Identification of the First PAX4-MODY Family Reported in Brazil

References

• Yamagata K, Furuta H, Oda N, et al. Mutations in the hepatocyte nuclear factor-4α gene in maturity-onset diabetes of the young (MODY1). Nature. 1996;384(6608):458–460. doi:10.1038/384458a0
   PubMed Web of Science ®Google Scholar
• Yamagata K, Oda N, Kaisaki PJ, et al. Mutations in the hepatocyte nuclear factor-1α gene in maturity-onset diabetes of the young (MODY3). Nature. 1996;384(6608):455–458. doi:10.1038/384455a0
   PubMed Web of Science ®Google Scholar
• Horikawa Y, Iwasaki N, Hara M, et al. Mutation in hepatocyte nuclear factor–1β gene (TCF2) associated with MODY. Nat Genet. 1997;17(4):384–385. doi:10.1038/ng1297-384
   PubMed Web of Science ®Google Scholar
• Stoffers DA, Ferrer J, Clarke WL, Haneber JF. Early-onset type-II diabetes mellitus (MODY4) linked to IPF1. Nat Genet. 1997;17:138–139. doi:10.1038/ng0797-270
   PubMed Web of Science ®Google Scholar
• Malecki MT, Jhala US, Antonellis A, et al. Mutations in NEUROD1 are associated with the development of type 2 diabetes mellitus. Nat Genet. 1999;23(3):323–328. doi:10.1038/15500
   PubMedGoogle Scholar
• Neve B, Fernandez-Zapico ME, Ashkenazi-Katalan V, et al. From the cover: role of transcription factor KLF11 and its diabetes-associated gene variants in pancreatic beta cell function. Proc Natl Acad Sci. 2005;102(13):4807–4812. doi:10.1073/pnas.0409177102
   PubMedGoogle Scholar
• Plengvidhya N, Kooptiwut S, Songtawee N, et al. PAX4 mutations in thais with maturity onset diabetes of the young. J Clin Endocrinol Metab. 2007;92(7):2821–2826. doi:10.1210/jc.2006-1927
   PubMed Web of Science ®Google Scholar
• Sosa-Pineda B, Chowdhury K, Torres M, Oliver G, Gruss P. The Pax4 gene is essential for differentiation of insulin-producing β cells in the mammalian pancreas. Nature. 1997;386(6623):399–402. doi:10.1038/386399a0
   PubMed Web of Science ®Google Scholar
• Biason-Lauber A, Boehm B, Lang-Muritano M. et al. Association of childhood type 1 diabetes mellitus with a variant of PAX4: possible link to beta cell regenerative capacity. Diabetologia. 2005;48(5):900–905. doi:10.1007/s00125-005-1723-5
   PubMedGoogle Scholar
• Shimajiri Y, Sanke T, Furuta H, et al. A missense mutation of Pax4 gene (R121W) is associated with type 2 diabetes in Japanese. Diabetes. 2001;50(12):2864–2869. doi:10.2337/diabetes.50.12.2864
   PubMedGoogle Scholar
• Mauvais-Jarvis F, Smith SB, Le May C, et al. PAX4 gene variations predispose to ketosis-prone diabetes. Hum Mol Genet. 2004;13(24):3151–3159. doi:10.1093/hmg/ddh341
   PubMed Web of Science ®Google Scholar
• Jo W, Endo M, Ishizu K, Nakamura A, Tajima T. A novel PAX4 mutation in a Japanese patient with maturity-onset diabetes of the young. Tohoku J Exp Med. 2011;223(2):113–118. doi:10.1620/tjem.223.113
   PubMedGoogle Scholar
• Sujjitjoon J, Kooptiwut S, Chongjaroen N, Tangjittipokin W, Plengvidhya N, Yenchitsomanus PT. Aberrant mRNA splicing of paired box 4 (PAX4) IVS7-1G>A mutation causing maturity-onset diabetes of the young, type 9. Acta Diabetol. 2016;53(2):205–216. doi:10.1007/s00592-015-0760-x
   PubMedGoogle Scholar
• Chapla A, Mruthyunjaya MD, Asha HS, et al. Maturity onset diabetes of the young in India - a distinctive mutation pattern identified through targeted next-generation sequencing. Clin Endocrinol (Oxf). 2015;82(4):533–542. doi:10.1111/cen.12541
   PubMedGoogle Scholar
• Ang SF, Lim SC, Tan CS, et al. A preliminary study to evaluate the strategy of combining clinical criteria and next generation sequencing (NGS) for the identification of monogenic diabetes among multi-ethnic Asians. Diabetes Res Clin Pract. 2016;119:13–22. doi:10.1016/j.diabres.2016.06.008
   PubMedGoogle Scholar
• Tarantino RM, Abreu GDM, Fonseca A, et al. MODY probability calculator for GCK and HNF1A screening in a multiethnic background population. Arch Endocrinol Metab. 2019;1(8):1–7. doi:10.20945/2359-3997000000173
   Google Scholar
• Aidar M, Line SRP. A simple and cost-effective protocol for DNA isolation from buccal epithelial cells. Braz Dent J. 2007;18(2):148–152. doi:10.1590/S0103-64402007000200012
   PubMedGoogle Scholar
• Naslavsky MS, Yamamoto GL, de Almeida TF, et al. Exomic variants of an elderly cohort of Brazilians in the ABraOM database. Hum Mutat. 2017;38(7):751–763. doi:10.1002/humu.23220
   PubMed Web of Science ®Google Scholar
• Sim N-L, 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(W1):W452–W457. doi:10.1093/nar/gks539
   PubMed Web of Science ®Google Scholar
• Adzhubei IA, Schmidt S, Peshkin L, et al. A method and server for predicting damaging missense mutations. Nat Methods. 2010;7(4):248–249. doi:10.1038/nmeth0410-248
   PubMed Web of Science ®Google Scholar
• 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
   PubMed Web of Science ®Google Scholar
• Ioannidis NM, Rothstein JH, Pejaver V, et al. REVEL: an ensemble method for predicting the pathogenicity of rare missense variants. Am J Hum Genet. 2016;99(4):877–885. doi:10.1016/j.ajhg.2016.08.016
   PubMed Web of Science ®Google Scholar
• Capriotti E, Calabrese R, Fariselli P, Martelli P, 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
   PubMed Web of Science ®Google Scholar
• Pejaver V, Urresti J, Lugo-Martinez J, et al. MutPred2: inferring the molecular and phenotypic impact of amino acid variants. bioRxiv. 2017:134981. doi:10.1101/134981.
   Google Scholar
• Bromberg Y, Rost B. SNAP: predict effect of non-synonymous polymorphisms on function. Nucleic Acids Res. 2007;35(11):3823–3835. doi:10.1093/nar/gkm238
   PubMed Web of Science ®Google Scholar
• Shihab HA, Gough J, Cooper DN, et al. Predicting the functional, molecular, and phenotypic consequences of amino acid substitutions using hidden markov models. Hum Mutat. 2013;34(1):57–65. doi:10.1002/humu.22225
   PubMed Web of Science ®Google Scholar
• Jagadeesh KA, Wenger AM, Berger MJ, et al. M-CAP eliminates a majority of variants of uncertain significance in clinical exomes at high sensitivity. Nat Genet. 2016;48(12):1581–1586. doi:10.1038/ng.3703
   PubMed Web of Science ®Google Scholar
• Rentzsch P, Witten D, Cooper GM, Shendure J, Kircher M. CADD: predicting the deleteriousness of variants throughout the human genome. Nucleic Acids Res. 2019;47(D1):D886–D894. doi:10.1093/nar/gky1016
   PubMed Web of Science ®Google Scholar
• Reva B, Antipin Y, Sander C. Predicting the functional impact of protein mutations: application to cancer genomics. Nucleic Acids Res. 2011;39(17):e118. doi:10.1093/nar/gkr407
   PubMed Web of Science ®Google Scholar
• 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):1342–1354. doi:10.1002/humu.20896
   PubMed Web of Science ®Google Scholar
• Tang H, Thomas PD. PANTHER-PSEP: predicting disease-causing genetic variants using position-specific evolutionary preservation. Bioinformatics. 2016;32(14):2230–2232. doi:10.1093/bioinformatics/btw222
   PubMed Web of Science ®Google Scholar
• Schwarz JM, Cooper DN, Schuelke M, Seelow D. MutationTaster2: mutation prediction for the deep-sequencing age. Nat Methods. 2014;11(4):361–362. doi:10.1038/nmeth.2890
   PubMed Web of Science ®Google Scholar
• Schmidt W, Lankers H. Co-inheritance of PAX4 and BLK mutations (MODY 7 and 9) in a 38 year old African Patient with Ketosis-Prone Diabetes. Presented at European Congress of Endocrinology ECE Munick, DEU. Endocrine abstract. 2016;41:EP401. doi:10.1530/endoabs.41.EP401
   Google Scholar