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ChIP-nexus data for Oct4, Sox2, Nanog and Klf4 in mouse embryonic stem cells

ABSTRACT: The goal of this study was discover the transcription binding synthax for the key differentiation TFs in mouse embryonic stem cells. Genes are regulated through enhancer sequences, in which transcription factor binding motifs and their specific arrangements (syntax) form a cis-regulatory code. To understand the relationship between motif syntax and transcription factor binding, we train a deep learning model that uses DNA sequence to predict base-resolution binding profiles of four pluripotency transcription factors Oct4, Sox2, Nanog, and Klf4. We interpret the model to accurately map hundreds of thousands of motifs in the genome, learn novel motif representations and identify rules by which motifs and syntax influence transcription factor binding. We find that instances of strict motif spacing are largely due to retrotransposons, but that soft motif syntax influences motif interactions at protein and nucleosome range. Most strikingly, Nanog binding is driven by motifs with a strong preference for ~10.5 bp spacings corresponding to helical periodicity. Interpreting deep learning models applied to high-resolution binding data is a powerful and versatile approach to uncover the motifs and syntax of cis-regulatory sequences.

ORGANISM(S): Mus musculus

PROVIDER: GSE137193 | GEO | 2020/11/21

REPOSITORIES: GEO

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Project description:Spatiotemporal gene regulation during embryonic development is driven by cis-regulatory DNA sequences called enhancers. Enhancers are activated through a combination of transcription factors (TFs) that bind to short sequence motifs within these sequences, but the order of events by which TFs read out motifs is not clear. Some TFs can only bind chromatin that is already accessible, while other TFs called pioneers can open chromatin themselves. Identifying motifs and the order by which they drive chromatin accessibility is very challenging. The recent implementation of convolutional neural networks, which learn complex cis-regulatory sequence rules that are predictive for genomics data, provides an unprecedented opportunity to dissect this problem. Here, we trained base-resolution deep learning models and applied them to high-resolution TF binding and chromatin accessibility data from the well-studied early Drosophila embryo. We uncover a clear hierarchical relationship between the pioneer Zelda and the TFs involved in the spatiotemporal patterning of the embryo, consistent with Zelda being a pioneer. However, the models predict that patterning TFs can also augment chromatin accessibility in a context-specific manner. Using a series of Drosophila mutant strains, we find that the two types of TFs increase chromatin accessibility by distinct mechanisms. Zelda’s pioneering is proportional to motif affinity, while the patterning TFs specifically increase chromatin accessibility when they mediate enhancer activation. This was conclusive because Dorsal can function both as activator and repressor, and the effect on chromatin accessibility depended on Dorsal’s transactivation effect and not on its binding per se. In conclusion, chromatin accessibility occurs in two phases: one through pioneering, which makes regions first accessible but not necessarily active, and a second when the correct combination of transcription factors lead to enhancer activation.

Project description:Contemporary high throughput technologies permit the rapid identification of transcription factor (TF) target genes on a genome-wide scale, yet the functional significance of TFs requires knowledge of target gene expression patterns, cooperating TFs and cis-regulatory element (CRE) structures. Here we investigated the myogenic regulatory network downstream of the Drosophila zinc finger TF Lame duck (Lmd) by combining both previously published and newly performed genomic data sets, including chromatin immunoprecipitation sequencing (ChIP-seq), genome-wide mRNA profiling, cell-specific expression patterns of putative transcriptional targets, analysis of histone mark signatures, studies of TF co-occupancy by additional mesodermal regulators, TF binding site determination using protein binding microarrays (PBMs), and machine learning of candidate CRE motif compositions. Our findings suggest that Lmd orchestrates an extensive myogenic regulatory network, a conclusion supported by the identification of Lmd-dependent genes, histone signatures of Lmd-bound genomic regions, and the relationship of these features to cell-specific gene expression patterns. The heterogeneous co-occupancy of Lmd-bound regions with additional mesodermal regulators revealed that different transcriptional inputs are used to mediate similar myogenic gene expression patterns. Machine learning further demonstrated diverse combinatorial motif patterns within tissue-specific Lmd-bound regions. PBM analysis established the complete spectrum of Lmd DNA binding specificities, and site-directed mutagenesis of Lmd and additional, newly discovered motifs in known enhancers demonstrated the critical role of these TF binding sites in supporting full enhancer activity. Collectively, these findings provide new insights into the transcriptional codes regulating muscle gene expression, and offer a generalizable approach for similar studies in other systems. Examination of Lmd occupancy to genomic DNA from sorted mesodermal cells

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ChIP-nexus data for Oct4, Sox2, Nanog and Klf4 in mouse embryonic stem cells

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