Spatiotemporal clustering of the epigenome reveals rules of dynamic gene regulation

Abstract

Spatial organization of different epigenomic marks was used to infer functions of the epigenome. It remains unclear what can be learned from the temporal changes of the epigenome. Here, we developed a probabilistic model to cluster genomic sequences based on the similarity of temporal changes of multiple epigenomic marks during a cellular differentiation process. We differentiated mouse embryonic stem (ES) cells into mesendoderm cells. At three time points during this differentiation process, we used high-throughput sequencing to measure seven histone modifications and variants-H3K4me1/2/3, H3K27ac, H3K27me3, H3K36me3, and H2A.Z; two DNA modifications-5-mC and 5-hmC; and transcribed mRNAs and noncoding RNAs (ncRNAs). Genomic sequences were clustered based on the spatiotemporal epigenomic information. These clusters not only clearly distinguished gene bodies, promoters, and enhancers, but also were predictive of bidirectional promoters, miRNA promoters, and piRNAs. This suggests specific epigenomic patterns exist on piRNA genes much earlier than germ cell development. Temporal changes of H3K4me2, unmethylated CpG, and H2A.Z were predictive of 5-hmC changes, suggesting unmethylated CpG and H3K4me2 as potential upstream signals guiding TETs to specific sequences. Several rules on combinatorial epigenomic changes and their effects on mRNA expression and ncRNA expression were derived, including a simple rule governing the relationship between 5-hmC and gene expression levels. A Sox17 enhancer containing a FOXA2 binding site and a Foxa2 enhancer containing a SOX17 binding site were identified, suggesting a positive feedback loop between the two mesendoderm transcription factors. These data illustrate the power of using epigenome dynamics to investigate regulatory functions.

Characterization of the neural stem cell gene regulatory network identifies OLIG2 as a multifunctional regulator of self-renewal

“…We focused on enhancer CREs, since these were expected to contribute more to cell type-specific gene expression (Heintzman et al 2009;Hawkins et al 2011;Yu et al 2013), and selected a set of TFs whose motifs were enriched in enhancers, which were robustly expressed in self-renewing NS cells (see FPKM expression values from RNAseq, below) and for which we could obtain ChIP-grade antibodies. We selected an antibody that specifically recognizes all four NFI family members (NFIA [FPKM = 36], NFIB [FPKM = 28], NFIC [FPKM = 19], NFIX [FPKM = 28]) .…”

Exposure to the widely used herbicide atrazine results in deregulation of global tissue-specific RNA transcription in the third generation and is associated with a global decrease of histone trimethylation in mice

“…To classify the differential regions as enhancers or promoters, we used a published dataset, which includes both H3K4me3 and H3K4me1 ChIP-seq data of mouse embryonic stem cells (75). For each region with altered H3K4me3 occupancy, we calculated the intensities of H3K4me3 and H3K4me1 marks from mouse embryonic stem cells.…”

Cotranscriptional histone H2B monoubiquitylation is tightly coupled with RNA polymerase II elongation rate

“…However, most of the studies examine H3K36me3 levels under steady-state conditions, and the few studies that did test the dynamic behavior of H3K36me3 reached contradicting conclusions (Edmunds et al 2008;Wada et al 2009;de Almeida et al 2011;Le Martelot et al 2012;Yu et al 2013). Thus, we examined the dynamics of H3K36me3 during transcription elongation using the same assay as above.…”