[1372] Genome-Wide mRNA Profiling To Define Shared Transcriptional Relationships and Renal Function in Multiple Renal Diseases

JB Hodgin, S Martini, S Bhavnani, A Henger, F Eichinger, CC Berthier, K Shedden, CD Cohen, M Kretzler, European Renal cDNA Bank Consortium. University of Michigan, Ann Arbor; University of Zurich

Background: Morphologic examination of renal biopsies provides key information for the diagnosis and effective therapeutic management of renal disease. However, prognostic information has been limited. An international multicenter study was established and protocols developed to identify and validate molecular diagnostic markers and outcome predictors. Human renal biopsies have been obtained from 24 medical centers spanning 4 continents. More than 2000 biopsies have undergone manual microdissection of nephron segments and are available for gene expression analysis.
Design: Affymetrix HG-U133 based gene expression profiles from nine different renal diseases (focal and segmental glomerulosclerosis (FSGS), minimal change disease, membranous nephropathy (MGN), diabetic nephropathy, lupus nephritis (LN), IgA nephropathy, thin basement membrane disease, and controls) has allowed a network analysis approach to define relatedness of major human diseases on a molecular level. A bipartite network analysis, using the renal disease entities and mRNA expression regulation as nodes, creates an easily navigable graph demonstrating similarity of renal diseases on the transcriptomic level, revealing a shared transcriptional response among some diseases (FSGS, MGN, and LN).
Results: The bipartite network confirmed established progression factors of renal disease (e. g. apoptosis). But it also may implicate specific disease mechanism. In addition, expression profiles reflect the functional status irrespective of the cause of renal disease. Kidney tissues from six different diseases with a wide spectrum of creatinine clearances were studied. Using the ridge regression model for glomerular filtration rate (GFR) prediction, a marker panel of 40 mRNAs correlated with the GFR independent of histopathology (r=0.78, p<0.001). This panel performed equally well in a second series with three independent disease cohorts (r=0.69, p<0.001). and allowed the classification of chronic kidney disease (CKD) stage III-V versus stage I-II with a sensitivity of 0.73 and specificity of 0.82. In an independent cohort this mRNA marker panel was able to correctly predict the CKD stage 36 months after renal biopsy (sensitivity 0.77, specificity 0.75).
Conclusions: This demonstrates the feasibility of using cross-sectional mRNA markers to predict disease progression. S Martini and S Bhavnani contributed equally.
Category: Kidney (does not include tumors)

Monday, March 9, 2009 8:30 AM

Platform Session: Section G 1, Monday Morning