Recent technological advances that led to ‘omics’ revolution have enabled large-scale data generation in different areas of biology. Thousands of high-throughput datasets are available that contain the expression levels of all genes of an organism under various experimental conditions. Expression of each gene is a complex process that requires coordination of many factors for maintaining the basic mechanisms of development and controlled by two important classes of regulators: microRNAs (miRNAs) and transcription factors (TFs).
Based on protein-protein interactions, miRNA-targets and TF-targets, we constructed transfection experiment specific models that connect the transfecting miRNA via causal relationships to the TFs that were detected as active using the proposed statistical tests.
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.
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.
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.
Model of miRNA actions
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
File S1 - Additional Data information
The File S1 describes the information contained in additional data files including figures, tables and results extracted from the miRNA transfection experiments analyzed in the present paper using MIRTFnet.
File S2 - miRNA-target associations
The File S2 contains the transfecting miRNA target genes including TFs, kinases and other differentially regulated miRNA target genes. For more details see File S1: Table S8.
File S3 - Kinase-TF relationships
The File S3 lists the kinase-TF associations necessary to link active TF to the transfecting miRNA in each miRNA-transfection experiment. For more details see File S1: Table S9.
File S4 - Significant TFs
The File S4 contains the TFs identified as active (either based on the Wilcoxon test or based on the fold change criterion) in each miRNA transfection experiment. For more details see File S1: Table S10.
File S5 - TF-target relationships
The identified active TFs and their target genes (in addition to the direct miRNA target genes) provide a potential explanation for the majority of the observed differential expression in the examined miRNA transfection experiments. The File S5 contains the active TF regulated target genes information. For more details see File S1: Table S11.
S6 - Model of miRNA action
The file S6.zip contains the miRNA-kinase-TF models complied using the Wilcoxon (WS) test. Each transfection model file contains miRNA-TF, miRNA-kinase, kinase-TF and TF-TF associations. The model file name is titled as transfecting miRNA name-time point (hr)-dataset-ID.
S7 - Model of miRNA action
The file S7.zip contains the miRNA-kinase-TF models complied applying the MIRTFnet (Wilcoxon (WS) and Kolmogorov-Smirnov (KS) tests). Each transfection model file contains miRNA-TF, miRNA-kinase, kinase-TF and TF-TF associations. Each model file name is titled as: transfecting miRNA name-time point (hr)-dataset-ID.