Cell type-specific binding patterns reveal that TCF7L2 can be tethered to the genome by association with GATA3

BACKGROUND: The TCF7L2 transcription factor is linked to a variety of human diseases, including type 2 diabetes and cancer. One mechanism by which TCF7L2 could influence expression of genes involved in diverse diseases is by binding to distinct regulatory regions in different tissues. To test this hypothesis, we performed ChIP-seq for TCF7L2 in six human cell lines. RESULTS: We identified 116,000 non-redundant TCF7L2 binding sites, with only 1,864 sites common to the six cell lines. Using ChIP-seq, we showed that many genomic regions that are marked by both H3K4me1 and H3K27Ac are also bound by TCF7L2, suggesting that TCF7L2 plays a critical role in enhancer activity. Bioinformatic analysis of the cell type-specific TCF7L2 binding sites revealed enrichment for multiple transcription factors, including HNF4alpha and FOXA2 motifs in HepG2 cells and the GATA3 motif in MCF7 cells. ChIP-seq analysis revealed that TCF7L2 co-localizes with HNF4alpha and FOXA2 in HepG2 cells and with GATA3 in MCF7 cells. Interestingly, in MCF7 cells the TCF7L2 motif is enriched in most TCF7L2 sites but is not enriched in the sites bound by both GATA3 and TCF7L2. This analysis suggested that GATA3 might tether TCF7L2 to the genome at these sites. To test this hypothesis, we depleted GATA3 in MCF7 cells and showed that TCF7L2 binding was lost at a subset of sites. RNA-seq analysis suggested that TCF7L2 represses transcription when tethered to the genome via GATA3. CONCLUSIONS: Our studies demonstrate a novel relationship between GATA3 and TCF7L2, and reveal important insights into TCF7L2-mediated gene regulation

Comprehensive functional annotation of 77 prostate cancer risk loci.

Genome-wide association studies (GWAS) have revolutionized the field of cancer genetics, but the causal links between increased genetic risk and onset/progression of disease processes remain to be identified. Here we report the first step in such an endeavor for prostate cancer. We provide a comprehensive annotation of the 77 known risk loci, based upon highly correlated variants in biologically relevant chromatin annotations--we identified 727 such potentially functional SNPs. We also provide a detailed account of possible protein disruption, microRNA target sequence disruption and regulatory response element disruption of all correlated SNPs at r(2) ≥ 0.88%. 88% of the 727 SNPs fall within putative enhancers, and many alter critical residues in the response elements of transcription factors known to be involved in prostate biology. We define as risk enhancers those regions with enhancer chromatin biofeatures in prostate-derived cell lines with prostate-cancer correlated SNPs. To aid the identification of these enhancers, we performed genomewide ChIP-seq for H3K27-acetylation, a mark of actively engaged enhancers, as well as the transcription factor TCF7L2. We analyzed in depth three variants in risk enhancers, two of which show significantly altered androgen sensitivity in LNCaP cells. This includes rs4907792, that is in linkage disequilibrium (r(2) = 0.91) with an eQTL for NUDT11 (on the X chromosome) in prostate tissue, and rs10486567, the index SNP in intron 3 of the JAZF1 gene on chromosome 7. Rs4907792 is within a critical residue of a strong consensus androgen response element that is interrupted in the protective allele, resulting in a 56% decrease in its androgen sensitivity, whereas rs10486567 affects both NKX3-1 and FOXA-AR motifs where the risk allele results in a 39% increase in basal activity and a 28% fold-increase in androgen stimulated enhancer activity. Identification of such enhancer variants and their potential target genes represents a preliminary step in connecting risk to disease process

Comprehensive functional annotation of 77 prostate cancer risk loci

Genome-wide association studies (GWAS) have revolutionized the field of cancer genetics, but the causal links between increased genetic risk and onset/progression of disease processes remain to be identified. Here we report the first step in such an endeavor for prostate cancer. We provide a comprehensive annotation of the 77 known risk loci, based upon highly correlated variants in biologically relevant chromatin annotations— we identified 727 such potentially functional SNPs. We also provide a detailed account of possible protein disruption, microRNA target sequence disruption and regulatory response element disruption of all correlated SNPs at r2 ≥ 0.5. 88% of the 727 SNPs fall within putative enhancers, and many alter critical residues in the response elements of transcription factors known to be involved in prostate biology. We define as risk enhancers those regions with enhancer chromatin biofeatures in prostate-derived cell lines with prostate-cancer correlated SNPs. To aid the identification of these enhancers, we performed genomewide ChIP-seq for H3K27-acetylation, a mark of actively engaged enhancers, as well as the transcription factor TCF7L2. We analyzed in depth three variants in risk enhancers, two of which show significantly altered androgen sensitivity in LNCaP cells. This includes rs4907792, that is in linkage disequilibrium (r2 = 0.91) with an eQTL for NUDT11 (on the X chromosome) in prostate tissue, and rs10486567, the index SNP in intron 3 of the JAZF1 gene on chromosome 7. Rs4907792 is within a critical residue of a strong consensus androgen response element that is interrupted in the protective allele, resulting in a 56% decrease in its androgen sensitivity, whereas rs10486567 affects both NKX3-1 and FOXA-AR motifs where the risk allele results in a 39% increase in basal activity and a 28% fold-increase in androgen stimulated enhancer activity. Identification of such enhancer variants and their potential target genes represents a preliminary step in connecting risk to disease process

Primer sequences.

<p><b>Primers used in cloning enhancers for reporter assays.</b> The underlined portion highlights the and sites used for site-directed cloning of the PCR product. The PSA control is described in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004102#pgen.1004102-Jia1" target="_blank">[7]</a>.</p

Enrichment of Gene Ontology.

<p>Representative ontology clusters from DAVID <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004102#pgen.1004102-Huang1" target="_blank">[37]</a> enrichment analysis of nearby genes given in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004102#pgen-1004102-t001" target="_blank">Table 1</a>. Green boxes indicate membership of the genes (as columns) with the annotations (as rows). A. Transcription factor cluster. B. Male gonad development cluster.</p

Independent risk loci.

<p><b>Independent GWAS Loci.</b> Table of independent associations with prostate cancer. Index SNPs with are grouped together, and shown with source citations. A locus with a significant number of correlated SNPs at for two index SNPs that don't meet the cutoff are also considered the same locus. Also shown are the nearby genes (Gene) and population in which the associations were reported (Ethn).</p

miR-target variants.

<p><b>SNPs in miR target sequences.</b> Table of SNPs affecting putative miR target sequences in untranslated coding regions, and the potentially affected target genes.</p

Transcription Factor Response Elements are not enriched in PCa GWAS SNPs.

<p> express number of observed response element disruptions as a proportion relative to the standard deviation from the background distribution. The regression line is shown in blue with 95% confidence interval. Transcription factors of interest are highlighted with blue text. The inner box (dotted line) demarcates the 95% C.I. of a bootstrapped distribution for each PWM. A bonferroni box is outside the bounds of the graphic.</p

Results of <i>Funci{SNP}</i> analysis of GWAS correlated SNPs.

<p>Index SNPs with biofeatures and correlated SNPs at are combined and summarized in A–D. A. SNP counts by value. B. SNP counts by biofeature. Some SNPs map to more than one biofeature, hence the total does not sum to 727. C. Classification of 727 SNPs by <i>putative</i> functional category. D. Supervised clustering of SNPs by biofeature.</p

Annotation of the 8q24.21 region.

<p>The intergenic region between FAM84B and MYC is shown with biofeatures indicated as colored hashes in the inside tracks. Index SNPs are black, correlated enhancer snps are in green according to the convention in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004102#pgen-1004102-g003" target="_blank">Figure 3</a>. Chromatin capture 5C data are indicated as links (light blue) in the center, showing interactions between regions. Histogram (inset) indicates the distribution of the dataset, showing the tag density on the -axis <i>vs.</i> number of regions. The dotted line indicates <i>min.</i> tag-density cutoff for the display.</p