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A Comprehensive Profile of ChIP-Seq-Based STAT1 Target Genes Suggests the Complexity of STAT1-Mediated Gene Regulatory Mechanisms.

ABSTRACT: Interferon-gamma (IFN?) plays a key role in macrophage activation, T helper and regulatory cell differentiation, defense against intracellular pathogens, tissue remodeling, and tumor surveillance. The diverse biological functions of IFN? are mediated by direct activation of signal transducer and activator of transcription 1 (STAT1) as well as numerous downstream effector genes. Because a perturbation in STAT1 target gene networks is closely associated with development of autoimmune diseases and cancers, it is important to characterize the global picture of these networks. Chromatin immunoprecipitation followed by deep sequencing (ChIP-Seq) provides a highly efficient method for genome-wide profiling of DNA-binding proteins. We analyzed the STAT1 ChIP-Seq dataset of IFN?-stimulated HeLa S3 cells derived from the ENCODE project, along with transcriptome analysis on microarray. We identified 1,441 stringent ChIP-Seq peaks of protein-coding genes. They were located in the promoter (21.5%) and more often in intronic regions (72.2%) with an existence of IFN?-activated site (GAS) elements. Among the 1,441 STAT1 target genes, 212 genes are known IFN-regulated genes (IRGs) and 194 genes (13.5%) are actually upregulated in response to IFN? by transcriptome analysis. The panel of upregulated genes constituted IFN-signaling molecular networks pivotal for host defense against infections, where interferon-regulatory factor (IRF) and STAT transcription factors serve as a hub on which biologically important molecular connections concentrate. The genes with the peak location in intronic regions showed significantly lower expression levels in response to IFN?. These results indicate that the binding of STAT1 to GAS is not sufficient to fully activate target genes, suggesting the high complexity of STAT1-mediated gene regulatory mechanisms.

SUBMITTER: Satoh J

PROVIDER: S-EPMC3623615 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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A Comprehensive Profile of ChIP-Seq-Based STAT1 Target Genes Suggests the Complexity of STAT1-Mediated Gene Regulatory Mechanisms.

Satoh Jun-Ichi J Tabunoki Hiroko H

Gene regulation and systems biology 20130326

Interferon-gamma (IFNγ) plays a key role in macrophage activation, T helper and regulatory cell differentiation, defense against intracellular pathogens, tissue remodeling, and tumor surveillance. The diverse biological functions of IFNγ are mediated by direct activation of signal transducer and activator of transcription 1 (STAT1) as well as numerous downstream effector genes. Because a perturbation in STAT1 target gene networks is closely associated with development of autoimmune diseases and ...[more]

PMID: 23645984

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Project description:BackgroundChIPx (i.e., ChIP-seq and ChIP-chip) is increasingly used to map genome-wide transcription factor (TF) binding sites. A single ChIPx experiment can identify thousands of TF bound genes, but typically only a fraction of these genes are functional targets that respond transcriptionally to perturbations of TF expression. To identify promising functional target genes for follow-up studies, researchers usually collect gene expression data from TF perturbation experiments to determine which of the TF targets respond transcriptionally to binding. Unfortunately, approximately 40% of ChIPx studies do not have accompanying gene expression data from TF perturbation experiments. For these studies, genes are often prioritized solely based on the binding strengths of ChIPx signals in order to choose follow-up candidates. ChIPXpress is a novel method that improves upon this ChIPx-only ranking approach by integrating ChIPx data with large amounts of Publicly available gene Expression Data (PED).ResultsWe demonstrate that PED does contain useful information to identify functional TF target genes despite its inherent heterogeneity. A truncated absolute correlation measure is developed to better capture the regulatory relationships between TFs and their target genes in PED. By integrating the information from ChIPx and PED, ChIPXpress can significantly increase the chance of finding functional target genes responsive to TF perturbation among the top ranked genes. ChIPXpress is implemented as an easy-to-use R/Bioconductor package. We evaluate ChIPXpress using 10 different ChIPx datasets in mouse and human and find that ChIPXpress rankings are more accurate than rankings based solely on ChIPx data and may result in substantial improvement in prediction accuracy, irrespective of which peak calling algorithm is used to analyze the ChIPx data.ConclusionsChIPXpress provides a new tool to better prioritize TF bound genes from ChIPx experiments for follow-up studies when investigators do not have their own gene expression data. It demonstrates that the regulatory information from PED can be used to boost ChIPx data analyses. It also represents an important step towards more fully utilizing the valuable, but highly heterogeneous data contained in public gene expression databases.

Dataset Information

A Comprehensive Profile of ChIP-Seq-Based STAT1 Target Genes Suggests the Complexity of STAT1-Mediated Gene Regulatory Mechanisms.

Publications

A Comprehensive Profile of ChIP-Seq-Based STAT1 Target Genes Suggests the Complexity of STAT1-Mediated Gene Regulatory Mechanisms.

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