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

ABSTRACT

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.