Skip to main content

Advertisement

SpringerLink
Log in

Defining human diabetic nephropathy on the molecular level: Integration of transcriptomic profiles with biological knowledge

  • Published:
Reviews in Endocrine and Metabolic Disorders Aims and scope Submit manuscript

Abstract

Diabetic nephropathy (DN) is the most common cause for end stage renal disease (ESRD). Next to environmental factors, genetic predispositions determine the susceptibility for DN and its rate of progression to ESRD. With the availability of genome wide expression profiling we have the opportunity to define relevant pathways activated in the individual diabetic patient, integrating both environmental exposure and genetic background. In this review we summarize current understanding of how to link comprehensive gene expression data sets with biomedical knowledge and present strategies to build a transcriptional network of DN. Information about the individual disease processes of DN might allow the implementation of a personalized molecular medicine approach with mechanism-based patient management. Web based search engines like Nephromine are essential tools to facilitate access to molecular data of genomics, proteomics and metabolomics of DN.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. USRDS. 2007 Annual Data Report: Atlas of Chronic Kidney Disease and End-Stage Renal Disease in the United States. Bethseda, MD: National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases; 2007.

    Google Scholar 

  2. Ritz E, Orth SR. Nephropathy in patients with type 2 diabetes mellitus. N Engl J Med. 1999;341:1127–33. doi:10.1056/NEJM199910073411506.

    Article  PubMed  CAS  Google Scholar 

  3. Patel A, MacMahon S, Chalmers J, Neal B, et al. Intensive blood glucose control and vascular outcomes in patients with type 2 diabetes. N Engl J Med. 2008;358:2560–72. doi:10.1056/NEJMicm066227.

    Article  PubMed  CAS  Google Scholar 

  4. American Diabetes Association. Economic costs of diabetes in the U.S. in 2007. Diabetes Care 2008;31:596–615. doi:10.2337/dc08-9017.

    Article  Google Scholar 

  5. Iyengar SK, Schelling JR, Sedor JR. Approaches to understanding susceptibility to nephropathy: from genetics to genomics. Kidney Int. 2002;61:S61–67. doi:10.1046/j.1523-1755.2002.0610s1061.x.

    Article  PubMed  Google Scholar 

  6. Ng DP, Krolewski AS. Molecular genetic approaches for studying the etiology of diabetic nephropathy. Curr Mol Med. 2005;5:509–25. doi:10.2174/1566524054553504.

    Article  PubMed  CAS  Google Scholar 

  7. Rogus JJ, Poznik GD, Pezzolesi MG, Smiles AM, et al. High-density SNP genome wide linkage scan for susceptibility genes for diabetic nephropathy in type 1 diabetes: discordant sib-pair approach. Diabetes 2008. doi:10.2337/db07-1086.

  8. Bleyer AJ, Sedor JR, Freedman BI, O’Brien A, et al. Risk factors for development and progression of diabetic kidney disease and treatment patterns among diabetic siblings of patients with diabetic kidney disease. Am J Kidney Dis. 2008;51:29–37. doi:10.1053/j.ajkd.2007.10.029.

    Article  PubMed  Google Scholar 

  9. Schelling JR, Abboud HE, Nicholas SB, Pahl MV, et al. Genome-wide scan for estimated glomerular filtration rate in multi-ethnic diabetic populations: the Family Investigation of Nephropathy and Diabetes (FIND). Diabetes. 2008;57:235–43. doi:10.2337/db07-0313.

    Article  PubMed  CAS  Google Scholar 

  10. Tanaka N, Babazono T. Assessing genetic susceptibility to diabetic nephropathy. Nephrology (Carlton) 2005;10(Suppl):S17–21. doi:10.1111/j.1440-1797.2005.00451.x.

    Article  CAS  Google Scholar 

  11. Iyengar SK, Adler SG. The application of the HapMap to diabetic nephropathy and other causes of chronic renal failure. Semin Nephrol. 2007;27:223–36. doi:10.1016/j.semnephrol.2007.01.003.

    Article  PubMed  CAS  Google Scholar 

  12. Luetscher JA, Kraemer FB, Wilson DM, Schwartz HC, et al. Increased plasma inactive renin in diabetes mellitus. A marker of microvascular complications. N Engl J Med. 1985;312:1412–7.

    PubMed  CAS  Google Scholar 

  13. Sarafidis PA, Stafylas PC, Kanaki AI, Lasaridis AN. Effects of renin–angiotensin system blockers on renal outcomes and all-cause mortality in patients with diabetic nephropathy: an updated meta-analysis. Am J Hypertens. 2008;21(8):922–9.

    Article  PubMed  CAS  Google Scholar 

  14. Parving HH, Persson F, Lewis JB, Lewis EJ, et al. Aliskiren combined with losartan in type 2 diabetes and nephropathy. N Engl J Med. 2008;358:2433–46. doi:10.1056/NEJMoa0708379.

    Article  PubMed  CAS  Google Scholar 

  15. Rosario RF, Prabhakar S. Lipids and diabetic nephropathy. Curr Diab Rep. 2006;6:455–62. doi:10.1007/s11892-006-0079-7.

    Article  PubMed  CAS  Google Scholar 

  16. Forbes JM, Fukami K, Cooper ME. Diabetic nephropathy: where hemodynamics meets metabolism. Exp Clin Endocrinol Diabetes. 2007;115:69–84. doi:10.1055/s-2007-949721.

    Article  PubMed  CAS  Google Scholar 

  17. Maric C. Vasoactive hormones and the diabetic kidney. ScientificWorldJournal. 2008;8:470–85. doi:10.1100/tsw.2008.57.

    Article  PubMed  CAS  Google Scholar 

  18. Wolf G, Ziyadeh FN. Cellular and molecular mechanisms of proteinuria in diabetic nephropathy. Nephron Physiol. 2007;106:26–31. doi:10.1159/000101797.

    Article  Google Scholar 

  19. Niehof M, Borlak J. HNF4 alpha and the Ca-channel TRPC1 are novel disease candidate genes in diabetic nephropathy. Diabetes. 2008;57:1069–77. doi:10.2337/db07-1065.

    Article  PubMed  CAS  Google Scholar 

  20. Sieberts SK, Schadt EE. Moving toward a system genetics view of disease. Mamm Genome. 2007;18:389–401. doi:10.1007/s00335-007-9040-6.

    Article  PubMed  Google Scholar 

  21. Morrison N, Cochrane G, Faruque N, Tatusova T, et al. Concept of sample in OMICS technology. OMICS. 2006;10:127–37. doi:10.1089/omi.2006.10.127.

    Article  PubMed  CAS  Google Scholar 

  22. Malinowski DP. Multiple biomarkers in molecular oncology. II. Molecular diagnostics applications in breast cancer management. Expert Rev Mol Diagn. 2007;7:269–80. doi:10.1586/14737159.7.3.269.

    Article  PubMed  CAS  Google Scholar 

  23. Martinelli G, Iacobucci I, Soverini S, Cilloni D, et al. Monitoring minimal residual disease and controlling drug resistance in chronic myeloid leukaemia patients in treatment with imatinib as a guide to clinical management. Hematol Oncol. 2006;24:196–204. doi:10.1002/hon.792.

    Article  PubMed  CAS  Google Scholar 

  24. Sanga S, Frieboes HB, Zheng X, Gatenby R, et al. Predictive oncology: a review of multidisciplinary, multiscale in silico modeling linking phenotype, morphology and growth. Neuroimage. 2007;37(Suppl 1):S120–134. doi:10.1016/j.neuroimage.2007.05.043.

    Article  PubMed  Google Scholar 

  25. Wulfkuhle JD, Speer R, Pierobon M, Laird J, et al. Multiplexed cell signaling analysis of human breast cancer applications for personalized therapy. J Proteome Res. 2008;7:1508–17. doi:10.1021/pr7008127.

    Article  PubMed  CAS  Google Scholar 

  26. Kretzler M, Cohen CD, Doran P, Henger A, et al. Repuncturing the renal biopsy: strategies for molecular diagnosis in nephrology. J Am Soc Nephrol. 2002;13:1961–72. doi:10.1097/01.ASN.0000020390.29418.70.

    Article  PubMed  Google Scholar 

  27. Chabardes-Garonne D, Mejean A, Aude JC, Cheval L, et al. A panoramic view of gene expression in the human kidney. Proc Natl Acad Sci U S A. 2003;100:13710–5. doi:10.1073/pnas.2234604100.

    Article  PubMed  CAS  Google Scholar 

  28. Chan YH. Biostatistics. 302 Principal component and factor analysis. Singapore Med J. 2004;45:558–65.

    PubMed  CAS  Google Scholar 

  29. Gasch AP, Eisen MB. Exploring the conditional coregulation of yeast gene expression through fuzzy k-means clustering. Genome Biol. 2002;3(11):1–22.

    Article  Google Scholar 

  30. Qin ZS. Clustering microarray gene expression data using weighted Chinese restaurant process. Bioinformatics. 2006;22:1988–97. doi:10.1093/bioinformatics/btl284.

    Article  PubMed  CAS  Google Scholar 

  31. Kanehisa M. The KEGG database. Novartis Found Symp. 2002;247:91–101. discussion 101–103, 119–128, 244–152.

    Article  PubMed  CAS  Google Scholar 

  32. Werner T. Bioinformatics applications for pathway analysis of microarray data. Curr Opin Biotechnol. 2008;19:50–4. doi:10.1016/j.copbio.2007.11.005.

    Article  PubMed  CAS  Google Scholar 

  33. Schena M, Shalon D, Davis RW, Brown PO. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science. 1995;270:467–70. doi:10.1126/science.270.5235.467.

    Article  PubMed  CAS  Google Scholar 

  34. Baelde HJ, Eikmans M, Doran PP, Lappin DW, et al. Gene expression profiling in glomeruli from human kidneys with diabetic nephropathy. Am J Kidney Dis. 2004;43:636–50. doi:10.1053/j.ajkd.2003.12.028.

    Article  PubMed  CAS  Google Scholar 

  35. Qi W, Chen X, Gilbert RE, Zhang Y, et al. High glucose-induced thioredoxin-interacting protein in renal proximal tubule cells is independent of transforming growth factor-beta1. Am J Pathol. 2007;171:744–54. doi:10.2353/ajpath.2007.060813.

    Article  PubMed  CAS  Google Scholar 

  36. Panchapakesan U, Sumual S, Pollock CA, Chen X. PPARgamma agonists exert antifibrotic effects in renal tubular cells exposed to high glucose. Am J Physiol Renal Physiol. 2005;289:F1153–1158. doi:10.1152/ajprenal.00097.2005.

    Article  PubMed  CAS  Google Scholar 

  37. Srinivasan S, Bolick DT, Hatley ME, Natarajan R, et al. Glucose regulates interleukin-8 production in aortic endothelial cells through activation of the p38 mitogen-activated protein kinase pathway in diabetes. J Biol Chem. 2004;279:31930–6. doi:10.1074/jbc.M400753200.

    Article  PubMed  CAS  Google Scholar 

  38. Mora C, Navarro JF. Inflammation and diabetic nephropathy. Curr Diab Rep. 2006;6:463–8. doi:10.1007/s11892-006-0080-1.

    Article  PubMed  CAS  Google Scholar 

  39. Moczulski DK, Fojcik H, Wielgorecki A, Trautsolt W, et al. Expression pattern of genes in peripheral blood mononuclear cells in diabetic nephropathy. Diabet Med. 2007;24:266–71. doi:10.1111/j.1464-5491.2006.02067.x.

    Article  PubMed  CAS  Google Scholar 

  40. Tone A, Shikata K, Ogawa D, Sasaki S, et al. Changes of gene expression profiles in macrophages stimulated by angiotensin II–angiotensin II induces MCP-2 through AT1-receptor. J Renin Angiotensin Aldosterone Syst. 2007;8:45–50. doi:10.3317/jraas.2007.007.

    Article  PubMed  CAS  Google Scholar 

  41. Huang C, Kim Y, Caramori ML, Moore JH, et al. Diabetic nephropathy is associated with gene expression levels of oxidative phosphorylation and related pathways. Diabetes. 2006;55:1826–31. doi:10.2337/db05-1438.

    Article  PubMed  CAS  Google Scholar 

  42. de Borst MH, Benigni A, Remuzzi G. Primer: strategies for identifying genes involved in renal disease. Nat Clin Pract Nephrol. 2008;4:265–76. doi:10.1038/ncpneph0785.

    Article  PubMed  Google Scholar 

  43. Werner T, Fessele S, Maier H, Nelson PJ. Computer modeling of promoter organization as a tool to study transcriptional coregulation. FASEB J. 2003;17:1228–37. doi:10.1096/fj.02-0955rev.

    Article  PubMed  CAS  Google Scholar 

  44. Schmid H, Boucherot A, Yasuda Y, Henger A, et al. Modular activation of nuclear factor-kappaB transcriptional programs in human diabetic nephropathy. Diabetes. 2006;55:2993–3003. doi:10.2337/db06-0477.

    Article  PubMed  CAS  Google Scholar 

  45. Mezzano S, Aros C, Droguett A, Burgos ME, et al. NF-kappaB activation and overexpression of regulated genes in human diabetic nephropathy. Nephrol Dial Transplant. 2004;19:2505–12. doi:10.1093/ndt/gfh207.

    Article  PubMed  CAS  Google Scholar 

  46. Sakai N, Wada T, Furuichi K, Iwata Y, et al. Involvement of extracellular signal-regulated kinase and p38 in human diabetic nephropathy. Am J Kidney Dis. 2005;45:54–65. doi:10.1053/j.ajkd.2004.08.039.

    Article  PubMed  CAS  Google Scholar 

  47. Yang B, Hodgkinson A, Oates PJ, Millward BA, et al. High glucose induction of DNA-binding activity of the transcription factor NFkappaB in patients with diabetic nephropathy. Biochim Biophys Acta. 2008;1782:295–302.

    PubMed  CAS  Google Scholar 

  48. Nagai N, Izumi-Nagai K, Oike Y, Koto T, et al. Suppression of diabetes-induced retinal inflammation by blocking the angiotensin II type 1 receptor or its downstream nuclear factor-kappaB pathway. Invest Ophthalmol Vis Sci. 2007;48:4342–50. doi:10.1167/iovs.06-1473.

    Article  PubMed  Google Scholar 

  49. Yoshida A, Yoshida S, Ishibashi T, Kuwano M, et al. Suppression of retinal neovascularization by the NF-kappaB inhibitor pyrrolidine dithiocarbamate in mice. Invest Ophthalmol Vis Sci. 1999;40:1624–9.

    PubMed  CAS  Google Scholar 

  50. Chen L, Zhang J, Zhang Y, Wang Y, et al. Improvement of inflammatory responses associated with NF-kappaB pathway in kidneys from diabetic rats. Inflamm Res 2008;57(5):199–204.

    Article  PubMed  CAS  Google Scholar 

  51. Saisho Y, Hirose H, Horimai C, Miyashita K, et al. Effects of DHMEQ, a novel nuclear factor-kappab inhibitor, on beta cell dysfunction in INS-1 cells. Endocr J. 2008;55:433–8. doi:10.1507/endocrj.K07E-036.

    Article  PubMed  CAS  Google Scholar 

  52. Lorz C, Benito-Martin A, Boucherot A, Ucero AC, et al. The death ligand TRAIL in diabetic nephropathy. J Am Soc Nephrol. 2008;19:904–14. doi:10.1681/ASN.2007050581.

    Article  PubMed  CAS  Google Scholar 

  53. Rhodes DR, Kalyana-Sundaram S, Mahavisno V, Varambally R, et al. Oncomine 3.0: genes, pathways, and networks in a collection of 18,000 cancer gene expression profiles. Neoplasia. 2007;9:166–80. doi:10.1593/neo.07112.

    Article  PubMed  CAS  Google Scholar 

  54. Peng F, Wu D, Gao B, Ingram AJ, et al. RhoA/Rho-kinase contribute to the pathogenesis of diabetic renal disease. Diabetes. 2008;57:1683–92. doi:10.2337/db07-1149.

    Article  PubMed  CAS  Google Scholar 

Download references

Acknowledgements

We thank Anna Henger and Celine Berthier for helpful discussion and Chrysta Lienczewski for carefully proofreading the manuscript. This study was supported in part by NIHU54 DA021519 ‘National Center for Integrative Biomedical Informatics’ and by a MICHR Multidisciplinary Research Grant to MK.

Author information

Authors and Affiliations

  1. Division of Nephrology, Department of Internal Medicine, University of Michigan, 1150 W. Medical Center Drive, 1552 MSRB II, Ann Arbor, MI, 48109-0676, USA

    Sebastian Martini, Felix Eichinger, Viji Nair & Matthias Kretzler

Authors
  1. Sebastian Martini
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Felix Eichinger
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Viji Nair
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Matthias Kretzler
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Matthias Kretzler.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Martini, S., Eichinger, F., Nair, V. et al. Defining human diabetic nephropathy on the molecular level: Integration of transcriptomic profiles with biological knowledge. Rev Endocr Metab Disord 9, 267–274 (2008). https://doi.org/10.1007/s11154-008-9103-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11154-008-9103-3

Keywords

Search

Navigation