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

Autoři: Zhenyu Wang aff001;  Chen Huang aff001;  Huibin Lv aff001;  Mingzhou Zhang aff001;  Xuemin Li aff001
Působiště autorů: Department of Ophthalmology, Peking University Third Hospital, Beijing, China aff001;  Beijing Key Laboratory of Restoration of Damaged Ocular Nerve, Peking University Third Hospital, Beijing, China aff002;  Medical Research Center, Peking University Third Hospital, Beijing, China aff003
Vyšlo v časopise: PLoS ONE 15(1)
Kategorie: Research Article


In order to provide a cost-effective method to narrow down the number of pathogenic Crystallin beta A4 (CRYBA4) non-synonymous single nucleotide polymorphisms (nsSNPs), we collected nsSNP information of the CRYBA4 gene from SNP databases and literature, predicting the pathogenicity and possible changes of protein properties and structures using multiple bioinformatics tools. The nsSNP data of the CRYBA4 gene were collected from 4 databases and published literature. According to 12 criteria, six bioinformatics tools were chosen to predict the pathogenicity. I-Mutant 2.0, Mupro and INPS online tools were used to analyze the effects of amino acid substitution on protein stability by calculating the value of ΔΔG. ConSurf, SOPMA, GETAREA and HOPE online tools were used to predict the evolutionary conservation of amino acids, solvent accessible surface areas, and the physical and chemical properties and changes of protein structure. All 157 CRYBA4 nsSNPs were analyzed. Forty-four CRYBA4 high-risk pathogenic nsSNPs (predicted to be pathogenic by all six software tools) were detected out of the 157 CRYBA4 nsSNPs, four of which (c.283C>T, p.R95W; c.449T>A, p.V150D; c.475G>A, p.G159R; c.575G>C, p.R192P) should be focused on because of their high potential pathogenicity and possibility of changing protein properties. Thirty high-risk nsSNPs were predicted to cause a decrease of protein stability. Twenty-nine high-risk nsSNPs occurred in evolutionary conserved positions. Twenty-two high-risk nsSNPs occurred in the core of the protein. It is predicted that these high-risk pathogenic nsSNPs can cause changes in the physical and chemical properties of amino acids, resulting in structural changes of proteins and changes in the interactions between domains and other molecules, thus affecting the function of proteins. This study provides important reference value when narrowing down the number of pathogenic CRYBA4 nsSNPs and studying the pathogenesis of congenital cataracts. By using this method, we can easily find 44 high-risk pathogenic nsSNPs out of 157 CRYBA4 nsSNPs.

Klíčová slova:

Amino acid analysis – Cataracts – Mutation databases – Pathogenesis – Protein interactions – Protein structure – Protein structure prediction – Software tools


1. Sheeladevi S, Lawrenson JG, Fielder AR, Suttle CM. Global prevalence of childhood cataract: a systematic review. Eye (Lond). 2016;30(9):1160–9.

2. Shiels A, Hejtmancik JF. Mutations and mechanisms in congenital and age-related cataracts. Exp Eye Res. 2017;156:95–102. doi: 10.1016/j.exer.2016.06.011 27334249

3. Pichi F, Lembo A, Serafino M, Nucci P. Genetics of Congenital Cataract. Dev Ophthalmol. 2016;57:1–14. doi: 10.1159/000442495 27043388

4. Gilbert C, Foster A. Childhood blindness in the context of VISION 2020—the right to sight. Bull World Health Organ. 2001;79(3):227–32. 11285667

5. Yi J, Yun J, Li ZK, Xu CT, Pan BR. Epidemiology and molecular genetics of congenital cataracts. Int J Ophthalmol. 2011;4(4):422–32. doi: 10.3980/j.issn.2222-3959.2011.04.20 22553694

6. Huang B, He W. Molecular characteristics of inherited congenital cataracts. Eur J Med Genet. 2010;53(6):347–57. doi: 10.1016/j.ejmg.2010.07.001 20624502

7. Santana A, Waiswo M. The genetic and molecular basis of congenital cataract. Arq Bras Oftalmol. 2011;74(2):136–42. doi: 10.1590/s0004-27492011000200016 21779674

8. Gill D, Klose R, Munier FL, McFadden M, Priston M, Billingsley G, et al. Genetic heterogeneity of the Coppock-like cataract: a mutation in CRYBB2 on chromosome 22q11.2. Invest Ophthalmol Vis Sci. 2000;41(1):159–65. 10634616

9. Graw J. Genetics of crystallins: cataract and beyond. Exp Eye Res. 2009;88(2):173–89. doi: 10.1016/j.exer.2008.10.011 19007775

10. Lampi KJ, Ma Z, Shih M, Shearer TR, Smith JB, Smith DL, et al. Sequence analysis of betaA3, betaB3, and betaA4 crystallins completes the identification of the major proteins in young human lens. J Biol Chem. 1997;272(4):2268–75. doi: 10.1074/jbc.272.4.2268 8999933

11. Billingsley G, Santhiya ST, Paterson AD, Ogata K, Wodak S, Hosseini SM, et al. CRYBA4, a novel human cataract gene, is also involved in microphthalmia. Am J Hum Genet. 2006;79(4):702–9. doi: 10.1086/507712 16960806

12. Collins FS, Brooks LD, Chakravarti A. A DNA polymorphism discovery resource for research on human genetic variation. Genome Res. 1998;8(12):1229–31. doi: 10.1101/gr.8.12.1229 9872978

13. Sun W, Xiao X, Li S, Guo X, Zhang Q. Exome sequencing of 18 Chinese families with congenital cataracts: a new sight of the NHS gene. PLoS One. 2014;9(6):e100455. doi: 10.1371/journal.pone.0100455 24968223

14. Zhou G, Zhou N, Hu S, Zhao L, Zhang C, Qi Y. A missense mutation in CRYBA4 associated with congenital cataract and microcornea. Mol Vis. 2010;16:1019–24. 20577656

15. Kumar M, Agarwal T, Kaur P, Kumar M, Khokhar S, Dada R. Molecular and structural analysis of genetic variations in congenital cataract. Mol Vis. 2013;19:2436–50. 24319337

16. Chen J, Wang Q, Cabrera PE, Zhong Z, Sun W, Jiao X, et al. Molecular Genetic Analysis of Pakistani Families With Autosomal Recessive Congenital Cataracts by Homozygosity Screening. Invest Ophthalmol Vis Sci. 2017;58(4):2207–17. doi: 10.1167/iovs.17-21469 28418495

17. Li J, Leng Y, Han S, Yan L, Lu C, Luo Y, et al. Clinical and genetic characteristics of Chinese patients with familial or sporadic pediatric cataract. Orphanet J Rare Dis. 2018;13(1):94. doi: 10.1186/s13023-018-0828-0 29914532

18. Li J, Zhao T, Zhang Y, Zhang K, Shi L, Chen Y, et al. Performance evaluation of pathogenicity-computation methods for missense variants. Nucleic Acids Res. 2018;46(15):7793–804. doi: 10.1093/nar/gky678 30060008

19. Luxembourg B, D'Souza M, Korber S, Seifried E. Prediction of the pathogenicity of antithrombin sequence variations by in silico methods. Thromb Res. 2015;135(2):404–9. doi: 10.1016/j.thromres.2014.11.022 25496998

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

21. Landrum MJ, Lee JM, Benson M, Brown GR, Chao C, Chitipiralla S, et al. ClinVar: improving access to variant interpretations and supporting evidence. Nucleic Acids Res. 2018;46(D1):D1062–D7. doi: 10.1093/nar/gkx1153 29165669

22. Stenson PD, Mort M, Ball EV, Evans K, Hayden M, Heywood S, et al. The Human Gene Mutation Database: towards a comprehensive repository of inherited mutation data for medical research, genetic diagnosis and next-generation sequencing studies. Hum Genet. 2017;136(6):665–77. doi: 10.1007/s00439-017-1779-6 28349240

23. Pinero J, Bravo A, Queralt-Rosinach N, Gutierrez-Sacristan A, Deu-Pons J, Centeno E, et al. DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants. Nucleic Acids Res. 2017;45(D1):D833–D9. doi: 10.1093/nar/gkw943 27924018

24. Pejaver V UJ, Lugo-Martinez J, Pagel KA, Lin GN, Nam HJ, Mort M, Cooper DN, Sebat J, Iakoucheva LM, Mooney SD, Radivojac P. MutPred2: inferring the molecular and phenotypic impact of amino acid variants.

25. 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 27193693

26. 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(Web Server issue):W306–10. doi: 10.1093/nar/gki375 15980478

27. 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

28. 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. doi: 10.1093/bioinformatics/btv195 25851949

29. 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(Web Server issue):W452–7. doi: 10.1093/nar/gks539 22689647

30. 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

31. 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

32. Savojardo C, Fariselli P, Martelli PL, Casadio R. INPS-MD: a web server to predict stability of protein variants from sequence and structure. Bioinformatics. 2016;32(16):2542–4. doi: 10.1093/bioinformatics/btw192 27153629

33. Ashkenazy H, Abadi S, Martz E, Chay O, Mayrose I, Pupko T, et al. 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 27166375

34. Geourjon C, Deleage G. SOPMA: significant improvements in protein secondary structure prediction by consensus prediction from multiple alignments. Comput Appl Biosci. 1995;11(6):681–4. doi: 10.1093/bioinformatics/11.6.681 8808585

35. Fraczkiewicz RaB W. Exact and Efficient Analytical Calculation of the Accessible Surface Areas and Their Gradients for Macromolecules. J Comp Chem. 1998;19:319–33.

36. Moritz C, Brown WM. Tandem duplications in animal mitochondrial DNAs: variation in incidence and gene content among lizards. Proc Natl Acad Sci U S A. 1987;84(20):7183–7. doi: 10.1073/pnas.84.20.7183 3478691

37. Hamosh A, Scott AF, Amberger JS, Bocchini CA, McKusick VA. Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res. 2005;33(Database issue):D514–7. doi: 10.1093/nar/gki033 15608251

38. Chen X, Sullivan PF. Single nucleotide polymorphism genotyping: biochemistry, protocol, cost and throughput. Pharmacogenomics J. 2003;3(2):77–96. doi: 10.1038/sj.tpj.6500167 12746733

39. Bloemendal H, de Jong W, Jaenicke R, Lubsen NH, Slingsby C, Tardieu A. Ageing and vision: structure, stability and function of lens crystallins. Prog Biophys Mol Biol. 2004;86(3):407–85. doi: 10.1016/j.pbiomolbio.2003.11.012 15302206

40. Hejtmancik JF. Congenital cataracts and their molecular genetics. Semin Cell Dev Biol. 2008;19(2):134–49. doi: 10.1016/j.semcdb.2007.10.003 18035564

41. Aguayo-Ortiz R, Gonzalez-Navejas A, Palomino-Vizcaino G, Rodriguez-Meza O, Costas M, Quintanar L, et al. Thermodynamic Stability of Human gammaD-crystallin Mutants Using Alchemical Free Energy Calculations. J Phys Chem B. 2019.

42. Khan S, Vihinen M. Performance of protein stability predictors. Hum Mutat. 2010;31(6):675–84. doi: 10.1002/humu.21242 20232415

43. Arcanjo C, Armant O, Floriani M, Cavalie I, Camilleri V, Simon O, et al. Tritiated water exposure disrupts myofibril structure and induces mis-regulation of eye opacity and DNA repair genes in zebrafish early life stages. Aquat Toxicol. 2018;200:114–26. doi: 10.1016/j.aquatox.2018.04.012 29751158

44. Li W, Ji Q, Wei Z et al., Biochemical characterization of G64W mutant of acidic beta-crystallin 4. Exp Eye Res, 2019. 186:107712. doi: 10.1016/j.exer.2019.107712 31254514

45. Zhai Y, Li J, Yu W et al., Targeted Exome Sequencing of Congenital Cataracts Related Genes: Broadening the Mutation Spectrum and Genotype-Phenotype Correlations in 27 Chinese Han Families. Sci Rep, 2017. 7(1):1219. doi: 10.1038/s41598-017-01182-9 28450710

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