Unbiased kidney-centric molecular categorization of chronic kidney disease as a step towards precision medicine.

Current classification of chronic kidney disease (CKD) into stages using indirect systemic measures (estimated glomerular filtration rate (eGFR) and albuminuria) is agnostic to the heterogeneity of underlying molecular processes in the kidney thereby limiting precision medicine approaches. To generate a novel CKD categorization that directly reflects within kidney disease drivers we analyzed publicly available transcriptomic data from kidney biopsy tissue. A Self-Organizing Maps unsupervised artificial neural network machine-learning algorithm was used to stratify a total of 369 patients with CKD and 46 living kidney donors as healthy controls. Unbiased stratification of the discovery cohort resulted in identification of four novel molecular categories of disease termed CKD-Blue, CKD-Gold, CKD-Olive, CKD-Plum that were replicated in independent CKD and diabetic kidney disease datasets and can be further tested on any external data at kidneyclass.org. Each molecular category spanned across CKD stages and histopathological diagnoses and represented transcriptional activation of distinct biological pathways. Disease progression rates were highly significantly different between the molecular categories. CKD-Gold displayed rapid progression, with significant eGFR-adjusted Cox regression hazard ratio of 5.6 [1.01-31.3] for kidney failure and hazard ratio of 4.7 [1.3-16.5] for composite of kidney failure or a 40% or more eGFR decline. Urine proteomics revealed distinct patterns between the molecular categories, and a 25-protein signature was identified to distinguish CKD-Gold from other molecular categories. Thus, patient stratification based on kidney tissue omics offers a gateway to non-invasive biomarker-driven categorization and the potential for future clinical implementation, as a key step towards precision medicine in CKD.

Novel insights into the disease transcriptome of human diabetic glomeruli and tubulointerstitium.

BACKGROUND: Diabetic nephropathy (DN) is the most common cause of end-stage renal disease, affecting ∼30% of the rapidly growing diabetic population, and strongly associated with cardiovascular risk. Despite this, the molecular mechanisms of disease remain unknown. METHODS: RNA sequencing (RNAseq) was performed on paired, micro-dissected glomerular and tubulointerstitial tissue from patients diagnosed with DN [n = 19, 15 males, median (range) age: 61 (30-85) years, chronic kidney disease stages 1-4] and living kidney donors [n = 20, 12 males, median (range) age: 56 (30-70) years]. RESULTS: Principal component analysis showed a clear separation between glomeruli and tubulointerstitium transcriptomes. Differential expression analysis identified 1550 and 4530 differentially expressed genes, respectively (adjusted P < 0.01). Gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses highlighted activation of inflammation and extracellular matrix (ECM) organization pathways in glomeruli, and immune and apoptosis pathways in tubulointerstitium of DN patients. Specific gene modules were associated with renal function in weighted gene co-expression network analysis. Increased messengerRNA (mRNA) expression of renal damage markers lipocalin 2 (LCN) and hepatitis A virus cellular receptor1 (HAVCR1) in the tubulointerstitial fraction was observed alongside higher urinary concentrations of the corresponding proteins neutrophil gelatinase-associated lipocalin (NGAL) and kidney injury molecule-1 (KIM-1) in DN patients. CONCLUSIONS: Here we present the first RNAseq experiment performed on paired glomerular and tubulointerstitial samples from DN patients. We show that prominent disease-specific changes occur in both compartments, including relevant cellular processes such as reorganization of ECM and inflammation (glomeruli) as well as apoptosis (tubulointerstitium). The results emphasize the potential of utilizing high-throughput transcriptomics to decipher disease pathways and treatment targets in this high-risk patient population.

Targeted Analysis of Serum Proteins Encoded at Known Inflammatory Bowel Disease Risk Loci.

Background: Few studies have investigated the blood proteome of inflammatory bowel disease (IBD). We characterized the serum abundance of proteins encoded at 163 known IBD risk loci and tested these proteins for their biomarker discovery potential. Methods: Based on the Human Protein Atlas (HPA) antibody availability, 218 proteins from genes mapping at 163 IBD risk loci were selected. Targeted serum protein profiles from 49 Crohn's disease (CD) patients, 51 ulcerative colitis (UC) patients, and 50 sex- and age-matched healthy individuals were obtained using multiplexed antibody suspension bead array assays. Differences in relative serum abundance levels between disease groups and controls were examined. Replication was attempted for CD-UC comparisons (including disease subtypes) by including 64 additional patients (33 CD and 31 UC). Antibodies targeting a potentially novel risk protein were validated by paired antibodies, Western blot, immuno-capture mass spectrometry, and epitope mapping. Results: By univariate analysis, 13 proteins mostly related to neutrophil, T-cell, and B-cell activation and function were differentially expressed in IBD patients vs healthy controls, 3 in CD patients vs healthy controls and 2 in UC patients vs healthy controls (q < 0.01). Multivariate analyses further differentiated disease groups from healthy controls and CD subtypes from UC (P < 0.05). Extended characterization of an antibody targeting a novel, discriminative serum marker, the laccase (multicopper oxidoreductase) domain containing 1 (LACC1) protein, provided evidence for antibody on-target specificity. Conclusions: Using affinity proteomics, we identified a set of IBD-associated serum proteins encoded at IBD risk loci. These candidate proteins hold the potential to be exploited as diagnostic biomarkers of IBD.

Lectin complement pathway gene profile of the donor and recipient does not influence graft outcome after kidney transplantation.

In kidney transplantation, complement activation was found to be induced by donor brain death, renal ischemia-reperfusion injury and allograft rejection. There are three known pathways of complement activation: the classical, lectin and the alternative pathway. The lectin complement pathway can be activated upon pattern recognition by mannan binding lectin (MBL) or ficolins (FCN). Single nucleotide polymorphisms (SNPs) in the genes encoding the lectin pathway proteins determine their functional activity and serum levels. The aim of this study was to investigate the role of the lectin gene profile of the donor and recipient on post-transplant outcome. A total of 12 functional SNPs in the MBL2, FCN2 and MBL-associated serine proteases 2 (MASP2) genes of 1271 donor-recipient pairs were determined. Lectin genotypic variants were analyzed for association with primary non-function (PNF), delayed graft function (DGF), biopsy proven acute rejection, death-censored graft survival and patient survival. Multivariate analyses found no association of donor and recipient MBL2 and MASP2 genotype with allograft outcome. Analysis of separate functional SNPs and haplotypes in the FCN2 gene of the donor and recipient did not reveal an association with transplant outcome. Also, the joint effect of the MBL2 and FCN2 genotype was not associated with allograft outcome.This study shows that the genetic profile of the lectin pathway of complement activation of the donor and recipient is not associated with allograft outcome after kidney transplantation.