The motivation for applying a more advanced method like ICA for miRNA target prediction than negative correlation was that surprisingly few mRNAs have clear negative correlations with their targeting miRNAs. This is probably due to the mRNA profiles being influenced by a number of factors e.g. miRNA regulation, transcription factor binding and site accessibility. Here, ICA was used as an attempt to filter the factors influencing mRNA expression. Decomposition of microarray data using ICA has been shown to outperform other linear data representations, such as PCA [16–19]. Several target prediction methods have incorporated miRNA and mRNA profiling data. However, none of them uses ICA.
The miRNA array profiling identified eight miRNAs with differential expression in a type 1 diabetes model. Performing ICA on the mRNA expressions from the same samples resulted in five ICs that correlated with the experimental conditions studied. Comparing the two target prediction methods indicated that ICA was better at capturing miRNA activity than negative correlation. Seven miRNAs showed a significant enrichment of sequence-predicted targets when using ICA, as compared to only four by use of negative correlation. Interestingly, the ICs were enriched for miRNA targets with functional roles in diabetes-relevant pathways e.g. the pathways T1D, T2D, MODY, oxidative phosphorylation, insulin, cytokine-cytokine receptors and type 1 interferon. This supports that the eight miRNAs are implicated in disease mechanisms in diabetes. Additionally, targets of five of the eight miRNAs were significantly regulated by cytokines in models of β-cell destruction e.g. in human islets.
miRNAs fine tune the expression of genes in a combinatorial manner, meaning that several miRNAs can target the same mRNA transcript . Furthermore, a cluster of co-expressed miRNAs can regulate functionally related genes . In this study, we observe small expression changes in the miRNAs. However, even minute changes in miRNA expressions might have impact on mRNA expression, and miRNAs acting in a cooperative manner can most likely induce biologically relevant expression changes in their targets. ICA can uncover these more complex interactions. Interestingly, it was recently suggested that cooperativity could be incorporated for prediction of target interactions between different miRNAs . For the eight miRNAs we identified, there is a significant overlap in the mRNAs they target. We have incorporated cooperativity between miRNAs pairwise and identified four miRNA pairs (miR-375/672, miR-194/375, miR-192/375, miR-124/194) that had a significant co-regulatory effect on their common targets in IC 1.
Of the eight significant miRNAs, miR-124 and miR-375 have previously been identified in β-cells [33, 35, 45, 46]. Further, the expression of miR-204 has been shown to be induced in insulinomas, where its expression correlated with insulin expression .
That miR-375 was significantly regulated strengthens our model since a previous study observed interaction between Pdx-1 (and NeuroD1) and the miR-375 locus . However, no Pdx-1 consensus binding sites were identified, but binding elements for other transcription factors have been identified in the miR-375 locus . The decreased miR-375 expression could, at least in part, be mediated through NeuroD1, since we observed a decreased NeuroD1 expression in response to Pdx-1 induction. Additionally, the decreased miR-375 expression is in agreement with miR-375 having a higher expression level in non-β-cells than in β-cells . This is also supported by our findings in α-cells versus β-cells (Additional file 7). Interestingly, both miR-375 and Pax6, a key factor in α-cell development, had negative loads in IC 1, i.e. both were down-regulated in response to Pdx-1. The decreased miR-375 expression is also in compliance with the function of miR-375 as a negative regulator of insulin exocytosis , since it correlates with the need for an increased insulin secretion in the mature β-cell phenotype. Similarly, miR-124 has been shown to modulate insulin secretion by targeting Foxa2 . miR-194 is highly expressed in liver and in intestinal epithelial cells, where it is under regulation by Hnf1α [49, 50]. Interestingly, Hnf1α is required for proper β-cell function and mutations in this gene cause MODY . miR-192 is also expressed in the liver and is in cluster with miR-194, suggesting co-regulation [49, 50]. Furthermore, miR-128 has been shown to induce apoptosis in kidney cells through interaction with Bax . So far, miR-672 and miR-708 have not been examined in β-cells. The likely involvement of miR-128/192/194/204/708 in β-cell regulatory networks (Figure 5) and the high expression in β-cells compared to α-cells for miR-672/204 (Additional file 7) make them interesting candidates for further studies.
We have used ICA for bioinformatics investigation of the functional roles of the miRNAs and their targets. ICA in combination with pathway analysis indicates that the eight miRNAs, through their mRNA targets, are implicated in several diabetes relevant pathways.
The transcriptional changes mediated by miRNAs on their targets may not be entirely explained by direct repression but may also reflect indirect mechanisms such as activation by feedback and feed-forward transcriptional loops within regulatory networks [52, 53]. miRNAs can be important players in these networks. By use of a combined bioinformatics approach we identified miRNA regulatory networks. The results suggest connections between seven of the eight miRNAs through interactions with key pancreatic transcription factors, cytokine signalling molecules and insulin (Figure 5).