Volume 27, Issue 5 (9-2023)                   IBJ 2023, 27(5): 307-319 | Back to browse issues page


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Maryami F, Rismani E, Davoudi-Dehaghani E, Khalesi N, Talebi S, Mahdian R et al . In silico Analysis of Two Novel Variants in the Pyruvate Carboxylase (PC) Gene Associated with the Severe Form of PC Deficiency. IBJ 2023; 27 (5) :307-319
URL: http://ibj.pasteur.ac.ir/article-1-3935-en.html
Abstract:  
Background: Inborne errors of metabolism are a common cause of neonatal death. This study evaluated the acute early-onset metabolic derangement and death in two unrelated neonates.
Methods: Whole-exome sequencing (WES), Sanger sequencing, homology modeling, and in silico bioinformatics analysis were employed to assess the effects of variants on protein structure and function.
Results: WES revealed a novel homozygous variant, p.G303Afs*40 and p.R156P, in the pyruvate carboxylase (PC) gene of each neonate,  which both were confirmed by Sanger sequencing. Based on the American College of Medical Genetics and Genomics guidelines, the p.G303Afs*40 was likely pathogenic, and the p.R156P was a variant of uncertain significance (VUS). Nevertheless, a known variant at position 156, the p.R156Q, was also a VUS. Protein secondary structure prediction showed changes in p.R156P and p.R156Q variants compared to the wild-type protein. However, p.G303Afs*40 depicted significant changes at C-terminal. Furthermore, comparing the interaction of wild-type and variant proteins with the ATP ligand during simulations, revealed a decreased affinity to the ATP in all the variants. Moreover, analysis of Single nucleotide polymorphism impacts on PC protein using Polyphen-2, SNAP2, FATHMM, and SNPs&GO servers predicted both R156P and R156Q as damaging variants. Likewise, free energy calculations demonstrated the destabilizing effect of both variants on PC.
Conclusion: This study confirmed the pathogenicity of both variants and suggested them as a cause of type B Pyruvate carboxylase deficiency. The results of this study would provide the family with prenatal diagnosis and expand the variant spectrum in the PC gene,which is  beneficial for geneticists and endocrinologists.

References
1. Wallace JC, Jitrapakdee S, Chapman-Smith A. Pyruvate carboxylase. The international journal of biochemistry and cell biology 1998; 30(1): 1-5. [DOI:10.1016/S1357-2725(97)00147-7]
2. Wexler ID, Kerr DS, Du Y, Kaung MM, Stephenson W, Lusk MM, Wappner RS, Higgins JJ. Molecular characterization of pyruvate carboxylase deficiency in two consanguineous families. Pediatric research 1998; 43(5): 579-584. [DOI:10.1203/00006450-199805000-00004]
3. Coci EG, Gapsys V, Shur N, Shin-Podskarbi Y, de Groot BL, Miller K, Vockley J, Sondheimer N, Ganetzky R, Freisinger P. Pyruvate carboxylase deficiency type A and type C: Characterization of five novel pathogenic variants in PC and analysis of the genotype-phenotype correlation. Human mutation 2019; 40(6): 816-827. [DOI:10.1002/humu.23742]
4. Lee SH, Davis EJ. Carboxylation and decarboxylation reactions. Anaplerotic flux and removal of citrate cycle intermediates in skeletal muscle. The journal of biological chemistry 1979; 254(2): 420-430. [DOI:10.1016/S0021-9258(17)37934-6]
5. Crabtree B, Higgins SJ, Newsholme EA. The activities of pyruvate carboxylase, phosphoenolpyruvate carboxylase and fructose diphosphatase in muscles from vertebrates and invertebrates. Biochemical journal 1972; 130(2): 391-396. [DOI:10.1042/bj1300391]
6. Marin-Valencia I, Roe CR, Pascual JM. Pyruvate carboxylase deficiency: mechanisms, mimics and anaplerosis. Molecular genetics and metabolism 2010; 101(1): 9-17. [DOI:10.1016/j.ymgme.2010.05.004]
7. Monnot S, Serre V, Chadefaux-Vekemans B, Aupetit J, Romano S, De Lonlay P, Rival JM, Munnich A, Steffann J, Bonnefont JP. Structural insights on pathogenic effects of novel mutations causing pyruvate carboxylase deficiency. Human mutation 2009; 30(5): 734-740. [DOI:10.1002/humu.20908]
8. Wang D, Yang H, De Braganca KC, Lu J, Shih LY, Briones P, Lang T, De Vivo DC. The molecular basis of pyruvate carboxylase deficiency: mosaicism correlates with prolonged survival. Molecular genetics and metabolism 2008; 95(1-2): 31-38. [DOI:10.1016/j.ymgme.2008.06.006]
9. Carbone, M.A., D.A. Applegarth, and B.H. Robinson, Intron retention and frameshift mutations result in severe pyruvate carboxylase deficiency in two male siblings. Human mutation 2002; 20(1): 48-56. [DOI:10.1002/humu.10093]
10. Carbone MA, MacKay N, Ling M, Cole DE, Douglas C, Rigat B, Feigenbaum A, Clarke JT, Haworth JC, Greenberg CR, Seargeant L, Robinson BH. Amerindian pyruvate carboxylase deficiency is associated with two distinct missense mutations. American journal of human genetics 1998; 62(6): 1312-1319. [DOI:10.1086/301884]
11. Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 2009; 25(14): 1754-1760. [DOI:10.1093/bioinformatics/btp324]
12. DePristo MA, Banks E, Poplin R, Garimella KV, Maguire JR, Hart C, Philippakis AA, del Angel G, Rivas MA, Hanna M, McKenna A, Fennell TJ, Kernytsky AM, Sivachenko AY, Cibulskis K, Gabriel SB, Altshuler D, Daly MJ. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nature genetics 2011; 43(5): 491-498. [DOI:10.1038/ng.806]
13. Wang K, Li M, Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic acids research 2010; 38(16): e164. [DOI:10.1093/nar/gkq603]
14. 1000 Genomes Project Consortium, Abecasis GR, Altshuler D, Auton A, Brooks LD, Durbin RM, Gibbs RA, Hurles ME, McVean GA. A map of human genome variation from population-scale sequencing. Nature 2010; 467(7319): 1061-1073. [DOI:10.1038/nature09534]
15. Lek M, Karczewski KJ, Minikel EV, Samocha KE, Banks E, Fennell T, O'Donnell-Luria AH, Ware JS, Hill AJ, Cummings BB, Tukiainen T, Birnbaum DP, Kosmicki JA, Duncan LE, Estrada K, Zhao F, Zou J, Pierce-Hoffman E, Berghout J, Cooper DN, Deflaux N, DePristo M, Do R, Flannick J, Fromer M, Gauthier L, Goldstein J, Gupta N, Howrigan D, Kiezun A, KurkiMI, Moonshine AL, Natarajan P, Orozco L, Peloso GM, Poplin R, Rivas MA, Ruano-Rubio V, Rose SA, Ruderfer DM, Shakir K, Stenson PD, Stevens C, Thomas BP, Tiao G, Tusie-Luna MT, Weisburd B, Won HH, Yu D, Altshuler DM, Ardissino D, Boehnke M, Danesh J, Donnelly S, Elosua R, Florez JC, Gabriel SB, Getz G, Glatt SJ, Hultman CM, Kathiresan S, Laakso M, McCarroll S, McCarthy MI, McGovern D, McPherson R, Neale BM, Palotie A, Purcell SM, Saleheen D, Scharf JM, Sklar P, Sullivan PF, Tuomilehto J, Tsuang MT, Watkins HC, Wilson JG, Daly MJ, MacArthur DG; Exome Aggregation Consortium. Analysis of protein-coding genetic variation in 60,706 humans. Nature 2016; 536(7616): 285-291. [DOI:10.1038/nature19057]
16. Havrilla JM, Pedersen BS, Layer RM, Quinlan AR. A map of constrained coding regions in the human genome. Nature genetics 2019; 51(1): 88-95. [DOI:10.1038/s41588-018-0294-6]
17. Glusman G, Caballero J, Mauldin DE, Hood L, Roach JC. Kaviar: an accessible system for testing SNV novelty. Bioinformatics 2011; 27(22): 3216-3217. [DOI:10.1093/bioinformatics/btr540]
18. Ng PC, Henikoff S. SIFT: Predicting amino acid changes that affect protein function. Nucleic acids research 2003; 31(13): 3812-4. [DOI:10.1093/nar/gkg509]
19. Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE, Gerasimova A, Bork P, Kondrashov AS, Sunyaev SR. A method and server for predicting damaging missense mutations. Nature methods 2010; 7(4): 248-249. [DOI:10.1038/nmeth0410-248]
20. Schwarz JM, Cooper DN, Schuelke M, Seelow D. MutationTaster2: mutation prediction for the deep-sequencing age. Nature methods 2014; 11(4): 361-362. [DOI:10.1038/nmeth.2890]
21. Mi H, Guo N, Kejariwal A, Thomas PD. PANTHER version 6: protein sequence and function evolution data with expanded representation of biological pathways. Nucleic acids research 2007; 35(Database issue): D247- D252. [DOI:10.1093/nar/gkl869]
22. Choi Y,Chan AP. PROVEAN web server: a tool to predict the functional effect of amino acid substitutions and indels. Bioinformatics 2015; 31(16): 2745-2747. [DOI:10.1093/bioinformatics/btv195]
23. Kopanos C, Tsiolkas V, Kouris A, Chapple CE, Aguilera MA, Meyer R, Massouras A. VarSome: the human genomic variant search engine. Bioinformatics 2019; 35(11): 1978-1980. [DOI:10.1093/bioinformatics/bty897]
24. Richards S, Aziz N, Bale S, Bick D, Das S, Gastier-Foster J, Grody WW, Hegde M, Lyon E, Spector E, Voelkerding K, Rehm HL, ACMG Laboratory Quality Assurance Committee. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genetics in medicine 2015; 17(5): 405-424. [DOI:10.1038/gim.2015.30]
25. Waterhouse A, Bertoni M, Bienert S, Studer G, Tauriello G, Gumienny R, Heer FT, de Beer TAP, Rempfer C, Bordoli L, Lepore R, Schwede T. SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic acids research 2018; 46(W1): W296-W303. [DOI:10.1093/nar/gky427]
26. Buchan DWA, Jones DT. The PSIPRED protein analysis workbench: 20 years on. Nucleic acids research 2019; 47(W1): W402-W407. [DOI:10.1093/nar/gkz297]
27. McGuffin LJ, Bryson K, Jones DT. The PSIPRED protein structure prediction server. Bioinformatics 2000; 16(4): 404-405. [DOI:10.1093/bioinformatics/16.4.404]
28. Pettersen EF, Goddard TD, Huang CC, Couch GS, Greenblatt DM, Meng EC, Ferrin TE. UCSF Chimera--a visualization system for exploratory research and analysis. Journal of computational chemistry 2004; 25(13): 1605-12. [DOI:10.1002/jcc.20084]
29. Xue LC, Rodrigues JP, Kastritis PL, Bonvin AM, Vangone A. PRODIGY: a web server for predicting the binding affinity of protein-protein complexes. Bioinformatics 2016; 32(23): 3676-3678. [DOI:10.1093/bioinformatics/btw514]
30. Kuriata A, Gierut AM, Oleniecki T, Ciemny MP, Kolinski A, Kurcinski M, Kmiecik S. CABS-flex 2.0: a web server for fast simulations of flexibility of protein structures. Nucleic acids research 2018; 46(W1): W338-W343. [DOI:10.1093/nar/gky356]
31. Hecht M, Bromberg Y, Rost B. Better prediction of functional effects for sequence variants. BMC genomics 2015; 16 Suppl 8 (Suppl 8): S1. [DOI:10.1186/1471-2164-16-S8-S1]
32. Capriotti E, Calabrese R, Fariselli P, Martelli PL, Altman RB, Casadio R. WS-SNPs&GO: a web server for predicting the deleterious effect of human protein variants using functional annotation. BMC genomics 2013; 14 (Suppl 3): S6. [DOI:10.1186/1471-2164-14-S3-S6]
33. Capriotti E, Fariselli P, Casadio R. I-Mutant2.0: predicting stability changes upon mutation from the protein sequence or structure. Nucleic acids research 2005; 33(Web Server issue): W306-310. [DOI:10.1093/nar/gki375]
34. Shihab HA, Gough J, Cooper DN, Stenson PD, Barker GLA, Edwards KJ, Day INM, Gaunt TR. Predicting the functional, molecular, and phenotypic consequences of amino acid substitutions using hidden Markov models. Human mutation 2013; 34(1): 57-65. [DOI:10.1002/humu.22225]
35. Pejaver V, Byrne AB, Feng BL, Pagel KA, Mooney SD, Karchin R, O'Donnell-Luria A, Harrison SM, Tavtigian SV, Greenblatt MS, Biesecker LG, Radivojac P, Brenner SE, ClinGen Sequence Variant Interpretation Working Group. Calibration of computational tools for missense variant pathogenicity classification and ClinGen recommendations for PP3/BP4 criteria. American journal of human genetics 2022; 109(12): 2163-2177. [DOI:10.1016/j.ajhg.2022.10.013]
36. Preston CG, Wright MW, Madhavrao R, Harrison SM, Goldstein JL, Luo X, Wand H, Wulf B, Cheung G, Mark E Mandell ME, Tong H, Cheng S, Iacocca MA, Pineda AL, Popejoy AB, Dalton K, Zhen J, Dwight SS, Babb L, DiStefano M, O'Daniel JM, Lee K, Riggs ER, Zastrow DB, Mester JL, Ritter DI, Patel RY, Subramanian SL, Milosavljevic A, Berg JS, Rehm HL, Plon SE, Cherry JM, Bustamante CD, Costa HA, Clinical Genome Resource (ClinGen). ClinGen variant curation interface: a variant classification platform for the application of evidence criteria from ACMG/AMP guidelines. Genome medicine 2022; 14(1): 6. [DOI:10.1186/s13073-021-01004-8]
37. Tavtigian SV, Greenblatt MS, Harrison SM, Nussbaum RL, Prabhu SA, Boucher KM, Biesecker LG, ClinGen Sequence Variant Interpretation Working Group (ClinGen SVI). Modeling the ACMG/AMP variant classification guidelines as a Bayesian classification framework. Genetics in medicine 2018; 20(9): 1054-1060. [DOI:10.1038/gim.2017.210]
38. Xiang S, Tong L. Crystal structures of human and Staphylococcus aureus pyruvate carboxylase and molecular insights into the carboxyltransfer reaction. Nature structural & molecular biology 2008;15(3): 295-302. [DOI:10.1038/nsmb.1393]
39. Kondo S, Nakajima Y, Sugio S, Sueda S, Islam MN, Kondo H. Structure of the biotin carboxylase domain of pyruvate carboxylase from Bacillus thermodenitrificans. Acta crystallographica 2007; 63(Pt 8): 885-890. [DOI:10.1107/S0907444907029423]
40. Mochalkin I, Miller JR, Evdokimov A, Lightle S, Yan C, Stover CK, Waldrop GL. Structural evidence for substrate-induced synergism and half-sites reactivity in biotin carboxylase. Protein science 2008; 17(10): 1706-1718. [DOI:10.1110/ps.035584.108]
41. Tsygankova P, Bychkov I, Minzhenkova M, Pechatnikova N, Bessonova L, Buyanova G, Naumchik I, Beskorovainiy N, Tabakov V, Itkis Y, Shilova N, Zakharova E. Expanding the genetic spectrum of the pyruvate carboxylase deficiency with novel missense, deep intronic and structural variants. Molecular genetics and metabolism reports 2022; 32: 100889. [DOI:10.1016/j.ymgmr.2022.100889]
42. Babanejad M, Beheshtian M, Jamshidi F, M Mohseni M, Booth KT, Kahrizi K, Najmabadi H. Genetic etiology of hearing loss in Iran. Human genetics 2022; 141(3-4): 623-631. [DOI:10.1007/s00439-021-02421-w]
43. Cheraghi, S, Moghbelinejad S, Najmabadi N, Kimia Kahrizi , R Najafipour. The PTRHD1 Mutation in Intellectual Disability. Archives of Iranian medicine 2021; 24(10): 747-751. [DOI:10.34172/aim.2021.110]
44. Tavasoli B, Safa M, Dorgalaleh A, Ghasemi JB, Makhouri FM, Rezvani MR, Ahmadi A, Tabibian S, Jazebi M, Baghaipour MR, Zaker F. Molecular and clinical profile of congenital fibrinogen disorders in Iran, identification of two novel mutations. International journal of laboratory hematology 2020; 42(5): 619-627. [DOI:10.1111/ijlh.13258]
45. Bayat R, Koohmanaee S, Nejat M, Kharaee F, Shahrokhi M, Hassanzadeh Rad A, Chakoosari SN, Dalili S, Hoseini Nouri SA. A case of pyruvate carboxylase deficiency with longer survival and normal laboratory findings. Acta medica iranica 2021; 59(10): 4. [DOI:10.18502/acta.v59i10.7772]
46. Ostergaard E, Duno M, Møller LB, Kalkanoglu-Sivri HS, Dursun A, Aliefendioglu D, Leth H, Dahl M, Christensen E, Wibrand F. Novel mutations in the PC gene in patients with type B pyruvate carboxylase deficiency. JIMD reports 2013; 9: 1-5. [DOI:10.1007/8904_2012_173]
47. Breen C, White FJ, Scott CAB, Heptinstall L, Walter JH, Jones SA, Morris AAM. Unsuccessful treatment of severe pyruvate carboxylase deficiency with triheptanoin. European journal of pediatrics 2014; 173(3): 361-6. [DOI:10.1007/s00431-013-2166-5]
48. Hauser K, Negron C, Albanese SK, Ray S, Steinbrecher T, Abel R, Chodera JD, Wang L. Predicting resistance of clinical Abl mutations to targeted kinase inhibitors using alchemical free-energy calculations. Communications biology 2018; 1: 70. [DOI:10.1038/s42003-018-0075-x]
49. Fowler PW, Cole K, Gordon NC, Kearns AM, Llewelyn MJ, Peto TEA, Crook DW, Walker AS. Robust prediction of resistance to trimethoprim in Staphylococcus aureus. Cell chemical biology 2018; 25(3): 339-349 e4. [DOI:10.1016/j.chembiol.2017.12.009]
50. Gapsys V, Michielssens S, Seeliger D, de Groot BL. Accurate and rigorous prediction of the changes in protein free energies in a large-scale mutation scan. Angewandte chemie 2016; 55(26): 7364-7368. [DOI:10.1002/anie.201510054]
51. Dietrich J, Lovell S, Veatch OJ, Butler MG. PHIP gene variants with protein modeling, interactions, and clinical phenotypes. American journal of medical genetics. Part A 2022; 188(2): 579-589. [DOI:10.1002/ajmg.a.62557]
52. Wang Y, Ma C, Jiang C, Zhang Y, Wu D. A novel IRF6 variant detected in a family with nonsyndromic cleft lip and palate by whole exome sequencing. The Journal of craniofacial surgery 2021; 32(1): 265-269. [DOI:10.1097/SCS.0000000000007000]

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