1
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Kumar Halder A, Agarwal A, Jodkowska K, Plewczynski D. A systematic analyses of different bioinformatics pipelines for genomic data and its impact on deep learning models for chromatin loop prediction. Brief Funct Genomics 2024; 23:538-548. [PMID: 38555493 DOI: 10.1093/bfgp/elae009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 02/07/2024] [Accepted: 03/04/2024] [Indexed: 04/02/2024] Open
Abstract
Genomic data analysis has witnessed a surge in complexity and volume, primarily driven by the advent of high-throughput technologies. In particular, studying chromatin loops and structures has become pivotal in understanding gene regulation and genome organization. This systematic investigation explores the realm of specialized bioinformatics pipelines designed specifically for the analysis of chromatin loops and structures. Our investigation incorporates two protein (CTCF and Cohesin) factor-specific loop interaction datasets from six distinct pipelines, amassing a comprehensive collection of 36 diverse datasets. Through a meticulous review of existing literature, we offer a holistic perspective on the methodologies, tools and algorithms underpinning the analysis of this multifaceted genomic feature. We illuminate the vast array of approaches deployed, encompassing pivotal aspects such as data preparation pipeline, preprocessing, statistical features and modelling techniques. Beyond this, we rigorously assess the strengths and limitations inherent in these bioinformatics pipelines, shedding light on the interplay between data quality and the performance of deep learning models, ultimately advancing our comprehension of genomic intricacies.
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Affiliation(s)
- Anup Kumar Halder
- Laboratory of Bioinformatics and Computational Genomics, Faculty of Mathematics and Information Science, Warsaw University of Technology, Koszykowa 75, 00-662 Warsaw, Poland
- Laboratory of Functional and Structural Genomics, Centre of New Technologies, University of Warsaw, Banacha 2c, 02-097 Warsaw, Poland
| | - Abhishek Agarwal
- Laboratory of Functional and Structural Genomics, Centre of New Technologies, University of Warsaw, Banacha 2c, 02-097 Warsaw, Poland
| | - Karolina Jodkowska
- Laboratory of Functional and Structural Genomics, Centre of New Technologies, University of Warsaw, Banacha 2c, 02-097 Warsaw, Poland
| | - Dariusz Plewczynski
- Laboratory of Bioinformatics and Computational Genomics, Faculty of Mathematics and Information Science, Warsaw University of Technology, Koszykowa 75, 00-662 Warsaw, Poland
- Laboratory of Functional and Structural Genomics, Centre of New Technologies, University of Warsaw, Banacha 2c, 02-097 Warsaw, Poland
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2
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Yu Z, Wang Q, Zhang Q, Tian Y, Yan G, Zhu J, Zhu G, Zhang Y. Decoding the genomic landscape of chromatin-associated biomolecular condensates. Nat Commun 2024; 15:6952. [PMID: 39138204 PMCID: PMC11322608 DOI: 10.1038/s41467-024-51426-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 08/05/2024] [Indexed: 08/15/2024] Open
Abstract
Biomolecular condensates play a significant role in chromatin activities, primarily by concentrating and compartmentalizing proteins and/or nucleic acids. However, their genomic landscapes and compositions remain largely unexplored due to a lack of dedicated computational tools for systematic identification in vivo. To address this, we develop CondSigDetector, a computational framework designed to detect condensate-like chromatin-associated protein co-occupancy signatures (CondSigs), to predict genomic loci and component proteins of distinct chromatin-associated biomolecular condensates. Applying this framework to mouse embryonic stem cells (mESC) and human K562 cells enable us to depict the high-resolution genomic landscape of chromatin-associated biomolecular condensates, and uncover both known and potentially unknown biomolecular condensates. Multi-omics analysis and experimental validation further verify the condensation properties of CondSigs. Additionally, our investigation sheds light on the impact of chromatin-associated biomolecular condensates on chromatin activities. Collectively, CondSigDetector provides an approach to decode the genomic landscape of chromatin-associated condensates, facilitating a deeper understanding of their biological functions and underlying mechanisms in cells.
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Affiliation(s)
- Zhaowei Yu
- State Key Laboratory of Cardiovascular Diseases and Medical Innovation Center, Institute for Regenerative Medicine, Department of Neurosurgery, Shanghai East Hospital, Shanghai Key Laboratory of Signaling and Disease Research, Frontier Science Center for Stem Cell Research, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Qi Wang
- State Key Laboratory of Cardiovascular Diseases and Medical Innovation Center, Institute for Regenerative Medicine, Department of Neurosurgery, Shanghai East Hospital, Shanghai Key Laboratory of Signaling and Disease Research, Frontier Science Center for Stem Cell Research, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Qichen Zhang
- Pancreatic Intensive Care Unit, Changhai hospital, Naval Medical University, Shanghai, 200433, China
- Lingang Laboratory, Shanghai, 200031, China
| | - Yawen Tian
- Lingang Laboratory, Shanghai, 200031, China
| | - Guo Yan
- Lingang Laboratory, Shanghai, 200031, China
| | - Jidong Zhu
- Etern Biopharma, Shanghai, 201203, China
| | - Guangya Zhu
- Lingang Laboratory, Shanghai, 200031, China.
| | - Yong Zhang
- State Key Laboratory of Cardiovascular Diseases and Medical Innovation Center, Institute for Regenerative Medicine, Department of Neurosurgery, Shanghai East Hospital, Shanghai Key Laboratory of Signaling and Disease Research, Frontier Science Center for Stem Cell Research, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
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3
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Zhang J, Hu G, Lu Y, Ren H, Huang Y, Wen Y, Ji B, Wang D, Wang H, Liu H, Ma N, Zhang L, Pan G, Qu Y, Wang H, Zhang W, Miao Z, Yao H. CTCF mutation at R567 causes developmental disorders via 3D genome rearrangement and abnormal neurodevelopment. Nat Commun 2024; 15:5524. [PMID: 38951485 PMCID: PMC11217373 DOI: 10.1038/s41467-024-49684-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 06/14/2024] [Indexed: 07/03/2024] Open
Abstract
The three-dimensional genome structure organized by CTCF is required for development. Clinically identified mutations in CTCF have been linked to adverse developmental outcomes. Nevertheless, the underlying mechanism remains elusive. In this investigation, we explore the regulatory roles of a clinically relevant R567W point mutation, located within the 11th zinc finger of CTCF, by introducing this mutation into both murine models and human embryonic stem cell-derived cortical organoid models. Mice with homozygous CTCFR567W mutation exhibit growth impediments, resulting in postnatal mortality, and deviations in brain, heart, and lung development at the pathological and single-cell transcriptome levels. This mutation induces premature stem-like cell exhaustion, accelerates the maturation of GABAergic neurons, and disrupts neurodevelopmental and synaptic pathways. Additionally, it specifically hinders CTCF binding to peripheral motifs upstream to the core consensus site, causing alterations in local chromatin structure and gene expression, particularly at the clustered protocadherin locus. Comparative analysis using human cortical organoids mirrors the consequences induced by this mutation. In summary, this study elucidates the influence of the CTCFR567W mutation on human neurodevelopmental disorders, paving the way for potential therapeutic interventions.
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Affiliation(s)
- Jie Zhang
- State Key Laboratory of Respiratory Disease, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
- The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Gongcheng Hu
- Department of Basic Research, Guangzhou National Laboratory, Guangzhou, China
| | - Yuli Lu
- State Key Laboratory of Respiratory Disease, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Huawei Ren
- College of Veterinary Medicine, Shanxi Agricultural University, Jinzhong, China
| | - Yin Huang
- Department of Basic Research, Guangzhou National Laboratory, Guangzhou, China
| | - Yulin Wen
- State Key Laboratory of Respiratory Disease, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Binrui Ji
- State Key Laboratory of Respiratory Disease, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
- Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Diyang Wang
- Key Laboratory of CNS Regeneration (Ministry of Education), Guangdong-Hong Kong-Macau Institute of CNS Regeneration, Jinan University, Guangzhou, China
| | - Haidong Wang
- College of Veterinary Medicine, Shanxi Agricultural University, Jinzhong, China
| | - Huisheng Liu
- Department of Basic Research, Guangzhou National Laboratory, Guangzhou, China
| | - Ning Ma
- Department of Basic Research, Guangzhou National Laboratory, Guangzhou, China
| | - Lingling Zhang
- Institute of Clinical Pharmacology, Key Laboratory of Anti-Inflammatory and Immune Medicine (Ministry of Education), Anhui Medical University, Hefei, China
| | - Guangjin Pan
- State Key Laboratory of Respiratory Disease, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Yibo Qu
- Key Laboratory of CNS Regeneration (Ministry of Education), Guangdong-Hong Kong-Macau Institute of CNS Regeneration, Jinan University, Guangzhou, China
| | - Hua Wang
- Institute of Clinical Pharmacology, Key Laboratory of Anti-Inflammatory and Immune Medicine (Ministry of Education), Anhui Medical University, Hefei, China
| | - Wei Zhang
- Department of Basic Research, Guangzhou National Laboratory, Guangzhou, China
| | - Zhichao Miao
- Department of Basic Research, Guangzhou National Laboratory, Guangzhou, China
| | - Hongjie Yao
- State Key Laboratory of Respiratory Disease, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China.
- The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
- Department of Basic Research, Guangzhou National Laboratory, Guangzhou, China.
- University of Chinese Academy of Sciences, Beijing, China.
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4
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Hu S, Liu Y, Zhang Q, Bai J, Xu C. A continuum of zinc finger transcription factor retention on native chromatin underlies dynamic genome organization. Mol Syst Biol 2024; 20:799-824. [PMID: 38745107 PMCID: PMC11220090 DOI: 10.1038/s44320-024-00038-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 04/10/2024] [Accepted: 04/15/2024] [Indexed: 05/16/2024] Open
Abstract
Transcription factor (TF) residence on chromatin translates into quantitative transcriptional or structural outcomes on genome. Commonly used formaldehyde crosslinking fixes TF-DNA interactions cumulatively and compromises the measured occupancy level. Here we mapped the occupancy level of global or individual zinc finger TFs like CTCF and MAZ, in the form of highly resolved footprints, on native chromatin. By incorporating reinforcing perturbation conditions, we established S-score, a quantitative metric to proxy the continuum of CTCF or MAZ retention across different motifs on native chromatin. The native chromatin-retained CTCF sites harbor sequence features within CTCF motifs better explained by S-score than the metrics obtained from other crosslinking or native assays. CTCF retention on native chromatin correlates with local SUMOylation level, and anti-correlates with transcriptional activity. The S-score successfully delineates the otherwise-masked differential stability of chromatin structures mediated by CTCF, or by MAZ independent of CTCF. Overall, our study established a paradigm continuum of TF retention across binding sites on native chromatin, explaining the dynamic genome organization.
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Affiliation(s)
- Siling Hu
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
- China National Center for Bioinformation, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Yangying Liu
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
- China National Center for Bioinformation, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Qifan Zhang
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
- China National Center for Bioinformation, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Juan Bai
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
- China National Center for Bioinformation, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Chenhuan Xu
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China.
- China National Center for Bioinformation, Beijing, China.
- University of Chinese Academy of Sciences, Beijing, China.
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5
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Wang W, Ye Y, Gao L. Statistical modeling and significance estimation of multi-way chromatin contacts with HyperloopFinder. Brief Bioinform 2024; 25:bbae341. [PMID: 39003726 PMCID: PMC11246602 DOI: 10.1093/bib/bbae341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Revised: 06/12/2024] [Accepted: 07/03/2024] [Indexed: 07/16/2024] Open
Abstract
Recent advances in chromatin conformation capture technologies, such as SPRITE and Pore-C, have enabled the detection of simultaneous contacts among multiple chromatin loci. This has made it possible to investigate the cooperative transcriptional regulation involving multiple genes and regulatory elements at the resolution of a single molecule. However, these technologies are unavoidably subject to the random polymer looping effect and technical biases, making it challenging to distinguish genuine regulatory relationships directly from random polymer interactions. Here, we present HyperloopFinder, a method for identifying regulatory multi-way chromatin contacts (hyperloops) by jointly modeling the random polymer looping effect and technical biases to estimate the statistical significance of multi-way contacts. The results show that our model can accurately estimate the expected interaction frequency of multi-way contacts based on the distance distribution of pairwise contacts, revealing that most multi-way contacts can be formed by randomly linking the pairwise contacts adjacent to each other. Moreover, we observed the spatial colocalization of the interaction sites of hyperloops from image-based data. Our results also revealed that hyperloops can function as scaffolds for the cooperation among multiple genes and regulatory elements. In summary, our work contributes novel insights into higher-order chromatin structures and functions and has the potential to enhance our understanding of transcriptional regulation and other cellular processes.
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Affiliation(s)
- Weibing Wang
- Department of Computer Science, School of Computer Science and Technology, Xidian University, Xi'an, Shaanxi, China
| | - Yusen Ye
- Department of Computer Science, School of Computer Science and Technology, Xidian University, Xi'an, Shaanxi, China
| | - Lin Gao
- Department of Computer Science, School of Computer Science and Technology, Xidian University, Xi'an, Shaanxi, China
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6
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Guo R, Dong X, Chen F, Ji T, He Q, Zhang J, Sheng Y, Liu Y, Yang S, Liang W, Song Y, Fang K, Zhang L, Hu G, Yao H. TEAD2 initiates ground-state pluripotency by mediating chromatin looping. EMBO J 2024; 43:1965-1989. [PMID: 38605224 PMCID: PMC11099042 DOI: 10.1038/s44318-024-00086-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Revised: 02/26/2024] [Accepted: 03/03/2024] [Indexed: 04/13/2024] Open
Abstract
The transition of mouse embryonic stem cells (ESCs) between serum/LIF and 2i(MEK and GSK3 kinase inhibitor)/LIF culture conditions serves as a valuable model for exploring the mechanisms underlying ground and confused pluripotent states. Regulatory networks comprising core and ancillary pluripotency factors drive the gene expression programs defining stable naïve pluripotency. In our study, we systematically screened factors essential for ESC pluripotency, identifying TEAD2 as an ancillary factor maintaining ground-state pluripotency in 2i/LIF ESCs and facilitating the transition from serum/LIF to 2i/LIF ESCs. TEAD2 exhibits increased binding to chromatin in 2i/LIF ESCs, targeting active chromatin regions to regulate the expression of 2i-specific genes. In addition, TEAD2 facilitates the expression of 2i-specific genes by mediating enhancer-promoter interactions during the serum/LIF to 2i/LIF transition. Notably, deletion of Tead2 results in reduction of a specific set of enhancer-promoter interactions without significantly affecting binding of chromatin architecture proteins, CCCTC-binding factor (CTCF), and Yin Yang 1 (YY1). In summary, our findings highlight a novel prominent role of TEAD2 in orchestrating higher-order chromatin structures of 2i-specific genes to sustain ground-state pluripotency.
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Affiliation(s)
- Rong Guo
- State Key Laboratory of Respiratory Disease, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou National Laboratory, Guangzhou Medical University, Guangzhou, China
- Center for Health Research, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
| | - Xiaotao Dong
- Center for Health Research, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
- School of Basic Medical Science, Henan University, Kaifeng, China
| | - Feng Chen
- State Key Laboratory of Respiratory Disease, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou National Laboratory, Guangzhou Medical University, Guangzhou, China
| | - Tianrong Ji
- Center for Health Research, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
| | - Qiannan He
- State Key Laboratory of Respiratory Disease, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou National Laboratory, Guangzhou Medical University, Guangzhou, China
- Center for Health Research, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
| | - Jie Zhang
- State Key Laboratory of Respiratory Disease, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou National Laboratory, Guangzhou Medical University, Guangzhou, China
- Center for Health Research, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
| | - Yingliang Sheng
- State Key Laboratory of Respiratory Disease, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou National Laboratory, Guangzhou Medical University, Guangzhou, China
- Center for Health Research, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
- Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Yanjiang Liu
- State Key Laboratory of Respiratory Disease, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou National Laboratory, Guangzhou Medical University, Guangzhou, China
- Center for Health Research, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
| | - Shengxiong Yang
- State Key Laboratory of Respiratory Disease, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou National Laboratory, Guangzhou Medical University, Guangzhou, China
- Center for Health Research, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
| | - Weifang Liang
- College of Veterinary Medicine, Shanxi Agricultural University, Jinzhong, China
| | - Yawei Song
- Center for Health Research, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
| | - Ke Fang
- Center for Health Research, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
| | - Lingling Zhang
- Institute of Clinical Pharmacology, Anhui Medical University, Hefei, China
| | - Gongcheng Hu
- State Key Laboratory of Respiratory Disease, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou National Laboratory, Guangzhou Medical University, Guangzhou, China
- Center for Health Research, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
| | - Hongjie Yao
- State Key Laboratory of Respiratory Disease, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou National Laboratory, Guangzhou Medical University, Guangzhou, China.
- Center for Health Research, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China.
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7
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Tang L, Liao J, Hill MC, Hu J, Zhao Y, Ellinor P, Li M. MMCT-Loop: a mix model-based pipeline for calling targeted 3D chromatin loops. Nucleic Acids Res 2024; 52:e25. [PMID: 38281134 PMCID: PMC10954456 DOI: 10.1093/nar/gkae029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Revised: 12/03/2023] [Accepted: 01/12/2024] [Indexed: 01/30/2024] Open
Abstract
Protein-specific Chromatin Conformation Capture (3C)-based technologies have become essential for identifying distal genomic interactions with critical roles in gene regulation. The standard techniques include Chromatin Interaction Analysis by Paired-End Tag (ChIA-PET), in situ Hi-C followed by chromatin immunoprecipitation (HiChIP) also known as PLAC-seq. To identify chromatin interactions from these data, a variety of computational methods have emerged. Although these state-of-art methods address many issues with loop calling, only few methods can fit different data types simultaneously, and the accuracy as well as the efficiency these approaches remains limited. Here we have generated a pipeline, MMCT-Loop, which ensures the accurate identification of strong loops as well as dynamic or weak loops through a mixed model. MMCT-Loop outperforms existing methods in accuracy, and the detected loops show higher activation functionality. To highlight the utility of MMCT-Loop, we applied it to conformational data derived from neural stem cell (NSCs) and uncovered several previously unidentified regulatory regions for key master regulators of stem cell identity. MMCT-Loop is an accurate and efficient loop caller for targeted conformation capture data, which supports raw data or pre-processed valid pairs as input, the output interactions are formatted and easily uploaded to a genome browser for visualization.
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Affiliation(s)
- Li Tang
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Jiaqi Liao
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Matthew C Hill
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA 02129, USA
- Cardiovascular Disease Initiative, The Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
| | - Jiaxin Hu
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Yichao Zhao
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Patrick T Ellinor
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA 02129, USA
- Cardiovascular Disease Initiative, The Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
| | - Min Li
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China
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8
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Zhang L, Zhao R, Liang J, Cai X, Zhang L, Guo H, Zhang Z, Wu J, Wang X. BL-Hi-C reveals the 3D genome structure of Brassica crops with high sensitivity. HORTICULTURE RESEARCH 2024; 11:uhae017. [PMID: 38464474 PMCID: PMC10923644 DOI: 10.1093/hr/uhae017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 01/03/2024] [Indexed: 03/12/2024]
Abstract
High-throughput Chromatin Conformation Capture (Hi-C) technologies can be used to investigate the three-dimensional genomic structure of plants. However, the practical utility of these technologies is impeded by significant background noise, hindering their capability in detecting fine 3D genomic structures. In this study, we optimized the Bridge Linker Hi-C technology (BL-Hi-C) to comprehensively investigate the 3D chromatin landscape of Brassica rapa and Brassica oleracea. The Bouquet configuration of both B. rapa and B. oleracea was elucidated through the construction of a 3D genome simulation. The optimized BL-Hi-C exhibited lower background noise compared to conventional Hi-C methods. Taking this advantage, we used BL-Hi-C to identify FLC gene loops in Arabidopsis, B. rapa, and B. oleracea. We observed that gene loops of FLC2 exhibited conservation across Arabidopsis, B. rapa, and B. oleracea. While gene loops of syntenic FLCs exhibited conservation across B. rapa and B. oleracea, variations in gene loops were evident among multiple paralogs FLCs within the same species. Collectively, our findings highlight the high sensitivity of optimized BL-Hi-C as a powerful tool for investigating the fine 3D genomic organization.
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Affiliation(s)
- Lupeng Zhang
- State Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Ranze Zhao
- State Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Jianli Liang
- State Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Xu Cai
- State Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Lei Zhang
- State Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Huiling Guo
- State Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Zhicheng Zhang
- State Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Jian Wu
- State Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Xiaowu Wang
- State Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing 100081, China
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9
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Abbas A, Chandratre K, Gao Y, Yuan J, Zhang MQ, Mani RS. ChIPr: accurate prediction of cohesin-mediated 3D genome organization from 2D chromatin features. Genome Biol 2024; 25:15. [PMID: 38217027 PMCID: PMC10785520 DOI: 10.1186/s13059-023-03158-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Accepted: 12/22/2023] [Indexed: 01/14/2024] Open
Abstract
The three-dimensional genome organization influences diverse nuclear processes. Here we present Chromatin Interaction Predictor (ChIPr), a suite of regression models based on deep neural networks, random forest, and gradient boosting to predict cohesin-mediated chromatin interaction strength between any two loci in the genome. The predictions of ChIPr correlate well with ChIA-PET data in four cell lines. The standard ChIPr model requires three experimental inputs: ChIP-Seq signals for RAD21, H3K27ac, and H3K27me3 but works well with just RAD21 signal. Integrative analysis reveals novel insights into the role of CTCF motif, its orientation, and CTCF binding on cohesin-mediated chromatin interactions.
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Affiliation(s)
- Ahmed Abbas
- Department of Pathology, UT Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Khyati Chandratre
- Department of Biological Sciences, Center for Systems Biology, The University of Texas at Dallas, Richardson, TX, 75080, USA
| | - Yunpeng Gao
- Department of Pathology, UT Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Jiapei Yuan
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, 300020, China
| | - Michael Q Zhang
- Department of Biological Sciences, Center for Systems Biology, The University of Texas at Dallas, Richardson, TX, 75080, USA.
| | - Ram S Mani
- Department of Pathology, UT Southwestern Medical Center, Dallas, TX, 75390, USA.
- Department of Urology, UT Southwestern Medical Center, Dallas, TX, 75390, USA.
- Harold C. Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, TX, 75390, USA.
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10
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Ren R, Hua Y, Wang H. Protocol to capture transcription factor-mediated 3D chromatin interactions using affinity tag-based BL-HiChIP. STAR Protoc 2023; 4:102589. [PMID: 37738118 PMCID: PMC10519843 DOI: 10.1016/j.xpro.2023.102589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 07/26/2023] [Accepted: 09/01/2023] [Indexed: 09/24/2023] Open
Abstract
Pioneer transcription factors (TFs) can directly establish higher-order chromatin interactions to instruct gene transcription. Here, we present a protocol for capturing TF-mediated 3D chromatin interactions using affinity tag-based bridge linker (BL)-Hi-chromatin immunoprecipitation (HiChIP). We describe steps for constructing FLAG-tagged TF, performing BL-HiChIP, and preparing the library. We then detail procedures for sequencing, data analysis, and quality control. This protocol has potential applications in 3D chromatin analysis centered on any specific TF in any type of cells without the need of optimal antibodies. For complete details on the use and execution of this protocol, please refer to Ren et al. (2022).1.
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Affiliation(s)
- Ruimin Ren
- College of Animal Science and Technology, Hunan Agricultural University, Changsha, China.
| | - Yao Hua
- College of Animal Science and Technology, Shandong Agricultural University, Taian, China
| | - Heng Wang
- College of Animal Science and Technology, Shandong Agricultural University, Taian, China.
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11
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Zhang W, Wang H, Ma Y, Gao B, Guan P, Huang X, Ouyang W, Guo M, Chen G, Li G, Li X. Domains Rearranged Methylase 2 maintains DNA methylation at large DNA hypomethylated shores and long-range chromatin interactions in rice. PLANT BIOTECHNOLOGY JOURNAL 2023; 21:2333-2347. [PMID: 37539491 PMCID: PMC10579712 DOI: 10.1111/pbi.14134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 06/25/2023] [Accepted: 07/08/2023] [Indexed: 08/05/2023]
Abstract
DNA methylation plays an important role in gene regulation and genomic stability. However, large DNA hypomethylated regions known as DNA methylation valleys (DMVs) or canyons have also been suggested to serve unique regulatory functions, largely unknown in rice (Oryza sativa). Here, we describe the DMVs in rice seedlings, which were highly enriched with developmental and transcription regulatory genes. Further detailed analysis indicated that grand DMVs (gDMVs) might be derived from nuclear integrants of organelle DNA (NORGs). Furthermore, Domains Rearranged Methylase 2 (OsDRM2) maintained DNA methylation at short DMV (sDMV) shores. Epigenetic maps indicated that sDMVs were marked with H3K4me3 and/or H3K27me3, although the loss of DNA methylation had a negligible effect on histone modification within these regions. In addition, we constructed H3K27me3-associated interaction maps for homozygous T-DNA insertion mutant of the gene (osdrm2) and wild type (WT). From a global perspective, most (90%) compartments were stable between osdrm2 and WT plants. At a high resolution, we observed a dramatic loss of long-range chromatin loops in osdrm2, which suffered an extensive loss of non-CG (CHG and CHH, H = A, T, or C) methylation. From another viewpoint, the loss of non-CG methylation at sDMV shores in osdrm2 could disrupt H3K27me3-mediated chromatin interaction networks. Overall, our results demonstrated that DMVs are a key genomic feature in rice and are precisely regulated by epigenetic modifications, including DNA methylation and histone modifications. OsDRM2 maintained DNA methylation at sDMV shores, while OsDRM2 deficiency strongly affected three-dimensional (3D) genome architectures.
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Affiliation(s)
- Wei Zhang
- National Key Laboratory of Crop Genetic ImprovementHubei Hongshan LaboratoryHuazhong Agricultural UniversityWuhanChina
| | - Huanhuan Wang
- Hubei Key Laboratory of Agricultural Bioinformatics, Hubei Engineering Technology Research Center of Agricultural Big Data, College of InformaticsHuazhong Agricultural UniversityWuhanChina
| | - Yuning Ma
- National Key Laboratory of Crop Genetic ImprovementHubei Hongshan LaboratoryHuazhong Agricultural UniversityWuhanChina
| | - Baibai Gao
- National Key Laboratory of Crop Genetic ImprovementHubei Hongshan LaboratoryHuazhong Agricultural UniversityWuhanChina
| | - Pengpeng Guan
- Hubei Key Laboratory of Agricultural Bioinformatics, Hubei Engineering Technology Research Center of Agricultural Big Data, College of InformaticsHuazhong Agricultural UniversityWuhanChina
| | - Xingyu Huang
- Hubei Key Laboratory of Agricultural Bioinformatics, Hubei Engineering Technology Research Center of Agricultural Big Data, College of InformaticsHuazhong Agricultural UniversityWuhanChina
| | - Weizhi Ouyang
- National Key Laboratory of Crop Genetic ImprovementHubei Hongshan LaboratoryHuazhong Agricultural UniversityWuhanChina
| | - Minrong Guo
- National Key Laboratory of Crop Genetic ImprovementHubei Hongshan LaboratoryHuazhong Agricultural UniversityWuhanChina
| | - Guoting Chen
- Hubei Key Laboratory of Agricultural Bioinformatics, Hubei Engineering Technology Research Center of Agricultural Big Data, College of InformaticsHuazhong Agricultural UniversityWuhanChina
| | - Guoliang Li
- National Key Laboratory of Crop Genetic ImprovementHubei Hongshan LaboratoryHuazhong Agricultural UniversityWuhanChina
- Hubei Key Laboratory of Agricultural Bioinformatics, Hubei Engineering Technology Research Center of Agricultural Big Data, College of InformaticsHuazhong Agricultural UniversityWuhanChina
- Shenzhen BranchGuangdong Laboratory for Lingnan Modern AgricultureGenome Analysis Laboratory of the Ministry of AgricultureAgricultural Genomics Institute at ShenzhenChinese Academy of Agricultural SciencesShenzhenChina
- Shenzhen Institute of Nutrition and HealthHuazhong Agricultural UniversityWuhanChina
| | - Xingwang Li
- National Key Laboratory of Crop Genetic ImprovementHubei Hongshan LaboratoryHuazhong Agricultural UniversityWuhanChina
- Shenzhen BranchGuangdong Laboratory for Lingnan Modern AgricultureGenome Analysis Laboratory of the Ministry of AgricultureAgricultural Genomics Institute at ShenzhenChinese Academy of Agricultural SciencesShenzhenChina
- Shenzhen Institute of Nutrition and HealthHuazhong Agricultural UniversityWuhanChina
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12
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Zhang Y, Chen G, Deng L, Gao B, Yang J, Ding C, Zhang Q, Ouyang W, Guo M, Wang W, Liu B, Zhang Q, Sung WK, Yan J, Li G, Li X. Integrated 3D genome, epigenome and transcriptome analyses reveal transcriptional coordination of circadian rhythm in rice. Nucleic Acids Res 2023; 51:9001-9018. [PMID: 37572350 PMCID: PMC10516653 DOI: 10.1093/nar/gkad658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 07/11/2023] [Accepted: 08/01/2023] [Indexed: 08/14/2023] Open
Abstract
Photoperiods integrate with the circadian clock to coordinate gene expression rhythms and thus ensure plant fitness to the environment. Genome-wide characterization and comparison of rhythmic genes under different light conditions revealed delayed phase under constant darkness (DD) and reduced amplitude under constant light (LL) in rice. Interestingly, ChIP-seq and RNA-seq profiling of rhythmic genes exhibit synchronous circadian oscillation in H3K9ac modifications at their loci and long non-coding RNAs (lncRNAs) expression at proximal loci. To investigate how gene expression rhythm is regulated in rice, we profiled the open chromatin regions and transcription factor (TF) footprints by time-series ATAC-seq. Although open chromatin regions did not show circadian change, a significant number of TFs were identified to rhythmically associate with chromatin and drive gene expression in a time-dependent manner. Further transcriptional regulatory networks mapping uncovered significant correlation between core clock genes and transcription factors involved in light/temperature signaling. In situ Hi-C of ZT8-specific expressed genes displayed highly connected chromatin association at the same time, whereas this ZT8 chromatin connection network dissociates at ZT20, suggesting the circadian control of gene expression by dynamic spatial chromatin conformation. These findings together implicate the existence of a synchronization mechanism between circadian H3K9ac modifications, chromatin association of TF and gene expression, and provides insights into circadian dynamics of spatial chromatin conformation that associate with gene expression rhythms.
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Affiliation(s)
- Ying Zhang
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Guoting Chen
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
- Laboratory of Agricultural Bioinformatics, Hubei Engineering Technology Research Center of Agricultural Big Data, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Li Deng
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Baibai Gao
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Jing Yang
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Cheng Ding
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Qing Zhang
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Weizhi Ouyang
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Minrong Guo
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Wenxia Wang
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Beibei Liu
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Qinghua Zhang
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Wing-Kin Sung
- Department of Chemical Pathology, Chinese University of Hong Kong, Hong Kong, China
| | - Jiapei Yan
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Guoliang Li
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
- Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan, China
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
- Laboratory of Agricultural Bioinformatics, Hubei Engineering Technology Research Center of Agricultural Big Data, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Xingwang Li
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
- Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan, China
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
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13
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Xu H, Yi X, Fan X, Wu C, Wang W, Chu X, Zhang S, Dong X, Wang Z, Wang J, Zhou Y, Zhao K, Yao H, Zheng N, Wang J, Chen Y, Plewczynski D, Sham PC, Chen K, Huang D, Li MJ. Inferring CTCF-binding patterns and anchored loops across human tissues and cell types. PATTERNS (NEW YORK, N.Y.) 2023; 4:100798. [PMID: 37602215 PMCID: PMC10436006 DOI: 10.1016/j.patter.2023.100798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 01/25/2023] [Accepted: 06/20/2023] [Indexed: 08/22/2023]
Abstract
CCCTC-binding factor (CTCF) is a transcription regulator with a complex role in gene regulation. The recognition and effects of CTCF on DNA sequences, chromosome barriers, and enhancer blocking are not well understood. Existing computational tools struggle to assess the regulatory potential of CTCF-binding sites and their impact on chromatin loop formation. Here we have developed a deep-learning model, DeepAnchor, to accurately characterize CTCF binding using high-resolution genomic/epigenomic features. This has revealed distinct chromatin and sequence patterns for CTCF-mediated insulation and looping. An optimized implementation of a previous loop model based on DeepAnchor score excels in predicting CTCF-anchored loops. We have established a compendium of CTCF-anchored loops across 52 human tissue/cell types, and this suggests that genomic disruption of these loops could be a general mechanism of disease pathogenesis. These computational models and resources can help investigate how CTCF-mediated cis-regulatory elements shape context-specific gene regulation in cell development and disease progression.
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Affiliation(s)
- Hang Xu
- Department of Epidemiology and Biostatistics, Key Laboratory of Prevention and Control of Human Major Diseases (Ministry of Education), National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin 300070, China
- Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A∗STAR), Singapore 138648, Singapore
| | - Xianfu Yi
- Department of Bioinformatics, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Xutong Fan
- Department of Bioinformatics, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Chengyue Wu
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Wei Wang
- Department of Epidemiology and Biostatistics, Key Laboratory of Prevention and Control of Human Major Diseases (Ministry of Education), National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin 300070, China
| | - Xinlei Chu
- Department of Epidemiology and Biostatistics, Key Laboratory of Prevention and Control of Human Major Diseases (Ministry of Education), National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin 300070, China
| | - Shijie Zhang
- Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Xiaobao Dong
- Department of Genetics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Zhao Wang
- Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Jianhua Wang
- Department of Bioinformatics, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Yao Zhou
- Department of Bioinformatics, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Ke Zhao
- Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Hongcheng Yao
- Centre for PanorOmic Sciences-Genomics and Bioinformatics Cores, The University of Hong Kong, Hong Kong 999077, China
| | - Nan Zheng
- Department of Network Security and Informatization, Tianjin Medical University, Tianjin 300070, China
| | - Junwen Wang
- Department of Health Sciences Research and Center for Individualized Medicine, Mayo Clinic, Scottsdale, AZ 85259, USA
| | - Yupeng Chen
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Dariusz Plewczynski
- Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland
| | - Pak Chung Sham
- Centre for PanorOmic Sciences-Genomics and Bioinformatics Cores, The University of Hong Kong, Hong Kong 999077, China
| | - Kexin Chen
- Department of Epidemiology and Biostatistics, Key Laboratory of Prevention and Control of Human Major Diseases (Ministry of Education), National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin 300070, China
| | - Dandan Huang
- Wuxi School of Medicine, Jiangnan University, Wuxi 214122, China
| | - Mulin Jun Li
- Department of Epidemiology and Biostatistics, Key Laboratory of Prevention and Control of Human Major Diseases (Ministry of Education), National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin 300070, China
- Department of Bioinformatics, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
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14
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Deng L, Zhou Q, Zhou J, Zhang Q, Jia Z, Zhu G, Cheng S, Cheng L, Yin C, Yang C, Shen J, Nie J, Zhu JK, Li G, Zhao L. 3D organization of regulatory elements for transcriptional regulation in Arabidopsis. Genome Biol 2023; 24:181. [PMID: 37550699 PMCID: PMC10405511 DOI: 10.1186/s13059-023-03018-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Accepted: 07/20/2023] [Indexed: 08/09/2023] Open
Abstract
BACKGROUND Although spatial organization of compartments and topologically associating domains at large scale is relatively well studied, the spatial organization of regulatory elements at fine scale is poorly understood in plants. RESULTS Here we perform high-resolution chromatin interaction analysis using paired-end tag sequencing approach. We map chromatin interactions tethered with RNA polymerase II and associated with heterochromatic, transcriptionally active, and Polycomb-repressive histone modifications in Arabidopsis. Analysis of the regulatory repertoire shows that distal active cis-regulatory elements are linked to their target genes through long-range chromatin interactions with increased expression of the target genes, while poised cis-regulatory elements are linked to their target genes through long-range chromatin interactions with depressed expression of the target genes. Furthermore, we demonstrate that transcription factor MYC2 is critical for chromatin spatial organization, and propose that MYC2 occupancy and MYC2-mediated chromatin interactions coordinately facilitate transcription within the framework of 3D chromatin architecture. Analysis of functionally related gene-defined chromatin connectivity networks reveals that genes implicated in flowering-time control are functionally compartmentalized into separate subdomains via their spatial activity in the leaf or shoot apical meristem, linking active mark- or Polycomb-repressive mark-associated chromatin conformation to coordinated gene expression. CONCLUSION The results reveal that the regulation of gene transcription in Arabidopsis is not only by linear juxtaposition, but also by long-range chromatin interactions. Our study uncovers the fine scale genome organization of Arabidopsis and the potential roles of such organization in orchestrating transcription and development.
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Affiliation(s)
- Li Deng
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Qiangwei Zhou
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
- Agricultural Bioinformatics Key Laboratory of Hubei Province and Hubei Engineering Technology Research Center of Agricultural Big Data, 3D Genomics Research Center, Huazhong Agricultural University, Wuhan, 430070, China
| | - Jie Zhou
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Qing Zhang
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Zhibo Jia
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Guangfeng Zhu
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Sheng Cheng
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
- Agricultural Bioinformatics Key Laboratory of Hubei Province and Hubei Engineering Technology Research Center of Agricultural Big Data, 3D Genomics Research Center, Huazhong Agricultural University, Wuhan, 430070, China
| | - Lulu Cheng
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Caijun Yin
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Chao Yang
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Jinxiong Shen
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Junwei Nie
- Vazyme Biotech Co., Ltd., Nanjing, 210000, China
| | - Jian-Kang Zhu
- Institute of Advanced Biotechnology and School of Life Sciences, Southern University of Science and Technology, Shenzhen, 518055, China.
- Center for Advanced Bioindustry Technologies, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
| | - Guoliang Li
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China.
- Agricultural Bioinformatics Key Laboratory of Hubei Province and Hubei Engineering Technology Research Center of Agricultural Big Data, 3D Genomics Research Center, Huazhong Agricultural University, Wuhan, 430070, China.
| | - Lun Zhao
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China.
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15
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Liang Z, Gilbreath C, Liu W, Wang Y, Zhang MQ, Zhang DE, Wu S, Fu XD. Chromatin-associated RNA Dictates the ecDNA Interactome in the Nucleus. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.27.550855. [PMID: 37547001 PMCID: PMC10402128 DOI: 10.1101/2023.07.27.550855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
Extrachromosomal DNA (ecDNA) promotes cancer by driving copy number heterogeneity and amplifying oncogenes along with functional enhancers. More recent studies suggest two additional mechanisms for further enhancing their oncogenic potential, one via forming ecDNA hubs to augment oncogene expression 1 and the other through acting as portable enhancers to trans-activate target genes 2. However, it has remained entirely elusive about how ecDNA explores the three-dimensional space of the nucleus and whether different ecDNA have distinct interacting mechanisms. Here, by profiling the DNA-DNA and DNA-RNA interactomes in tumor cells harboring different types of ecDNAs in comparison with similarly amplified homogenously staining regions (HSRs) in the chromosome, we show that specific ecDNA interactome is dictated by ecDNA-borne nascent RNA. We demonstrate that the ecDNA co-amplifying PVT1 and MYC utilize nascent noncoding PVT1 transcripts to mediate specific trans-activation of both ecDNA and chromosomal genes. In contrast, the ecDNA amplifying EGFR is weak in this property because of more efficient splicing to remove chromatin-associated nascent RNA. These findings reveal a noncoding RNA-orchestrated program hijacked by cancer cells to enhance the functional impact of amplified oncogenes and associated regulatory elements.
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Affiliation(s)
- Zhengyu Liang
- Department of System Biology, School of Life Science, Southern University of Science and Technology, Shenzhen, 518055, China
- Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA 92093, USA
| | - Collin Gilbreath
- Children’s Medical Center Research Institute, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Wenyue Liu
- Department of Cell Biology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Yan Wang
- Samara Inc., San Francisco, CA, USA
| | - Michael Q. Zhang
- Department of Biological Sciences, Center for Systems Biology, University of Texas, Dallas, TX 75080, USA
| | - Dong-Er Zhang
- Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA 92093, USA
| | - Sihan Wu
- Children’s Medical Center Research Institute, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Xiang-Dong Fu
- Department Westlake Laboratory of Life Sciences and Biomedicine, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China
- Lead contact
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16
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Wang R, Xu Q, Wang C, Tian K, Wang H, Ji X. Multiomic analysis of cohesin reveals that ZBTB transcription factors contribute to chromatin interactions. Nucleic Acids Res 2023; 51:6784-6805. [PMID: 37264934 PMCID: PMC10359638 DOI: 10.1093/nar/gkad401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 05/23/2023] [Indexed: 06/03/2023] Open
Abstract
One bottleneck in understanding the principles of 3D chromatin structures is caused by the paucity of known regulators. Cohesin is essential for 3D chromatin organization, and its interacting partners are candidate regulators. Here, we performed proteomic profiling of the cohesin in chromatin and identified transcription factors, RNA-binding proteins and chromatin regulators associated with cohesin. Acute protein degradation followed by time-series genomic binding quantitation and BAT Hi-C analysis were conducted, and the results showed that the transcription factor ZBTB21 contributes to cohesin chromatin binding, 3D chromatin interactions and transcriptional repression. Strikingly, multiomic analyses revealed that the other four ZBTB factors interacted with cohesin, and double degradation of ZBTB21 and ZBTB7B led to a further decrease in cohesin chromatin occupancy. We propose that multiple ZBTB transcription factors orchestrate the chromatin binding of cohesin to regulate chromatin interactions, and we provide a catalog of many additional proteins associated with cohesin that warrant further investigation.
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Affiliation(s)
- Rui Wang
- Key Laboratory of Cell Proliferation and Differentiation of the Ministry of Education, School of Life Sciences, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, China
| | - Qiqin Xu
- Key Laboratory of Cell Proliferation and Differentiation of the Ministry of Education, School of Life Sciences, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, China
| | - Chenlu Wang
- Key Laboratory of Cell Proliferation and Differentiation of the Ministry of Education, School of Life Sciences, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, China
| | - Kai Tian
- Key Laboratory of Cell Proliferation and Differentiation of the Ministry of Education, School of Life Sciences, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, China
| | - Hui Wang
- Key Laboratory of Cell Proliferation and Differentiation of the Ministry of Education, School of Life Sciences, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, China
| | - Xiong Ji
- Key Laboratory of Cell Proliferation and Differentiation of the Ministry of Education, School of Life Sciences, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, China
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17
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Yuan XQ, Zhou N, Wang JP, Yang XZ, Wang S, Zhang CY, Li GC, Peng L. Anchoring super-enhancer-driven oncogenic lncRNAs for anti-tumor therapy in hepatocellular carcinoma. Mol Ther 2023; 31:1756-1774. [PMID: 36461633 PMCID: PMC10277835 DOI: 10.1016/j.ymthe.2022.11.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 10/19/2022] [Accepted: 11/28/2022] [Indexed: 12/04/2022] Open
Abstract
Super-enhancer (SE) plays a vital role in the determination of cell identity and fate. Up-regulated expression of coding genes is frequently associated with SE. However, the transcription dysregulation driven by SE, from the viewpoint of long non-coding RNA (lncRNA), remains unclear. Here, SE-associated lncRNAs in HCC are comprehensively outlined for the first time. This study integrally screens and identifies several novel SE-associated lncRNAs that are highly abundant and sensitive to JQ1. Especially, HSAL3 is identified as an uncharacterized SE-driven oncogenic lncRNA, which is activated by transcription factors HCFC1 and HSF1 via its super-enhancer. HSAL3 interference negatively regulates NOTCH signaling, implying the potential mechanism of its tumor-promoting role. The expression of HSAL3 is increased in HCC samples, and higher HSAL3 expression indicates an inferior overall survival of HCC patients. Furthermore, siHSAL3 loaded nanoparticles exert anti-tumor effect on HCC in vitro and in vivo. In conclusion, this is the first comprehensive survey of SE-associated lncRNAs in HCC. HSAL3 is a novel SE-driven oncogenic lncRNA, and siHSAL3 loaded nanoparticles are therapeutic candidates for HCC. This work sheds lights on the merit of anchoring SE-driven oncogenic lncRNAs for HCC treatment.
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Affiliation(s)
- Xiao-Qing Yuan
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, P. R. China; Breast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510120, P. R. China
| | - Nan Zhou
- Department of Research, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou 510370, P. R. China
| | - Jun-Pu Wang
- Department of Pathology, Xiang-ya Hospital, Central South University, Changsha 410008, P. R. China; Department of Pathology, School of Basic Medicine, Central South University, Changsha 410013, P. R. China
| | - Xian-Zhu Yang
- School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou 511442, P. R. China
| | - Shan Wang
- Department of Pathology, Xiang-ya Hospital, Central South University, Changsha 410008, P. R. China; Department of Pathology, School of Basic Medicine, Central South University, Changsha 410013, P. R. China
| | - Chao-Yang Zhang
- Division of Functional Genome Analysis, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany
| | - Guan-Cheng Li
- Key Laboratory of Carcinogenesis of the Chinese Ministry of Health and the Key Laboratory of Carcinogenesis and Cancer Invasion of Chinese Ministry of Education, Central South University, Changsha 410078, P. R. China; Cancer Research Institute, Central South University, Changsha 410078, P. R. China
| | - Li Peng
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, P. R. China; Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, P. R. China.
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18
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Zhang Y, Zhang J, Zhang W, Wang M, Wang S, Xu Y, Zhao L, Li X, Li G. Mapping Multi-factor-mediated Chromatin Interactions to Assess Dysregulation of Lung Cancer-related Genes. GENOMICS, PROTEOMICS & BIOINFORMATICS 2023; 21:573-588. [PMID: 36702236 PMCID: PMC10787015 DOI: 10.1016/j.gpb.2023.01.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 11/30/2022] [Accepted: 01/17/2023] [Indexed: 01/25/2023]
Abstract
Studies on the lung cancer genome are indispensable for developing a cure for lung cancer. Whole-genome resequencing, genome-wide association studies, and transcriptome sequencing have greatly improved our understanding of the cancer genome. However, dysregulation of long-range chromatin interactions in lung cancer remains poorly described. To better understand the three-dimensional (3D) genomic interaction features of the lung cancer genome, we used the A549 cell line as a model system and generated high-resolution chromatin interactions associated with RNA polymerase II (RNAPII), CCCTC-binding factor (CTCF), enhancer of zeste homolog 2 (EZH2), and histone 3 lysine 27 trimethylation (H3K27me3) using long-read chromatin interaction analysis by paired-end tag sequencing (ChIA-PET). Analysis showed that EZH2/H3K27me3-mediated interactions further repressed target genes, either through loops or domains, and their distributions along the genome were distinct from and complementary to those associated with RNAPII. Cancer-related genes were highly enriched with chromatin interactions, and chromatin interactions specific to the A549 cell line were associated with oncogenes and tumor suppressor genes, such as additional repressive interactions on FOXO4 and promoter-promoter interactions between NF1 and RNF135. Knockout of an anchor associated with chromatin interactions reversed the dysregulation of cancer-related genes, suggesting that chromatin interactions are essential for proper expression of lung cancer-related genes. These findings demonstrate the 3D landscape and gene regulatory relationships of the lung cancer genome.
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Affiliation(s)
- Yan Zhang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; Hubei Key Laboratory of Agricultural Bioinformatics and Hubei Engineering Technology Research Center of Agricultural Big Data, 3D Genomics Research Center, Huazhong Agricultural University, Wuhan 430070, China
| | - Jingwen Zhang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; Hubei Key Laboratory of Agricultural Bioinformatics and Hubei Engineering Technology Research Center of Agricultural Big Data, 3D Genomics Research Center, Huazhong Agricultural University, Wuhan 430070, China
| | - Wei Zhang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
| | - Mohan Wang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; Hubei Key Laboratory of Agricultural Bioinformatics and Hubei Engineering Technology Research Center of Agricultural Big Data, 3D Genomics Research Center, Huazhong Agricultural University, Wuhan 430070, China
| | - Shuangqi Wang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; Hubei Key Laboratory of Agricultural Bioinformatics and Hubei Engineering Technology Research Center of Agricultural Big Data, 3D Genomics Research Center, Huazhong Agricultural University, Wuhan 430070, China
| | - Yao Xu
- Hubei Key Laboratory of Agricultural Bioinformatics and Hubei Engineering Technology Research Center of Agricultural Big Data, 3D Genomics Research Center, Huazhong Agricultural University, Wuhan 430070, China
| | - Lun Zhao
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
| | - Xingwang Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
| | - Guoliang Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; Hubei Key Laboratory of Agricultural Bioinformatics and Hubei Engineering Technology Research Center of Agricultural Big Data, 3D Genomics Research Center, Huazhong Agricultural University, Wuhan 430070, China.
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19
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Ouyang W, Zhang X, Guo M, Wang J, Wang X, Gao R, Ma M, Xiang X, Luan S, Xing F, Cao Z, Yan J, Li G, Li X. Haplotype mapping of H3K27me3-associated chromatin interactions defines topological regulation of gene silencing in rice. Cell Rep 2023; 42:112350. [PMID: 37071534 DOI: 10.1016/j.celrep.2023.112350] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Revised: 02/06/2023] [Accepted: 03/20/2023] [Indexed: 04/19/2023] Open
Abstract
Histone modification H3K27me3 is an important chromatin mark that plays vital roles in repressing expression of developmental genes. Here, we construct high-resolution 3D genome maps using long-read chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) and characterize H3K27me3-associated chromatin interactions in an elite rice hybrid, Shanyou 63. We find that many H3K27me3-marked regions may function as silencer-like regulatory elements. The silencer-like elements can come into proximity with distal target genes via forming chromatin loops in 3D space of the nuclei, regulating gene silencing and plant traits. Natural and induced deletion of silencers upregulate expression of distal connected genes. Furthermore, we identify extensive allele-specific chromatin loops. We find that genetic variations alter allelic chromatin topology, thus modulating allelic gene imprinting in rice hybrids. In conclusion, the characterization of silencer-like regulatory elements and haplotype-resolved chromatin interaction maps provide insights into the understanding of molecular mechanisms underlying allelic gene silencing and plant trait controlling.
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Affiliation(s)
- Weizhi Ouyang
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China
| | - Xiwen Zhang
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China
| | - Minrong Guo
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China
| | - Jing Wang
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China
| | - Xiaoting Wang
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China
| | - Runxin Gao
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China
| | - Meng Ma
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China
| | - Xu Xiang
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China
| | - Shiping Luan
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China
| | - Feng Xing
- College of Life Science, Xinyang Normal University, Xinyang 464000, China
| | - Zhilin Cao
- Department of Resources and Environment, Henan University of Engineering, Zhengzhou 451191, China
| | - Jiapei Yan
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China
| | - Guoliang Li
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China; Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan, China; Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China.
| | - Xingwang Li
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China; Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan, China; Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China.
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20
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Zhang X, Zhu W, Sun H, Ding Y, Liu L. Prediction of CTCF loop anchor based on machine learning. Front Genet 2023; 14:1181956. [PMID: 37077544 PMCID: PMC10106609 DOI: 10.3389/fgene.2023.1181956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 03/24/2023] [Indexed: 04/05/2023] Open
Abstract
Introduction: Various activities in biological cells are affected by three-dimensional genome structure. The insulators play an important role in the organization of higher-order structure. CTCF is a representative of mammalian insulators, which can produce barriers to prevent the continuous extrusion of chromatin loop. As a multifunctional protein, CTCF has tens of thousands of binding sites in the genome, but only a portion of them can be used as anchors of chromatin loops. It is still unclear how cells select the anchor in the process of chromatin looping.Methods: In this paper, a comparative analysis is performed to investigate the sequence preference and binding strength of anchor and non-anchor CTCF binding sites. Furthermore, a machine learning model based on the CTCF binding intensity and DNA sequence is proposed to predict which CTCF sites can form chromatin loop anchors.Results: The accuracy of the machine learning model that we constructed for predicting the anchor of the chromatin loop mediated by CTCF reached 0.8646. And we find that the formation of loop anchor is mainly influenced by the CTCF binding strength and binding pattern (which can be interpreted as the binding of different zinc fingers).Discussion: In conclusion, our results suggest that The CTCF core motif and it’s flanking sequence may be responsible for the binding specificity. This work contributes to understanding the mechanism of loop anchor selection and provides a reference for the prediction of CTCF-mediated chromatin loops.
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Affiliation(s)
- Xiao Zhang
- School of Mathematics and Statistics, Hainan Normal University, Haikou, China
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
- Key Laboratory of Computational Science and Application of Hainan Province, Haikou, China
| | - Wen Zhu
- School of Mathematics and Statistics, Hainan Normal University, Haikou, China
- Key Laboratory of Computational Science and Application of Hainan Province, Haikou, China
- *Correspondence: Wen Zhu,
| | - Huimin Sun
- School of Physical Science and Technology, Inner Mongolia University, Hohhot, China
| | - Yijie Ding
- Key Laboratory of Computational Science and Application of Hainan Province, Haikou, China
| | - Li Liu
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
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21
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Wayman JA, Thomas A, Bejjani A, Katko A, Almanan M, Godarova A, Korinfskaya S, Cazares TA, Yukawa M, Kottyan LC, Barski A, Chougnet CA, Hildeman DA, Miraldi ER. An atlas of gene regulatory networks for memory CD4 + T cells in youth and old age. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.07.531590. [PMID: 36945549 PMCID: PMC10028906 DOI: 10.1101/2023.03.07.531590] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2023]
Abstract
Aging profoundly affects immune-system function, promoting susceptibility to pathogens, cancers and chronic inflammation. We previously identified a population of IL-10-producing, T follicular helper-like cells (" Tfh10 "), linked to suppressed vaccine responses in aged mice. Here, we integrate single-cell ( sc )RNA-seq, scATAC-seq and genome-scale modeling to characterize Tfh10 - and the full CD4 + memory T cell ( CD4 + TM ) compartment - in young and old mice. We identified 13 CD4 + TM populations, which we validated through cross-comparison to prior scRNA-seq studies. We built gene regulatory networks ( GRNs ) that predict transcription-factor control of gene expression in each T-cell population and how these circuits change with age. Through integration with pan-cell aging atlases, we identified intercellular-signaling networks driving age-dependent changes in CD4 + TM. Our atlas of finely resolved CD4 + TM subsets, GRNs and cell-cell communication networks is a comprehensive resource of predicted regulatory mechanisms operative in memory T cells, presenting new opportunities to improve immune responses in the elderly.
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22
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An autoimmune pleiotropic SNP modulates IRF5 alternative promoter usage through ZBTB3-mediated chromatin looping. Nat Commun 2023; 14:1208. [PMID: 36869052 PMCID: PMC9984425 DOI: 10.1038/s41467-023-36897-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 02/22/2023] [Indexed: 03/05/2023] Open
Abstract
Genetic sharing is extensively observed for autoimmune diseases, but the causal variants and their underlying molecular mechanisms remain largely unknown. Through systematic investigation of autoimmune disease pleiotropic loci, we found most of these shared genetic effects are transmitted from regulatory code. We used an evidence-based strategy to functionally prioritize causal pleiotropic variants and identify their target genes. A top-ranked pleiotropic variant, rs4728142, yielded many lines of evidence as being causal. Mechanistically, the rs4728142-containing region interacts with the IRF5 alternative promoter in an allele-specific manner and orchestrates its upstream enhancer to regulate IRF5 alternative promoter usage through chromatin looping. A putative structural regulator, ZBTB3, mediates the allele-specific loop to promote IRF5-short transcript expression at the rs4728142 risk allele, resulting in IRF5 overactivation and M1 macrophage polarization. Together, our findings establish a causal mechanism between the regulatory variant and fine-scale molecular phenotype underlying the dysfunction of pleiotropic genes in human autoimmunity.
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23
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Gong H, Li M, Ji M, Zhang X, Yuan Z, Zhang S, Yang Y, Li C, Chen Y. MINE is a method for detecting spatial density of regulatory chromatin interactions based on a multi-modal network. CELL REPORTS METHODS 2023; 3:100386. [PMID: 36814847 PMCID: PMC9939382 DOI: 10.1016/j.crmeth.2022.100386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 09/15/2022] [Accepted: 12/16/2022] [Indexed: 06/18/2023]
Abstract
Chromatin interactions play essential roles in chromatin conformation and gene expression. However, few tools exist to analyze the spatial density of regulatory chromatin interactions (SD-RCI). Here, we present the multi-modal network (MINE) toolkit, including MINE-Loop, MINE-Density, and MINE-Viewer. The MINE-Loop network aims to enhance the detection of RCIs, MINE-Density quantifies the SD--RCI, and MINE-Viewer facilitates 3D visualization of the density of chromatin interactions and participating regulatory factors (e.g., transcription factors). We applied MINE to investigate the relationship between the SD-RCI and chromatin volume change in HeLa cells before and after liquid-liquid phase separation. Changes in SD-RCI before and after treating the HeLa cells with 1,6-hexanediol suggest that changes in chromatin organization was related to the degree of activation or repression of genes. Together, the MINE toolkit enables quantitative studies on different aspects of chromatin conformation and regulatory activity.
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Affiliation(s)
- Haiyan Gong
- Beijing Advanced Innovation Center for Materials Genome Engineering, School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Minghong Li
- Beijing Advanced Innovation Center for Materials Genome Engineering, School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Mengdie Ji
- State Key Laboratory of Medical Molecular Biology, Department of Biochemistry and Molecular Biology, Institute of Basic Medical Sciences, School of Basic Medicine, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100005, China
| | - Xiaotong Zhang
- Beijing Advanced Innovation Center for Materials Genome Engineering, School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
- Shunde Innovation School, University of Science and Technology Beijing, Foshan 528399, China
| | - Zan Yuan
- State Key Laboratory of Medical Molecular Biology, Department of Biochemistry and Molecular Biology, Institute of Basic Medical Sciences, School of Basic Medicine, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100005, China
| | - Sichen Zhang
- Beijing Advanced Innovation Center for Materials Genome Engineering, School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Yi Yang
- Beijing Advanced Innovation Center for Materials Genome Engineering, School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Chun Li
- School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Yang Chen
- State Key Laboratory of Medical Molecular Biology, Department of Biochemistry and Molecular Biology, Institute of Basic Medical Sciences, School of Basic Medicine, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100005, China
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24
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Yuan J, Houlahan KE, Ramanand SG, Lee S, Baek G, Yang Y, Chen Y, Strand DW, Zhang MQ, Boutros PC, Mani RS. Prostate Cancer Transcriptomic Regulation by the Interplay of Germline Risk Alleles, Somatic Mutations, and 3D Genomic Architecture. Cancer Discov 2022; 12:2838-2855. [PMID: 36108240 PMCID: PMC9722594 DOI: 10.1158/2159-8290.cd-22-0027] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Revised: 07/18/2022] [Accepted: 09/15/2022] [Indexed: 01/12/2023]
Abstract
Prostate cancer is one of the most heritable human cancers. Genome-wide association studies have identified at least 185 prostate cancer germline risk alleles, most noncoding. We used integrative three-dimensional (3D) spatial genomics to identify the chromatin interaction targets of 45 prostate cancer risk alleles, 31 of which were associated with the transcriptional regulation of target genes in 565 localized prostate tumors. To supplement these 31, we verified transcriptional targets for 56 additional risk alleles using linear proximity and linkage disequilibrium analysis in localized prostate tumors. Some individual risk alleles influenced multiple target genes; others specifically influenced only distal genes while leaving proximal ones unaffected. Several risk alleles exhibited widespread germline-somatic interactions in transcriptional regulation, having different effects in tumors with loss of PTEN or RB1 relative to those without. These data clarify functional prostate cancer risk alleles in large linkage blocks and outline a strategy to model multidimensional transcriptional regulation. SIGNIFICANCE Many prostate cancer germline risk alleles are enriched in the noncoding regions of the genome and are hypothesized to regulate transcription. We present a 3D genomics framework to unravel risk SNP function and describe the widespread germline-somatic interplay in transcription control. This article is highlighted in the In This Issue feature, p. 2711.
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Affiliation(s)
- Jiapei Yuan
- Department of Pathology, UT Southwestern Medical Center, Dallas, Texas,State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College., Tianjin, China
| | - Kathleen E Houlahan
- Department of Human Genetics, University of California, Los Angeles, California,Jonsson Comprehensive Cancer Centre, University of California, Los Angeles, California,Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada,Vector Institute, Toronto, ON M5G 1M1, Canada,Ontario Institute for Cancer Research, Toronto, ON M5G 0A3, Canada
| | | | - Sora Lee
- Department of Pathology, UT Southwestern Medical Center, Dallas, Texas
| | - GuemHee Baek
- Department of Pathology, UT Southwestern Medical Center, Dallas, Texas
| | - Yang Yang
- The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Key Laboratory of Inflammation Biology, Department of Bioinformatics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China,Department of Geriatrics, Tianjin Medical University General Hospital, Tianjin Medical University, Tianjin, China
| | - Yong Chen
- Department of Molecular and Cellular Biosciences, Rowan University, Glassboro, New Jersey
| | - Douglas W. Strand
- Department of Urology, UT Southwestern Medical Center, Dallas, Texas
| | - Michael Q. Zhang
- Department of Biological Sciences, Center for Systems Biology, The University of Texas at Dallas, Richardson, Texas,MOE Key Laboratory of Bioinformatics and Bioinformatics Division, Center for Synthetic and System Biology, TNLIST/Department Automation, Tsinghua University, Beijing 100084, China
| | - Paul C. Boutros
- Department of Human Genetics, University of California, Los Angeles, California,Jonsson Comprehensive Cancer Centre, University of California, Los Angeles, California,Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada,Vector Institute, Toronto, ON M5G 1M1, Canada,Department of Urology, University of California, Los Angeles, California,Institute for Precision Health, University of California, Los Angeles, California
| | - Ram S. Mani
- Department of Pathology, UT Southwestern Medical Center, Dallas, Texas,Department of Urology, UT Southwestern Medical Center, Dallas, Texas,Harold C. Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, Texas
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25
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Peng J, Cai D, Zeng R, Zhang C, Li G, Chen S, Yuan X, Peng L. Upregulation of Superenhancer-Driven LncRNA FASRL by USF1 Promotes De Novo Fatty Acid Biosynthesis to Exacerbate Hepatocellular Carcinoma. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2022; 10:e2204711. [PMID: 36307901 PMCID: PMC9811444 DOI: 10.1002/advs.202204711] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 10/02/2022] [Indexed: 06/16/2023]
Abstract
Superenhancers drive abnormal gene expression in tumors and promote malignancy. However, the relationship between superenhancer-associated long noncoding RNA (lncRNA) and abnormal metabolism is unknown. This study identifies a novel lncRNA, fatty acid synthesis-related lncRNA (FASRL), whose expression is driven by upstream stimulatory factor 1 (USF1) through its superenhancer. FASRL promotes hepatocellular carcinoma (HCC) cell proliferation in vitro and in vivo. Furthermore, FASRL binds to acetyl-CoA carboxylase 1 (ACACA), a fatty acid synthesis rate-limiting enzyme, increasing fatty acid synthesis via the fatty acid metabolism pathway. Moreover, the expression of FASRL, USF1, and ACACA is increased, and their high expression indicates a worse prognosis in HCC patients. In summary, USF1 drives FASRL transcription via a superenhancer. FASRL binding to ACACA increases fatty acid synthesis and lipid accumulation to mechanistically exacerbate HCC. FASRL may serve as a novel prognostic marker and treatment target in HCC.
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Affiliation(s)
- Jiang‐Yun Peng
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene RegulationGuangdong‐Hong Kong Joint Laboratory for RNA MedicineSun Yat‐sen Memorial HospitalSun Yat‐sen UniversityGuangzhou510120P. R. China
- Medical Research CenterSun Yat‐sen Memorial HospitalSun Yat‐sen UniversityGuangzhou510120P. R. China
| | - Dian‐Kui Cai
- Department of Hepatobiliary SurgerySun Yat‐sen Memorial HospitalSun Yat‐sen UniversityGuangzhou510120P. R. China
| | - Ren‐Li Zeng
- Department of EndocrinologySun Yat‐sen Memorial HospitalSun Yat‐sen UniversityGuangzhou510120P. R. China
| | - Chao‐Yang Zhang
- Division of Functional Genome AnalysisGerman Cancer Research Center (DKFZ)69120HeidelbergGermany
| | - Guan‐Cheng Li
- Key Laboratory of Carcinogenesis of the Chinese Ministry of Health and the Key Laboratory of Carcinogenesis and Cancer Invasion of Chinese Ministry of EducationCentral South UniversityChangsha410078P. R. China
- Cancer Research InstituteCentral South UniversityChangsha410078P. R. China
| | - Si‐Fan Chen
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene RegulationGuangdong‐Hong Kong Joint Laboratory for RNA MedicineSun Yat‐sen Memorial HospitalSun Yat‐sen UniversityGuangzhou510120P. R. China
- Medical Research CenterSun Yat‐sen Memorial HospitalSun Yat‐sen UniversityGuangzhou510120P. R. China
| | - Xiao‐Qing Yuan
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene RegulationGuangdong‐Hong Kong Joint Laboratory for RNA MedicineSun Yat‐sen Memorial HospitalSun Yat‐sen UniversityGuangzhou510120P. R. China
- Breast Tumor CenterSun Yat‐sen Memorial HospitalSun Yat‐sen UniversityGuangzhou510120P. R. China
| | - Li Peng
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene RegulationGuangdong‐Hong Kong Joint Laboratory for RNA MedicineSun Yat‐sen Memorial HospitalSun Yat‐sen UniversityGuangzhou510120P. R. China
- Medical Research CenterSun Yat‐sen Memorial HospitalSun Yat‐sen UniversityGuangzhou510120P. R. China
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26
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Gong S, Hu G, Guo R, Zhang J, Yang Y, Ji B, Li G, Yao H. CTCF acetylation at lysine 20 is required for the early cardiac mesoderm differentiation of embryonic stem cells. CELL REGENERATION 2022; 11:34. [PMID: 36117192 PMCID: PMC9482892 DOI: 10.1186/s13619-022-00131-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Accepted: 07/31/2022] [Indexed: 11/15/2022]
Abstract
The CCCTC-binding factor (CTCF) protein and its modified forms regulate gene expression and genome organization. However, information on CTCF acetylation and its biological function is still lacking. Here, we show that CTCF can be acetylated at lysine 20 (CTCF-K20) by CREB-binding protein (CBP) and deacetylated by histone deacetylase 6 (HDAC6). CTCF-K20 is required for the CTCF interaction with CBP. A CTCF point mutation at lysine 20 had no effect on self-renewal but blocked the mesoderm differentiation of mouse embryonic stem cells (mESCs). The CTCF-K20 mutation reduced CTCF binding to the promoters and enhancers of genes associated with early cardiac mesoderm differentiation, resulting in diminished chromatin accessibility and decreased enhancer-promoter interactions, impairing gene expression. In summary, this study reveals the important roles of CTCF-K20 in regulating CTCF genomic functions and mESC differentiation into mesoderm.
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27
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Zhang S, Plummer D, Lu L, Cui J, Xu W, Wang M, Liu X, Prabhakar N, Shrinet J, Srinivasan D, Fraser P, Li Y, Li J, Jin F. DeepLoop robustly maps chromatin interactions from sparse allele-resolved or single-cell Hi-C data at kilobase resolution. Nat Genet 2022; 54:1013-1025. [PMID: 35817982 PMCID: PMC10082397 DOI: 10.1038/s41588-022-01116-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Accepted: 05/30/2022] [Indexed: 11/09/2022]
Abstract
Mapping chromatin loops from noisy Hi-C heatmaps remains a major challenge. Here we present DeepLoop, which performs rigorous bias correction followed by deep-learning-based signal enhancement for robust chromatin interaction mapping from low-depth Hi-C data. DeepLoop enables loop-resolution, single-cell Hi-C analysis. It also achieves a cross-platform convergence between different Hi-C protocols and micrococcal nuclease (micro-C). DeepLoop allowed us to map the genetic and epigenetic determinants of allele-specific chromatin interactions in the human genome. We nominate new loci with allele-specific interactions governed by imprinting or allelic DNA methylation. We also discovered that, in the inactivated X chromosome (Xi), local loops at the DXZ4 'megadomain' boundary escape X-inactivation but the FIRRE 'superloop' locus does not. Importantly, DeepLoop can pinpoint heterozygous single-nucleotide polymorphisms and large structure variants that cause allelic chromatin loops, many of which rewire enhancers with transcription consequences. Taken together, DeepLoop expands the use of Hi-C to provide loop-resolution insights into the genetics of the three-dimensional genome.
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Affiliation(s)
- Shanshan Zhang
- Department of Genetics and Genome Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA.,The Biomedical Sciences Training Program, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Dylan Plummer
- Department of Computer and Data Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Leina Lu
- Department of Genetics and Genome Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Jian Cui
- Department of Genetics and Genome Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Wanying Xu
- Department of Genetics and Genome Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA.,The Biomedical Sciences Training Program, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Miao Wang
- Department of Biological Science, Florida State University, Tallahassee, FL, USA
| | - Xiaoxiao Liu
- Department of Genetics and Genome Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Nachiketh Prabhakar
- Department of Computer and Data Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Jatin Shrinet
- Department of Biological Science, Florida State University, Tallahassee, FL, USA
| | - Divyaa Srinivasan
- Department of Biological Science, Florida State University, Tallahassee, FL, USA
| | - Peter Fraser
- Department of Biological Science, Florida State University, Tallahassee, FL, USA
| | - Yan Li
- Department of Genetics and Genome Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA.
| | - Jing Li
- Department of Computer and Data Sciences, Case Western Reserve University, Cleveland, OH, USA. .,Department of Population and Quantitative Health Sciences, Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, OH, USA.
| | - Fulai Jin
- Department of Genetics and Genome Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA. .,Department of Computer and Data Sciences, Case Western Reserve University, Cleveland, OH, USA. .,Department of Population and Quantitative Health Sciences, Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, OH, USA.
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28
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Wei C, Jia L, Huang X, Tan J, Wang M, Niu J, Hou Y, Sun J, Zeng P, Wang J, Qing L, Ma L, Liu X, Tang X, Li F, Jiang S, Liu J, Li T, Fan L, Sun Y, Gao J, Li C, Ding J. CTCF organizes inter-A compartment interactions through RYBP-dependent phase separation. Cell Res 2022; 32:744-760. [PMID: 35768498 PMCID: PMC9343660 DOI: 10.1038/s41422-022-00676-0] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 05/10/2022] [Indexed: 12/13/2022] Open
Abstract
Chromatin is spatially organized into three-dimensional structures at different levels including A/B compartments, topologically associating domains and loops. The canonical CTCF-mediated loop extrusion model can explain the formation of loops. However, the organization mechanisms underlying long-range chromatin interactions such as interactions between A-A compartments are still poorly understood. Here we show that different from the canonical loop extrusion model, RYBP-mediated phase separation of CTCF organizes inter-A compartment interactions. Based on this model, we designed and verified an induced CTCF phase separation system in embryonic stem cells (ESCs), which facilitated inter-A compartment interactions, improved self-renewal of ESCs and inhibited their differentiation toward neural progenitor cells. These findings support a novel and non-canonical role of CTCF in organizing long-range chromatin interactions via phase separation.
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Affiliation(s)
- Chao Wei
- RNA Biomedical Institute, Sun Yat-Sen Memorial Hospital, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, Guangdong, China.,Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China.,Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Lumeng Jia
- School of Life Sciences, Peking University, Beijing, China
| | - Xiaona Huang
- RNA Biomedical Institute, Sun Yat-Sen Memorial Hospital, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, Guangdong, China.,Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China.,Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Jin Tan
- RNA Biomedical Institute, Sun Yat-Sen Memorial Hospital, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, Guangdong, China.,Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China.,Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Mulan Wang
- RNA Biomedical Institute, Sun Yat-Sen Memorial Hospital, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, Guangdong, China.,Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China.,Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Jing Niu
- School of Medicine, Tsinghua University, Beijing, China
| | - Yingping Hou
- Peking-Tsinghua Center for Life Sciences; Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Jun Sun
- RNA Biomedical Institute, Sun Yat-Sen Memorial Hospital, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, Guangdong, China.,Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China.,Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Pengguihang Zeng
- RNA Biomedical Institute, Sun Yat-Sen Memorial Hospital, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, Guangdong, China.,Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China.,Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, Guangdong, China.,Department of Cell Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, Guangdong, China
| | - Jia Wang
- RNA Biomedical Institute, Sun Yat-Sen Memorial Hospital, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, Guangdong, China.,Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China.,Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Li Qing
- RNA Biomedical Institute, Sun Yat-Sen Memorial Hospital, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, Guangdong, China.,Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China.,Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Lin Ma
- RNA Biomedical Institute, Sun Yat-Sen Memorial Hospital, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, Guangdong, China.,Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China.,Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Xinyi Liu
- RNA Biomedical Institute, Sun Yat-Sen Memorial Hospital, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, Guangdong, China.,Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China.,Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Xiuxiao Tang
- RNA Biomedical Institute, Sun Yat-Sen Memorial Hospital, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, Guangdong, China.,Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China.,Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Fenjie Li
- RNA Biomedical Institute, Sun Yat-Sen Memorial Hospital, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, Guangdong, China.,Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China.,Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, Guangdong, China.,Department of Pediatric Surgery, Guangdong Provincial Key Laboratory of Research in Structural Birth Defect Disease, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Shaoshuai Jiang
- RNA Biomedical Institute, Sun Yat-Sen Memorial Hospital, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, Guangdong, China.,Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China.,Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Jingxin Liu
- RNA Biomedical Institute, Sun Yat-Sen Memorial Hospital, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, Guangdong, China.,Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China.,Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Tingting Li
- State Key Laboratory of Proteomics, National Center of Biomedical Analysis, Institute of Basic Medical Sciences, Beijing, China
| | - Lili Fan
- Guangzhou Key Laboratory of Formula-Pattern of Traditional Chinese Medicine, School of Traditional Chinese Medicine, Jinan University, Guangzhou, Guangdong, China
| | - Yujie Sun
- School of Life Sciences, Peking University, Beijing, China.,State Key Laboratory of Membrane Biology, Biomedical pioneering innovation center (BIOPIC), Peking University, Beijing, China
| | - Juntao Gao
- MOE Key Laboratory of Bioinformatics; Bioinformatics Division, BNRist; Department of Automation; Center for Synthetic & Systems Biology, Tsinghua University, Beijing, China
| | - Cheng Li
- School of Life Sciences, Peking University, Beijing, China. .,Center for Bioinformatics, Center for Statistical Science, Peking University, Beijing, China.
| | - Junjun Ding
- RNA Biomedical Institute, Sun Yat-Sen Memorial Hospital, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, Guangdong, China. .,Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China. .,Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, Guangdong, China.
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29
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Li J, Xiang Y, Zhang L, Qi X, Zheng Z, Zhou P, Tang Z, Jin Y, Zhao Q, Fu Y, Zhao Y, Li X, Fu L, Zhao S. Enhancer-promoter interaction maps provide insights into skeletal muscle-related traits in pig genome. BMC Biol 2022; 20:136. [PMID: 35681201 PMCID: PMC9185926 DOI: 10.1186/s12915-022-01322-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 05/06/2022] [Indexed: 12/03/2022] Open
Abstract
Background Gene expression programs are intimately linked to the interplay of active cis regulatory elements mediated by chromatin contacts and associated RNAs. Genome-wide association studies (GWAS) have identified many variants in these regulatory elements that can contribute to phenotypic diversity. However, the functional interpretation of these variants remains nontrivial due to the lack of chromatin contact information or limited contact resolution. Furthermore, the distribution and role of chromatin-associated RNAs in gene expression and chromatin conformation remain poorly understood. To address this, we first present a comprehensive interaction map of nuclear dynamics of 3D chromatin-chromatin interactions (H3K27ac BL-HiChIP) and RNA-chromatin interactions (GRID-seq) to reveal genomic variants that contribute to complex skeletal muscle traits. Results In a genome-wide scan, we provide systematic fine mapping and gene prioritization from GWAS leading signals that underlie phenotypic variability of growth rate, meat quality, and carcass performance. A set of candidate functional variants and 54 target genes previously not detected were identified, with 71% of these candidate functional variants choosing to skip over their nearest gene to regulate the target gene in a long-range manner. The effects of three functional variants regulating KLF6 (related to days to 100 kg), MXRA8 (related to lean meat percentage), and TAF11 (related to loin muscle depth) were observed in two pig populations. Moreover, we find that this multi-omics interaction map consists of functional communities that are enriched in specific biological functions, and GWAS target genes can serve as core genes for exploring peripheral trait-relevant genes. Conclusions Our results provide a valuable resource of candidate functional variants for complex skeletal muscle-related traits and establish an integrated approach to complement existing 3D genomics by exploiting RNA-chromatin and chromatin-chromatin interactions for future association studies. Supplementary Information The online version contains supplementary material available at 10.1186/s12915-022-01322-2.
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Affiliation(s)
- Jingjin Li
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China
| | - Yue Xiang
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China
| | - Lu Zhang
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China
| | - Xiaolong Qi
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China
| | - Zhuqing Zheng
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China
| | - Peng Zhou
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China
| | - Zhenshuang Tang
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China
| | - Yi Jin
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China
| | - Qiulin Zhao
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China
| | - Yuhua Fu
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China
| | - Yunxia Zhao
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China
| | - Xinyun Li
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China. .,The Cooperative Innovation Center for Sustainable Pig Production, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China. .,Hubei Hongshan Laboratory, 430070, Wuhan, People's Republic of China.
| | - Liangliang Fu
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China. .,The Cooperative Innovation Center for Sustainable Pig Production, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China. .,Hubei Hongshan Laboratory, 430070, Wuhan, People's Republic of China.
| | - Shuhong Zhao
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China. .,The Cooperative Innovation Center for Sustainable Pig Production, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China. .,Hubei Hongshan Laboratory, 430070, Wuhan, People's Republic of China.
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30
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Deng S, Feng Y, Pauklin S. 3D chromatin architecture and transcription regulation in cancer. J Hematol Oncol 2022; 15:49. [PMID: 35509102 PMCID: PMC9069733 DOI: 10.1186/s13045-022-01271-x] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 04/21/2022] [Indexed: 12/18/2022] Open
Abstract
Chromatin has distinct three-dimensional (3D) architectures important in key biological processes, such as cell cycle, replication, differentiation, and transcription regulation. In turn, aberrant 3D structures play a vital role in developing abnormalities and diseases such as cancer. This review discusses key 3D chromatin structures (topologically associating domain, lamina-associated domain, and enhancer-promoter interactions) and corresponding structural protein elements mediating 3D chromatin interactions [CCCTC-binding factor, polycomb group protein, cohesin, and Brother of the Regulator of Imprinted Sites (BORIS) protein] with a highlight of their associations with cancer. We also summarise the recent development of technologies and bioinformatics approaches to study the 3D chromatin interactions in gene expression regulation, including crosslinking and proximity ligation methods in the bulk cell population (ChIA-PET and HiChIP) or single-molecule resolution (ChIA-drop), and methods other than proximity ligation, such as GAM, SPRITE, and super-resolution microscopy techniques.
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Affiliation(s)
- Siwei Deng
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, University of Oxford, Old Road, Headington, Oxford, OX3 7LD, UK
| | - Yuliang Feng
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, University of Oxford, Old Road, Headington, Oxford, OX3 7LD, UK
| | - Siim Pauklin
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, University of Oxford, Old Road, Headington, Oxford, OX3 7LD, UK.
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31
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Qin T, Lee C, Li S, Cavalcante RG, Orchard P, Yao H, Zhang H, Wang S, Patil S, Boyle AP, Sartor MA. Comprehensive enhancer-target gene assignments improve gene set level interpretation of genome-wide regulatory data. Genome Biol 2022; 23:105. [PMID: 35473573 PMCID: PMC9044877 DOI: 10.1186/s13059-022-02668-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Accepted: 04/06/2022] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Revealing the gene targets of distal regulatory elements is challenging yet critical for interpreting regulome data. Experiment-derived enhancer-gene links are restricted to a small set of enhancers and/or cell types, while the accuracy of genome-wide approaches remains elusive due to the lack of a systematic evaluation. We combined multiple spatial and in silico approaches for defining enhancer locations and linking them to their target genes aggregated across >500 cell types, generating 1860 human genome-wide distal enhancer-to-target gene definitions (EnTDefs). To evaluate performance, we used gene set enrichment (GSE) testing on 87 independent ENCODE ChIP-seq datasets of 34 transcription factors (TFs) and assessed concordance of results with known TF Gene Ontology annotations, and other benchmarks. RESULTS The top ranked 741 (40%) EnTDefs significantly outperform the common, naïve approach of linking distal regions to the nearest genes, and the top 10 EnTDefs perform well when applied to ChIP-seq data of other cell types. The GSE-based ranking of EnTDefs is highly concordant with ranking based on overlap with curated benchmarks of enhancer-gene interactions. Both our top general EnTDef and cell-type-specific EnTDefs significantly outperform seven independent computational and experiment-based enhancer-gene pair datasets. We show that using our top EnTDefs for GSE with either genome-wide DNA methylation or ATAC-seq data is able to better recapitulate the biological processes changed in gene expression data performed in parallel for the same experiment than our lower-ranked EnTDefs. CONCLUSIONS Our findings illustrate the power of our approach to provide genome-wide interpretation regardless of cell type.
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Affiliation(s)
- Tingting Qin
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA.
| | - Christopher Lee
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA
- Department of Biostatistics, School of Public Health, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Shiting Li
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Raymond G Cavalcante
- Biomedical Research Core Facilities, Epigenomics Core, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Peter Orchard
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Heming Yao
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Hanrui Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Shuze Wang
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Snehal Patil
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Alan P Boyle
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA
- Department of Human Genetics, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Maureen A Sartor
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA.
- Department of Biostatistics, School of Public Health, University of Michigan Medical School, Ann Arbor, MI, USA.
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32
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Dong X, Guo R, Ji T, Zhang J, Xu J, Li Y, Sheng Y, Wang Y, Fang K, Wen Y, Liu B, Hu G, Deng H, Yao H. YY1 safeguard multidimensional epigenetic landscape associated with extended pluripotency. Nucleic Acids Res 2022; 50:12019-12038. [PMID: 35425987 PMCID: PMC9756953 DOI: 10.1093/nar/gkac230] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 03/21/2022] [Accepted: 03/27/2022] [Indexed: 12/24/2022] Open
Abstract
Although extended pluripotent stem cells (EPSCs) have the potential to form both embryonic and extraembryonic lineages, how their transcriptional regulatory mechanism differs from that of embryonic stem cells (ESCs) remains unclear. Here, we discovered that YY1 binds to specific open chromatin regions in EPSCs. Yy1 depletion in EPSCs leads to a gene expression pattern more similar to that of ESCs than control EPSCs. Moreover, Yy1 depletion triggers a series of epigenetic crosstalk activities, including changes in DNA methylation, histone modifications and high-order chromatin structures. Yy1 depletion in EPSCs disrupts the enhancer-promoter (EP) interactions of EPSC-specific genes, including Dnmt3l. Yy1 loss results in DNA hypomethylation and dramatically reduces the enrichment of H3K4me3 and H3K27ac on the promoters of EPSC-specific genes by upregulating the expression of Kdm5c and Hdac6 through facilitating the formation of CCCTC-binding factor (CTCF)-mediated EP interactions surrounding their loci. Furthermore, single-cell RNA sequencing (scRNA-seq) experiments revealed that YY1 is required for the derivation of extraembryonic endoderm (XEN)-like cells from EPSCs in vitro. Together, this study reveals that YY1 functions as a key regulator of multidimensional epigenetic crosstalk associated with extended pluripotency.
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Affiliation(s)
| | | | - Tianrong Ji
- CAS Key Laboratory of Regenerative Biology, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou Medical University, Guangzhou 510530, China,Bioland Laboratory (Guangzhou Regenerative Medicine and Health GuangDong Laboratory), Guangzhou 510005, China,Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, GIBH-CUHK Joint Research Laboratory on Stem Cell and Regenerative Medicine, State Key Laboratory of Respiratory Disease, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China,Institute of Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing 100101, China
| | - Jie Zhang
- CAS Key Laboratory of Regenerative Biology, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou Medical University, Guangzhou 510530, China,Bioland Laboratory (Guangzhou Regenerative Medicine and Health GuangDong Laboratory), Guangzhou 510005, China,Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, GIBH-CUHK Joint Research Laboratory on Stem Cell and Regenerative Medicine, State Key Laboratory of Respiratory Disease, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China,University of Chinese Academy of Sciences, Beijing 100049, China,Institute of Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing 100101, China
| | - Jun Xu
- School of Life Sciences, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, China
| | - Yaoyi Li
- CAS Key Laboratory of Regenerative Biology, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou Medical University, Guangzhou 510530, China,Bioland Laboratory (Guangzhou Regenerative Medicine and Health GuangDong Laboratory), Guangzhou 510005, China,Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, GIBH-CUHK Joint Research Laboratory on Stem Cell and Regenerative Medicine, State Key Laboratory of Respiratory Disease, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China,Institute of Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing 100101, China
| | - Yingliang Sheng
- CAS Key Laboratory of Regenerative Biology, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou Medical University, Guangzhou 510530, China,Bioland Laboratory (Guangzhou Regenerative Medicine and Health GuangDong Laboratory), Guangzhou 510005, China,Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, GIBH-CUHK Joint Research Laboratory on Stem Cell and Regenerative Medicine, State Key Laboratory of Respiratory Disease, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China
| | - Yuxiang Wang
- CAS Key Laboratory of Regenerative Biology, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou Medical University, Guangzhou 510530, China,Bioland Laboratory (Guangzhou Regenerative Medicine and Health GuangDong Laboratory), Guangzhou 510005, China,Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, GIBH-CUHK Joint Research Laboratory on Stem Cell and Regenerative Medicine, State Key Laboratory of Respiratory Disease, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China,University of Chinese Academy of Sciences, Beijing 100049, China,Institute of Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing 100101, China
| | - Ke Fang
- CAS Key Laboratory of Regenerative Biology, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou Medical University, Guangzhou 510530, China,Bioland Laboratory (Guangzhou Regenerative Medicine and Health GuangDong Laboratory), Guangzhou 510005, China,Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, GIBH-CUHK Joint Research Laboratory on Stem Cell and Regenerative Medicine, State Key Laboratory of Respiratory Disease, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China,Institute of Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing 100101, China
| | - Yulin Wen
- CAS Key Laboratory of Regenerative Biology, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou Medical University, Guangzhou 510530, China,Bioland Laboratory (Guangzhou Regenerative Medicine and Health GuangDong Laboratory), Guangzhou 510005, China,Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, GIBH-CUHK Joint Research Laboratory on Stem Cell and Regenerative Medicine, State Key Laboratory of Respiratory Disease, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China,University of Chinese Academy of Sciences, Beijing 100049, China,Institute of Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing 100101, China
| | - Bei Liu
- School of Life Sciences, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, China
| | - Gongcheng Hu
- CAS Key Laboratory of Regenerative Biology, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou Medical University, Guangzhou 510530, China,Bioland Laboratory (Guangzhou Regenerative Medicine and Health GuangDong Laboratory), Guangzhou 510005, China,Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, GIBH-CUHK Joint Research Laboratory on Stem Cell and Regenerative Medicine, State Key Laboratory of Respiratory Disease, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China,Institute of Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing 100101, China
| | - Hongkui Deng
- School of Life Sciences, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, China
| | - Hongjie Yao
- To whom correspondence should be addressed. Tel: +86 20 32015279;
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Clow PA, Du M, Jillette N, Taghbalout A, Zhu JJ, Cheng AW. CRISPR-mediated multiplexed live cell imaging of nonrepetitive genomic loci with one guide RNA per locus. Nat Commun 2022; 13:1871. [PMID: 35387989 PMCID: PMC8987088 DOI: 10.1038/s41467-022-29343-z] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 03/08/2022] [Indexed: 12/20/2022] Open
Abstract
Three-dimensional (3D) structures of the genome are dynamic, heterogeneous and functionally important. Live cell imaging has become the leading method for chromatin dynamics tracking. However, existing CRISPR- and TALE-based genomic labeling techniques have been hampered by laborious protocols and are ineffective in labeling non-repetitive sequences. Here, we report a versatile CRISPR/Casilio-based imaging method that allows for a nonrepetitive genomic locus to be labeled using one guide RNA. We construct Casilio dual-color probes to visualize the dynamic interactions of DNA elements in single live cells in the presence or absence of the cohesin subunit RAD21. Using a three-color palette, we track the dynamic 3D locations of multiple reference points along a chromatin loop. Casilio imaging reveals intercellular heterogeneity and interallelic asynchrony in chromatin interaction dynamics, underscoring the importance of studying genome structures in 4D.
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Affiliation(s)
- Patricia A Clow
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA
| | - Menghan Du
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA
- Department of Genetics and Genome Sciences, University of Connecticut Health Center, Farmington, CT, 06030, USA
| | | | - Aziz Taghbalout
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA
| | - Jacqueline J Zhu
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA.
- School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ, 85281, USA.
| | - Albert W Cheng
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA.
- Department of Genetics and Genome Sciences, University of Connecticut Health Center, Farmington, CT, 06030, USA.
- School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ, 85281, USA.
- The Jackson Laboratory Cancer Center, Bar Harbor, ME, 04609, USA.
- Institute for Systems Genomics, University of Connecticut Health Center, Farmington, CT, 06030, USA.
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34
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Song Y, Liang Z, Zhang J, Hu G, Wang J, Li Y, Guo R, Dong X, Babarinde IA, Ping W, Sheng YL, Li H, Chen Z, Gao M, Chen Y, Shan G, Zhang MQ, Hutchins AP, Fu XD, Yao H. CTCF functions as an insulator for somatic genes and a chromatin remodeler for pluripotency genes during reprogramming. Cell Rep 2022; 39:110626. [PMID: 35385732 DOI: 10.1016/j.celrep.2022.110626] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Revised: 01/13/2022] [Accepted: 03/14/2022] [Indexed: 12/15/2022] Open
Abstract
CTCF mediates chromatin insulation and long-distance enhancer-promoter (EP) interactions; however, little is known about how these regulatory functions are partitioned among target genes in key biological processes. Here, we show that Ctcf expression is progressively increased during induced pluripotency. In this process, CTCF first functions as a chromatin insulator responsible for direct silencing of the somatic gene expression program and, interestingly, elevated Ctcf expression next ensures chromatin accessibility and contributes to increased EP interactions for a fraction of pluripotency-associated genes. Therefore, CTCF functions in a context-specific manner to modulate the 3D genome to enable cellular reprogramming. We further discover that these context-specific CTCF functions also enlist SMARCA5, an imitation switch (ISWI) chromatin remodeler, together rewiring the epigenome to facilitate cell-fate switch. These findings reveal the dual functions of CTCF in conjunction with a key chromatin remodeler to drive reprogramming toward pluripotency.
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Affiliation(s)
- Yawei Song
- CAS Key Laboratory of Regenerative Biology, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou Medical University, Guangzhou 510530, China; Bioland Laboratory (Guangzhou Regenerative Medicine and Health GuangDong Laboratory), Guangzhou 510005, China; Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, GIBH-CUHK Joint Research Laboratory on Stem Cell and Regenerative Medicine, State Key Laboratory of Respiratory Disease, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China; Institute of Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing 100101, China
| | - Zhengyu Liang
- Department of Cellular and Molecular Medicine, Institute of Genomic Medicine, University of California, San Diego, La Jolla, CA 92093-0651, USA
| | - Jie Zhang
- CAS Key Laboratory of Regenerative Biology, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou Medical University, Guangzhou 510530, China; Bioland Laboratory (Guangzhou Regenerative Medicine and Health GuangDong Laboratory), Guangzhou 510005, China; Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, GIBH-CUHK Joint Research Laboratory on Stem Cell and Regenerative Medicine, State Key Laboratory of Respiratory Disease, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China; Institute of Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Gongcheng Hu
- CAS Key Laboratory of Regenerative Biology, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou Medical University, Guangzhou 510530, China; Bioland Laboratory (Guangzhou Regenerative Medicine and Health GuangDong Laboratory), Guangzhou 510005, China; Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, GIBH-CUHK Joint Research Laboratory on Stem Cell and Regenerative Medicine, State Key Laboratory of Respiratory Disease, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China; Institute of Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing 100101, China
| | - Juehan Wang
- CAS Key Laboratory of Regenerative Biology, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou Medical University, Guangzhou 510530, China; Bioland Laboratory (Guangzhou Regenerative Medicine and Health GuangDong Laboratory), Guangzhou 510005, China; Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, GIBH-CUHK Joint Research Laboratory on Stem Cell and Regenerative Medicine, State Key Laboratory of Respiratory Disease, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China; Institute of Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yaoyi Li
- CAS Key Laboratory of Regenerative Biology, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou Medical University, Guangzhou 510530, China; Bioland Laboratory (Guangzhou Regenerative Medicine and Health GuangDong Laboratory), Guangzhou 510005, China; Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, GIBH-CUHK Joint Research Laboratory on Stem Cell and Regenerative Medicine, State Key Laboratory of Respiratory Disease, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China; Institute of Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing 100101, China
| | - Rong Guo
- CAS Key Laboratory of Regenerative Biology, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou Medical University, Guangzhou 510530, China; Bioland Laboratory (Guangzhou Regenerative Medicine and Health GuangDong Laboratory), Guangzhou 510005, China; Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, GIBH-CUHK Joint Research Laboratory on Stem Cell and Regenerative Medicine, State Key Laboratory of Respiratory Disease, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China; Institute of Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiaotao Dong
- CAS Key Laboratory of Regenerative Biology, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou Medical University, Guangzhou 510530, China; Bioland Laboratory (Guangzhou Regenerative Medicine and Health GuangDong Laboratory), Guangzhou 510005, China; Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, GIBH-CUHK Joint Research Laboratory on Stem Cell and Regenerative Medicine, State Key Laboratory of Respiratory Disease, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China; Institute of Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing 100101, China
| | - Isaac A Babarinde
- Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China
| | - Wangfang Ping
- CAS Key Laboratory of Regenerative Biology, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou Medical University, Guangzhou 510530, China; Bioland Laboratory (Guangzhou Regenerative Medicine and Health GuangDong Laboratory), Guangzhou 510005, China; Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, GIBH-CUHK Joint Research Laboratory on Stem Cell and Regenerative Medicine, State Key Laboratory of Respiratory Disease, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China; Institute of Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Ying-Liang Sheng
- CAS Key Laboratory of Regenerative Biology, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou Medical University, Guangzhou 510530, China; Bioland Laboratory (Guangzhou Regenerative Medicine and Health GuangDong Laboratory), Guangzhou 510005, China; Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, GIBH-CUHK Joint Research Laboratory on Stem Cell and Regenerative Medicine, State Key Laboratory of Respiratory Disease, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China; Institute of Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing 100101, China; Department of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230027, China
| | - Huanhuan Li
- CAS Key Laboratory of Regenerative Biology, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou Medical University, Guangzhou 510530, China
| | - Zhaoming Chen
- CAS Key Laboratory of Regenerative Biology, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou Medical University, Guangzhou 510530, China; Bioland Laboratory (Guangzhou Regenerative Medicine and Health GuangDong Laboratory), Guangzhou 510005, China
| | - Minghui Gao
- CAS Key Laboratory of Regenerative Biology, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou Medical University, Guangzhou 510530, China
| | - Yang Chen
- MOE Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic & Systems Biology, BNRist, School of Medicine, Tsinghua University, Beijing 100084, China
| | - Ge Shan
- Department of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230027, China
| | - Michael Q Zhang
- MOE Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic & Systems Biology, BNRist, School of Medicine, Tsinghua University, Beijing 100084, China; Department of Biological Sciences, Center for Systems Biology, The University of Texas, Richardson, TX 75080-3021, USA
| | - Andrew P Hutchins
- Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China
| | - Xiang-Dong Fu
- Department of Cellular and Molecular Medicine, Institute of Genomic Medicine, University of California, San Diego, La Jolla, CA 92093-0651, USA.
| | - Hongjie Yao
- CAS Key Laboratory of Regenerative Biology, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou Medical University, Guangzhou 510530, China; Bioland Laboratory (Guangzhou Regenerative Medicine and Health GuangDong Laboratory), Guangzhou 510005, China; Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, GIBH-CUHK Joint Research Laboratory on Stem Cell and Regenerative Medicine, State Key Laboratory of Respiratory Disease, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China; Institute of Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China.
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35
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Qiu Z, Zhao L, Shen JZ, Liang Z, Wu Q, Yang K, Min L, Gimple RC, Yang Q, Bhargava S, Jin C, Kim C, Hinz D, Dixit D, Bernatchez JA, Prager BC, Zhang G, Dong Z, Lv D, Wang X, Kim LJ, Zhu Z, Jones KA, Zheng Y, Wang X, Siqueira-Neto JL, Chavez L, Fu XD, Spruck C, Rich JN. Transcription Elongation Machinery Is a Druggable Dependency and Potentiates Immunotherapy in Glioblastoma Stem Cells. Cancer Discov 2022; 12:502-521. [PMID: 34615656 PMCID: PMC8831451 DOI: 10.1158/2159-8290.cd-20-1848] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Revised: 07/03/2021] [Accepted: 10/01/2021] [Indexed: 11/16/2022]
Abstract
Glioblastoma (GBM) is the most lethal primary brain cancer characterized by therapeutic resistance, which is promoted by GBM stem cells (GSC). Here, we interrogated gene expression and whole-genome CRISPR/Cas9 screening in a large panel of patient-derived GSCs, differentiated GBM cells (DGC), and neural stem cells (NSC) to identify master regulators of GSC stemness, revealing an essential transcription state with increased RNA polymerase II-mediated transcription. The YY1 and transcriptional CDK9 complex was essential for GSC survival and maintenance in vitro and in vivo. YY1 interacted with CDK9 to regulate transcription elongation in GSCs. Genetic or pharmacologic targeting of the YY1-CDK9 complex elicited RNA m6A modification-dependent interferon responses, reduced regulatory T-cell infiltration, and augmented efficacy of immune checkpoint therapy in GBM. Collectively, these results suggest that YY1-CDK9 transcription elongation complex defines a targetable cell state with active transcription, suppressed interferon responses, and immunotherapy resistance in GBM. SIGNIFICANCE: Effective strategies to rewire immunosuppressive microenvironment and enhance immunotherapy response are still lacking in GBM. YY1-driven transcriptional elongation machinery represents a druggable target to activate interferon response and enhance anti-PD-1 response through regulating the m6A modification program, linking epigenetic regulation to immunomodulatory function in GBM.This article is highlighted in the In This Issue feature, p. 275.
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Affiliation(s)
- Zhixin Qiu
- Hillman Cancer Center and Department of Neurology, University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA.,Division of Regenerative Medicine, Department of Medicine, University of California, San Diego, La Jolla, CA 92037, USA
| | - Linjie Zhao
- Hillman Cancer Center and Department of Neurology, University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA.,Division of Regenerative Medicine, Department of Medicine, University of California, San Diego, La Jolla, CA 92037, USA
| | - Jia Z. Shen
- Tumor Initiation and Maintenance Program, NCI-Designated Cancer Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA 92037, USA
| | - Zhengyu Liang
- Department of Cellular and Molecular Medicine, Institute of Genomic Medicine, University of California, San Diego, La Jolla, CA 92093, USA
| | - Qiulian Wu
- Hillman Cancer Center and Department of Neurology, University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA.,Division of Regenerative Medicine, Department of Medicine, University of California, San Diego, La Jolla, CA 92037, USA
| | - Kailin Yang
- Department of Radiation Oncology, Taussig Cancer Center, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Lihua Min
- Hillman Cancer Center and Department of Neurology, University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
| | - Ryan C. Gimple
- Division of Regenerative Medicine, Department of Medicine, University of California, San Diego, La Jolla, CA 92037, USA.,Department of Pathology, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA
| | - Qiyuan Yang
- NOMIS Center for Immunobiology and Microbial Pathogenesis, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Shruti Bhargava
- Division of Regenerative Medicine, Department of Medicine, University of California, San Diego, La Jolla, CA 92037, USA
| | - Chunyu Jin
- Howard Hughes Medical Institute, Department of Medicine, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - Cheryl Kim
- Flow Cytometry Core Facility, La Jolla Institute for Immunology (LJI), La Jolla, CA 92037, USA
| | - Denise Hinz
- Flow Cytometry Core Facility, La Jolla Institute for Immunology (LJI), La Jolla, CA 92037, USA
| | - Deobrat Dixit
- Division of Regenerative Medicine, Department of Medicine, University of California, San Diego, La Jolla, CA 92037, USA
| | - Jean A. Bernatchez
- Center for Discovery and Innovation in Parasitic Diseases, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA 92037, USA
| | - Briana C. Prager
- Division of Regenerative Medicine, Department of Medicine, University of California, San Diego, La Jolla, CA 92037, USA.,Department of Pathology, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA
| | - Guoxin Zhang
- Division of Regenerative Medicine, Department of Medicine, University of California, San Diego, La Jolla, CA 92037, USA
| | - Zhen Dong
- Division of Regenerative Medicine, Department of Medicine, University of California, San Diego, La Jolla, CA 92037, USA
| | - Deguan Lv
- Hillman Cancer Center and Department of Neurology, University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA.,Division of Regenerative Medicine, Department of Medicine, University of California, San Diego, La Jolla, CA 92037, USA
| | - Xujun Wang
- SJTU-Yale Joint Center for Biostatistics, School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Leo J.Y. Kim
- Division of Regenerative Medicine, Department of Medicine, University of California, San Diego, La Jolla, CA 92037, USA.,Department of Pathology, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA
| | - Zhe Zhu
- Division of Regenerative Medicine, Department of Medicine, University of California, San Diego, La Jolla, CA 92037, USA
| | - Katherine A. Jones
- Regulatory Biology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Ye Zheng
- NOMIS Center for Immunobiology and Microbial Pathogenesis, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Xiuxing Wang
- Division of Regenerative Medicine, Department of Medicine, University of California, San Diego, La Jolla, CA 92037, USA.,School of Basic Medical Sciences, Nanjing Medical University, Nanjing, Jiangsu 211166, China
| | - Jair L. Siqueira-Neto
- Center for Discovery and Innovation in Parasitic Diseases, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA 92037, USA
| | - Lukas Chavez
- Department of Medicine, University of California, San Diego, La Jolla, CA 92093, USA
| | - Xiang-Dong Fu
- Department of Cellular and Molecular Medicine, Institute of Genomic Medicine, University of California, San Diego, La Jolla, CA 92093, USA
| | - Charles Spruck
- Tumor Initiation and Maintenance Program, NCI-Designated Cancer Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, California.
| | - Jeremy N. Rich
- Hillman Cancer Center and Department of Neurology, University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA.,Division of Regenerative Medicine, Department of Medicine, University of California, San Diego, La Jolla, CA 92037, USA.,Sanford Consortium for Regenerative Medicine, La Jolla, CA 92037, USA.,Department of Neurology, University of Pittsburgh, Pittsburgh, PA 15213, USA.,Corresponding Authors: Jeremy N. Rich: ; +1(412) 623-3364; Address: UPMC Hillman Cancer Center, 5115 Centre Ave, Pittsburgh, PA 15232; Charles Spruck: ; +1(858) 401-3459; Address: 10901 N Torrey Pines Rd, La Jolla, CA 92037
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36
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Tang L, Hill MC, Ellinor PT, Li M. Bacon: a comprehensive computational benchmarking framework for evaluating targeted chromatin conformation capture-specific methodologies. Genome Biol 2022; 23:30. [PMID: 35063001 PMCID: PMC8780810 DOI: 10.1186/s13059-021-02597-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 12/30/2021] [Indexed: 01/10/2023] Open
Abstract
Chromatin conformation capture (3C)-based technologies have enabled the accurate detection of topological genomic interactions, and the adoption of ChIP techniques to 3C-based protocols makes it possible to identify long-range interactions. To analyze these large and complex datasets, computational methods are undergoing rapid and expansive evolution. Thus, a thorough evaluation of these analytical pipelines is necessary to identify which commonly used algorithms and processing pipelines need to be improved. Here we present a comprehensive benchmark framework, Bacon, to evaluate the performance of several computational methods. Finally, we provide practical recommendations for users working with HiChIP and/or ChIA-PET analyses.
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Affiliation(s)
- Li Tang
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, 410083, China
| | - Matthew C Hill
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, 02129, USA
- Cardiovascular Disease Initiative, The Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Patrick T Ellinor
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, 02129, USA
- Cardiovascular Disease Initiative, The Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Min Li
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, 410083, China.
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37
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Deng L, Gao B, Zhao L, Zhang Y, Zhang Q, Guo M, Yang Y, Wang S, Xie L, Lou H, Ma M, Zhang W, Cao Z, Zhang Q, McClung CR, Li G, Li X. Diurnal RNAPII-tethered chromatin interactions are associated with rhythmic gene expression in rice. Genome Biol 2022; 23:7. [PMID: 34991658 PMCID: PMC8734370 DOI: 10.1186/s13059-021-02594-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 12/29/2021] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND The daily cycling of plant physiological processes is speculated to arise from the coordinated rhythms of gene expression. However, the dynamics of diurnal 3D genome architecture and their potential functions underlying the rhythmic gene expression remain unclear. RESULTS Here, we reveal the genome-wide rhythmic occupancy of RNA polymerase II (RNAPII), which precedes mRNA accumulation by approximately 2 h. Rhythmic RNAPII binding dynamically correlates with RNAPII-mediated chromatin architecture remodeling at the genomic level of chromatin interactions, spatial clusters, and chromatin connectivity maps, which are associated with the circadian rhythm of gene expression. Rhythmically expressed genes within the same peak phases of expression are preferentially tethered by RNAPII for coordinated transcription. RNAPII-associated chromatin spatial clusters (CSCs) show high plasticity during the circadian cycle, and rhythmically expressed genes in the morning phase and non-rhythmically expressed genes in the evening phase tend to be enriched in RNAPII-associated CSCs to orchestrate expression. Core circadian clock genes are associated with RNAPII-mediated highly connected chromatin connectivity networks in the morning in contrast to the scattered, sporadic spatial chromatin connectivity in the evening; this indicates that they are transcribed within physical proximity to each other during the AM circadian window and are located in discrete "transcriptional factory" foci in the evening, linking chromatin architecture to coordinated transcription outputs. CONCLUSION Our findings uncover fundamental diurnal genome folding principles in plants and reveal a distinct higher-order chromosome organization that is crucial for coordinating diurnal dynamics of transcriptional regulation.
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Affiliation(s)
- Li Deng
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, 1 Shizishan Street, Hongshan District, Wuhan, 430070, Hubei, China
| | - Baibai Gao
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, 1 Shizishan Street, Hongshan District, Wuhan, 430070, Hubei, China
| | - Lun Zhao
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, 1 Shizishan Street, Hongshan District, Wuhan, 430070, Hubei, China
| | - Ying Zhang
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, 1 Shizishan Street, Hongshan District, Wuhan, 430070, Hubei, China
| | - Qing Zhang
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, 1 Shizishan Street, Hongshan District, Wuhan, 430070, Hubei, China
| | - Minrong Guo
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, 1 Shizishan Street, Hongshan District, Wuhan, 430070, Hubei, China
| | - Yongqing Yang
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, 1 Shizishan Street, Hongshan District, Wuhan, 430070, Hubei, China
| | - Shuangqi Wang
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, 1 Shizishan Street, Hongshan District, Wuhan, 430070, Hubei, China
| | - Liang Xie
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, 1 Shizishan Street, Hongshan District, Wuhan, 430070, Hubei, China
| | - Hao Lou
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, 1 Shizishan Street, Hongshan District, Wuhan, 430070, Hubei, China
| | - Meng Ma
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, 1 Shizishan Street, Hongshan District, Wuhan, 430070, Hubei, China
| | - Wei Zhang
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, 1 Shizishan Street, Hongshan District, Wuhan, 430070, Hubei, China
| | - Zhilin Cao
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, 1 Shizishan Street, Hongshan District, Wuhan, 430070, Hubei, China
- Department of Resources and Environment, Henan University of Engineering, 1 Xianghe Road, Longhu Town, Zhengzhou, 451191, Henan, China
| | - Qinghua Zhang
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, 1 Shizishan Street, Hongshan District, Wuhan, 430070, Hubei, China
| | - C Robertson McClung
- Department of Biological Sciences, Dartmouth College, Hanover, NH, 03755, USA
| | - Guoliang Li
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, 1 Shizishan Street, Hongshan District, Wuhan, 430070, Hubei, China
- Agricultural Bioinformatics Key Laboratory of Hubei Province and Hubei Engineering Technology Research Center of Agricultural Big Data, 3D Genomics Research Center, Huazhong Agricultural University, 1 Shizishan Street, Hongshan District, Wuhan, 430070, Hubei, China
| | - Xingwang Li
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, 1 Shizishan Street, Hongshan District, Wuhan, 430070, Hubei, China.
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38
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Di Giammartino DC, Polyzos A, Apostolou E. Assessing Specific Networks of Chromatin Interactions with HiChIP. Methods Mol Biol 2022; 2532:113-141. [PMID: 35867248 DOI: 10.1007/978-1-0716-2497-5_7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The introduction of chromosome conformation capture (3C)-based technologies coupled with next-generation sequencing have significantly advanced our understanding of how the genetic material is organized within the eukaryotic nucleus. Three-dimensional (3D) genomic organization occurs at hierarchical levels, ranging from chromosome territories and subnuclear compartments to smaller self-associated domains and fine-scale chromatin interactions. The latter can be further categorized into different subtypes, such as structural or regulatory, based either on their presumed functionality and/or the factors that mediate their formation. Various enrichment strategies coupled with 3C-based technologies have been developed to prospectively isolate and quantify chromatin interactions around regions occupied by specific proteins or marks of interest. These approaches not only enable high-resolution characterization of the selected chromatin contacts at a cost-effective manner, but also offer important biological insights into their organizational principles and regulatory function. In this chapter, we will focus on the recently developed HiChIP technology with an emphasis on the discovery of putative active enhancers and promoter interactions in cell types of interest. We will describe the specific steps for designing, performing and analyzing successful HiChIP experiments as well as important limitations and considerations.
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Affiliation(s)
- Dafne Campigli Di Giammartino
- Sanford I. Weill Department of Medicine, Division of Hematology/Oncology, Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
| | - Alexander Polyzos
- Sanford I. Weill Department of Medicine, Division of Hematology/Oncology, Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
| | - Effie Apostolou
- Sanford I. Weill Department of Medicine, Division of Hematology/Oncology, Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA.
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39
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Shi M, You K, Chen T, Hou C, Liang Z, Liu M, Wang J, Wei T, Qin J, Chen Y, Zhang MQ, Li T. Quantifying the phase separation property of chromatin-associated proteins under physiological conditions using an anti-1,6-hexanediol index. Genome Biol 2021; 22:229. [PMID: 34404448 PMCID: PMC8369651 DOI: 10.1186/s13059-021-02456-2] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Accepted: 07/30/2021] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND Liquid-liquid phase separation (LLPS) is an important organizing principle for biomolecular condensation and chromosome compartmentalization. However, while many proteins have been reported to undergo LLPS, quantitative and global analysis of chromatin LLPS property remains absent. RESULTS Here, by combining chromatin-associated protein pull-down, quantitative proteomics and 1,6-hexanediol (1,6-HD) treatment, we develop Hi-MS and define an anti-1,6-HD index of chromatin-associated proteins (AICAP) to quantify 1,6-HD sensitivity of chromatin-associated proteins under physiological conditions. Compared with known physicochemical properties involved in phase separation, we find that proteins with lower AICAP are associated with higher content of disordered regions, higher hydrophobic residue preference, higher mobility and higher predicted LLPS potential. We also construct BL-Hi-C libraries following 1,6-HD treatment to study the sensitivity of chromatin conformation to 1,6-HD treatment. We find that the active chromatin and high-order structures, as well as the proteins enriched in corresponding regions, are more sensitive to 1,6-HD treatment. CONCLUSIONS Our work provides a global quantitative measurement of LLPS properties of chromatin-associated proteins and higher-order chromatin structure. Hi-MS and AICAP data provide an experimental tool and quantitative resources valuable for future studies of biomolecular condensates.
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Affiliation(s)
- Minglei Shi
- MOE Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic & Systems Biology, BNRist, School of Medicine, Tsinghua University, Beijing, 100084, China.
| | - Kaiqiang You
- Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, 100191, China
| | - Taoyu Chen
- Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, 100191, China
| | - Chao Hou
- Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, 100191, China
| | - Zhengyu Liang
- Department of Cellular and Molecular Medicine, Institute of Genomic Medicine, University of California, La Jolla, San Diego, CA, 92093, USA
| | - Mingwei Liu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China
| | - Jifeng Wang
- Laboratory of Proteomics, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China
| | - Taotao Wei
- National Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China
| | - Jun Qin
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China
| | - Yang Chen
- MOE Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic & Systems Biology, BNRist, School of Medicine, Tsinghua University, Beijing, 100084, China.
- The State Key Laboratory of Medical Molecular Biology, Department of Molecular Biology and Biochemistry, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, Beijing, 100005, China.
| | - Michael Q Zhang
- MOE Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic & Systems Biology, BNRist, School of Medicine, Tsinghua University, Beijing, 100084, China.
- Department of Biological Sciences, Center for Systems Biology, The University of Texas, Richardson, TX, 75080-3021, USA.
| | - Tingting Li
- Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, 100191, China.
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40
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Wang W, Gao L, Ye Y, Gao Y. CCIP: Predicting CTCF-mediated chromatin loops with transitivity. Bioinformatics 2021; 37:4635-4642. [PMID: 34289010 PMCID: PMC8665748 DOI: 10.1093/bioinformatics/btab534] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 06/18/2021] [Accepted: 07/19/2021] [Indexed: 11/14/2022] Open
Abstract
Motivation CTCF-mediated chromatin loops underlie the formation of topological associating domains and serve as the structural basis for transcriptional regulation. However, the formation mechanism of these loops remains unclear, and the genome-wide mapping of these loops is costly and difficult. Motivated by the recent studies on the formation mechanism of CTCF-mediated loops, we studied the possibility of making use of transitivity-related information of interacting CTCF anchors to predict CTCF loops computationally. In this context, transitivity arises when two CTCF anchors interact with the same third anchor by the loop extrusion mechanism and bring themselves close to each other spatially to form an indirect loop. Results To determine whether transitivity is informative for predicting CTCF loops and to obtain an accurate and low-cost predicting method, we proposed a two-stage random-forest-based machine learning method, CTCF-mediated Chromatin Interaction Prediction (CCIP), to predict CTCF-mediated chromatin loops. Our two-stage learning approach makes it possible for us to train a prediction model by taking advantage of transitivity-related information as well as functional genome data and genomic data. Experimental studies showed that our method predicts CTCF-mediated loops more accurately than other methods and that transitivity, when used as a properly defined attribute, is informative for predicting CTCF loops. Furthermore, we found that transitivity explains the formation of tandem CTCF loops and facilitates enhancer–promoter interactions. Our work contributes to the understanding of the formation mechanism and function of CTCF-mediated chromatin loops. Availability and implementation The source code of CCIP can be accessed at: https://github.com/GaoLabXDU/CCIP. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Weibing Wang
- School of Computer Science and Technology, Xidian University, Xi'an, Shaanxi, 710071, China
| | - Lin Gao
- School of Computer Science and Technology, Xidian University, Xi'an, Shaanxi, 710071, China
| | - Yusen Ye
- School of Computer Science and Technology, Xidian University, Xi'an, Shaanxi, 710071, China
| | - Yong Gao
- Department of Computer Science, The University of British Columbia Okanagan, Kelowna, BC, V1V 1V5, Canada
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41
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3D genome alterations associated with dysregulated HOXA13 expression in high-risk T-lineage acute lymphoblastic leukemia. Nat Commun 2021; 12:3708. [PMID: 34140506 PMCID: PMC8211852 DOI: 10.1038/s41467-021-24044-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Accepted: 06/01/2021] [Indexed: 02/06/2023] Open
Abstract
3D genome alternations can dysregulate gene expression by rewiring enhancer-promoter interactions and lead to diseases. We report integrated analyses of 3D genome alterations and differential gene expressions in 18 newly diagnosed T-lineage acute lymphoblastic leukemia (T-ALL) patients and 4 healthy controls. 3D genome organizations at the levels of compartment, topologically associated domains and loop could hierarchically classify different subtypes of T-ALL according to T cell differentiation trajectory, similar to gene expressions-based classification. Thirty-four previously unrecognized translocations and 44 translocation-mediated neo-loops are mapped by Hi-C analysis. We find that neo-loops formed in the non-coding region of the genome could potentially regulate ectopic expressions of TLX3, TAL2 and HOXA transcription factors via enhancer hijacking. Importantly, both translocation-mediated neo-loops and NUP98-related fusions are associated with HOXA13 ectopic expressions. Patients with HOXA11-A13 expressions, but not other genes in the HOXA cluster, have immature immunophenotype and poor outcomes. Here, we highlight the potentially important roles of 3D genome alterations in the etiology and prognosis of T-ALL. The non-coding genome of T-ALL has not been extensively studied. Here, the authors conduct RNA-seq, ATAC-seq and Hi-C seq analyses and find that T-ALL associated neo-loops may regulate key transcription factors including HOXA13; the aberrant expression of which is associated with poor prognosis.
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42
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Phase separation of OCT4 controls TAD reorganization to promote cell fate transitions. Cell Stem Cell 2021; 28:1868-1883.e11. [PMID: 34038708 DOI: 10.1016/j.stem.2021.04.023] [Citation(s) in RCA: 62] [Impact Index Per Article: 20.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Revised: 08/06/2020] [Accepted: 04/19/2021] [Indexed: 02/05/2023]
Abstract
Topological-associated domains (TADs) are thought to be relatively stable across cell types, although some TAD reorganization has been observed during cellular differentiation. However, little is known about the mechanisms through which TAD reorganization affects cell fate or how master transcription factors affect TAD structures during cell fate transitions. Here, we show extensive TAD reorganization during somatic cell reprogramming, which is correlated with gene transcription and changes in cellular identity. Manipulating TAD reorganization promotes reprogramming, and the dynamics of concentrated chromatin loops in OCT4 phase separated condensates contribute to TAD reorganization. Disrupting OCT4 phase separation attenuates TAD reorganization and reprogramming, which can be rescued by fusing an intrinsically disordered region (IDR) to OCT4. We developed an approach termed TAD reorganization-based multiomics analysis (TADMAN), which identified reprogramming regulators. Together, these findings elucidate a role and mechanism of TAD reorganization, regulated by OCT4 phase separation, in cellular reprogramming.
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43
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Loop competition and extrusion model predicts CTCF interaction specificity. Nat Commun 2021; 12:1046. [PMID: 33594051 PMCID: PMC7886907 DOI: 10.1038/s41467-021-21368-0] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Accepted: 01/22/2021] [Indexed: 12/20/2022] Open
Abstract
Three-dimensional chromatin looping interactions play an important role in constraining enhancer–promoter interactions and mediating transcriptional gene regulation. CTCF is thought to play a critical role in the formation of these loops, but the specificity of which CTCF binding events form loops and which do not is difficult to predict. Loops often have convergent CTCF binding site motif orientation, but this constraint alone is only weakly predictive of genome-wide interaction data. Here we present an easily interpretable and simple mathematical model of CTCF mediated loop formation which is consistent with Cohesin extrusion and can predict ChIA-PET CTCF looping interaction measurements with high accuracy. Competition between overlapping loops is a critical determinant of loop specificity. We show that this model is consistent with observed chromatin interaction frequency changes induced by CTCF binding site deletion, inversion, and mutation, and is also consistent with observed constraints on validated enhancer–promoter interactions. Boundaries of topologically associated domains in genomes are marked by CTCF and cohesin binding. Here the authors predict CTCF interaction specificity by building a simple mathematical model with features including loop competition and extrusion.
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44
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Li F, Yuan Q, Di W, Xia X, Liu Z, Mao N, Li L, Li C, He J, Li Y, Guo W, Zhang X, Zhu Y, Aji R, Wang S, Tong X, Ji H, Chi P, Carver B, Wang Y, Chen Y, Gao D. ERG orchestrates chromatin interactions to drive prostate cell fate reprogramming. J Clin Invest 2021; 130:5924-5941. [PMID: 32701507 DOI: 10.1172/jci137967] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Accepted: 07/22/2020] [Indexed: 12/21/2022] Open
Abstract
Although cancer is commonly perceived as a disease of dedifferentiation, the hallmark of early-stage prostate cancer is paradoxically the loss of more plastic basal cells and the abnormal proliferation of more differentiated secretory luminal cells. However, the mechanism of prostate cancer proluminal differentiation is largely unknown. Through integrating analysis of the transcription factors (TFs) from 806 human prostate cancers, we found that ERG was highly correlated with prostate cancer luminal subtyping. ERG overexpression in luminal epithelial cells inhibited those cells' normal plasticity to transdifferentiate into a basal lineage, and ERG superseded PTEN loss, which favored basal differentiation. ERG KO disrupted prostate cell luminal differentiation, whereas AR KO had no such effects. Trp63 is a known master regulator of the prostate basal lineage. Through analysis of 3D chromatin architecture, we found that ERG bound and inhibited the enhancer activity and chromatin looping of a Trp63 distal enhancer, thereby silencing its gene expression. Specific deletion of the distal ERG binding site resulted in the loss of ERG-mediated inhibition of basal differentiation. Thus, ERG, in its fundamental role in lineage differentiation in prostate cancer initiation, orchestrated chromatin interactions and regulated prostate cell lineage toward a proluminal program.
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Affiliation(s)
- Fei Li
- State Key Laboratory of Cell Biology, Shanghai Key Laboratory of Molecular Andrology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Qiuyue Yuan
- Center for Excellence in Mathematical Sciences (CEMS), National Center for Mathematics and Interdisciplinary Sciences (NCMIS), Key Laboratory of Management, Decision and Information Systems (MDIS)., Academy of Mathematics and Systems Science, National Center for Mathematics and Interdisciplinary Sciences, and.,School of Mathematical Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing, China
| | - Wei Di
- State Key Laboratory of Cell Biology, Shanghai Key Laboratory of Molecular Andrology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Xinyi Xia
- State Key Laboratory of Cell Biology, Shanghai Key Laboratory of Molecular Andrology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Zhuang Liu
- State Key Laboratory of Cell Biology, Shanghai Key Laboratory of Molecular Andrology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Ninghui Mao
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Lin Li
- State Key Laboratory of Cell Biology, Shanghai Key Laboratory of Molecular Andrology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Chunfeng Li
- State Key Laboratory of Cell Biology, Shanghai Key Laboratory of Molecular Andrology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Juan He
- State Key Laboratory of Cell Biology, Shanghai Key Laboratory of Molecular Andrology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Yunguang Li
- State Key Laboratory of Cell Biology, Shanghai Key Laboratory of Molecular Andrology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Wangxin Guo
- State Key Laboratory of Cell Biology, Shanghai Key Laboratory of Molecular Andrology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Xiaoyu Zhang
- State Key Laboratory of Cell Biology, Shanghai Key Laboratory of Molecular Andrology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Yiqin Zhu
- State Key Laboratory of Cell Biology, Shanghai Key Laboratory of Molecular Andrology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Rebiguli Aji
- State Key Laboratory of Cell Biology, Shanghai Key Laboratory of Molecular Andrology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Shangqian Wang
- Department of Urology, First Affiliated Hospital, Nanjing Medical University, Nanjing, China
| | - Xinyuan Tong
- State Key Laboratory of Cell Biology, Shanghai Key Laboratory of Molecular Andrology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Hongbin Ji
- State Key Laboratory of Cell Biology, Shanghai Key Laboratory of Molecular Andrology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Ping Chi
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, New York, USA.,Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, USA.,Department of Medicine and.,Department of Cell and Developmental Biology, Weill Cornell Medical College and New York-Presbyterian Hospital, New York, New York, USA
| | - Brett Carver
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, New York, USA.,Division of Urology, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Yong Wang
- Center for Excellence in Mathematical Sciences (CEMS), National Center for Mathematics and Interdisciplinary Sciences (NCMIS), Key Laboratory of Management, Decision and Information Systems (MDIS)., Academy of Mathematics and Systems Science, National Center for Mathematics and Interdisciplinary Sciences, and.,School of Mathematical Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing, China.,Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China.,Key Laboratory of Systems Biology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, China
| | - Yu Chen
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, New York, USA.,Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, USA.,Department of Medicine and.,Department of Cell and Developmental Biology, Weill Cornell Medical College and New York-Presbyterian Hospital, New York, New York, USA
| | - Dong Gao
- State Key Laboratory of Cell Biology, Shanghai Key Laboratory of Molecular Andrology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, China.,University of Chinese Academy of Sciences, Beijing, China.,Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, China
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45
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Cavallaro M, Walsh MD, Jones M, Teahan J, Tiberi S, Finkenstädt B, Hebenstreit D. 3 '-5 ' crosstalk contributes to transcriptional bursting. Genome Biol 2021; 22:56. [PMID: 33541397 PMCID: PMC7860045 DOI: 10.1186/s13059-020-02227-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2020] [Accepted: 12/08/2020] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Transcription in mammalian cells is a complex stochastic process involving shuttling of polymerase between genes and phase-separated liquid condensates. It occurs in bursts, which results in vastly different numbers of an mRNA species in isogenic cell populations. Several factors contributing to transcriptional bursting have been identified, usually classified as intrinsic, in other words local to single genes, or extrinsic, relating to the macroscopic state of the cell. However, some possible contributors have not been explored yet. Here, we focus on processes at the 3 ' and 5 ' ends of a gene that enable reinitiation of transcription upon termination. RESULTS Using Bayesian methodology, we measure the transcriptional bursting in inducible transgenes, showing that perturbation of polymerase shuttling typically reduces burst size, increases burst frequency, and thus limits transcriptional noise. Analysis based on paired-end tag sequencing (PolII ChIA-PET) suggests that this effect is genome wide. The observed noise patterns are also reproduced by a generative model that captures major characteristics of the polymerase flux between the ends of a gene and a phase-separated compartment. CONCLUSIONS Interactions between the 3 ' and 5 ' ends of a gene, which facilitate polymerase recycling, are major contributors to transcriptional noise.
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Affiliation(s)
- Massimo Cavallaro
- School of Life Sciences, University of Warwick, Coventry, UK.
- Mathematics Institute and Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Coventry, UK.
- Department of Statistics, University of Warwick, Coventry, UK.
| | - Mark D Walsh
- School of Life Sciences, University of Warwick, Coventry, UK
| | - Matt Jones
- School of Life Sciences, University of Warwick, Coventry, UK
| | - James Teahan
- Department of Chemistry, University of Warwick, Coventry, UK
| | - Simone Tiberi
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, Zurich, Switzerland
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46
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Ramanand SG, Chen Y, Yuan J, Daescu K, Lambros MB, Houlahan KE, Carreira S, Yuan W, Baek G, Sharp A, Paschalis A, Kanchwala M, Gao Y, Aslam A, Safdar N, Zhan X, Raj GV, Xing C, Boutros PC, de Bono J, Zhang MQ, Mani RS. The landscape of RNA polymerase II-associated chromatin interactions in prostate cancer. J Clin Invest 2021; 130:3987-4005. [PMID: 32343676 DOI: 10.1172/jci134260] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Accepted: 04/23/2020] [Indexed: 12/15/2022] Open
Abstract
Transcriptional dysregulation is a hallmark of prostate cancer (PCa). We mapped the RNA polymerase II-associated (RNA Pol II-associated) chromatin interactions in normal prostate cells and PCa cells. We discovered thousands of enhancer-promoter, enhancer-enhancer, as well as promoter-promoter chromatin interactions. These transcriptional hubs operate within the framework set by structural proteins - CTCF and cohesins - and are regulated by the cooperative action of master transcription factors, such as the androgen receptor (AR) and FOXA1. By combining analyses from metastatic castration-resistant PCa (mCRPC) specimens, we show that AR locus amplification contributes to the transcriptional upregulation of the AR gene by increasing the total number of chromatin interaction modules comprising the AR gene and its distal enhancer. We deconvoluted the transcription control modules of several PCa genes, notably the biomarker KLK3, lineage-restricted genes (KRT8, KRT18, HOXB13, FOXA1, ZBTB16), the drug target EZH2, and the oncogene MYC. By integrating clinical PCa data, we defined a germline-somatic interplay between the PCa risk allele rs684232 and the somatically acquired TMPRSS2-ERG gene fusion in the transcriptional regulation of multiple target genes - VPS53, FAM57A, and GEMIN4. Our studies implicate changes in genome organization as a critical determinant of aberrant transcriptional regulation in PCa.
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Affiliation(s)
- Susmita G Ramanand
- Department of Pathology, UT Southwestern Medical Center, Dallas, Texas, USA
| | - Yong Chen
- Department of Biological Sciences, Center for Systems Biology, University of Texas at Dallas, Richardson, Texas, USA.,Department of Molecular and Cellular Biosciences, Rowan University, Glassboro, New Jersey, USA
| | - Jiapei Yuan
- Department of Pathology, UT Southwestern Medical Center, Dallas, Texas, USA
| | - Kelly Daescu
- Department of Biological Sciences, Center for Systems Biology, University of Texas at Dallas, Richardson, Texas, USA
| | - Maryou Bk Lambros
- Prostate Cancer Targeted Therapy and Cancer Biomarkers Group, Institute of Cancer Research (ICR) and Royal Marsden NHS Foundation Trust, Sutton, United Kingdom
| | - Kathleen E Houlahan
- Ontario Institute for Cancer Research, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.,Vector Institute, Toronto, Ontario, Canada.,Department of Urology.,Department of Human Genetics, and.,Jonsson Comprehensive Cancer Center, UCLA, Los Angeles, California, USA
| | - Suzanne Carreira
- Prostate Cancer Targeted Therapy and Cancer Biomarkers Group, Institute of Cancer Research (ICR) and Royal Marsden NHS Foundation Trust, Sutton, United Kingdom
| | - Wei Yuan
- Prostate Cancer Targeted Therapy and Cancer Biomarkers Group, Institute of Cancer Research (ICR) and Royal Marsden NHS Foundation Trust, Sutton, United Kingdom
| | - GuemHee Baek
- Department of Pathology, UT Southwestern Medical Center, Dallas, Texas, USA
| | - Adam Sharp
- Prostate Cancer Targeted Therapy and Cancer Biomarkers Group, Institute of Cancer Research (ICR) and Royal Marsden NHS Foundation Trust, Sutton, United Kingdom
| | - Alec Paschalis
- Prostate Cancer Targeted Therapy and Cancer Biomarkers Group, Institute of Cancer Research (ICR) and Royal Marsden NHS Foundation Trust, Sutton, United Kingdom
| | | | - Yunpeng Gao
- Department of Pathology, UT Southwestern Medical Center, Dallas, Texas, USA
| | - Adam Aslam
- Department of Pathology, UT Southwestern Medical Center, Dallas, Texas, USA
| | - Nida Safdar
- Department of Pathology, UT Southwestern Medical Center, Dallas, Texas, USA
| | | | | | - Chao Xing
- Department of Urology.,Department of Human Genetics, and.,Department of Bioinformatics, UT Southwestern Medical Center, Dallas, Texas, USA
| | - Paul C Boutros
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.,Vector Institute, Toronto, Ontario, Canada.,Department of Urology.,Department of Human Genetics, and.,Jonsson Comprehensive Cancer Center, UCLA, Los Angeles, California, USA
| | - Johann de Bono
- Prostate Cancer Targeted Therapy and Cancer Biomarkers Group, Institute of Cancer Research (ICR) and Royal Marsden NHS Foundation Trust, Sutton, United Kingdom
| | - Michael Q Zhang
- Department of Biological Sciences, Center for Systems Biology, University of Texas at Dallas, Richardson, Texas, USA.,MOE Key Laboratory of Bioinformatics and Bioinformatics Division, Center for Synthetic and Systems Biology, TNLIST/Department of Automation, Tsinghua University, Beijing, China
| | - Ram S Mani
- Department of Pathology, UT Southwestern Medical Center, Dallas, Texas, USA.,Department of Urology, and.,Harold C. Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, Texas, USA
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47
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Li XC, Tang ZD, Peng L, Li YY, Qian FC, Zhao JM, Ding LW, Du XJ, Li M, Zhang J, Bai XF, Zhu J, Feng CC, Wang QY, Pan J, Li CQ. Integrative Epigenomic Analysis of Transcriptional Regulation of Human CircRNAs. Front Genet 2021; 11:590672. [PMID: 33569079 PMCID: PMC7868561 DOI: 10.3389/fgene.2020.590672] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Accepted: 12/02/2020] [Indexed: 12/25/2022] Open
Abstract
Circular RNAs (circRNAs) are evolutionarily conserved and abundant non-coding RNAs whose functions and regulatory mechanisms remain largely unknown. Here, we identify and characterize an epigenomically distinct group of circRNAs (TAH-circRNAs), which are transcribed to a higher level than their host genes. By integrative analysis of cistromic and transcriptomic data, we find that compared with other circRNAs, TAH-circRNAs are expressed more abundantly and have more transcription factors (TFs) binding sites and lower DNA methylation levels. Concordantly, TAH-circRNAs are enriched in open and active chromatin regions. Importantly, ChIA-PET results showed that 23–52% of transcription start sites (TSSs) of TAH-circRNAs have direct interactions with cis-regulatory regions, strongly suggesting their independent transcriptional regulation from host genes. In addition, we characterize molecular features of super-enhancer-driven circRNAs in cancer biology. Together, this study comprehensively analyzes epigenomic characteristics of circRNAs and identifies a distinct group of TAH-circRNAs that are independently transcribed via enhancers and super-enhancers by TFs. These findings substantially advance our understanding of the regulatory mechanism of circRNAs and may have important implications for future investigations of this class of non-coding RNAs.
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Affiliation(s)
- Xue-Cang Li
- School of Medical Informatics, Harbin Medical University, Daqing, China
| | - Zhi-Dong Tang
- School of Medical Informatics, Harbin Medical University, Daqing, China
| | - Li Peng
- Guangdong Province Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yan-Yu Li
- School of Medical Informatics, Harbin Medical University, Daqing, China
| | - Feng-Cui Qian
- School of Medical Informatics, Harbin Medical University, Daqing, China
| | - Jian-Mei Zhao
- School of Medical Informatics, Harbin Medical University, Daqing, China
| | - Ling-Wen Ding
- Cancer Science Institute of Singapore, National University of Singapore, Singapore, Singapore
| | - Xiao-Juan Du
- The 942 Hospital of Joint Logistic Support Force of PLA, Yinchuan, China
| | - Meng Li
- School of Medical Informatics, Harbin Medical University, Daqing, China
| | - Jian Zhang
- School of Medical Informatics, Harbin Medical University, Daqing, China
| | - Xue-Feng Bai
- School of Medical Informatics, Harbin Medical University, Daqing, China
| | - Jiang Zhu
- School of Medical Informatics, Harbin Medical University, Daqing, China
| | - Chen-Chen Feng
- School of Medical Informatics, Harbin Medical University, Daqing, China
| | - Qiu-Yu Wang
- School of Medical Informatics, Harbin Medical University, Daqing, China
| | - Jian Pan
- Department of Hematology and Oncology, Children's Hospital of Soochow University, Suzhou, China
| | - Chun-Quan Li
- School of Medical Informatics, Harbin Medical University, Daqing, China
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48
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Zhang MQ. A personal journey on cracking the genomic codes. QUANTITATIVE BIOLOGY 2021. [DOI: 10.15302/j-qb-021-0245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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49
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Arega Y, Jiang H, Wang S, Zhang J, Niu X, Li G. ChIAMM: A Mixture Model for Statistical Analysis of Long-Range Chromatin Interactions From ChIA-PET Experiments. Front Genet 2021; 11:616160. [PMID: 33381154 PMCID: PMC7767989 DOI: 10.3389/fgene.2020.616160] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2020] [Accepted: 11/11/2020] [Indexed: 11/13/2022] Open
Abstract
Chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) is an important experimental method for detecting specific protein-mediated chromatin loops genome-wide at high resolution. Here, we proposed a new statistical approach with a mixture model, chromatin interaction analysis using mixture model (ChIAMM), to detect significant chromatin interactions from ChIA-PET data. The statistical model is cast into a Bayesian framework to consider more systematic biases: the genomic distance, local enrichment, mappability, and GC content. Using different ChIA-PET datasets, we evaluated the performance of ChIAMM and compared it with the existing methods, including ChIA-PET Tool, ChiaSig, Mango, ChIA-PET2, and ChIAPoP. The result showed that the new approach performed better than most top existing methods in detecting significant chromatin interactions in ChIA-PET experiments.
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Affiliation(s)
- Yibeltal Arega
- Agricultural Bioinformatics Key Laboratory of Hubei Province, Hubei Engineering Technology Research Center of Agricultural Big Data, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Hao Jiang
- Agricultural Bioinformatics Key Laboratory of Hubei Province, Hubei Engineering Technology Research Center of Agricultural Big Data, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Shuangqi Wang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
| | - Jingwen Zhang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
| | - Xiaohui Niu
- Agricultural Bioinformatics Key Laboratory of Hubei Province, Hubei Engineering Technology Research Center of Agricultural Big Data, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Guoliang Li
- Agricultural Bioinformatics Key Laboratory of Hubei Province, Hubei Engineering Technology Research Center of Agricultural Big Data, College of Informatics, Huazhong Agricultural University, Wuhan, China.,National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
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50
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Yang B, Li B, Jia L, Jiang Y, Wang X, Jiang S, Du S, Ji X, Yang P. 3D landscape of Hepatitis B virus interactions with human chromatins. Cell Discov 2020; 6:95. [PMID: 33372176 PMCID: PMC7769987 DOI: 10.1038/s41421-020-00218-1] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Accepted: 08/26/2020] [Indexed: 12/18/2022] Open
Abstract
Hepatitis B viral (HBV) DNAs, including covalently closed circular DNA (cccDNA) and integrated HBV DNA forms, are considered to be primary contributors to the development and progression of HBV-associated liver diseases. However, it remains largely unclear how HBV DNAs communicate with human chromatin. Here we employed a highly sensitive technology, 3C-high-throughput genome-wide translocation sequencing (3C-HTGTS), to globally identify HBV DNA-host DNA contacts in cellular models of HBV infection. HBV DNA does not randomly position in host genome but instead preferentially establishes contacts with the host DNA at active chromatin regions. HBV DNA-host DNA contacts are significantly enriched at H3K4me1-marked regions modified by KMT2C/D; this histone modification is also observed in the HBV cccDNA mini-chromosome and strongly influences HBV transcription. On the other hand, chromatin loop formed by integrated HBV DNA with host genomic DNA was found in transcriptionally active regions. Furthermore, HBV infection influences host gene expression accompanied with HBV DNA-host DNA contacts. Our study provides a 3D landscape of spatial organization of cccDNA and integrated HBV DNA within the human genome, which lays the foundation for a better understanding of the mechanisms how HBV involves in liver disease development and progression.
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Affiliation(s)
- Bo Yang
- CAS Key Laboratory of Infection and Immunity, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing, 100101, China
| | - Boyuan Li
- Key Laboratory of Cell Proliferation and Differentiation of the Ministry of Education, School of Life Sciences, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, 100871, China
| | - Liyang Jia
- CAS Key Laboratory of Infection and Immunity, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing, 100101, China
| | - Yongpeng Jiang
- Key Laboratory of Cell Proliferation and Differentiation of the Ministry of Education, School of Life Sciences, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, 100871, China
| | - Xin Wang
- CAS Key Laboratory of Infection and Immunity, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing, 100101, China
| | - Shaodong Jiang
- CAS Key Laboratory of Infection and Immunity, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing, 100101, China
| | - Shunda Du
- Department of Liver Surgery, Peking Union Medical College (PUMC) Hospital, Chinese Academy of Medical Science and PUMC, Beijing, 100730, China
| | - Xiong Ji
- Key Laboratory of Cell Proliferation and Differentiation of the Ministry of Education, School of Life Sciences, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, 100871, China.
| | - Pengyuan Yang
- CAS Key Laboratory of Infection and Immunity, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing, 100101, China.
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