bank. Nucleic Acids Res. 2000;28(1):235-42. doi: 10.1093/nar/28.1.235, PMID 10592235.