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Computational Inference and Modularity Analysis of the ERα Transcription Regulatory Network

Research Scholar

Binhua Tang, Biomedical Informatics (China)
Victor Xin, Faculty Mentor


Binhua Tang is a Postdoctoral Researcher at the Wexner Medical Center at The Ohio State University. Previously he received a PhD in biomedical engineering from Tongji University, Shanghai, and a master's in detection and automation from Sun Yat-Sen University, Guangzhou. His current research includes NGS data analysis and statistical modeling in cancer genomics.

What is the issue or problem addressed in your research?

Estrogen receptor α (ERα) is an estrogen (E2)-inducible transcription factor (TF) and member of the nuclear receptor superfamily, the dysfunction of which accounts for 70% breast tumors. ERα binds to estrogen response elements (EREs) at target gene's regulatory regions, and works with other signaling components to control downstream transcriptional and translational processes.

Many recent genome-wide profiling studies of ERα have shown a highly complex regulation network involved with both ERα and other TFs. These studies revealed that many ERα binding sites could be located far away, up to 50-100kb, from a known transcription start site (TSS) and a large number of other TF binding sites (TFBSs) could be co-enriched with ERα binding sites, which constitute a hierarchical regulatory network with target hubs. However, the static network fails to capture dynamic properties of transcriptional regulation responses to estrogens, due to lack of time-series ChIP-seq data.

What methodology did you use in your research?

In this study, we integrated both E2-stimulated time-series ERα ChIP-seq data conducted in our laboratory and publically available E2-stimulated time-series gene expression data for reverse engineering the ERα-mediated transcriptional regulatory network.

We identified the ERα-centered TF hubs and their target genes from the ERα ChIP-seq data at the four time points after estrogen stimulation, and inferred the time-variant hierarchical network structures using a Bayesian multivariate modeling approach. Furthermore we analyzed the properties of network structures including global connectivity distribution, the correlation between the regulatory coefficients and components' signal-to-noise ratios with respect to absolute rank value distribution of regulatory strength. Finally, we used inherent recurrent motif patterns to determine self-bedded regulatory modules within the hierarchical networks. The Gene Ontology (GO) analyses were also performed to reveal distinct biological functions of ERα genes regulated by each module at different time. Together the survival analysis for the module-regulated targets based on three breast cancer patient data sets reveals statistically significant clinical outcomes.

What are the purpose/rationale and implications of your research?

In summary, our Bayesian statistical modeling and modularity analysis not only reveals the dynamic properties of the ERα-centered regulatory network and associated distinct biological functions, but also provides a reliable and effective genomic analytical approach for the analysis of dynamic regulatory network for any given TF.