Abstract 16771: Inferring Dynamic Gene-microRNA Regulatory Networks in Cardiac Differentiation by Integrating Multi-Dimensional Data
Background: Decoding the temporal control of gene and microRNA expression patterns are key to understanding the complex mechanism of developmental decisions during heart development. High-throughput methods have been employed to systematically study the dynamic and coordinated nature of cardiac differentiation at the global level with multiple dimensions. There is pressing need to develop systems way to integrate these data from individual studies and infer the dynamic regulatory networks in an unbiased fashion.
Methods: We developed a two-step strategy to integrate data from (1) temporal RNA-seq, (2) temporal histone modifications ChIP-seq, (3) temporal microRNA microarray, (4) microRNA target sites, (5) transcription factor (TF) ChIP-seq and (6) gene perturbation, to reconstruct the dynamic network. First, we trained a logistic regression model to predict the probability (LR score) of any base being bound by 543 TFs with known positional weight matrices. Second, six dimensions of data were combined by time-varying dynamic Bayesian network model to infer the dynamic networks. Our method not only infers the time-varying networks between different stages of heart development, but also identifies the TF binding sites.
Results: The LR scores of known ESC and heart enhancers were significantly higher than random regions (p <10-100), suggesting that a high LR score is a reliable indicator for functional TF binding sites. Our network inference model identified a region with elevated LR score approximately -9400 bp upstream of the transcriptional start site of Nkx2-5, which overlapped with a previously reported enhancer region (-9435 to -8922 bp). TFs such as Tead1, Gata4, Msx2, and Tgif1 were predicted to bind to this region and participate in the regulation of Nkx2-5. Our model also predicted 435 significant TF-microRNA-gene network motifs that may be important for the differentiation from cardiac progenitors to cardiomyocytes.
Conclusions: We report a novel method to systematically integrate multi-dimensional omics data and reconstruct the gene-microRNA regulatory networks. This method will allow one to rapidly determine the cis-modules and TF-microRNA-gene network motifs that regulate key genes during cardiac differentiation.
Author Disclosures: W. Gong: None. N. Koyano-Nakagawa: None. D.J. Garry: None.
- © 2014 by American Heart Association, Inc.