Chronic kidney disease (CKD) affects 8 to 16% people worldwide, with an increasing incidence and prevalence of end-stage kidney disease (ESKD). The effective management of CKD is confounded by the inability to identify patients at high risk of progression while in early stages of CKD. To address this challenge, a renal biopsy transcriptome-driven approach was applied to develop noninvasive prognostic biomarkers for CKD progression. Expression of intrarenal transcripts was correlated with the baseline estimated glomerular filtration rate (eGFR) in 261 patients. Proteins encoded by eGFR-associated transcripts were tested in urine for association with renal tissue injury and baseline eGFR. The ability to predict CKD progression, defined as the composite of ESKD or 40% reduction of baseline eGFR, was then determined in three independent CKD cohorts. A panel of intrarenal transcripts, including epidermal growth factor (EGF), a tubule-specific protein critical for cell differentiation and regeneration, predicted eGFR. The amount of EGF protein in urine (uEGF) showed significant correlation (P < 0.001) with intrarenal EGF mRNA, interstitial fibrosis/tubular atrophy, eGFR, and rate of eGFR loss. Prediction of the composite renal end point by age, gender, eGFR, and albuminuria was significantly (P < 0.001) improved by addition of uEGF, with an increase of the C-statistic from 0.75 to 0.87. Outcome predictions were replicated in two independent CKD cohorts. Our approach identified uEGF as an independent risk predictor of CKD progression. Addition of uEGF to standard clinical parameters improved the prediction of disease events in diverse CKD populations with a wide spectrum of causes and stages.

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Cell-lineage–specific transcripts are essential for differentiated tissue function, implicated in hereditary organ failure, and mediate acquired chronic diseases. However, experimental identification of cell-lineage–specific genes in a genome-scale manner is infeasible for most solid human tissues. We developed the first genome-scale method to identify genes with cell-lineage–specific expression, even in lineages not separable by experimental microdissection. Our machine-learning–based approach leverages high-throughput data from tissue homogenates in a novel iterative statistical framework. We applied this method to chronic kidney disease and identified transcripts specific to podocytes, key cells in the glomerular filter responsible for hereditary and most acquired glomerular kidney disease. In a systematic evaluation of our predictions by immunohistochemistry, our in silico approach was significantly more accurate (65% accuracy in human) than predictions based on direct measurement of in vivo fluorescence-tagged murine podocytes (23%). Our method identified genes implicated as causal in hereditary glomerular disease and involved in molecular pathways of acquired and chronic renal diseases. Furthermore, based on expression analysis of human kidney disease biopsies, we demonstrated that expression of the podocyte genes identified by our approach is significantly related to the degree of renal impairment in patients. Our approach is broadly applicable to define lineage specificity in both cell physiology and human disease contexts. We provide a user-friendly website that enables researchers to apply this method to any cell-lineage or tissue of interest. Identified cell-lineage–specific transcripts are expected to play essential tissue-specific roles in organogenesis and disease and can provide starting points for the development of organ-specific diagnostics and therapies.

Diabetic nephropathy (DN) is a frequent complication in patients with diabetes. Although the majority of DN models and human studies have focused on glomeruli, tubulointerstitial damage is a major feature of DN and an important predictor of renal dysfunction. This study sought to investigate molecular markers of pathogenic pathways in the renal interstitium of patients with DN. Microdissected tubulointerstitial compartments from biopsies with established DN and control kidneys were subjected to expression profiling. Analysis of candidate genes, potentially involved in DN on the basis of common hypotheses, identified 49 genes with significantly altered expression levels in established DN in comparison with controls. In contrast to some rodent models, the growth factors vascular endothelial growth factor A (VEGF-A) and epidermal growth factor (EGF) showed a decrease in mRNA expression in DN. This was validated on an independent cohort of patients with DN by real-time reverse transcriptase-PCR. Immunohistochemical staining for VEGF-A and EGF also showed a reduced expression in DN. The decrease of renal VEGF-A expression was associated with a reduction in peritubular capillary densities shown by platelet-endothelial cell adhesion molecule-1/CD31 staining. Furthermore, a significant inverse correlation between VEGF-A and proteinuria, as well as EGF and proteinuria, and a positive correlation between VEGF-A and hypoxia-inducible factor-1␣ mRNA was found. Thus, in human DN, a decrease of VEGF-A, rather than the reported increase as described in some rodent models, may contribute to the progressive disease. These findings and the questions about rodent models in DN raise a note of caution regarding the proposal to inhibit VEGF-A to prevent progression of DN.