Felix Eichinger

Modular Activation of Nuclear Factor-κB Transcriptional Programs in Human Diabetic Nephropathy

Diabetic nephropathy (DN) is the leading cause of end-stage renal failure and a major risk factor for cardiovascular mortality in diabetic patients. To evaluate the multiple pathogenetic factors implicated in DN, unbiased mRNA expression screening of tubulointerstitial compartments of human renal biopsies was combined with hypothesis-driven pathway analysis. Expression fingerprints obtained from biopsies with histological diagnosis of DN (n = 13) and from control subjects (pretransplant kidney donors [n = 7] and minimal change disease [n = 4]) allowed us to segregate the biopsies by disease state and stage by the specific expression signatures. Functional categorization showed regulation of genes linked to inflammation in progressive DN. Pathway mapping of nuclear factor-κB (NF-κB), a master transcriptional switch in inflammation, segregated progressive from mild DN and control subjects by showing upregulation of 54 of 138 known NF-κB targets. The promoter regions of regulated NF-κB targets were analyzed using ModelInspector, and the NF-κB module NFKB_IRFF_01 was found to be specifically enriched in progressive disease. Using this module, the induction of eight NFKB_IRFF_01–dependant genes was correctly predicted in progressive DN (B2M, CCL5/RANTES, CXCL10/IP10, EDN1, HLA-A, HLA-B, IFNB1, and VCAM1). The identification of a specific NF-κB promoter module activated in the inflammatory stress response of progressive DN has helped to characterize upstream pathways as potential targets for the treatment of progressive renal diseases such as DN.

Interstitial Vascular Rarefaction and Reduced VEGF-A Expression in Human Diabetic Nephropathy

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-1alpha 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.

Tissue transcriptome-driven identification of epidermal growth factor as a chronic kidney disease biomarker

Renal and urinary EGF can serve as biomarkers for prediction of outcomes in chronic kidney disease. Urine marker to the rescue Chronic kidney disease is a common medical problem worldwide, but it is difficult to predict which patients are more likely to progress to end-stage disease and need aggressive management. Ju et al. have now drawn on four independent cohorts totaling hundreds of patients from around the world to identify the expression of epidermal growth factor (EGF) in the kidneys as a marker of kidney disease progression. Moreover, the authors demonstrated that the amount of EGF in the urine is just as useful, providing a biomarker that can be easily tracked over time without requiring invasive biopsies. 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.

A Molecular Profile of Focal Segmental Glomerulosclerosis from Formalin-Fixed, Paraffin-Embedded Tissue

Focal segmental glomerulosclerosis (FSGS) is a common form of idiopathic nephrotic syndrome defined by the characteristic lesions of focal glomerular sclerosis and foot process effacement; however, its etiology and pathogenesis are unknown. We used mRNA isolated from laser-captured glomeruli from archived formalin-fixed, paraffin-embedded renal biopsies, until recently considered an unsuitable source of mRNA for microarray analysis, to investigate the glomerular gene expression profiles of patients with primary classic FSGS, collapsing FSGS (COLL), minimal change disease (MCD), and normal controls (Normal). Amplified mRNA was hybridized to an Affymetrix Human X3P array. Unsupervised (unbiased) hierarchical clustering revealed two distinct clusters delineating FSGS and COLL from Normal and MCD. Class comparison analysis of FSGS + COLL combined versus Normal + MCD revealed 316 significantly differentially regulated genes (134 up-regulated, 182 down-regulated). Among the differentially regulated genes were those known to be part of the slit diaphragm junctional complex and those previously described in the dysregulated podocyte phenotype. Analysis based on Gene Ontology categories revealed overrepresented biological processes of development, differentiation and morphogenesis, cell motility and migration, cytoskeleton organization, and signal transduction. Transcription factors associated with developmental processes were heavily overrepresented, indicating the importance of reactivation of developmental programs in the pathogenesis of FSGS. Our findings reveal novel insights into the molecular pathogenesis of glomerular injury and structural degeneration in FSGS.

Defining cell-type specificity at the transcriptional level in human disease

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.

Improved elucidation of biological processes linked to diabetic nephropathy by single probe-based microarray data analysis.

Background Diabetic nephropathy (DN) is a complex and chronic metabolic disease that evolves into a progressive fibrosing renal disorder. Effective transcriptomic profiling of slowly evolving disease processes such as DN can be problematic. The changes that occur are often subtle and can escape detection by conventional oligonucleotide DNA array analyses. Methodology/Principal Findings We examined microdissected human renal tissue with or without DN using Affymetrix oligonucleotide microarrays (HG-U133A) by standard Robust Multi-array Analysis (RMA). Subsequent gene ontology analysis by Database for Annotation, Visualization and Integrated Discovery (DAVID) showed limited detection of biological processes previously identified as central mechanisms in the development of DN (e.g. inflammation and angiogenesis). This apparent lack of sensitivity may be associated with the gene-oriented averaging of oligonucleotide probe signals, as this includes signals from cross-hybridizing probes and gene annotation that is based on out of date genomic data. We then examined the same CEL file data using a different methodology to determine how well it could correlate transcriptomic data with observed biology. ChipInspector (CI) is based on single probe analysis and de novo gene annotation that bypasses probe set definitions. Both methods, RMA and CI, used at default settings yielded comparable numbers of differentially regulated genes. However, when verified by RT-PCR, the single probe based analysis demonstrated reduced background noise with enhanced sensitivity and fewer false positives. Conclusions/Significance Using a single probe based analysis approach with de novo gene annotation allowed an improved representation of the biological processes linked to the development and progression of DN. The improved analysis was exemplified by the detection of Wnt signaling pathway activation in DN, a process not previously reported to be involved in this disease.

Periostin Is Induced in Glomerular Injury and Expressed de Novo in Interstitial Renal Fibrosis

Matricellular proteins participate in the pathogenesis of chronic kidney diseases. We analyzed glomerular gene expression profiles from patients with proteinuric diseases to identify matricellular proteins contributing to the progression of human nephropathies. Several genes encoding matricellular proteins, such as SPARC, THBS1, and CTGF, were induced in progressive nephropathies, but not in nonprogressive minimal-change disease. Periostin showed the highest induction, and its transcript levels correlated negatively with glomerular filtration rate in both glomerular and tubulointerstitial specimen. In well-preserved renal tissue, periostin localized to the glomerular tuft, the vascular pole, and along Bowmans capsule; no signal was detected in the tubulointerstitial compartment. Biopsies from patients with glomerulopathies and renal dysfunction showed enhanced periostin expression in the mesangium, tubular interstitium, and sites of fibrosis. Periostin staining correlated negatively with renal function. α-smooth muscle actin-positive mesangial and interstitial cells localized close to periostin-positive sites, as indicated by co-immunofluorescence. In vitro stimulation of mesangial cells by external addition of TGF-β1 resulted in robust induction of periostin. Addition of periostin to mesangial cells induced cell proliferation and decreased the number of cells expressing activated caspase-3, a marker of apoptosis. These human data indicate for the first time a role of periostin in glomerular and interstitial injury in acquired nephropathies.

Renal gene and protein expression signatures for prediction of kidney disease progression.

Although chronic kidney disease (CKD) is common, only a fraction of CKD patients progress to end-stage renal disease. Molecular predictors to stratify CKD populations according to their risk of progression remain undiscovered. Here we applied transcriptional profiling of kidneys from transforming growth factor-beta1 transgenic (Tg) mice, characterized by heterogeneity of kidney disease progression, to identify 43 genes that discriminate kidneys by severity of glomerular apoptosis before the onset of tubulointerstitial fibrosis in 2-week-old animals. Among the genes examined, 19 showed significant correlation between mRNA expression in uninephrectomized left kidneys at 2 weeks of age and renal disease severity in right kidneys of Tg mice at 4 weeks of age. Gene expression profiles of human orthologs of the 43 genes in kidney biopsies were highly significantly related (R(2) = 0.53; P < 0.001) to the estimated glomerular filtration rates in patients with CKD stages I to V, and discriminated groups of CKD stages I/II and III/IV/V with positive and negative predictive values of 0.8 and 0.83, respectively. Protein expression patterns for selected genes were successfully validated by immunohistochemistry in kidneys of Tg mice and kidney biopsies of patients with IgA nephropathy and CKD stages I to V, respectively. In conclusion, we developed novel mRNA and protein expression signatures that predict progressive renal fibrosis in mice and may be useful molecular predictors of CKD progression in humans.

Systematic analysis of a novel human renal glomerulus-enriched gene expression dataset.

Glomerular diseases account for the majority of cases with chronic renal failure. Several genes have been identified with key relevance for glomerular function. Quite a few of these genes show a specific or preferential mRNA expression in the renal glomerulus. To identify additional candidate genes involved in glomerular function in humans we generated a human renal glomerulus-enriched gene expression dataset (REGGED) by comparing gene expression profiles from human glomeruli and tubulointerstitium obtained from six transplant living donors using Affymetrix HG-U133A arrays. This analysis resulted in 677 genes with prominent overrepresentation in the glomerulus. Genes with ‘a priori’ known prominent glomerular expression served for validation and were all found in the novel dataset (e.g. CDKN1, DAG1, DDN, EHD3, MYH9, NES, NPHS1, NPHS2, PDPN, PLA2R1, PLCE1, PODXL, PTPRO, SYNPO, TCF21, TJP1, WT1). The mRNA expression of several novel glomerulus-enriched genes in REGGED was validated by qRT-PCR. Gene ontology and pathway analysis identified biological processes previously not reported to be of relevance in glomeruli of healthy human adult kidneys including among others axon guidance. This finding was further validated by assessing the expression of the axon guidance molecules neuritin (NRN1) and roundabout receptor ROBO1 and -2. In diabetic nephropathy, a prevalent glomerulopathy, differential regulation of glomerular ROBO2 mRNA was found. In summary, novel transcripts with predominant expression in the human glomerulus could be identified using a comparative strategy on microdissected nephrons. A systematic analysis of this glomerulus-specifc gene expression dataset allows the detection of target molecules and biological processes involved in glomerular biology and renal disease.

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

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.