Dataset Information

Reconstructing dynamic microRNA-regulated interaction networks.

ABSTRACT: The regulation of gene expression in cells, including by microRNAs (miRNAs), is a dynamic process. Current methods for identifying miRNA targets by combining sequence and miRNA and mRNA expression data do not adequately use the temporal information and thus miss important miRNAs and their targets. We developed the MIRna Dynamic Regulatory Events Miner (mirDREM), a probabilistic modeling method that uses input-output hidden Markov models to reconstruct dynamic regulatory networks that explain how temporal gene expression is jointly regulated by miRNAs and transcription factors. We measured miRNA and mRNA expression for postnatal lung development in mice and used mirDREM to study the regulation of this process. The reconstructed dynamic network correctly identified known miRNAs and transcription factors. The method has also provided predictions about additional miRNAs regulating this process and the specific developmental phases they regulate, several of which were experimentally validated. Our analysis uncovered links between miRNAs involved in lung development and differentially expressed miRNAs in idiopathic pulmonary fibrosis patients, some of which we have experimentally validated using proliferation assays. These results indicate that some disease progression pathways in idiopathic pulmonary fibrosis may represent partial reversal of lung differentiation.

SUBMITTER: Schulz MH

PROVIDER: S-EPMC3785769 | biostudies-literature | 2013 Sep

REPOSITORIES: biostudies-literature

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Reconstructing dynamic microRNA-regulated interaction networks.

Schulz Marcel H MH Pandit Kusum V KV Lino Cardenas Christian L CL Ambalavanan Namasivayam N Kaminski Naftali N Bar-Joseph Ziv Z

Proceedings of the National Academy of Sciences of the United States of America 20130828 39

The regulation of gene expression in cells, including by microRNAs (miRNAs), is a dynamic process. Current methods for identifying miRNA targets by combining sequence and miRNA and mRNA expression data do not adequately use the temporal information and thus miss important miRNAs and their targets. We developed the MIRna Dynamic Regulatory Events Miner (mirDREM), a probabilistic modeling method that uses input-output hidden Markov models to reconstruct dynamic regulatory networks that explain how ...[more]

PMID: 23986498

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Project description:BackgroundOne of the crucial steps toward understanding the biological functions of a cellular system is to investigate protein-protein interaction (PPI) networks. As an increasing number of reliable PPIs become available, there is a growing need for discovering PPIs to reconstruct PPI networks of interesting organisms. Some interolog-based methods and homologous PPI families have been proposed for predicting PPIs from the known PPIs of source organisms.ResultsHere, we propose a multiple-strategy scoring method to identify reliable PPIs for reconstructing the mouse PPI network from two well-known organisms: human and fly. We firstly identified the PPI candidates of target organisms based on homologous PPIs, sharing significant sequence similarities (joint E-value ? 1 × 10(-40)), from source organisms using generalized interolog mapping. These PPI candidates were evaluated by our multiple-strategy scoring method, combining sequence similarities, normalized ranks, and conservation scores across multiple organisms. According to 106,825 PPI candidates in yeast derived from human and fly, our scoring method can achieve high prediction accuracy and outperform generalized interolog mapping. Experiment results show that our multiple-strategy score can avoid the influence of the protein family size and length to significantly improve PPI prediction accuracy and reflect the biological functions. In addition, the top-ranked and conserved PPIs are often orthologous/essential interactions and share the functional similarity. Based on these reliable predicted PPIs, we reconstructed a comprehensive mouse PPI network, which is a scale-free network and can reflect the biological functions and high connectivity of 292 KEGG modules, including 216 pathways and 76 structural complexes.ConclusionsExperimental results show that our scoring method can improve the predicting accuracy based on the normalized rank and evolutionary conservation from multiple organisms. Our predicted PPIs share similar biological processes and cellular components, and the reconstructed genome-wide PPI network can reflect network topology and modularity. We believe that our method is useful for inferring reliable PPIs and reconstructing a comprehensive PPI network of an interesting organism.

Dataset Information

Reconstructing dynamic microRNA-regulated interaction networks.

Publications

Reconstructing dynamic microRNA-regulated interaction networks.

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