201
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Borsari B, Villegas-Mirón P, Pérez-Lluch S, Turpin I, Laayouni H, Segarra-Casas A, Bertranpetit J, Guigó R, Acosta S. Enhancers with tissue-specific activity are enriched in intronic regions. Genome Res 2021; 31:1325-1336. [PMID: 34290042 PMCID: PMC8327915 DOI: 10.1101/gr.270371.120] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Accepted: 06/23/2021] [Indexed: 01/07/2023]
Abstract
Tissue function and homeostasis reflect the gene expression signature by which the combination of ubiquitous and tissue-specific genes contribute to the tissue maintenance and stimuli-responsive function. Enhancers are central to control this tissue-specific gene expression pattern. Here, we explore the correlation between the genomic location of enhancers and their role in tissue-specific gene expression. We find that enhancers showing tissue-specific activity are highly enriched in intronic regions and regulate the expression of genes involved in tissue-specific functions, whereas housekeeping genes are more often controlled by intergenic enhancers, common to many tissues. Notably, an intergenic-to-intronic active enhancers continuum is observed in the transition from developmental to adult stages: the most differentiated tissues present higher rates of intronic enhancers, whereas the lowest rates are observed in embryonic stem cells. Altogether, our results suggest that the genomic location of active enhancers is key for the tissue-specific control of gene expression.
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Affiliation(s)
- Beatrice Borsari
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona 08003, Catalonia, Spain
| | - Pablo Villegas-Mirón
- Institut de Biologia Evolutiva (UPF-CSIC), Universitat Pompeu Fabra, Barcelona 08003, Catalonia, Spain
| | - Sílvia Pérez-Lluch
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona 08003, Catalonia, Spain
| | - Isabel Turpin
- Institut de Biologia Evolutiva (UPF-CSIC), Universitat Pompeu Fabra, Barcelona 08003, Catalonia, Spain
| | - Hafid Laayouni
- Institut de Biologia Evolutiva (UPF-CSIC), Universitat Pompeu Fabra, Barcelona 08003, Catalonia, Spain.,Bioinformatic Studies, ESCI-UPF, 08003, Barcelona, Spain
| | - Alba Segarra-Casas
- Institut de Biologia Evolutiva (UPF-CSIC), Universitat Pompeu Fabra, Barcelona 08003, Catalonia, Spain
| | - Jaume Bertranpetit
- Institut de Biologia Evolutiva (UPF-CSIC), Universitat Pompeu Fabra, Barcelona 08003, Catalonia, Spain
| | - Roderic Guigó
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona 08003, Catalonia, Spain.,Universitat Pompeu Fabra (UPF), Barcelona 08003, Catalonia, Spain
| | - Sandra Acosta
- Institut de Biologia Evolutiva (UPF-CSIC), Universitat Pompeu Fabra, Barcelona 08003, Catalonia, Spain.,Department of Pathology and Experimental Therapeutics, Medical School, University of Barcelona, 08907, L'Hospitalet de Llobregat, Catalonia, Spain
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202
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Chen J, Dong G, Song L, Zhao X, Cao J, Luo X, Feng J, Zhao XM. Integration of Multimodal Data for Deciphering Brain Disorders. Annu Rev Biomed Data Sci 2021; 4:43-56. [PMID: 34465176 DOI: 10.1146/annurev-biodatasci-092820-020354] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The accumulation of vast amounts of multimodal data for the human brain, in both normal and disease conditions, has provided unprecedented opportunities for understanding why and how brain disorders arise. Compared with traditional analyses of single datasets, the integration of multimodal datasets covering different types of data (i.e., genomics, transcriptomics, imaging, etc.) has shed light on the mechanisms underlying brain disorders in greater detail across both the microscopic and macroscopic levels. In this review, we first briefly introduce the popular large datasets for the brain. Then, we discuss in detail how integration of multimodal human brain datasets can reveal the genetic predispositions and the abnormal molecular pathways of brain disorders. Finally, we present an outlook on how future data integration efforts may advance the diagnosis and treatment of brain disorders.
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Affiliation(s)
- Jingqi Chen
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China; , .,MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science, Fudan University, Ministry of Education, Shanghai 200433, China.,Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai 200433, China
| | - Guiying Dong
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China; ,
| | - Liting Song
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China; ,
| | - Xingzhong Zhao
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China; ,
| | - Jixin Cao
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China; ,
| | - Xiaohui Luo
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China; ,
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China; , .,MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science, Fudan University, Ministry of Education, Shanghai 200433, China.,Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai 200433, China.,Department of Computer Science, University of Warwick, Coventry CV4 7AL, United Kingdom
| | - Xing-Ming Zhao
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China; , .,MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science, Fudan University, Ministry of Education, Shanghai 200433, China.,Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai 200433, China
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203
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Mandla R, Jung C, Vedantham V. Transcriptional and Epigenetic Landscape of Cardiac Pacemaker Cells: Insights Into Cellular Specialization in the Sinoatrial Node. Front Physiol 2021; 12:712666. [PMID: 34335313 PMCID: PMC8322687 DOI: 10.3389/fphys.2021.712666] [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: 05/20/2021] [Accepted: 06/23/2021] [Indexed: 01/23/2023] Open
Abstract
Cardiac pacemaker cells differentiate and functionally specialize early in embryonic development through activation of critical gene regulatory networks. In general, cellular specification and differentiation require that combinations of cell type-specific transcriptional regulators activate expression of key effector genes by binding to DNA regulatory elements including enhancers and promoters. However, because genomic DNA is tightly packaged by histones that must be covalently modified in order to render DNA regulatory elements and promoters accessible for transcription, the process of development and differentiation is intimately connected to the epigenetic regulation of chromatin accessibility. Although the difficulty of obtaining sufficient quantities of pure populations of pacemaker cells has limited progress in this field, the advent of low-input genomic technologies has the potential to catalyze a rapid growth of knowledge in this important area. The goal of this review is to outline the key transcriptional networks that control pacemaker cell development, with particular attention to our emerging understanding of how chromatin accessibility is modified and regulated during pacemaker cell differentiation. In addition, we will discuss the relevance of these findings to adult sinus node function, sinus node diseases, and origins of genetic variation in heart rhythm. Lastly, we will outline the current challenges facing this field and promising directions for future investigation.
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Affiliation(s)
- Ravi Mandla
- Division of Cardiology, Department of Medicine and Cardiovascular Research Institute, University of California, San Francisco, San Francisco, CA, United States
| | - Catherine Jung
- Division of Cardiology, Department of Medicine and Cardiovascular Research Institute, University of California, San Francisco, San Francisco, CA, United States
| | - Vasanth Vedantham
- Division of Cardiology, Department of Medicine and Cardiovascular Research Institute, University of California, San Francisco, San Francisco, CA, United States
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204
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Williams K, Carrasquilla GD, Ingerslev LR, Hochreuter MY, Hansson S, Pillon NJ, Donkin I, Versteyhe S, Zierath JR, Kilpeläinen TO, Barrès R. Epigenetic rewiring of skeletal muscle enhancers after exercise training supports a role in whole-body function and human health. Mol Metab 2021; 53:101290. [PMID: 34252634 PMCID: PMC8355925 DOI: 10.1016/j.molmet.2021.101290] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Revised: 06/30/2021] [Accepted: 07/05/2021] [Indexed: 12/13/2022] Open
Abstract
Objectives Regular physical exercise improves health by reducing the risk of a plethora of chronic disorders. We hypothesized that endurance exercise training remodels the activity of gene enhancers in skeletal muscle and that this remodeling contributes to the beneficial effects of exercise on human health. Methods and results By studying changes in histone modifications, we mapped the genome-wide positions and activities of enhancers in skeletal muscle biopsies collected from young sedentary men before and after 6 weeks of endurance exercise. We identified extensive remodeling of enhancer activities after exercise training, with a large subset of the remodeled enhancers located in the proximity of genes transcriptionally regulated after exercise. By overlapping the position of enhancers with genetic variants, we identified an enrichment of disease-associated genetic variants within the exercise-remodeled enhancers. Conclusion Our data provide evidence of a functional link between epigenetic rewiring of enhancers to control their activity after exercise training and the modulation of disease risk in humans. Exercise training changes in skeletal muscle gene expression is enriched for secreted factors. The activity of skeletal muscle enhancers undergoes substantial remodeling after exercise training. Skeletal muscle enhancer activity and gene transcription are strongly associated. Exercise training-remodeled enhancer regions are enriched for GWAS SNPs associated with human traits and diseases.
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Affiliation(s)
- Kristine Williams
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3B, 2200, Copenhagen N, Denmark
| | - Germán D Carrasquilla
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3B, 2200, Copenhagen N, Denmark
| | - Lars Roed Ingerslev
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3B, 2200, Copenhagen N, Denmark
| | - Mette Yde Hochreuter
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3B, 2200, Copenhagen N, Denmark
| | - Svenja Hansson
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3B, 2200, Copenhagen N, Denmark
| | - Nicolas J Pillon
- Department of Physiology and Pharmacology, Karolinska Institutet, 171 77, Stockholm, Sweden
| | - Ida Donkin
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3B, 2200, Copenhagen N, Denmark
| | - Soetkin Versteyhe
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3B, 2200, Copenhagen N, Denmark
| | - Juleen R Zierath
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3B, 2200, Copenhagen N, Denmark; Department of Physiology and Pharmacology, Karolinska Institutet, 171 77, Stockholm, Sweden; Department of Molecular Medicine and Surgery, Karolinska University Hospital, 171 76, Stockholm, Sweden
| | - Tuomas O Kilpeläinen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3B, 2200, Copenhagen N, Denmark
| | - Romain Barrès
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3B, 2200, Copenhagen N, Denmark.
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205
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Yuan R, Zhang J, Wang Y, Zhu X, Hu S, Zeng J, Liang F, Tang Q, Chen Y, Chen L, Zhu W, Li M, Mo D. Reorganization of chromatin architecture during prenatal development of porcine skeletal muscle. DNA Res 2021; 28:6261936. [PMID: 34009337 PMCID: PMC8154859 DOI: 10.1093/dnares/dsab003] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Accepted: 04/26/2021] [Indexed: 11/18/2022] Open
Abstract
Myofibres (primary and secondary myofibre) are the basic structure of muscle and the determinant of muscle mass. To explore the skeletal muscle developmental processes from primary myofibres to secondary myofibres in pigs, we conducted an integrative three-dimensional structure of genome and transcriptomic characterization of longissimus dorsi muscle of pig from primary myofibre formation stage [embryonic Day 35 (E35)] to secondary myofibre formation stage (E80). In the hierarchical genomic structure, we found that 11.43% of genome switched compartment A/B status, 14.53% of topologically associating domains are changed intradomain interactions (D-scores) and 2,730 genes with differential promoter–enhancer interactions and (or) enhancer activity from E35 to E80. The alterations of genome architecture were found to correlate with expression of genes that play significant roles in neuromuscular junction, embryonic morphogenesis, skeletal muscle development or metabolism, typically, NEFL, MuSK, SLN, Mef2D and GCK. Significantly, Sox6 and MATN2 play important roles in the process of primary to secondary myofibres formation and increase the regulatory potential score and genes expression in it. In brief, we reveal the genomic reorganization from E35 to E80 and construct genome-wide high-resolution interaction maps that provide a resource for studying long-range control of gene expression from E35 to E80.
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Affiliation(s)
- Renqiang Yuan
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou 510006, China.,Institute of Animal Genetics and Breeding, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China
| | - Jiaman Zhang
- Institute of Animal Genetics and Breeding, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China
| | - Yujie Wang
- Institute of Animal Genetics and Breeding, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China
| | - Xingxing Zhu
- Institute of Animal Genetics and Breeding, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China
| | - Silu Hu
- Institute of Animal Genetics and Breeding, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China
| | - Jianhua Zeng
- Guangdong YIHAO Food Co., Ltd, Guangzhou 510620, China
| | - Feng Liang
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou 510006, China
| | - Qianzi Tang
- Institute of Animal Genetics and Breeding, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China
| | - Yaosheng Chen
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou 510006, China
| | - Luxi Chen
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou 510006, China.,Guangdong Key Laboratory of Pharmaceutical Bioactive Substances, Guangdong Pharmaceutical University, Guangzhou 510006, China
| | - Wei Zhu
- Institute of Animal Genetics and Breeding, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China
| | - Mingzhou Li
- Institute of Animal Genetics and Breeding, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China
| | - Delin Mo
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou 510006, China
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206
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Bai WY, Xia JW, Rong XL, Cong PK, Khederzadeh S, Zheng HF. Integrative analysis of genomic and epigenomic data reveals underlying super-enhancer-mediated microRNA regulatory network for human bone mineral density. Hum Mol Genet 2021; 30:2177-2189. [PMID: 34230965 PMCID: PMC8561425 DOI: 10.1093/hmg/ddab181] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 06/17/2021] [Accepted: 06/29/2021] [Indexed: 12/24/2022] Open
Abstract
Bone mineral density (BMD) is a highly heritable complex trait and is a key indicator for diagnosis and treatment for osteoporosis. In the last decade, numerous susceptibility loci for BMD and fracture have been identified by genome wide association studies (GWAS); however, fine mapping of these loci is challengeable. Here, we proposed a new long-range fine-mapping approach that combined super-enhancers (SEs) and microRNAs (miRNAs) data, which were two important factors in control of cell identity and specific differentiation, with the GWAS summary datasets in cell-type-restricted way. Genome-wide SE-based analysis found that the BMD-related variants were significantly enriched in the osteoblast SE regions, indicative of potential long-range effects of such SNPs. With the SNP-mapped SEs (mSEs), 13 accessible long-range mSE-interacted miRNAs (mSE-miRNAs) were identified by integrating osteoblast Hi-C and ATAC-seq data, including three known bone-related miRNAs (miR-132-3p, miR-212-3p and miR-125b-5p). The putative targets of the two newly identified mSE-miRNAs (miR-548aj-3p and miR-190a-3p) were found largely enriched in osteogenic-related pathway and processes, suggesting that these mSE-miRNAs could be functional in the regulation of osteoblast differentiation. Furthermore, we identified 54 genes with the long-range 'mSE-miRNA' approach, and 24 of them were previously reported to be related to skeletal development. Besides, enrichment analysis found that these genes were specifically enriched in the post-transcriptional regulation and bone formation processes. This study provided a new insight into the approach of fine-mapping of GWAS loci. A tool was provided for the genome-wide SE-based analysis and the detection of long-range osteoblast-restricted mSE-miRNAs (https://github.com/Zheng-Lab-Westlake/Osteo-Fine-Mapp-SNP2SE2miRNA).
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Affiliation(s)
- Wei-Yang Bai
- Fudan University, Shanghai, China.,Diseases & Population (DaP) Geninfo Lab, School of Life Sciences, Westlake University, 18 Shilongshan Road, Xihu District, Hangzhou 310024, Zhejiang, China.,Westlake Laboratory of Life Sciences and Biomedicine, 18 Shilongshan Road, Xihu District, Hangzhou 310024, Zhejiang, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Xihu District, Hangzhou 310024, Zhejiang, China
| | - Jiang-Wei Xia
- Diseases & Population (DaP) Geninfo Lab, School of Life Sciences, Westlake University, 18 Shilongshan Road, Xihu District, Hangzhou 310024, Zhejiang, China.,Westlake Laboratory of Life Sciences and Biomedicine, 18 Shilongshan Road, Xihu District, Hangzhou 310024, Zhejiang, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Xihu District, Hangzhou 310024, Zhejiang, China
| | - Xiao-Li Rong
- Institute for Cell Engineering, The Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
| | - Pei-Kuan Cong
- Diseases & Population (DaP) Geninfo Lab, School of Life Sciences, Westlake University, 18 Shilongshan Road, Xihu District, Hangzhou 310024, Zhejiang, China.,Westlake Laboratory of Life Sciences and Biomedicine, 18 Shilongshan Road, Xihu District, Hangzhou 310024, Zhejiang, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Xihu District, Hangzhou 310024, Zhejiang, China
| | - Saber Khederzadeh
- Diseases & Population (DaP) Geninfo Lab, School of Life Sciences, Westlake University, 18 Shilongshan Road, Xihu District, Hangzhou 310024, Zhejiang, China.,Westlake Laboratory of Life Sciences and Biomedicine, 18 Shilongshan Road, Xihu District, Hangzhou 310024, Zhejiang, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Xihu District, Hangzhou 310024, Zhejiang, China
| | - Hou-Feng Zheng
- Fudan University, Shanghai, China.,Diseases & Population (DaP) Geninfo Lab, School of Life Sciences, Westlake University, 18 Shilongshan Road, Xihu District, Hangzhou 310024, Zhejiang, China.,Westlake Laboratory of Life Sciences and Biomedicine, 18 Shilongshan Road, Xihu District, Hangzhou 310024, Zhejiang, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Xihu District, Hangzhou 310024, Zhejiang, China
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207
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Huang Y, Sicar S, Ramirez-Prado JS, Manza-Mianza D, Antunez-Sanchez J, Brik-Chaouche R, Rodriguez-Granados NY, An J, Bergounioux C, Mahfouz MM, Hirt H, Crespi M, Concia L, Barneche F, Amiard S, Probst AV, Gutierrez-Marcos J, Ariel F, Raynaud C, Latrasse D, Benhamed M. Polycomb-dependent differential chromatin compartmentalization determines gene coregulation in Arabidopsis. Genome Res 2021; 31:1230-1244. [PMID: 34083408 PMCID: PMC8256866 DOI: 10.1101/gr.273771.120] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Accepted: 05/20/2021] [Indexed: 11/24/2022]
Abstract
In animals, distant H3K27me3-marked Polycomb targets can establish physical interactions forming repressive chromatin hubs. In plants, growing evidence suggests that H3K27me3 acts directly or indirectly to regulate chromatin interactions, although how this histone modification modulates 3D chromatin architecture remains elusive. To decipher the impact of the dynamic deposition of H3K27me3 on the Arabidopsis thaliana nuclear interactome, we combined genetics, transcriptomics, and several 3D epigenomic approaches. By analyzing mutants defective for histone H3K27 methylation or demethylation, we uncovered the crucial role of this chromatin mark in short- and previously unnoticed long-range chromatin loop formation. We found that a reduction in H3K27me3 levels led to a decrease in the interactions within Polycomb-associated repressive domains. Regions with lower H3K27me3 levels in the H3K27 methyltransferase clf mutant established new interactions with regions marked with H3K9ac, a histone modification associated with active transcription, indicating that a reduction in H3K27me3 levels induces a global reconfiguration of chromatin architecture. Altogether, our results reveal that the 3D genome organization is tightly linked to reversible histone modifications that govern chromatin interactions. Consequently, nuclear organization dynamics shapes the transcriptional reprogramming during plant development and places H3K27me3 as a key feature in the coregulation of distant genes.
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Affiliation(s)
- Ying Huang
- Université Paris-Saclay, CNRS, INRAE, Univ Evry, Institute of Plant Sciences Paris-Saclay (IPS2), 91405, Orsay, France
| | - Sanchari Sicar
- Université Paris-Saclay, CNRS, INRAE, Univ Evry, Institute of Plant Sciences Paris-Saclay (IPS2), 91405, Orsay, France
| | - Juan S Ramirez-Prado
- Université Paris-Saclay, CNRS, INRAE, Univ Evry, Institute of Plant Sciences Paris-Saclay (IPS2), 91405, Orsay, France
| | - Deborah Manza-Mianza
- Université Paris-Saclay, CNRS, INRAE, Univ Evry, Institute of Plant Sciences Paris-Saclay (IPS2), 91405, Orsay, France
| | | | - Rim Brik-Chaouche
- Université Paris-Saclay, CNRS, INRAE, Univ Evry, Institute of Plant Sciences Paris-Saclay (IPS2), 91405, Orsay, France
| | - Natalia Y Rodriguez-Granados
- Université Paris-Saclay, CNRS, INRAE, Univ Evry, Institute of Plant Sciences Paris-Saclay (IPS2), 91405, Orsay, France
| | - Jing An
- Université Paris-Saclay, CNRS, INRAE, Univ Evry, Institute of Plant Sciences Paris-Saclay (IPS2), 91405, Orsay, France
| | - Catherine Bergounioux
- Université Paris-Saclay, CNRS, INRAE, Univ Evry, Institute of Plant Sciences Paris-Saclay (IPS2), 91405, Orsay, France
| | - Magdy M Mahfouz
- Division of Biological and Environmental Sciences and Engineering, King Abdullah University of Science and Technology, Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Heribert Hirt
- Université Paris-Saclay, CNRS, INRAE, Univ Evry, Institute of Plant Sciences Paris-Saclay (IPS2), 91405, Orsay, France
- Division of Biological and Environmental Sciences and Engineering, King Abdullah University of Science and Technology, Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Martin Crespi
- Université Paris-Saclay, CNRS, INRAE, Univ Evry, Institute of Plant Sciences Paris-Saclay (IPS2), 91405, Orsay, France
| | - Lorenzo Concia
- Université Paris-Saclay, CNRS, INRAE, Univ Evry, Institute of Plant Sciences Paris-Saclay (IPS2), 91405, Orsay, France
- Institut de Biologie de l'Ecole Normale Supérieure (IBENS), ENS, CNRS UMR8197, INSERM U1024, PSL Research University, 75005, Paris, France
| | - Fredy Barneche
- Institut de Biologie de l'Ecole Normale Supérieure (IBENS), ENS, CNRS UMR8197, INSERM U1024, PSL Research University, 75005, Paris, France
| | - Simon Amiard
- GReD, Université Clermont Auvergne, CNRS, INSERM, BP 38, 63001, Clermont-Ferrand, France
| | - Aline V Probst
- GReD, Université Clermont Auvergne, CNRS, INSERM, BP 38, 63001, Clermont-Ferrand, France
| | | | - Federico Ariel
- Instituto de Agrobiotecnología del Litoral, CONICET, Universidad Nacional del Litoral, 3000, Santa Fe, Argentina
| | - Cécile Raynaud
- Université Paris-Saclay, CNRS, INRAE, Univ Evry, Institute of Plant Sciences Paris-Saclay (IPS2), 91405, Orsay, France
| | - David Latrasse
- Université Paris-Saclay, CNRS, INRAE, Univ Evry, Institute of Plant Sciences Paris-Saclay (IPS2), 91405, Orsay, France
| | - Moussa Benhamed
- Université Paris-Saclay, CNRS, INRAE, Univ Evry, Institute of Plant Sciences Paris-Saclay (IPS2), 91405, Orsay, France
- Université de Paris, Institute of Plant Sciences Paris-Saclay (IPS2), F-75006 Paris, France
- Institut Universitaire de France (IUF)
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208
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Davidson IF, Peters JM. Genome folding through loop extrusion by SMC complexes. Nat Rev Mol Cell Biol 2021; 22:445-464. [PMID: 33767413 DOI: 10.1038/s41580-021-00349-7] [Citation(s) in RCA: 218] [Impact Index Per Article: 72.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/03/2021] [Indexed: 02/02/2023]
Abstract
Genomic DNA is folded into loops and topologically associating domains (TADs), which serve important structural and regulatory roles. It has been proposed that these genomic structures are formed by a loop extrusion process, which is mediated by structural maintenance of chromosomes (SMC) protein complexes. Recent single-molecule studies have shown that the SMC complexes condensin and cohesin are indeed able to extrude DNA into loops. In this Review, we discuss how the loop extrusion hypothesis can explain key features of genome architecture; cellular functions of loop extrusion, such as separation of replicated DNA molecules, facilitation of enhancer-promoter interactions and immunoglobulin gene recombination; and what is known about the mechanism of loop extrusion and its regulation, for example, by chromatin boundaries that depend on the DNA binding protein CTCF. We also discuss how the loop extrusion hypothesis has led to a paradigm shift in our understanding of both genome architecture and the functions of SMC complexes.
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Affiliation(s)
- Iain F Davidson
- Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC), Vienna, Austria
| | - Jan-Michael Peters
- Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC), Vienna, Austria.
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209
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Cao CH, Wei Y, Liu R, Lin XR, Luo JQ, Zhang QJ, Lin SR, Geng L, Ye SK, Shi Y, Xia X. Three-Dimensional Genome Interactions Identify Potential Adipocyte Metabolism-Associated Gene STON1 and Immune-Correlated Gene FSHR at the rs13405728 Locus in Polycystic Ovary Syndrome. Front Endocrinol (Lausanne) 2021; 12:686054. [PMID: 34248847 PMCID: PMC8264658 DOI: 10.3389/fendo.2021.686054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Accepted: 06/03/2021] [Indexed: 12/03/2022] Open
Abstract
Background rs13405728 was identified as one of the most prevalent susceptibility loci for polycystic ovary syndrome (PCOS) in Han Chinese and Caucasian women. However, the target genes and potential mechanisms of the rs13405728 locus remain to be determined. Methods Three-dimensional (3D) genome interactions from the ovary tissue were characterized via high-through chromosome conformation capture (Hi-C) and Capture Hi-C technologies to identify putative targets at the rs13405728 locus. Combined analyses of eQTL, RNA-Seq, DNase-Seq, ChIP-Seq, and sing-cell sequencing were performed to explore the molecular roles of these target genes in PCOS. PCOS-like mice were applied to verify the expression patterns. Results Generally, STON1 and FSHR were identified as potential targets of the rs13405728 locus in 3D genomic interactions with epigenomic regulatory peaks, with STON1 (P=0.0423) and FSHR (P=0.0013) being highly expressed in PCOS patients. STON1 co-expressed genes were associated with metabolic processes (P=0.0008) in adipocytes (P=0.0001), which was validated in the fat tissue (P<0.0001) and ovary (P=0.0035) from fat-diet mice. The immune system process (GO:0002376) was enriched in FSHR co-expressed genes (P=0.0002) and PCOS patients (P=0.0002), with CD4 high expression in PCOS patients (P=0.0316) and PCOS-like models (P=0.0079). Meanwhile, FSHR expression was positively correlated with CD4 expression in PCOS patients (P=0.0252) and PCOS-like models (P=0.0178). Furthermore, androgen receptor (AR) was identified as the common transcription factor for STON1 and FSHR and positively correlated with the expression of STON1 (P=0.039) and FSHR (P=4e-06) in ovary tissues and PCOS-like mice. Conclusion Overall, we identified STON1 and FSHR as potential targets for the rs13405728 locus and their roles in the processes of adipocyte metabolism and CD4 immune expression in PCOS, which provides 3D genomic insight into the pathogenesis of PCOS.
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Affiliation(s)
- Can-hui Cao
- Center for Reproductive Medicine, Department of Obstetrics and Gynecology, Peking University Shenzhen Hospital, Shenzhen Peking University-The Hong Kong University of Science and Technology Medical Center, Shenzhen, China
- Department of Gynecologic Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ye Wei
- Department of Gynecologic Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Rang Liu
- Center for Reproductive Medicine, Department of Obstetrics and Gynecology, Peking University Shenzhen Hospital, Shenzhen Peking University-The Hong Kong University of Science and Technology Medical Center, Shenzhen, China
| | - Xin-ran Lin
- Center for Reproductive Medicine, Department of Obstetrics and Gynecology, Peking University Shenzhen Hospital, Shenzhen Peking University-The Hong Kong University of Science and Technology Medical Center, Shenzhen, China
| | - Jia-qi Luo
- Center for Reproductive Medicine, Department of Obstetrics and Gynecology, Peking University Shenzhen Hospital, Shenzhen Peking University-The Hong Kong University of Science and Technology Medical Center, Shenzhen, China
| | - Qiu-ju Zhang
- Center for Reproductive Medicine, Department of Obstetrics and Gynecology, Peking University Shenzhen Hospital, Shenzhen Peking University-The Hong Kong University of Science and Technology Medical Center, Shenzhen, China
| | - Shou-ren Lin
- Center for Reproductive Medicine, Department of Obstetrics and Gynecology, Peking University Shenzhen Hospital, Shenzhen Peking University-The Hong Kong University of Science and Technology Medical Center, Shenzhen, China
| | - Lan Geng
- Center for Reproductive Medicine, Department of Obstetrics and Gynecology, Peking University Shenzhen Hospital, Shenzhen Peking University-The Hong Kong University of Science and Technology Medical Center, Shenzhen, China
| | - Si-kang Ye
- Department of Critical Care Medicine, The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
| | - Yu Shi
- Department of Ultrasonography, Peking University Shenzhen Hospital, Shenzhen Peking University-The Hong Kong University of Science and Technology Medical Center, Shenzhen, China
| | - Xi Xia
- Center for Reproductive Medicine, Department of Obstetrics and Gynecology, Peking University Shenzhen Hospital, Shenzhen Peking University-The Hong Kong University of Science and Technology Medical Center, Shenzhen, China
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210
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Wang J, Clay-Gilmour AI, Karaesmen E, Rizvi A, Zhu Q, Yan L, Preus L, Liu S, Wang Y, Griffiths E, Stram DO, Pooler L, Sheng X, Haiman C, Van Den Berg D, Webb A, Brock G, Spellman S, Pasquini M, McCarthy P, Allan J, Stölzel F, Onel K, Hahn T, Sucheston-Campbell LE. Genome-Wide Association Analyses Identify Variants in IRF4 Associated With Acute Myeloid Leukemia and Myelodysplastic Syndrome Susceptibility. Front Genet 2021; 12:554948. [PMID: 34220922 PMCID: PMC8248805 DOI: 10.3389/fgene.2021.554948] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Accepted: 04/19/2021] [Indexed: 12/22/2022] Open
Abstract
The role of common genetic variation in susceptibility to acute myeloid leukemia (AML), and myelodysplastic syndrome (MDS), a group of rare clonal hematologic disorders characterized by dysplastic hematopoiesis and high mortality, remains unclear. We performed AML and MDS genome-wide association studies (GWAS) in the DISCOVeRY-BMT cohorts (2,309 cases and 2,814 controls). Association analysis based on subsets (ASSET) was used to conduct a summary statistics SNP-based analysis of MDS and AML subtypes. For each AML and MDS case and control we used PrediXcan to estimate the component of gene expression determined by their genetic profile and correlate this imputed gene expression level with risk of developing disease in a transcriptome-wide association study (TWAS). ASSET identified an increased risk for de novo AML and MDS (OR = 1.38, 95% CI, 1.26-1.51, Pmeta = 2.8 × 10-12) in patients carrying the T allele at s12203592 in Interferon Regulatory Factor 4 (IRF4), a transcription factor which regulates myeloid and lymphoid hematopoietic differentiation. Our TWAS analyses showed increased IRF4 gene expression is associated with increased risk of de novo AML and MDS (OR = 3.90, 95% CI, 2.36-6.44, Pmeta = 1.0 × 10-7). The identification of IRF4 by both GWAS and TWAS contributes valuable insight on the role of genetic variation in AML and MDS susceptibility.
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Affiliation(s)
- Junke Wang
- College of Pharmacy, The Ohio State University, Columbus, OH, United States
| | - Alyssa I. Clay-Gilmour
- Department of Epidemiology, Mayo Clinic, Rochester, MN, United States
- Department of Epidemiology & Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, United States
| | - Ezgi Karaesmen
- College of Pharmacy, The Ohio State University, Columbus, OH, United States
| | - Abbas Rizvi
- College of Pharmacy, The Ohio State University, Columbus, OH, United States
| | - Qianqian Zhu
- Department of Biostatistics and Bioinformatics, Roswell Park Comprehensive Cancer Center, Buffalo, NY, United States
| | - Li Yan
- Department of Biostatistics and Bioinformatics, Roswell Park Comprehensive Cancer Center, Buffalo, NY, United States
| | - Leah Preus
- College of Pharmacy, The Ohio State University, Columbus, OH, United States
| | - Song Liu
- Department of Biostatistics and Bioinformatics, Roswell Park Comprehensive Cancer Center, Buffalo, NY, United States
| | - Yiwen Wang
- College of Pharmacy, The Ohio State University, Columbus, OH, United States
| | - Elizabeth Griffiths
- Department of Medicine, Roswell Park Comprehensive Cancer Center, Buffalo, NY, United States
| | - Daniel O. Stram
- Department of Preventive Medicine, University of Southern California, Los Angeles, CA, United States
| | - Loreall Pooler
- Department of Preventive Medicine, University of Southern California, Los Angeles, CA, United States
| | - Xin Sheng
- Department of Preventive Medicine, University of Southern California, Los Angeles, CA, United States
| | - Christopher Haiman
- Department of Preventive Medicine, University of Southern California, Los Angeles, CA, United States
| | - David Van Den Berg
- Department of Preventive Medicine, University of Southern California, Los Angeles, CA, United States
| | - Amy Webb
- Department on Biomedical Informatics, The Ohio State University, Columbus, OH, United States
| | - Guy Brock
- Department on Biomedical Informatics, The Ohio State University, Columbus, OH, United States
| | - Stephen Spellman
- Center for International Blood and Marrow Transplant Research, Minneapolis, MN, United States
| | - Marcelo Pasquini
- Center for International Blood and Marrow Transplant Research, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Philip McCarthy
- Department of Medicine, Roswell Park Comprehensive Cancer Center, Buffalo, NY, United States
| | - James Allan
- Northern Institute for Cancer Research, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Friedrich Stölzel
- Department of Internal Medicine I, University Hospital Carl Gustav Carus Dresden, Technical University Dresden, Dresden, Germany
| | - Kenan Onel
- Department of Pediatrics, Mount Sinai Medical Center, Miami Beach, NY, United States
| | - Theresa Hahn
- Department of Medicine, Roswell Park Comprehensive Cancer Center, Buffalo, NY, United States
| | - Lara E. Sucheston-Campbell
- College of Pharmacy, The Ohio State University, Columbus, OH, United States
- College of Veterinary Medicine, The Ohio State University, Columbus, OH, United States
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211
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Pei L, Li G, Lindsey K, Zhang X, Wang M. Plant 3D genomics: the exploration and application of chromatin organization. THE NEW PHYTOLOGIST 2021; 230:1772-1786. [PMID: 33560539 PMCID: PMC8252774 DOI: 10.1111/nph.17262] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Accepted: 02/01/2021] [Indexed: 05/29/2023]
Abstract
Eukaryotic genomes are highly folded for packing into higher-order chromatin structures in the nucleus. With the emergence of state-of-the-art chromosome conformation capture methods and microscopic imaging techniques, the spatial organization of chromatin and its functional implications have been interrogated. Our knowledge of 3D chromatin organization in plants has improved dramatically in the past few years, building on the early advances in animal systems. Here, we review recent advances in 3D genome mapping approaches, our understanding of the sophisticated organization of spatial structures, and the application of 3D genomic principles in plants. We also discuss directions for future developments in 3D genomics in plants.
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Affiliation(s)
- Liuling Pei
- National Key Laboratory of Crop Genetic ImprovementHuazhong Agricultural UniversityWuhanHubei430070China
| | - Guoliang Li
- Hubei Key Laboratory of Agricultural BioinformaticsCollege of InformaticsHuazhong Agricultural UniversityWuhanHubei430070China
| | - Keith Lindsey
- Department of BiosciencesDurham UniversitySouth RoadDurhamDH1 3LEUK
| | - Xianlong Zhang
- National Key Laboratory of Crop Genetic ImprovementHuazhong Agricultural UniversityWuhanHubei430070China
| | - Maojun Wang
- National Key Laboratory of Crop Genetic ImprovementHuazhong Agricultural UniversityWuhanHubei430070China
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212
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Abstract
In eukaryotes, the genome is hierarchically packed inside the nucleus, which facilitates physical contact between cis-regulatory elements (CREs), such as enhancers and promoters. Accumulating evidence highlights the critical role of higher-order chromatin structure in precise regulation of spatiotemporal gene expression under diverse biological contexts including lineage commitment and cell activation by external stimulus. Genomics and imaging-based technologies, such as Hi-C and DNA fluorescence in situ hybridization (FISH), have revealed the key principles of genome folding, while newly developed tools focus on improvement in resolution, throughput and modality at single-cell and population levels, and challenge the knowledge obtained through conventional approaches. In this review, we discuss recent advances in our understanding of principles of higher-order chromosome conformation and technologies to investigate 4D chromatin interactions.
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Affiliation(s)
- Namyoung Jung
- Department of Life Sciences, Pohang University of Science and Technology (POSTECH), Pohang 37673, Korea
| | - Tae-Kyung Kim
- Department of Life Sciences, Pohang University of Science and Technology (POSTECH), Pohang 37673, Korea
- Yonsei University, Seoul 03722, Korea
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213
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Zha M, Wang N, Zhang C, Wang Z. Inferring Single-Cell 3D Chromosomal Structures Based on the Lennard-Jones Potential. Int J Mol Sci 2021; 22:ijms22115914. [PMID: 34072879 PMCID: PMC8199262 DOI: 10.3390/ijms22115914] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Revised: 05/23/2021] [Accepted: 05/28/2021] [Indexed: 11/16/2022] Open
Abstract
Reconstructing three-dimensional (3D) chromosomal structures based on single-cell Hi-C data is a challenging scientific problem due to the extreme sparseness of the single-cell Hi-C data. In this research, we used the Lennard-Jones potential to reconstruct both 500 kb and high-resolution 50 kb chromosomal structures based on single-cell Hi-C data. A chromosome was represented by a string of 500 kb or 50 kb DNA beads and put into a 3D cubic lattice for simulations. A 2D Gaussian function was used to impute the sparse single-cell Hi-C contact matrices. We designed a novel loss function based on the Lennard-Jones potential, in which the ε value, i.e., the well depth, was used to indicate how stable the binding of every pair of beads is. For the bead pairs that have single-cell Hi-C contacts and their neighboring bead pairs, the loss function assigns them stronger binding stability. The Metropolis-Hastings algorithm was used to try different locations for the DNA beads, and simulated annealing was used to optimize the loss function. We proved the correctness and validness of the reconstructed 3D structures by evaluating the models according to multiple criteria and comparing the models with 3D-FISH data.
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Affiliation(s)
- Mengsheng Zha
- School of Computing Sciences and Computer Engineering, University of Southern Mississippi, 118 College Dr, Hattiesburg, MS 39406, USA; (M.Z.); (C.Z.)
| | - Nan Wang
- Department of Computer Science, New Jersey City University, 2039 Kennedy Blvd, Jersey City, NJ 07305, USA;
| | - Chaoyang Zhang
- School of Computing Sciences and Computer Engineering, University of Southern Mississippi, 118 College Dr, Hattiesburg, MS 39406, USA; (M.Z.); (C.Z.)
| | - Zheng Wang
- Department of Computer Science, University of Miami, 1364 Memorial Drive, Coral Gables, FL 33124, USA
- Correspondence:
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214
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covNorm: An R package for coverage based normalization of Hi-C and capture Hi-C data. Comput Struct Biotechnol J 2021; 19:3149-3159. [PMID: 34141136 PMCID: PMC8188117 DOI: 10.1016/j.csbj.2021.05.041] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Revised: 05/05/2021] [Accepted: 05/23/2021] [Indexed: 11/24/2022] Open
Abstract
Hi-C and capture Hi-C have greatly advanced our understanding of the principles of higher-order chromatin structure. In line with the evolution of the Hi-C protocols, there is a demand for an advanced computational method that can be applied to the various forms of Hi-C protocols and effectively remove innate biases. To resolve this issue, we developed an implicit normalization method named “covNorm” and implemented it as an R package. The proposed method can perform a complete procedure of data processing for Hi-C and its variants. Starting from the negative binomial model-based normalization for DNA fragment coverages, removal of genomic distance-dependent background and calling of the significant interactions can be applied sequentially. The performance evaluation of covNorm showed enhanced or similar reproducibility in terms of HiC-spector score, correlation of compartment A/B profiles, and detection of reproducible significant long-range chromatin contacts compared to baseline methods in the benchmark datasets. The developed method is powerful in terms of effective normalization of Hi-C and capture Hi-C data, detection of long-range chromatin contacts, and readily extendibility to the other derivative Hi-C protocols. The covNorm R package is freely available at GitHub: https://github.com/kaistcbfg/covNormRpkg.
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215
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Lu J, Wang X, Sun K, Lan X. Chrom-Lasso: a lasso regression-based model to detect functional interactions using Hi-C data. Brief Bioinform 2021; 22:6278150. [PMID: 34013331 PMCID: PMC8574949 DOI: 10.1093/bib/bbab181] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Revised: 05/13/2021] [Indexed: 01/02/2023] Open
Abstract
Hi-C is a genome-wide assay based on Chromosome Conformation Capture and high-throughput sequencing to decipher 3D chromatin organization in the nucleus. However, computational methods to detect functional interactions utilizing Hi-C data face challenges including the correction for various sources of biases and the identification of functional interactions with low counts of interacting fragments. We present Chrom-Lasso, a lasso linear regression model that removes complex biases assumption-free and identifies functional interacting loci with increased power by combining information of local reads distribution surrounding the area of interest. We showed that interacting regions identified by Chrom-Lasso are more enriched for 5C validated interactions and functional GWAS hits than that of GOTHiC and Fit-Hi-C. To further demonstrate the ability of Chrom-Lasso to detect interactions of functional importance, we performed time-series Hi-C and RNA-seq during T cell activation and exhaustion. We showed that the dynamic changes in gene expression and chromatin interactions identified by Chrom-Lasso were largely concordant with each other. Finally, we experimentally confirmed Chrom-Lasso’s finding that Erbb3 was co-regulated with distinct neighboring genes at different states during T cell activation. Our results highlight Chrom-Lasso’s utility in detecting weak functional interaction between cis-regulatory elements, such as promoters and enhancers.
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Affiliation(s)
- Jingzhe Lu
- School of Medicine, Tsinghua University, Beijing, China
| | - Xu Wang
- School of Medicine and the Tsinghua-Peking Center for Life science, Tsinghua University, Beijing, China
| | - Keyong Sun
- School of Medicine and the Tsinghua-Peking Center for Life science, Tsinghua University, Beijing, China
| | - Xun Lan
- School of Medicine and the Tsinghua-Peking Center for Life science, Tsinghua University, Beijing, China
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216
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Kranas H, Tuszynska I, Wilczynski B. HiCEnterprise: identifying long range chromosomal contacts in Hi-C data. PeerJ 2021; 9:e10558. [PMID: 33981483 PMCID: PMC8083178 DOI: 10.7717/peerj.10558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Accepted: 11/22/2020] [Indexed: 11/20/2022] Open
Abstract
Motivation Computational analysis of chromosomal contact data is currently gaining popularity with the rapid advance in experimental techniques providing access to a growing body of data. An important problem in this area is the identification of long range contacts between distinct chromatin regions. Such loops were shown to exist at different scales, either mediating relatively short range interactions between enhancers and promoters or providing interactions between much larger, distant chromosome domains. A proper statistical analysis as well as availability to a wide research community are crucial in a tool for this task. Results We present HiCEnterprise, a first freely available software tool for identification of long range chromatin contacts not only between small regions, but also between chromosomal domains. It implements four different statistical tests for identification of significant contacts for user defined regions or domains as well as necessary functions for input, output and visualization of chromosome contacts. Availability The software and the corresponding documentation are available at: github.com/regulomics/HiCEnterprise. Supplementary information Supplemental data are available in the online version of the article and at the website regulomics.mimuw.edu.pl/wp/hicenterprise.
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Affiliation(s)
- Hanna Kranas
- Institute of Informatics, University of Warsaw, Warsaw, Poland.,Current Address: Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Irina Tuszynska
- Institute of Informatics, University of Warsaw, Warsaw, Poland
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217
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Zhu I, Song W, Ovcharenko I, Landsman D. A model of active transcription hubs that unifies the roles of active promoters and enhancers. Nucleic Acids Res 2021; 49:4493-4505. [PMID: 33872375 PMCID: PMC8096258 DOI: 10.1093/nar/gkab235] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2020] [Revised: 01/27/2021] [Accepted: 03/22/2021] [Indexed: 12/31/2022] Open
Abstract
An essential questions of gene regulation is how large number of enhancers and promoters organize into gene regulatory loops. Using transcription-factor binding enrichment as an indicator of enhancer strength, we identified a portion of H3K27ac peaks as potentially strong enhancers and found a universal pattern of promoter and enhancer distribution: At actively transcribed regions of length of ∼200-300 kb, the numbers of active promoters and enhancers are inversely related. Enhancer clusters are associated with isolated active promoters, regardless of the gene's cell-type specificity. As the number of nearby active promoters increases, the number of enhancers decreases. At regions where multiple active genes are closely located, there are few distant enhancers. With Hi-C analysis, we demonstrate that the interactions among the regulatory elements (active promoters and enhancers) occur predominantly in clusters and multiway among linearly close elements and the distance between adjacent elements shows a preference of ∼30 kb. We propose a simple rule of spatial organization of active promoters and enhancers: Gene transcriptions and regulations mainly occur at local active transcription hubs contributed dynamically by multiple elements from linearly close enhancers and/or active promoters. The hub model can be represented with a flower-shaped structure and implies an enhancer-like role of active promoters.
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Affiliation(s)
- Iris Zhu
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20892, USA
| | - Wei Song
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20892, USA
| | - Ivan Ovcharenko
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20892, USA
| | - David Landsman
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20892, USA
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218
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Syreeni A, Sandholm N, Sidore C, Cucca F, Haukka J, Harjutsalo V, Groop PH. Genome-wide search for genes affecting the age at diagnosis of type 1 diabetes. J Intern Med 2021; 289:662-674. [PMID: 33179336 PMCID: PMC8247053 DOI: 10.1111/joim.13187] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Revised: 09/07/2020] [Accepted: 09/09/2020] [Indexed: 12/16/2022]
Abstract
BACKGROUND Type 1 diabetes (T1D) is an autoimmune disease affecting individuals in the early years of life. Although previous studies have identified genetic loci influencing T1D diagnosis age, these studies did not investigate the genome with high resolution. OBJECTIVE AND METHODS We performed a genome-wide meta-analysis for age at diagnosis with cohorts from Finland (Finnish Diabetic Nephropathy Study), the United Kingdom (UK Genetic Resource Investigating Diabetes) and Sardinia. Through SNP associations, transcriptome-wide association analysis linked T1D diagnosis age and gene expression. RESULTS We identified two chromosomal regions associated with T1D diagnosis age: multiple independent variants in the HLA region on chromosome 6 and a locus on chromosome 17q12. We performed gene-level association tests with transcriptome prediction models from two whole blood datasets, lymphocyte cell line, spleen, pancreas and small intestine tissues. Of the non-HLA genes, lower PNMT expression in whole blood, and higher IKZF3 and ZPBP2, and lower ORMDL3 and GSDMB transcription levels in multiple tissues were associated with lower T1D diagnosis age (FDR = 0.05). These genes lie on chr17q12 which is associated with T1D, other autoimmune diseases, and childhood asthma. Additionally, higher expression of PHF20L1, a gene not previously implicated in T1D, was associated with lower diagnosis age in lymphocytes, pancreas, and spleen. Altogether, the non-HLA associations were enriched in open chromatin in various blood cells, blood vessel tissues and foetal thymus tissue. CONCLUSION Multiple genes on chr17q12 and PHF20L1 on chr8 were associated with T1D diagnosis age and only further studies may elucidate the role of these genes for immunity and T1D onset.
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Affiliation(s)
- A Syreeni
- From the, Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland.,Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland.,Abdominal Center, Nephrology, University of Helsinki and Helsinki University Central Hospital, Helsinki, Finland
| | - N Sandholm
- From the, Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland.,Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland.,Abdominal Center, Nephrology, University of Helsinki and Helsinki University Central Hospital, Helsinki, Finland
| | - C Sidore
- Instituto di Ricerca Genetica e Biomedica, CNR, Monserrato, Italy
| | - F Cucca
- Instituto di Ricerca Genetica e Biomedica, CNR, Monserrato, Italy.,Dipartimento di Scienze Biomediche, Università degli Studi di Sassari, Sassari, Italy
| | - J Haukka
- From the, Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland.,Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland.,Abdominal Center, Nephrology, University of Helsinki and Helsinki University Central Hospital, Helsinki, Finland
| | - V Harjutsalo
- From the, Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland.,Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland.,Abdominal Center, Nephrology, University of Helsinki and Helsinki University Central Hospital, Helsinki, Finland.,National Institute for Health and Welfare, Helsinki, Finland
| | - P-H Groop
- From the, Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland.,Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland.,Abdominal Center, Nephrology, University of Helsinki and Helsinki University Central Hospital, Helsinki, Finland.,Department of Diabetes, Central Clinical School, Monash University, Melbourne, Victoria, Australia
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219
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Shew CJ, Carmona-Mora P, Soto DC, Mastoras M, Roberts E, Rosas J, Jagannathan D, Kaya G, O'Geen H, Dennis MY. Diverse Molecular Mechanisms Contribute to Differential Expression of Human Duplicated Genes. Mol Biol Evol 2021; 38:3060-3077. [PMID: 34009325 PMCID: PMC8321529 DOI: 10.1093/molbev/msab131] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2021] [Revised: 04/08/2021] [Accepted: 04/28/2021] [Indexed: 12/24/2022] Open
Abstract
Emerging evidence links genes within human-specific segmental duplications (HSDs) to traits and diseases unique to our species. Strikingly, despite being nearly identical by sequence (>98.5%), paralogous HSD genes are differentially expressed across human cell and tissue types, though the underlying mechanisms have not been examined. We compared cross-tissue mRNA levels of 75 HSD genes from 30 families between humans and chimpanzees and found expression patterns consistent with relaxed selection on or neofunctionalization of derived paralogs. In general, ancestral paralogs exhibited greatest expression conservation with chimpanzee orthologs, though exceptions suggest certain derived paralogs may retain or supplant ancestral functions. Concordantly, analysis of long-read isoform sequencing data sets from diverse human tissues and cell lines found that about half of derived paralogs exhibited globally lower expression. To understand mechanisms underlying these differences, we leveraged data from human lymphoblastoid cell lines (LCLs) and found no relationship between paralogous expression divergence and post-transcriptional regulation, sequence divergence, or copy-number variation. Considering cis-regulation, we reanalyzed ENCODE data and recovered hundreds of previously unidentified candidate CREs in HSDs. We also generated large-insert ChIP-sequencing data for active chromatin features in an LCL to better distinguish paralogous regions. Some duplicated CREs were sufficient to drive differential reporter activity, suggesting they may contribute to divergent cis-regulation of paralogous genes. This work provides evidence that cis-regulatory divergence contributes to novel expression patterns of recent gene duplicates in humans.
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Affiliation(s)
- Colin J Shew
- Genome Center, University of California Davis, CA, USA.,Integrative Genetics and Genomics Graduate Group, University of California Davis, CA, USA
| | - Paulina Carmona-Mora
- Genome Center, University of California Davis, CA, USA.,MIND Institute, University of California, Davis, CA, USA.,Autism Research Training Program, University of California, Davis, CA, USA
| | - Daniela C Soto
- Genome Center, University of California Davis, CA, USA.,Integrative Genetics and Genomics Graduate Group, University of California Davis, CA, USA
| | - Mira Mastoras
- Genome Center, University of California Davis, CA, USA
| | | | - Joseph Rosas
- Genome Center, University of California Davis, CA, USA.,Postbaccalaureate Research Education Program, University of California, Davis, CA, USA
| | | | - Gulhan Kaya
- Genome Center, University of California Davis, CA, USA
| | | | - Megan Y Dennis
- Genome Center, University of California Davis, CA, USA.,Integrative Genetics and Genomics Graduate Group, University of California Davis, CA, USA.,MIND Institute, University of California, Davis, CA, USA.,Autism Research Training Program, University of California, Davis, CA, USA.,Postbaccalaureate Research Education Program, University of California, Davis, CA, USA.,Department of Biochemistry & Molecular Medicine, University of California, Davis, CA, USA
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220
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Liu S, Zhao K. The Toolbox for Untangling Chromosome Architecture in Immune Cells. Front Immunol 2021; 12:670884. [PMID: 33995409 PMCID: PMC8120992 DOI: 10.3389/fimmu.2021.670884] [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: 02/22/2021] [Accepted: 04/06/2021] [Indexed: 12/19/2022] Open
Abstract
The code of life is not only encrypted in the sequence of DNA but also in the way it is organized into chromosomes. Chromosome architecture is gradually being recognized as an important player in regulating cell activities (e.g., controlling spatiotemporal gene expression). In the past decade, the toolbox for elucidating genome structure has been expanding, providing an opportunity to explore this under charted territory. In this review, we will introduce the recent advancements in approaches for mapping spatial organization of the genome, emphasizing applications of these techniques to immune cells, and trying to bridge chromosome structure with immune cell activities.
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Affiliation(s)
- Shuai Liu
- Laboratory of Epigenome Biology, Systems Biology Center, NHLBI, NIH, Bethesda, MD, United States
| | - Keji Zhao
- Laboratory of Epigenome Biology, Systems Biology Center, NHLBI, NIH, Bethesda, MD, United States
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221
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Wu H, Wang X, Chu M, Li D, Cheng L, Zhou K. HCMB: A stable and efficient algorithm for processing the normalization of highly sparse Hi-C contact data. Comput Struct Biotechnol J 2021; 19:2637-2645. [PMID: 34025950 PMCID: PMC8120939 DOI: 10.1016/j.csbj.2021.04.064] [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: 01/15/2021] [Revised: 04/11/2021] [Accepted: 04/24/2021] [Indexed: 11/17/2022] Open
Abstract
The high-throughput genome-wide chromosome conformation capture (Hi-C) method has recently become an important tool to study chromosomal interactions where one can extract meaningful biological information including P(s) curve, topologically associated domains, A/B compartments, and other biologically relevant signals. Normalization is a critical pre-processing step of downstream analyses for the elimination of systematic and technical biases from chromatin contact matrices due to different mappability, GC content, and restriction fragment lengths. Especially, the problem of high sparsity puts forward a huge challenge on the correction, indicating the urgent need for a stable and efficient method for Hi-C data normalization. Recently, some matrix balancing methods have been developed to normalize Hi-C data, such as the Knight-Ruiz (KR) algorithm, but it failed to normalize contact matrices with high sparsity. Here, we presented an algorithm, Hi-C Matrix Balancing (HCMB), based on an iterative solution of equations, combining with linear search and projection strategy to normalize the Hi-C original interaction data. Both the simulated and experimental data demonstrated that HCMB is robust and efficient in normalizing Hi-C data and preserving the biologically relevant Hi-C features even facing very high sparsity. HCMB is implemented in Python and is freely accessible to non-commercial users at GitHub: https://github.com/HUST-DataMan/HCMB.
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Affiliation(s)
- Honglong Wu
- Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, Hubei 430000, China
- BGI PathoGenesis Pharmaceutical Technology, BGI-Shenzhen, Shenzhen 518083, China
| | - Xuebin Wang
- BGI PathoGenesis Pharmaceutical Technology, BGI-Shenzhen, Shenzhen 518083, China
| | - Mengtian Chu
- BGI PathoGenesis Pharmaceutical Technology, BGI-Shenzhen, Shenzhen 518083, China
| | - Dongfang Li
- Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, Hubei 430000, China
- BGI PathoGenesis Pharmaceutical Technology, BGI-Shenzhen, Shenzhen 518083, China
| | - Lixin Cheng
- Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen 518020, China
| | - Ke Zhou
- Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, Hubei 430000, China
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222
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Labreche K, Daniau M, Sud A, Law PJ, Royer-Perron L, Holroyd A, Broderick P, Went M, Benazra M, Ahle G, Soubeyran P, Taillandier L, Chinot OL, Casasnovas O, Bay JO, Jardin F, Oberic L, Fabbro M, Damaj G, Brion A, Mokhtari K, Philippe C, Sanson M, Houillier C, Soussain C, Hoang-Xuan K, Houlston RS, Alentorn A. A genome-wide association study identifies susceptibility loci for primary central nervous system lymphoma at 6p25.3 and 3p22.1: a LOC Network study. Neuro Oncol 2021; 21:1039-1048. [PMID: 31102405 DOI: 10.1093/neuonc/noz088] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023] Open
Abstract
BACKGROUND Primary central nervous system lymphoma (PCNSL) is a rare form of extra-nodal non-Hodgkin lymphoma. PCNSL is a distinct subtype of non-Hodgkin lymphoma, with over 95% of tumors belonging to the diffuse large B-cell lymphoma (DLBCL) group. We have conducted a genome-wide association study (GWAS) on immunocompetent patients to address the possibility that common genetic variants influence the risk of developing PCNSL. METHODS We performed a meta-analysis of 2 new GWASs of PCNSL totaling 475 cases and 1134 controls of European ancestry. To increase genomic resolution, we imputed >10 million single nucleotide polymorphisms using the 1000 Genomes Project combined with UK10K as reference. In addition we performed a transcription factor binding disruption analysis and investigated the patterns of local chromatin by Capture Hi-C data. RESULTS We identified independent risk loci at 3p22.1 (rs41289586, ANO10, P = 2.17 × 10-8) and 6p25.3 near EXOC2 (rs116446171, P = 1.95 x 10-13). In contrast, the lack of an association between rs41289586 and DLBCL suggests distinct germline predisposition to PCNSL and DLBCL. We found looping chromatin interactions between noncoding regions at 6p25.3 (rs11646171) with the IRF4 promoter and at 8q24.21 (rs13254990) with the MYC promoter, both genes with strong relevance to B-cell tumorigenesis. CONCLUSION To our knowledge this is the first study providing insight into the genetic predisposition to PCNSL. Our findings represent an important step in defining the contribution of common genetic variation to the risk of developing PCNSL.
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Affiliation(s)
- Karim Labreche
- Division of Genetics and Epidemiology, Institute of Cancer Research, Sutton, Surrey, UK.,(i) National Institute of Health and Medical Research (Inserm) U 1127, Paris, France, (ii) National Center for Scientific Research, Joint Research Unit 7225, Paris, France, (iii) Brain and Spine Institute (ICM), Paris, France, and (iv) Sorbonne University, Pierre and Marie Curie University, Paris 6, Paris, France
| | - Mailys Daniau
- (i) National Institute of Health and Medical Research (Inserm) U 1127, Paris, France, (ii) National Center for Scientific Research, Joint Research Unit 7225, Paris, France, (iii) Brain and Spine Institute (ICM), Paris, France, and (iv) Sorbonne University, Pierre and Marie Curie University, Paris 6, Paris, France.,ICM, iGenSeq Platform, Paris, France
| | - Amit Sud
- Division of Genetics and Epidemiology, Institute of Cancer Research, Sutton, Surrey, UK
| | - Philip J Law
- Division of Genetics and Epidemiology, Institute of Cancer Research, Sutton, Surrey, UK
| | - Louis Royer-Perron
- (i) National Institute of Health and Medical Research (Inserm) U 1127, Paris, France, (ii) National Center for Scientific Research, Joint Research Unit 7225, Paris, France, (iii) Brain and Spine Institute (ICM), Paris, France, and (iv) Sorbonne University, Pierre and Marie Curie University, Paris 6, Paris, France.,Neurology Service 2 (Mazarin), Public Assistance-Hospitals of Paris, Hospital Group of Pitié-Salpêtrière, Paris, France; Pierre and Marie Curie University, Paris 6, Paris, France
| | - Amy Holroyd
- Division of Genetics and Epidemiology, Institute of Cancer Research, Sutton, Surrey, UK
| | - Peter Broderick
- Division of Genetics and Epidemiology, Institute of Cancer Research, Sutton, Surrey, UK
| | - Molly Went
- Division of Genetics and Epidemiology, Institute of Cancer Research, Sutton, Surrey, UK
| | - Marion Benazra
- (i) National Institute of Health and Medical Research (Inserm) U 1127, Paris, France, (ii) National Center for Scientific Research, Joint Research Unit 7225, Paris, France, (iii) Brain and Spine Institute (ICM), Paris, France, and (iv) Sorbonne University, Pierre and Marie Curie University, Paris 6, Paris, France.,ICM, iGenSeq Platform, Paris, France
| | - Guido Ahle
- Department of Neurology, Colmar Civil Hospitals, Colmar Cedex, France
| | - Pierre Soubeyran
- Department of Medical Oncology, Bergnoié Institute, Bordeaux, France.,Inserm Research Unit U1218, Bordeaux, France
| | - Luc Taillandier
- Neuro-oncology Department, Nancy University Hospital and The Center of Research in Automatic Control of Nancy, Joint Research Unit 7039, National Center for Scientific Research, SBS BEAM Department, Nancy University, Vandoeuvre-lès-Nancy, France
| | - Olivier L Chinot
- Department of Pathology and Neuropathology, Timone Hospital, Aix-Marseille University (AMU), Public Assistance-Hospitals of Marseille, Marseille, France.,AMU Research Center in Oncology Biology and Oncopharmacology, Marseille, France
| | | | - Jacques-Olivier Bay
- Department of Hematology, Clermont-Ferrand University Hospital, Clermont-Ferrand, France
| | - Fabrice Jardin
- Department of Hematology, Henri Becquerel Cancer Center, Rouen, France and Inserm U1245, Henri Becquerel Cancer Center, Institute of Research and Innovation in Biomedicine, University of Normandy, Rouen, France
| | - Lucie Oberic
- Department of Hematology, University Cancer Institute of Toulouse-Oncopole, Toulouse, France
| | | | - Gandhi Damaj
- Department of Hematology, University Hospital of Caen, Caen, France
| | - Annie Brion
- Department of Hematology, Regional and University Hospitals Besançon, Besançon, France
| | - Karima Mokhtari
- (i) National Institute of Health and Medical Research (Inserm) U 1127, Paris, France, (ii) National Center for Scientific Research, Joint Research Unit 7225, Paris, France, (iii) Brain and Spine Institute (ICM), Paris, France, and (iv) Sorbonne University, Pierre and Marie Curie University, Paris 6, Paris, France.,Raymond Escourolle Department of Neuropathology, Public Assistance-Hospitals of Paris, Hospital Group of Pitié-Salpêtrière, Paris, France.,OncoNeuroTek, ICM, Paris, France
| | | | - Marc Sanson
- (i) National Institute of Health and Medical Research (Inserm) U 1127, Paris, France, (ii) National Center for Scientific Research, Joint Research Unit 7225, Paris, France, (iii) Brain and Spine Institute (ICM), Paris, France, and (iv) Sorbonne University, Pierre and Marie Curie University, Paris 6, Paris, France.,Neurology Service 2 (Mazarin), Public Assistance-Hospitals of Paris, Hospital Group of Pitié-Salpêtrière, Paris, France; Pierre and Marie Curie University, Paris 6, Paris, France.,OncoNeuroTek, ICM, Paris, France
| | - Caroline Houillier
- (i) National Institute of Health and Medical Research (Inserm) U 1127, Paris, France, (ii) National Center for Scientific Research, Joint Research Unit 7225, Paris, France, (iii) Brain and Spine Institute (ICM), Paris, France, and (iv) Sorbonne University, Pierre and Marie Curie University, Paris 6, Paris, France
| | - Carole Soussain
- Department of Hematology, René Huguenin Hospital, Curie Institute, Saint-Cloud, France
| | - Khê Hoang-Xuan
- (i) National Institute of Health and Medical Research (Inserm) U 1127, Paris, France, (ii) National Center for Scientific Research, Joint Research Unit 7225, Paris, France, (iii) Brain and Spine Institute (ICM), Paris, France, and (iv) Sorbonne University, Pierre and Marie Curie University, Paris 6, Paris, France.,Neurology Service 2 (Mazarin), Public Assistance-Hospitals of Paris, Hospital Group of Pitié-Salpêtrière, Paris, France; Pierre and Marie Curie University, Paris 6, Paris, France
| | - Richard S Houlston
- Division of Genetics and Epidemiology, Institute of Cancer Research, Sutton, Surrey, UK
| | - Agusti Alentorn
- (i) National Institute of Health and Medical Research (Inserm) U 1127, Paris, France, (ii) National Center for Scientific Research, Joint Research Unit 7225, Paris, France, (iii) Brain and Spine Institute (ICM), Paris, France, and (iv) Sorbonne University, Pierre and Marie Curie University, Paris 6, Paris, France.,Neurology Service 2 (Mazarin), Public Assistance-Hospitals of Paris, Hospital Group of Pitié-Salpêtrière, Paris, France; Pierre and Marie Curie University, Paris 6, Paris, France
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223
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Feng Y, McQuillan MA, Tishkoff SA. Evolutionary genetics of skin pigmentation in African populations. Hum Mol Genet 2021; 30:R88-R97. [PMID: 33438000 PMCID: PMC8117430 DOI: 10.1093/hmg/ddab007] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 01/07/2021] [Accepted: 01/07/2021] [Indexed: 12/14/2022] Open
Abstract
Skin color is a highly heritable human trait, and global variation in skin pigmentation has been shaped by natural selection, migration and admixture. Ethnically diverse African populations harbor extremely high levels of genetic and phenotypic diversity, and skin pigmentation varies widely across Africa. Recent genome-wide genetic studies of skin pigmentation in African populations have advanced our understanding of pigmentation biology and human evolutionary history. For example, novel roles in skin pigmentation for loci near MFSD12 and DDB1 have recently been identified in African populations. However, due to an underrepresentation of Africans in human genetic studies, there is still much to learn about the evolutionary genetics of skin pigmentation. Here, we summarize recent progress in skin pigmentation genetics in Africans and discuss the importance of including more ethnically diverse African populations in future genetic studies. In addition, we discuss methods for functional validation of adaptive variants related to skin pigmentation.
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Affiliation(s)
- Yuanqing Feng
- Department of Genetics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Michael A McQuillan
- Department of Genetics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sarah A Tishkoff
- Department of Genetics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA
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224
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Integration of Multiple Resolution Data in 3D Chromatin Reconstruction Using ChromStruct. BIOLOGY 2021; 10:biology10040338. [PMID: 33923796 PMCID: PMC8072831 DOI: 10.3390/biology10040338] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 03/30/2021] [Accepted: 04/15/2021] [Indexed: 11/29/2022]
Abstract
The three-dimensional structure of chromatin in the cellular nucleus carries important information that is connected to physiological and pathological correlates and dysfunctional cell behaviour. As direct observation is not feasible at present, on one side, several experimental techniques have been developed to provide information on the spatial organization of the DNA in the cell; on the other side, several computational methods have been developed to elaborate experimental data and infer 3D chromatin conformations. The most relevant experimental methods are Chromosome Conformation Capture and its derivatives, chromatin immunoprecipitation and sequencing techniques (CHIP-seq), RNA-seq, fluorescence in situ hybridization (FISH) and other genetic and biochemical techniques. All of them provide important and complementary information that relate to the three-dimensional organization of chromatin. However, these techniques employ very different experimental protocols and provide information that is not easily integrated, due to different contexts and different resolutions. Here, we present an open-source tool, which is an expansion of the previously reported code ChromStruct, for inferring the 3D structure of chromatin that, by exploiting a multilevel approach, allows an easy integration of information derived from different experimental protocols and referred to different resolution levels of the structure, from a few kilobases up to Megabases. Our results show that the introduction of chromatin modelling features related to CTCF CHIA-PET data, histone modification CHIP-seq, and RNA-seq data produce appreciable improvements in ChromStruct’s 3D reconstructions, compared to the use of HI-C data alone, at a local level and at a very high resolution.
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225
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Watt S, Vasquez L, Walter K, Mann AL, Kundu K, Chen L, Sims Y, Ecker S, Burden F, Farrow S, Farr B, Iotchkova V, Elding H, Mead D, Tardaguila M, Ponstingl H, Richardson D, Datta A, Flicek P, Clarke L, Downes K, Pastinen T, Fraser P, Frontini M, Javierre BM, Spivakov M, Soranzo N. Genetic perturbation of PU.1 binding and chromatin looping at neutrophil enhancers associates with autoimmune disease. Nat Commun 2021; 12:2298. [PMID: 33863903 PMCID: PMC8052402 DOI: 10.1038/s41467-021-22548-8] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2019] [Accepted: 03/17/2021] [Indexed: 02/06/2023] Open
Abstract
Neutrophils play fundamental roles in innate immune response, shape adaptive immunity, and are a potentially causal cell type underpinning genetic associations with immune system traits and diseases. Here, we profile the binding of myeloid master regulator PU.1 in primary neutrophils across nearly a hundred volunteers. We show that variants associated with differential PU.1 binding underlie genetically-driven differences in cell count and susceptibility to autoimmune and inflammatory diseases. We integrate these results with other multi-individual genomic readouts, revealing coordinated effects of PU.1 binding variants on the local chromatin state, enhancer-promoter contacts and downstream gene expression, and providing a functional interpretation for 27 genes underlying immune traits. Collectively, these results demonstrate the functional role of PU.1 and its target enhancers in neutrophil transcriptional control and immune disease susceptibility.
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Affiliation(s)
- Stephen Watt
- Human Genetics, Wellcome Sanger Institute, Genome Campus, Hinxton, UK
| | - Louella Vasquez
- Human Genetics, Wellcome Sanger Institute, Genome Campus, Hinxton, UK
| | - Klaudia Walter
- Human Genetics, Wellcome Sanger Institute, Genome Campus, Hinxton, UK
| | - Alice L Mann
- Human Genetics, Wellcome Sanger Institute, Genome Campus, Hinxton, UK
| | - Kousik Kundu
- Human Genetics, Wellcome Sanger Institute, Genome Campus, Hinxton, UK
- School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Lu Chen
- Human Genetics, Wellcome Sanger Institute, Genome Campus, Hinxton, UK
- School of Clinical Medicine, University of Cambridge, Cambridge, UK
- Key Laboratory of Birth Defects and Related Diseases of Women and Children, Department of Laboratory Medicine, West China Second University Hospital, State Key Laboratory of Biotherapy, Sichuan University, Chengdu, China
| | - Ying Sims
- Human Genetics, Wellcome Sanger Institute, Genome Campus, Hinxton, UK
| | | | - Frances Burden
- Department of Haematology, University of Cambridge, Cambridge, UK
- National Health Service Blood and Transplant (NHSBT), Cambridge, UK
| | - Samantha Farrow
- Department of Haematology, University of Cambridge, Cambridge, UK
- National Health Service Blood and Transplant (NHSBT), Cambridge, UK
| | - Ben Farr
- Human Genetics, Wellcome Sanger Institute, Genome Campus, Hinxton, UK
| | - Valentina Iotchkova
- Human Genetics, Wellcome Sanger Institute, Genome Campus, Hinxton, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge, UK
- MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, University of Oxford, John Radcliffe Hospital, Headington, Oxford, UK
| | - Heather Elding
- Human Genetics, Wellcome Sanger Institute, Genome Campus, Hinxton, UK
| | - Daniel Mead
- Human Genetics, Wellcome Sanger Institute, Genome Campus, Hinxton, UK
| | - Manuel Tardaguila
- Human Genetics, Wellcome Sanger Institute, Genome Campus, Hinxton, UK
| | - Hannes Ponstingl
- Human Genetics, Wellcome Sanger Institute, Genome Campus, Hinxton, UK
| | - David Richardson
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge, UK
| | - Avik Datta
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge, UK
| | - Paul Flicek
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge, UK
| | - Laura Clarke
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge, UK
| | - Kate Downes
- Department of Haematology, University of Cambridge, Cambridge, UK
- National Health Service Blood and Transplant (NHSBT), Cambridge, UK
| | - Tomi Pastinen
- Center for Pediatric Genomic Medicine, Children's Mercy, Kansas City, MO, USA
| | - Peter Fraser
- Nuclear Dynamics Programme, Babraham Institute, Cambridge, UK
- Department of Biological Science, Florida State University, Tallahassee, FL, USA
| | - Mattia Frontini
- Department of Haematology, University of Cambridge, Cambridge, UK
- National Health Service Blood and Transplant (NHSBT), Cambridge, UK
- British Heart Foundation Centre of Excellence, Division of Cardiovascular Medicine, Addenbrooke's Hospital, Cambridge, UK
- Institute of Biomedical & Clinical Science, College of Medicine and Health, University of Exeter Medical School, RILD Building, Exeter, UK
| | - Biola-Maria Javierre
- Nuclear Dynamics Programme, Babraham Institute, Cambridge, UK.
- Josep Carreras Leukaemia Research Institute, Badalona, Barcelona, Spain.
| | - Mikhail Spivakov
- Nuclear Dynamics Programme, Babraham Institute, Cambridge, UK.
- Functional Gene Control Group, MRC London Institute of Medical Sciences (LMS), London, UK.
- Institute of Clinical Sciences, Imperial College Faculty of Medicine, London, UK.
| | - Nicole Soranzo
- Human Genetics, Wellcome Sanger Institute, Genome Campus, Hinxton, UK.
- School of Clinical Medicine, University of Cambridge, Cambridge, UK.
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226
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A compendium and comparative epigenomics analysis of cis-regulatory elements in the pig genome. Nat Commun 2021; 12:2217. [PMID: 33850120 PMCID: PMC8044108 DOI: 10.1038/s41467-021-22448-x] [Citation(s) in RCA: 59] [Impact Index Per Article: 19.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Accepted: 03/15/2021] [Indexed: 02/01/2023] Open
Abstract
Although major advances in genomics have initiated an exciting new era of research, a lack of information regarding cis-regulatory elements has limited the genetic improvement or manipulation of pigs as a meat source and biomedical model. Here, we systematically characterize cis-regulatory elements and their functions in 12 diverse tissues from four pig breeds by adopting similar strategies as the ENCODE and Roadmap Epigenomics projects, which include RNA-seq, ATAC-seq, and ChIP-seq. In total, we generate 199 datasets and identify more than 220,000 cis-regulatory elements in the pig genome. Surprisingly, we find higher conservation of cis-regulatory elements between human and pig genomes than those between human and mouse genomes. Furthermore, the differences of topologically associating domains between the pig and human genomes are associated with morphological evolution of the head and face. Beyond generating a major new benchmark resource for pig epigenetics, our study provides basic comparative epigenetic data relevant to using pigs as models in human biomedical research.
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227
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Gong H, Yang Y, Zhang S, Li M, Zhang X. Application of Hi-C and other omics data analysis in human cancer and cell differentiation research. Comput Struct Biotechnol J 2021; 19:2070-2083. [PMID: 33995903 PMCID: PMC8086027 DOI: 10.1016/j.csbj.2021.04.016] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 04/04/2021] [Accepted: 04/04/2021] [Indexed: 02/07/2023] Open
Abstract
With the development of 3C (chromosome conformation capture) and its derivative technology Hi-C (High-throughput chromosome conformation capture) research, the study of the spatial structure of the genomic sequence in the nucleus helps researchers understand the functions of biological processes such as gene transcription, replication, repair, and regulation. In this paper, we first introduce the research background and purpose of Hi-C data visualization analysis. After that, we discuss the Hi-C data analysis methods from genome 3D structure, A/B compartment, TADs (topologically associated domain), and loop detection. We also discuss how to apply genome visualization technologies to the identification of chromosome feature structures. We continue with a review of correlation analysis differences among multi-omics data, and how to apply Hi-C and other omics data analysis into cancer and cell differentiation research. Finally, we summarize the various problems in joint analyses based on Hi-C and other multi-omics data. We believe this review can help researchers better understand the progress and applications of 3D genome technology.
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Affiliation(s)
- Haiyan Gong
- Department of Computer Science and Technology, University of Science and Technology Beijing, Beijing 100083, China
- Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing 100083, China
- Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing 100083, China
- Shunde Graduate School of University of Science and Technology Beijing, Foshan 528000, China
| | - Yi Yang
- Department of Computer Science and Technology, University of Science and Technology Beijing, Beijing 100083, China
| | - Sichen Zhang
- Department of Computer Science and Technology, University of Science and Technology Beijing, Beijing 100083, China
| | - Minghong Li
- Department of Computer Science and Technology, University of Science and Technology Beijing, Beijing 100083, China
| | - Xiaotong Zhang
- Department of Computer Science and Technology, University of Science and Technology Beijing, Beijing 100083, China
- Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing 100083, China
- Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing 100083, China
- Shunde Graduate School of University of Science and Technology Beijing, Foshan 528000, China
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228
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Chovanec P, Collier AJ, Krueger C, Várnai C, Semprich CI, Schoenfelder S, Corcoran AE, Rugg-Gunn PJ. Widespread reorganisation of pluripotent factor binding and gene regulatory interactions between human pluripotent states. Nat Commun 2021; 12:2098. [PMID: 33828098 PMCID: PMC8026613 DOI: 10.1038/s41467-021-22201-4] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Accepted: 03/05/2021] [Indexed: 01/10/2023] Open
Abstract
The transition from naive to primed pluripotency is accompanied by an extensive reorganisation of transcriptional and epigenetic programmes. However, the role of transcriptional enhancers and three-dimensional chromatin organisation in coordinating these developmental programmes remains incompletely understood. Here, we generate a high-resolution atlas of gene regulatory interactions, chromatin profiles and transcription factor occupancy in naive and primed human pluripotent stem cells, and develop a network-graph approach to examine the atlas at multiple spatial scales. We uncover highly connected promoter hubs that change substantially in interaction frequency and in transcriptional co-regulation between pluripotent states. Small hubs frequently merge to form larger networks in primed cells, often linked by newly-formed Polycomb-associated interactions. We identify widespread state-specific differences in enhancer activity and interactivity that correspond with an extensive reconfiguration of OCT4, SOX2 and NANOG binding and target gene expression. These findings provide multilayered insights into the chromatin-based gene regulatory control of human pluripotent states.
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Affiliation(s)
- Peter Chovanec
- Lymphocyte Signalling and Development Programme, Babraham Institute, Cambridge, UK
- Nuclear Dynamics Programme, Babraham Institute, Cambridge, UK
| | | | | | - Csilla Várnai
- Nuclear Dynamics Programme, Babraham Institute, Cambridge, UK
- Centre for Computational Biology, University of Birmingham, Birmingham, UK
| | | | - Stefan Schoenfelder
- Nuclear Dynamics Programme, Babraham Institute, Cambridge, UK
- Epigenetics Programme, Babraham Institute, Cambridge, UK
| | - Anne E Corcoran
- Lymphocyte Signalling and Development Programme, Babraham Institute, Cambridge, UK
- Nuclear Dynamics Programme, Babraham Institute, Cambridge, UK
| | - Peter J Rugg-Gunn
- Epigenetics Programme, Babraham Institute, Cambridge, UK.
- Wellcome - MRC Cambridge Stem Cell Institute, Cambridge, UK.
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229
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Holgersen EM, Gillespie A, Leavy OC, Baxter JS, Zvereva A, Muirhead G, Johnson N, Sipos O, Dryden NH, Broome LR, Chen Y, Kozin I, Dudbridge F, Fletcher O, Haider S. Identifying high-confidence capture Hi-C interactions using CHiCANE. Nat Protoc 2021; 16:2257-2285. [PMID: 33837305 DOI: 10.1038/s41596-021-00498-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Accepted: 01/12/2021] [Indexed: 02/07/2023]
Abstract
The ability to identify regulatory interactions that mediate gene expression changes through distal elements, such as risk loci, is transforming our understanding of how genomes are spatially organized and regulated. Capture Hi-C (CHi-C) is a powerful tool to delineate such regulatory interactions. However, primary analysis and downstream interpretation of CHi-C profiles remains challenging and relies on disparate tools with ad-hoc input/output formats and specific assumptions for statistical modeling. Here we present a data processing and interaction calling toolkit (CHiCANE), specialized for the analysis and meaningful interpretation of CHi-C assays. In this protocol, we demonstrate applications of CHiCANE to region capture Hi-C (rCHi-C) and promoter capture Hi-C (pCHi-C) libraries, followed by quality assessment of interaction peaks, as well as downstream analysis specific to rCHi-C and pCHi-C to aid functional interpretation. For a typical rCHi-C/pCHi-C dataset this protocol takes up to 3 d for users with a moderate understanding of R programming and statistical concepts, although this is dependent on dataset size and compute power available. CHiCANE is freely available at https://cran.r-project.org/web/packages/chicane .
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Affiliation(s)
- Erle M Holgersen
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK
| | - Andrea Gillespie
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK
| | - Olivia C Leavy
- Department of Non-communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK.,Department of Health Sciences, University of Leicester, Leicester, UK
| | - Joseph S Baxter
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK
| | - Alisa Zvereva
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK
| | - Gareth Muirhead
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK
| | - Nichola Johnson
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK
| | - Orsolya Sipos
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK
| | - Nicola H Dryden
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK
| | - Laura R Broome
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK
| | - Yi Chen
- Scientific Computing, The Institute of Cancer Research, London, UK
| | - Igor Kozin
- Scientific Computing, The Institute of Cancer Research, London, UK
| | - Frank Dudbridge
- Department of Health Sciences, University of Leicester, Leicester, UK
| | - Olivia Fletcher
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK.
| | - Syed Haider
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK.
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230
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Mendieta-Esteban J, Di Stefano M, Castillo D, Farabella I, Marti-Renom MA. 3D reconstruction of genomic regions from sparse interaction data. NAR Genom Bioinform 2021; 3:lqab017. [PMID: 33778492 PMCID: PMC7985034 DOI: 10.1093/nargab/lqab017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2020] [Revised: 02/08/2021] [Accepted: 03/02/2021] [Indexed: 01/04/2023] Open
Abstract
Chromosome conformation capture (3C) technologies measure the interaction frequency between pairs of chromatin regions within the nucleus in a cell or a population of cells. Some of these 3C technologies retrieve interactions involving non-contiguous sets of loci, resulting in sparse interaction matrices. One of such 3C technologies is Promoter Capture Hi-C (pcHi-C) that is tailored to probe only interactions involving gene promoters. As such, pcHi-C provides sparse interaction matrices that are suitable to characterize short- and long-range enhancer-promoter interactions. Here, we introduce a new method to reconstruct the chromatin structural (3D) organization from sparse 3C-based datasets such as pcHi-C. Our method allows for data normalization, detection of significant interactions and reconstruction of the full 3D organization of the genomic region despite of the data sparseness. Specifically, it builds, with as low as the 2-3% of the data from the matrix, reliable 3D models of similar accuracy of those based on dense interaction matrices. Furthermore, the method is sensitive enough to detect cell-type-specific 3D organizational features such as the formation of different networks of active gene communities.
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Affiliation(s)
- Julen Mendieta-Esteban
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), 08028 Barcelona, Spain
| | - Marco Di Stefano
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), 08028 Barcelona, Spain
| | - David Castillo
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), 08028 Barcelona, Spain
| | - Irene Farabella
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), 08028 Barcelona, Spain
| | - Marc A Marti-Renom
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), 08028 Barcelona, Spain
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231
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Giudicelli F, Roest Crollius H. On the importance of evolutionary constraint for regulatory sequence identification. Brief Funct Genomics 2021:elab015. [PMID: 33754633 DOI: 10.1093/bfgp/elab015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 01/15/2021] [Accepted: 02/19/2021] [Indexed: 11/13/2022] Open
Abstract
Regulation of gene expression relies on the activity of specialized genomic elements, enhancers or silencers, distributed over sometimes large distance from their target gene promoters. A significant part of vertebrate genomes consists in such regulatory elements, but their identification and that of their target genes remains challenging, due to the lack of clear signature at the nucleotide level. For many years the main hallmark used for identifying functional elements has been their sequence conservation between genomes of distant species, indicative of purifying selection. More recently, genome-wide biochemical assays have opened new avenues for detecting regulatory regions, shifting attention away from evolutionary constraints. Here, we review the respective contributions of comparative genomics and biochemical assays for the definition of regulatory elements and their targets and advocate that both sequence conservation and preserved synteny, taken as signature of functional constraint, remain essential tools in this task.
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232
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Górski AZ, Piwowar M. Nucleotide spacing distribution analysis for human genome. Mamm Genome 2021; 32:123-128. [PMID: 33723659 PMCID: PMC8012312 DOI: 10.1007/s00335-021-09865-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Accepted: 03/02/2021] [Indexed: 11/30/2022]
Abstract
The distribution of nucleotides spacing in human genome was investigated. An analysis of the frequency of occurrence in the human genome of different sequence lengths flanked by one type of nucleotide was carried out showing that the distribution has no self-similar (fractal) structure. The results nevertheless revealed several characteristic features: (i) the distribution for short-range spacing is quite similar to the purely stochastic sequences; (ii) the distribution for long-range spacing essentially deviates from the random sequence distribution, showing strong long-range correlations; (iii) the differences between (A, T) and (C, G) nucleotides are quite significant; (iv) the spacing distribution displays tiny oscillations.
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Affiliation(s)
- Andrzej Z Górski
- Polish Academy of Sciences, Institute of Nuclear Physics, Radzikowskiego 152 st, 31-342, Kraków, Poland
| | - Monika Piwowar
- Jagiellonian University, Collegium Medicum, Kopernika 7E st, 31-034, Kraków, Poland.
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233
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Jung S, Appleton E, Ali M, Church GM, Del Sol A. A computer-guided design tool to increase the efficiency of cellular conversions. Nat Commun 2021; 12:1659. [PMID: 33712564 PMCID: PMC7954801 DOI: 10.1038/s41467-021-21801-4] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Accepted: 02/09/2021] [Indexed: 02/07/2023] Open
Abstract
Human cell conversion technology has become an important tool for devising new cell transplantation therapies, generating disease models and testing gene therapies. However, while transcription factor over-expression-based methods have shown great promise in generating cell types in vitro, they often endure low conversion efficiency. In this context, great effort has been devoted to increasing the efficiency of current protocols and the development of computational approaches can be of great help in this endeavor. Here we introduce a computer-guided design tool that combines a computational framework for prioritizing more efficient combinations of instructive factors (IFs) of cellular conversions, called IRENE, with a transposon-based genomic integration system for efficient delivery. Particularly, IRENE relies on a stochastic gene regulatory network model that systematically prioritizes more efficient IFs by maximizing the agreement of the transcriptional and epigenetic landscapes between the converted and target cells. Our predictions substantially increased the efficiency of two established iPSC-differentiation protocols (natural killer cells and melanocytes) and established the first protocol for iPSC-derived mammary epithelial cells with high efficiency.
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Affiliation(s)
- Sascha Jung
- Computational Biology Group, CIC bioGUNE-BRTA (Basque Research and Technology Alliance), Bizkaia Technology Park, Derio, Spain
| | - Evan Appleton
- Wyss Institute for Biologically Inspired Engineering at Harvard University, Boston, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Muhammad Ali
- Computational Biology Group, Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Maastricht University School for Mental Health and Neuroscience (MHeNs), Department of Psychiatry and Neuropsychology, Maastricht University, Maastricht, the Netherlands
| | - George M Church
- Wyss Institute for Biologically Inspired Engineering at Harvard University, Boston, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
- GC Therapeutics, Inc, Cambridge, MA, USA
| | - Antonio Del Sol
- Computational Biology Group, CIC bioGUNE-BRTA (Basque Research and Technology Alliance), Bizkaia Technology Park, Derio, Spain.
- Computational Biology Group, Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg.
- IKERBASQUE, Basque Foundation for Science, Bilbao, Spain.
- Moscow Institute of Physics and Technology, Dolgoprudny, Russia.
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234
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Hall ND, Patel JD, McElroy JS, Goertzen LR. Detection of subgenome bias using an anchored syntenic approach in Eleusine coracana (finger millet). BMC Genomics 2021; 22:175. [PMID: 33706694 PMCID: PMC7953713 DOI: 10.1186/s12864-021-07447-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Accepted: 02/16/2021] [Indexed: 01/15/2023] Open
Abstract
Background Finger millet (Eleusine coracana 2n = 4x = 36) is a hardy, nutraceutical, climate change tolerant, orphan crop that is consumed throughout eastern Africa and India. Its genome has been sequenced multiple times, but A and B subgenomes could not be separated because no published genome for E. indica existed. The classification of A and B subgenomes is important for understanding the evolution of this crop and provide a means to improve current and future breeding programs. Results We produced subgenome calls for 704 syntenic blocks and inferred A or B subgenomic identity for 59,377 genes 81% of the annotated genes. Phylogenetic analysis of a super matrix containing 455 genes shows high support for A and B divergence within the Eleusine genus. Synonymous substitution rates between A and B genes support A and B calls. The repetitive content on highly supported B contigs is higher than that on similar A contigs. Analysis of syntenic singletons showed evidence of biased fractionation showed a pattern of A genome dominance, with 61% A, 37% B and 1% unassigned, and was further supported by the pattern of loss observed among cyto-nuclear interacting genes. Conclusion The evidence of individual gene calls within each syntenic block, provides a powerful tool for inference for subgenome classification. Our results show the utility of a draft genome in resolving A and B subgenomes calls, primarily it allows for the proper polarization of A and B syntenic blocks. There have been multiple calls for the use of phylogenetic inference in subgenome classification, our use of synteny is a practical application in a system that has only one parental genome available. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-021-07447-y.
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Affiliation(s)
- Nathan D Hall
- Department of Crop, Soil and Environmental Science Auburn University, Auburn, AL, USA.
| | - Jinesh D Patel
- Department of Crop, Soil and Environmental Science Auburn University, Auburn, AL, USA
| | - J Scott McElroy
- Department of Crop, Soil and Environmental Science Auburn University, Auburn, AL, USA
| | - Leslie R Goertzen
- Department of Biological Sciences, Auburn University, Auburn, AL, USA
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235
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Yuan X, Scott IC, Wilson MD. Heart Enhancers: Development and Disease Control at a Distance. Front Genet 2021; 12:642975. [PMID: 33777110 PMCID: PMC7987942 DOI: 10.3389/fgene.2021.642975] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Accepted: 01/29/2021] [Indexed: 12/14/2022] Open
Abstract
Bound by lineage-determining transcription factors and signaling effectors, enhancers play essential roles in controlling spatiotemporal gene expression profiles during development, homeostasis and disease. Recent synergistic advances in functional genomic technologies, combined with the developmental biology toolbox, have resulted in unprecedented genome-wide annotation of heart enhancers and their target genes. Starting with early studies of vertebrate heart enhancers and ending with state-of-the-art genome-wide enhancer discovery and testing, we will review how studying heart enhancers in metazoan species has helped inform our understanding of cardiac development and disease.
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Affiliation(s)
- Xuefei Yuan
- Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON, Canada
- Program in Developmental and Stem Cell Biology, The Hospital for Sick Children, Toronto, ON, Canada
| | - Ian C. Scott
- Program in Developmental and Stem Cell Biology, The Hospital for Sick Children, Toronto, ON, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Michael D. Wilson
- Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
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236
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Wang YN, Zhou XJ, Chen P, Yu GZ, Zhang X, Hou P, Liu LJ, Shi SF, Lv JC, Zhang H. Interaction between GALNT12 and C1GALT1 Associates with Galactose-Deficient IgA1 and IgA Nephropathy. J Am Soc Nephrol 2021; 32:545-552. [PMID: 33593824 PMCID: PMC7920185 DOI: 10.1681/asn.2020060823] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2020] [Accepted: 12/01/2020] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND Galactose-deficient IgA1 plays a key role in the pathogenesis of IgA nephropathy, the most common primary GN worldwide. Although serum levels of galactose-deficient IgA1 have a strong genetic component, the genetic link between this molecule and IgA nephropathy has not yet been clearly established. METHODS To identify novel loci associated with galactose-deficient IgA1, we performed a quantitative genome-wide association study for serum galactose-deficient IgA1 levels, on the basis of two different genome-wide association study panels conducted in 1127 patients with IgA nephropathy. To test genetic associations with susceptibility to IgA nephropathy, we also enrolled 2352 patients with biopsy-diagnosed IgA nephropathy and 2632 healthy controls. Peripheral blood samples from 59 patients and 27 healthy controls were also collected for gene expression analysis. RESULTS We discovered two loci, in C1GALT1 and GALNT12, that achieved genome-wide significance, explaining about 3.7% and 3.4% of variance in serum galactose-deficient IgA1 levels, respectively. We confirmed the previously reported association of C1GALT1 with serum galactose-deficient IgA1 levels, but with a different lead single-nucleotide polymorphism (rs10238682; β=0.26, P=1.20×10-9); the locus we identified at GALNT12 (rs7856182; β=0.73, P=2.38×10-9) was novel. Of more interest, we found that GALNT12 exhibits genetic interactions with C1GALT1 in both galactose-deficient IgA1 levels (P=1.40×10-2) and disease risk (P=6.55×10-3). GALNT12 mRNA expression in patients with IgA nephropathy was significantly lower compared with healthy controls. CONCLUSIONS Our data identify GALNT12 as a novel gene associated with galactose-deficient IgA1 and suggest novel genetic interactions. These findings support a key role of genetically conferred dysregulation of galactose-deficient IgA1 in the development of IgA nephropathy.
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Affiliation(s)
- Yan-Na Wang
- Renal Division, Peking University First Hospital, Beijing, China,Peking University Institute of Nephrology, Beijing, China,Key Laboratory of Renal Disease, Ministry of Health of China, Beijing, China,Key Laboratory of Chronic Kidney Disease Prevention and Treatment (Peking University), Ministry of Education, Beijing, China
| | - Xu-Jie Zhou
- Renal Division, Peking University First Hospital, Beijing, China,Peking University Institute of Nephrology, Beijing, China,Key Laboratory of Renal Disease, Ministry of Health of China, Beijing, China,Key Laboratory of Chronic Kidney Disease Prevention and Treatment (Peking University), Ministry of Education, Beijing, China
| | - Pei Chen
- Renal Division, Peking University First Hospital, Beijing, China,Peking University Institute of Nephrology, Beijing, China,Key Laboratory of Renal Disease, Ministry of Health of China, Beijing, China,Key Laboratory of Chronic Kidney Disease Prevention and Treatment (Peking University), Ministry of Education, Beijing, China
| | - Gui-Zhen Yu
- Renal Division, Peking University First Hospital, Beijing, China,Peking University Institute of Nephrology, Beijing, China,Key Laboratory of Renal Disease, Ministry of Health of China, Beijing, China,Key Laboratory of Chronic Kidney Disease Prevention and Treatment (Peking University), Ministry of Education, Beijing, China
| | - Xue Zhang
- Renal Division, Peking University First Hospital, Beijing, China,Peking University Institute of Nephrology, Beijing, China,Key Laboratory of Renal Disease, Ministry of Health of China, Beijing, China,Key Laboratory of Chronic Kidney Disease Prevention and Treatment (Peking University), Ministry of Education, Beijing, China
| | - Ping Hou
- Renal Division, Peking University First Hospital, Beijing, China,Peking University Institute of Nephrology, Beijing, China,Key Laboratory of Renal Disease, Ministry of Health of China, Beijing, China,Key Laboratory of Chronic Kidney Disease Prevention and Treatment (Peking University), Ministry of Education, Beijing, China
| | - Li-Jun Liu
- Renal Division, Peking University First Hospital, Beijing, China,Peking University Institute of Nephrology, Beijing, China,Key Laboratory of Renal Disease, Ministry of Health of China, Beijing, China,Key Laboratory of Chronic Kidney Disease Prevention and Treatment (Peking University), Ministry of Education, Beijing, China
| | - Su-Fang Shi
- Renal Division, Peking University First Hospital, Beijing, China,Peking University Institute of Nephrology, Beijing, China,Key Laboratory of Renal Disease, Ministry of Health of China, Beijing, China,Key Laboratory of Chronic Kidney Disease Prevention and Treatment (Peking University), Ministry of Education, Beijing, China
| | - Ji-Cheng Lv
- Renal Division, Peking University First Hospital, Beijing, China,Peking University Institute of Nephrology, Beijing, China,Key Laboratory of Renal Disease, Ministry of Health of China, Beijing, China,Key Laboratory of Chronic Kidney Disease Prevention and Treatment (Peking University), Ministry of Education, Beijing, China
| | - Hong Zhang
- Renal Division, Peking University First Hospital, Beijing, China,Peking University Institute of Nephrology, Beijing, China,Key Laboratory of Renal Disease, Ministry of Health of China, Beijing, China,Key Laboratory of Chronic Kidney Disease Prevention and Treatment (Peking University), Ministry of Education, Beijing, China
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237
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Guo X, Lin W, Wen W, Huyghe J, Bien S, Cai Q, Harrison T, Chen Z, Qu C, Bao J, Long J, Yuan Y, Wang F, Bai M, Abecasis GR, Albanes D, Berndt SI, Bézieau S, Bishop DT, Brenner H, Buch S, Burnett-Hartman A, Campbell PT, Castellví-Bel S, Chan AT, Chang-Claude J, Chanock SJ, Cho SH, Conti DV, Chapelle ADL, Feskens EJM, Gallinger SJ, Giles GG, Goodman PJ, Gsur A, Guinter M, Gunter MJ, Hampe J, Hampel H, Hayes RB, Hoffmeister M, Kampman E, Kang HM, Keku TO, Kim HR, Le Marchand L, Lee SC, Li CI, Li L, Lindblom A, Lindor N, Milne RL, Moreno V, Murphy N, Newcomb PA, Nickerson DA, Offit K, Pearlman R, Pharoah PDP, Platz EA, Potter JD, Rennert G, Sakoda LC, Schafmayer C, Schmit SL, Schoen RE, Schumacher FR, Slattery ML, Su YR, Tangen CM, Ulrich CM, van Duijnhoven FJB, Van Guelpen B, Visvanathan K, Vodicka P, Vodickova L, Vymetalkova V, Wang X, White E, Wolk A, Woods MO, Casey G, Hsu L, Jenkins MA, Gruber SB, Peters U, Zheng W. Identifying Novel Susceptibility Genes for Colorectal Cancer Risk From a Transcriptome-Wide Association Study of 125,478 Subjects. Gastroenterology 2021; 160:1164-1178.e6. [PMID: 33058866 PMCID: PMC7956223 DOI: 10.1053/j.gastro.2020.08.062] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Revised: 08/20/2020] [Accepted: 08/28/2020] [Indexed: 01/07/2023]
Abstract
BACKGROUND AND AIMS Susceptibility genes and the underlying mechanisms for the majority of risk loci identified by genome-wide association studies (GWAS) for colorectal cancer (CRC) risk remain largely unknown. We conducted a transcriptome-wide association study (TWAS) to identify putative susceptibility genes. METHODS Gene-expression prediction models were built using transcriptome and genetic data from the 284 normal transverse colon tissues of European descendants from the Genotype-Tissue Expression (GTEx), and model performance was evaluated using data from The Cancer Genome Atlas (n = 355). We applied the gene-expression prediction models and GWAS data to evaluate associations of genetically predicted gene-expression with CRC risk in 58,131 CRC cases and 67,347 controls of European ancestry. Dual-luciferase reporter assays and knockdown experiments in CRC cells and tumor xenografts were conducted. RESULTS We identified 25 genes associated with CRC risk at a Bonferroni-corrected threshold of P < 9.1 × 10-6, including genes in 4 novel loci, PYGL (14q22.1), RPL28 (19q13.42), CAPN12 (19q13.2), MYH7B (20q11.22), and MAP1L3CA (20q11.22). In 9 known GWAS-identified loci, we uncovered 9 genes that have not been reported previously, whereas 4 genes remained statistically significant after adjusting for the lead risk variant of the locus. Through colocalization analysis in GWAS loci, we additionally identified 12 putative susceptibility genes that were supported by TWAS analysis at P < .01. We showed that risk allele of the lead risk variant rs1741640 affected the promoter activity of CABLES2. Knockdown experiments confirmed that CABLES2 plays a vital role in colorectal carcinogenesis. CONCLUSIONS Our study reveals new putative susceptibility genes and provides new insight into the biological mechanisms underlying CRC development.
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Affiliation(s)
- Xingyi Guo
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, and Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee.
| | - Weiqiang Lin
- The Kidney Disease Center, the First Affiliated Hospital, Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou, China
| | - Wanqing Wen
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, and Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Jeroen Huyghe
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Stephanie Bien
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Qiuyin Cai
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, and Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Tabitha Harrison
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Zhishan Chen
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, and Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Conghui Qu
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Jiandong Bao
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, and Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Jirong Long
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, and Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Yuan Yuan
- The Kidney Disease Center, the First Affiliated Hospital, Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou, China
| | - Fangqin Wang
- The Kidney Disease Center, the First Affiliated Hospital, Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou, China
| | - Mengqiu Bai
- The Kidney Disease Center, the First Affiliated Hospital, Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou, China
| | - Goncalo R Abecasis
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan
| | - Demetrius Albanes
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Sonja I Berndt
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Stéphane Bézieau
- Service de Génétique Médicale, Centre Hospitalier Universitaire, Nantes, France
| | - D Timothy Bishop
- Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, United Kingdom
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center, Heidelberg, Germany; Division of Preventive Oncology, German Cancer Research Center and National Center for Tumor Diseases, Heidelberg, Germany
| | - Stephan Buch
- Department of Medicine I, University Hospital Dresden, Technische Universität Dresden, Dresden, Germany
| | | | - Peter T Campbell
- Behavioral and Epidemiology Research Group, American Cancer Society, Atlanta, Georgia
| | - Sergi Castellví-Bel
- Gastroenterology Department, Hospital Clínic, Institut d'Investigacions Biomèdiques August Pi i Sunyer, Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas, University of Barcelona, Barcelona, Spain
| | - Andrew T Chan
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts; Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Jenny Chang-Claude
- Division of Cancer Epidemiology, German Cancer Research Center, Heidelberg, Germany; University Medical Centre Hamburg-Eppendorf, University Cancer Centre Hamburg, Hamburg, Germany
| | - Stephen J Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Sang Hee Cho
- Department of Hematology-Oncology, Chonnam National University Hospital, Hwasun, South Korea
| | - David V Conti
- Department of Preventive Medicine and University of Southern California Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Albert de la Chapelle
- Department of Cancer Biology and Genetics and the Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio
| | - Edith J M Feskens
- Division of Human Nutrition and Health, Wageningen University and Research, Wageningen, the Netherlands
| | - Steven J Gallinger
- Lunenfeld Tanenbaum Research Institute, Mount Sinai Hospital, University of Toronto, Toronto, Ontario, Canada
| | - Graham G Giles
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia; Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Phyllis J Goodman
- SWOG Statistical Center, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Andrea Gsur
- Institute of Cancer Research, Department of Medicine I, Medical University Vienna, Vienna, Austria
| | - Mark Guinter
- Behavioral and Epidemiology Research Group, American Cancer Society, Atlanta, Georgia
| | - Marc J Gunter
- Nutrition and Metabolism Section, International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - Jochen Hampe
- Department of Medicine I, University Hospital Dresden, Technische Universität Dresden, Dresden, Germany
| | - Heather Hampel
- Division of Human Genetics, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio
| | - Richard B Hayes
- Division of Epidemiology, Department of Population Health, New York University School of Medicine, New York, New York
| | - Michael Hoffmeister
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center, Heidelberg, Germany
| | - Ellen Kampman
- Division of Human Nutrition and Health, Wageningen University and Research, Wageningen, the Netherlands
| | - Hyun Min Kang
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan
| | - Temitope O Keku
- Center for Gastrointestinal Biology and Disease, University of North Carolina, Chapel Hill, North Carolina
| | - Hyeong Rok Kim
- Department of Surgery, Chonnam National University Hwasun Hospital and Medical School, Hwasun, Korea
| | | | - Soo Chin Lee
- National University Cancer Institute, Singapore; Cancer Science Institute of Singapore, National University of Singapore, Singapore
| | - Christopher I Li
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Li Li
- Department of Family Medicine, University of Virginia, Charlottesville, Virginia
| | - Annika Lindblom
- Department of Clinical Genetics, Karolinska University Hospital, Stockholm, Sweden; Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
| | | | - Roger L Milne
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia; Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Victor Moreno
- Oncology Data Analytics Program, Catalan Institute of Oncology-IDIBELL, L'Hospitalet de Llobregat, Barcelona, Spain; CIBER Epidemiología y Salud Pública, Madrid, Spain
| | - Neil Murphy
- Behavioral and Epidemiology Research Group, American Cancer Society, Atlanta, Georgia
| | - Polly A Newcomb
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington; School of Public Health, University of Washington, Seattle, Washington
| | - Deborah A Nickerson
- Department of Genome Sciences, University of Washington, Seattle, Washington
| | - Kenneth Offit
- Clinical Genetics Service, Department of Medicine, Memorial Sloan-Kettering Cancer Center, New York, New York; Department of Medicine, Weill Cornell Medical College, New York, New York
| | - Rachel Pearlman
- Division of Human Genetics, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio
| | - Paul D P Pharoah
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Elizabeth A Platz
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - John D Potter
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Gad Rennert
- Department of Community Medicine and Epidemiology, Lady Davis Carmel Medical Center, Haifa, Israel; Ruth and Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel
| | - Lori C Sakoda
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington; Division of Research, Kaiser Permanente Northern California, Oakland, California
| | - Clemens Schafmayer
- Department of General Surgery, University Hospital Rostock, Rostock, Germany
| | - Stephanie L Schmit
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida
| | - Robert E Schoen
- Department of Medicine and Epidemiology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
| | - Fredrick R Schumacher
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio
| | - Martha L Slattery
- Department of Internal Medicine, University of Utah, Salt Lake City, Utah
| | - Yu-Ru Su
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Catherine M Tangen
- SWOG Statistical Center, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Cornelia M Ulrich
- Huntsman Cancer Institute and Department of Population Health Sciences, University of Utah, Salt Lake City, Utah
| | | | - Bethany Van Guelpen
- Department of Radiation Sciences, Oncology Unit, Umeå University, Umeå, Sweden
| | - Kala Visvanathan
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Pavel Vodicka
- Department of Molecular Biology of Cancer, Institute of Experimental Medicine of the Czech Academy of Sciences, Prague, Czech Republic; Institute of Biology and Medical Genetics, First Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Ludmila Vodickova
- Department of Molecular Biology of Cancer, Institute of Experimental Medicine of the Czech Academy of Sciences, Prague, Czech Republic; Institute of Biology and Medical Genetics, First Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Veronika Vymetalkova
- Department of Molecular Biology of Cancer, Institute of Experimental Medicine of the Czech Academy of Sciences, Prague, Czech Republic; Institute of Biology and Medical Genetics, First Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Xiaoliang Wang
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Emily White
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington; Department of Epidemiology, University of Washington School of Public Health, Seattle, Washington
| | - Alicja Wolk
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Michael O Woods
- Memorial University of Newfoundland, Discipline of Genetics, St John's, Newfoundland and Labrador, Canada
| | - Graham Casey
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia
| | - Li Hsu
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Mark A Jenkins
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Stephen B Gruber
- Department of Preventive Medicine and University of Southern California Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Ulrike Peters
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington; Department of Epidemiology, University of Washington School of Public Health, Seattle, Washington
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, and Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee
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238
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Lv H, Dao FY, Zulfiqar H, Su W, Ding H, Liu L, Lin H. A sequence-based deep learning approach to predict CTCF-mediated chromatin loop. Brief Bioinform 2021; 22:6149346. [PMID: 33634313 DOI: 10.1093/bib/bbab031] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Revised: 12/01/2020] [Accepted: 01/21/2021] [Indexed: 12/13/2022] Open
Abstract
Three-dimensional (3D) architecture of the chromosomes is of crucial importance for transcription regulation and DNA replication. Various high-throughput chromosome conformation capture-based methods have revealed that CTCF-mediated chromatin loops are a major component of 3D architecture. However, CTCF-mediated chromatin loops are cell type specific, and most chromatin interaction capture techniques are time-consuming and labor-intensive, which restricts their usage on a very large number of cell types. Genomic sequence-based computational models are sophisticated enough to capture important features of chromatin architecture and help to identify chromatin loops. In this work, we develop Deep-loop, a convolutional neural network model, to integrate k-tuple nucleotide frequency component, nucleotide pair spectrum encoding, position conservation, position scoring function and natural vector features for the prediction of chromatin loops. By a series of examination based on cross-validation, Deep-loop shows excellent performance in the identification of the chromatin loops from different cell types. The source code of Deep-loop is freely available at the repository https://github.com/linDing-group/Deep-loop.
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Affiliation(s)
- Hao Lv
- Informational Biology at University of Electronic Science and Technology of China
| | - Fu-Ying Dao
- Informational Biology at University of Electronic Science and Technology of China
| | - Hasan Zulfiqar
- Informational Biology at University of Electronic Science and Technology of China
| | - Wei Su
- Informational Biology at University of Electronic Science and Technology of China
| | - Hui Ding
- Informational Biology at University of Electronic Science and Technology of China
| | - Li Liu
- Laboratory of Theoretical Biophysics at Inner Mongolia University
| | - Hao Lin
- Informational Biology at University of Electronic Science and Technology of China
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239
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Ohnuki H, Venzon DJ, Lobanov A, Tosato G. Iterative epigenomic analyses in the same single cell. Genome Res 2021; 31:1819-1830. [PMID: 33627472 DOI: 10.1101/gr.269068.120] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Accepted: 01/14/2021] [Indexed: 11/24/2022]
Abstract
Gene expression in individual cells is epigenetically regulated by DNA modifications, histone modifications, transcription factors, and other DNA-binding proteins. It has been shown that multiple histone modifications can predict gene expression and reflect future responses of bulk cells to extracellular cues. However, the predictive ability of epigenomic analysis is still limited for mechanistic research at a single cell level. To overcome this limitation, it would be useful to acquire reliable signals from multiple epigenetic marks in the same single cell. Here, we propose a new approach and a new method for analysis of several components of the epigenome in the same single cell. The new method allows reanalysis of the same single cell. We found that reanalysis of the same single cell is feasible, provides confirmation of the epigenetic signals, and allows application of statistical analysis to identify reproduced reads using data sets generated only from the single cell. Reanalysis of the same single cell is also useful to acquire multiple epigenetic marks from the same single cells. The method can acquire at least five epigenetic marks: H3K27ac, H3K27me3, mediator complex subunit 1, a DNA modification, and a DNA-interacting protein. We can predict active signaling pathways in K562 single cells using the epigenetic data and confirm that the predicted results strongly correlate with actual active signaling pathways identified by RNA-seq results. These results suggest that the new method provides mechanistic insights for cellular phenotypes through multilayered epigenome analysis in the same single cells.
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Affiliation(s)
- Hidetaka Ohnuki
- Laboratory of Cellular Oncology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - David J Venzon
- Biostatistics and Data Management Section, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Rockville, Maryland 20850, USA
| | - Alexei Lobanov
- CCR Collaborative Bioinformatics Resource (CCBR), Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA.,Advanced Biomedical Computational Science, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Frederick, Maryland 21702, USA
| | - Giovanna Tosato
- Laboratory of Cellular Oncology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
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240
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Roller M, Stamper E, Villar D, Izuogu O, Martin F, Redmond AM, Ramachanderan R, Harewood L, Odom DT, Flicek P. LINE retrotransposons characterize mammalian tissue-specific and evolutionarily dynamic regulatory regions. Genome Biol 2021; 22:62. [PMID: 33602314 PMCID: PMC7890895 DOI: 10.1186/s13059-021-02260-y] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Accepted: 01/04/2021] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND To investigate the mechanisms driving regulatory evolution across tissues, we experimentally mapped promoters, enhancers, and gene expression in the liver, brain, muscle, and testis from ten diverse mammals. RESULTS The regulatory landscape around genes included both tissue-shared and tissue-specific regulatory regions, where tissue-specific promoters and enhancers evolved most rapidly. Genomic regions switching between promoters and enhancers were more common across species, and less common across tissues within a single species. Long Interspersed Nuclear Elements (LINEs) played recurrent evolutionary roles: LINE L1s were associated with tissue-specific regulatory regions, whereas more ancient LINE L2s were associated with tissue-shared regulatory regions and with those switching between promoter and enhancer signatures across species. CONCLUSIONS Our analyses of the tissue-specificity and evolutionary stability among promoters and enhancers reveal how specific LINE families have helped shape the dynamic mammalian regulome.
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Affiliation(s)
- Maša Roller
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Ericca Stamper
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
- Cancer Research UK Cambridge Institute, University of Cambridge, Robinson Way, Cambridge, CB2 0RE, UK
- Present address: Harriet L. Wilkes Honors College, Florida Atlantic University, Jupiter, FL, 33458, USA
| | - Diego Villar
- Cancer Research UK Cambridge Institute, University of Cambridge, Robinson Way, Cambridge, CB2 0RE, UK
- Present address: Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, E1 2AT, UK
| | - Osagie Izuogu
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Fergal Martin
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Aisling M Redmond
- Cancer Research UK Cambridge Institute, University of Cambridge, Robinson Way, Cambridge, CB2 0RE, UK
- Present address: MRC Cancer Unit, Hutchison-MRC Research Centre, University of Cambridge, Cambridge, CB2 0XZ, UK
| | - Raghavendra Ramachanderan
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Louise Harewood
- Cancer Research UK Cambridge Institute, University of Cambridge, Robinson Way, Cambridge, CB2 0RE, UK
- Present address: Precision Medicine Centre of Excellence, Queen's University Belfast, Belfast, BT9 7AE, UK
| | - Duncan T Odom
- Cancer Research UK Cambridge Institute, University of Cambridge, Robinson Way, Cambridge, CB2 0RE, UK.
- German Cancer Research Center (DKFZ), Division of Regulatory Genomics and Cancer Evolution, Im Neuenheimer Feld 280, 69120, Heidelberg, Germany.
| | - Paul Flicek
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.
- Cancer Research UK Cambridge Institute, University of Cambridge, Robinson Way, Cambridge, CB2 0RE, UK.
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.
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241
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Nancy Z, Yan L, Hui S, Paul B, Liye C. From the Genetics of Ankylosing Spondylitis to New Biology and Drug Target Discovery. Front Immunol 2021; 12:624632. [PMID: 33679768 PMCID: PMC7925991 DOI: 10.3389/fimmu.2021.624632] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2020] [Accepted: 01/22/2021] [Indexed: 12/31/2022] Open
Abstract
Genome-wide association studies (GWAS) have identified 113 single nucleotide polymorphisms (SNPs) affecting the risk of developing ankylosing spondylitis (AS), and an on-going GWAS study will likely identify 100+ new risk loci. The translation of genetic findings to novel disease biology and treatments has been difficult due to the following challenges: (1) difficulties in determining the causal genes regulated by disease-associated SNPs, (2) difficulties in determining the relevant cell-type(s) that causal genes exhibit their function(s), (3) difficulties in determining appropriate cellular contexts to interrogate the functional role of causal genes in disease biology. This review will discuss recent progress and unanswered questions with a focus on these challenges. Additionally, we will review the investigation of biology and the development of drugs related to the IL-23/IL-17 pathway, which has been partially driven by the AS genetics, and discuss what can be learned from these studies for the future functional and translational study of AS-associated genes.
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Affiliation(s)
- Zaarour Nancy
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, United Kingdom
| | - Li Yan
- Department of Rheumatology, The First Affiliated Hospital of Xiamen University, Medical College of Xiamen University, Xiamen, China
| | - Shi Hui
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, United Kingdom
| | - Bowness Paul
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, United Kingdom
| | - Chen Liye
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, United Kingdom
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242
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Bertero A, Rosa-Garrido M. Three-dimensional chromatin organization in cardiac development and disease. J Mol Cell Cardiol 2021; 151:89-105. [PMID: 33242466 PMCID: PMC11056610 DOI: 10.1016/j.yjmcc.2020.11.008] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/08/2020] [Revised: 11/10/2020] [Accepted: 11/18/2020] [Indexed: 02/07/2023]
Abstract
Recent technological advancements in the field of chromatin biology have rewritten the textbook on nuclear organization. We now appreciate that the folding of chromatin in the three-dimensional space (i.e. its 3D "architecture") is non-random, hierarchical, and highly complex. While 3D chromatin structure is partially encoded in the primary sequence and thereby broadly conserved across cell types and states, a substantial portion of the genome seems to be dynamic during development or in disease. Moreover, there is growing evidence that at least some of the 3D structure of chromatin is functionally linked to gene regulation, both being modulated by and impacting on multiple nuclear processes (including DNA replication, transcription, and RNA splicing). In recent years, these new concepts have nourished several investigations about the functional role of 3D chromatin topology dynamics in the heart during development and disease. This review aims to provide a comprehensive overview of our current understanding in this field, and to discuss how this knowledge can inform further research as well as clinical practice.
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Affiliation(s)
- Alessandro Bertero
- Department of Laboratory Medicine and Pathology, University of Washington, 1959 NE Pacific Street, Seattle, WA 98195, USA; Institute for Stem Cell and Regenerative Medicine, University of Washington, 850 Republican Street, Seattle, WA 98109, USA; Center for Cardiovascular Biology, University of Washington, 850 Republican Street, Brotman Building, Seattle, WA 98109, USA.
| | - Manuel Rosa-Garrido
- Department of Anesthesiology and Perioperative Medicine, David Geffen School of Medicine, University of California, 650 Charles Young Dr, Los Angeles, CA 90095, USA.
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243
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H3K27me3-rich genomic regions can function as silencers to repress gene expression via chromatin interactions. Nat Commun 2021; 12:719. [PMID: 33514712 PMCID: PMC7846766 DOI: 10.1038/s41467-021-20940-y] [Citation(s) in RCA: 123] [Impact Index Per Article: 41.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Accepted: 01/04/2021] [Indexed: 12/29/2022] Open
Abstract
The mechanisms underlying gene repression and silencers are poorly understood. Here we investigate the hypothesis that H3K27me3-rich regions of the genome, defined from clusters of H3K27me3 peaks, may be used to identify silencers that can regulate gene expression via proximity or looping. We find that H3K27me3-rich regions are associated with chromatin interactions and interact preferentially with each other. H3K27me3-rich regions component removal at interaction anchors by CRISPR leads to upregulation of interacting target genes, altered H3K27me3 and H3K27ac levels at interacting regions, and altered chromatin interactions. Chromatin interactions did not change at regions with high H3K27me3, but regions with low H3K27me3 and high H3K27ac levels showed changes in chromatin interactions. Cells with H3K27me3-rich regions knockout also show changes in phenotype associated with cell identity, and altered xenograft tumor growth. Finally, we observe that H3K27me3-rich regions-associated genes and long-range chromatin interactions are susceptible to H3K27me3 depletion. Our results characterize H3K27me3-rich regions and their mechanisms of functioning via looping.
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244
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Pan Q, Liu YJ, Bai XF, Han XL, Jiang Y, Ai B, Shi SS, Wang F, Xu MC, Wang YZ, Zhao J, Chen JX, Zhang J, Li XC, Zhu J, Zhang GR, Wang QY, Li CQ. VARAdb: a comprehensive variation annotation database for human. Nucleic Acids Res 2021; 49:D1431-D1444. [PMID: 33095866 PMCID: PMC7779011 DOI: 10.1093/nar/gkaa922] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2020] [Revised: 09/28/2020] [Accepted: 10/22/2020] [Indexed: 01/08/2023] Open
Abstract
With the study of human diseases and biological processes increasing, a large number of non-coding variants have been identified and facilitated. The rapid accumulation of genetic and epigenomic information has resulted in an urgent need to collect and process data to explore the regulation of non-coding variants. Here, we developed a comprehensive variation annotation database for human (VARAdb, http://www.licpathway.net/VARAdb/), which specifically considers non-coding variants. VARAdb provides annotation information for 577,283,813 variations and novel variants, prioritizes variations based on scores using nine annotation categories, and supports pathway downstream analysis. Importantly, VARAdb integrates a large amount of genetic and epigenomic data into five annotation sections, which include ‘Variation information’, ‘Regulatory information’, ‘Related genes’, ‘Chromatin accessibility’ and ‘Chromatin interaction’. The detailed annotation information consists of motif changes, risk SNPs, LD SNPs, eQTLs, clinical variant-drug-gene pairs, sequence conservation, somatic mutations, enhancers, super enhancers, promoters, transcription factors, chromatin states, histone modifications, chromatin accessibility regions and chromatin interactions. This database is a user-friendly interface to query, browse and visualize variations and related annotation information. VARAdb is a useful resource for selecting potential functional variations and interpreting their effects on human diseases and biological processes.
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Affiliation(s)
- Qi Pan
- School of Medical Informatics, Daqing Campus, Harbin Medical University. Daqing 163319, China
| | - Yue-Juan Liu
- School of Medical Informatics, Daqing Campus, Harbin Medical University. Daqing 163319, China
| | - Xue-Feng Bai
- School of Medical Informatics, Daqing Campus, Harbin Medical University. Daqing 163319, China
| | - Xiao-Le Han
- School of Medical Informatics, Daqing Campus, Harbin Medical University. Daqing 163319, China
| | - Yong Jiang
- School of Medical Informatics, Daqing Campus, Harbin Medical University. Daqing 163319, China
| | - Bo Ai
- School of Medical Informatics, Daqing Campus, Harbin Medical University. Daqing 163319, China
| | - Shan-Shan Shi
- School of Medical Informatics, Daqing Campus, Harbin Medical University. Daqing 163319, China
| | - Fan Wang
- School of Medical Informatics, Daqing Campus, Harbin Medical University. Daqing 163319, China
| | - Ming-Cong Xu
- School of Medical Informatics, Daqing Campus, Harbin Medical University. Daqing 163319, China
| | - Yue-Zhu Wang
- School of Medical Informatics, Daqing Campus, Harbin Medical University. Daqing 163319, China
| | - Jun Zhao
- School of Medical Informatics, Daqing Campus, Harbin Medical University. Daqing 163319, China
| | - Jia-Xin Chen
- School of Medical Informatics, Daqing Campus, Harbin Medical University. Daqing 163319, China
| | - Jian Zhang
- School of Medical Informatics, Daqing Campus, Harbin Medical University. Daqing 163319, China
| | - Xue-Cang Li
- School of Medical Informatics, Daqing Campus, Harbin Medical University. Daqing 163319, China
| | - Jiang Zhu
- School of Medical Informatics, Daqing Campus, Harbin Medical University. Daqing 163319, China
| | - Guo-Rui Zhang
- School of Medical Informatics, Daqing Campus, Harbin Medical University. Daqing 163319, China
| | - Qiu-Yu Wang
- School of Medical Informatics, Daqing Campus, Harbin Medical University. Daqing 163319, China
| | - Chun-Quan Li
- School of Medical Informatics, Daqing Campus, Harbin Medical University. Daqing 163319, China
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245
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Downes DJ, Beagrie RA, Gosden ME, Telenius J, Carpenter SJ, Nussbaum L, De Ornellas S, Sergeant M, Eijsbouts CQ, Schwessinger R, Kerry J, Roberts N, Shivalingam A, El-Sagheer A, Oudelaar AM, Brown T, Buckle VJ, Davies JOJ, Hughes JR. High-resolution targeted 3C interrogation of cis-regulatory element organization at genome-wide scale. Nat Commun 2021; 12:531. [PMID: 33483495 PMCID: PMC7822813 DOI: 10.1038/s41467-020-20809-6] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Accepted: 12/16/2020] [Indexed: 01/30/2023] Open
Abstract
Chromosome conformation capture (3C) provides an adaptable tool for studying diverse biological questions. Current 3C methods generally provide either low-resolution interaction profiles across the entire genome, or high-resolution interaction profiles at limited numbers of loci. Due to technical limitations, generation of reproducible high-resolution interaction profiles has not been achieved at genome-wide scale. Here, to overcome this barrier, we systematically test each step of 3C and report two improvements over current methods. We show that up to 30% of reporter events generated using the popular in situ 3C method arise from ligations between two individual nuclei, but this noise can be almost entirely eliminated by isolating intact nuclei after ligation. Using Nuclear-Titrated Capture-C, we generate reproducible high-resolution genome-wide 3C interaction profiles by targeting 8055 gene promoters in erythroid cells. By pairing high-resolution 3C interaction calls with nascent gene expression we interrogate the role of promoter hubs and super-enhancers in gene regulation.
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Affiliation(s)
- Damien J Downes
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - Robert A Beagrie
- MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - Matthew E Gosden
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - Jelena Telenius
- MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - Stephanie J Carpenter
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - Lea Nussbaum
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - Sara De Ornellas
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
- Chemistry Research Laboratory, Department of Chemistry, University of Oxford, Oxford, UK
| | - Martin Sergeant
- MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - Chris Q Eijsbouts
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Ron Schwessinger
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
- MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - Jon Kerry
- MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - Nigel Roberts
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - Arun Shivalingam
- Chemistry Research Laboratory, Department of Chemistry, University of Oxford, Oxford, UK
| | - Afaf El-Sagheer
- Chemistry Research Laboratory, Department of Chemistry, University of Oxford, Oxford, UK
| | - A Marieke Oudelaar
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
- MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - Tom Brown
- Chemistry Research Laboratory, Department of Chemistry, University of Oxford, Oxford, UK
| | - Veronica J Buckle
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - James O J Davies
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - Jim R Hughes
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK.
- MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK.
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246
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Amjadi-Moheb F, Paniri A, Akhavan-Niaki H. Insights into the Links between MYC and 3D Chromatin Structure and Epigenetics Regulation: Implications for Cancer Therapy. Cancer Res 2021; 81:1925-1936. [PMID: 33472888 DOI: 10.1158/0008-5472.can-20-3613] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Revised: 12/21/2020] [Accepted: 01/06/2021] [Indexed: 11/16/2022]
Abstract
MYC is embedded in the transcriptional oasis of the 8q24 gene desert. A plethora of genomic elements has roles in MYC aberrant expression in cancer development by interacting with transcription factors and epigenetics regulators as well as altering the structure of chromatin at the MYC locus and tissue-specific long-range enhancer-promoter contacts. Furthermore, MYC is a master regulator of several human cancers by modulating the transcription of numerous cancer-related genes through epigenetic mechanisms. This review provides a comprehensive overview of the three-dimensional genomic organization around MYC and the role of epigenetic machinery in transcription and function of MYC as well as discusses various epigenetic-targeted therapeutic strategies in MYC-driven cancers.
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Affiliation(s)
- Fatemeh Amjadi-Moheb
- Department of Genetics, Faculty of Medicine, Babol University of Medical Sciences, Babol, Iran
| | - Alireza Paniri
- Department of Genetics, Faculty of Medicine, Babol University of Medical Sciences, Babol, Iran
| | - Haleh Akhavan-Niaki
- Department of Genetics, Faculty of Medicine, Babol University of Medical Sciences, Babol, Iran.
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247
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Kim K, Jang I, Kim M, Choi J, Kim MS, Lee B, Jung I. 3DIV update for 2021: a comprehensive resource of 3D genome and 3D cancer genome. Nucleic Acids Res 2021; 49:D38-D46. [PMID: 33245777 PMCID: PMC7778885 DOI: 10.1093/nar/gkaa1078] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2020] [Revised: 10/20/2020] [Accepted: 10/26/2020] [Indexed: 12/21/2022] Open
Abstract
Three-dimensional (3D) genome organization is tightly coupled with gene regulation in various biological processes and diseases. In cancer, various types of large-scale genomic rearrangements can disrupt the 3D genome, leading to oncogenic gene expression. However, unraveling the pathogenicity of the 3D cancer genome remains a challenge since closer examinations have been greatly limited due to the lack of appropriate tools specialized for disorganized higher-order chromatin structure. Here, we updated a 3D-genome Interaction Viewer and database named 3DIV by uniformly processing ∼230 billion raw Hi-C reads to expand our contents to the 3D cancer genome. The updates of 3DIV are listed as follows: (i) the collection of 401 samples including 220 cancer cell line/tumor Hi-C data, 153 normal cell line/tissue Hi-C data, and 28 promoter capture Hi-C data, (ii) the live interactive manipulation of the 3D cancer genome to simulate the impact of structural variations and (iii) the reconstruction of Hi-C contact maps by user-defined chromosome order to investigate the 3D genome of the complex genomic rearrangement. In summary, the updated 3DIV will be the most comprehensive resource to explore the gene regulatory effects of both the normal and cancer 3D genome. ‘3DIV’ is freely available at http://3div.kr.
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Affiliation(s)
- Kyukwang Kim
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Insu Jang
- Korean Bioinformation Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon 34141, Korea
| | - Mooyoung Kim
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Jinhyuk Choi
- Korean Bioinformation Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon 34141, Korea
| | - Min-Seo Kim
- Korean Bioinformation Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon 34141, Korea
| | - Byungwook Lee
- Korean Bioinformation Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon 34141, Korea
| | - Inkyung Jung
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
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248
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Crump NT, Ballabio E, Godfrey L, Thorne R, Repapi E, Kerry J, Tapia M, Hua P, Lagerholm C, Filippakopoulos P, Davies JOJ, Milne TA. BET inhibition disrupts transcription but retains enhancer-promoter contact. Nat Commun 2021; 12:223. [PMID: 33431820 PMCID: PMC7801379 DOI: 10.1038/s41467-020-20400-z] [Citation(s) in RCA: 67] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2019] [Accepted: 12/01/2020] [Indexed: 12/13/2022] Open
Abstract
Enhancers are DNA sequences that enable complex temporal and tissue-specific regulation of genes in higher eukaryotes. Although it is not entirely clear how enhancer-promoter interactions can increase gene expression, this proximity has been observed in multiple systems at multiple loci and is thought to be essential for the maintenance of gene expression. Bromodomain and Extra-Terminal domain (BET) and Mediator proteins have been shown capable of forming phase condensates and are thought to be essential for super-enhancer function. Here, we show that targeting of cells with inhibitors of BET proteins or pharmacological degradation of BET protein Bromodomain-containing protein 4 (BRD4) has a strong impact on transcription but very little impact on enhancer-promoter interactions. Dissolving phase condensates reduces BRD4 and Mediator binding at enhancers and can also strongly affect gene transcription, without disrupting enhancer-promoter interactions. These results suggest that activation of transcription and maintenance of enhancer-promoter interactions are separable events. Our findings further indicate that enhancer-promoter interactions are not dependent on high levels of BRD4 and Mediator, and are likely maintained by a complex set of factors including additional activator complexes and, at some sites, CTCF and cohesin.
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Affiliation(s)
- Nicholas T Crump
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, NIHR Oxford Biomedical Research Centre Haematology Theme, Radcliffe Department of Medicine, University of Oxford, Oxford, OX3 9DS, UK
| | - Erica Ballabio
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, NIHR Oxford Biomedical Research Centre Haematology Theme, Radcliffe Department of Medicine, University of Oxford, Oxford, OX3 9DS, UK
| | - Laura Godfrey
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, NIHR Oxford Biomedical Research Centre Haematology Theme, Radcliffe Department of Medicine, University of Oxford, Oxford, OX3 9DS, UK
| | - Ross Thorne
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, NIHR Oxford Biomedical Research Centre Haematology Theme, Radcliffe Department of Medicine, University of Oxford, Oxford, OX3 9DS, UK
| | - Emmanouela Repapi
- MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, OX3 9DS, UK
| | - Jon Kerry
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, NIHR Oxford Biomedical Research Centre Haematology Theme, Radcliffe Department of Medicine, University of Oxford, Oxford, OX3 9DS, UK
| | - Marta Tapia
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, NIHR Oxford Biomedical Research Centre Haematology Theme, Radcliffe Department of Medicine, University of Oxford, Oxford, OX3 9DS, UK
- The Finsen Laboratory, Rigshospitalet, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark
- Biotech Research and Innovation Centre (BRIC), Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Peng Hua
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, OX3 9DS, UK
| | - Christoffer Lagerholm
- Wolfson Imaging Centre Oxford, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, OX3 9DS, UK
| | - Panagis Filippakopoulos
- Structural Genomics Consortium, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, OX3 7DQ, UK
| | - James O J Davies
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, OX3 9DS, UK
| | - Thomas A Milne
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, NIHR Oxford Biomedical Research Centre Haematology Theme, Radcliffe Department of Medicine, University of Oxford, Oxford, OX3 9DS, UK.
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249
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Zhang J, Yao W, Pan Z, Liu H. Long-distance chromatin interaction of IGF1 during embryonic and postnatal development in the liver of Sus scrofa. Funct Integr Genomics 2021; 21:59-72. [PMID: 33404915 DOI: 10.1007/s10142-020-00761-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Revised: 10/25/2020] [Accepted: 11/12/2020] [Indexed: 11/25/2022]
Abstract
The dynamics of chromatin have been the focus of studies aimed at characterizing gene regulation. Among various chromosome conformation capture methods, 4C-seq is a powerful technique to identify genome-wide interactions with a single locus of interest. Insulin-like growth factor 1 (IGF1) is a member of the somatotropin axis that plays a significant role in cell proliferation and growth. Determining the IGF1-involved genome-wide chromatin interaction profile at different growth stages not only is important for understanding IGF1 transcriptional regulation but also provides a representation of genome-wide chromatin transformation during development. Using the IGF1 promoter as a "bait", we identified genome-wide interactomes of embryonic (E70) and postnatal (P1 and P70) pig liver cells by 4C-seq. The IGF1 promoter interactomes varied significantly among the three developmental stages. The most active chromatin interaction was observed in the P1 stage, while the highest interaction variability was observed in the P70 stage. The identified 4C sites were enriched around transcription start sites, CpG sites and functional pig QTLs. In addition, the genes located in the interacting regions and the involved pathways were also analysed. Overall, our work reveals a distinct long-distance regulatory pattern in pig liver during development for the first time, and the identified interacting sites and genes may serve as candidate targets in further transcriptional mechanism studies and effective molecular markers for functional traits.
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Affiliation(s)
- Jinbi Zhang
- College of Animal Science and Technology, Jinling Institute of Technology, Nanjing, 211169, People's Republic of China
- College of Animal Science and Technology, Nanjing Agriculture University, Nanjing, 210095, People's Republic of China
| | - Wang Yao
- College of Animal Science and Technology, Nanjing Agriculture University, Nanjing, 210095, People's Republic of China
| | - Zengxiang Pan
- College of Animal Science and Technology, Nanjing Agriculture University, Nanjing, 210095, People's Republic of China.
| | - Honglin Liu
- College of Animal Science and Technology, Nanjing Agriculture University, Nanjing, 210095, People's Republic of China
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250
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Ferchen K, Song B, Grimes HL. A primer on single-cell genomics in myeloid biology. Curr Opin Hematol 2021; 28:11-17. [PMID: 33186153 PMCID: PMC9205579 DOI: 10.1097/moh.0000000000000623] [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: 11/26/2022]
Abstract
PURPOSE OF REVIEW Understanding the fast-moving field of single-cell technologies, as applied to myeloid biology, requires an appreciation of basic molecular, informatics, and biological concepts. Here, we highlight both key and recent articles to illustrate basic concepts for those new to molecular single-cell analyses in myeloid hematology. RECENT FINDINGS Recent studies apply single-cell omics to discover novel cell populations, construct relationships between cell populations, reconfigure the organization of hematopoiesis, and study hematopoietic lineage tree and fate choices. Accompanying development of technologies, new informatic tools have emerged, providing exciting new insights. SUMMARY Hematopoietic stem and progenitor cells are regulated by complex intrinsic and extrinsic factors to produce blood cell types. In this review, we discuss recent advances in single-cell omics to profile these cells, methods to infer cell type identify, and trajectories from molecular omics data to ultimately derive new insights into hematopoietic stem and progenitor cell biology. We further discuss future applications of these technologies to understand hematopoietic cell interactions, function, and development. The goal is to offer a comprehensive overview of current single-cell technologies and their impact on our understanding of myeloid cell development for those new to single-cell analyses.
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Affiliation(s)
- Kyle Ferchen
- Division of Immunobiology, Cincinnati Children’s Hospital, Cincinnati, OH, USA
- Department of Cancer Biology, University of Cincinnati, Cincinnati, OH, USA
| | - Baobao Song
- Division of Immunobiology, Cincinnati Children’s Hospital, Cincinnati, OH, USA
- Immunology Graduate Program, University of Cincinnati College of Medicine, Cincinnati, OH 45229, USA
| | - H. Leighton Grimes
- Division of Immunobiology, Cincinnati Children’s Hospital, Cincinnati, OH, USA
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