Project description:BackgroundAs one of the most common types of co-regulatory motifs, feed-forward loops (FFLs) control many cell functions and play an important role in human cancers. Therefore, it is crucial to reconstruct and analyze cancer-related FFLs that are controlled by transcription factor (TF) and microRNA (miRNA) simultaneously, in order to find out how miRNAs and TFs cooperate with each other in cancer cells and how they contribute to carcinogenesis. Current FFL studies rely on predicted regulation information and therefore suffer the false positive issue in prediction results. More critically, FFLs generated by existing approaches cannot represent the dynamic and conditional regulation relationship under different experimental conditions.Methodology/principal findingsIn this study, we proposed a novel filter-wrapper feature selection method to accurately identify co-regulatory mechanism by incorporating prior information from predicted regulatory interactions with parallel miRNA/mRNA expression datasets. By applying this method, we reconstructed 208 and 110 TF-miRNA co-regulatory FFLs from human pan-cancer and prostate datasets, respectively. Further analysis of these cancer-related FFLs showed that the top-ranking TF STAT3 and miRNA hsa-let-7e are key regulators implicated in human cancers, which have regulated targets significantly enriched in cellular process regulations and signaling pathways that are involved in carcinogenesis.Conclusions/significanceIn this study, we introduced an efficient computational approach to reconstruct co-regulatory FFLs by accurately identifying gene co-regulatory interactions. The strength of the proposed feature selection method lies in the fact it can precisely filter out false positives in predicted regulatory interactions by quantitatively modeling the complex co-regulation of target genes mediated by TFs and miRNAs simultaneously. Moreover, the proposed feature selection method can be generally applied to other gene regulation studies using parallel expression data with respect to different biological contexts.

Project description:Post-transcriptional regulation of gene expression accomplished by microRNAs (miRNAs) importantly affects the complex gene regulatory network. In particular, miRNAs are known to be involved in recurrent motifs named miRNA-mediated feed forward loops (FFLs) where a transcription factor (TF) regulates a miRNA and they both regulate the expression level of a target RNA. Here, we focus on the identification of active FFLs during adipogenic differentiation. A list of putative feed-forward loops was generated based on sequence analysis of conserved and overrepresented motifs in the regulatory regions. Since this approach is not specific for adipogenesis and is known to generate false positive feed-forward loops, an experiment was designed to select active ones based on their dynamics using a model-based approach. Microarray time series of gene and miRNA expression data were collected at seven time points on human multipotent adipose-derived stem (hMADS) cells upon adipogenic differentiation. Three different dynamic models, sharing the same FFL topology but incorporating descriptions of increasing complexity of miRNA and mRNA dynamics, are identified on miRNA and mRNA expression data and compared based on identification criteria, namely: goodness of fit, precision of the estimates and comparison with submodels. 24 FFLs, able to properly reproduce data, are selected as active out of the 329 putative ones. This method considerably reduces the search space for new interactions between TFs, miRNAs and mRNAs and provides interesting biological results identifying genes known from the literature to be regulators in adipogenesis and adipocyte-related functions that can be interpreted as positive control of the validity of the apporoach. Therefore, the genes in the selected FFLs that are not yet known to be involved in this context are potential novel players in this regulatory network of adipogenesis and adipocyte function. Two independent cell culture experiments were performed as biological replicates during adipogenic differentiation of human mesenchymal stem cell. Cells where harvested at the pre-confluent stage as reference (day -3) and at seven subsequent time points during human adipogenic differentiation: day -2 and 0 before, and 1, 2, 5, 10, 15 days after induction of differentiation. All hybridizations were repeated with reversed dye assignment (dye-swap).

Dataset Information

CircuitsDB: a database of mixed microRNA/transcription factor feed-forward regulatory circuits in human and mouse.

Background

Description

Conclusions

Publications

CircuitsDB: a database of mixed microRNA/transcription factor feed-forward regulatory circuits in human and mouse.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets