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Hierarchical modularity in ER? transcriptional network is associated with distinct functions and implicates clinical outcomes.

ABSTRACT: Recent genome-wide profiling reveals highly complex regulation networks among ER? and its targets. We integrated estrogen (E2)-stimulated time-series ER? ChIP-seq and gene expression data to identify the ER?-centered transcription factor (TF) hubs and their target genes, and inferred the time-variant hierarchical network structures using a Bayesian multivariate modeling approach. With its recurrent motif patterns, we determined three embedded regulatory modules from the ER? core transcriptional network. The GO analyses revealed the distinct biological function associated with each of three embedded modules. The survival analysis showed the genes in each module were able to render a significant survival correlation in breast cancer patient cohorts. In summary, our Bayesian statistical modeling and modularity analysis not only reveals the dynamic properties of the ER?-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.

SUBMITTER: Tang B

PROVIDER: S-EPMC3500769 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Hierarchical modularity in ERα transcriptional network is associated with distinct functions and implicates clinical outcomes.

Tang Binhua B Hsu Hang-Kai HK Hsu Pei-Yin PY Bonneville Russell R Chen Su-Shing SS Huang Tim H-M TH Jin Victor X VX

Scientific reports 20121119

Recent genome-wide profiling reveals highly complex regulation networks among ERα and its targets. We integrated estrogen (E2)-stimulated time-series ERα ChIP-seq and gene expression data to identify the ERα-centered transcription factor (TF) hubs and their target genes, and inferred the time-variant hierarchical network structures using a Bayesian multivariate modeling approach. With its recurrent motif patterns, we determined three embedded regulatory modules from the ERα core transcriptional ...[more]

PMID: 23166858

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Project description:In biological networks of molecular interactions in a cell, network motifs that are biologically relevant are also functionally coherent, or form functional modules. These functionally coherent modules combine in a hierarchical manner into larger, less cohesive subsystems, thus revealing one of the essential design principles of system-level cellular organization and function-hierarchical modularity. Arguably, hierarchical modularity has not been explicitly taken into consideration by most, if not all, functional annotation systems. As a result, the existing methods would often fail to assign a statistically significant functional coherence score to biologically relevant molecular machines. We developed a methodology for hierarchical functional annotation. Given the hierarchical taxonomy of functional concepts (e.g., Gene Ontology) and the association of individual genes or proteins with these concepts (e.g., GO terms), our method will assign a Hierarchical Modularity Score (HMS) to each node in the hierarchy of functional modules; the HMS score and its p-value measure functional coherence of each module in the hierarchy. While existing methods annotate each module with a set of "enriched" functional terms in a bag of genes, our complementary method provides the hierarchical functional annotation of the modules and their hierarchically organized components. A hierarchical organization of functional modules often comes as a bi-product of cluster analysis of gene expression data or protein interaction data. Otherwise, our method will automatically build such a hierarchy by directly incorporating the functional taxonomy information into the hierarchy search process and by allowing multi-functional genes to be part of more than one component in the hierarchy. In addition, its underlying HMS scoring metric ensures that functional specificity of the terms across different levels of the hierarchical taxonomy is properly treated. We have evaluated our method using Saccharomyces cerevisiae data from KEGG and MIPS databases and several other computationally derived and curated datasets. The code and additional supplemental files can be obtained from http://code.google.com/p/functional-annotation-of-hierarchical-modularity/ (Accessed 2012 March 13).

Project description:The two estrogen receptors ERα and ERβ are nuclear receptors that bind estrogen (E2) and function as ligand-inducible transcription factors. They are homologues and can form dimers with each other and bind to the same estrogen-response element motifs in the DNA. ERα drives breast cancer growth whereas ERβ has been reported to be anti-proliferative. However, they are rarely expressed in the same cells, and it is not fully investigated to which extent their functions are different because of inherent differences or because of different cellular context. To dissect their similarities and differences, we here generated a novel estrogen-dependent cell model where ERα homodimers can be directly compared to ERβ homodimers within the identical cellular context. By using CRISPR-cas9 to delete ERα in breast cancer MCF7 cells with Tet-Off-inducible ERβ expression, we generated MCF7 cells that express ERβ but not ERα. MCF7 (ERβ only) cells exhibited regulation of estrogen-responsive targets in a ligand-dependent manner. We demonstrated that either ER was required for MCF7 proliferation, but while E2 increased proliferation via ERα, it reduced proliferation through a G2/M arrest via ERβ. The two ERs also impacted migration differently. In absence of ligand, ERβ increased migration, but upon E2 treatment, ERβ reduced migration. E2 via ERα, on the other hand, had no significant impact on migration. RNA sequencing revealed that E2 regulated a transcriptome of around 800 genes via each receptor, but over half were specific for either ERα or ERβ (417 and 503 genes, respectively). Functional gene ontology enrichment analysis reinforced that E2 regulated cell proliferation in opposite directions depending on the ER, and that ERβ specifically impacted extracellular matrix organization. We corroborated that ERβ bound to cis-regulatory chromatin of its unique proposed migration-related direct targets ANXA9 and TFAP2C. In conclusion, we demonstrate that within the same cellular context, the two ERs regulate cell proliferation in the opposite manner, impact migration differently, and each receptor also regulates a distinct set of target genes in response to E2. The developed cell model provides a novel and valuable resource to further complement the mechanistic understanding of the two different ER isoforms.

Dataset Information

Hierarchical modularity in ER? transcriptional network is associated with distinct functions and implicates clinical outcomes.

Publications

Hierarchical modularity in ERα transcriptional network is associated with distinct functions and implicates clinical outcomes.

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