Urinary microRNA profiling in the nephropathy of type 1 diabetes

Abstract

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.

Methods and findings: 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.

Figure 1. Schematic representation of the normalization procedure and estimation of relative fold changes adopted in the manuscript.

Replicate qPCR reactions were analyzed with a hierarchical linear mixed model in order to estimate panel specific correction factors that were subtracted from the raw C_q signals of unreplicated reactions (first step), while simultaneously estimating the difference (ΔC_q) between an experimental and referent state. In the second step, the ΔC_q of the spiked in control was subtracted from the non-control ΔC_q values to calibrate the relative fold changes according to the Delta-Delta method. Both steps of the normalization procedure acknowledged the uncertainty implicit in estimating the ΔC_q of both control and non-control signals (shown as a density plot at the bottom part of the figure), by performing this subtraction probabilistically i.e. by Monte Carlo methods.

Figure 2. Results of Principal Component Analysis applied to all urine samples analyzed in this study.

To present the results of the five dimensional PCA, we utilized bivariate projections in which each component is plotted against all e.g. the second plot in the first row plots the first principal component (PC1) against the second (PC2). Each individual urine sample is color and symbol coded according to the disease classification at the time it was collected. N: patients without nephropathy, DN: patients with overt nephropathy, IMA(B): normoalbuminuric samples from patients who had intermittent microalbuminuria, PMA(B): last normoalbuminuric samples from patients who had persistent albuminuria, IMA: micro-albuminuric samples from patients who had intermittent micro-albuminuria, PMA: micro-albuminuric samples from patients who had persistent microalbuminuria.

Figure 3. Results of Principal Component Analysis rendered according to pair identification number.

This figure utilizes the same bivariate projection setup as Figure 2, but points are symbol coded according to the unique identifier used when matching patients into pairs. For patients with MA who contributed two samples (one at the baseline and one at the microalbuminuric state) there are more than 2 points with the same symbol.

Figure 4. Distribution of the number of mRNAs targeted by differentially regulated microRNAs in diabetic urine.