Computational analysis of functional single nucleotide polymorphisms associated with SLC26A4 gene

English version

Autoři: Mirza Jawad Ul Hasnain ^aff001; Muhammad Shoaib ^aff002; Salman Qadri ^aff003; Bakhtawar Afzal ^aff004; Tehreem Anwar ^aff001; Syed Hassan Abbas ^aff001; Amina Sarwar ^aff001; Hafiz Muhammad Talha Malik ^aff005; Muhammad Tariq Pervez ^aff001
Působiště autorů: Department of Bioinformatics, Virtual University of Pakistan, Lahore, Pakistan ^aff001; Department of Computer Science and Engineering, UET, Lahore, Pakistan ^aff002; Department of CS & IT, The Islamia University of Bahawalpur, Bahawalpur, Pakistan ^aff003; Department of Biosciences, COMSATS University, Islamabad, Pakistan ^aff004; Alpha Genomics Private Limited, PWD, Islamabad, Pakistan ^aff005
Vyšlo v časopise: PLoS ONE 15(1)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0225368

Souhrn

Single Nucleotide Polymorphisms (SNPs) are the most common candidate mutations in human beings that play a vital role in the genetic basis of certain diseases. Previous studies revealed that Solute Carrier Family 26 Member 4 (SLC26A4) being an essential gene of the multi-faceted transporter family SLC26 facilitates reflexive movement of Iodide into follicular lumen through apical membrane of thyrocyte. SLC26A4 gene encodes Pendred protein, a membrane glycoprotein, highly hydrophobic in nature, present at the apical membrane of thyrocyte functioning as transporter of iodide for thyroid cells. A minor genetic variation in SLC26A4 can cause Pendred syndrome, a syndrome associated with thyroid glands and deafness. In this study, we performed in-silico analysis of 674 missense SNPs of SLC26A4 using different computational platforms. The bunch of tools including SNPNEXUS, SNAP-2, PhD-SNP, SNPs&GO, I-Mutant, ConSurf, and ModPred were used to predict 23 highly confident damaging and disease causing nsSNPs (G209V, G197R, L458P, S427P, Q101P, W472R, N392Y, V359E, R409C, Q235R, R409P, G139V, G497S, H723R, D87G, Y127H, F667C, G334A, G95R, S427C, R291W, Q383H and E384G) that could potentially alter the SLC26A4 gene. Moreover, protein structure prediction, protein-ligand docking and Molecular Dynamics simulation were performed to confirm the impact of two evident alterations (Y127H and G334A) on the protein structure and function.

Klíčová slova:

Biochemical simulations – Molecular dynamics – Molecular genetics – Mutation – Protein structure – Protein structure comparison – Protein structure prediction – Structural proteins

Zdroje

1. Manolio TA, Brooks LD, Collins FS. A HapMap harvest of insights into the genetics of common disease. J Clin Invest. 2008;118(5):1590–605. doi: 10.1172/JCI34772 18451988

2. McCarthy MI, Hirschhorn JN. Genome-wide association studies: potential next steps on a genetic journey. Hum Mol Genet. 2008;17(R2):R156–65. doi: 10.1093/hmg/ddn289 18852205

3. Cho JH. The genetics and immunopathogenesis of inflammatory bowel disease. Nat Rev Immunol. 2008;8(6):458–66. doi: 10.1038/nri2340 18500230

4. Krawczak M, Ball E. V., Fenton I., Stenson P. D., Abeysinghe S., Thomas N., & Cooper D. N. Human gene mutation database—a biomedical information and research resource. Human mutation. 2000;15(1):7. doi: 10.1002/(SICI)1098-1004(200001)15:1<7::AID-HUMU4>3.0.CO;2-N

5. Ng PC, Henikoff S. Predicting deleterious amino acid substitutions. Genome Res. 2001;11(5):863–74. doi: 10.1101/gr.176601 11337480

6. Ng PC, Henikoff S. Accounting for human polymorphisms predicted to affect protein function. Genome Res. 2002;12(3):436–46. doi: 10.1101/gr.212802 11875032

7. Ng PC. SIFT: predicting amino acid changes that affect protein function. Nucleic Acids Research. 2003;31(13):3812–4. doi: 10.1093/nar/gkg509 12824425

8. Klein J, Naoyuki Takahata, and Francisco J. Ayala. Molecular genetics of speciation and human origins. Proc Natl Acad Sci. 1994.

9. Risch N, Merikangas K. The future of genetic studies of complex human diseases. Science. 1996;273(5281):1516–7. doi: 10.1126/science.273.5281.1516 8801636

10. Dossena S, Rodighiero S, Vezzoli V, Nofziger C, Salvioni E, Boccazzi M, et al. Functional characterization of wild-type and mutated pendrin (SLC26A4), the anion transporter involved in Pendred syndrome. J Mol Endocrinol. 2009;43(3):93–103. doi: 10.1677/JME-08-0175 19608655

11. Collins FS, Guyer MS, Charkravarti A. VSariations on a theme: cataloging human DNA sequence variation. Science. 1997;278(5343):1580–1. doi: 10.1126/science.278.5343.1580 9411782

12. Abe S, Usami S, Shinkawa H. Three familial cases of hearing loss associated with enlargement of the vestibular aqueduct. Ann Otol Rhinol Laryngol. 1997;106(12):1063–9. doi: 10.1177/000348949710601210 9415602

13. Tsukamoto K, Suzuki H, Harada D, Namba A, Abe S, Usami S. Distribution and frequencies of PDS (SLC26A4) mutations in Pendred syndrome and nonsyndromic hearing loss associated with enlarged vestibular aqueduct: a unique spectrum of mutations in Japanese. Eur J Hum Genet. 2003;11(12):916–22. doi: 10.1038/sj.ejhg.5201073 14508505

14. Everett LA, Glaser B, Beck JC, Idol JR, Buchs A, Heyman M, et al. Pendred syndrome is caused by mutations in a putative sulphate transporter gene (PDS). Nat Genet. 1997;17(4):411–22. doi: 10.1038/ng1297-411 9398842

15. Van Hauwe P, Everett LA, Coucke P, Scott DA, Kraft ML, Ris-Stalpers C, et al. Two frequent missense mutations in Pendred syndrome. Hum Mol Genet. 1998;7(7):1099–104. doi: 10.1093/hmg/7.7.1099 9618166

16. Coyle B, Reardon W, Herbrick JA, Tsui LC, Gausden E, Lee J, et al. Molecular analysis of the PDS gene in Pendred syndrome. Hum Mol Genet. 1998;7(7):1105–12. doi: 10.1093/hmg/7.7.1105 9618167

17. Everett LA, Morsli H, Wu DK, Green ED. Expression pattern of the mouse ortholog of the Pendred's syndrome gene (Pds) suggests a key role for pendrin in the inner ear. Proc Natl Acad Sci U S A. 1999;96(17):9727–32. doi: 10.1073/pnas.96.17.9727 10449762

18. Bidart JM, Mian C, Lazar V, Russo D, Filetti S, Caillou B, et al. Expression of pendrin and the Pendred syndrome (PDS) gene in human thyroid tissues. J Clin Endocrinol Metab. 2000;85(5):2028–33. doi: 10.1210/jcem.85.5.6519 10843192

19. Valvassori GE, Clemis JD. The large vestibular aqueduct syndrome. Laryngoscope. 1978;88(5):723–8. doi: 10.1002/lary.1978.88.5.723 306012

20. Campbell C, Cucci RA, Prasad S, Green GE, Edeal JB, Galer CE, et al. Pendred syndrome, DFNB4, and PDS/SLC26A4 identification of eight novel mutations and possible genotype-phenotype correlations. Hum Mutat. 2001;17(5):403–11. doi: 10.1002/humu.1116 11317356

21. Desai M, Chauhan JB. Computational analysis for the determination of deleterious nsSNPs in human MTHFR gene. Comput Biol Chem. 2018;74:20–30. doi: 10.1016/j.compbiolchem.2018.02.022 29524840

22. Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE, Gerasimova A, Bork P, et al. A method and server for predicting damaging missense mutations. Nat Methods. 2010;7(4):248–9. doi: 10.1038/nmeth0410-248 20354512

23. Dayem Ullah AZ, Oscanoa J, Wang J, Nagano A, Lemoine NR, Chelala C. SNPnexus: assessing the functional relevance of genetic variation to facilitate the promise of precision medicine. Nucleic Acids Res. 2018;46(W1):W109–W13. doi: 10.1093/nar/gky399 29757393

24. Johnson AD, Handsaker RE, Pulit SL, Nizzari MM, O'Donnell CJ, de Bakker PI. SNAP: a web-based tool for identification and annotation of proxy SNPs using HapMap. Bioinformatics. 2008;24(24):2938–9. doi: 10.1093/bioinformatics/btn564 18974171

25. 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. doi: 10.1002/humu.21047 19514061

26. 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. doi: 10.1093/bioinformatics/btl423 16895930

27. 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 16372356

28. Armon A, Graur D, Ben-Tal N. ConSurf: an algorithmic tool for the identification of functional regions in proteins by surface mapping of phylogenetic information. J Mol Biol. 2001;307(1):447–63. doi: 10.1006/jmbi.2000.4474 11243830

29. Pejaver V, Hsu WL, Xin F, Dunker AK, Uversky VN, Radivojac P. The structural and functional signatures of proteins that undergo multiple events of post-translational modification. Protein Sci. 2014;23(8):1077–93. doi: 10.1002/pro.2494 24888500

30. Xu D, Zhang Y. Improving the physical realism and structural accuracy of protein models by a two-step atomic-level energy minimization. Biophys J. 2011;101(10):2525–34. doi: 10.1016/j.bpj.2011.10.024 22098752

31. Blaszczyk M., Jamroz M., Kmiecik S., & Kolinski A. CABS-fold: server for the de novo and consensus-based prediction of protein structure. Nucleic acids research.2013; 41(W1), W406–W411.

32. Lill M. A., & Danielson M. L.Computer-aided drug design platform using PyMOL. Journal of computer-aided molecular design. 2011;25(1), 13–19. doi: 10.1007/s10822-010-9395-8 21053052

33. Abraham MJ, Murtola T, Schulz R, Páll S, Smith JC, Hess B, Lindahl E. GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX. 2015 Sep 1;1:19–25.

34. Kleywegt GJ, Jones TA. Phi/psi-chology: Ramachandran revisited. Structure. 1996;4(12):1395–400. doi: 10.1016/s0969-2126(96)00147-5 8994966

35. Yang J, Roy A, Zhang Y. BioLiP: a semi-manually curated database for biologically relevant ligand-protein interactions. Nucleic Acids Res. 2013;41(Database issue):D1096–103. doi: 10.1093/nar/gks966 23087378

36. Everett LA, Glaser B, Beck JC, Idol JR, Buchs A, Heyman M, et al. Pendred syndrome is caused by mutations in a putative sulphate transporter gene (PDS). Nature Genetics. 1997;17(4):13.

37. Kim HM, Wangemann P. Failure of fluid absorption in the endolymphatic sac initiates cochlear enlargement that leads to deafness in mice lacking pendrin expression. PLoS One. 2010;5(11):e14041. doi: 10.1371/journal.pone.0014041 21103348

38. Wangemann P, Nakaya K, Wu T, Maganti RJ, Itza EM, Sanneman JD, et al. Loss of cochlear HCO3- secretion causes deafness via endolymphatic acidification and inhibition of Ca2+ reabsorption in a Pendred syndrome mouse model. Am J Physiol Renal Physiol. 2007;292(5):F1345–53. doi: 10.1152/ajprenal.00487.2006 17299139

39. Wall SM. The renal physiology of pendrin (SLC26A4) and its role in hypertension. Novartis Found Symp. 2006;273:231–9; discussion 9–43, 61–4. 17120771

40. Mohd Hassan Baig D. Raja Sudhakar, Ponnusamy Kalaiarasan, et al. Insight into the Effect of Inhibitor Pesistant S130G Mutant on Physico-Chemical Properties of SHV Type Beta-Lactamase: A Molecular Dynamics Study. Plos-One. 2014; 9.12;p.e112456. doi: 10.1371/journal.pone.0112456 25479359

41. Yuan X, Waterworth D, Perry JR, Lim N, Song K, Chambers JC, et al. Population-based genome-wide association studies reveal six loci influencing plasma levels of liver enzymes. Am J Hum Genet. 2008;83(4):520–8. doi: 10.1016/j.ajhg.2008.09.012 18940312

Computational analysis of functional single nucleotide polymorphisms associated with SLC26A4 gene

Souhrn

Klíčová slova:

Zdroje

PLOS One

INSIGHTS from European Respiratory Congress

Současné pohledy na riziko v parodontologii

Svět praktické medicíny 3/2024 (znalostní test z časopisu)

Kardiologické projevy hypereozinofilií

Střevní příprava před kolonoskopií