Evaluation of Nonsynonymous Single Nucleotide Variations in NOS2 Gene Identified Through Whole Exome Sequencing: A Bioinformatics Approach

Authors

  • Tejaswini Prakash Genetics and Genomics Lab, Department of Studies in Genetics and Genomics, University of Mysore, Manasagangothri, Mysuru, Karnataka 570006, India
  • M S Basavaraj University Health Centre, University of Mysore, Mysuru, Karnataka 570005, India
  • Nallur B Ramachandra Genetics and Genomics Lab, Department of Studies in Genetics and Genomics, University of Mysore, Manasagangothri, Mysuru, Karnataka 570006, India

DOI:

https://doi.org/10.48165/

Keywords:

Coronary artery disease, Whole Exome Sequencing, In Silico analyses, Bioinformatics, NOS2

Abstract

Majority of the human diseases are accounted for  by nonsynonymous single nucleotide variations  (nsSNVs) that occur in the coding region of genes  and alter the amino acid residues at specific  positions. The future of genomics research is in  identification of nsSNVs that contribute to  disease pathophysiology by disrupting protein  function. A robust pipeline with several  integrated computational prediction tools  facilitates prioritization of significant disease associated nsSNVs. In this study, we analysed 30  rare frequency missense variations in NOS2 identified through Whole Exome Sequencing  (WES) of six coronary artery disease (CAD) using  multiple bioinformatics softwares and tools. We  employed a stringent filtering workflow to  identify and assess the pathogenic effect of  nsSNVs on NOS2 structure and function. We  used a combination of deleterious variation  detection tools, protein stability changes  prediction, post-translational modification site  prediction, protein-protein interaction and  enrichment analyses to discern disease-associated  variations. Our findings implicate four amino  acid substitutions - K730N, P769R, P958S and  L1012S as candidates in pathological process of  NOS2. 

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References

Adzhubei, I., Jordan, D.M. and Sunyaev, S,R. (2013). Predicting functional effect of human missense mutations using PolyPhen-2. Curr Protoc Hum Genet. 7: 7.20. doi:10.1002/0471142905.hg0720s76.

Bulletin of Pure and Applied Sciences / Vol.39A (Zoology), No.1 / January-June 2020 185

Evaluation of Nonsynonymous Single Nucleotide Variations in NOS2 Gene Identified Through Whole Exome Sequencing: A Bioinformatics Approach

Aldons, J.L. (2012). Genetics of Atherosclerosis. Trends Genet. 28(6): 267-275. 3. Ashkenazy, H., Abadi, S., Martz, E., Chay, O., Mayrose, I., Pupko, T. and Ben-Tal, N. (2016). ConSurf 2016: an improved methodology to estimate and visualize evolutionary conservation in macromolecules. Nucleic Acids Res. 44(W1): W344-350.

Benjamin, E.J., Muntner, P., Alonso, A., Bittencourt, M.S., Callaway, C.W., Carson, A.P., Chamberlain, A.M., Chang, A.R., Cheng, S., Das, S.R., Delling, F.N., Djousse, L., Elkind, M.S.V., Ferguson, J.F., Fornage, M., Jordan, L.C., Khan, S.S., Kissela, B.M., Knutson, K.L., Kwan, T.W., Lackland, D.T., Lewis, T.T., Lichtman, J.H., Longenecker, C.T., Loop, M.S., Lutsey, P.L., Martin, S.S., Matsushita, K., Moran, A.E., Mussolino, M.E., O'Flaherty, M., Pandey, A., Perak, A.M., Rosamond, W.D., Roth, G.A., Sampson, U.K.A., Satou, G.M.,

Schroeder, E.B., Shah, S.H., Spartano, N.L., Stokes, A., Tirschwell, D.L., Tsao, C.W., Turakhia, M.P., VanWagner, L.B., Wilkins, J.T., Wong, S.S. and Virani, S.S. (2019). American Heart Association Council on Epidemiology & Prevention Statistics Committee and Stroke Statistics Subcommittee, Heart Disease and Stroke Statistics-2019 Update: A Report From the American Heart Association. Circulation. 139(10): e56-e528.

Bolli, R. (2001). Cardioprotective function of inducible nitric oxide synthase and role of nitric oxide in myocardial ischemia and preconditioning: an overview of a decade of research. J Mol Cell Cardiol. 33(11): 1897-1918.

Bomba, L., Walter, K. and Soranzo, N. (2017). The impact of rare and low-frequency genetic variants in common disease. Genome Biol. 18(1): 77.

Capriotti, E., Calabrese, R. and Casadio, R. (2006). Predicting the insurgence of human genetic diseases associated to single point protein mutations with support vector machines and evolutionary information. Bioinformatics. 22(22): 2729-2734.

Capriotti, E., Fariselli, P. and Casadio, R. (2005). I-Mutant2.0: predicting stability changes upon mutation from the protein sequence or structure. Nucleic Acids Res. 33: W306-10. 9. Chang, X. and Wang, K. (2012). wANNOVAR: annotating genetic variants for personal genomes via the web. J Med Genet. 49(7): 433-436.

Cheng, J., Randall, A. and Baldi, P. (2006). Prediction of protein stability changes for single site mutations using support vector machines. Proteins. 62(4): 1125-1132.

Choi, Y. and Chan, A.P. (2015). PROVEAN web server: a tool to predict the functional effect of amino acid substitutions and indels. Bioinformatics. 31(16): 2745-2747.

Do, R., Kathiresan, S. and Abecasis, G.R. (2012). Exome sequencing and complex disease: practical aspects of rare variant association studies. Hum Mol Genet. 21(R1): R1-9. 13. Elkhattabi, L., Morjane, I., Charoute, H., Amghar, S., Bouafi, H., Elkarhat, Z., Saile, R., Rouba, H. and Barakat, A. (2019). In Silico Analysis of Coding/Noncoding SNPs of Human RETN Gene and Characterization of Their Impact on Resistin Stability and Structure. J Diabetes Res. 2019. https://doi.org/10.1155/2019/4951627.

Ewing, B. and Green, P. (1998). Base-calling of automated sequencer traces using phred. II. Error probabilities. Genome Res. 8(3): 186-194.

Fernandes, M., Patel, A. and Husi, H. (2018). C/VDdb: A multi-omics expression profiling database for a knowledge-driven approach in cardiovascular disease (CVD). PLoS One. 13(11): e0207371.

Hajar, R. (2017). Risk Factors for Coronary Artery Disease: Historical Perspectives. Heart Views. 18(3): 109-114.

Hecht, M., Bromberg, Y. and Rost, B. (2015). Better prediction of functional effects for sequence variants. BMC Genomics. 16: S1.

Huang, W., Zhang, K., Zhu, Y., Wang, Z., Li, Z. and Zhang, J. (2018). Genetic polymorphisms of NOS2 and predisposition to fracture non-union: A case control study based on Han Chinese population. PLoS One. 13(3): e0193673.

Jagadeesh, K.A., Wenger, A.M., Berger, M.J., Guturu, H., Stenson, P.D., Cooper, D.N., Bernstein, J.A. and Bejerano, G. (2016). M-CAP eliminates a majority of variants with uncertain significance in clinical exomes at high sensitivity. Nat Genet. 48(12): 1581-1586.

Kalia, N., Sharma, A., Kaur, M., Kamboj, S.S. and Singh, J. (2016). A comprehensive in silico analysis of non-synonymous and regulatory SNPs of human MBL2 gene. Springerplus. 5(1): 811.

Karczewski, K.J., Francioli, L.C., Tiao, G., Cummings, B.B., Alföldi, J., Wang, Q., Collins, R.L., Laricchia, K.M., Ganna, A., Birnbaum, D.P., Gauthier, L.D., Brand, H., Solomonson, M., Watts, N.A., Rhodes, D., Singer-Berk, M., England, E.M., Seaby, E.G., Kosmicki, J.A., …MacArthur, D.G. (2019). The mutational constraint spectrum quantified from variation in 141,456 humans. In bioRxiv (p. 531210). https://doi.org/10.1101/531210.

Kröncke, K.D., Fehsel, K. and Kolb-Bachofen, V. (1998). Inducible nitric oxide synthase in human diseases. Clin Exp Immunol. 113(2): 147-156.

Kuleshov, M.V., Jones, M.R., Rouillard, A.D., Fernandez, N.F., Duan, Q., Wang, Z., Koplev, S., Jenkins, S.L., Jagodnik, K.M., Lachmann, A., McDermott, M.G., Monteiro, C.D., Gundersen, G.W. and Ma'ayan, A. (2016). Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 44(W1): W90-97.

Levy, A.S., Chung, J.C., Kroetsch, J.T. and Rush, J.W. (2009). Nitric oxide and coronary vascular endothelium adaptations in hypertension. Vasc Health Risk Manag. 5: 1075-1087. 25. Liu, H., Liu, W., Liao, Y., Cheng, L., Liu, Q., Ren, X., Shi, L., Tu, X., Wang, Q.K. and Guo, A.Y. (2011). CADgene: a comprehensive database for coronary artery disease genes. Nucleic Acids Res. 39: D991-996.

López-Ferrando, V., Gazzo, A., de la Cruz, X., Orozco, M. and Gelpí, J.L. (2017). PMut: a web based tool for the annotation of pathological variants on proteins, 2017 update. Nucleic Acids Res. 45(W1): W222-228.

Pacher, P., Beckman, J.S. and Liaudet, L. (2007). Nitric oxide and peroxynitrite in health and disease. Physiol Rev. 87(1): 315-424.

Pejaver, V., Hsu, W.L., Xin, F., Dunker, A.K., Uversky, V.N. and Radivojac, P. (2014). The structural and functional signatures of proteins that undergo multiple events of post translational modification. Protein Sci. 23(8): 1077-1093.

Rani, J., Mittal, I., Pramanik, A., Singh, N., Dube, N., Sharma, S., Puniya, B.L., Raghunandanan, M.V., Mobeen, A. and Ramachandran, S. (2017). T2DiACoD: A Gene Atlas of Type 2 Diabetes Mellitus Associated Complex Disorders. Sci Rep. 7(1): 6892.

Reva, B., Antipin, Y. and Sander, C. (2011). Predicting the functional impact of protein mutations: application to cancer genomics. Nucleic Acids Res. 39(17): e118.

Schwarz, J.M., Cooper, D.N., Schuelke, M. and Seelow, D. (2014). MutationTaster2: mutation prediction for the deep-sequencing age. Nat Methods. 11(4): 361-362.

Seifi, M. and Walter, M.A. (2018). Accurate prediction of functional, structural, and stability changes in PITX2 mutations using in silico bioinformatics algorithms. PLoS One. 13(4): e0195971.

Sherry, S.T., Ward, M.H., Kholodov, M., Baker, J., Phan, L., Smigielski, E.M. and Sirotkin, K. (2001). dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 29(1): 308-311. 34. Shihab, H.A., Gough, J., Cooper, D.N., Stenson, P.D., Barker, G.L., Edwards, K.J., Day, I.N.,

and Gaunt, T.R. (2013). Predicting the functional, molecular, and phenotypic consequences of amino acid substitutions using hidden Markov models. Hum Mutat. 34(1): 57-65. 35. Sim, N.L., Kumar, P., Hu, J., Henikoff, S., Schneider, G. and Ng, P.C. (2012). SIFT web server: predicting effects of amino acid substitutions on proteins. Nucleic Acids Res. 40: W452-457. 36. Singh, R.B., Mengi, S.A., Xu, Y-J., Arneja, A.S. and Dhalla, N.S. (2002). Pathogenesis of atherosclerosis: A multifactorial process. Exp Clin Cardiol. 7(1): 40-53.

Strand Life Sciences Pvt. Ltd., Strand NGS. ©Strand Life Sciences, Bangalore, India, 2016. 38. Szklarczyk, D., Gable, A.L., Lyon, D., Junge, A., Wyder, S., Huerta-Cepas, J., Simonovic, M., Doncheva, N.T., Morris, J.H., Bork, P., Jensen, L.J. and Mering, C.V. (2019). STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 47(D1): D607-613. 39. Topchieva, L.V., Balan, O.V., Korneeva, V.A. and Malysheva, I.E. (2019). The Role of Inducible NOS2 Gene Polymorphism in the Development of Essential Arterial Hypertension. Bull Exp Biol Med. 168(1): 79-83.

Venselaar, H., Te Beek, T.A., Kuipers, R.K., Hekkelman, M.L. and Vriend, G. (2010). Protein structure analysis of mutations causing inheritable diseases. An e-Science approach with life scientist friendly interfaces. BMC Bioinformatics. 11: 548.

Wang, Z., Huang, C., Lv, H., Zhang, M. and Li, X. (2020). In silico analysis and high-risk

pathogenic phenotype predictions of non-synonymous single nucleotide polymorphisms in

human Crystallin beta A4 gene associated with congenital cataract. PLoS One. 15(1): e0227859.

Xi, L., Jarrett, N.C., Hess, M.L. and Kukreja, R.C. (1999). Essential role of inducible nitric oxide

synthase in monophosphoryl lipid A–induced late cardioprotection: evidence from

pharmacological inhibition and gene knockout mice. Circulation. 99(16): 2157-2163.

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Published

2020-06-15

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Vol. 39 No. 1 (2020): Bulletin of Pure and Applied Sciences Zoology (Jan-Jun) 2020

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Articles

How to Cite

Evaluation of Nonsynonymous Single Nucleotide Variations in NOS2 Gene Identified Through Whole Exome Sequencing: A Bioinformatics Approach . (2020). Bulletin of Pure & Applied Sciences- Zoology , 39(1), 172–188. https://doi.org/10.48165/