Clinically significant findings of high-risk mutations in human SLC29A4 gene associated with diabetes mellitus type 2 in Pakistani population

References

• Ali, O. (2013). Genetics of type 2 diabetes. World Journal of Diabetes, 4(4), 114–123. https://doi.org/10.4239/wjd.v4.i4.114
   PubMedGoogle Scholar
• Brunetti, A., Chiefari, E., & Foti, D. (2014). Recent advances in the molecular genetics of type 2 diabetes mellitus. World Journal of Diabetes, 5(2), 128–140.
   PubMedGoogle Scholar
• Cheng, P. N., Liu, C., Zhao, M., Eisenberg, D., & Nowick, J. S. (2012). Amyloid β-sheet mimics that antagonize protein aggregation and reduce amyloid toxicity. Nature Chemistry, 4(11), 927–933. https://doi.org/10.1038/nchem.1433
   PubMed Web of Science ®Google Scholar
• Choi, Y., & Chan, A. P. (2015). PROVEAN web server: A tool to predict the functional effect of amino acid substitutions and indels. Bioinformatics (Oxford, England), 31(16), 2745–2747.
   PubMed Web of Science ®Google Scholar
• Creative Research Systems. (2017). Creative Research Systems. [online][cited 2017 Jun 12]. Available from: URL:www.surveysystem.com.
   Google Scholar
• Dawed, A. Y., Zhou, K., van Leeuwen, N., Mahajan, A., Robertson, N., Koivula, R., Elders, P. J. M., Rauh, S. P., Jones, A. G., Holl, R. W., Stingl, J. C., Franks, P. W., McCarthy, M. I., ‘T Hart, L. M., & Pearson, E. R. (2019). Variation in the plasma membrane monoamine transporter (PMAT, encoded in SLC29A4) and organic cation transporter 1 (OCT1, Encoded in SLC22A1) and gastrointestinal intolerance to metformin in type 2 diabetes: An IMI DIRECT study. Diabetes Care, 42(6), 1027–1033. https://doi.org/10.2337/dc18-2182
   PubMed Web of Science ®Google Scholar
• Dayem Ullah, A. Z., Lemoine, N. R., & Chelala, C. (2012). SNPnexus: A web server for functional annotation of novel and publicly known genetic variants (2012 update). Nucleic Acids Research, 40(Web Server issue), W65–W70.
   PubMedGoogle Scholar
• Feingold KR, Anawalt B, Boyce A, (Eds.). (2000). Chronic fatigue syndrome. MDText.com, Inc. (Last Update: July 12, 2020).
   Google Scholar
• Garbuzynskiy, S. O., Lobanov, M. Y., & Galzitskaya, O. V. (2010). FoldAmyloid: A method of prediction of amyloidogenic regions from protein sequence. Bioinformatics (Oxford, England), 26(3), 326–332.
   PubMed Web of Science ®Google Scholar
• Glaser, F., Pupko, T., Paz, I., Bell, R. E., Bechor-Shental, D., Martz, E., & Ben-Tal, N. (2003). ConSurf: Identification of functional regions in proteins by surface-mapping of phylogenetic information. Bioinformatics (Oxford, England), 19(1), 163–164. https://doi.org/10.1093/bioinformatics/19.1.163
   PubMed Web of Science ®Google Scholar
• Gong, L., Goswami, S., Giacomini, K. M., Altman, R. B., & Klein, T. E. (2012). Metformin pathways: Pharmacokinetics and pharmacodynamics. Pharmacogenetics and Genomics, 22(11), 820–827.
   PubMed Web of Science ®Google Scholar
• Graham, G. G., Punt, J., Arora, M., Day, R. O., Doogue, M. P., Duong, J. K., Furlong, T. J., Greenfield, J. R., Greenup, L. C., Kirkpatrick, C. M., Ray, J. E., Timmins, P., & Williams, K. M. (2011). Clinical pharmacokinetics of metformin. Clinical Pharmacokinetics, 50(2), 81–98. https://doi.org/10.2165/11534750-000000000-00000
   PubMed Web of Science ®Google Scholar
• Hu, L., Zhuo, W., He, Y.-J., Zhou, H.-H., & Fan, L. (2012). Pharmacogenetics of P450 oxidoreductase: implications in drug metabolism and therapy. Pharmacogenetics and Genomics, 22(11), 812–819. https://doi.org/10.1097/FPC.0b013e328358d92b
   PubMed Web of Science ®Google Scholar
• Inzucchi, S. E., Bergenstal, R. M., Buse, J. B., Diamant, M., Ferrannini, E., Nauck, M., Peters, A. L., Tsapas, A., Wender, R., & Matthews, D. R. (2012). Management of hyperglycemia in type 2 diabetes: A patient-centered approach: Position statement of the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD). Diabetes Spectrum, 25(3), 154–171. https://doi.org/10.2337/diaspect.25.3.154
   Google Scholar
• Kirby, A., Gebski, V., & Keech, A. C. (2002). Determining the sample size in a clinical trial. The Medical Journal of Australia, 177(5), 256–257. https://doi.org/10.5694/j.1326-5377.2002.tb04759.x
   PubMed Web of Science ®Google Scholar
• Kozakov, D., Hall, D. R., Xia, B., Porter, K. A., Padhorny, D., Yueh, C., Beglov, D., & Vajda, S. (2017). The ClusPro web server for protein–protein docking. Nature Protocols, 12(2), 255–278. https://doi.org/10.1038/nprot.2016.169
   PubMed Web of Science ®Google Scholar
• Kumar, C. V., Swetha, R. G., Anbarasu, A., & Ramaiah, S. (2014). Computational analysis reveals the association of threonine 118 methionine mutation in PMP22 resulting in CMT-1A. Advances in Bioinformatics, 2014, 502618. https://doi.org/10.1155/2014/502618
   PubMedGoogle Scholar
• Kuriata, A., Gierut, A. M., Oleniecki, T., Ciemny, M. P., Kolinski, A., Kurcinski, M., & Kmiecik, S. (2018). CABS-flex 2.0: A web server for fast simulations of flexibility of protein structures. Nucleic Acids Research, 46(W1), W338–43. https://doi.org/10.1093/nar/gky356
   PubMed Web of Science ®Google Scholar
• Laskowski, R. A., & Swindells, M. B. (2011). LigPlot+: multiple ligand-protein interaction diagrams for drug discovery. Journal of Chemical Information and Modeling, 51(10), 2778–2786. https://doi.org/10.1021/ci200227u
   PubMed Web of Science ®Google Scholar
• Mahjabeen, I., Baig, R. M., Masood, N., Sabir, M., Inayat, U., Malik, F. A., & Kayani, M. A. (2013). Genetic variations in XRCC1 gene in sporadic head and neck cancer (HNC) patients. Pathology Oncology Research : POR, 19(2), 183–188. https://doi.org/10.1007/s12253-012-9567-z
   PubMed Web of Science ®Google Scholar
• Manning, A. K., LaValley, M., Liu, C.-T., Rice, K., An, P., Liu, Y., Miljkovic, I., Rasmussen-Torvik, L., Harris, T. B., Province, M. A., Borecki, I. B., Florez, J. C., Meigs, J. B., Cupples, L. A., & Dupuis, J. (2011). Meta‐analysis of gene‐environment interaction: Joint estimation of SNP and SNP × environment regression coefficients. Genetic Epidemiology, 35(1), 11–18. https://doi.org/10.1002/gepi.20546
   PubMed Web of Science ®Google Scholar
• Mukherjee, A., Morales-Scheihing, D., Butler, P. C., & Soto, C. (2015). Type 2 diabetes as a protein misfolding disease. Trends in Molecular Medicine, 21(7), 439–449. https://doi.org/10.1016/j.molmed.2015.04.005
   PubMed Web of Science ®Google Scholar
• Nathan, D. M., Buse, J. B., Davidson, M. B., Heine, R. J., Holman, R. R., Sherwin, R., & Zinman, B. (2006). Management of hyperglycemia in type 2 diabetes: A consensus algorithm for the initiation and adjustment of therapy: A consensus statement from the American Diabetes Association and the European Association for the Study of Diabetes. Diabetes Care, 29(8), 1963–1972. https://doi.org/10.2337/dc06-9912
   PubMed Web of Science ®Google Scholar
• Ngan, C. H., Hall, D. R., Zerbe, B., Grove, L. E., Kozakov, D., & Vajda, S. (2012). FTSite: High accuracy detection of ligand binding sites on unbound protein structures. Bioinformatics (Oxford, England), 28(2), 286–287. https://doi.org/10.1093/bioinformatics/btr651
   PubMed Web of Science ®Google Scholar
• Olokoba, A. B., Obateru, O. A., & Olokoba, L. B. (2012). Type 2 diabetes mellitus: A review of current trends. Oman Medical Journal, 27(4), 269–273. https://doi.org/10.5001/omj.2012.68
   PubMedGoogle Scholar
• Pandey, B., Grover, S., Tyagi, C., Goyal, S., Jamal, S., Singh, A., Kaur, J., & Grover, A. (2016). Molecular principles behind pyrazinamide resistance due to mutations in panD gene in Mycobacterium tuberculosis. Gene, 581(1), 31–42. Apr 25https://doi.org/10.1016/j.gene.2016.01.024
   PubMed Web of Science ®Google Scholar
• Pereira, G. R., Da Silva, A. N., Do Nascimento, S. S., & De Mesquita, J. F. (2019). In silico analysis and molecular dynamics simulation of human superoxide dismutase 3 (SOD3) genetic variants. Journal of Cellular Biochemistry, 120(3), 3583–3598. https://doi.org/10.1002/jcb.27636
   PubMed Web of Science ®Google Scholar
• Pettersen, E. F., Goddard, T. D., Huang, C. C., Couch, G. S., Greenblatt, D. M., Meng, E. C., & Ferrin, T. E. (2004). UCSF Chimera—a visualization system for exploratory research and analysis. Journal of Computational Chemistry, 25(13), 1605–1612.
   PubMed Web of Science ®Google Scholar
• Pires, D. E., Ascher, D. B., & Blundell, T. L. (2014). mCSM: Predicting the effects of mutations in proteins using graph-based signatures. Bioinformatics (Oxford, England), 30(3), 335–342.
   PubMed Web of Science ®Google Scholar
• Raj, M. G., Mathaiyan, J., Mukta, V., & Priyadarshini, R. (2018). Lack of effect of the SLC47A1 and SLC47A2 gene polymorphisms on the glycemic response to metformin in type 2 diabetes mellitus patients. Drug Metabolism and Personalized Therapy, 33(4), 175–185. https://doi.org/10.1515/dmpt-2018-0030
   PubMedGoogle Scholar
• Scarpello, J. H., & Howlett, H. C. (2008). Metformin therapy and clinical uses. Diabetes & Vascular Disease Research, 5(3), 157–167. https://doi.org/10.3132/dvdr.2008.027
   PubMed Web of Science ®Google Scholar
• Semiz, S., Dujic, T., & Causevic, A. (2013). Pharmacogenetics and personalized treatment of type 2 diabetes. Biochemia Medica, 23(2), 154–171.
   Web of Science ®Google Scholar
• Shu, Y., Sheardown, S. A., Brown, C., Owen, R. P., Zhang, S., Castro, R. A., Ianculescu, A. G., Yue, L., Lo, J. C., Burchard, E. G., Brett, C. M., & Giacomini, K. M. (2007). Effect of genetic variation in the organic cation transporter 1 (OCT1) on metformin action. The Journal of Clinical Investigation, 117(5), 1422–1431.
   PubMed Web of Science ®Google Scholar
• Sissung, T. M., Troutman, S. M., Campbell, T. J., Pressler, H. M., Sung, H., Bates, S. E., & Figg, W. D. (2012). Transporter polymorphisms affect normal physiology, diseases, and pharmacotherapy. Discovery Medicine, 13(68), 19–34.
   PubMed Web of Science ®Google Scholar
• Stumvoll, M., Goldstein, B. J., & Van Haeften, T. W. (2005). Type 2 diabetes: Principles of pathogenesis and therapy. The Lancet, 365(9467), 1333–1346. https://doi.org/10.1016/S0140-6736(05)61032-X
   PubMed Web of Science ®Google Scholar
• Tarasova, L., Kalnina, I., Geldnere, K., Bumbure, A., Ritenberga, R., Nikitina-Zake, L., Fridmanis, D., Vaivade, I., Pirags, V., & Klovins, J. (2012). Association of genetic variation in the organic cation transporters OCT1, OCT2 and multidrug and toxin extrusion 1 transporter protein genes with the gastrointestinal side effects and lower BMI in metformin-treated type 2 diabetes patients. Pharmacogenetics and Genomics, 22(9), 659–666. https://doi.org/10.1097/FPC.0b013e3283561666
   PubMed Web of Science ®Google Scholar
• Thule, P. M. (2012). Mechanisms of current therapies for diabetes mellitus type 2. Advances in Physiology Education, 36(4), 275–283.
   PubMed Web of Science ®Google Scholar
• Trott, O., & Olson, A. J. (2010). AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of Computational Chemistry, 31(2), 455–461. https://doi.org/10.1002/jcc.21334
   PubMed Web of Science ®Google Scholar
• Venselaar, H., Te Beek, T. A., Kuipers, R. K., Hekkelman, M. L., & Vriend, G. (2010). Protein structure analysis of mutations causing inheritable diseases. An e-Science approach with life scientist friendly interfaces. BMC Bioinformatics, 11(1), 548. https://doi.org/10.1186/1471-2105-11-548
   PubMed Web of Science ®Google Scholar
• Viollet, B., Guigas, B., Garcia, N. S., Leclerc, J., Foretz, M., & Andreelli, F. (2012). Cellular and molecular mechanisms of metformin: An overview. Clinical Science (London, England : 1979), 122(6), 253–270. https://doi.org/10.1042/CS20110386
   PubMed Web of Science ®Google Scholar
• Vitarani, D. A., Ningrum, R. I., & Rheza, F. (2019). Allele Frequency of SLC22A1 Met420del Metformin Main Transporter Encoding Gene among Javanese-Indonesian Population. Open Access Macedonian Journal of Medical Sciences, 7(3), 378–383. https://doi.org/10.3889/oamjms.2019.087
   PubMedGoogle Scholar
• Wild, S., Roglic, G., Green, A., Sicree, R., & King, H. (2004). Global prevalence of diabetes: Estimates for the year 2000 and projections for 2030. Diabetes Care, 27(5), 1047–1053. https://doi.org/10.2337/diacare.27.5.1047
   PubMed Web of Science ®Google Scholar
• Yang, J., Yan, R., Roy, A., Xu, D., Poisson, J., & Zhang, Y. (2015). The I-TASSER Suite: Protein structure and function prediction. Nature Methods, 12(1), 7–8. https://doi.org/10.1038/nmeth.3213
   PubMed Web of Science ®Google Scholar
• Zhou, M., Xia, L., & Wang, J. (2007). Metformin transport by a newly cloned proton-stimulated organic cation transporter (plasma membrane monoamine transporter) expressed in human intestine. Drug Metabolism and Disposition: The Biological Fate of Chemicals, 35(10), 1956–1962. https://doi.org/10.1124/dmd.107.015495
   PubMed Web of Science ®Google Scholar