Abstract
BackgroundSeveral approaches have been developed for miRNA target prediction, including methods that incorporate expression profiling. However the methods are still in need of improvements due to a high false discovery rate. So far, none of the methods have used independent component analysis (ICA). Here, we developed a novel target prediction method based on ICA that incorporates both seed matching and expression profiling of miRNA and mRNA expressions. The method was applied on a cellular model of type 1 diabetes.ResultsMicrorray profiling identified eight miRNAs (miR-124/128/192/194/204/375/672/708) with differential expression. Applying ICA on the mRNA profiling data revealed five significant independent components (ICs) correlating to the experimental conditions. The five ICs also captured the miRNA expressions by explaining >97% of their variance. By using ICA, seven of the eight miRNAs showed significant enrichment of sequence predicted targets, compared to only four miRNAs when using simple negative correlation. The ICs were enriched for miRNA targets that function in diabetes-relevant pathways e.g. type 1 and type 2 diabetes and maturity onset diabetes of the young (MODY).ConclusionsIn this study, ICA was applied as an attempt to separate the various factors that influence the mRNA expression in order to identify miRNA targets. The results suggest that ICA is better at identifying miRNA targets than negative correlation. Additionally, combining ICA and pathway analysis constitutes a means for prioritizing between the predicted miRNA targets. Applying the method on a model of type 1 diabetes resulted in identification of eight miRNAs that appear to affect pathways of relevance to disease mechanisms in diabetes.
Highlights
Several approaches have been developed for miRNA target prediction, including methods that incorporate expression profiling
We found that the expression of miR-375 was significantly decreased in response to Pancreatic and duodenal homeobox 1 (Pdx-1) induction, whereas the miR-194 expression was significantly increased in response to IL-1b treatment, see Figure 3
Cardozo et al [41] performed mRNA profiling on b-cells from 10 weeks old male Wistar rats, un-stimulated or stimulated with cytokines (IL-1b and/or IFNg). Using their mRNA data, we found that targets of miR192 were significantly down-regulated in response to IL1b stimulation when comparing to non-targets (q = 0.02)
Summary
Several approaches have been developed for miRNA target prediction, including methods that incorporate expression profiling. We developed a novel target prediction method based on ICA that incorporates both seed matching and expression profiling of miRNA and mRNA expressions. ICA is a computational method for separating mixed independent signals and can be used to decompose the expression matrix into independent components [10]. This decomposition has been shown to be informative in several studies [11,12,13,14,15], and superior to clustering and PCA [16,17,18,19]. The representation of gene expression as a mix of independent, possibly overlapping, transcriptional programs captures the differential regulation of well-defined biological processes and metabolic pathways [19,20]
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