Abstract
Chronic kidney disease has severe impacts on the patient and represents a major burden to the health care systems worldwide. Despite an increased knowledge of pathophysiological processes involved in kidney diseases, the progress in defining novel treatment strategies has been limited. One reason is the descriptive disease categorization used in nephrology based on clinical findings or histopathological categories irrespective of potential different molecular disease mechanisms. To accelerate progress toward a targeted treatment, a definition of human disease extending from phenotypic disease classification to mechanism-based disease definitions is needed. In recent years, we have witnessed a major transition in biomedical research from a single gene research to an information rich and collaborative science. Tissue-based analysis in renal disease allows to link structure to molecular function. In our review, we introduce the concept of precision medicine in nephrology, describe several large cohort studies established for molecular analysis of kidney diseases, and highlight examples of renal biopsy-driven target identification by integrative systems biology approaches. Furthermore, we give an outlook on how the new disease definitions can be used for patient stratification in clinical trial design. Finally, we introduce the concept of an informational commons of renal precision medicine for joint analyses of large-scale data sets in renal failure.
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Ayeni D, Politi K, Goldberg SB (2015) Emerging agents and new mutations in EGFR-mutant lung cancer. Clin Cancer Res 21:3818–3820. doi:10.1158/1078-0432.CCR-15-1211
Barisoni L, Gimpel C, Kain R, Laurinavicius A, Bueno G, Caihong Z, Zhihong L, Schaefer F, Kretzler M, Holzman LB, Hewitt SM (2017) Digital pathology imaging as a novel platform for standardization and globalization of quantitative nephropathology. Clin Kidney J 10:176–187
Barisoni L, Nast CC, Jennette JC, Hodgin JB, Herzenberg AM, Lemley KV, Conway CM, Kopp JB, Kretzler M, Lienczewski C, Avila-Casado C, Bagnasco S, Sethi S, Tomaszewski J, Gasim AH, Hewitt SM (2013) Digital pathology evaluation in the multicenter nephrotic syndrome study network (NEPTUNE). Clin J Am Soc Nephrol 8:1449–1459. doi:10.2215/CJN.08370812
Baron M, Veres A, Wolock SL, Faust AL, Gaujoux R, Vetere A, Ryu JH, Wagner BK, Shen-Orr SS, Klein AM, Melton DA, Yanai I (2016) A single-cell transcriptomic map of the human and mouse pancreas reveals inter- and intra-cell population structure. Cell Syst 3:346–360 e344. doi:10.1016/j.cels.2016.08.011
Betz B, Conway BR (2016) An update on the use of animal models in diabetic nephropathy research. Curr Diab Rep 16:18. doi:10.1007/s11892-015-0706-2
Boerries M, Grahammer F, Eiselein S, Buck M, Meyer C, Goedel M, Bechtel W, Zschiedrich S, Pfeifer D, Laloe D, Arrondel C, Goncalves S, Kruger M, Harvey SJ, Busch H, Dengjel J, Huber TB (2013) Molecular fingerprinting of the podocyte reveals novel gene and protein regulatory networks. Kidney Int 83:1052–1064. doi:10.1038/ki.2012.487
Brunskill EW, Park JS, Chung E, Chen F, Magella B, Potter SS (2014) Single cell dissection of early kidney development: multilineage priming. Development 141:3093–3101. doi:10.1242/dev.110601
Buettner R, Wolf J, Thomas RK (2013) Lessons learned from lung cancer genomics: the emerging concept of individualized diagnostics and treatment. J Clin Oncol 31:1858–1865. doi:10.1200/JCO.2012.45.9867
Cohen CD, Frach K, Schlondorff D, Kretzler M (2002) Quantitative gene expression analysis in renal biopsies: a novel protocol for a high-throughput multicenter application. Kidney Int 61:133–140. doi:10.1046/j.1523-1755.2002.00113.x
Collins FS, Varmus H (2015) A new initiative on precision medicine. N Engl J Med 372:793–795. doi:10.1056/NEJMp1500523
de Zeeuw D, Bekker P, Henkel E, Hasslacher C, Gouni-Berthold I, Mehling H, Potarca A, Tesar V, Heerspink HJ, Schall TJ (2015) The effect of CCR2 inhibitor CCX140-B on residual albuminuria in patients with type 2 diabetes and nephropathy: a randomised trial. Lancet Diabetes Endocrinol 3:687–696. doi:10.1016/S2213-8587(15)00261-2
Fernandes M, Husi H (2017) Establishment of a integrative multi-omics expression database CKDdb in the context of chronic kidney disease (CKD). Sci Rep 7:40367. doi:10.1038/srep40367
Freedman BI, Bowden DW, Sale MM, Langefeld CD, Rich SS (2006) Genetic susceptibility contributes to renal and cardiovascular complications of type 2 diabetes mellitus. Hypertension 48:8–13. doi:10.1161/01.HYP.0000227047.26988.3e
Fu J, Wei C, Lee K, Zhang W, He W, Chuang P, Liu Z, He JC (2016) Comparison of glomerular and podocyte mRNA profiles in Streptozotocin-induced diabetes. J Am Soc Nephrol 27:1006–1014. doi:10.1681/ASN.2015040421
Gadegbeku CA, Gipson DS, Holzman LB, Ojo AO, Song PX, Barisoni L, Sampson MG, Kopp JB, Lemley KV, Nelson PJ, Lienczewski CC, Adler SG, Appel GB, Cattran DC, Choi MJ, Contreras G, Dell KM, Fervenza FC, Gibson KL, Greenbaum LA, Hernandez JD, Hewitt SM, Hingorani SR, Hladunewich M, Hogan MC, Hogan SL, Kaskel FJ, Lieske JC, Meyers KE, Nachman PH, Nast CC, Neu AM, Reich HN, Sedor JR, Sethna CB, Trachtman H, Tuttle KR, Zhdanova O, Zilleruelo GE, Kretzler M (2013) Design of the Nephrotic Syndrome Study Network (NEPTUNE) to evaluate primary glomerular nephropathy by a multidisciplinary approach. Kidney Int 83:749–756. doi:10.1038/ki.2012.428
Gohda T, Niewczas MA, Ficociello LH, Walker WH, Skupien J, Rosetti F, Cullere X, Johnson AC, Crabtree G, Smiles AM, Mayadas TN, Warram JH, Krolewski AS (2012) Circulating TNF receptors 1 and 2 predict stage 3 CKD in type 1 diabetes. J Am Soc Nephrol 23:516–524. doi:10.1681/ASN.2011060628
Hirakawa Y, Tanaka T, Nangaku M (2017) Mechanisms of metabolic memory and renal hypoxia as a therapeutic target in diabetic kidney disease. J Diabetes Investig. doi:10.1111/jdi.12624
Hodgin JB, Nair V, Zhang H, Randolph A, Harris RC, Nelson RG, Weil EJ, Cavalcoli JD, Patel JM, Brosius FC 3rd, Kretzler M (2013) Identification of cross-species shared transcriptional networks of diabetic nephropathy in human and mouse glomeruli. Diabetes 62:299–308. doi:10.2337/db11-1667
Hu FB, Satija A, Manson JE (2015) Curbing the diabetes pandemic: the need for global policy solutions. JAMA 313:2319–2320. doi:10.1001/jama.2015.5287
Jha V, Garcia-Garcia G, Iseki K, Li Z, Naicker S, Plattner B, Saran R, Wang AY, Yang CW (2013) Chronic kidney disease: global dimension and perspectives. Lancet 382:260–272. doi:10.1016/S0140-6736(13)60687-X
Ju W, Nair V, Smith S, Zhu L, Shedden K, Song PX, Mariani LH, Eichinger FH, Berthier CC, Randolph A, Lai JY, Zhou Y, Hawkins JJ, Bitzer M, Sampson MG, Thier M, Solier C, Duran-Pacheco GC, Duchateau-Nguyen G, Essioux L, Schott B, Formentini I, Magnone MC, Bobadilla M, Cohen CD, Bagnasco SM, Barisoni L, Lv J, Zhang H, Wang HY, Brosius FC, Gadegbeku CA, Kretzler M, Ercb CPN, Consortium PK-I (2015) Tissue transcriptome-driven identification of epidermal growth factor as a chronic kidney disease biomarker. Sci Transl Med 7:316ra193. doi:10.1126/scitranslmed.aac7071
Keller BJ, Martini S, Sedor JR, Kretzler M (2012) A systems view of genetics in chronic kidney disease. Kidney Int 81:14–21. doi:10.1038/ki.2011.359
Kikuchi M, Wickman L, Hodgin JB, Wiggins RC (2015) Podometrics as a potential clinical tool for glomerular disease management. Semin Nephrol 35:245–255. doi:10.1016/j.semnephrol.2015.04.004
Klein J, Jupp S, Moulos P, Fernandez M, Buffin-Meyer B, Casemayou A, Chaaya R, Charonis A, Bascands JL, Stevens R, Schanstra JP (2012) The KUPKB: a novel web application to access multiomics data on kidney disease. FASEB J 26:2145–2153. doi:10.1096/fj.11-194381
Ledo N, Ko YA, Park AS, Kang HM, Han SY, Choi P, Susztak K (2015) Functional genomic annotation of genetic risk loci highlights inflammation and epithelial biology networks in CKD. J Am Soc Nephrol 26:692–714. doi:10.1681/ASN.2014010028
Levey AS, Coresh J (2012) Chronic kidney disease. Lancet 379:165–180. doi:10.1016/S0140-6736(11)60178-5
Levin A, Tonelli M, Bonventre J, Coresh J, Donner JA, Fogo AB, Fox CS, Gansevoort RT, Heerspink HJL, Jardine M, Kasiske B, Kottgen A, Kretzler M, Levey AS, Luyckx VA, Mehta R, Moe O, Obrador G, Pannu N, Parikh CR, Perkovic V, Pollock C, Stenvinkel P, Tuttle KR, Wheeler DC, Eckardt KU (2017) Global kidney health 2017 and beyond: a roadmap for closing gaps in care, research, and policy. Lancet. doi:10.1016/S0140-6736(17)30788-2
Mariani LH, Martini S, Barisoni L, Canetta PA, Troost JP, Hodgin JB, Palmer M, Rosenberg AZ, Lemley KV, Chien HP, Zee J, Smith A, Appel GB, Trachtman H, Hewitt SM, Kretzler M, Bagnasco SM (2017) Interstitial fibrosis scored on whole-slide digital imaging of kidney biopsies is a predictor of outcome in proteinuric glomerulopathies. Nephrol Dial Transplant. doi:10.1093/ndt/gfw443
Martini S, Nair V, Keller BJ, Eichinger F, Hawkins JJ, Randolph A, Boger CA, Gadegbeku CA, Fox CS, Cohen CD, Kretzler M, European Renal c DNAB, Cohort CP, Consortium CK (2014) Integrative biology identifies shared transcriptional networks in CKD. J Am Soc Nephrol 25:2559–2572. doi:10.1681/ASN.2013080906
Menne J, Eulberg D, Beyer D, Baumann M, Saudek F, Valkusz Z, Wiecek A, Haller H (2016) C-C motif-ligand 2 inhibition with emapticap pegol (NOX-E36) in type 2 diabetic patients with albuminuria. Nephrol Dial Transplant. doi:10.1093/ndt/gfv459
Niewczas MA, Gohda T, Skupien J, Smiles AM, Walker WH, Rosetti F, Cullere X, Eckfeldt JH, Doria A, Mayadas TN, Warram JH, Krolewski AS (2012) Circulating TNF receptors 1 and 2 predict ESRD in type 2 diabetes. J Am Soc Nephrol 23:507–515. doi:10.1681/ASN.2011060627
Pavkov ME, Knowler WC, Hanson RL, Nelson RG (2008) Diabetic nephropathy in American Indians, with a special emphasis on the pima Indians. Curr Diab Rep 8:486–493
Pavkov ME, Nelson RG, Knowler WC, Cheng Y, Krolewski AS, Niewczas MA (2015) Elevation of circulating TNF receptors 1 and 2 increases the risk of end-stage renal disease in American Indians with type 2 diabetes. Kidney Int 87:812–819. doi:10.1038/ki.2014.330
Schmid H, Boucherot A, Yasuda Y, Henger A, Brunner B, Eichinger F, Nitsche A, Kiss E, Bleich M, Grone HJ, Nelson PJ, Schlondorff D, Cohen CD, Kretzler M, European Renal c DNABC (2006) Modular activation of nuclear factor-kappaB transcriptional programs in human diabetic nephropathy. Diabetes 55:2993–3003. doi:10.2337/db06-0477
Shen-Orr SS, Gaujoux R (2013) Computational deconvolution: extracting cell type-specific information from heterogeneous samples. Curr Opin Immunol 25:571–578. doi:10.1016/j.coi.2013.09.015
Shen-Orr SS, Tibshirani R, Khatri P, Bodian DL, Staedtler F, Perry NM, Hastie T, Sarwal MM, Davis MM, Butte AJ (2010) Cell type-specific gene expression differences in complex tissues. Nat Methods 7:287–289. doi:10.1038/nmeth.1439
Speeckaert MM, Speeckaert R, Laute M, Vanholder R, Delanghe JR (2012) Tumor necrosis factor receptors: biology and therapeutic potential in kidney diseases. Am J Nephrol 36:261–270. doi:10.1159/000342333
Teng J, Dwyer KM, Hill P, See E, Ekinci EI, Jerums G, MacIsaac RJ (2014) Spectrum of renal disease in diabetes. Nephrology (Carlton) 19:528–536. doi:10.1111/nep.12288
Theilig F (2010) Spread of glomerular to tubulointerstitial disease with a focus on proteinuria. Ann Anat 192:125–132. doi:10.1016/j.aanat.2010.03.003
Tuttle KB, Brosius FC, Adler SG, Kretzler M, Mehta RL, Tumlin JA, Liu J, Silk ME, Cardillo TE, Duffin KL, Haas JV, Macias WL, Janes JM (2015) Baricitinib in diabetic kidney disease: results from a phase 2 multicenter, randomized double-blind, placebo-controlled study. In: 75th Scientific Sessions of the American Diabetes Association, Boston, MA (Abstract) 114-LB
USRDS (2016) United States Renal Data System. 2016 USRDS annual data report: epidemiology of kidney disease in the United States. National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD. http://www.usrds.org/atlas.aspx.
Walsh M, Sar A, Lee D, Yilmaz S, Benediktsson H, Manns B, Hemmelgarn B (2010) Histopathologic features aid in predicting risk for progression of IgA nephropathy. Clin J Am Soc Nephrol 5:425–430. doi:10.2215/CJN.06530909
Weil EJ, Fufaa G, Jones LI, Lovato T, Lemley KV, Hanson RL, Knowler WC, Bennett PH, Yee B, Myers BD, Nelson RG (2013) Effect of losartan on prevention and progression of early diabetic nephropathy in American Indians with type 2 diabetes. Diabetes 62:3224–3231. doi:10.2337/db12-1512
Werner T (2003) Promoters can contribute to the elucidation of protein function. Trends Biotechnol 21:9–13
WHO WHO (2016) Global report on diabetes. http://www.who.int/diabetes/publications/grd-2016/en/
Wickman L, Afshinnia F, Wang SQ, Yang Y, Wang F, Chowdhury M, Graham D, Hawkins J, Nishizono R, Tanzer M, Wiggins J, Escobar GA, Rovin B, Song P, Gipson D, Kershaw D, Wiggins RC (2013) Urine podocyte mRNAs, proteinuria, and progression in human glomerular diseases. J Am Soc Nephrol 24:2081–2095. doi:10.1681/ASN.2013020173
Wolkow PP, Niewczas MA, Perkins B, Ficociello LH, Lipinski B, Warram JH, Krolewski AS (2008) Association of urinary inflammatory markers and renal decline in microalbuminuric type 1 diabetics. J Am Soc Nephrol 19:789–797. doi:10.1681/ASN.2007050556
Zhang H, Saha J, Atkins KB, Brosius FC (2012) Podocyte JAK2 augments glomerular injury induced by diabetes and angiotensin II. J Am Soc Nephrol (Abstract) 23:203A
Zhang Q, Yang B, Chen X, Xu J, Mei C, Mao Z (2014) Renal Gene expression database (RGED): a relational database of gene expression profiles in kidney disease. Database (Oxford) 2014. doi:10.1093/database/bau092
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This work has been supported by the Else-Kröner-Fresenius Foundation and the European Consortium for High-Throughput Research in Rare Kidney Diseases (EURenOmics; European Union FP 7:305608).
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This article is part of the special issue on Functional Anatomy of the Kidney in Health and Disease in Pflügers Archiv—European Journal of Physiology.
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Lindenmeyer, M.T., Kretzler, M. Renal biopsy-driven molecular target identification in glomerular disease. Pflugers Arch - Eur J Physiol 469, 1021–1028 (2017). https://doi.org/10.1007/s00424-017-2006-y
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DOI: https://doi.org/10.1007/s00424-017-2006-y