17 research outputs found

    Fine-Resolution Mapping of TF Binding and Chromatin Interactions

    No full text
    Summary: Transcription factor (TF) binding to DNA is crucial for transcriptional regulation. There are multiple methods for mapping such binding. These methods balance between input requirements, spatial resolution, and compatibility with high-throughput automation. Here, we describe SLIM-ChIP (short-fragment-enriched, low-input, indexed MNase ChIP), which combines enzymatic fragmentation of chromatin and on-bead indexing to address these desiderata. SLIM-ChIP reproduces a high-resolution binding map of yeast Reb1 comparable with existing methods, yet with less input material and full compatibility with high-throughput procedures. We demonstrate the robustness and flexibility of SLIM-ChIP by probing additional factors in yeast and mouse. Finally, we show that SLIM-ChIP provides information on the chromatin landscape surrounding the bound transcription factor. We identify a class of Reb1 sites where the proximal −1 nucleosome tightly interacts with Reb1 and maintains unidirectional transcription. SLIM-ChIP is an attractive solution for mapping DNA binding proteins and charting the surrounding chromatin occupancy landscape at a single-cell level. : Mapping transcription factors binding to DNA by chromatin immunoprecipitation sequencing is a key step in studying transcriptional programs. Gutin et al. introduce SLIM-ChIP, a simple, automation compatible protocol, that provides insights about the chromatin landscape at the bound sites. Using this protocol, they discover promoter architectures that enforce unidirectional transcription. Keywords: Reb1, CTCF, ChIP-seq, chromatin, transcription factor, DNA-binding, promoter directionality, nucleosome

    Fine-Resolution Mapping of TF Binding and Chromatin Interactions

    No full text
    Summary: Transcription factor (TF) binding to DNA is crucial for transcriptional regulation. There are multiple methods for mapping such binding. These methods balance between input requirements, spatial resolution, and compatibility with high-throughput automation. Here, we describe SLIM-ChIP (short-fragment-enriched, low-input, indexed MNase ChIP), which combines enzymatic fragmentation of chromatin and on-bead indexing to address these desiderata. SLIM-ChIP reproduces a high-resolution binding map of yeast Reb1 comparable with existing methods, yet with less input material and full compatibility with high-throughput procedures. We demonstrate the robustness and flexibility of SLIM-ChIP by probing additional factors in yeast and mouse. Finally, we show that SLIM-ChIP provides information on the chromatin landscape surrounding the bound transcription factor. We identify a class of Reb1 sites where the proximal −1 nucleosome tightly interacts with Reb1 and maintains unidirectional transcription. SLIM-ChIP is an attractive solution for mapping DNA binding proteins and charting the surrounding chromatin occupancy landscape at a single-cell level. : Mapping transcription factors binding to DNA by chromatin immunoprecipitation sequencing is a key step in studying transcriptional programs. Gutin et al. introduce SLIM-ChIP, a simple, automation compatible protocol, that provides insights about the chromatin landscape at the bound sites. Using this protocol, they discover promoter architectures that enforce unidirectional transcription. Keywords: Reb1, CTCF, ChIP-seq, chromatin, transcription factor, DNA-binding, promoter directionality, nucleosome

    Deciphering eukaryotic gene-regulatory logic with 100 million random promoters

    No full text
    How transcription factors (TFs) interpret cis-regulatory DNA sequence to control gene expression remains unclear, largely because past studies using native and engineered sequences had insufficient scale. Here, we measure the expression output of >100 million synthetic yeast promoter sequences that are fully random. These sequences yield diverse, reproducible expression levels that can be explained by their chance inclusion of functional TF binding sites. We use machine learning to build interpretable models of transcriptional regulation that predict ~94% of the expression driven from independent test promoters and ~89% of the expression driven from native yeast promoter fragments. These models allow us to characterize each TF’s specificity, activity and interactions with chromatin. TF activity depends on binding-site strand, position, DNA helical face and chromatin context. Notably, expression level is influenced by weak regulatory interactions, which confound designed-sequence studies. Our analyses show that massive-throughput assays of fully random DNA can provide the big data necessary to develop complex, predictive models of gene regulation. ©2019, The Author(s), under exclusive licence to Springer Nature America, Inc.NIH (grant no. K99-HG009920-01)Fellowship from the Canadian Institutes for Health ResearchMIT Presidential Fellowshi
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