MIRTFnet: Analysis of miRNA regulated transcription factors

Background Several expression datasets of miRNA transfection experiments are available to analyze the regulatory mechanisms downstream of miRNA effects. The miRNA induced regulatory effects can be propagated via transcription factors (TFs). We propose the method MIRTFnet to identify miRNA controlled TFs as active regulators if their downstream target genes are differentially expressed.

Methodology/Principal Findings MIRTFnet enables the determination of active transcription factors (TFs) and is sensitive enough to exploit the small expression changes induced by the activity of miRNAs. For this purpose, different statistical tests were evaluated and compared. Based on the identified TFs, databases, computational predictions and the literature we construct regulatory models downstream of miRNA actions. Transfecting miRNAs are connected to active regulators via a network of miRNA-TF, miRNA-kinase-TF as well as TF-TF relationships. Based on 43 transfection experiments involving 17 cancer relevant miRNAs we show that MIRTFnet detects active regulators reliably.

Conclusions/Significance The consensus of the individual regulatory models shows that the examined miRNAs induce activity changes in a common core of transcription factors involved in cancer related processes such as proliferation or apoptosis.

Modelling miRNA actions from expression measurements. Active regulators such as miRNAs and TFs are detected by their effect on the expression of downstream targets, here exemplified by the Wilcoxon test. In step 1 just the direct miRNA targets (kinases and significant TFs) are added to the model. Additional significant TFs are included if they can be connected to the model by interactions, i.e. by repeating steps 2 and 3. The model of miR-155 transfection (8hr), for instance, includes 14 kinases and 24 out of 27 TFs detected as active by MIRTFnet. The remaining 3 TFs could not be connected by known interactions. Using these models we consider gene expression changes observed after miRNA transfection as explained if they satisfy two constrains: (1) such a gene must be targeted by an active TF, and (2) such a TF must be connectable to the transfecting miRNA by a path of known or predicted miRNA-TF, TF-TF and kinase-TF interactions.

Model of the regulatory network induced by miR-155 (8-hr) transfection. The red box shows the transfected human (hsa) miR-155; orange box nodes indicate the miRNA-regulated kinases; blue circle nodes represent TFs that are regulated either directly by the transfected miR-155 or by miRNA-regulated kinases; the green circle nodes represent TFs that are regulated by miRNA-targeted TFs, subject to indirect regulation of miR-155 on TFs. All TFs were identified as active TFs by MIRTFnet (applying the Wilcoxon (WR) and Kolmogorov-Smirnov (KS) tests) (section 4.3). The active TFs were connected to the transfected miRNA by interactions extracted from databases or computational predictions (Table 12). Additionally, kinases were connected via miRNA-kinase-TF causal relationships i.e. they usually do not receive direct support from the expression profile. 55% of the differential expression pattern can be explained by the extracted miR-155-TF regulatory network model. Here, genes are regarded as differentially expression if they exhibit a fold change of at least 2 or less than 0.5