Identifying common and specific microRNAs expressed in peripheral blood mononuclear cell of type 1, type 2, and gestational diabetes mellitus patients

Abstract

Background: Regardless the regulatory function of microRNAs (miRNA), their differential expression pattern has been used to define miRNA signatures and to disclose disease biomarkers. To address the question of whether patients presenting the different types of diabetes mellitus could be distinguished on the basis of their miRNA and mRNA expression profiling, we obtained peripheral blood mononuclear cell (PBMC) RNAs from 7 type 1 (T1D), 7 type 2 (T2D), and 6 gestational diabetes (GDM) patients, which were hybridized to Agilent miRNA and mRNA microarrays. Data quantification and quality control were obtained using the Feature Extraction software, and data distribution was normalized using quantile function implemented in the Aroma light package. Differentially expressed miRNAs/mRNAs were identified using Rank products, comparing T1DxGDM, T2DxGDM and T1DxT2D. Hierarchical clustering was performed using the average linkage criterion with Pearson uncentered distance as metrics.

Results: The use of the same microarrays platform permitted the identification of sets of shared or specific miRNAs/mRNA interaction for each type of diabetes. Nine miRNAs (hsa-miR-126, hsa-miR-1307, hsa-miR-142-3p, hsa-miR-142-5p, hsa-miR-144, hsa-miR-199a-5p, hsa-miR-27a, hsa-miR-29b, and hsa-miR-342-3p) were shared among T1D, T2D and GDM, and additional specific miRNAs were identified for T1D (20 miRNAs), T2D (14) and GDM (19) patients. ROC curves allowed the identification of specific and relevant (greater AUC values) miRNAs for each type of diabetes, including: i) hsa-miR-1274a, hsa-miR-1274b and hsa-let-7f for T1D; ii) hsa-miR-222, hsa-miR-30e and hsa-miR-140-3p for T2D, and iii) hsa-miR-181a and hsa-miR-1268 for GDM. Many of these miRNAs targeted mRNAs associated with diabetes pathogenesis.

Conclusions: These results indicate that PBMC can be used as reporter cells to characterize the miRNA expression profiling disclosed by the different diabetes mellitus manifestations. Shared miRNAs may characterize diabetes as a metabolic and inflammatory disorder, whereas specific miRNAs may represent biological markers for each type of diabetes, deserving further attention.

Figure 1

Hierarchical clustering of mRNA (upper dendrograms) and microRNAs (lower dendrograms). Clustering analyses refer to the comparisons of the transcript profiles (mRNA and miRNA) between T1D versus GDM (1A and 1D), between T2D versus GDM (1B and 1E), and between T1D versus T2D (1C and 1F). As observed, the mRNA and miRNA profiles were distinct for each type of diabetes.

Figure 2

Networks between miRNAs and mRNAs. The relationship between miRNAs and mRNAs were evaluated by constructing networks using the Cytoscape software. (A) Upper networks show all interactions described in Tables 1, 2 and 3, and (B) lower networks show only the negative correlations, i.e., increased miRNA versus decreased mRNA or vice-versa. Red circles represent miRNAs and the grey ones represent mRNA.

Figure 3

Venn diagrams showing common and specific microRNAs for the three types of diabetes. The central intersection of the upper diagram shows the nine shared miRNAs among T1D, T2D and GDM, the upper right intersection shows the 5 miRNAs specific for T2D, the upper left intersection shows the 11 miRNAs specific for T1D, and the middle lower intersection shows the 10 miRNAs specific for GDM patients. Lower Venn diagrams identify specific miRNAs for each type of diabetes (bold letters), as well as the shared ones.

Figure 4

Identification of most relevant specific miRNAs for each diabetes type. The values of the area under the curve (AUC) were estimated for all specific miRNAs obtained after the multiple comparisons among the three types of diabetes as shown in Figure 3. MiRNAs exhibiting high AUC values are highlighted within blue rectangles.