dCaP: detecting differential binding events in multiple conditions and proteins.

BACKGROUND: Current ChIP-seq studies are interested in comparing multiple epigenetic profiles across several cell types and tissues simultaneously for studying constitutive and differential regulation. Simultaneous analysis of multiple epigenetic features in many samples can gain substantial power and specificity than analyzing individual features and/or samples separately. Yet there are currently few tools can perform joint inference of constitutive and differential regulation in multi-feature-multi-condition contexts with statistical testing. Existing tools either test regulatory variation for one factor in multiple samples at a time, or for multiple factors in one or two samples. Many of them only identify binary rather than quantitative variation, which are sensitive to threshold choices. RESULTS: We propose a novel and powerful method called dCaP for simultaneously detecting constitutive and differential regulation of multiple epigenetic factors in multiple samples. Using simulation, we demonstrate the superior power of dCaP compared to existing methods. We then apply dCaP to two datasets from human and mouse ENCODE projects to demonstrate its utility. We show in the human dataset that the cell-type specific regulatory loci detected by dCaP are significantly enriched near genes with cell-type specific functions and disease relevance. We further show in the mouse dataset that dCaP captures genomic regions showing significant signal variations for TAL1 occupancy between two mouse erythroid cell lines. The novel TAL1 occupancy loci detected only by dCaP are highly enriched with GATA1 occupancy and differential gene expression, while those detected only by other methods are not. CONCLUSIONS: Here, we developed a novel approach to utilize the cooperative property of proteins to detect differential binding given multivariate ChIP-seq samples to provide better power, aiming for complementing existing approaches and providing new insights in the method development in this field.

Dynamics of the epigenetic landscape during erythroid differentiation after GATA1 restoration.

Interplays among lineage-specific nuclear proteins, chromatin modifying enzymes, and the basal transcription machinery govern cellular differentiation, but their dynamics of action and coordination with transcriptional control are not fully understood. Alterations in chromatin structure appear to establish a permissive state for gene activation at some loci, but they play an integral role in activation at other loci. To determine the predominant roles of chromatin states and factor occupancy in directing gene regulation during differentiation, we mapped chromatin accessibility, histone modifications, and nuclear factor occupancy genome-wide during mouse erythroid differentiation dependent on the master regulatory transcription factor GATA1. Notably, despite extensive changes in gene expression, the chromatin state profiles (proportions of a gene in a chromatin state dominated by activating or repressive histone modifications) and accessibility remain largely unchanged during GATA1-induced erythroid differentiation. In contrast, gene induction and repression are strongly associated with changes in patterns of transcription factor occupancy. Our results indicate that during erythroid differentiation, the broad features of chromatin states are established at the stage of lineage commitment, largely independently of GATA1. These determine permissiveness for expression, with subsequent induction or repression mediated by distinctive combinations of transcription factors.

Erythroid GATA1 function revealed by genome-wide analysis of transcription factor occupancy, histone modifications, and mRNA expression.

The transcription factor GATA1 regulates an extensive program of gene activation and repression during erythroid development. However, the associated mechanisms, including the contributions of distal versus proximal cis-regulatory modules, co-occupancy with other transcription factors, and the effects of histone modifications, are poorly understood. We studied these problems genome-wide in a Gata1 knockout erythroblast cell line that undergoes GATA1-dependent terminal maturation, identifying 2616 GATA1-responsive genes and 15,360 GATA1-occupied DNA segments after restoration of GATA1. Virtually all occupied DNA segments have high levels of H3K4 monomethylation and low levels of H3K27me3 around the canonical GATA binding motif, regardless of whether the nearby gene is induced or repressed. Induced genes tend to be bound by GATA1 close to the transcription start site (most frequently in the first intron), have multiple GATA1-occupied segments that are also bound by TAL1, and show evolutionary constraint on the GATA1-binding site motif. In contrast, repressed genes are further away from GATA1-occupied segments, and a subset shows reduced TAL1 occupancy and increased H3K27me3 at the transcription start site. Our data expand the repertoire of GATA1 action in erythropoiesis by defining a new cohort of target genes and determining the spatial distribution of cis-regulatory modules throughout the genome. In addition, we begin to establish functional criteria and mechanisms that distinguish GATA1 activation from repression at specific target genes. More broadly, these studies illustrate how a "master regulator" transcription factor coordinates tissue differentiation through a panoply of DNA and protein interactions.

A varying threshold method for ChIP peak-calling using multiple sources of information.

MOTIVATION: Gene regulation commonly involves interaction among DNA, proteins and biochemical conditions. Using chromatin immunoprecipitation (ChIP) technologies, protein-DNA interactions are routinely detected in the genome scale. Computational methods that detect weak protein-binding signals and simultaneously maintain a high specificity yet remain to be challenging. An attractive approach is to incorporate biologically relevant data, such as protein co-occupancy, to improve the power of protein-binding detection. We call the additional data related with the target protein binding as supporting tracks. RESULTS: We propose a novel but rigorous statistical method to identify protein occupancy in ChIP data using multiple supporting tracks (PASS2). We demonstrate that utilizing biologically related information can significantly increase the discovery of true protein-binding sites, while still maintaining a desired level of false positive calls. Applying the method to GATA1 restoration in mouse erythroid cell line, we detected many new GATA1-binding sites using GATA1 co-occupancy data. AVAILABILITY: http://stat.psu.edu/ approximately yuzhang/pass2.tar.

Repression of Stress-Induced LINE-1 Expression Protects Cancer Cell Subpopulations from Lethal Drug Exposure.

Maintenance of phenotypic heterogeneity within cell populations is an evolutionarily conserved mechanism that underlies population survival upon stressful exposures. We show that the genomes of a cancer cell subpopulation that survives treatment with otherwise lethal drugs, the drug-tolerant persisters (DTPs), exhibit a repressed chromatin state characterized by increased methylation of histone H3 lysines 9 and 27 (H3K9 and H3K27). We also show that survival of DTPs is, in part, maintained by regulators of H3K9me3-mediated heterochromatin formation and that the observed increase in H3K9me3 in DTPs is most prominent over long interspersed repeat element 1 (LINE-1). Disruption of the repressive chromatin over LINE-1 elements in DTPs results in DTP ablation, which is partially rescued by reducing LINE-1 expression or function.

Occupancy by key transcription factors is a more accurate predictor of enhancer activity than histone modifications or chromatin accessibility.

BACKGROUND: Regulated gene expression controls organismal development, and variation in regulatory patterns has been implicated in complex traits. Thus accurate prediction of enhancers is important for further understanding of these processes. Genome-wide measurement of epigenetic features, such as histone modifications and occupancy by transcription factors, is improving enhancer predictions, but the contribution of these features to prediction accuracy is not known. Given the importance of the hematopoietic transcription factor TAL1 for erythroid gene activation, we predicted candidate enhancers based on genomic occupancy by TAL1 and measured their activity. Contributions of multiple features to enhancer prediction were evaluated based on the results of these and other studies. RESULTS: TAL1-bound DNA segments were active enhancers at a high rate both in transient transfections of cultured cells (39 of 79, or 56%) and transgenic mice (43 of 66, or 65%). The level of binding signal for TAL1 or GATA1 did not help distinguish TAL1-bound DNA segments as active versus inactive enhancers, nor did the density of regulation-related histone modifications. A meta-analysis of results from this and other studies (273 tested predicted enhancers) showed that the presence of TAL1, GATA1, EP300, SMAD1, H3K4 methylation, H3K27ac, and CAGE tags at DNase hypersensitive sites gave the most accurate predictors of enhancer activity, with a success rate over 80% and a median threefold increase in activity. Chromatin accessibility assays and the histone modifications H3K4me1 and H3K27ac were sensitive for finding enhancers, but they have high false positive rates unless transcription factor occupancy is also included. CONCLUSIONS: Occupancy by key transcription factors such as TAL1, GATA1, SMAD1, and EP300, along with evidence of transcription, improves the accuracy of enhancer predictions based on epigenetic features.