Foxa2 and H2A.Z Mediate Nucleosome Depletion during Embryonic Stem Cell Differentiation

SummaryNucleosome occupancy is fundamental for establishing chromatin architecture. However, little is known about the relationship between nucleosome dynamics and initial cell lineage specification. Here, we determine the mechanisms that control global nucleosome dynamics during embryonic stem (ES) cell differentiation into endoderm. Both nucleosome depletion and de novo occupation occur during the differentiation process, with higher overall nucleosome density after differentiation. The variant histone H2A.Z and the winged helix transcription factor Foxa2 both act to regulate nucleosome depletion and gene activation, thus promoting ES cell differentiation, whereas DNA methylation promotes nucleosome occupation and suppresses gene expression. Nucleosome depletion during ES cell differentiation is dependent on Nap1l1-coupled SWI/SNF and INO80 chromatin remodeling complexes. Thus, both epigenetic and genetic regulators cooperate to control nucleosome dynamics during ES cell fate decisions

Transcriptional regulation of Satb1 in mouse trophoblast stem cells

SATB homeobox proteins are important regulators of developmental gene expression. Among the stem cell lineages that emerge during early embryonic development, trophoblast stem (TS) cells exhibit robust SATB expression. Both SATB1 and SATB2 act to maintain the trophoblast stem-state. However, the molecular mechanisms that regulate TS-specific Satb expression are not yet known. We identified Satb1 variant 2 as the predominant transcript in trophoblasts. Histone marks, and RNA polymerase II occupancy in TS cells indicated an active state of the promoter. A novel cis-regulatory region with active histone marks was identified ∼21 kbp upstream of the variant 2 promoter. CRISPR/Cas9 mediated disruption of this sequence decreased Satb1 expression in TS cells and chromosome conformation capture analysis confirmed looping of this distant regulatory region into the proximal promoter. Scanning position weight matrices across the enhancer predicted two ELF5 binding sites in close proximity to SATB1 sites, which were confirmed by chromatin immunoprecipitation. Knockdown of ELF5 downregulated Satb1 expression in TS cells and overexpression of ELF5 increased the enhancer-reporter activity. Interestingly, ELF5 interacts with SATB1 in TS cells, and the enhancer activity was upregulated following SATB overexpression. Our findings indicate that trophoblast-specific Satb1 expression is regulated by long-range chromatin looping of an enhancer that interacts with ELF5 and SATB proteins

Illuminating transcriptional regulation with genome -wide binding data

Over the last several years, the development of high-throughput in vivo techniques has significantly enhanced our ability to identify targets for transcription factors across the genome. In the first part of this thesis, I use ChIP-on-chip technology to identify targets in the adult mouse liver for Foxa2, a transcription factor that is known to regulate hepatic gene expression. I then introduce an analysis method that demonstrates that Foxa2-containing cis-regulatory modules are not constructed from a random assortment of binding sites for other transcription factors, but rather that the module composition depends on the strength of the Foxa2 consensus site present. Genes containing a cis-regulatory module with a medium or weak Foxa2 consensus site are much more liver-specific than the genes with a strong consensus site. These data show that Foxa2 may have different mechanisms by which it binds to and regulates genes. ChIP-on-chip is rapidly being replaced by ChIP-Seq technology, which combines ChIP with massively parallel sequencing. Identifying bound regions from the large number of sequence tags produced by ChIP-Seq is a challenging tank. Therefore, in the second part of my thesis, I develop GLITR ( GLobal Identifier of Target Regions), an algorithm that accurately identifies enriched regions in target data compared to control data and requires minimal user-defined thresholds. I compare GLITR to several existing methods and show that GLITR has improved sensitivity for identifying bound regions as compared to other algorithms. I also use GLITR to address the issue of sequencing depth, and show that sequencing biological replicates identifies far more binding regions than re-sequencing the same sample. Although ChIP-Seq technology is in its early stages of development, I discuss many of the fundamental aspects of both the experimental and analytical approaches to target identification. The novel methods I present will have a significant impact for future studies that investigate gene regulation by transcription factors

Illuminating transcriptional regulation with genome -wide binding data

Over the last several years, the development of high-throughput in vivo techniques has significantly enhanced our ability to identify targets for transcription factors across the genome. In the first part of this thesis, I use ChIP-on-chip technology to identify targets in the adult mouse liver for Foxa2, a transcription factor that is known to regulate hepatic gene expression. I then introduce an analysis method that demonstrates that Foxa2-containing cis-regulatory modules are not constructed from a random assortment of binding sites for other transcription factors, but rather that the module composition depends on the strength of the Foxa2 consensus site present. Genes containing a cis-regulatory module with a medium or weak Foxa2 consensus site are much more liver-specific than the genes with a strong consensus site. These data show that Foxa2 may have different mechanisms by which it binds to and regulates genes. ChIP-on-chip is rapidly being replaced by ChIP-Seq technology, which combines ChIP with massively parallel sequencing. Identifying bound regions from the large number of sequence tags produced by ChIP-Seq is a challenging tank. Therefore, in the second part of my thesis, I develop GLITR ( GLobal Identifier of Target Regions), an algorithm that accurately identifies enriched regions in target data compared to control data and requires minimal user-defined thresholds. I compare GLITR to several existing methods and show that GLITR has improved sensitivity for identifying bound regions as compared to other algorithms. I also use GLITR to address the issue of sequencing depth, and show that sequencing biological replicates identifies far more binding regions than re-sequencing the same sample. Although ChIP-Seq technology is in its early stages of development, I discuss many of the fundamental aspects of both the experimental and analytical approaches to target identification. The novel methods I present will have a significant impact for future studies that investigate gene regulation by transcription factors

Combined analysis of dissimilar promoter accessibility and gene expression profiles identifies tissue-specific genes and actively repressed networks

Abstract Background The assay for transposase-accessible chromatin (ATAC-seq) is a powerful method to examine chromatin accessibility. While many studies have reported a positive correlation between gene expression and promoter accessibility, few have investigated the genes that deviate from this trend. In this study, we aimed to understand the relationship between gene expression and promoter accessibility in multiple cell types while also identifying gene regulatory networks in the placenta, an understudied organ that is critical for a successful pregnancy. Results We started by assaying the open chromatin landscape in the mid-gestation placenta, when the fetal vasculature has started developing. After incorporating transcriptomic data generated in the placenta at the same time point, we grouped genes based on their expression levels and ATAC-seq promoter coverage. We found that the genes with the strongest correlation (high expression and high coverage) are likely involved in housekeeping functions, whereas tissue-specific genes were highly expressed and had only medium–low coverage. We also predicted that genes with medium–low expression and high promoter coverage were actively repressed. Within this group, we extracted a protein–protein interaction network enriched for neuronal functions, likely preventing the cells from adopting a neuronal fate. We further confirmed that a repressive histone mark is bound to the promoters of genes in this network. Finally, we ran our pipeline using ATAC-seq and RNA-seq data generated in ten additional cell types. We again found that genes with the strongest correlation are enriched for housekeeping functions and that genes with medium–low promoter coverage and high expression are more likely to be tissue-specific. These results demonstrate that only two data types, both of which require relatively low starting material to generate and are becoming more commonly available, can be integrated to understand multiple aspects of gene regulation. Conclusions Within the placenta, we identified an active placenta-specific gene network as well as a repressed neuronal network. Beyond the placenta, we demonstrate that ATAC-seq data and RNA-seq data can be integrated to identify tissue-specific genes and actively repressed gene networks in multiple cell types