Urinary microRNA profiling in the nephropathy of type 1 diabetes.
BACKGROUND
Patients with Type 1 Diabetes (T1D) are particularly vulnerable to development of Diabetic nephropathy (DN) leading to End Stage Renal Disease. Hence a better understanding of the factors affecting kidney disease progression in T1D is urgently needed. In recent years microRNAs have emerged as important post-transcriptional regulators of gene expression in many different health conditions. We hypothesized that urinary microRNA profile of patients will differ in the different stages of diabetic renal disease.


RESULTS
We studied urine microRNA profiles with qPCR in 40 T1D with >20 year follow up 10 who never developed renal disease (N) matched against 10 patients who went on to develop overt nephropathy (DN), 10 patients with intermittent microalbuminuria (IMA) matched against 10 patients with persistent (PMA) microalbuminuria. A Bayesian procedure was used to normalize and convert raw signals to expression ratios. We applied formal statistical techniques to translate fold changes to profiles of microRNA targets which were then used to make inferences about biological pathways in the Gene Ontology and REACTOME structured vocabularies. A total of 27 microRNAs were found to be present at significantly different levels in different stages of untreated nephropathy. These microRNAs mapped to overlapping pathways pertaining to growth factor signaling and renal fibrosis known to be targeted in diabetic kidney disease.


CONCLUSIONS
Urinary microRNA profiles differ across the different stages of diabetic nephropathy. Previous work using experimental, clinical chemistry or biopsy samples has demonstrated differential expression of many of these microRNAs in a variety of chronic renal conditions and diabetes. Combining expression ratios of microRNAs with formal inferences about their predicted mRNA targets and associated biological pathways may yield useful markers for early diagnosis and risk stratification of DN in T1D by inferring the alteration of renal molecular processes.
Renal and Electrolyte Division, Department of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
Christos
C
Argyropoulos
Kai
K
Wang
Sara
S
McClarty
David
D
Huang
Jose
J
Bernardo
Demetrius
D
Ellis
Trevor
T
Orchard
David
D
Galas
John
J
Johnson
D001499:Bayes Theorem; D003922:Diabetes Mellitus, Type 1 / Q000652:urine*; D003928:Diabetic Nephropathies / Q000652:urine*; D020869:Gene Expression Profiling; D006801:Humans; D035683:MicroRNAs / Q000652:urine*; D016133:Polymerase Chain Reaction; D025341:Principal Component Analysis
D035683:MicroRNAs