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. 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. © 2013 Argyropoulos et al

The list of miRNAs with the most and least concentration variations in serum and plasma samples and measurement platforms.

*<p>Coefficient of variation (standard deviation/mean) was used to measure the variability.</p>#<p>Common miRNA species between serum and plasma were listed in boldface characters.</p

Comparing the individual miRNA concentrations between serum and plasma.

<p>The average concentrations (40-Ct values) of detected miRNAs in all serum and plasma samples within each platform were plotted – each point represents the average from all individuals of a specific miRNA. The Y-axis represents the average miRNA concentrations in serum and X-axis is the average concentration of corresponding plasma. The results from Taqman are showed in panel A and Exiqon are showed in panel B.</p

General information on samples and measurement results.

<p>General information on samples and measurement results.</p

Examples of miRNA concentration differences between serum and plasma using individual Exiqon QPCR primers.

<p>The sample IDs were listed on the X-axis and the miRNA concentrations were displayed on the Y-axis (in 40-Ct value). The miRNA IDs were indicated on top of the graph. Open bars represent plasma samples and the solid bars represent the corresponding serum samples. The values of standard derivation were obtained from three independent measurements. Two-way ANOVA was used to determine the statistical significance of the miRNA concentration differences between serum and plasma (p-values are shown in the figure).</p

Examples of miRNA concentration differences between serum and plasma using individual Taqman QPCR primers.

<p>The sample IDs were listed on the X-axis and the miRNA concentrations were displayed on the Y-axis (in 40-Ct value). The miRNA IDs were indicated on top of the graph. Open bars represent plasma samples and the solid bars represent the corresponding serum samples. The values of standard derivation were obtained from three independent measurements. The original measurement results from Taqman card showed higher 40-Ct values since a pre-amplification step was employed. Two-way ANOVA was used to determine the statistical significance of the miRNA concentration differences between serum and plasma (p-values are shown in the figure).</p

Differentially expressed miRNAs between albuminuric and non-albuminuric (reference) samples from patients with MA.

<p>For miRNAs whose name changed after the introduction of the 18^th version of MiRBase, we provide both the previous (in <i>italics</i>) and the recent (regular font) name.</p

REACTOME pathway terms enriched in targets of differentially expressed miRNAs.

<p>P-value: the p-value of the hypergeometric test unadjusted for multiple comparisons, Fraction: number of proteins in the pathway that are targets of differentially expressed miRNAs over the total number of proteins in each pathway.</p