Myles Brown

Model-based analysis of ChIP-Seq (MACS).

We present Model-based Analysis of ChIP-Seq data, MACS, which analyzes data generated by short read sequencers such as Solexas Genome Analyzer. MACS empirically models the shift size of ChIP-Seq tags, and uses it to improve the spatial resolution of predicted binding sites. MACS also uses a dynamic Poisson distribution to effectively capture local biases in the genome, allowing for more robust predictions. MACS compares favorably to existing ChIP-Seq peak-finding algorithms, and is freely available.

Cofactor Dynamics and Sufficiency in Estrogen Receptor–Regulated Transcription

Many cofactors bind the hormone-activated estrogen receptor (ER), yet the specific regulators of endogenous ER-mediated gene transcription are unknown. Using chromatin immunoprecipitation (ChIP), we find that ER and a number of coactivators rapidly associate with estrogen responsive promoters following estrogen treatment in a cyclic fashion that is not predicted by current models of hormone activation. Cycles of ER complex assembly are followed by transcription. In contrast, the anti-estrogen tamoxifen (TAM) recruits corepressors but not coactivators. Using a genetic approach, we show that recruitment of the p160 class of coactivators is sufficient for gene activation and for the growth stimulatory actions of estrogen in breast cancer supporting a model in which ER cofactors play unique roles in estrogen signaling.

Genome-wide analysis of estrogen receptor binding sites

The estrogen receptor is the master transcriptional regulator of breast cancer phenotype and the archetype of a molecular therapeutic target. We mapped all estrogen receptor and RNA polymerase II binding sites on a genome-wide scale, identifying the authentic cis binding sites and target genes, in breast cancer cells. Combining this unique resource with gene expression data demonstrates distinct temporal mechanisms of estrogen-mediated gene regulation, particularly in the case of estrogen-suppressed genes. Furthermore, this resource has allowed the identification of cis-regulatory sites in previously unexplored regions of the genome and the cooperating transcription factors underlying estrogen signaling in breast cancer.

Chromosome-Wide Mapping of Estrogen Receptor Binding Reveals Long-Range Regulation Requiring the Forkhead Protein FoxA1

Estrogen plays an essential physiologic role in reproduction and a pathologic one in breast cancer. The completion of the human genome has allowed the identification of the expressed regions of protein-coding genes; however, little is known concerning the organization of their cis-regulatory elements. We have mapped the association of the estrogen receptor (ER) with the complete nonrepetitive sequence of human chromosomes 21 and 22 by combining chromatin immunoprecipitation (ChIP) with tiled microarrays. ER binds selectively to a limited number of sites, the majority of which are distant from the transcription start sites of regulated genes. The unbiased sequence interrogation of the genuine chromatin binding sites suggests that direct ER binding requires the presence of Forkhead factor binding in close proximity. Furthermore, knockdown of FoxA1 expression blocks the association of ER with chromatin and estrogen-induced gene expression demonstrating the necessity of FoxA1 in mediating an estrogen response in breast cancer cells.

FoxA1 Translates Epigenetic Signatures into Enhancer-Driven Lineage-Specific Transcription

Complex organisms require tissue-specific transcriptional programs, yet little is known about how these are established. The transcription factor FoxA1 is thought to contribute to gene regulation through its ability to act as a pioneer factor binding to nucleosomal DNA. Through genome-wide positional analyses, we demonstrate that FoxA1 cell type-specific functions rely primarily on differential recruitment to chromatin predominantly at distant enhancers rather than proximal promoters. This differential recruitment leads to cell type-specific changes in chromatin structure and functional collaboration with lineage-specific transcription factors. Despite the ability of FoxA1 to bind nucleosomes, its differential binding to chromatin sites is dependent on the distribution of histone H3 lysine 4 dimethylation. Together, our results suggest that methylation of histone H3 lysine 4 is part of the epigenetic signature that defines lineage-specific FoxA1 recruitment sites in chromatin. FoxA1 translates this epigenetic signature into changes in chromatin structure thereby establishing lineage-specific transcriptional enhancers and programs.

Differential Activation of Peroxisome Proliferator-activated Receptors by Eicosanoids

Peroxisome proliferator-activated receptors (PPARs) are nuclear hormone receptors that regulate gene transcription in response to peroxisome proliferators and fatty acids. PPARs also play an important role in the regulation of adipocyte differentiation. It is unclear, however, what naturally occurring compounds activate each of the PPAR subtypes. To address this issue, a screening assay was established using heterologous fusions of the bacterial tetracycline repressor to several members of the peroxisome proliferator-activated receptor (PPAR) family. This assay was employed to compare the activation of PPAR family members by known PPAR activators including peroxisome proliferators and fatty acids. Interestingly, the activation of PPARs by fatty acids was partially inhibited by the cyclooxygenase inhibitor indomethacin, which prevents prostaglandin synthesis. Indeed, prostaglandins PGA1 and 2, PGD1 and 2, and PGJ2-activated PPARs, while a number of other prostaglandins had no effect. We also screened a variety of hydroxyeicosatetraenoic acids (HETEs) for the ability to activate PPARs. 8(S)-HETE, but not other (S)-HETEs, was a strong activator of PPARα. Remarkably, PPAR activation by 8(S)-HETE was stereoselective. In addition, 8(S)-HETE was able to induce differentiation of 3T3-L1 preadipocytes. These results indicate that PPARs are differentially activated by naturally occurring eicosanoids and related molecules.

Androgen Receptor Regulates a Distinct Transcription Program in Androgen-Independent Prostate Cancer

The evolution of prostate cancer from an androgen-dependent state to one that is androgen-independent marks its lethal progression. The androgen receptor (AR) is essential in both, though its function in androgen-independent cancers is poorly understood. We have defined the direct AR-dependent target genes in both androgen-dependent and -independent cancer cells by generating AR-dependent gene expression profiles and AR cistromes. In contrast to what is found in androgen-dependent cells, AR selectively upregulates M-phase cell-cycle genes in androgen-independent cells, including UBE2C, a gene that inactivates the M-phase checkpoint. We find that epigenetic marks at the UBE2C enhancer, notably histone H3K4 methylation and FoxA1 transcription factor binding, are present in androgen-independent cells and direct AR-enhancer binding and UBE2C activation. Thus, the role of AR in androgen-independent cancer cells is not to direct the androgen-dependent gene expression program without androgen, but rather to execute a distinct program resulting in androgen-independent growth.

Formation of the Androgen Receptor Transcription Complex

Androgen receptor (AR) is required for sexual differentiation and is implicated in the development of prostate cancer. Here we describe distinct functions for cofactor proteins and gene regulatory elements in the assembly of AR-mediated transcription complexes. The formation of an activation complex involves AR, coactivators, and RNA polymerase II recruitment to both the enhancer and promoter, whereas the formation of a repression complex involves factors bound only at the promoter and not the enhancer. These results suggest a model for the functional coordination between the promoter and enhancer in which communication between these elements is established through shared coactivators in the AR transcription complex.

AIB1 Is a Conduit for Kinase-Mediated Growth Factor Signaling to the Estrogen Receptor

ABSTRACT Growth factor modulation of estrogen receptor (ER) activity plays an important role in both normal estrogen physiology and the pathogenesis of breast cancer. Growth factors are known to stimulate the ligand-independent activity of ER through the activation of mitogen-activated protein kinase (MAPK) and the direct phosphorylation of ER. We found that the transcriptional activity of AIB1, a ligand-dependent ER coactivator and a gene amplified preferentially in ER-positive breast cancers, is enhanced by MAPK phosphorylation. We demonstrate that AIB1 is a phosphoprotein in vivo and can be phosphorylated in vitro by MAPK. Finally, we observed that MAPK activation of AIB1 stimulates the recruitment of p300 and associated histone acetyltransferase activity. These results suggest that the ability of growth factors to modulate estrogen action may be mediated through MAPK activation of the nuclear receptor coactivator AIB1.

Model-based analysis of tiling-arrays for ChIP-chip

We propose a fast and powerful analysis algorithm, titled Model-based Analysis of Tiling-arrays (MAT), to reliably detect regions enriched by transcription factor chromatin immunoprecipitation (ChIP) on Affymetrix tiling arrays (ChIP-chip). MAT models the baseline probe behavior by considering probe sequence and copy number on each array. It standardizes the probe value through the probe model, eliminating the need for sample normalization. MAT uses an innovative function to score regions for ChIP enrichment, which allows robust P value and false discovery rate calculations. MAT can detect ChIP regions from a single ChIP sample, multiple ChIP samples, or multiple ChIP samples with controls with increasing accuracy. The single-array ChIP region detection feature minimizes the time and monetary costs for laboratories newly adopting ChIP-chip to test their protocols and antibodies and allows established ChIP-chip laboratories to identify samples with questionable quality that might contaminate their data. MAT is developed in open-source Python and is available at http://chip.dfci.harvard.edu/∼wli/MAT. The general framework presented here can be extended to other oligonucleotide microarrays and tiling array platforms.