Region Capture Micro-C reveals coalescence of enhancers and promoters into nested microcompartments

Goel, Viraat Y.; Huseyin, Miles K.; Hansen, Anders S.

doi:10.1101/2022.07.12.499637

Cited by 48 publications

(70 citation statements)

References 53 publications

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“…In contrast, we found that PLAC-seq shows a very weak enrichment of interactions at DHSs, even for those bound by CTCF. Thus, in line with other recent reports on MNase-based C-methods 6,30,31,[39][40][41] , our results suggest that MChIP-C achieves superior sensitivity and resolution compared to C-methods based on standard restriction enzymes.…”

Section: Mchip-c Provides a Sensitive Genome-wide View Of Promoter-ce...supporting

confidence: 92%

A genome-wide nucleosome-resolution map of promoter-centered interactions in human cells corroborates the enhancer-promoter looping model

Golov

Гаврилов

Kaplan

et al. 2023

Preprint

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The enhancer-promoter looping model, in which enhancers activate their target genes via physical contact, has long dominated the field of gene regulation. However, the ubiquity of this model has been questioned due to evidence of alternative mechanisms and the lack of its systematic validation, primarily owing to the absence of suitable experimental techniques. In this study, we present a new MNase-based proximity ligation method called MChIP-C, allowing for the measurement of protein-mediated chromatin interactions at single-nucleosome resolution on a genome-wide scale. By applying MChIP-C to study H3K4me3 promoter-centered interactions in K562 cells, we found that it had greatly improved resolution and sensitivity compared to restriction endonuclease-based C-methods. This allowed us to identify EP300 histone acetyltransferase and the SWI/SNF remodeling complex as potential candidates for establishing and/or maintaining enhancer-promoter interactions. Finally, leveraging data from published CRISPRi screens, we found that most functionally-verified enhancers do physically interact with their cognate promoters, supporting the enhancer-promoter looping model.

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Section: Mchip-c Provides a Sensitive Genome-wide View Of Promoter-ce...supporting

confidence: 92%

A genome-wide nucleosome-resolution map of promoter-centered interactions in human cells corroborates the enhancer-promoter looping model

Golov

Гаврилов

Kaplan

et al. 2023

Preprint

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“…5, Additional file 2: Figure S7). Previous capture Micro-C and MNase 3C capture studies [39][40][41] were focused on targeted regions and may provide higher sensitive signals using a relatively small number of reads. However, these methods detect limited chromatin interactions at the target regions while promoter capture Micro-C detects chromatin interactions of all promoter regions throughout the genome.…”

Section: Discussionmentioning

confidence: 99%

Characterizing chromatin interactions of regulatory elements and nucleosome positions, using Hi-C, Micro-C, and promoter capture Micro-C

Lee

Rhie

2022

Epigenetics & Chromatin

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Background Regulatory elements such as promoters, enhancers, and insulators interact each other to mediate molecular processes. To capture chromatin interactions of regulatory elements, 3C-derived methods such as Hi-C and Micro-C are developed. Here, we generated and analyzed Hi-C, Micro-C, and promoter capture Micro-C datasets with different sequencing depths to study chromatin interactions of regulatory elements and nucleosome positions in human prostate cancer cells. Results Compared to Hi-C, Micro-C identifies more high-resolution loops, including ones around structural variants. By evaluating the effect of sequencing depth, we revealed that more than 2 billion reads of Micro-C are needed to detect chromatin interactions at 1 kb resolution. Moreover, we found that deep-sequencing identifies additional long-range loops that are longer than 1 Mb in distance. Furthermore, we found that more than 50% of the loops are involved in insulators while less than 10% of the loops are promoter–enhancer loops. To comprehensively capture chromatin interactions that promoters are involved in, we performed promoter capture Micro-C. Promoter capture Micro-C identifies loops near promoters with a lower amount of sequencing reads. Sequencing of 160 million reads of promoter capture Micro-C resulted in reaching a plateau of identifying loops. However, there was still a subset of promoters that are not involved in loops even after deep-sequencing. By integrating Micro-C with NOMe-seq and ChIP-seq, we found that active promoters involved in loops have a more accessible region with lower levels of DNA methylation and more highly phased nucleosomes, compared to active promoters that are not involved in loops. Conclusion We determined the required sequencing depth for Micro-C and promoter capture Micro-C to generate high-resolution chromatin interaction maps and loops. We also investigated the effect of sequencing coverage of Hi-C, Micro-C, and promoter capture Micro-C on detecting chromatin loops. Our analyses suggest the presence of distinct regulatory element groups, which are differently involved in nucleosome positions and chromatin interactions. This study does not only provide valuable insights on understanding chromatin interactions of regulatory elements, but also present guidelines for designing research projects on chromatin interactions among regulatory elements.

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“…Additionally, different from models that are pre-trained and fine-tuned on DNA sequence only [9, 15], EPCOT learns cell-type specific representations from DNA sequence with chromatin accessibility signals, which is generalizable to new, unseen human cell and tissue types by only requiring cell-type specific chromatin accessibility data as input. Here, chromatin accessibility is leveraged to provide cell-type specific information due to its connection with TF binding, gene regulation, and chromatin contacts [16, 17, 18]. Furthermore, EPCOT trained on human cell types can be generalized to mouse cell and tissue types.…”

Section: Mainmentioning

confidence: 99%

A generalizable framework to comprehensively predict epigenome, chromatin organization, and transcriptome

Zhang

Qiu

Feng

et al. 2022

Preprint

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Many deep learning approaches have been proposed to connect DNA sequence, epigenetic profiles, chromatin organization and transcription activities. While these approaches achieve satisfactory performance in predicting one modality from another, the representations learned are not generalizable across predictive tasks or across cell types. In this paper, we propose a deep learning approach named EPCOT which employs a pre-training and fine-tuning framework, and comprehensively predicts epigenome, chromatin organization, transcriptome, and enhancer activity in one framework, which is also generalizable to new cell types. EPCOT not only achieves superior predictive performance in individual predictive tasks, it also produces globally optimized sequence representations that are generalizable across different predictive tasks. Interpreting EPCOT model also allows us to provide a number of tools and services to the research community including mapping between different genomic modalities, identifying TF sequence binding patterns, and analyzing cell-type specific TF impacts to enhancer activity.

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Region Capture Micro-C reveals coalescence of enhancers and promoters into nested microcompartments

Cited by 48 publications

References 53 publications

A genome-wide nucleosome-resolution map of promoter-centered interactions in human cells corroborates the enhancer-promoter looping model

A genome-wide nucleosome-resolution map of promoter-centered interactions in human cells corroborates the enhancer-promoter looping model

Characterizing chromatin interactions of regulatory elements and nucleosome positions, using Hi-C, Micro-C, and promoter capture Micro-C

A generalizable framework to comprehensively predict epigenome, chromatin organization, and transcriptome

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