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Journal of Biosciences

, 44:21 | Cite as

Overview of genomics and post-genomics research on type 2 diabetes mellitus: Future perspectives and a framework for further studies

  • Battini Mohan ReddyEmail author
  • Rayabarapu Pranavchand
  • S A A Latheef
Review
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Abstract

In this review, we briefly outlined salient features of pathophysiology and results of the genetic association studies hitherto conducted on type 2 diabetes. Primarily focusing on the current status of genomic research, we briefly discussed the limited progress made during the post-genomic era and tried to identify the limitations of the post-genomic research strategies. We suggested reanalysis of the existing genomic data through advanced statistical and computational methods and recommended integrated genomics-metabolomics approaches for future studies to facilitate understanding of the gene-environment interactions in the manifestation of the disease. We also propose a framework for research that may be apt for determining the effects of urbanization and changing lifestyles in the manifestation of complex genetic disorders like type 2 diabetes in the Indian populations and offset the confounding effects of both genetic and environmental factors in the natural way.

Keywords

Complex genetic disorders genomics Indian scenario metabolomics proteomics transcriptomics type 2 diabetes mellitus 
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Notes

References

  1. Abu Bakar MH, Sarmidi MR, Cheng KK, Ali Khan A, Suan CL, ZamanHuri H and Yaakob H 2015 Metabolomics - the complementary field in systems biology a review on obesity and type 2 diabetes. Mol. Biosyst. 11 1742–1774CrossRefGoogle Scholar
  2. Ali O 2013 Genetics of type 2 diabetes. World J. Diabetes 4 114–123CrossRefGoogle Scholar
  3. Ali S, Chopra R, Manvati S, Singh YP, Kaul N, Behura A, Mahajan A, Sehajpal P et al. 2013 Replication of type 2 diabetes candidate genes variations in three geographically unrelated Indian population groups. PLoS ONE 8 e58881CrossRefGoogle Scholar
  4. Alonso A, Marsal S and Julià A 2015 Analytical methods in untargeted metabolomics state of the art in 2015. Front. Bioeng. Biotechnol. 3 23.CrossRefGoogle Scholar
  5. Aretz I and Meierhofer D 2016 Advantages and pitfalls of mass spectrometry based metabolome profiling in systems Biology. Int. J. Mol. Sci. 17 632CrossRefGoogle Scholar
  6. Ashcroft FM and Rorsman P 2012 Diabetes mellitus and the β cell the last ten years. Cell 148 1160–1171CrossRefGoogle Scholar
  7. Bodhini D, Radha V, Dhar M, Narayani N and Mohan V 2007 The rs12255372(G/T) and rs7903146(C/T) polymorphisms of the TCF7L2 gene are associated with type 2 diabetes mellitus in Asian Indians. Metabolism 56 1174–1178CrossRefGoogle Scholar
  8. Boj SF, van Es JH, Huch M, Li VS, José A, Hatzis P et al. 2012 Diabetes risk gene and Wnt effector TCF7L2/TCF4 controls hepatic response to perinatal and adult metabolic demand. Cell 151 1595–1607CrossRefGoogle Scholar
  9. Chandak GR, Janipalli CS, Bhaskar S, Kulkarni SR, Mohankrishna P, Hattersley AT, et al. 2007 Common variants in the TCF7L2 gene are strongly associated with type 2 diabetes mellitus in the Indian population. Diabetologia 50 63–67CrossRefGoogle Scholar
  10. Chauhan G, Spurgeon CJ, Tabassum R, Bhaskar S, Kulkarni SR, Mahajan A, et al. 2010 Impact of common variants of PPARG, KCNJ11, TCF7L2, SLC30A8, HHEX, CDKN2A, IGF2BP2 and CDKAL1 on the risk of type 2 diabetes in 5,164 Indians. Diabetes 59 2068–2074CrossRefGoogle Scholar
  11. Chen BH, Hivert MF, Peters MJ, Pilling LC, Hogan JD, Pham LM, Harries LW, Fox CS, et al. 2016 Peripheral blood transcriptomic signatures of fasting glucose and insulin concentrations. Diabetes 65 3794–3804CrossRefGoogle Scholar
  12. Chen GB, Xu Y, Xu HN, Li MD, Zhu J, Lou XY 2011 Practical and theoretical considerations in study design for detecting gene-gene interactions using MDR and GMDR approaches. PLoS ONE 6 e16981CrossRefGoogle Scholar
  13. Chen L, Magliano DJ and Zimmet PZ 2012 The worldwide epidemiology of type 2 diabetes mellitus-present and future perspectives. Nat. Rev. Endocrinol. 8 228–236CrossRefGoogle Scholar
  14. Cheng S, Rhee EP, Larson MG, Lewis GD, McCabe EL, Shen D, et al. 2012 Metabolite profiling identifies pathways associated with metabolic risk in humans. Circulation 125 2222–2231CrossRefGoogle Scholar
  15. Chidambaram M, Radha V and Mohan V 2010 Replication of recently described type 2 diabetes gene variants in a south Indian population. Metabolism 59 1760–1766CrossRefGoogle Scholar
  16. Delgado-Lista J, Perez-Martinez P, García-Rios A, Phillips CM, Williams CM, Gulseth HL et al. 2011 Pleiotropic effects of TCF7L2 gene variants and its modulation in the metabolic syndrome from the LIPGENE study. Atherosclerosis 214 110–116CrossRefGoogle Scholar
  17. Garbis S, Lubec G and Fountoulakis M 2005 Limitations of current proteomics technologies. J. Chromatogr. A 1077 1–18CrossRefGoogle Scholar
  18. Genetic testing time tool 2017 A resource from American College of Preventive Medicine www.acpm.org
  19. Genetic testing registry 2017 https // www.ncbi.nlm.nih.gov/gtr/
  20. Gogna N, Krishna M, Oommen AM, Dorai K 2015 Investigating correlations in the altered metabolic profiles of obese and diabetic subjects in a south Indian Asian population using an NMR-based metabolomics approach. Mol. Biosyst. 11 505–606Google Scholar
  21. Grarup N, Sandholt CH, Hansen T and Pedersen O 2014 Genetic susceptibility to type 2 diabetes and obesity from genome-wide association studies to rare variants and beyond. Diabetologia 57 1528–1541CrossRefGoogle Scholar
  22. Grant SF, Thorleifsson G, Reynisdottir I, Benediktsson R, Manolescu A, Sainz J et al. 2006 Variant of transcription factor 7-like 2 (TCF7L2) gene confers risk of type 2 diabetes. Nat. Genet. 38 320–323CrossRefGoogle Scholar
  23. Guan M, Xie L, Diao C, et al.2013 Systemic perturbations of key metabolites in diabetic rats during the evolution of diabetes studied by urine metabonomics. PLoS ONE 8 e60409CrossRefGoogle Scholar
  24. Gupta V, Khadgawat R, Saraswathy KN, Sachdeva MP and Kalla AK 2008 Emergence of TCF7L2 as a most promising gene in predisposition of diabetes type II. Int. J. Hum. Genet. 8 199–215CrossRefGoogle Scholar
  25. Ha CY, Kim JY, Paik JK, Kim OY, Paik YH, Lee EJ and Lee JH. 2012 The association of specific metabolites of lipid metabolism with markers of oxidative stress, inflammation and arterial stiffness in men with newly diagnosed type 2 diabetes. Clin. Endocrinol. 76 674–682CrossRefGoogle Scholar
  26. Hartstra AV, Bouter KEC, Backhed F and Nieuwdorp M. 2015 Insights into the role of the microbiome in obesity and type 2 diabetes. Diabetes Care 38 159–165CrossRefGoogle Scholar
  27. Hernández-Alvarez MI, Díaz-Ramos A, Berdasco M, Cobb J, Planet E, Cooper D, Pazderska A, Wanic K et al. 2017 Early-onset and classical forms of type 2 diabetes show impaired expression of genes involved in muscle branched-chain amino acids metabolism. Sci. Rep. 7 13850CrossRefGoogle Scholar
  28. International Diabetes Federation 2015 IDF Diabetes Atlas 7th edition (Brussels, International Diabetes Federation)Google Scholar
  29. Jain S, Rajput A, Kumar Y, Uppuluri N, Arvind AS and Tatu U 2005 Proteomic analysis of urinary protein markers for accurate prediction of diabetic kidney disorder. J. Assoc. Physicians India 53 513–520PubMedGoogle Scholar
  30. Jenkinson CP, Göring HHH, Arya R, Blangero J, Duggirala R and DeFronzob RA 2016 Transcriptomics in type 2 diabetes bridging the gap between genotype and phenotype. Genomics Data 8 25–36CrossRefGoogle Scholar
  31. Kaput J and Dawson K 2007 Complexity of type 2 diabetes mellitus data sets emerging from nutrigenomic research a case for dimensionality reduction? Mutat. Res. 622 19–32CrossRefGoogle Scholar
  32. Klein MS and,Shearer J 2016 Metabolomics and type 2 diabetes translating basic research into clinical application. J. Diabetes Res. 2016. http //dx.doi.org/ https://doi.org/10.1155/2016/3898502
  33. Kommoju UJ and Reddy BM 2011 Genetic etiology of type 2 diabetes mellitus A review. Int. J. Diabetes Dev. Countries 31 51–64CrossRefGoogle Scholar
  34. Kommoju UJ, Maruda J, Subbaraj SK, Irgam K, Kotla JP, Velaga L and Reddy BM 2013 No detectable association of IGF2BP2 and SLC30A8 genes with type 2 diabetes in the population of Hyderabad, India. Meta Gene 1 15–23CrossRefGoogle Scholar
  35. Kommoju UJ, Maruda J, Subbaraj SK, Irgam K, Kotla JP and Reddy BM 2014 Association of IRS1, CAPN10, and PPARG gene polymorphisms with type 2 diabetes mellitus in the high-risk population of Hyderabad, Indian J. Diabetes 6 564–573CrossRefGoogle Scholar
  36. Kommoju UJ, Subbaraj SK, Maruda J, Irgam K, Kotla JP, Velaga L and Reddy BM 2016 Association of CDKAL1, CDKN2A/B & HHEX gene polymorphisms with type 2 diabetes mellitus in the population of Hyderabad, India. Indian J. Med. Res. 143 455–63CrossRefGoogle Scholar
  37. Kretowski A, Francisco J. Ruperez and Michal Ciborowski 2016 Genomics and metabolomics in obesity and type 2 diabetes. J. Diabetes Res.  https://doi.org/10.1155/2016/9415645
  38. Lasonder E 2017 Clinical proteomics from biological sample to clinical exploitation. Proteomes 5 10CrossRefGoogle Scholar
  39. Li JW, Lee HM, Wang Y, Tong AH, Yip KY, Tsui SK, Lok S, Ozaki R et al.2016 Interactome-transcriptome analysis discovers signatures complementary to GWAS Loci of Type 2 diabetes. Sci. Rep. 18 35228CrossRefGoogle Scholar
  40. Lim JE, Hong K-W, Jin H-S, Kim YS, Park HK and Oh B 2010. Type 2 diabetes genetic association database manually curated for the study design and odds ratio. BMC Med. Inform. Decis. Mak. 10 76CrossRefGoogle Scholar
  41. Lin Y and Sun Z 2010 Current views on type 2 diabetes. J. Endocrinol 204 1–11CrossRefGoogle Scholar
  42. Liu X, Gao J, Chen J, Wang Z, Shi Q, Man H, Guo S, Wang Y, Li Z and Wang W 2016 Identification of metabolic biomarkers in patients with type 2 diabetic coronary heart diseases based on metabolomic approach. Sci. Rep. 29 30785CrossRefGoogle Scholar
  43. Lu J, Xie G, Jia W and Jia W 2013 Metabolomics in human type 2 diabetes research. Front. Med. 7 4–13CrossRefGoogle Scholar
  44. Mao J, Ai J, Zhou X, Zhou X, Shenwu M, Ong M, Blue M, Washington JT, Wang X and Deng Y 2011 Transcriptomic profiles of peripheral white blood cells in type II diabetes and racial differences in expression profiles. BMC Genomics 12 S12CrossRefGoogle Scholar
  45. Matone A, Derlindati E, Marchetti L, Spigoni V, Dei Cas A, Montanini B, Ardigo D, Zavaroni I et al. 2017 Identification of an early transcriptomic signature of insulin resistance and related diseases in lymphomonocytes of healthy subjects. PLoS ONE 12 e0182559CrossRefGoogle Scholar
  46. Mischak H, Kaiser T, Walden M, Hillmann M, Wittke S, Herrmann A, Knueppel S, Haller H and Fliser D 2004 Proteomic analysis for the assessment of diabetic renal damage in humans. Clin. Sci. 107 485–495CrossRefGoogle Scholar
  47. Nakanishi T, Koyama R, Ikeda T and, Shimizua A 2002 Catalogue of soluble proteins in the human vitreous humor comparison between diabetic retinopathy and macular hole. J. Chromatogr. B 776 89–100CrossRefGoogle Scholar
  48. Olokoba AB, Obateru OA and Olokoba LB 2012 Type 2 diabetes mellitus: A review of current trends. Oman Med. J. 27 269–273CrossRefGoogle Scholar
  49. Park S, Sadanala KC and Kim E-K 2015 A metabolomic approach to understanding the metabolic link between obesity and diabetes. Mol. Cells 38 587–596CrossRefGoogle Scholar
  50. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, Bender D, Maller J, Sklar P et al. 2007 PLINK a tool set for whole-genome association and population-based linkage analysis. Am. J. Hum. Genet. 81 559–575CrossRefGoogle Scholar
  51. Qiu G, Zheng Y, Wang H, Sun J, Ma H, Xiao Y, Li Y, Yuan Y et al.2016 Plasma metabolomics identified novel metabolites associated with risk of type 2 diabetes in two prospective cohorts of Chinese adults. Int. J. Epidemiol . 45 1507–1516CrossRefGoogle Scholar
  52. Rao PV, Lu X, Standley M, Pattee P, Neelima G, Girisesh G, Dakshinamurthy KV, Roberts CT Jr and Nagalla SR. 2007 Proteomic identification of urinary biomarkers of diabetic nephropathy. Diabetes Care 30 629–637CrossRefGoogle Scholar
  53. Rao PV, Reddy AP, Lu X, Dasari S, Krishnaprasad A, Biggs E, Roberts CT and Nagalla SR. 2009 Proteomic identification of salivary biomarkers of type-2 diabetes. J. Proteome Res. 8 239–245CrossRefGoogle Scholar
  54. Reddy BM 2013 Genetic etiology of complex genetic disorders A framework for future Indian studies; In Proceedings of UGC Sponsored National Seminar on Incidence and Prevalence of Mendelian Traits & Diseases in People of Odisha (ed) B Das (Cuttack, Agraduta, Banka Bazar) pp 96–119Google Scholar
  55. Riaz S, Alam SS and Akhtar MW 2010 Proteomic identification of human serum biomarkers in diabetes mellitus type2. J. Pharm. Biomed. Anal. 51 1103–1107CrossRefGoogle Scholar
  56. Richards S, Aziz N, Bale S, Bick D, Das S, Gastier-Foster J, Grody WW, Hegde M et al 2015 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. Genet. Med. 17 405–424CrossRefGoogle Scholar
  57. Rossing K, Mischak H, Parving HH, Christensen PK, Walden M, Hillmann M and Kaiser T 2005 Impact of diabetic nephropathy and angiotensin II receptor blockade on urinary polypeptide patterns. Kidney Int. 68 193–205CrossRefGoogle Scholar
  58. Sanghera DK, Nath SK, Ortega L, Gambarelli M, Kim-Howard X, Singh JR, Ralhan SK, Wander GS et al. 2008a TCF7L2 polymorphisms are associated with type 2 diabetes in Khatri Sikhs from North India: genetic variation affects lipid levels. Ann. Hum. Genet. 72 499–509CrossRefGoogle Scholar
  59. Sanghera DK, Ortega L, Han S, Singh J, Ralhan SK, Wander GS, Mehra NK, Mulvihill et al. 2008b Impact of nine common type 2 diabetes risk polymorphisms in Asian Indian Sikhs: PPARG2 Pro12Ala),IGF2BP2, TCF7L2 and FTO variants confer a significant risk. BMC Med. Genet. 9 59CrossRefGoogle Scholar
  60. Sas KM, Karnovsky A, Michailidis G and Pennathur S 2015 Metabolomics and diabetes analytical and computational approaches. Diabetes: 64 718–732CrossRefGoogle Scholar
  61. Savolainen O, Fagerberg B, Vendelbo Lind M, Sandberg A-S, Ross AB and Bergstroem G 2017 Biomarkers for predicting type 2 diabetes development can metabolomics improve on existing biomarkers? PLoS ONE 12 e0177738CrossRefGoogle Scholar
  62. Savic D, Ye H, Aneas I, Park SY, Bell GI and Nobrega M A 2011. Alterations in TCF7L2 expression define its role as a key regulator of glucose metabolism. Genome Res. 21 1417–1425CrossRefGoogle Scholar
  63. Saxena R, Saleheen D, Been LF, Garavito ML, Braun T, Bjonnes A, Young R, Ho WK et al 2013 Genome-wide association study identifies a novel locus contributing to type 2 diabetes susceptibility in Sikhs of Punjabi origin from India. Diabetes 62 1746–1755CrossRefGoogle Scholar
  64. Scalbert A, Brennan L, Fiehn O, Hankemeier T, Kristal BB,van Ommen B, Pujos-Guillot E, Verheij E et al. 2009 Mass-spectrometry-based metabolomics limitations and recommendations for future progress with particular focus on nutrition research. Metabolomics 5 435–458CrossRefGoogle Scholar
  65. Scott LJ, Bonnycastle LL, Willer CJ, Sprau AG, Jackson AU, Narisu N, Duren WL, Chines PS et al. 2006 Association of transcription factor 7- like 2 (TCF7L2) variants with Type 2 diabetes in a Finnish sample. Diabetes 55 2649–2653CrossRefGoogle Scholar
  66. Scott LJ, Erdos MR, Huyghe JR, Welch RP, Beck AT, Wolford BN, Chines PS and Didion JP et al. 2016 The genetic regulatory signature of type 2 diabetes in human skeletal muscle. Nat. Commun. 29 11764CrossRefGoogle Scholar
  67. Segerstolpe Å, Palasantza A, Eliasson P, Andersson EM, Andréasson AC, Sun X, Picelli S and Sabirsh A et al. 2016 Single-cell transcriptome profiling of human pancreatic islets in health and type 2 diabetes. Cell Metab. 24 593–607CrossRefGoogle Scholar
  68. Sidoli S, Kulej K and Garcia BA 2017 Why proteomics is not the new genomics and the future of mass spectrometry in cell biology. J. Cell Biol. 216 21–24CrossRefGoogle Scholar
  69. Silander K, Tang H, Myles S, Jakkula E, Timpson NJ, Cavalli-Sforza L and Peltonen L 2009 Worldwide patterns of haplotype diversity at 9p21.3, a locus associated with type 2 diabetes and coronary heart disease. Genome Med. 1 51CrossRefGoogle Scholar
  70. Sonksen P and Sonksen J 2000 Insulin understanding its action in health and disease. Br. J. Anaesth. 85 69–79CrossRefGoogle Scholar
  71. Stephen LA, Kathy B, Barb S and Laura W 2004 Glucose metabolism and regulation beyond insulin and glucagon. Diabetes Spec. 17 183–190CrossRefGoogle Scholar
  72. Tabassum R, Chauhan G, Dwivedi OP, Mahajan A, Jaiswal A, Kaur I, Bandesh K, Singh T, et al. 2013 Genome-wide association study for type 2 diabetes in Indians identifies a new susceptibility locus at 2q21. Diabetes 62 977–86CrossRefGoogle Scholar
  73. Tibaldi JM 2013 The future of insulin therapy for patients with type 2 diabetes mellitus. J. Am. Osteopath. Assoc. 113 S29–S39PubMedGoogle Scholar
  74. Trougakos IP, Poulakoua M, Stathatosa M, Chalikiab A, Melidonisb A and Gonosa FS 2002 Serum levels of the senescence biomarker clusterin/apolipoprotein J increase significantly in diabetes type II and during development of coronary heart disease or at myocardial infarction. Exp, Gerontol. 37 1175–1187CrossRefGoogle Scholar
  75. Uma Jyothi K, Jayaraj M, Subburaj KS, Prasad KJ, Kumuda I, Lakshmi V and Reddy BM 2013 Association of TCF7L2 gene polymorphisms with T2DM in the population of Hyderabad, India. PLoS ONE 8 e60212CrossRefGoogle Scholar
  76. Uma Jyothi K and Reddy BM 2015 Gene–gene and gene–environment interactions in the etiology of type 2 diabetes mellitus in the population of Hyderabad, India. Meta Gene 5 9–20CrossRefGoogle Scholar
  77. Unnikrishnan R, Anjana RM and Mohan V 2014 Diabetes in South Asians: is the phenotype different? Diabetes 63 53–55CrossRefGoogle Scholar
  78. Wang TJ, Ngo D, Psychogios N, DejamA, Larson MG, Vasan RS, Ghorbani A, O’Sullivan J et al. 2013 2-Aminoadipic acid is a biomarker for diabetes risk. J. Clin. Invest. 123 4309 –4317CrossRefGoogle Scholar
  79. Wang YJ, Schug J, Won KJ, Liu C, Naji A, Avrahami D, Golson ML and Kaestner KH 2016 Single-cell transcriptomics of the human endocrine pancreas. Diabetes 65 3028–38CrossRefGoogle Scholar
  80. Wang-Sattler R, Yu Z, Herder C, Messias AC, Floegel A, He Y, Heim K, Campillos M et al. 2012 Novel biomarkers for pre-diabetes identified by metabolomics. Mol. Syst. Biol. 8 615CrossRefGoogle Scholar
  81. Winnier DA, Fourcaudot M, Norton L, Abdul-Ghani MA, Hu SL, Farook VS, Coletta DK, Kumar S et al 2015 Transcriptomic identification of ADH1B as a novel candidate gene for obesity and insulin resistance in human adipose tissue in Mexican Americans from the Veterans Administration Genetic Epidemiology Study (VAGES). PLoS One 10 e0119941CrossRefGoogle Scholar
  82. Yang Z, Yang J, Liu w, Wu L, Zing L, Wang Y, Fan X and Cheng Y 2013 T2D@ZJU A knowledgebase integrating heterogeneous connections associated with type 2 diabetes mellitus. Database 2013 bat052.  https://doi.org/10.1093/database/bat052
  83. Yeh S-H, Chang W-C, Chuang H, Huang H-C, Liu R-T and Yang KD 2016 Differentiation of type 2 diabetes mellitus with different complications by proteomic analysis of plasma low abundance proteins. J. Diabetes Metab. Dis. 15 24CrossRefGoogle Scholar
  84. Zhang C, Qi L, Hunter DJ, Meigs ZB, Manson JAE, van Dam RM, Hu FB 2006 Variant of transcription factor 7-like 2 (TCFL2) gene and the risk of type 2 diabetes in large cohorts of US women and men. Diabetes 55 2645–2648CrossRefGoogle Scholar
  85. Zhao Y, Barrere-Cain RE and Yang X 2015 Nutritional systems biology of type 2 diabetes. Genes Nutr. 10 31CrossRefGoogle Scholar
  86. Zheng Y and Hu FB 2015 Comprehensive metabolomic profiling of type 2 diabetes. Clin. Chem. 61 45Google Scholar

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© Indian Academy of Sciences 2019

Authors and Affiliations

  • Battini Mohan Reddy
    • 1
    Email author
  • Rayabarapu Pranavchand
    • 1
  • S A A Latheef
    • 1
  1. 1.Department of GeneticsOsmania UniversityHyderabadIndia

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