Dataset Information

Deciphering miRNA transcription factor feed-forward loops to identify drug repurposing candidates for cystic fibrosis.

ABSTRACT: Cystic fibrosis (CF) is a fatal genetic disorder caused by mutations in the CF transmembrane conductance regulator (CFTR) gene that primarily affects the lungs and the digestive system, and the current drug treatment is mainly able to alleviate symptoms. To improve disease management for CF, we considered the repurposing of approved drugs and hypothesized that specific microRNA (miRNA) transcription factors (TF) gene networks can be used to generate feed-forward loops (FFLs), thus providing treatment opportunities on the basis of disease specific FFLs.Comprehensive database searches revealed significantly enriched TFs and miRNAs in CF and CFTR gene networks. The target genes were validated using ChIPBase and by employing a consensus approach of diverse algorithms to predict miRNA gene targets. STRING analysis confirmed protein-protein interactions (PPIs) among network partners and motif searches defined composite FFLs. Using information extracted from SM2miR and Pharmaco-miR, an in silico drug repurposing pipeline was established based on the regulation of miRNA/TFs in CF/CFTR networks.In human airway epithelium, a total of 15 composite FFLs were constructed based on CFTR specific miRNA/TF gene networks. Importantly, nine of them were confirmed in patient samples and CF epithelial cells lines, and STRING PPI analysis provided evidence that the targets interacted with each other. Functional analysis revealed that ubiquitin-mediated proteolysis and protein processing in the endoplasmic reticulum dominate the composite FFLs, whose major functions are folding, sorting, and degradation. Given that the mutated CFTR gene disrupts the function of the chloride channel, the constructed FFLs address mechanistic aspects of the disease and, among 48 repurposing drug candidates, 26 were confirmed with literature reports and/or existing clinical trials relevant to the treatment of CF patients.The construction of FFLs identified promising drug repurposing candidates for CF and the developed strategy may be applied to other diseases as well.

SUBMITTER: Liu Z

PROVIDER: S-EPMC4256829 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Deciphering miRNA transcription factor feed-forward loops to identify drug repurposing candidates for cystic fibrosis.

Liu Zhichao Z Borlak Jürgen J Tong Weida W

Genome medicine 20141202 12

<h4>Background</h4>Cystic fibrosis (CF) is a fatal genetic disorder caused by mutations in the CF transmembrane conductance regulator (CFTR) gene that primarily affects the lungs and the digestive system, and the current drug treatment is mainly able to alleviate symptoms. To improve disease management for CF, we considered the repurposing of approved drugs and hypothesized that specific microRNA (miRNA) transcription factors (TF) gene networks can be used to generate feed-forward loops (FFLs), ...[more]

PMID: 25484921

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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).

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.

Dataset Information

Deciphering miRNA transcription factor feed-forward loops to identify drug repurposing candidates for cystic fibrosis.

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Deciphering miRNA transcription factor feed-forward loops to identify drug repurposing candidates for cystic fibrosis.

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