1
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James C, Trevisan-Herraz M, Juan D, Rico D. Evolutionary analysis of gene ages across TADs associates chromatin topology with whole-genome duplications. Cell Rep 2024; 43:113895. [PMID: 38517894 DOI: 10.1016/j.celrep.2024.113895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2022] [Revised: 11/03/2023] [Accepted: 02/16/2024] [Indexed: 03/24/2024] Open
Abstract
Topologically associated domains (TADs) are interaction subnetworks of chromosomal regions in 3D genomes. TAD boundaries frequently coincide with genome breaks while boundary deletion is under negative selection, suggesting that TADs may facilitate genome rearrangements and evolution. We show that genes co-localize by evolutionary age in humans and mice, resulting in TADs having different proportions of younger and older genes. We observe a major transition in the age co-localization patterns between the genes born during vertebrate whole-genome duplications (WGDs) or before and those born afterward. We also find that genes recently duplicated in primates and rodents are more frequently essential when they are located in old-enriched TADs and interact with genes that last duplicated during the WGD. Therefore, the evolutionary relevance of recent genes may increase when located in TADs with established regulatory networks. Our data suggest that TADs could play a role in organizing ancestral functions and evolutionary novelty.
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Affiliation(s)
- Caelinn James
- Biosciences Institute, Newcastle University, Newcastle Upon Tyne, UK; Scotland's Rural College (SRUC), The Roslin Institute Building, Easter Bush, Midlothian, UK
| | - Marco Trevisan-Herraz
- Biosciences Institute, Newcastle University, Newcastle Upon Tyne, UK; Translational and Clinical Research Institute, Newcastle University, Newcastle Upon Tyne, UK
| | - David Juan
- Institut de Biologia Evolutiva, Consejo Superior de Investigaciones Científicas-Universitat Pompeu Fabra, Parc de Recerca Biomèdica de Barcelona, Barcelona, Spain; Systems Biology Department, Spanish National Centre for Biotechnology (CNB-CSIC), Madrid, Spain
| | - Daniel Rico
- Biosciences Institute, Newcastle University, Newcastle Upon Tyne, UK; Centro Andaluz de Biología Molecular y Medicina Regenerativa (CABIMER), CSIC-Universidad de Sevilla-Universidad Pablo de Olavide-Junta de Andalucía, Seville, Spain.
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2
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Liu R, Xu R, Yan S, Li P, Jia C, Sun H, Sheng K, Wang Y, Zhang Q, Guo J, Xin X, Li X, Guo D. Hi-C, a chromatin 3D structure technique advancing the functional genomics of immune cells. Front Genet 2024; 15:1377238. [PMID: 38586584 PMCID: PMC10995239 DOI: 10.3389/fgene.2024.1377238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Accepted: 03/13/2024] [Indexed: 04/09/2024] Open
Abstract
The functional performance of immune cells relies on a complex transcriptional regulatory network. The three-dimensional structure of chromatin can affect chromatin status and gene expression patterns, and plays an important regulatory role in gene transcription. Currently available techniques for studying chromatin spatial structure include chromatin conformation capture techniques and their derivatives, chromatin accessibility sequencing techniques, and others. Additionally, the recently emerged deep learning technology can be utilized as a tool to enhance the analysis of data. In this review, we elucidate the definition and significance of the three-dimensional chromatin structure, summarize the technologies available for studying it, and describe the research progress on the chromatin spatial structure of dendritic cells, macrophages, T cells, B cells, and neutrophils.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | - Dianhao Guo
- School of Clinical and Basic Medical Sciences, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
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3
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Zhigulev A, Norberg Z, Cordier J, Spalinskas R, Bassereh H, Björn N, Pradhananga S, Gréen H, Sahlén P. Enhancer mutations modulate the severity of chemotherapy-induced myelosuppression. Life Sci Alliance 2024; 7:e202302244. [PMID: 38228368 PMCID: PMC10796589 DOI: 10.26508/lsa.202302244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 12/19/2023] [Accepted: 12/21/2023] [Indexed: 01/18/2024] Open
Abstract
Non-small cell lung cancer is often diagnosed at advanced stages, and many patients are still treated with classical chemotherapy. The unselective nature of chemotherapy often results in severe myelosuppression. Previous studies showed that protein-coding mutations could not fully explain the predisposition to myelosuppression. Here, we investigate the possible role of enhancer mutations in myelosuppression susceptibility. We produced transcriptome and promoter-interaction maps (using HiCap) of three blood stem-like cell lines treated with carboplatin or gemcitabine. Taking advantage of publicly available enhancer datasets, we validated HiCap results in silico and in living cells using epigenetic CRISPR technology. We also developed a network approach for interactome analysis and detection of differentially interacting genes. Differential interaction analysis provided additional information on relevant genes and pathways for myelosuppression compared with differential gene expression analysis at the bulk level. Moreover, we showed that enhancers of differentially interacting genes are highly enriched for variants associated with differing levels of myelosuppression. Altogether, our work represents a prominent example of integrative transcriptome and gene regulatory datasets analysis for the functional annotation of noncoding mutations.
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Affiliation(s)
- Artemy Zhigulev
- https://ror.org/026vcq606 Royal Institute of Technology - KTH, School of Chemistry, Biotechnology and Health, Science for Life Laboratory, Stockholm, Sweden
| | - Zandra Norberg
- https://ror.org/026vcq606 Royal Institute of Technology - KTH, School of Chemistry, Biotechnology and Health, Science for Life Laboratory, Stockholm, Sweden
| | - Julie Cordier
- https://ror.org/026vcq606 Royal Institute of Technology - KTH, School of Chemistry, Biotechnology and Health, Science for Life Laboratory, Stockholm, Sweden
| | - Rapolas Spalinskas
- https://ror.org/026vcq606 Royal Institute of Technology - KTH, School of Chemistry, Biotechnology and Health, Science for Life Laboratory, Stockholm, Sweden
| | - Hassan Bassereh
- https://ror.org/026vcq606 Royal Institute of Technology - KTH, School of Chemistry, Biotechnology and Health, Science for Life Laboratory, Stockholm, Sweden
| | - Niclas Björn
- Division of Clinical Chemistry and Pharmacology, Department of Biomedical and Clinical Sciences, Faculty of Medicine and Health Sciences, Linköping University, Linköping, Sweden
| | - Sailendra Pradhananga
- https://ror.org/026vcq606 Royal Institute of Technology - KTH, School of Chemistry, Biotechnology and Health, Science for Life Laboratory, Stockholm, Sweden
| | - Henrik Gréen
- Division of Clinical Chemistry and Pharmacology, Department of Biomedical and Clinical Sciences, Faculty of Medicine and Health Sciences, Linköping University, Linköping, Sweden
- Department of Forensic Genetics and Forensic Toxicology, National Board of Forensic Medicine, Linköping, Sweden
| | - Pelin Sahlén
- https://ror.org/026vcq606 Royal Institute of Technology - KTH, School of Chemistry, Biotechnology and Health, Science for Life Laboratory, Stockholm, Sweden
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4
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Baden T, Briseño J, Coffing G, Cohen-Bodénès S, Courtney A, Dickerson D, Dölen G, Fiorito G, Gestal C, Gustafson T, Heath-Heckman E, Hua Q, Imperadore P, Kimbara R, Król M, Lajbner Z, Lichilín N, Macchi F, McCoy MJ, Nishiguchi MK, Nyholm SV, Otjacques E, Pérez-Ferrer PA, Ponte G, Pungor JR, Rogers TF, Rosenthal JJC, Rouressol L, Rubas N, Sanchez G, Santos CP, Schultz DT, Seuntjens E, Songco-Casey JO, Stewart IE, Styfhals R, Tuanapaya S, Vijayan N, Weissenbacher A, Zifcakova L, Schulz G, Weertman W, Simakov O, Albertin CB. Cephalopod-omics: Emerging Fields and Technologies in Cephalopod Biology. Integr Comp Biol 2023; 63:1226-1239. [PMID: 37370232 PMCID: PMC10755191 DOI: 10.1093/icb/icad087] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 06/09/2023] [Accepted: 06/13/2023] [Indexed: 06/29/2023] Open
Abstract
Few animal groups can claim the level of wonder that cephalopods instill in the minds of researchers and the general public. Much of cephalopod biology, however, remains unexplored: the largest invertebrate brain, difficult husbandry conditions, and complex (meta-)genomes, among many other things, have hindered progress in addressing key questions. However, recent technological advancements in sequencing, imaging, and genetic manipulation have opened new avenues for exploring the biology of these extraordinary animals. The cephalopod molecular biology community is thus experiencing a large influx of researchers, emerging from different fields, accelerating the pace of research in this clade. In the first post-pandemic event at the Cephalopod International Advisory Council (CIAC) conference in April 2022, over 40 participants from all over the world met and discussed key challenges and perspectives for current cephalopod molecular biology and evolution. Our particular focus was on the fields of comparative and regulatory genomics, gene manipulation, single-cell transcriptomics, metagenomics, and microbial interactions. This article is a result of this joint effort, summarizing the latest insights from these emerging fields, their bottlenecks, and potential solutions. The article highlights the interdisciplinary nature of the cephalopod-omics community and provides an emphasis on continuous consolidation of efforts and collaboration in this rapidly evolving field.
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Affiliation(s)
- Tom Baden
- School of Life Sciences, University of Sussex, Brighton BN1 9QG, UK
| | - John Briseño
- Molecular and Cell Biology Department, University of Connecticut, Storrs, CT 06269, USA
| | - Gabrielle Coffing
- Biology Department: Institute of Ecology and Evolution, University of Oregon, Eugene, OR 97403-5289, USA
| | - Sophie Cohen-Bodénès
- Laboratoire des Systèmes Perceptifs, Département d'Etudes Cognitives, Ecole Normale Supérieure, PSL University, CNRS, 75005 Paris, France
| | - Amy Courtney
- MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge CB2 0QH, UK
| | - Dominick Dickerson
- Friday Harbor Laboratory, University of Washington, Seattle, WA 98250, USA
| | - Gül Dölen
- Department of Neuroscience, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Graziano Fiorito
- Department of Biology and Evolution of Marine Organisms, Stazione Zoologica Anton Dohrn, 80121 Napoli, Italy
| | - Camino Gestal
- Laboratory of Marine Molecular Pathobiology, Institute of Marine Research (IIM), Spanish National Research Council (CSIC), Vigo 36208, Spain
| | | | - Elizabeth Heath-Heckman
- Departments of Integrative Biology and Microbiology and Molecular Genetics, Michigan State University, East Lansing, MI 48824, USA
| | - Qiaz Hua
- Department of Ecology and Evolution, University of Adelaide, Adelaide, South Australia 5000, Australia
| | - Pamela Imperadore
- Department of Biology and Evolution of Marine Organisms, Stazione Zoologica Anton Dohrn, 80121 Napoli, Italy
| | - Ryosuke Kimbara
- Misaki Marine Biological Station, School of Science, The University of Tokyo, Miura, Kanagawa 238-0225, Japan
| | - Mirela Król
- Adam Mickiewicz University in Poznań, Poznań 61-712, Poland
| | - Zdeněk Lajbner
- Physics and Biology Unit, Okinawa Institute of Science and Technology Graduate University, 1919-1 Tancha, Onna, Kunigami District, Okinawa 904-0495, Japan
| | - Nicolás Lichilín
- Department of Neurosciences and Developmental Biology, University of Vienna, Vienna 1010, Austria
| | - Filippo Macchi
- Program in Biology, New York University Abu Dhabi, P.O. Box 129188 Abu Dhabi, United Arab Emirates
| | - Matthew J McCoy
- Department of Pathology, Stanford University, Stanford, CA 94305, USA
| | - Michele K Nishiguchi
- Department of Molecular and Cell Biology, School of Natural Sciences, University of California, Merced, 5200 N. Lake Blvd., Merced, CA 95343, USA
| | - Spencer V Nyholm
- Molecular and Cell Biology Department, University of Connecticut, Storrs, CT 06269, USA
| | - Eve Otjacques
- MARE—Marine and Environmental Sciences Centre & ARNET—Aquatic Research Network, Laboratório Marítimo da Guia, Faculdade de Ciências, Universidade de Lisboa, Av. Nossa Senhora do Cabo, 939, 2750-374 Cascais, Portugal
- Division of Biosphere Sciences and Engineering, Carnegie Institution for Science, 1200 E. California Blvd, Pasadena, CA 91125, USA
| | - Pedro Antonio Pérez-Ferrer
- Department of Molecular and Cell Biology, School of Natural Sciences, University of California, Merced, 5200 N. Lake Blvd., Merced, CA 95343, USA
| | - Giovanna Ponte
- Department of Biology and Evolution of Marine Organisms, Stazione Zoologica Anton Dohrn, 80121 Napoli, Italy
| | - Judit R Pungor
- Biology Department: Institute of Ecology and Evolution, University of Oregon, Eugene, OR 97403-5289, USA
| | - Thea F Rogers
- Department of Neurosciences and Developmental Biology, University of Vienna, Vienna 1010, Austria
| | - Joshua J C Rosenthal
- Marine Biological Laboratory, The Eugene Bell Center for Regenerative Biology and Tissue Engineering, Woods Hole, MA 02543-1015, USA
| | - Lisa Rouressol
- Department of Neurosciences and Developmental Biology, University of Vienna, Vienna 1010, Austria
| | - Noelle Rubas
- Department of Molecular Biosciences and Bioengineering, University of Hawaii Manoa, Honolulu, HI 96822, USA
| | - Gustavo Sanchez
- Molecular Genetics Unit, Okinawa Institute of Science and Technology Graduate University, Onna, Okinawa 904-0495, Japan
| | - Catarina Pereira Santos
- MARE—Marine and Environmental Sciences Centre & ARNET—Aquatic Research Network, Laboratório Marítimo da Guia, Faculdade de Ciências, Universidade de Lisboa, Av. Nossa Senhora do Cabo, 939, 2750-374 Cascais, Portugal
| | - Darrin T Schultz
- Department of Neurosciences and Developmental Biology, University of Vienna, Vienna 1010, Austria
| | - Eve Seuntjens
- Laboratory of Developmental Neurobiology, Department of Biology, KU Leuven, Leuven 3000, Belgium
| | - Jeremea O Songco-Casey
- Biology Department: Institute of Ecology and Evolution, University of Oregon, Eugene, OR 97403-5289, USA
| | - Ian Erik Stewart
- Neural Circuits and Behaviour Lab, Max‐Delbrück‐Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin 13125, Germany
| | - Ruth Styfhals
- Laboratory of Developmental Neurobiology, Department of Biology, KU Leuven, Leuven 3000, Belgium
| | - Surangkana Tuanapaya
- Laboratory of genetics and applied breeding of molluscs, Fisheries College, Ocean University of China, Qingdao 266100, China
| | - Nidhi Vijayan
- Molecular and Cell Biology Department, University of Connecticut, Storrs, CT 06269, USA
| | | | - Lucia Zifcakova
- Physics and Biology Unit, Okinawa Institute of Science and Technology Graduate University, 1919-1 Tancha, Onna, Kunigami District, Okinawa 904-0495, Japan
| | | | - Willem Weertman
- Friday Harbor Laboratory, University of Washington, Seattle, WA 98250, USA
| | - Oleg Simakov
- Department of Neurosciences and Developmental Biology, University of Vienna, Vienna 1010, Austria
| | - Caroline B Albertin
- Marine Biological Laboratory, The Eugene Bell Center for Regenerative Biology and Tissue Engineering, Woods Hole, MA 02543-1015, USA
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5
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Qiu Y, Feng D, Jiang W, Zhang T, Lu Q, Zhao M. 3D genome organization and epigenetic regulation in autoimmune diseases. Front Immunol 2023; 14:1196123. [PMID: 37346038 PMCID: PMC10279977 DOI: 10.3389/fimmu.2023.1196123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 05/17/2023] [Indexed: 06/23/2023] Open
Abstract
Three-dimensional (3D) genomics is an emerging field of research that investigates the relationship between gene regulatory function and the spatial structure of chromatin. Chromatin folding can be studied using chromosome conformation capture (3C) technology and 3C-based derivative sequencing technologies, including chromosome conformation capture-on-chip (4C), chromosome conformation capture carbon copy (5C), and high-throughput chromosome conformation capture (Hi-C), which allow scientists to capture 3D conformations from a single site to the entire genome. A comprehensive analysis of the relationships between various regulatory components and gene function also requires the integration of multi-omics data such as genomics, transcriptomics, and epigenomics. 3D genome folding is involved in immune cell differentiation, activation, and dysfunction and participates in a wide range of diseases, including autoimmune diseases. We describe hierarchical 3D chromatin organization in this review and conclude with characteristics of C-techniques and multi-omics applications of the 3D genome. In addition, we describe the relationship between 3D genome structure and the differentiation and maturation of immune cells and address how changes in chromosome folding contribute to autoimmune diseases.
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Affiliation(s)
- Yueqi Qiu
- Institute of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, China
- Key Laboratory of Basic and Translational Research on Immune-Mediated Skin Diseases, Institute of Dermatology, Chinese Academy of Medical Sciences, Nanjing, China
| | - Delong Feng
- Department of Dermatology, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Wenjuan Jiang
- Institute of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, China
- Key Laboratory of Basic and Translational Research on Immune-Mediated Skin Diseases, Institute of Dermatology, Chinese Academy of Medical Sciences, Nanjing, China
| | - Tingting Zhang
- Institute of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, China
- Key Laboratory of Basic and Translational Research on Immune-Mediated Skin Diseases, Institute of Dermatology, Chinese Academy of Medical Sciences, Nanjing, China
- State Key Laboratory of Natural Medicines, School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Qianjin Lu
- Institute of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, China
- Key Laboratory of Basic and Translational Research on Immune-Mediated Skin Diseases, Institute of Dermatology, Chinese Academy of Medical Sciences, Nanjing, China
- Department of Dermatology, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Ming Zhao
- Institute of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, China
- Key Laboratory of Basic and Translational Research on Immune-Mediated Skin Diseases, Institute of Dermatology, Chinese Academy of Medical Sciences, Nanjing, China
- Department of Dermatology, The Second Xiangya Hospital of Central South University, Changsha, China
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6
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Hamley JC, Li H, Denny N, Downes D, Davies JOJ. Determining chromatin architecture with Micro Capture-C. Nat Protoc 2023; 18:1687-1711. [PMID: 36991220 DOI: 10.1038/s41596-023-00817-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Accepted: 01/11/2023] [Indexed: 03/30/2023]
Abstract
Micro Capture-C (MCC) is a chromatin conformation capture (3C) method for visualizing reproducible three-dimensional contacts of specified regions of the genome at base pair resolution. These methods are an established family of techniques that use proximity ligation to assay the topology of chromatin. MCC can generate data at substantially higher resolution than previous techniques through multiple refinements of the 3C method. Using a sequence agnostic nuclease, the maintenance of cellular integrity and full sequencing of the ligation junctions, MCC achieves subnucleosomal levels of resolution, which can be used to reveal transcription factor binding sites analogous to DNAse I footprinting. Gene dense regions, close-range enhancer-promoter contacts, individual enhancers within super-enhancers and multiple other types of loci or regulatory regions that were previously challenging to assay with conventional 3C techniques, are readily observed using MCC. MCC requires training in common molecular biology techniques and bioinformatics to perform the experiment and analyze the data. The protocol can be expected to be completed in a 3 week timeframe for experienced molecular biologists.
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Affiliation(s)
- Joseph C Hamley
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - Hangpeng Li
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - Nicholas Denny
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - Damien Downes
- 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.
- Oxford Biomedical Research Centre, Genomic Medicine and Cell and Gene Therapy Themes, Oxford, UK.
- National Institute of Health Research Blood and Transplant Research Unit, Oxford, UK.
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7
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Guo Q, Wu S, Geschwind DH. Characterization of Gene Regulatory Elements in Human Fetal Cortical Development: Enhancing Our Understanding of Neurodevelopmental Disorders and Evolution. Dev Neurosci 2023; 46:69-83. [PMID: 37231806 DOI: 10.1159/000530929] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 04/24/2023] [Indexed: 05/27/2023] Open
Abstract
The neocortex is the region that most distinguishes human brain from other mammals and primates [Annu Rev Genet. 2021 Nov;55(1):555-81]. Studying the development of human cortex is important in understanding the evolutionary changes occurring in humans relative to other primates, as well as in elucidating mechanisms underlying neurodevelopmental disorders. Cortical development is a highly regulated process, spatially and temporally coordinated by expression of essential transcriptional factors in response to signaling pathways [Neuron. 2019 Sep;103(6):980-1004]. Enhancers are the most well-understood cis-acting, non-protein-coding regulatory elements that regulate gene expression [Nat Rev Genet. 2014 Apr;15(4):272-86]. Importantly, given the conservation of both DNA sequence and molecular function of the majority of proteins across mammals [Genome Res. 2003 Dec;13(12):2507-18], enhancers [Science. 2015 Mar;347(6226):1155-9], which are far more divergent at the sequence level, likely account for the phenotypes that distinguish the human brain by changing the regulation of gene expression. In this review, we will revisit the conceptual framework of gene regulation during human brain development, as well as the evolution of technologies to study transcriptional regulation, with recent advances in genome biology that open a window allowing us to systematically characterize cis-regulatory elements in developing human brain [Hum Mol Genet. 2022 Oct;31(R1):R84-96]. We provide an update on work to characterize the suite of all enhancers in the developing human brain and the implications for understanding neuropsychiatric disorders. Finally, we discuss emerging therapeutic ideas that utilize our emerging knowledge of enhancer function.
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Affiliation(s)
- Qiuyu Guo
- Center for Neurobehavioral Genetics, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, California, USA
- Center for Autism Research and Treatment, Semel Institute, University of California Los Angeles, Los Angeles, California, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Sarah Wu
- Center for Neurobehavioral Genetics, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, California, USA
| | - Daniel H Geschwind
- Center for Neurobehavioral Genetics, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, California, USA
- Center for Autism Research and Treatment, Semel Institute, University of California Los Angeles, Los Angeles, California, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
- Institute of Precision Health, University of California Los Angeles, Los Angeles, California, USA
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8
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Zheng L, Liu L, Zhu W, Ding Y, Wu F. Predicting enhancer-promoter interaction based on epigenomic signals. Front Genet 2023; 14:1133775. [PMID: 37144127 PMCID: PMC10151517 DOI: 10.3389/fgene.2023.1133775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Accepted: 04/04/2023] [Indexed: 05/06/2023] Open
Abstract
Introduction: The physical interactions between enhancers and promoters are often involved in gene transcriptional regulation. High tissue-specific enhancer-promoter interactions (EPIs) are responsible for the differential expression of genes. Experimental methods are time-consuming and labor-intensive in measuring EPIs. An alternative approach, machine learning, has been widely used to predict EPIs. However, most existing machine learning methods require a large number of functional genomic and epigenomic features as input, which limits the application to different cell lines. Methods: In this paper, we developed a random forest model, HARD (H3K27ac, ATAC-seq, RAD21, and Distance), to predict EPI using only four types of features. Results: Independent tests on a benchmark dataset showed that HARD outperforms other models with the fewest features. Discussion: Our results revealed that chromatin accessibility and the binding of cohesin are important for cell-line-specific EPIs. Furthermore, we trained the HARD model in the GM12878 cell line and performed testing in the HeLa cell line. The cross-cell-lines prediction also performs well, suggesting it has the potential to be applied to other cell lines.
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Affiliation(s)
- Leqiong Zheng
- School of Mathematics and Statistics, Hainan Normal University, Haikou, China
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
- Key Laboratory of Computational Science and Application of Hainan Province, Haikou, China
| | - Li Liu
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
| | - Wen Zhu
- School of Mathematics and Statistics, Hainan Normal University, Haikou, China
- Key Laboratory of Computational Science and Application of Hainan Province, Haikou, China
| | - Yijie Ding
- Key Laboratory of Computational Science and Application of Hainan Province, Haikou, China
| | - Fangxiang Wu
- School of Mathematics and Statistics, Hainan Normal University, Haikou, China
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9
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Chen M, Liu X, Liu Q, Shi D, Li H. 3D genomics and its applications in precision medicine. Cell Mol Biol Lett 2023; 28:19. [PMID: 36879202 PMCID: PMC9987123 DOI: 10.1186/s11658-023-00428-x] [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: 11/16/2022] [Accepted: 02/06/2023] [Indexed: 03/08/2023] Open
Abstract
Three-dimensional (3D) genomics is an emerging discipline that studies the three-dimensional structure of chromatin and the three-dimensional and functions of genomes. It mainly studies the three-dimensional conformation and functional regulation of intranuclear genomes, such as DNA replication, DNA recombination, genome folding, gene expression regulation, transcription factor regulation mechanism, and the maintenance of three-dimensional conformation of genomes. Self-chromosomal conformation capture (3C) technology has been developed, and 3D genomics and related fields have developed rapidly. In addition, chromatin interaction analysis techniques developed by 3C technologies, such as paired-end tag sequencing (ChIA-PET) and whole-genome chromosome conformation capture (Hi-C), enable scientists to further study the relationship between chromatin conformation and gene regulation in different species. Thus, the spatial conformation of plant, animal, and microbial genomes, transcriptional regulation mechanisms, interaction patterns of chromosomes, and the formation mechanism of spatiotemporal specificity of genomes are revealed. With the help of new experimental technologies, the identification of key genes and signal pathways related to life activities and diseases is sustaining the rapid development of life science, agriculture, and medicine. In this paper, the concept and development of 3D genomics and its application in agricultural science, life science, and medicine are introduced, which provides a theoretical basis for the study of biological life processes.
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Affiliation(s)
- Mengjie Chen
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, College of Animal Science and Technology, Guangxi University, Nanning, 530004, Guangxi Province, China
| | - Xingyu Liu
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, College of Animal Science and Technology, Guangxi University, Nanning, 530004, Guangxi Province, China
| | - Qingyou Liu
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, College of Animal Science and Technology, Guangxi University, Nanning, 530004, Guangxi Province, China.,Guangdong Provincial Key Laboratory of Animal Molecular Design and Precise Breeding, School of Life Science and Engineering, Foshan University, Foshan, 528225, China
| | - Deshun Shi
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, College of Animal Science and Technology, Guangxi University, Nanning, 530004, Guangxi Province, China.
| | - Hui Li
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, College of Animal Science and Technology, Guangxi University, Nanning, 530004, Guangxi Province, China.
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10
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Liu S, Tang Q, Zhao K. Analysis of Chromatin Interaction and Accessibility by Trac-Looping. Methods Mol Biol 2023; 2611:85-97. [PMID: 36807066 DOI: 10.1007/978-1-0716-2899-7_7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/23/2023]
Abstract
Spatial organization of the genome modulates pivotal biological processes. The emerging new technologies have provided novel insights into genome structure and its role in regulating cell activities. To examine the genome-wide chromatin interactions at accessible chromatin regions, we developed a DNA transposase-mediated analysis of chromatin looping (Trac-looping) method for simultaneously detecting chromatin interactions and chromatin accessibility. Here, we describe a detailed protocol of generating Trac-looping libraries.
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Affiliation(s)
- Shuai Liu
- Laboratory of Epigenome Biology, Systems Biology Center, Division of Intramural Research, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Qingsong Tang
- Laboratory of Epigenome Biology, Systems Biology Center, Division of Intramural Research, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Keji Zhao
- Laboratory of Epigenome Biology, Systems Biology Center, Division of Intramural Research, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA.
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11
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Chai H, Tjong H, Li P, Liao W, Wang P, Wong CH, Ngan CY, Leonard WJ, Wei CL, Ruan Y. ChIATAC is an efficient strategy for multi-omics mapping of 3D epigenomes from low-cell inputs. Nat Commun 2023; 14:213. [PMID: 36639381 PMCID: PMC9839710 DOI: 10.1038/s41467-023-35879-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 01/05/2023] [Indexed: 01/15/2023] Open
Abstract
Connecting genes to their cis-regulatory elements has been enabled by genome-wide mapping of chromatin interactions using proximity ligation in ChIA-PET, Hi-C, and their derivatives. However, these methods require millions of input cells for high-quality data and thus are unsuitable for many studies when only limited cells are available. Conversely, epigenomic profiling via transposase digestion in ATAC-seq requires only hundreds to thousands of cells to robustly map open chromatin associated with transcription activity, but it cannot directly connect active genes to their distal enhancers. Here, we combine proximity ligation in ChIA-PET and transposase accessibility in ATAC-seq into ChIATAC to efficiently map interactions between open chromatin loci in low numbers of input cells. We validate ChIATAC in Drosophila cells and optimize it for mapping 3D epigenomes in human cells robustly. Applying ChIATAC to primary human T cells, we reveal mechanisms that topologically regulate transcriptional programs during T cell activation.
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Affiliation(s)
- Haoxi Chai
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Harianto Tjong
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Peng Li
- Laboratory of Molecular Immunology and the Immunology Center, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Wei Liao
- Laboratory of Molecular Immunology and the Immunology Center, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Ping Wang
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Chee Hong Wong
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Chew Yee Ngan
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Warren J Leonard
- Laboratory of Molecular Immunology and the Immunology Center, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Chia-Lin Wei
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA.
| | - Yijun Ruan
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA.
- Life Sciences Institute, Zhejiang University, Hangzhou, Zhejiang Province, P. R. China.
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12
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Liu K, Li HD, Li Y, Wang J, Wang J. A Comparison of Topologically Associating Domain Callers Based on Hi-C Data. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:15-29. [PMID: 35104223 DOI: 10.1109/tcbb.2022.3147805] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Topologically associating domains (TADs) are local chromatin interaction domains, which have been shown to play an important role in gene expression regulation. TADs were originally discovered in the investigation of 3D genome organization based on High-throughput Chromosome Conformation Capture (Hi-C) data. Continuous considerable efforts have been dedicated to developing methods for detecting TADs from Hi-C data. Different computational methods for TADs identification vary in their assumptions and criteria in calling TADs. As a consequence, the TADs called by these methods differ in their similarities and biological features they are enriched in. In this work, we performed a systematic comparison of twenty-six TAD callers. We first compared the TADs and gaps between adjacent TADs across different methods, resolutions, and sequencing depths. We then assessed the quality of TADs and TAD boundaries according to three criteria: the decay of contact frequencies over the genomic distance, enrichment and depletion of regulatory elements around TAD boundaries, and reproducibility of TADs and TAD boundaries in replicate samples. Last, due to the lack of a gold standard of TADs, we also evaluated the performance of the methods on synthetic datasets. We discussed the key principles of TAD callers, and pinpointed current situation in the detection of TADs. We provide a concise, comprehensive, and systematic framework for evaluating the performance of TAD callers, and expect our work will provide useful guidance in choosing suitable approaches for the detection and evaluation of TADs.
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13
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Liang W, Wang S, Wang H, Li X, Meng Q, Zhao Y, Zheng C. When 3D genome technology meets viral infection, including SARS-CoV-2. J Med Virol 2022; 94:5627-5639. [PMID: 35916043 PMCID: PMC9538846 DOI: 10.1002/jmv.28040] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 07/09/2022] [Accepted: 07/30/2022] [Indexed: 01/06/2023]
Abstract
Mammalian chromosomes undergo varying degrees of compression to form three-dimensional genome structures. These three-dimensional structures undergo dynamic and precise chromatin interactions to achieve precise spatial and temporal regulation of gene expression. Most eukaryotic DNA viruses can invade their genomes into the nucleus. However, it is still poorly understood how the viral genome is precisely positioned after entering the host cell nucleus to find the most suitable location and whether it can specifically interact with the host genome to hijack the host transcriptional factories or even integrate into the host genome to complete its transcription and replication rapidly. Chromosome conformation capture technology can reveal long-range chromatin interactions between different chromosomal sites in the nucleus, potentially providing a reference for viral DNA-host chromatin interactions. This review summarized the research progress on the three-dimensional interaction between virus and host genome and the impact of virus integration into the host genome on gene transcription regulation, aiming to provide new insights into chromatin interaction and viral gene transcription regulation, laying the foundation for the treatment of infectious diseases.
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Affiliation(s)
- Weizheng Liang
- Central LaboratoryThe First Affiliated Hospital of Hebei North UniversityZhangjiakouChina
- Department of Immunology, School of Basic Medical SciencesFujian Medical UniversityFuzhouChina
| | - Shuangqing Wang
- Department of NeurologyShenzhen University General Hospital, Shenzhen UniversityShenzhen, Guangdong ProvinceChina
| | - Hao Wang
- Department of Obstetrics and GynecologyShenzhen University General HospitalShenzhen, GuangdongChina
| | - Xiushen Li
- Department of Obstetrics and GynecologyShenzhen University General HospitalShenzhen, GuangdongChina
- Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical EngineeringShenzhen University Health Science CenterShenzhen, GuangdongChina
- Shenzhen Key LaboratoryShenzhen University General HospitalShenzhen, GuangdongChina
| | - Qingxue Meng
- Central LaboratoryThe First Affiliated Hospital of Hebei North UniversityZhangjiakouChina
| | - Yan Zhao
- Department of Mathematics and Computer ScienceFree University BerlinBerlinGermany
| | - Chunfu Zheng
- Department of Immunology, School of Basic Medical SciencesFujian Medical UniversityFuzhouChina
- Department of Microbiology, Immunology and Infectious DiseasesUniversity of CalgaryCalgaryAlbertaCanada
- The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, School of Life SciencesInner Mongolia UniversityHohhotChina
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14
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Wang S, Teng D, Li X, Yang P, Da W, Zhang Y, Zhang Y, Liu G, Zhang X, Wan W, Dong Z, Wang D, Huang S, Jiang Z, Wang Q, Lohman DJ, Wu Y, Zhang L, Jia F, Westerman E, Zhang L, Wang W, Zhang W. The evolution and diversification of oakleaf butterflies. Cell 2022; 185:3138-3152.e20. [PMID: 35926506 DOI: 10.1016/j.cell.2022.06.042] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Revised: 01/20/2022] [Accepted: 06/22/2022] [Indexed: 10/16/2022]
Abstract
Oakleaf butterflies in the genus Kallima have a polymorphic wing phenotype, enabling these insects to masquerade as dead leaves. This iconic example of protective resemblance provides an interesting evolutionary paradigm that can be employed to study biodiversity. We integrated multi-omic data analyses and functional validation to infer the evolutionary history of Kallima species and investigate the genetic basis of their variable leaf wing patterns. We find that Kallima butterflies diversified in the eastern Himalayas and dispersed to East and Southeast Asia. Moreover, we find that leaf wing polymorphism is controlled by the wing patterning gene cortex, which has been maintained in Kallima by long-term balancing selection. Our results provide macroevolutionary and microevolutionary insights into a model species originating from a mountain ecosystem.
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Affiliation(s)
- Shuting Wang
- State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing 100871, China; Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Dequn Teng
- State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing 100871, China
| | - Xueyan Li
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China
| | - Peiwen Yang
- State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing 100871, China
| | - Wa Da
- Tibet Plateau Institute of Biology, Lhasa, Tibet 850001, China
| | - Yiming Zhang
- State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing 100871, China; Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Yubo Zhang
- State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing 100871, China; Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Guichun Liu
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China
| | | | - Wenting Wan
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China
| | - Zhiwei Dong
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China
| | - Donghui Wang
- State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing 100871, China; National Teaching Center for Experimental Biology, Peking University, Beijing 100871, China
| | - Shun Huang
- State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing 100871, China
| | - Zhisheng Jiang
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Qingyi Wang
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - David J Lohman
- Biology Department, City College of New York, City University of New York, New York, NY 10031, USA; Ph.D. Program in Biology, Graduate Center, City University of New York, New York, NY 10016, USA; Entomology Section, National Museum of Natural History, Manila 1000, Philippines
| | - Yongjie Wu
- Key Laboratory of Bio-Resources and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu 610065, China
| | - Linlin Zhang
- CAS and Shandong Province Key Laboratory of Experimental Marine Biology, Center for Ocean Mega-Science, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China; Laboratory for Marine Biology and Biotechnology, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266071, China
| | - Fenghai Jia
- Jiangxi University of Traditional Chinese Medicine, Nanchang 330004, China
| | - Erica Westerman
- Department of Biological Sciences, University of Arkansas, Fayetteville, AR 72701, USA
| | - Li Zhang
- Chinese Institute for Brain Research, Beijing 100871, China
| | - Wen Wang
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China; School of Ecology and Environment, Northwestern Polytechnical University, Xi'an 710072, China; Center for Excellence in Animal Evolution and Genetics, Kunming 650223, China
| | - Wei Zhang
- State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing 100871, China; Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China; Institute of Ecology, Peking University, Beijing 100871, China; Institute for Tibetan Plateau Research, Peking University, Beijing 100871, China.
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15
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Liu T, Wang Z. scHiCEmbed: Bin-Specific Embeddings of Single-Cell Hi-C Data Using Graph Auto-Encoders. Genes (Basel) 2022; 13:genes13061048. [PMID: 35741810 PMCID: PMC9222580 DOI: 10.3390/genes13061048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 06/08/2022] [Accepted: 06/09/2022] [Indexed: 02/05/2023] Open
Abstract
Most publicly accessible single-cell Hi-C data are sparse and cannot reach a higher resolution. Therefore, learning latent representations (bin-specific embeddings) of sparse single-cell Hi-C matrices would provide us with a novel way of mining valuable information hidden in the limited number of single-cell Hi-C contacts. We present scHiCEmbed, an unsupervised computational method for learning bin-specific embeddings of single-cell Hi-C data, and the computational system is applied to the tasks of 3D structure reconstruction of whole genomes and detection of topologically associating domains (TAD). The only input of scHiCEmbed is a raw or scHiCluster-imputed single-cell Hi-C matrix. The main process of scHiCEmbed is to embed each node/bin in a higher dimensional space using graph auto-encoders. The learned n-by-3 bin-specific embedding/latent matrix is considered the final reconstructed 3D genome structure. For TAD detection, we use constrained hierarchical clustering on the latent matrix to classify bins: S_Dbw is used to determine the optimal number of clusters, and each cluster is considered as one potential TAD. Our reconstructed 3D structures for individual chromatins at different cell stages reveal the expanding process of chromatins during the cell cycle. We observe that the TADs called from single-cell Hi-C data are not shared across individual cells and that the TAD boundaries called from raw or imputed single-cell Hi-C are significantly different from those called from bulk Hi-C, confirming the cell-to-cell variability in terms of TAD definitions. The source code for scHiCEmbed is publicly available, and the URL can be found in the conclusion section.
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16
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Sefer E. A comparison of topologically associating domain callers over mammals at high resolution. BMC Bioinformatics 2022; 23:127. [PMID: 35413815 PMCID: PMC9006547 DOI: 10.1186/s12859-022-04674-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 04/07/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Topologically associating domains (TADs) are locally highly-interacting genome regions, which also play a critical role in regulating gene expression in the cell. TADs have been first identified while investigating the 3D genome structure over High-throughput Chromosome Conformation Capture (Hi-C) interaction dataset. Substantial degree of efforts have been devoted to develop techniques for inferring TADs from Hi-C interaction dataset. Many TAD-calling methods have been developed which differ in their criteria and assumptions in TAD inference. Correspondingly, TADs inferred via these callers vary in terms of both similarities and biological features they are enriched in. RESULT We have carried out a systematic comparison of 27 TAD-calling methods over mammals. We use Micro-C, a recent high-resolution variant of Hi-C, to compare TADs at a very high resolution, and classify the methods into 3 categories: feature-based methods, Clustering methods, Graph-partitioning methods. We have evaluated TAD boundaries, gaps between adjacent TADs, and quality of TADs across various criteria. We also found particularly CTCF and Cohesin proteins to be effective in formation of TADs with corner dots. We have also assessed the callers performance on simulated datasets since a gold standard for TADs is missing. TAD sizes and numbers change remarkably between TAD callers and dataset resolutions, indicating that TADs are hierarchically-organized domains, instead of disjoint regions. A core subset of feature-based TAD callers regularly perform the best while inferring reproducible domains, which are also enriched for TAD related biological properties. CONCLUSION We have analyzed the fundamental principles of TAD-calling methods, and identified the existing situation in TAD inference across high resolution Micro-C interaction datasets over mammals. We come up with a systematic, comprehensive, and concise framework to evaluate the TAD-calling methods performance across Micro-C datasets. Our research will be useful in selecting appropriate methods for TAD inference and evaluation based on available data, experimental design, and biological question of interest. We also introduce our analysis as a benchmarking tool with publicly available source code.
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Affiliation(s)
- Emre Sefer
- Department of Computer Science, Ozyegin University, Istanbul, Turkey.
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17
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Melixetian M, Pelicci PG, Lanfrancone L. Regulation of LncRNAs in Melanoma and Their Functional Roles in the Metastatic Process. Cells 2022; 11:cells11030577. [PMID: 35159386 PMCID: PMC8834033 DOI: 10.3390/cells11030577] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 02/02/2022] [Accepted: 02/05/2022] [Indexed: 02/06/2023] Open
Abstract
Long non-coding RNAs (lncRNAs) are key regulators of numerous intracellular processes leading to tumorigenesis. They are frequently deregulated in cancer, functioning as oncogenes or tumor suppressors. As they act through multiple mechanisms, it is not surprising that they may exert dual functions in the same tumor. In melanoma, a highly invasive and metastatic tumor with the propensity to rapidly develop drug resistance, lncRNAs play different roles in: (i) guiding the phenotype switch and leading to metastasis formation; (ii) predicting the response of melanoma patients to immunotherapy; (iii) triggering adaptive responses to therapy and acquisition of drug resistance phenotypes. In this review we summarize the most recent findings on the lncRNAs involved in melanoma growth and spreading to distant sites, focusing on their role as biomarkers for disease diagnosis and patient prognosis, or targets for novel therapeutic approaches.
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Affiliation(s)
- Marine Melixetian
- Department of Experimental Oncology, IEO, European Institute of Oncology IRCCS, 20139 Milan, Italy; (M.M.); (P.G.P.)
| | - Pier Giuseppe Pelicci
- Department of Experimental Oncology, IEO, European Institute of Oncology IRCCS, 20139 Milan, Italy; (M.M.); (P.G.P.)
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
| | - Luisa Lanfrancone
- Department of Experimental Oncology, IEO, European Institute of Oncology IRCCS, 20139 Milan, Italy; (M.M.); (P.G.P.)
- Correspondence: ; Tel.: +39-02-94375011
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18
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Downes DJ, Smith AL, Karpinska MA, Velychko T, Rue-Albrecht K, Sims D, Milne TA, Davies JOJ, Oudelaar AM, Hughes JR. Capture-C: a modular and flexible approach for high-resolution chromosome conformation capture. Nat Protoc 2022; 17:445-475. [PMID: 35121852 PMCID: PMC7613269 DOI: 10.1038/s41596-021-00651-w] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Accepted: 10/27/2021] [Indexed: 12/16/2022]
Abstract
Chromosome conformation capture (3C) methods measure the spatial proximity between DNA elements in the cell nucleus. Many methods have been developed to sample 3C material, including the Capture-C family of protocols. Capture-C methods use oligonucleotides to enrich for interactions of interest from sequencing-ready 3C libraries. This approach is modular and has been adapted and optimized to work for sampling of disperse DNA elements (NuTi Capture-C), including from low cell inputs (LI Capture-C), as well as to generate Hi-C like maps for specific regions of interest (Tiled-C) and to interrogate multiway interactions (Tri-C). We present the design, experimental protocol and analysis pipeline for NuTi Capture-C in addition to the variations for generation of LI Capture-C, Tiled-C and Tri-C data. The entire procedure can be performed in 3 weeks and requires standard molecular biology skills and equipment, access to a next-generation sequencing platform, and basic bioinformatic skills. Implemented with other sequencing technologies, these methods can be used to identify regulatory interactions and to compare the structural organization of the genome in different cell types and genetic models.
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Affiliation(s)
- Damien J Downes
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - Alastair L Smith
- 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
| | | | - Taras Velychko
- Department of Molecular Biology, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany
| | - Kevin Rue-Albrecht
- MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - David Sims
- MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - Thomas A Milne
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
- NIHR Oxford Biomedical Research Centre, Haematology Theme, 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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19
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Huang N, Seow WQ, Appert A, Dong Y, Stempor P, Ahringer J. Accessible Region Conformation Capture (ARC-C) gives high-resolution insights into genome architecture and regulation. Genome Res 2022; 32:357-366. [PMID: 34933938 PMCID: PMC8805715 DOI: 10.1101/gr.275669.121] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 12/19/2021] [Indexed: 11/25/2022]
Abstract
Nuclear organization and chromatin interactions are important for genome function, yet determining chromatin connections at high resolution remains a major challenge. To address this, we developed Accessible Region Conformation Capture (ARC-C), which profiles interactions between regulatory elements genome-wide without a capture step. Applied to Caenorhabditis elegans, ARC-C identifies approximately 15,000 significant interactions between regulatory elements at 500-bp resolution. Of 105 TFs or chromatin regulators tested, we find that the binding sites of 60 are enriched for interacting with each other, making them candidates for mediating interactions. These include cohesin and condensin II. Applying ARC-C to a mutant of transcription factor BLMP-1 detected changes in interactions between its targets. ARC-C simultaneously profiles domain-level architecture, and we observe that C. elegans chromatin domains defined by either active or repressive modifications form topologically associating domains (TADs) that interact with A/B (active/inactive) compartment-like structure. Furthermore, we discover that inactive compartment interactions are dependent on H3K9 methylation. ARC-C is a powerful new tool to interrogate genome architecture and regulatory interactions at high resolution.
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Affiliation(s)
- Ni Huang
- The Gurdon Institute and Department of Genetics, University of Cambridge, Cambridge CB2 1QN, United Kingdom
| | - Wei Qiang Seow
- The Gurdon Institute and Department of Genetics, University of Cambridge, Cambridge CB2 1QN, United Kingdom
| | - Alex Appert
- The Gurdon Institute and Department of Genetics, University of Cambridge, Cambridge CB2 1QN, United Kingdom
| | - Yan Dong
- The Gurdon Institute and Department of Genetics, University of Cambridge, Cambridge CB2 1QN, United Kingdom
| | - Przemyslaw Stempor
- The Gurdon Institute and Department of Genetics, University of Cambridge, Cambridge CB2 1QN, United Kingdom
| | - Julie Ahringer
- The Gurdon Institute and Department of Genetics, University of Cambridge, Cambridge CB2 1QN, United Kingdom
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20
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Ma Q, Yang L, Tolentino K, Wang G, Zhao Y, Litzenburger UM, Shi Q, Zhu L, Yang C, Jiao H, Zhang F, Li R, Tsai MC, Chen JA, Lai I, Zeng H, Li L, Chang HY. Inducible lncRNA transgenic mice reveal continual role of HOTAIR in promoting breast cancer metastasis. eLife 2022; 11:79126. [PMID: 36579891 PMCID: PMC9831604 DOI: 10.7554/elife.79126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 12/28/2022] [Indexed: 12/30/2022] Open
Abstract
HOTAIR is a 2.2-kb long noncoding RNA (lncRNA) whose dysregulation has been linked to oncogenesis, defects in pattern formation during early development, and irregularities during the process of epithelial-to-mesenchymal transition (EMT). However, the oncogenic transformation determined by HOTAIR in vivo and its impact on chromatin dynamics are incompletely understood. Here, we generate a transgenic mouse model with doxycycline-inducible expression of human HOTAIR in the context of the MMTV-PyMT breast cancer-prone background to systematically interrogate the cellular mechanisms by which human HOTAIR lncRNA acts to promote breast cancer progression. We show that sustained high levels of HOTAIR over time increased breast metastatic capacity and invasiveness in breast cancer cells, promoting migration and subsequent metastasis to the lung. Subsequent withdrawal of HOTAIR overexpression reverted the metastatic phenotype, indicating oncogenic lncRNA addiction. Furthermore, HOTAIR overexpression altered both the cellular transcriptome and chromatin accessibility landscape of multiple metastasis-associated genes and promoted EMT. These alterations are abrogated within several cell cycles after HOTAIR expression is reverted to basal levels, indicating an erasable lncRNA-associated epigenetic memory. These results suggest that a continual role for HOTAIR in programming a metastatic gene regulatory program. Targeting HOTAIR lncRNA may potentially serve as a therapeutic strategy to ameliorate breast cancer progression.
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Affiliation(s)
- Qing Ma
- Shenzhen Key Laboratory of Synthetic Genomics, Guangdong Provincial Key Laboratory of Synthetic Genomics, CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of SciencesShenzhenChina
| | - Liuyi Yang
- Shenzhen Key Laboratory of Synthetic Genomics, Guangdong Provincial Key Laboratory of Synthetic Genomics, CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of SciencesShenzhenChina
| | - Karen Tolentino
- Center for Personal Dynamic Regulomes and Program in Epithelial Biology, Stanford University School of MedicineStanfordUnited States
| | - Guiping Wang
- Center for Personal Dynamic Regulomes and Program in Epithelial Biology, Stanford University School of MedicineStanfordUnited States
| | - Yang Zhao
- Center for Personal Dynamic Regulomes and Program in Epithelial Biology, Stanford University School of MedicineStanfordUnited States
| | - Ulrike M Litzenburger
- Center for Personal Dynamic Regulomes and Program in Epithelial Biology, Stanford University School of MedicineStanfordUnited States
| | - Quanming Shi
- Center for Personal Dynamic Regulomes and Program in Epithelial Biology, Stanford University School of MedicineStanfordUnited States
| | - Lin Zhu
- Shenzhen Key Laboratory of Synthetic Genomics, Guangdong Provincial Key Laboratory of Synthetic Genomics, CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of SciencesShenzhenChina
| | - Chen Yang
- Department of Histoembryology, Genetics and Developmental Biology, Shanghai Key Laboratory of Reproductive Medicine, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education,Shanghai Jiao Tong University School of MedicineShanghaiChina
| | - Huiyuan Jiao
- Department of Histoembryology, Genetics and Developmental Biology, Shanghai Key Laboratory of Reproductive Medicine, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education,Shanghai Jiao Tong University School of MedicineShanghaiChina
| | - Feng Zhang
- Department of Histoembryology, Genetics and Developmental Biology, Shanghai Key Laboratory of Reproductive Medicine, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education,Shanghai Jiao Tong University School of MedicineShanghaiChina
| | - Rui Li
- Center for Personal Dynamic Regulomes and Program in Epithelial Biology, Stanford University School of MedicineStanfordUnited States
| | - Miao-Chih Tsai
- Center for Personal Dynamic Regulomes and Program in Epithelial Biology, Stanford University School of MedicineStanfordUnited States
| | - Jun-An Chen
- Institute of Molecular Biology, Academia SinicaTaipeiTaiwan
| | - Ian Lai
- Transgenic, Knockout, and Tumor Model Center, Stanford University School of MedicineStanfordUnited States,Stanford Cancer Institute, Stanford University School of MedicineStanfordUnited States
| | - Hong Zeng
- Transgenic, Knockout, and Tumor Model Center, Stanford University School of MedicineStanfordUnited States,Stanford Cancer Institute, Stanford University School of MedicineStanfordUnited States
| | - Lingjie Li
- Department of Histoembryology, Genetics and Developmental Biology, Shanghai Key Laboratory of Reproductive Medicine, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education,Shanghai Jiao Tong University School of MedicineShanghaiChina
| | - Howard Y Chang
- Center for Personal Dynamic Regulomes and Program in Epithelial Biology, Stanford University School of MedicineStanfordUnited States,Howard Hughes Medical Institute, Stanford UniversityStanfordUnited States
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21
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Lee S. Hi-C Data Formats. Methods Mol Biol 2022; 2301:133-141. [PMID: 34415533 DOI: 10.1007/978-1-0716-1390-0_6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Processing, storing, and visualizing high-resolution Hi-C data required development of efficient data formats. A sparse matrix format saving only nonzero values has become the norm. A "zoomable" matrix style also became popular, storing multiple resolutions in a single file for interactive visualization. This chapter discusses the latest matrix file formats such as .hic and .mcool, and other intermediate formats including SAM/BAM and random-accessible contact lists.
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Affiliation(s)
- Soohyun Lee
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
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22
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Kumar S, Kaur S, Seem K, Kumar S, Mohapatra T. Understanding 3D Genome Organization and Its Effect on Transcriptional Gene Regulation Under Environmental Stress in Plant: A Chromatin Perspective. Front Cell Dev Biol 2021; 9:774719. [PMID: 34957106 PMCID: PMC8692796 DOI: 10.3389/fcell.2021.774719] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2021] [Accepted: 11/23/2021] [Indexed: 01/17/2023] Open
Abstract
The genome of a eukaryotic organism is comprised of a supra-molecular complex of chromatin fibers and intricately folded three-dimensional (3D) structures. Chromosomal interactions and topological changes in response to the developmental and/or environmental stimuli affect gene expression. Chromatin architecture plays important roles in DNA replication, gene expression, and genome integrity. Higher-order chromatin organizations like chromosome territories (CTs), A/B compartments, topologically associating domains (TADs), and chromatin loops vary among cells, tissues, and species depending on the developmental stage and/or environmental conditions (4D genomics). Every chromosome occupies a separate territory in the interphase nucleus and forms the top layer of hierarchical structure (CTs) in most of the eukaryotes. While the A and B compartments are associated with active (euchromatic) and inactive (heterochromatic) chromatin, respectively, having well-defined genomic/epigenomic features, TADs are the structural units of chromatin. Chromatin architecture like TADs as well as the local interactions between promoter and regulatory elements correlates with the chromatin activity, which alters during environmental stresses due to relocalization of the architectural proteins. Moreover, chromatin looping brings the gene and regulatory elements in close proximity for interactions. The intricate relationship between nucleotide sequence and chromatin architecture requires a more comprehensive understanding to unravel the genome organization and genetic plasticity. During the last decade, advances in chromatin conformation capture techniques for unravelling 3D genome organizations have improved our understanding of genome biology. However, the recent advances, such as Hi-C and ChIA-PET, have substantially increased the resolution, throughput as well our interest in analysing genome organizations. The present review provides an overview of the historical and contemporary perspectives of chromosome conformation capture technologies, their applications in functional genomics, and the constraints in predicting 3D genome organization. We also discuss the future perspectives of understanding high-order chromatin organizations in deciphering transcriptional regulation of gene expression under environmental stress (4D genomics). These might help design the climate-smart crop to meet the ever-growing demands of food, feed, and fodder.
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Affiliation(s)
- Suresh Kumar
- Division of Biochemistry, ICAR-Indian Agricultural Research Institute, New Delhi, India
| | - Simardeep Kaur
- Division of Biochemistry, ICAR-Indian Agricultural Research Institute, New Delhi, India
| | - Karishma Seem
- Division of Biochemistry, ICAR-Indian Agricultural Research Institute, New Delhi, India
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23
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Yi X, Zheng Z, Xu H, Zhou Y, Huang D, Wang J, Feng X, Zhao K, Fan X, Zhang S, Dong X, Wang Z, Shen Y, Cheng H, Shi L, Li MJ. Interrogating cell type-specific cooperation of transcriptional regulators in 3D chromatin. iScience 2021; 24:103468. [PMID: 34888502 PMCID: PMC8634045 DOI: 10.1016/j.isci.2021.103468] [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: 08/02/2021] [Revised: 09/23/2021] [Accepted: 11/12/2021] [Indexed: 12/14/2022] Open
Abstract
Context-specific activities of transcription regulators (TRs) in the nucleus modulate spatiotemporal gene expression precisely. Using the largest ChIP-seq data and chromatin loops in the human K562 cell line, we initially interrogated TR cooperation in 3D chromatin via a graphical model and revealed many known and novel TRs manipulating context-specific pathways. To explore TR cooperation across broad tissue/cell types, we systematically leveraged large-scale open chromatin profiles, computational footprinting, and high-resolution chromatin interactions to investigate tissue/cell type-specific TR cooperation. We first delineated a landscape of TR cooperation across 40 human tissue/cell types. Network modularity analyses uncovered the commonality and specificity of TR cooperation in different conditions. We also demonstrated that TR cooperation information can better interpret the disease-causal variants identified by genome-wide association studies and recapitulate cell states during neural development. Our study characterizes shared and unique patterns of TR cooperation associated with the cell type specificity of gene regulation in 3D chromatin. Computational inference of transcriptional regulator (TR) cooperation in 3D chromatin A landscape of 3D TR cooperation across 40 human tissue/cell types TR cooperation can better interpret the disease-causal variants identified by GWAS Cooperation of certain TRs shapes context-specific gene regulation in cell development
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Affiliation(s)
- Xianfu Yi
- School of Biomedical Engineering and Technology, Tianjin Medical University, Tianjin 300070, China.,Department of Bioinformatics, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Medical University, Tianjin 300070, China
| | - Zhanye Zheng
- Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Hang Xu
- Department of Bioinformatics, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Medical University, Tianjin 300070, China
| | - Yao Zhou
- Department of Bioinformatics, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Medical University, Tianjin 300070, China.,Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Dandan Huang
- Department of Bioinformatics, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Medical University, Tianjin 300070, China.,Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Jianhua Wang
- Department of Bioinformatics, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Medical University, Tianjin 300070, China.,Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Xiangling Feng
- Department of Bioinformatics, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Medical University, Tianjin 300070, China.,Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Ke Zhao
- Department of Bioinformatics, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Medical University, Tianjin 300070, China.,Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Xutong Fan
- Department of Bioinformatics, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Medical University, Tianjin 300070, China.,Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Shijie Zhang
- Department of Bioinformatics, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Medical University, Tianjin 300070, China.,Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Xiaobao Dong
- Department of Bioinformatics, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Medical University, Tianjin 300070, China.,Department of Genetics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Zhao Wang
- Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Yujun Shen
- Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Hui Cheng
- State Key Laboratory of Experimental Hematology, Chinese Academy of Medical Sciences, Tianjin 300070, China
| | - Lei Shi
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Mulin Jun Li
- Department of Bioinformatics, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Medical University, Tianjin 300070, China.,Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China.,Department of Epidemiology and Biostatistics, Tianjin Key Laboratory of Molecular Cancer Epidemiology, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin 300070, China
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24
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Zhou H, Gao Y, Li X, Shang S, Wang P, Zhi H, Guo S, Sun D, Liu H, Li X, Zhang Y, Ning S. Identifying and characterizing lincRNA genomic clusters reveals its cooperative functions in human cancer. J Transl Med 2021; 19:509. [PMID: 34906173 PMCID: PMC8672572 DOI: 10.1186/s12967-021-03179-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 12/03/2021] [Indexed: 02/01/2023] Open
Abstract
Background Emerging evidence has revealed that some long intergenic non-coding RNAs (lincRNAs) are likely to form clusters on the same chromosome, and lincRNA genomic clusters might play critical roles in the pathophysiological mechanism. However, the comprehensive investigation of lincRNA clustering is rarely studied, particularly the characterization of their functional significance across different cancer types. Methods In this study, we firstly constructed a computational method basing a sliding window approach for systematically identifying lincRNA genomic clusters. We then dissected these lincRNA genomic clusters to identify common characteristics in cooperative expression, conservation among divergent species, targeted miRNAs, and CNV frequency. Next, we performed comprehensive analyses in differentially-expressed patterns and overall survival outcomes for patients from The Cancer Genome Atlas (TCGA) and The Genotype-Tissue Expression (GTEx) across multiple cancer types. Finally, we explored the underlying mechanisms of lincRNA genomic clusters by functional enrichment analysis, pathway analysis, and drug-target interaction. Results We identified lincRNA genomic clusters according to the algorithm. Clustering lincRNAs tended to be co-expressed, highly conserved, targeted by more miRNAs, and with similar deletion and duplication frequency, suggesting that lincRNA genomic clusters may exert their effects by acting in combination. We further systematically explored conserved and cancer-specific lincRNA genomic clusters, indicating they were involved in some important mechanisms of disease occurrence through diverse approaches. Furthermore, lincRNA genomic clusters can serve as biomarkers with potential clinical significance and involve in specific pathological processes in the development of cancer. Moreover, a lincRNA genomic cluster named Cluster127 in DLK1-DIO3 imprinted locus was discovered, which contained MEG3, MEG8, MEG9, MIR381HG, LINC02285, AL132709.5, and AL132709.1. Further analysis indicated that Cluster127 may have the potential for predicting prognosis in cancer and could play their roles by participating in the regulation of PI3K-AKT signaling pathway. Conclusions Clarification of the lincRNA genomic clusters specific roles in human cancers could be beneficial for understanding the molecular pathogenesis of different cancer types. Supplementary Information The online version contains supplementary material available at 10.1186/s12967-021-03179-5.
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Affiliation(s)
- Hanxiao Zhou
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China
| | - Yue Gao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China
| | - Xin Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China
| | - Shipeng Shang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China
| | - Peng Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China
| | - Hui Zhi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China
| | - Shuang Guo
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China
| | - Dailin Sun
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China
| | - Hongjia Liu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China
| | - Xia Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China.
| | - Yunpeng Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China.
| | - Shangwei Ning
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China.
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25
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Goel VY, Hansen AS. The macro and micro of chromosome conformation capture. WILEY INTERDISCIPLINARY REVIEWS. DEVELOPMENTAL BIOLOGY 2021; 10:e395. [PMID: 32987449 PMCID: PMC8236208 DOI: 10.1002/wdev.395] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Revised: 07/21/2020] [Accepted: 07/31/2020] [Indexed: 12/14/2022]
Abstract
The 3D organization of the genome facilitates gene regulation, replication, and repair, making it a key feature of genomic function and one that remains to be properly understood. Over the past two decades, a variety of chromosome conformation capture (3C) methods have delineated genome folding from megabase-scale compartments and topologically associating domains (TADs) down to kilobase-scale enhancer-promoter interactions. Understanding the functional role of each layer of genome organization is a gateway to understanding cell state, development, and disease. Here, we discuss the evolution of 3C-based technologies for mapping 3D genome organization. We focus on genomics methods and provide a historical account of the development from 3C to Hi-C. We also discuss ChIP-based techniques that focus on 3D genome organization mediated by specific proteins, capture-based methods that focus on particular regions or regulatory elements, 3C-orthogonal methods that do not rely on restriction digestion and proximity ligation, and methods for mapping the DNA-RNA and RNA-RNA interactomes. We consider the biological discoveries that have come from these methods, examine the mechanistic contributions of CTCF, cohesin, and loop extrusion to genomic folding, and detail the 3D genome field's current understanding of nuclear architecture. Finally, we give special consideration to Micro-C as an emerging frontier in chromosome conformation capture and discuss recent Micro-C findings uncovering fine-scale chromatin organization in unprecedented detail. This article is categorized under: Gene Expression and Transcriptional Hierarchies > Regulatory Mechanisms Gene Expression and Transcriptional Hierarchies > Gene Networks and Genomics.
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Affiliation(s)
- Viraat Y. Goel
- Department of Biological EngineeringMassachusetts Institute of TechnologyCambridgeMassachusettsUSA
| | - Anders S. Hansen
- Department of Biological EngineeringMassachusetts Institute of TechnologyCambridgeMassachusettsUSA
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26
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Li X, Pan X, Zhou H, Wang P, Gao Y, Shang S, Guo S, Sun J, Xiong Z, Ning S, Zhi H, Li X. Comprehensive characterization genetic regulation and chromatin landscape of enhancer-associated long non-coding RNAs and their implication in human cancer. Brief Bioinform 2021; 23:6375264. [PMID: 34581409 DOI: 10.1093/bib/bbab401] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 08/19/2021] [Accepted: 09/02/2021] [Indexed: 02/06/2023] Open
Abstract
Long non-coding RNAs (lncRNAs) that emanate from enhancer regions (defined as enhancer-associated lncRNAs, or elncRNAs) are emerging as critical regulators in disease progression. However, their biological characteristics and clinical relevance have not been fully portrayed. Here, based on the traditional expression quantitative loci (eQTL) and our optimized residual eQTL method, we comprehensively described the genetic effect on elncRNA expression in more than 300 lymphoblastoid cell lines. Meanwhile, a chromatin atlas of elncRNAs relative to the genetic regulation state was depicted. By applying the maximum likelihood estimate method, we successfully identified causal elncRNAs for protein-coding gene expression reprogramming and showed their associated single nucleotide polymorphisms (SNPs) favor binding of transcription factors. Further epigenome analysis revealed two immune-associated elncRNAs AL662844.4 and LINC01215 possess high levels of H3K27ac and H3K4me1 in human cancer. Besides, pan-cancer analysis of 3D genome, transcriptome, and regulatome data showed they potentially regulate tumor-immune cell interaction through affecting MHC class I genes and CD47, respectively. Moreover, our study showed there exist associations between elncRNA and patient survival. Finally, we made a user-friendly web interface available for exploring the regulatory relationship of SNP-elncRNA-protein-coding gene triplets (http://bio-bigdata.hrbmu.edu.cn/elncVarReg). Our study provides critical mechanistic insights for elncRNA function and illustrates their implications in human cancer.
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Affiliation(s)
- Xin Li
- College of Bioinformatics Science and Technology, Harbin Medical University, China
| | - Xu Pan
- College of Bioinformatics Science and Technology, Harbin Medical University, China
| | - Hanxiao Zhou
- College of Bioinformatics Science and Technology, Harbin Medical University, China
| | - Peng Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, China
| | - Yue Gao
- College of Bioinformatics Science and Technology, Harbin Medical University, China
| | - Shipeng Shang
- College of Bioinformatics Science and Technology, Harbin Medical University, China
| | - Shuang Guo
- College of Bioinformatics Science and Technology, Harbin Medical University, China
| | - Jie Sun
- College of Bioinformatics Science and Technology, Harbin Medical University, China
| | - Zhiying Xiong
- College of Bioinformatics Science and Technology, Harbin Medical University, China
| | - Shangwei Ning
- College of Bioinformatics Science and Technology, Harbin Medical University, China
| | - Hui Zhi
- College of Bioinformatics Science and Technology, Harbin Medical University, China
| | - Xia Li
- College of Bioinformatics Science and Technology, Harbin Medical University, China
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27
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Liu N, Low WY, Alinejad-Rokny H, Pederson S, Sadlon T, Barry S, Breen J. Seeing the forest through the trees: prioritising potentially functional interactions from Hi-C. Epigenetics Chromatin 2021; 14:41. [PMID: 34454581 PMCID: PMC8399707 DOI: 10.1186/s13072-021-00417-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Accepted: 08/19/2021] [Indexed: 11/30/2022] Open
Abstract
Eukaryotic genomes are highly organised within the nucleus of a cell, allowing widely dispersed regulatory elements such as enhancers to interact with gene promoters through physical contacts in three-dimensional space. Recent chromosome conformation capture methodologies such as Hi-C have enabled the analysis of interacting regions of the genome providing a valuable insight into the three-dimensional organisation of the chromatin in the nucleus, including chromosome compartmentalisation and gene expression. Complicating the analysis of Hi-C data, however, is the massive amount of identified interactions, many of which do not directly drive gene function, thus hindering the identification of potentially biologically functional 3D interactions. In this review, we collate and examine the downstream analysis of Hi-C data with particular focus on methods that prioritise potentially functional interactions. We classify three groups of approaches: structural-based discovery methods, e.g. A/B compartments and topologically associated domains, detection of statistically significant chromatin interactions, and the use of epigenomic data integration to narrow down useful interaction information. Careful use of these three approaches is crucial to successfully identifying potentially functional interactions within the genome.
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Affiliation(s)
- Ning Liu
- Computational & Systems Biology, Precision Medicine Theme, South Australian Health & Medical Research Institute, SA, 5000, Adelaide, Australia
- Robinson Research Institute, University of Adelaide, SA, 5005, Adelaide, Australia
- Adelaide Medical School, University of Adelaide, SA, 5005, Adelaide, Australia
| | - Wai Yee Low
- The Davies Research Centre, School of Animal and Veterinary Sciences, University of Adelaide, Roseworthy, SA, 5371, Australia
| | - Hamid Alinejad-Rokny
- BioMedical Machine Learning Lab, The Graduate School of Biomedical Engineering, The University of New South Wales, NSW, 2052, Sydney, Australia
- Core Member of UNSW Data Science Hub, The University of New South Wales, 2052, Sydney, Australia
| | - Stephen Pederson
- Adelaide Medical School, University of Adelaide, SA, 5005, Adelaide, Australia
- Dame Roma Mitchell Cancer Research Laboratories (DRMCRL), Adelaide Medical School, University of Adelaide, SA, 5005, Adelaide, Australia
| | - Timothy Sadlon
- Robinson Research Institute, University of Adelaide, SA, 5005, Adelaide, Australia
- Women's & Children's Health Network, SA, 5006, North Adelaide, Australia
| | - Simon Barry
- Robinson Research Institute, University of Adelaide, SA, 5005, Adelaide, Australia
- Core Member of UNSW Data Science Hub, The University of New South Wales, 2052, Sydney, Australia
- Women's & Children's Health Network, SA, 5006, North Adelaide, Australia
| | - James Breen
- Computational & Systems Biology, Precision Medicine Theme, South Australian Health & Medical Research Institute, SA, 5000, Adelaide, Australia.
- Robinson Research Institute, University of Adelaide, SA, 5005, Adelaide, Australia.
- Adelaide Medical School, University of Adelaide, SA, 5005, Adelaide, Australia.
- South Australian Genomics Centre (SAGC), South Australian Health & Medical Research Institute (SAHMRI), SA, 5000, Adelaide, Australia.
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28
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Cao F, Zhang Y, Cai Y, Animesh S, Zhang Y, Akincilar SC, Loh YP, Li X, Chng WJ, Tergaonkar V, Kwoh CK, Fullwood MJ. Chromatin interaction neural network (ChINN): a machine learning-based method for predicting chromatin interactions from DNA sequences. Genome Biol 2021; 22:226. [PMID: 34399797 PMCID: PMC8365954 DOI: 10.1186/s13059-021-02453-5] [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: 02/05/2021] [Accepted: 08/04/2021] [Indexed: 11/10/2022] Open
Abstract
Chromatin interactions play important roles in regulating gene expression. However, the availability of genome-wide chromatin interaction data is limited. We develop a computational method, chromatin interaction neural network (ChINN), to predict chromatin interactions between open chromatin regions using only DNA sequences. ChINN predicts CTCF- and RNA polymerase II-associated and Hi-C chromatin interactions. ChINN shows good across-sample performances and captures various sequence features for chromatin interaction prediction. We apply ChINN to 6 chronic lymphocytic leukemia (CLL) patient samples and a published cohort of 84 CLL open chromatin samples. Our results demonstrate extensive heterogeneity in chromatin interactions among CLL patient samples.
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Affiliation(s)
- Fan Cao
- Cancer Science Institute of Singapore, National University of Singapore, 14 Medical Dr, Singapore, 117599 Singapore
| | - Yu Zhang
- School of Computer Science and Engineering, Nanyang Technological University, Block N4, 50 Nanyang Avenue, Singapore, 639798 Singapore
| | - Yichao Cai
- Cancer Science Institute of Singapore, National University of Singapore, 14 Medical Dr, Singapore, 117599 Singapore
| | - Sambhavi Animesh
- Cancer Science Institute of Singapore, National University of Singapore, 14 Medical Dr, Singapore, 117599 Singapore
| | - Ying Zhang
- Cancer Science Institute of Singapore, National University of Singapore, 14 Medical Dr, Singapore, 117599 Singapore
| | - Semih Can Akincilar
- Institute of Molecular and Cell Biology, Agency for Science (IMCB), A*STAR (Agency for Science, Technology and Research,, Singapore, 138673 Singapore
| | - Yan Ping Loh
- Cancer Science Institute of Singapore, National University of Singapore, 14 Medical Dr, Singapore, 117599 Singapore
| | - Xinya Li
- School of Biological Sciences, Nanyang Technological University, 60 Nanyang Drive, Singapore, 637551 Singapore
| | - Wee Joo Chng
- Cancer Science Institute of Singapore, National University of Singapore, 14 Medical Dr, Singapore, 117599 Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, 1E Kent Ridge Road, Singapore, 119228 Singapore
- Department of Haematology-Oncology, National University Cancer Institute, National University Health System, NUH Zone B, Medical Centre, Singapore, 119074 Singapore
| | - Vinay Tergaonkar
- Institute of Molecular and Cell Biology, Agency for Science (IMCB), A*STAR (Agency for Science, Technology and Research,, Singapore, 138673 Singapore
- Department of Pathology, Yong Loo Lin School of Medicine, National University of Singapore (NUS), Singapore, 117597 Singapore
| | - Chee Keong Kwoh
- School of Computer Science and Engineering, Nanyang Technological University, Block N4, 50 Nanyang Avenue, Singapore, 639798 Singapore
| | - Melissa J. Fullwood
- Cancer Science Institute of Singapore, National University of Singapore, 14 Medical Dr, Singapore, 117599 Singapore
- Institute of Molecular and Cell Biology, Agency for Science (IMCB), A*STAR (Agency for Science, Technology and Research,, Singapore, 138673 Singapore
- School of Biological Sciences, Nanyang Technological University, 60 Nanyang Drive, Singapore, 637551 Singapore
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29
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Jerkovic I, Cavalli G. Understanding 3D genome organization by multidisciplinary methods. Nat Rev Mol Cell Biol 2021; 22:511-528. [PMID: 33953379 DOI: 10.1038/s41580-021-00362-w] [Citation(s) in RCA: 139] [Impact Index Per Article: 46.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/16/2021] [Indexed: 02/03/2023]
Abstract
Understanding how chromatin is folded in the nucleus is fundamental to understanding its function. Although 3D genome organization has been historically difficult to study owing to a lack of relevant methodologies, major technological breakthroughs in genome-wide mapping of chromatin contacts and advances in imaging technologies in the twenty-first century considerably improved our understanding of chromosome conformation and nuclear architecture. In this Review, we discuss methods of 3D genome organization analysis, including sequencing-based techniques, such as Hi-C and its derivatives, Micro-C, DamID and others; microscopy-based techniques, such as super-resolution imaging coupled with fluorescence in situ hybridization (FISH), multiplex FISH, in situ genome sequencing and live microscopy methods; and computational and modelling approaches. We describe the most commonly used techniques and their contribution to our current knowledge of nuclear architecture and, finally, we provide a perspective on up-and-coming methods that open possibilities for future major discoveries.
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Affiliation(s)
- Ivana Jerkovic
- Institute of Human Genetics, CNRS, University of Montpellier, Montpellier, France
| | - Giacomo Cavalli
- Institute of Human Genetics, CNRS, University of Montpellier, Montpellier, France.
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30
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Sun D, Weng J, Dong Y, Jiang Y. Three-dimensional genome organization in the central nervous system, implications for neuropsychological disorders. J Genet Genomics 2021; 48:1045-1056. [PMID: 34426099 DOI: 10.1016/j.jgg.2021.06.017] [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] [Received: 05/17/2021] [Revised: 06/11/2021] [Accepted: 06/17/2021] [Indexed: 12/27/2022]
Abstract
Chromosomes in eukaryotic cell nuclei are highly compacted and finely organized into hierarchical three-dimensional (3D) configuration. In recent years, scientists have gained deeper understandings of 3D genome structures and revealed novel evidence linking 3D genome organization to various important cell events on the molecular level. Most importantly, alteration of 3D genome architecture has emerged as an intriguing higher order mechanism that connects disease-related genetic variants in multiple heterogenous and polygenic neuropsychological disorders, delivering novel insights into the etiology. In this review, we provide a brief overview of the hierarchical structures of 3D genome and two proposed regulatory models, loop extrusion and phase separation. We then focus on recent Hi-C data in the central nervous system and discuss 3D genome alterations during normal brain development and in mature neurons. Most importantly, we make a comprehensive review on current knowledge and discuss the role of 3D genome in multiple neuropsychological disorders, including schizophrenia, repeat expansion disorders, 22q11 deletion syndrome, and others.
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Affiliation(s)
- Daijing Sun
- Institutes of Brain Science, State Key Laboratory of Medical Neurobiology, MOE Frontier Center for Brain Science, Fudan University, Shanghai, 200032, China
| | - Jie Weng
- Institutes of Brain Science, State Key Laboratory of Medical Neurobiology, MOE Frontier Center for Brain Science, Fudan University, Shanghai, 200032, China
| | - Yuhao Dong
- Institutes of Brain Science, State Key Laboratory of Medical Neurobiology, MOE Frontier Center for Brain Science, Fudan University, Shanghai, 200032, China
| | - Yan Jiang
- Institutes of Brain Science, State Key Laboratory of Medical Neurobiology, MOE Frontier Center for Brain Science, Fudan University, Shanghai, 200032, China.
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31
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Hu Y, Ma W. EnHiC: learning fine-resolution Hi-C contact maps using a generative adversarial framework. Bioinformatics 2021; 37:i272-i279. [PMID: 34252966 PMCID: PMC8382278 DOI: 10.1093/bioinformatics/btab272] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Motivation The high-throughput chromosome conformation capture (Hi-C) technique has enabled genome-wide mapping of chromatin interactions. However, high-resolution Hi-C data requires costly, deep sequencing; therefore, it has only been achieved for a limited number of cell types. Machine learning models based on neural networks have been developed as a remedy to this problem. Results In this work, we propose a novel method, EnHiC, for predicting high-resolution Hi-C matrices from low-resolution input data based on a generative adversarial network (GAN) framework. Inspired by non-negative matrix factorization, our model fully exploits the unique properties of Hi-C matrices and extracts rank-1 features from multi-scale low-resolution matrices to enhance the resolution. Using three human Hi-C datasets, we demonstrated that EnHiC accurately and reliably enhanced the resolution of Hi-C matrices and outperformed other GAN-based models. Moreover, EnHiC-predicted high-resolution matrices facilitated the accurate detection of topologically associated domains and fine-scale chromatin interactions. Availability and implementation EnHiC is publicly available at https://github.com/wmalab/EnHiC. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yangyang Hu
- Department of Computer Science and Engineering
| | - Wenxiu Ma
- Department of Statistics, University of California Riverside, Riverside, CA 92521, USA
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32
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Abstract
Metastasis is a major contributor to cancer-associated deaths. It is characterized by a multistep process that occurs through the acquisition of molecular and phenotypic changes enabling cancer cells from a primary tumour to disseminate and colonize at distant organ sites. Over the past decade, the discovery and characterization of long noncoding RNAs (lncRNAs) have revealed the diversity of their regulatory roles, including key contributions throughout the metastatic cascade. Here, we review how lncRNAs promote metastasis by functioning in discrete pro-metastatic steps including the epithelial-mesenchymal transition, invasion and migration and organotrophic colonization, and by influencing the metastatic tumour microenvironment, often by interacting within ribonucleoprotein complexes or directly with other nucleic acid entities. We discuss well-characterized lncRNAs with in vivo phenotypes and highlight mechanistic commonalities such as convergence with the TGFβ-ZEB1/ZEB2 axis or the nuclear factor-κB pathway, in addition to lncRNAs with controversial mechanisms and the influence of methodologies on mechanistic interpretation. Furthermore, some lncRNAs can help identify tumours with increased metastatic risk and spur novel therapeutic strategies, with several lncRNAs having shown potential as novel targets for antisense oligonucleotide therapy in animal models. In addition to well-characterized examples of lncRNAs functioning in metastasis, we discuss controversies and ongoing challenges in lncRNA biology. Finally, we present areas for future study for this rapidly evolving field.
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Affiliation(s)
- S John Liu
- Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA, USA
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
- Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, CA, USA
| | - Ha X Dang
- Department of Internal Medicine, Washington University in St Louis, St Louis, MO, USA
- Siteman Cancer Center, Washington University in St Louis, St Louis, MO, USA
- McDonnell Genome Institute, Washington University in St Louis, St Louis, MO, USA
| | - Daniel A Lim
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
- Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, CA, USA
- San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA
| | - Felix Y Feng
- Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
- Division of Hematology and Oncology, Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
- Department of Urology, University of California, San Francisco, San Francisco, CA, USA
| | - Christopher A Maher
- Department of Internal Medicine, Washington University in St Louis, St Louis, MO, USA.
- Siteman Cancer Center, Washington University in St Louis, St Louis, MO, USA.
- McDonnell Genome Institute, Washington University in St Louis, St Louis, MO, USA.
- Department of Biomedical Engineering, Washington University in St Louis, St Louis, MO, USA.
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33
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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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34
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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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35
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Ohno M, Ando T, Priest DG, Taniguchi Y. Hi-CO: 3D genome structure analysis with nucleosome resolution. Nat Protoc 2021; 16:3439-3469. [PMID: 34050337 DOI: 10.1038/s41596-021-00543-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Accepted: 03/23/2021] [Indexed: 01/03/2023]
Abstract
The nucleosome is the basic organizational unit of the genome. The folding structure of nucleosomes is closely related to genome functions, and has been reported to be in dynamic interplay with binding of various nuclear proteins to genomic loci. Here, we describe our high-throughput chromosome conformation capture with nucleosome orientation (Hi-CO) technology to derive 3D nucleosome positions with their orientations at every genomic locus in the nucleus. This technology consists of an experimental procedure for nucleosome proximity analysis and a computational procedure for 3D modeling. The experimental procedure is based on an improved method of high-throughput chromosome conformation capture (Hi-C) analysis. Whereas conventional Hi-C allows spatial proximity analysis among genomic loci with 1-10 kbp resolution, our Hi-CO allows proximity analysis among DNA entry or exit points at every nucleosome locus. This analysis is realized by carrying out ligations among the entry/exit points in every nucleosome in a micrococcal-nuclease-fragmented genome, and by quantifying frequencies of ligation products with next-generation sequencing. Our protocol has enabled this analysis by cleanly excluding unwanted non-ligation products that are abundant owing to the frequent genome fragmentation by micrococcal nuclease. The computational procedure is based on simulated annealing-molecular dynamics, which allows determination of optimized 3D positions and orientations of every nucleosome that satisfies the proximity ligation data sufficiently well. Typically, examination of the Saccharomyces cerevisiae genome with 130 million sequencing reads facilitates analysis of a total of 66,360 nucleosome loci with 6.8 nm resolution. The technique requires 2-3 weeks for sequencing library preparation and 2 weeks for simulation.
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Affiliation(s)
- Masae Ohno
- Laboratory for Cell Systems Control, RIKEN Center for Biosystems Dynamics Research, Osaka, Japan.,Institute for Integrated Cell-Material Sciences (iCeMS), Kyoto University, Kyoto, Japan
| | - Tadashi Ando
- Laboratory for Biomolecular Function Simulation, Quantitative Biology Center, RIKEN, Kobe, Japan.,Department of Applied Electronics, Faculty of Advanced Engineering, Tokyo University of Science, Tokyo, Japan
| | - David G Priest
- Laboratory for Cell Systems Control, RIKEN Center for Biosystems Dynamics Research, Osaka, Japan.,Department of Biochemistry and Molecular Biology, Bio21 Molecular Science and Biotechnology Institute, University of Melbourne, Melbourne, VIC, Australia
| | - Yuichi Taniguchi
- Laboratory for Cell Systems Control, RIKEN Center for Biosystems Dynamics Research, Osaka, Japan. .,Institute for Integrated Cell-Material Sciences (iCeMS), Kyoto University, Kyoto, Japan. .,Graduate School of Biostudies, Kyoto University, Kyoto, Japan.
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36
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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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37
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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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38
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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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39
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Gridina M, Mozheiko E, Valeev E, Nazarenko LP, Lopatkina ME, Markova ZG, Yablonskaya MI, Voinova VY, Shilova NV, Lebedev IN, Fishman V. A cookbook for DNase Hi-C. Epigenetics Chromatin 2021; 14:15. [PMID: 33743768 PMCID: PMC7981840 DOI: 10.1186/s13072-021-00389-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2020] [Accepted: 03/08/2021] [Indexed: 12/23/2022] Open
Abstract
Background The Hi-C technique is widely employed to study the 3-dimensional chromatin architecture and to assemble genomes. The conventional in situ Hi-C protocol employs restriction enzymes to digest chromatin, which results in nonuniform genomic coverage. Using sequence-agnostic restriction enzymes, such as DNAse I, could help to overcome this limitation. Results In this study, we compare different DNAse Hi-C protocols and identify the critical steps that significantly affect the efficiency of the protocol. In particular, we show that the SDS quenching strategy strongly affects subsequent chromatin digestion. The presence of biotinylated oligonucleotide adapters may lead to ligase reaction by-products, which can be avoided by rational design of the adapter sequences. Moreover, the use of nucleotide-exchange enzymes for biotin fill-in enables simultaneous labelling and repair of DNA ends, similar to the conventional Hi-C protocol. These improvements simplify the protocol, making it less expensive and time-consuming. Conclusions We propose a new robust protocol for the preparation of DNAse Hi-C libraries from cultured human cells and blood samples supplemented with experimental controls and computational tools for the evaluation of library quality. Supplementary Information The online version contains supplementary material available at 10.1186/s13072-021-00389-5.
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Affiliation(s)
- Maria Gridina
- Institute of Cytology and Genetics SB RAS, Lavrentjeva ave 10, Novosibirsk, Russia
| | - Evgeniy Mozheiko
- Institute of Cytology and Genetics SB RAS, Lavrentjeva ave 10, Novosibirsk, Russia
| | - Emil Valeev
- Institute of Cytology and Genetics SB RAS, Lavrentjeva ave 10, Novosibirsk, Russia.,Novosibirsk State University, Pirogova str., 2, Novosibirsk, Russia
| | - Ludmila P Nazarenko
- Research Institute of Medical Genetics, Tomsk National Research Medical Center, Kooperativny Str, 5, Tomsk, Russia
| | - Maria E Lopatkina
- Research Institute of Medical Genetics, Tomsk National Research Medical Center, Kooperativny Str, 5, Tomsk, Russia
| | - Zhanna G Markova
- Research Centre for Medical Genetics, Moskvorechie str., 1, Moscow, Russia
| | - Maria I Yablonskaya
- Clinical Research Institute of Pediatrics Named After Acad. Y.E. Veltischev, Moscow, Russia
| | - Viktoria Yu Voinova
- Clinical Research Institute of Pediatrics Named After Acad. Y.E. Veltischev, Moscow, Russia
| | - Nadezhda V Shilova
- Research Centre for Medical Genetics, Moskvorechie str., 1, Moscow, Russia
| | - Igor N Lebedev
- Research Institute of Medical Genetics, Tomsk National Research Medical Center, Kooperativny Str, 5, Tomsk, Russia
| | - Veniamin Fishman
- Institute of Cytology and Genetics SB RAS, Lavrentjeva ave 10, Novosibirsk, Russia. .,Novosibirsk State University, Pirogova str., 2, Novosibirsk, Russia.
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40
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Lee BH, Rhie SK. Molecular and computational approaches to map regulatory elements in 3D chromatin structure. Epigenetics Chromatin 2021; 14:14. [PMID: 33741028 PMCID: PMC7980343 DOI: 10.1186/s13072-021-00390-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Accepted: 03/08/2021] [Indexed: 12/19/2022] Open
Abstract
Epigenetic marks do not change the sequence of DNA but affect gene expression in a cell-type specific manner by altering the activities of regulatory elements. Development of new molecular biology assays, sequencing technologies, and computational approaches enables us to profile the human epigenome in three-dimensional structure genome-wide. Here we describe various molecular biology techniques and bioinformatic tools that have been developed to measure the activities of regulatory elements and their chromatin interactions. Moreover, we list currently available three-dimensional epigenomic data sets that are generated in various human cell types and tissues to assist in the design and analysis of research projects.
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Affiliation(s)
- Beoung Hun Lee
- Department of Biochemistry and Molecular Medicine and the Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90089, USA
| | - Suhn K Rhie
- Department of Biochemistry and Molecular Medicine and the Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90089, USA.
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41
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Ye T, Ma W. ASHIC: hierarchical Bayesian modeling of diploid chromatin contacts and structures. Nucleic Acids Res 2020; 48:e123. [PMID: 33074315 PMCID: PMC7708071 DOI: 10.1093/nar/gkaa872] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2020] [Revised: 09/15/2020] [Accepted: 09/24/2020] [Indexed: 12/29/2022] Open
Abstract
The recently developed Hi-C technique has been widely applied to map genome-wide chromatin interactions. However, current methods for analyzing diploid Hi-C data cannot fully distinguish between homologous chromosomes. Consequently, the existing diploid Hi-C analyses are based on sparse and inaccurate allele-specific contact matrices, which might lead to incorrect modeling of diploid genome architecture. Here we present ASHIC, a hierarchical Bayesian framework to model allele-specific chromatin organizations in diploid genomes. We developed two models under the Bayesian framework: the Poisson-multinomial (ASHIC-PM) model and the zero-inflated Poisson-multinomial (ASHIC-ZIPM) model. The proposed ASHIC methods impute allele-specific contact maps from diploid Hi-C data and simultaneously infer allelic 3D structures. Through simulation studies, we demonstrated that ASHIC methods outperformed existing approaches, especially under low coverage and low SNP density conditions. Additionally, in the analyses of diploid Hi-C datasets in mouse and human, our ASHIC-ZIPM method produced fine-resolution diploid chromatin maps and 3D structures and provided insights into the allelic chromatin organizations and functions. To summarize, our work provides a statistically rigorous framework for investigating fine-scale allele-specific chromatin conformations. The ASHIC software is publicly available at https://github.com/wmalab/ASHIC.
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Affiliation(s)
- Tiantian Ye
- Genetics, Genomics and Bioinformatics Program
| | - Wenxiu Ma
- Department of Statistics, University of California Riverside, Riverside, CA 92521, USA
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42
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Jiang G, Su Z, Liang X, Huang Y, Lan Z, Jiang X. Long non-coding RNAs in prostate tumorigenesis and therapy (Review). Mol Clin Oncol 2020; 13:76. [PMID: 33005410 DOI: 10.3892/mco.2020.2146] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2019] [Accepted: 06/18/2020] [Indexed: 12/19/2022] Open
Abstract
Prostate cancer (PCa) is one of the most frequently diagnosed malignancy. Although there have been many advances in PCa diagnosis and therapy, the concrete mechanism remains unknown. Long non-coding RNAs (lncRNAs) are novel biomarkers associated with PCa, and their dysregulated expression is closely associated with risk stratification, diagnosis and carcinogenesis. Accumulating evidence has suggested that lncRNAs play important roles in prostate tumorigenesis through relevant pathways, such as androgen receptor interaction and PI3K/Akt. The present review systematically summarized the potential clinical utility of lncRNAs and provided a novel guide for their function in PCa.
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Affiliation(s)
- Ganggang Jiang
- Department of Urology, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong 510700, P.R. China.,Department of Center Laboratory, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong 510700, P.R. China
| | - Zhengming Su
- Department of Urology, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong 510700, P.R. China.,Department of Center Laboratory, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong 510700, P.R. China
| | - Xue Liang
- Department of Center Laboratory, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong 510700, P.R. China
| | - Yiqiao Huang
- Department of Urology, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong 510700, P.R. China
| | - Ziquan Lan
- Department of Urology, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong 510700, P.R. China
| | - Xianhan Jiang
- Department of Urology, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong 510700, P.R. China.,Department of Center Laboratory, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong 510700, P.R. China
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43
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Takei R, Cadzow M, Markie D, Bixley M, Phipps-Green A, Major TJ, Li C, Choi HK, Li Z, Hu H, Guo H, He M, Shi Y, Stamp LK, Dalbeth N, Merriman TR, Wei WH. Trans-ancestral dissection of urate- and gout-associated major loci SLC2A9 and ABCG2 reveals primate-specific regulatory effects. J Hum Genet 2020; 66:161-169. [PMID: 32778763 DOI: 10.1038/s10038-020-0821-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Revised: 07/19/2020] [Accepted: 07/21/2020] [Indexed: 02/07/2023]
Abstract
Gout is a complex inflammatory arthritis affecting ~20% of people with an elevated serum urate level (hyperuricemia). Gout and hyperuricemia are essentially specific to humans and other higher primates, with varied prevalence across ancestral groups. SLC2A9 and ABCG2 are major loci associated with both urate and gout in multiple ancestral groups. However, fine mapping has been challenging due to extensive linkage disequilibrium underlying the associated regions. We used trans-ancestral fine mapping integrated with primate-specific genomic information to address this challenge. Trans-ancestral meta-analyses of GWAS cohorts of either European (EUR) or East Asian (EAS) ancestry resulted in single-variant resolution mappings for SLC2A9 (rs3775948 for urate and rs4697701 for gout) and ABCG2 (rs2622621 for gout). Tests of colocalization of variants in both urate and gout suggested existence of a shared candidate causal variant for SLC2A9 only in EUR and for ABCG2 only in EAS. The fine-mapped gout variant rs4697701 was within an ancient enhancer, whereas rs2622621 was within a primate-specific transposable element, both supported by functional evidence from the Roadmap Epigenomics project in human primary tissues relevant to urate and gout. Additional primate-specific elements were found near both loci and those adjacent to SLC2A9 overlapped with known statistical epistatic interactions associated with urate as well as multiple super-enhancers identified in urate-relevant tissues. We conclude that by leveraging ancestral differences trans-ancestral fine mapping has identified ancestral and functional variants for SLC2A9 or ABCG2 with primate-specific regulatory effects on urate and gout.
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Affiliation(s)
- Riku Takei
- Department of Women's and Children's Health, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand.,Department of Biochemistry, University of Otago, Dunedin, New Zealand
| | - Murray Cadzow
- Department of Biochemistry, University of Otago, Dunedin, New Zealand
| | - David Markie
- Department of Pathology, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand
| | - Matt Bixley
- Department of Biochemistry, University of Otago, Dunedin, New Zealand
| | | | - Tanya J Major
- Department of Biochemistry, University of Otago, Dunedin, New Zealand
| | - Changgui Li
- Shandong Gout Clinical Medical Center, Qingdao, 266003, China.,Shandong Provincial Key Laboratory of Metabolic Disease, The Affiliated Hospital of Qingdao University, Qingdao, 266003, China
| | - Hyon K Choi
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Zhiqiang Li
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, 200030, China.,Institute of Social Cognitive and Behavioral Sciences, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Hua Hu
- Department of Occupational and Environmental Health and the Ministry of Education Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science & Technology, Wuhan, 430030, Hubei, China
| | | | - Hui Guo
- Center for Biostatistics, School of Health Sciences, The University of Manchester, Manchester, UK
| | - Meian He
- Department of Occupational and Environmental Health and the Ministry of Education Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science & Technology, Wuhan, 430030, Hubei, China
| | - Yongyong Shi
- Shandong Gout Clinical Medical Center, Qingdao, 266003, China.,Shandong Provincial Key Laboratory of Metabolic Disease, The Affiliated Hospital of Qingdao University, Qingdao, 266003, China.,Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, 200030, China.,Institute of Social Cognitive and Behavioral Sciences, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Lisa K Stamp
- Department of Medicine, University of Otago, Christchurch, Christchurch, New Zealand
| | - Nicola Dalbeth
- Department of Medicine, University of Auckland, Auckland, New Zealand
| | - Tony R Merriman
- Department of Biochemistry, University of Otago, Dunedin, New Zealand
| | - Wen-Hua Wei
- Department of Women's and Children's Health, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand.
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Outeiro-Pinho G, Barros-Silva D, Correia MP, Henrique R, Jerónimo C. Renal Cell Tumors: Uncovering the Biomarker Potential of ncRNAs. Cancers (Basel) 2020; 12:cancers12082214. [PMID: 32784737 PMCID: PMC7465320 DOI: 10.3390/cancers12082214] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Revised: 07/30/2020] [Accepted: 08/05/2020] [Indexed: 12/24/2022] Open
Abstract
Renal cell tumors (RCT) remain as one of the most common and lethal urological tumors worldwide. Discrimination between (1) benign and malignant disease, (2) indolent and aggressive tumors, and (3) patient responsiveness to a specific therapy is of major clinical importance, allowing for a more efficient patient management. Nonetheless, currently available tools provide limited information and novel strategies are needed. Over the years, a putative role of non-coding RNAs (ncRNAs) as disease biomarkers has gained relevance and is now one of the most prolific fields in biological sciences. Herein, we extensively sought the most significant reports on ncRNAs as potential RCTs' diagnostic, prognostic, predictive, and monitoring biomarkers. We could conclude that ncRNAs, either alone or in combination with currently used clinical and pathological parameters, might represent key elements to improve patient management, potentiating the implementation of precision medicine. Nevertheless, most ncRNA biomarkers require large-scale validation studies, prior to clinical implementation.
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Affiliation(s)
- Gonçalo Outeiro-Pinho
- Cancer Biology and Epigenetics Group, IPO Porto Research Center (CI-IPOP), Portuguese Oncology Institute of Porto (IPO Porto), Rua Dr. António Bernardino de Almeida, 4200-072 Porto, Portugal; (G.O.-P.); (D.B.-S.); (M.P.C.); (R.H.)
| | - Daniela Barros-Silva
- Cancer Biology and Epigenetics Group, IPO Porto Research Center (CI-IPOP), Portuguese Oncology Institute of Porto (IPO Porto), Rua Dr. António Bernardino de Almeida, 4200-072 Porto, Portugal; (G.O.-P.); (D.B.-S.); (M.P.C.); (R.H.)
| | - Margareta P. Correia
- Cancer Biology and Epigenetics Group, IPO Porto Research Center (CI-IPOP), Portuguese Oncology Institute of Porto (IPO Porto), Rua Dr. António Bernardino de Almeida, 4200-072 Porto, Portugal; (G.O.-P.); (D.B.-S.); (M.P.C.); (R.H.)
| | - Rui Henrique
- Cancer Biology and Epigenetics Group, IPO Porto Research Center (CI-IPOP), Portuguese Oncology Institute of Porto (IPO Porto), Rua Dr. António Bernardino de Almeida, 4200-072 Porto, Portugal; (G.O.-P.); (D.B.-S.); (M.P.C.); (R.H.)
- Department of Pathology, Portuguese Oncology Institute of Porto, 4200-072 Porto, Portugal
- Department of Pathology and Molecular Immunology, Institute of Biomedical Sciences Abel Salazar-University of Porto (ICBAS-UP), Rua de Jorge Viterbo Ferreira n. 228, 4050-313 Porto, Portugal
| | - Carmen Jerónimo
- Cancer Biology and Epigenetics Group, IPO Porto Research Center (CI-IPOP), Portuguese Oncology Institute of Porto (IPO Porto), Rua Dr. António Bernardino de Almeida, 4200-072 Porto, Portugal; (G.O.-P.); (D.B.-S.); (M.P.C.); (R.H.)
- Department of Pathology and Molecular Immunology, Institute of Biomedical Sciences Abel Salazar-University of Porto (ICBAS-UP), Rua de Jorge Viterbo Ferreira n. 228, 4050-313 Porto, Portugal
- Correspondence: ; Tel.: +351-225084000; Fax: +351-225084199
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Yuan Y, Chung CYL, Chan TF. Advances in optical mapping for genomic research. Comput Struct Biotechnol J 2020; 18:2051-2062. [PMID: 32802277 PMCID: PMC7419273 DOI: 10.1016/j.csbj.2020.07.018] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2020] [Revised: 07/08/2020] [Accepted: 07/24/2020] [Indexed: 12/28/2022] Open
Abstract
Recent advances in optical mapping have allowed the construction of improved genome assemblies with greater contiguity. Optical mapping also enables genome comparison and identification of large-scale structural variations. Association of these large-scale genomic features with biological functions is an important goal in plant and animal breeding and in medical research. Optical mapping has also been used in microbiology and still plays an important role in strain typing and epidemiological studies. Here, we review the development of optical mapping in recent decades to illustrate its importance in genomic research. We detail its applications and algorithms to show its specific advantages. Finally, we discuss the challenges required to facilitate the optimization of optical mapping and improve its future development and application.
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Key Words
- 3D, three-dimensional
- DBG, de Bruijn graph
- DLS, direct label and strain
- DNA, deoxyribonucleic acid
- Genome assembly
- Hi-C, high-throughput chromosome conformation capture
- Mb, million base pair
- Next generation sequencing
- OLC, overlap-layout-consensus
- Optical mapping
- PCR, polymerase chain reaction
- PacBio, Pacific Biosciences
- SRS, short-read sequencing
- SV, structural variation
- Structural variation
- bp, base pair
- kb, kilobase pair
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Affiliation(s)
- Yuxuan Yuan
- School of Life Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
- State Key Laboratory for Agrobiotechnology, The Chinese University of Hong Kong, Hong Kong SAR, China
- AoE Centre for Genomic Studies on Plant-Environment Interaction for Sustainable Agriculture and Food Security, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Claire Yik-Lok Chung
- School of Life Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
- State Key Laboratory for Agrobiotechnology, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Ting-Fung Chan
- School of Life Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
- State Key Laboratory for Agrobiotechnology, The Chinese University of Hong Kong, Hong Kong SAR, China
- AoE Centre for Genomic Studies on Plant-Environment Interaction for Sustainable Agriculture and Food Security, The Chinese University of Hong Kong, Hong Kong SAR, China
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Kong S, Li Q, Zhang G, Li Q, Huang Q, Huang L, Zhang H, Huang Y, Peng Y, Qin B, Zhang Y. Exonuclease combinations reduce noises in 3D genomics technologies. Nucleic Acids Res 2020; 48:e44. [PMID: 32128590 PMCID: PMC7192622 DOI: 10.1093/nar/gkaa106] [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: 01/04/2019] [Revised: 02/04/2020] [Accepted: 02/19/2020] [Indexed: 12/21/2022] Open
Abstract
Chromosome conformation-capture technologies are widely used in 3D genomics; however, experimentally, such methods have high-noise limitations and, therefore, require significant bioinformatics efforts to extract reliable distal interactions. Miscellaneous undesired linear DNAs, present during proximity-ligation, represent a main noise source, which needs to be minimized or eliminated. In this study, different exonuclease combinations were tested to remove linear DNA fragments from a circularized DNA preparation. This method efficiently removed linear DNAs, raised the proportion of annulation and increased the valid-pairs ratio from ∼40% to ∼80% for enhanced interaction detection in standard Hi-C. This strategy is applicable for development of various 3D genomics technologies, or optimization of Hi-C sequencing efficiency.
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Affiliation(s)
- Siyuan Kong
- Animal Functional Genomics Group, Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Qing Li
- Animal Functional Genomics Group, Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Gaolin Zhang
- Animal Functional Genomics Group, Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Qiujia Li
- Animal Functional Genomics Group, Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Qitong Huang
- Animal Functional Genomics Group, Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Lei Huang
- Animal Functional Genomics Group, Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Hui Zhang
- CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China
| | - Yinghua Huang
- CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China
| | - Yanling Peng
- Animal Functional Genomics Group, Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Baoming Qin
- CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China
| | - Yubo Zhang
- Animal Functional Genomics Group, Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
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Zhang Y, Li Z, Bian S, Zhao H, Feng D, Chen Y, Hou Y, Liu Q, Hao B. HiCoP, a simple and robust method for detecting interactions of regulatory regions. Epigenetics Chromatin 2020; 13:27. [PMID: 32611439 PMCID: PMC7329539 DOI: 10.1186/s13072-020-00348-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Accepted: 06/12/2020] [Indexed: 01/10/2023] Open
Abstract
Background Chromatin physical interactions provide essential information for understanding the regulation of cis-elements like enhancers, promoters, and insulators in cell development and differentiation. The Hi-C assay is a technique detecting chromatin structures of the whole genome, but not sensitive to interactions of regulatory elements. Several methods, like HiChIP, DNase-C, and OCEAN-C, have been developed for enriching interactions of regulatory regions, but all of them have some shortcomings. New simple, efficient, and robust methods are still in need for detecting interactions of regulatory regions. Results We developed a new, simple, and robust assay called CoP (Column Purified chromatin) for profiling of open chromatin regions by directly purifying fragmentized crosslinked chromatin with a DNA purification column. The accessible chromatin regions, including active enhancers, promoters, and insulators, were significantly enriched in CoP chromatin. The CoP-seq assay can efficiently detect open chromatin regions, especially active promoters, with a high signal-to-noise ratio. We integrated the CoP-seq and Hi-C technique (HiCoP) to detect interactions of accessible chromatin regions, which represent active cis-regulatory elements in cells. We observed that the HiCoP captured the peaks in the promoters-associated enhancer regions. HiCoP detected more promoter–enhancer (P–E), promoter–promoter (P–P), and enhancer–enhancer (E–E) interactions within 20 kb–5 Mb than Hi-C. Most of the loops identified by HiCoP are associated with the expressed genes. Conclusion CoP assay can efficiently enrich open chromatin regions. When CoP assay was integrated with Hi-C assay, it provides a simple, robust, alternative technique for profiling accessible chromatin regions and chromatin conformation simultaneously.
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Affiliation(s)
- Yan Zhang
- Department of Hematology, Nanfang Hospital, Southern Medical University, Guangzhou, China.,Guangdong Provincial Key Laboratory of Tumor Immunotherapy, Cancer Research Institute, School of Basic Medical Sciences, Southern Medical University, 1023 Shatai Road, Guangzhou, 510515, Guangdong, People's Republic of China
| | - Zhaoqiang Li
- Guangdong Provincial Key Laboratory of Tumor Immunotherapy, Cancer Research Institute, School of Basic Medical Sciences, Southern Medical University, 1023 Shatai Road, Guangzhou, 510515, Guangdong, People's Republic of China
| | - Shasha Bian
- Medical Genetic Institute of Henan Province, Henan Provincial Key Laboratory of Genetic Diseases and Functional Genomics, Henan Provincial People's Hospital, Zhengzhou University People's Hospital, Henan University People's Hospital, Zhengzhou, 450003, Henan, China
| | - Hao Zhao
- Guangdong Provincial Key Laboratory of Tumor Immunotherapy, Cancer Research Institute, School of Basic Medical Sciences, Southern Medical University, 1023 Shatai Road, Guangzhou, 510515, Guangdong, People's Republic of China
| | - Delong Feng
- Guangdong Provincial Key Laboratory of Tumor Immunotherapy, Cancer Research Institute, School of Basic Medical Sciences, Southern Medical University, 1023 Shatai Road, Guangzhou, 510515, Guangdong, People's Republic of China
| | - Yanhong Chen
- Medical Genetic Institute of Henan Province, Henan Provincial Key Laboratory of Genetic Diseases and Functional Genomics, Henan Provincial People's Hospital, Zhengzhou University People's Hospital, Henan University People's Hospital, Zhengzhou, 450003, Henan, China
| | - Yuhe Hou
- Guangdong Provincial Key Laboratory of Tumor Immunotherapy, Cancer Research Institute, School of Basic Medical Sciences, Southern Medical University, 1023 Shatai Road, Guangzhou, 510515, Guangdong, People's Republic of China
| | - Qifa Liu
- Department of Hematology, Nanfang Hospital, Southern Medical University, Guangzhou, China.
| | - Bingtao Hao
- Guangdong Provincial Key Laboratory of Tumor Immunotherapy, Cancer Research Institute, School of Basic Medical Sciences, Southern Medical University, 1023 Shatai Road, Guangzhou, 510515, Guangdong, People's Republic of China. .,Medical Genetic Institute of Henan Province, Henan Provincial Key Laboratory of Genetic Diseases and Functional Genomics, Henan Provincial People's Hospital, Zhengzhou University People's Hospital, Henan University People's Hospital, Zhengzhou, 450003, Henan, China. .,National Health Commission Key Laboratory of Birth Defects Prevention, Henan Key Laboratory of Population Defects Prevention, Zhengzhou, 450003, Henan, China.
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48
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Yang J, Wan W, Xie M, Mao J, Dong Z, Lu S, He J, Xie F, Liu G, Dai X, Chang Z, Zhao R, Zhang R, Wang S, Zhang Y, Zhang W, Wang W, Li X. Chromosome‐level reference genome assembly and gene editing of the dead‐leaf butterfly
Kallima inachus. Mol Ecol Resour 2020; 20:1080-1092. [DOI: 10.1111/1755-0998.13185] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2019] [Revised: 04/30/2020] [Accepted: 05/05/2020] [Indexed: 01/26/2023]
Affiliation(s)
- Jie Yang
- School of Ecology and Environment Northwestern Polytechnical University Xi'an China
| | - Wenting Wan
- School of Ecology and Environment Northwestern Polytechnical University Xi'an China
- State Key Laboratory of Genetic Resources and Evolution Kunming Institute of Zoology Chinese Academy of Sciences Kunming China
| | - Meng Xie
- State Key Laboratory of Genetic Resources and Evolution Kunming Institute of Zoology Chinese Academy of Sciences Kunming China
- College of Life Sciences Sichuan Agricultural University Yaan China
| | - Junlai Mao
- School of Marine Science and Technology Zhejiang Ocean University Zhoushan China
| | - Zhiwei Dong
- State Key Laboratory of Genetic Resources and Evolution Kunming Institute of Zoology Chinese Academy of Sciences Kunming China
| | - Sihan Lu
- School of Ecology and Environment Northwestern Polytechnical University Xi'an China
| | - Jinwu He
- School of Ecology and Environment Northwestern Polytechnical University Xi'an China
- State Key Laboratory of Genetic Resources and Evolution Kunming Institute of Zoology Chinese Academy of Sciences Kunming China
| | - Feiang Xie
- School of Marine Science and Technology Zhejiang Ocean University Zhoushan China
| | - Guichun Liu
- School of Ecology and Environment Northwestern Polytechnical University Xi'an China
- State Key Laboratory of Genetic Resources and Evolution Kunming Institute of Zoology Chinese Academy of Sciences Kunming China
| | - Xuelei Dai
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province College of Animal Science and Technology Northwest A&F University Yangling China
| | - Zhou Chang
- State Key Laboratory of Genetic Resources and Evolution Kunming Institute of Zoology Chinese Academy of Sciences Kunming China
| | - Ruoping Zhao
- State Key Laboratory of Genetic Resources and Evolution Kunming Institute of Zoology Chinese Academy of Sciences Kunming China
| | - Ru Zhang
- School of Ecology and Environment Northwestern Polytechnical University Xi'an China
| | - Shuting Wang
- Peking‐Tsinghua Center for Life Sciences Peking University Beijing China
| | - Yiming Zhang
- Peking‐Tsinghua Center for Life Sciences Peking University Beijing China
| | - Wei Zhang
- State Key Laboratory of Protein and Plant Gene Research Peking‐Tsinghua Center for Life Sciences and School of Life Sciences Peking University Beijing China
| | - Wen Wang
- School of Ecology and Environment Northwestern Polytechnical University Xi'an China
- State Key Laboratory of Genetic Resources and Evolution Kunming Institute of Zoology Chinese Academy of Sciences Kunming China
- Center for Excellence in Animal Evolution and Genetics Kunming China
| | - Xueyan Li
- State Key Laboratory of Genetic Resources and Evolution Kunming Institute of Zoology Chinese Academy of Sciences Kunming China
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49
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Li M, Gan J, Sun Y, Xu Z, Yang J, Sun Y, Li C. Architectural proteins for the formation and maintenance of the 3D genome. SCIENCE CHINA. LIFE SCIENCES 2020; 63:795-810. [PMID: 32249389 DOI: 10.1007/s11427-019-1613-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/04/2019] [Accepted: 12/26/2019] [Indexed: 12/20/2022]
Abstract
Eukaryotic genomes are densely packaged into hierarchical three-dimensional (3D) structures that contain information about gene regulation and many other biological processes. With the development of imaging and sequencing-based technologies, 3D genome studies have revealed that the high-order chromatin structure is composed of hierarchical levels, including chromosome territories, A/B compartments, topologically associated domains, and chromatin loops. However, how this chromatin architecture is formed and maintained is not completely clear. In this review, we introduce experimental methods to investigate the 3D genome, review major architectural proteins that regulate 3D chromatin organization in mammalian cells, such as CTCF (CCCTC-binding factor), cohesin, lamins, and transcription factors, and discuss relevant mechanisms such as phase separation.
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Affiliation(s)
- Mengfan Li
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies; School of Life Sciences, Peking University, Beijing, 100871, China
| | - Jingbo Gan
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies; School of Life Sciences, Peking University, Beijing, 100871, China
| | - Yuao Sun
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies; School of Life Sciences, Peking University, Beijing, 100871, China
- State Key Laboratory of Membrane Biology, School of Life Sciences; Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing, 100871, China
| | - Zihan Xu
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies; School of Life Sciences, Peking University, Beijing, 100871, China
| | - Junsheng Yang
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies; School of Life Sciences, Peking University, Beijing, 100871, China
- State Key Laboratory of Membrane Biology, School of Life Sciences; Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing, 100871, China
| | - Yujie Sun
- State Key Laboratory of Membrane Biology, School of Life Sciences; Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing, 100871, China.
| | - Cheng Li
- Center for Statistical Science, Center for Bioinformatics, School of Life Sciences, Peking University, Beijing, 100871, China.
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50
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Übelmesser N, Papantonis A. Technologies to study spatial genome organization: beyond 3C. Brief Funct Genomics 2020; 18:395-401. [PMID: 31609405 DOI: 10.1093/bfgp/elz019] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2019] [Revised: 06/27/2019] [Accepted: 07/15/2019] [Indexed: 01/05/2023] Open
Abstract
The way that chromatin is organized in three-dimensional nuclear space is now acknowledged as a factor critical for the major cell processes, like transcription, replication and cell division. Researchers have been armed with new molecular and imaging technologies to study this structure-to-function link of genomes, spearheaded by the introduction of the 'chromosome conformation capture' technology more than a decade ago. However, this technology is not without shortcomings, and novel variants and orthogonal approaches are being developed to overcome these. As a result, the field of nuclear organization is constantly fueled by methods of increasing resolution and/or throughput that strive to eliminate systematic biases and increase precision. In this review, we attempt to highlight the most recent advances in technology that promise to provide novel insights on how chromosomes fold and function.
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Affiliation(s)
- Nadine Übelmesser
- Institute of Pathology, University Medical Center Göttingen, 37075 Göttingen, Germany
| | - Argyris Papantonis
- Institute of Pathology, University Medical Center Göttingen, 37075 Göttingen, Germany
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