101
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Kyrochristos ID, Ziogas DE, Roukos DH. Dynamic genome and transcriptional network-based biomarkers and drugs: precision in breast cancer therapy. Med Res Rev 2018; 39:1205-1227. [PMID: 30417574 DOI: 10.1002/med.21549] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2018] [Revised: 10/12/2018] [Accepted: 10/14/2018] [Indexed: 12/13/2022]
Abstract
Despite remarkable progress in medium-term overall survival benefit in the adjuvant, neoadjuvant and metastatic settings, with multiple recent targeted drug approvals, acquired resistance, late relapse, and cancer-related death rates remain challenging. Integrated technological systems have been developed to overcome these unmet needs. The characterization of structural and functional noncoding genome elements through next-generation sequencing (NGS) systems, Hi-C and CRISPR/Cas9, as well as computational models, allows for whole genome and transcriptome analysis. Rapid progress in large-scale single-biopsy genome analysis has identified several novel breast cancer driver genes and oncotargets. The exploration of spatiotemporal tumor evolution has returned exciting while inconclusive data on dynamic intratumor heterogeneity (ITH) through multiregional NGS and single-cell DNA/RNA sequencing and circulating genomic subclones (cGSs) by serial circulating cell-free DNA NGS to predict and overcome intrinsic and acquired therapeutic resistance. This review discusses reliable breast cancer genome analysis data and focuses on two major crucial perspectives. The validation of ITH, cGSs, and intrapatient genetic/genomic heterogeneity as predictive biomarkers, as well as the valid discovery of novel oncotargets within patient-centric genomic trials, encouraging early drug development, could optimize primary and secondary therapeutic decision-making. A longer-term goal is to identify the individualized landscape of both coding and noncoding key mutations. This progress will enable the understanding of molecular mechanisms perturbating regulatory networks, shaping the pharmaceutical controllability of deregulated transcriptional biocircuits.
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Affiliation(s)
- Ioannis D Kyrochristos
- Centre for Biosystems and Genome Network Medicine, Ioannina University, Ioannina, Greece.,Department of Surgery, Ioannina University Hospital, Ioannina, Greece
| | - Demosthenes E Ziogas
- Centre for Biosystems and Genome Network Medicine, Ioannina University, Ioannina, Greece.,Department of Surgery, 'G. Hatzikosta' General Hospital, Ioannina, Greece
| | - Dimitrios H Roukos
- Centre for Biosystems and Genome Network Medicine, Ioannina University, Ioannina, Greece.,Department of Surgery, Ioannina University Hospital, Ioannina, Greece.,Department of Systems Biology, Biomedical Research Foundation of the Academy of Athens (BRFAA), Athens, Greece
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102
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Diament A, Tuller T. Modeling three-dimensional genomic organization in evolution and pathogenesis. Semin Cell Dev Biol 2018; 90:78-93. [PMID: 30030143 DOI: 10.1016/j.semcdb.2018.07.008] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2018] [Accepted: 07/08/2018] [Indexed: 12/17/2022]
Abstract
The regulation of gene expression is mediated via the complex three-dimensional (3D) conformation of the genetic material and its interactions with various intracellular factors. Various experimental and computational approaches have been developed in recent years for understating the relation between the 3D conformation of the genome and the phenotypes of cells in normal condition and diseases. In this review, we will discuss novel approaches for analyzing and modeling the 3D genomic conformation, focusing on deciphering disease-causing mutations that affect gene expression. We conclude that as this is a very challenging mission, an important direction should involve the comparative analysis of various 3D models from various organisms or cells.
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Affiliation(s)
- Alon Diament
- Dept. of Biomedical Engineering, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Tamir Tuller
- Dept. of Biomedical Engineering, Tel Aviv University, Tel Aviv 6997801, Israel; The Sagol School of Neuroscience, Tel-Aviv University, Tel Aviv 6997801, Israel.
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103
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Chakraborty A, Ay F. The role of 3D genome organization in disease: From compartments to single nucleotides. Semin Cell Dev Biol 2018; 90:104-113. [PMID: 30017907 DOI: 10.1016/j.semcdb.2018.07.005] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2018] [Accepted: 07/03/2018] [Indexed: 12/26/2022]
Abstract
Since the advent of the chromosome conformation capture technology, our understanding of the human genome 3D organization has grown rapidly and we now know that human interphase chromosomes are folded into multiple layers of hierarchical structures and each layer can play a critical role in transcriptional regulation. Alterations in any one of these finely-tuned layers can lead to unwanted cascade of molecular events and ultimately drive the manifestation of diseases and phenotypes. Here we discuss, starting from chromosome level organization going down to single nucleotide changes, recent studies linking diseases or phenotypes to changes in the 3D genome architecture.
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Affiliation(s)
| | - Ferhat Ay
- La Jolla Institute for Allergy and Immunology, La Jolla, CA, 92037, USA; UC San Diego, School of Medicine, La Jolla, 92093, CA, USA.
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104
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Orlando G, Kinnersley B, Houlston RS. Capture Hi-C Library Generation and Analysis to Detect Chromatin Interactions. CURRENT PROTOCOLS IN HUMAN GENETICS 2018; 98:e63. [PMID: 29979818 DOI: 10.1002/cphg.63] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Chromosome conformation capture (3C), coupled with next-generation sequencing (Hi-C), provides a means for deciphering not only the principles underlying genome folding and architecture, but more broadly, the role 3D chromatin structure plays in gene regulation and the replication and repair of DNA. The recently implemented modification, in situ Hi-C, maintains nuclear integrity during digestion and ligation steps, reducing random ligation of Hi-C fragments. Although Hi-C allows for genome-wide characterization of chromatin contacts, it requires high-depth sequencing to discover significant contacts. To address this, Capture Hi-C (CHi-C) enriches standard Hi-C libraries for regions of biological interest, for example by specifically targeting gene promoters, aiding identification of biologically significant chromatin interactions compared to conventional Hi-C, for an equivalent number of sequence reads. Illustrating the application of CHi-C applied to genome-wide analysis of chromatin interactions with promoters, we detail the protocols for in situ Hi-C and CHi-C library generation for sequencing, as well as the bioinformatics tools for data analysis. © 2018 by John Wiley & Sons, Inc.
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Affiliation(s)
- Giulia Orlando
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, United Kingdom
| | - Ben Kinnersley
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, United Kingdom
| | - Richard S Houlston
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, United Kingdom
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105
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Wu C, Pan W. Integration of Enhancer-Promoter Interactions with GWAS Summary Results Identifies Novel Schizophrenia-Associated Genes and Pathways. Genetics 2018; 209:699-709. [PMID: 29728367 PMCID: PMC6028261 DOI: 10.1534/genetics.118.300805] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2018] [Accepted: 05/02/2018] [Indexed: 12/28/2022] Open
Abstract
It remains challenging to boost statistical power of genome-wide association studies (GWASs) to identify more risk variants or loci that can account for "missing heritability." Furthermore, since most identified variants are not in gene-coding regions, a biological interpretation of their function is largely lacking. On the other hand, recent biotechnological advances have made it feasible to experimentally measure the three-dimensional organization of the genome, including enhancer-promoter interactions in high resolutions. Due to the well-known critical roles of enhancer-promoter interactions in regulating gene expression programs, such data have been applied to link GWAS risk variants to their putative target genes, gaining insights into underlying biological mechanisms. However, their direct use in GWAS association testing is yet to be exploited. Here we propose integrating enhancer-promoter interactions into GWAS association analysis to both boost statistical power and enhance interpretability. We demonstrate that through an application to two large-scale schizophrenia (SCZ) GWAS summary data sets, the proposed method could identify some novel SCZ-associated genes and pathways (containing no significant SNPs). For example, after the Bonferroni correction, for the larger SCZ data set with 36,989 cases and 113,075 controls, our method applied to the gene body and enhancer regions identified 27 novel genes and 11 novel KEGG pathways to be significant, all missed by the transcriptome-wide association study (TWAS) approach. We conclude that our proposed method is potentially useful and is complementary to TWAS and other standard gene- and pathway-based methods.
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Affiliation(s)
- Chong Wu
- Division of Biostatistics, University of Minnesota, Minneapolis, Minnesota 55455
| | - Wei Pan
- Division of Biostatistics, University of Minnesota, Minneapolis, Minnesota 55455
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106
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Schoenfelder S, Javierre BM, Furlan-Magaril M, Wingett SW, Fraser P. Promoter Capture Hi-C: High-resolution, Genome-wide Profiling of Promoter Interactions. J Vis Exp 2018. [PMID: 30010637 PMCID: PMC6102006 DOI: 10.3791/57320] [Citation(s) in RCA: 52] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
The three-dimensional organization of the genome is linked to its function. For example, regulatory elements such as transcriptional enhancers control the spatio-temporal expression of their target genes through physical contact, often bridging considerable (in some cases hundreds of kilobases) genomic distances and bypassing nearby genes. The human genome harbors an estimated one million enhancers, the vast majority of which have unknown gene targets. Assigning distal regulatory regions to their target genes is thus crucial to understand gene expression control. We developed Promoter Capture Hi-C (PCHi-C) to enable the genome-wide detection of distal promoter-interacting regions (PIRs), for all promoters in a single experiment. In PCHi-C, highly complex Hi-C libraries are specifically enriched for promoter sequences through in-solution hybrid selection with thousands of biotinylated RNA baits complementary to the ends of all promoter-containing restriction fragments. The aim is to then pull-down promoter sequences and their frequent interaction partners such as enhancers and other potential regulatory elements. After high-throughput paired-end sequencing, a statistical test is applied to each promoter-ligated restriction fragment to identify significant PIRs at the restriction fragment level. We have used PCHi-C to generate an atlas of long-range promoter interactions in dozens of human and mouse cell types. These promoter interactome maps have contributed to a greater understanding of mammalian gene expression control by assigning putative regulatory regions to their target genes and revealing preferential spatial promoter-promoter interaction networks. This information also has high relevance to understanding human genetic disease and the identification of potential disease genes, by linking non-coding disease-associated sequence variants in or near control sequences to their target genes.
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Affiliation(s)
- Stefan Schoenfelder
- Nuclear Dynamics Programme, The Babraham Institute, Babraham Research Campus;
| | - Biola-Maria Javierre
- Nuclear Dynamics Programme, The Babraham Institute, Babraham Research Campus; IJC Building, Campus ICO-Germans Trias i Pujol, Josep Carreras Leukemia Research Institute;
| | - Mayra Furlan-Magaril
- Nuclear Dynamics Programme, The Babraham Institute, Babraham Research Campus; Departamento de Genética Molecular, Instituto de Fisiología Celular, Universidad Nacional Autónoma de México
| | - Steven W Wingett
- Nuclear Dynamics Programme, The Babraham Institute, Babraham Research Campus; Bioinformatics Group, The Babraham Institute, Babraham Research Campus
| | - Peter Fraser
- Nuclear Dynamics Programme, The Babraham Institute, Babraham Research Campus; Department of Biological Science, Florida State University;
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107
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Sun L, Yu R, Dang W. Chromatin Architectural Changes during Cellular Senescence and Aging. Genes (Basel) 2018; 9:genes9040211. [PMID: 29659513 PMCID: PMC5924553 DOI: 10.3390/genes9040211] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2018] [Revised: 04/02/2018] [Accepted: 04/12/2018] [Indexed: 12/21/2022] Open
Abstract
Chromatin 3D structure is highly dynamic and associated with many biological processes, such as cell cycle progression, cellular differentiation, cell fate reprogramming, cancer development, cellular senescence, and aging. Recently, by using chromosome conformation capture technologies, tremendous findings have been reported about the dynamics of genome architecture, their associated proteins, and the underlying mechanisms involved in regulating chromatin spatial organization and gene expression. Cellular senescence and aging, which involve multiple cellular and molecular functional declines, also undergo significant chromatin structural changes, including alternations of heterochromatin and disruption of higher-order chromatin structure. In this review, we summarize recent findings related to genome architecture, factors regulating chromatin spatial organization, and how they change during cellular senescence and aging.
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Affiliation(s)
- Luyang Sun
- Huffington Center on Aging, Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.
| | - Ruofan Yu
- Huffington Center on Aging, Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.
| | - Weiwei Dang
- Huffington Center on Aging, Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.
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108
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Izzi B, Noro F, Cludts K, Freson K, Hoylaerts MF. Cell-Specific PEAR1 Methylation Studies Reveal a Locus that Coordinates Expression of Multiple Genes. Int J Mol Sci 2018; 19:ijms19041069. [PMID: 29614055 PMCID: PMC5979289 DOI: 10.3390/ijms19041069] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2018] [Revised: 03/19/2018] [Accepted: 03/28/2018] [Indexed: 02/07/2023] Open
Abstract
Chromosomal interactions connect distant enhancers and promoters on the same chromosome, activating or repressing gene expression. PEAR1 encodes the Platelet-Endothelial Aggregation Receptor 1, a contact receptor involved in platelet function and megakaryocyte and endothelial cell proliferation. PEAR1 expression during megakaryocyte differentiation is controlled by DNA methylation at its first CpG island. We identified a PEAR1 cell-specific methylation sensitive region in endothelial cells and megakaryocytes that showed strong chromosomal interactions with ISGL20L2, RRNAD1, MRLP24, HDGF and PRCC, using available promoter capture Hi-C datasets. These genes are involved in ribosome processing, protein synthesis, cell cycle and cell proliferation. We next studied the methylation and expression profile of these five genes in Human Umbilical Vein Endothelial Cells (HUVECs) and megakaryocyte precursors. While cell-specific PEAR1 methylation corresponded to variability in expression for four out of five genes, no methylation change was observed in their promoter regions across cell types. Our data suggest that PEAR1 cell-type specific methylation changes may control long distance interactions with other genes. Further studies are needed to show whether such interaction data might be relevant for the genome-wide association data that showed a role for non-coding PEAR1 variants in the same region and platelet function, platelet count and cardiovascular risk.
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Affiliation(s)
- Benedetta Izzi
- Department of Cardiovascular Sciences, Center for Molecular and Vascular Biology, University of Leuven, 3000 Leuven, Belgium.
| | - Fabrizia Noro
- Department of Epidemiology and Prevention, IRCCS Istituto Neurologico Mediterraneo Neuromed, Via dell'Elettronica, 86077 Pozzilli (IS), Italy.
| | - Katrien Cludts
- Department of Cardiovascular Sciences, Center for Molecular and Vascular Biology, University of Leuven, 3000 Leuven, Belgium.
| | - Kathleen Freson
- Department of Cardiovascular Sciences, Center for Molecular and Vascular Biology, University of Leuven, 3000 Leuven, Belgium.
| | - Marc F Hoylaerts
- Department of Cardiovascular Sciences, Center for Molecular and Vascular Biology, University of Leuven, 3000 Leuven, Belgium.
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109
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Abstract
Genome-wide association studies (GWAS) have identified approximately 100 breast cancer risk loci. Translating these findings into a greater understanding of the mechanisms that influence disease risk requires identification of the genes or non-coding RNAs that mediate these associations. Here, we use Capture Hi-C (CHi-C) to annotate 63 loci; we identify 110 putative target genes at 33 loci. To assess the support for these target genes in other data sources we test for associations between levels of expression and SNP genotype (eQTLs), disease-specific survival (DSS), and compare them with somatically mutated cancer genes. 22 putative target genes are eQTLs, 32 are associated with DSS and 14 are somatically mutated in breast, or other, cancers. Identifying the target genes at GWAS risk loci will lead to a greater understanding of the mechanisms that influence breast cancer risk and prognosis.
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110
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Sadlon T, Brown CY, Bandara V, Hope CM, Schjenken JE, Pederson SM, Breen J, Forrest A, Beyer M, Robertson S, Barry SC. Unravelling the molecular basis for regulatory T-cell plasticity and loss of function in disease. Clin Transl Immunology 2018; 7:e1011. [PMID: 29497530 PMCID: PMC5827651 DOI: 10.1002/cti2.1011] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2017] [Revised: 01/28/2018] [Accepted: 01/29/2018] [Indexed: 12/14/2022] Open
Abstract
Regulatory T cells (Treg) are critical for preventing autoimmunity and curtailing responses of conventional effector T cells (Tconv). The reprogramming of T‐cell fate and function to generate Treg requires switching on and off of key gene regulatory networks, which may be initiated by a subtle shift in expression levels of specific genes. This can be achieved by intermediary regulatory processes that include microRNA and long noncoding RNA‐based regulation of gene expression. There are well‐documented microRNA profiles in Treg and Tconv, and these can operate to either reinforce or reduce expression of a specific set of target genes, including FOXP3 itself. This type of feedforward/feedback regulatory loop is normally stable in the steady state, but can alter in response to local cues or genetic risk. This may go some way to explaining T‐cell plasticity. In addition, in chronic inflammation or autoimmunity, altered Treg/Tconv function may be influenced by changes in enhancer–promoter interactions, which are highly cell type‐specific. These interactions are impacted by genetic risk based on genome‐wide association studies and may cause subtle alterations to the gene regulatory networks controlled by or controlling FOXP3 and its target genes. Recent insights into the 3D organisation of chromatin and the mapping of noncoding regulatory regions to the genes they control are shedding new light on the direct impact of genetic risk on T‐cell function and susceptibility to inflammatory and autoimmune conditions.
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Affiliation(s)
- Timothy Sadlon
- Women's and Children's Health Network North Adelaide SA Australia.,Molecular immunology Robinson Research Institute University of Adelaide Adelaide SA Australia
| | - Cheryl Y Brown
- Molecular immunology Robinson Research Institute University of Adelaide Adelaide SA Australia
| | - Veronika Bandara
- Molecular immunology Robinson Research Institute University of Adelaide Adelaide SA Australia
| | | | - John E Schjenken
- Reproductive Immunology Robinson Research Institute University of Adelaide Adelaide SA Australia
| | - Stephen M Pederson
- Molecular immunology Robinson Research Institute University of Adelaide Adelaide SA Australia.,University of Adelaide Bioinformatics Hub University of Adelaide Adelaide SA Australia
| | - James Breen
- Molecular immunology Robinson Research Institute University of Adelaide Adelaide SA Australia.,University of Adelaide Bioinformatics Hub University of Adelaide Adelaide SA Australia
| | - Alistair Forrest
- Harry Perkins Institute of Medical Research University of Western Australia Perth, WA Australia
| | - Marc Beyer
- Deutsches Zentrum fur Neurodegenerative Erkrankungen Bonn Germany
| | - Sarah Robertson
- Reproductive Immunology Robinson Research Institute University of Adelaide Adelaide SA Australia
| | - Simon C Barry
- Molecular immunology Robinson Research Institute University of Adelaide Adelaide SA Australia
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111
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Zhang Y, Li Y, Jiang S, Chen W, Lou J, Ke J, Li J, Zhu Y, Gong Y, Yang Y, Tian J, Peng X, Zou D, Gong J, Chang J, Miao X, Zhong R. A functional variant rs4442975 modulating FOXA1-binding affinity does not influence the risk or progression of breast cancer in Chinese Han population. Oncotarget 2018; 7:81691-81697. [PMID: 27835577 PMCID: PMC5348423 DOI: 10.18632/oncotarget.13168] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2016] [Accepted: 10/19/2016] [Indexed: 11/25/2022] Open
Abstract
The DNA-binding protein FOXA1 has been shown to regulate nearly all estrogen receptor-chromatin interactions, thereby influencing target gene expression levels in breast cancer (BC) cells. Recently, the rs4442975 T-allele, which disrupts the recruitment of FOXA1 and interacts with the IGFBP5 promoter, was associated to BC susceptibility in a European population. We conducted a hospital-based case-control study that included 1227 cases and 1285 controls to explore the potential association between rs4442975 and BC risk in Chinese Han population, and the effect of this SNP on BC progression was also observed in cases. No significant associations between rs4442975 and BC risk were observed under any genetic models, with an odds ratio of 0.96 (95% confidence interval = 0.81-1.15) under the additive model. When stratified based on estrogen or progesterone receptor expression, smoking or drinking habits, or menopausal status, similar negative associations were observed for all subgroups. No significant associations were observed between rs4442975 and traditional progression factors such as tumor size, nodal status, distant metastasis, or TNM staging. These results reveal that rs4442975 may not confer a risk of BC occurrence or progression in the Chinese Han population, which indicates a distinct association related to genetic heterogeneity across ethnic populations.
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Affiliation(s)
- Yi Zhang
- Department of Epidemiology and Biostatistics and State Key Laboratory of Environment Health (Incubation), Ministry of Education Key Laboratory of Environment & Health, Ministry of Environmental Protection Key Laboratory of Environment and Health (Wuhan), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, PR China.,Department of Preventive Medicine, School of Public Health, Zunyi Medical University, Zunyi, Guizhou, China
| | - Yan Li
- Department of Preventive Medicine, School of Public Health, Zunyi Medical University, Zunyi, Guizhou, China
| | - Shaojie Jiang
- Department of Gastroenterology, Jing Zhou Central Hospital, Jingzhou, China
| | - Wei Chen
- Department of Epidemiology and Biostatistics and State Key Laboratory of Environment Health (Incubation), Ministry of Education Key Laboratory of Environment & Health, Ministry of Environmental Protection Key Laboratory of Environment and Health (Wuhan), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, PR China.,Zhuhai Center for Chronic Disease Control, Zhuhai, China
| | - Jiao Lou
- Department of Epidemiology and Biostatistics and State Key Laboratory of Environment Health (Incubation), Ministry of Education Key Laboratory of Environment & Health, Ministry of Environmental Protection Key Laboratory of Environment and Health (Wuhan), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, PR China
| | - Juntao Ke
- Department of Epidemiology and Biostatistics and State Key Laboratory of Environment Health (Incubation), Ministry of Education Key Laboratory of Environment & Health, Ministry of Environmental Protection Key Laboratory of Environment and Health (Wuhan), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, PR China
| | - Jiaoyuan Li
- Department of Epidemiology and Biostatistics and State Key Laboratory of Environment Health (Incubation), Ministry of Education Key Laboratory of Environment & Health, Ministry of Environmental Protection Key Laboratory of Environment and Health (Wuhan), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, PR China
| | - Ying Zhu
- Department of Epidemiology and Biostatistics and State Key Laboratory of Environment Health (Incubation), Ministry of Education Key Laboratory of Environment & Health, Ministry of Environmental Protection Key Laboratory of Environment and Health (Wuhan), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, PR China
| | - Yajie Gong
- Department of Epidemiology and Biostatistics and State Key Laboratory of Environment Health (Incubation), Ministry of Education Key Laboratory of Environment & Health, Ministry of Environmental Protection Key Laboratory of Environment and Health (Wuhan), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, PR China
| | - Yang Yang
- Department of Epidemiology and Biostatistics and State Key Laboratory of Environment Health (Incubation), Ministry of Education Key Laboratory of Environment & Health, Ministry of Environmental Protection Key Laboratory of Environment and Health (Wuhan), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, PR China
| | - Jianbo Tian
- Department of Epidemiology and Biostatistics and State Key Laboratory of Environment Health (Incubation), Ministry of Education Key Laboratory of Environment & Health, Ministry of Environmental Protection Key Laboratory of Environment and Health (Wuhan), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, PR China
| | - Xiating Peng
- Department of Epidemiology and Biostatistics and State Key Laboratory of Environment Health (Incubation), Ministry of Education Key Laboratory of Environment & Health, Ministry of Environmental Protection Key Laboratory of Environment and Health (Wuhan), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, PR China
| | - Danyi Zou
- Department of Epidemiology and Biostatistics and State Key Laboratory of Environment Health (Incubation), Ministry of Education Key Laboratory of Environment & Health, Ministry of Environmental Protection Key Laboratory of Environment and Health (Wuhan), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, PR China
| | - Jing Gong
- Department of Epidemiology and Biostatistics and State Key Laboratory of Environment Health (Incubation), Ministry of Education Key Laboratory of Environment & Health, Ministry of Environmental Protection Key Laboratory of Environment and Health (Wuhan), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, PR China
| | - Jiang Chang
- Department of Epidemiology and Biostatistics and State Key Laboratory of Environment Health (Incubation), Ministry of Education Key Laboratory of Environment & Health, Ministry of Environmental Protection Key Laboratory of Environment and Health (Wuhan), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, PR China
| | - Xiaoping Miao
- Department of Epidemiology and Biostatistics and State Key Laboratory of Environment Health (Incubation), Ministry of Education Key Laboratory of Environment & Health, Ministry of Environmental Protection Key Laboratory of Environment and Health (Wuhan), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, PR China
| | - Rong Zhong
- Department of Epidemiology and Biostatistics and State Key Laboratory of Environment Health (Incubation), Ministry of Education Key Laboratory of Environment & Health, Ministry of Environmental Protection Key Laboratory of Environment and Health (Wuhan), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, PR China
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112
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Anil A, Spalinskas R, Åkerborg Ö, Sahlén P. HiCapTools: a software suite for probe design and proximity detection for targeted chromosome conformation capture applications. Bioinformatics 2018; 34:675-677. [PMID: 29444232 PMCID: PMC6368139 DOI: 10.1093/bioinformatics/btx625] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2017] [Revised: 09/05/2017] [Accepted: 09/29/2017] [Indexed: 12/27/2022] Open
Abstract
Summary Folding of eukaryotic genomes within nuclear space enables physical and functional contacts between regions that are otherwise kilobases away in sequence space. Targeted chromosome conformation capture methods (T2C, chi-C and HiCap) are capable of informing genomic contacts for a subset of regions targeted by probes. We here present HiCapTools, a software package that can design sequence capture probes for targeted chromosome capture applications and analyse sequencing output to detect proximities involving targeted fragments. Two probes are designed for each feature while avoiding repeat elements and non-unique regions. The data analysis suite processes alignment files to report genomic proximities for each feature at restriction fragment level and is isoform-aware for gene features. Statistical significance of contact frequencies is evaluated using an empirically derived background distribution. Targeted chromosome conformation capture applications are invaluable for locating target genes of disease-associated variants found by genome-wide association studies. Hence, we believe our software suite will prove to be useful for a wider user base within clinical and functional applications. Availability https://github.com/sahlenlab/HiCapTools. Contact pelinak@kth.se. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Anandashankar Anil
- KTH – Royal Institute of Technology, Science for Life Laboratory, School of Biotechnology, Solna, Sweden
| | - Rapolas Spalinskas
- KTH – Royal Institute of Technology, Science for Life Laboratory, School of Biotechnology, Solna, Sweden
| | - Örjan Åkerborg
- KTH – Royal Institute of Technology, Science for Life Laboratory, School of Biotechnology, Solna, Sweden
| | - Pelin Sahlén
- KTH – Royal Institute of Technology, Science for Life Laboratory, School of Biotechnology, Solna, Sweden
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113
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Kolovos P, Brouwer RWW, Kockx CEM, Lesnussa M, Kepper N, Zuin J, Imam AMA, van de Werken HJG, Wendt KS, Knoch TA, van IJcken WFJ, Grosveld F. Investigation of the spatial structure and interactions of the genome at sub-kilobase-pair resolution using T2C. Nat Protoc 2018; 13:459-477. [DOI: 10.1038/nprot.2017.132] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
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114
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Xia K. Sequence-based multiscale modeling for high-throughput chromosome conformation capture (Hi-C) data analysis. PLoS One 2018; 13:e0191899. [PMID: 29408904 PMCID: PMC5800693 DOI: 10.1371/journal.pone.0191899] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2017] [Accepted: 01/12/2018] [Indexed: 11/18/2022] Open
Abstract
In this paper, we introduce sequence-based multiscale modeling for biomolecular data analysis. We employ spectral clustering method in our modeling and reveal the difference between sequence-based global scale clustering and local scale clustering. Essentially, two types of distances, i.e., Euclidean (or spatial) distance and genomic (or sequential) distance, can be used in data clustering. Clusters from sequence-based global scale models optimize spatial distances, meaning spatially adjacent loci are more likely to be assigned into the same cluster. Sequence-based local scale models, on the other hand, result in clusters that optimize genomic distances. That is to say, in these models, sequentially adjoining loci tend to be cluster together. We propose two sequence-based multiscale models (SeqMMs) for the study of chromosome hierarchical structures, including genomic compartments and topological associated domains (TADs). We find that genomic compartments are determined only by global scale information in the Hi-C data. The removal of all the local interactions within a band region as large as 10 Mb in genomic distance has almost no significant influence on the final compartment results. Further, in TAD analysis, we find that when the sequential scale is small, a tiny variation of diagonal band region in a contact map will result in a great change in the predicted TAD boundaries. When the scale value is larger than a threshold value, the TAD boundaries become very consistent. This threshold value is highly related to TAD sizes. By the comparison of our results with those previously obtained using a spectral clustering model, we find that our method is more robust and reliable. Finally, we demonstrate that almost all TAD boundaries from both clustering methods are local minimum of a TAD summation function.
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Affiliation(s)
- Kelin Xia
- Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 637371, Singapore
- School of Biological Sciences, Nanyang Technological University, Singapore 637371, Singapore
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115
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Kinnersley B, Houlston RS, Bondy ML. Genome-Wide Association Studies in Glioma. Cancer Epidemiol Biomarkers Prev 2018; 27:418-428. [PMID: 29382702 DOI: 10.1158/1055-9965.epi-17-1080] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2017] [Revised: 01/12/2018] [Accepted: 01/17/2018] [Indexed: 01/23/2023] Open
Abstract
Since the first reports in 2009, genome-wide association studies (GWAS) have been successful in identifying germline variants associated with glioma susceptibility. In this review, we describe a chronological history of glioma GWAS, culminating in the most recent study comprising 12,496 cases and 18,190 controls. We additionally summarize associations at the 27 glioma-risk SNPs that have been reported so far. Future efforts are likely to be principally focused on assessing association of germline-risk SNPs with particular molecular subgroups of glioma, as well as investigating the functional basis of the risk loci in tumor formation. These ongoing studies will be important to maximize the impact of research into glioma susceptibility, both in terms of insight into tumor etiology as well as opportunities for clinical translation. Cancer Epidemiol Biomarkers Prev; 27(4); 418-28. ©2018 AACRSee all articles in this CEBP Focus section, "Genome-Wide Association Studies in Cancer."
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Affiliation(s)
- Ben Kinnersley
- Division of Genetics and Epidemiology, The Institute of Cancer Research, Sutton, Surrey, United Kingdom
| | - Richard S Houlston
- Division of Genetics and Epidemiology, The Institute of Cancer Research, Sutton, Surrey, United Kingdom
| | - Melissa L Bondy
- Department of Medicine, Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas.
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116
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Dekker J, Belmont AS, Guttman M, Leshyk VO, Lis JT, Lomvardas S, Mirny LA, O'Shea CC, Park PJ, Ren B, Politz JCR, Shendure J, Zhong S. The 4D nucleome project. Nature 2018; 549:219-226. [PMID: 28905911 DOI: 10.1038/nature23884] [Citation(s) in RCA: 437] [Impact Index Per Article: 72.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2017] [Accepted: 07/27/2017] [Indexed: 12/19/2022]
Abstract
The 4D Nucleome Network aims to develop and apply approaches to map the structure and dynamics of the human and mouse genomes in space and time with the goal of gaining deeper mechanistic insights into how the nucleus is organized and functions. The project will develop and benchmark experimental and computational approaches for measuring genome conformation and nuclear organization, and investigate how these contribute to gene regulation and other genome functions. Validated experimental technologies will be combined with biophysical approaches to generate quantitative models of spatial genome organization in different biological states, both in cell populations and in single cells.
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Affiliation(s)
- Job Dekker
- Program in Systems Biology, Department of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, Howard Hughes Medical Institute, Worcester, Massachusetts 01605, USA
| | - Andrew S Belmont
- Department of Cell and Developmental Biology, University of Illinois, Urbana-Champaign, Illinois 61801, USA
| | - Mitchell Guttman
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California 91125, USA
| | - Victor O Leshyk
- Department of Bioengineering, University of California San Diego, La Jolla, California 92093, USA
| | - John T Lis
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York 14853, USA
| | - Stavros Lomvardas
- Department of Biochemistry and Molecular Biophysics, Mortimer B. Zuckerman Mind Brain and Behavior Institute, Columbia University, New York, New York 10027, USA
| | - Leonid A Mirny
- Institute for Medical Engineering and Science, and Department of Physics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Clodagh C O'Shea
- Molecular and Cell Biology Laboratory, Salk Institute for Biological Studies, La Jolla, California 92037, USA
| | - Peter J Park
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Bing Ren
- Ludwig Institute for Cancer Research, Department of Cellular and Molecular Medicine, Institute of Genomic Medicine, Moores Cancer Center, University of California San Diego, La Jolla California 92093, USA
| | - Joan C Ritland Politz
- Basic Sciences, Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, Seattle, Washington 98109, USA
| | - Jay Shendure
- Department of Genome Sciences, University of Washington, Howard Hughes Medical Institute, Seattle, Washington 98109, USA
| | - Sheng Zhong
- Department of Bioengineering, University of California San Diego, La Jolla, California 92093, USA
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117
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Zhang H, Li F, Jia Y, Xu B, Zhang Y, Li X, Zhang Z. Characteristic arrangement of nucleosomes is predictive of chromatin interactions at kilobase resolution. Nucleic Acids Res 2018; 45:12739-12751. [PMID: 29036650 PMCID: PMC5727446 DOI: 10.1093/nar/gkx885] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2017] [Accepted: 09/24/2017] [Indexed: 01/29/2023] Open
Abstract
High-throughput chromosome conformation capture (3C) technologies, such as Hi-C, have made it possible to survey 3D genome structure. However, obtaining 3D profiles at kilobase resolution at low cost remains a major challenge. Therefore, we herein present an algorithm for precise identification of chromatin interaction sites at kilobase resolution from MNase-seq data, termed chromatin interaction site detector (CISD), and a CISD-based chromatin loop predictor (CISD_loop) that predicts chromatin–chromatin interactions (CCIs) from low-resolution Hi-C data. We show that the predictions of CISD and CISD_loop overlap closely with chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) anchors and loops, respectively. The validity of CISD/CISD_loop was further supported by a 3C assay at about 5 kb resolution. Finally, we demonstrate that only modest amounts of MNase-seq and Hi-C data are sufficient to achieve ultrahigh resolution CCI maps. Our results suggest that CCIs may result in characteristic nucleosomes arrangement patterns flanking the interaction sites, and our algorithms may facilitate precise and systematic investigations of CCIs on a larger scale than hitherto have been possible.
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Affiliation(s)
- Hui Zhang
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Feifei Li
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
| | - Yan Jia
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
| | - Bingxiang Xu
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yiqun Zhang
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiaoli Li
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhihua Zhang
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.,University of Chinese Academy of Sciences, Beijing 100049, China
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118
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Grob S, Cavalli G. Technical Review: A Hitchhiker's Guide to Chromosome Conformation Capture. Methods Mol Biol 2018; 1675:233-246. [PMID: 29052195 DOI: 10.1007/978-1-4939-7318-7_14] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
The introduction of chromosome conformation capture (3C) technologies boosted the field of 3D-genome research and significantly enhanced the available toolset to study chromosomal architecture. 3C technologies not only offer increased resolution compared to the previously dominant cytological approaches but also allow the simultaneous study of genome-wide 3D chromatin contacts, thereby enabling a candidate-free perspective on 3D-genome architecture. Since its introduction in 2002, 3C technologies evolved rapidly and now constitute a collection of tools, each with their strengths and pitfalls with respect to specific research questions. This chapter aims at guiding 3C novices through the labyrinth of potential applications of the various family members, hopefully providing a valuable basis for choosing the appropriate strategy for different research questions.
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Affiliation(s)
- Stefan Grob
- Institute of Human Genetics, Centre National de la Recherche UMR9002, Montpellier, France.
| | - Giacomo Cavalli
- Institute of Human Genetics, Centre National de la Recherche UMR9002, Montpellier, France
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119
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Abstract
Recent advances in next-generation sequencing (NGS) and chromosome conformation capture (3C) analysis have led to the development of Hi-C, a genome-wide version of the 3C method. Hi-C has identified new levels of chromosome organization such as A/B compartments, topologically associating domains (TADs) as well as large megadomains on the inactive X chromosome, while allowing the identification of chromatin loops at the genome scale. Despite its powerfulness, Hi-C data analysis is much more involved compared to conventional NGS applications such as RNA-seq or ChIP-seq and requires many more steps. This presents a significant hurdle for those who wish to implement Hi-C technology into their laboratory. On the other hand, genomics data repository sites sometimes contain processed Hi-C data sets, allowing researchers to perform further analysis without the need for high-spec workstations and servers. In this chapter, we provide a detailed description on how to calculate A/B compartment profiles from processed Hi-C data on the autosomes and the active/inactive X chromosomes.
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120
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Bartonicek N, Clark MB, Quek XC, Torpy JR, Pritchard AL, Maag JLV, Gloss BS, Crawford J, Taft RJ, Hayward NK, Montgomery GW, Mattick JS, Mercer TR, Dinger ME. Intergenic disease-associated regions are abundant in novel transcripts. Genome Biol 2017; 18:241. [PMID: 29284497 PMCID: PMC5747244 DOI: 10.1186/s13059-017-1363-3] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2017] [Accepted: 11/21/2017] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Genotyping of large populations through genome-wide association studies (GWAS) has successfully identified many genomic variants associated with traits or disease risk. Unexpectedly, a large proportion of GWAS single nucleotide polymorphisms (SNPs) and associated haplotype blocks are in intronic and intergenic regions, hindering their functional evaluation. While some of these risk-susceptibility regions encompass cis-regulatory sites, their transcriptional potential has never been systematically explored. RESULTS To detect rare tissue-specific expression, we employed the transcript-enrichment method CaptureSeq on 21 human tissues to identify 1775 multi-exonic transcripts from 561 intronic and intergenic haploblocks associated with 392 traits and diseases, covering 73.9 Mb (2.2%) of the human genome. We show that a large proportion (85%) of disease-associated haploblocks express novel multi-exonic non-coding transcripts that are tissue-specific and enriched for GWAS SNPs as well as epigenetic markers of active transcription and enhancer activity. Similarly, we captured transcriptomes from 13 melanomas, targeting nine melanoma-associated haploblocks, and characterized 31 novel melanoma-specific transcripts that include fusion proteins, novel exons and non-coding RNAs, one-third of which showed allelically imbalanced expression. CONCLUSIONS This resource of previously unreported transcripts in disease-associated regions ( http://gwas-captureseq.dingerlab.org ) should provide an important starting point for the translational community in search of novel biomarkers, disease mechanisms, and drug targets.
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Affiliation(s)
- N Bartonicek
- Garvan Institute of Medical Research, Sydney, NSW, Australia
- Faculty of Medicine, St Vincent's Clinical School, University of New South Wales, Sydney, NSW, Australia
| | - M B Clark
- Garvan Institute of Medical Research, Sydney, NSW, Australia
- Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, UK
| | - X C Quek
- Garvan Institute of Medical Research, Sydney, NSW, Australia
- Faculty of Medicine, St Vincent's Clinical School, University of New South Wales, Sydney, NSW, Australia
| | - J R Torpy
- Garvan Institute of Medical Research, Sydney, NSW, Australia
- Faculty of Medicine, St Vincent's Clinical School, University of New South Wales, Sydney, NSW, Australia
| | - A L Pritchard
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - J L V Maag
- Garvan Institute of Medical Research, Sydney, NSW, Australia
- Faculty of Medicine, St Vincent's Clinical School, University of New South Wales, Sydney, NSW, Australia
| | - B S Gloss
- Garvan Institute of Medical Research, Sydney, NSW, Australia
- Faculty of Medicine, St Vincent's Clinical School, University of New South Wales, Sydney, NSW, Australia
| | - J Crawford
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - R J Taft
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
- Illumina, Inc., San Diego, CA, USA
| | - N K Hayward
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - G W Montgomery
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - J S Mattick
- Garvan Institute of Medical Research, Sydney, NSW, Australia
- Faculty of Medicine, St Vincent's Clinical School, University of New South Wales, Sydney, NSW, Australia
| | - T R Mercer
- Garvan Institute of Medical Research, Sydney, NSW, Australia
- Faculty of Medicine, St Vincent's Clinical School, University of New South Wales, Sydney, NSW, Australia
- Altius Institute for Biomedical Sciences, Seattle, USA
| | - M E Dinger
- Garvan Institute of Medical Research, Sydney, NSW, Australia.
- Faculty of Medicine, St Vincent's Clinical School, University of New South Wales, Sydney, NSW, Australia.
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121
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Chen J, Chen X. MYBL2 Is Targeted by miR-143-3p and Regulates Breast Cancer Cell Proliferation and Apoptosis. Oncol Res 2017; 26:913-922. [PMID: 29268817 PMCID: PMC7844795 DOI: 10.3727/096504017x15135941182107] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
Breast cancer remains a public health issue on a global scale. The present study aimed to explore the functional role of MYB proto-oncogene like 2 (MYBL2) in breast cancer, as well as underlying mechanisms. The regulatory relationship between miR-143-3p and MYBL2 was analyzed, and the effects of dysregulation of miR-143-3p and MYBL2 on cell proliferation and apoptosis were investigated. The results showed that MYBL2 and miR-143-3p were inversely expressed in breast cancer tissues and cells: MYBL2 was highly expressed, whereas miR-143-3p was lowly expressed. MYBL2 was confirmed as a target gene of miR-143-3p. Suppression of MYBL2 inhibited proliferation and induced apoptosis of breast cancer cells, which was similar to the effects of overexpression of miR-143-3p. Our findings reveal that MYBL2 is targeted by miR-143-3p and regulates breast cancer cell proliferation and apoptosis.
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Affiliation(s)
- Jianli Chen
- The Third Department of Medical Oncology, The Third Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan, P.R. China
| | - Xiaowen Chen
- Department of Oncology Center, Affiliated Hospital of Guangdong Medical College, Zhanjiang, Guangdong, P.R. China
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122
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Wang XT, Cui W, Peng C. HiTAD: detecting the structural and functional hierarchies of topologically associating domains from chromatin interactions. Nucleic Acids Res 2017; 45:e163. [PMID: 28977529 PMCID: PMC5737579 DOI: 10.1093/nar/gkx735] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2017] [Accepted: 08/11/2017] [Indexed: 11/12/2022] Open
Abstract
A current question in the high-order organization of chromatin is whether topologically associating domains (TADs) are distinct from other hierarchical chromatin domains. However, due to the unclear TAD definition in tradition, the structural and functional uniqueness of TAD is not well studied. In this work, we refined TAD definition by further constraining TADs to the optimal separation on global intra-chromosomal interactions. Inspired by this constraint, we developed a novel method, called HiTAD, to detect hierarchical TADs from Hi-C chromatin interactions. HiTAD performs well in domain sensitivity, replicate reproducibility and inter cell-type conservation. With a novel domain-based alignment proposed by us, we defined several types of hierarchical TAD changes which were not systematically studied previously, and subsequently used them to reveal that TADs and sub-TADs differed statistically in correlating chromosomal compartment, replication timing and gene transcription. Finally, our work also has the implication that the refinement of TAD definition could be achieved by only utilizing chromatin interactions, at least in part. HiTAD is freely available online.
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Affiliation(s)
- Xiao-Tao Wang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Wang Cui
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Cheng Peng
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
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123
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Jia R, Chai P, Zhang H, Fan X. Novel insights into chromosomal conformations in cancer. Mol Cancer 2017; 16:173. [PMID: 29149895 PMCID: PMC5693495 DOI: 10.1186/s12943-017-0741-5] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2017] [Accepted: 11/06/2017] [Indexed: 12/20/2022] Open
Abstract
Exploring gene function is critical for understanding the complexity of life. DNA sequences and the three-dimensional organization of chromatin (chromosomal interactions) are considered enigmatic factors underlying gene function, and interactions between two distant fragments can regulate transactivation activity via mediator proteins. Thus, a series of chromosome conformation capture techniques have been developed, including chromosome conformation capture (3C), circular chromosome conformation capture (4C), chromosome conformation capture carbon copy (5C), and high-resolution chromosome conformation capture (Hi-C). The application of these techniques has expanded to various fields, but cancer remains one of the major topics. Interactions mediated by proteins or long noncoding RNAs (lncRNAs) are typically found using 4C-sequencing and chromatin interaction analysis by paired-end tag sequencing (ChIA-PET). Currently, Hi-C is used to identify chromatin loops between cancer risk-associated single-nucleotide polymorphisms (SNPs) found by genome-wide association studies (GWAS) and their target genes. Chromosomal conformations are responsible for altered gene regulation through several typical mechanisms and contribute to the biological behavior and malignancy of different tumors, particularly prostate cancer, breast cancer and hematologic neoplasms. Moreover, different subtypes may exhibit different 3D-chromosomal conformations. Thus, C-tech can be used to help diagnose cancer subtypes and alleviate cancer progression by destroying specific chromosomal conformations. Here, we review the fundamentals and improvements in chromosome conformation capture techniques and their clinical applications in cancer to provide insight for future research.
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Affiliation(s)
- Ruobing Jia
- Department of Ophthalmology, Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China.,Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, People's Republic of China
| | - Peiwei Chai
- Department of Ophthalmology, Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China.,Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, People's Republic of China
| | - He Zhang
- Department of Ophthalmology, Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China. .,Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, People's Republic of China.
| | - Xianqun Fan
- Department of Ophthalmology, Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China. .,Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, People's Republic of China.
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124
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Jamge S, Stam M, Angenent GC, Immink RGH. A cautionary note on the use of chromosome conformation capture in plants. PLANT METHODS 2017; 13:101. [PMID: 29177001 PMCID: PMC5691870 DOI: 10.1186/s13007-017-0251-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/24/2017] [Accepted: 11/08/2017] [Indexed: 06/07/2023]
Abstract
BACKGROUND The chromosome conformation capture (3C) technique is a method to study chromatin interactions at specific genomic loci. Initially established for yeast the 3C technique has been adapted to plants in recent years in order to study chromatin interactions and their role in transcriptional gene regulation. As the plant scientific community continues to implement this technology, a discussion on critical controls, validations steps and interpretation of 3C data is essential to fully benefit from 3C in plants. RESULTS Here we assess the reliability and robustness of the 3C technique for the detection of chromatin interactions in Arabidopsis. As a case study, we applied this methodology to the genomic locus of a floral integrator gene SUPPRESSOR OF OVEREXPRESSION OF CONSTANS1 (SOC1), and demonstrate the need of several controls and standard validation steps to allow a meaningful interpretation of 3C data. The intricacies of this promising but challenging technique are discussed in depth. CONCLUSIONS The 3C technique offers an interesting opportunity to study chromatin interactions at a resolution infeasible by microscopy. However, for interpretation of 3C interaction data and identification of true interactions, 3C technology demands a stringent experimental setup and extreme caution.
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Affiliation(s)
- Suraj Jamge
- Laboratory of Molecular Biology, Wageningen University & Research, Droevendaalsesteeg 1, 6708 PB Wageningen, The Netherlands
| | - Maike Stam
- Swammerdam Institute for Life Sciences, Universiteit van Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands
| | - Gerco C. Angenent
- Laboratory of Molecular Biology, Wageningen University & Research, Droevendaalsesteeg 1, 6708 PB Wageningen, The Netherlands
- Wageningen Plant Research, Bioscience, Wageningen University & Research, Droevendaalsesteeg 1, 6708 PB Wageningen, The Netherlands
| | - Richard G. H. Immink
- Laboratory of Molecular Biology, Wageningen University & Research, Droevendaalsesteeg 1, 6708 PB Wageningen, The Netherlands
- Wageningen Plant Research, Bioscience, Wageningen University & Research, Droevendaalsesteeg 1, 6708 PB Wageningen, The Netherlands
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125
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Sud A, Kinnersley B, Houlston RS. Genome-wide association studies of cancer: current insights and future perspectives. Nat Rev Cancer 2017; 17:692-704. [PMID: 29026206 DOI: 10.1038/nrc.2017.82] [Citation(s) in RCA: 224] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Genome-wide association studies (GWAS) provide an agnostic approach for investigating the genetic basis of complex diseases. In oncology, GWAS of nearly all common malignancies have been performed, and over 450 genetic variants associated with increased risks have been identified. As well as revealing novel pathways important in carcinogenesis, these studies have shown that common genetic variation contributes substantially to the heritable risk of many common cancers. The clinical application of GWAS is starting to provide opportunities for drug discovery and repositioning as well as for cancer prevention. However, deciphering the functional and biological basis of associations is challenging and is in part a barrier to fully unlocking the potential of GWAS.
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Affiliation(s)
- Amit Sud
- Division of Genetics and Epidemiology, The Institute of Cancer Research
| | - Ben Kinnersley
- Division of Genetics and Epidemiology, The Institute of Cancer Research
| | - Richard S Houlston
- Division of Genetics and Epidemiology, The Institute of Cancer Research
- Division of Molecular Pathology, The Institute of Cancer Research, 15 Cotswold Road, Sutton, London SM2 5NG, UK
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126
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Abstract
Animal development depends on not only the linear genome sequence that embeds millions of cis-regulatory elements, but also the three-dimensional (3D) chromatin architecture that orchestrates the interplay between cis-regulatory elements and their target genes. Compared to our knowledge of the cis-regulatory sequences, the understanding of the 3D genome organization in human and other eukaryotes is still limited. Recent advances in technologies to map the 3D genome architecture have greatly accelerated the pace of discovery. Here, we review emerging concepts of chromatin organization in mammalian cells, discuss the dynamics of chromatin conformation during development, and highlight important roles for chromatin organization in cancer and other human diseases.
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Affiliation(s)
- Miao Yu
- Ludwig Institute for Cancer Research, La Jolla, California 92093;
| | - Bing Ren
- Ludwig Institute for Cancer Research, La Jolla, California 92093;
- Center for Epigenomics, Department of Cellular and Molecular Medicine, and Institute of Genomic Medicine, and Moores Cancer Center, University of California at San Diego, La Jolla, California 92093
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127
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Mishra A, Hawkins RD. Three-dimensional genome architecture and emerging technologies: looping in disease. Genome Med 2017; 9:87. [PMID: 28964259 PMCID: PMC5623062 DOI: 10.1186/s13073-017-0477-2] [Citation(s) in RCA: 50] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Genome compaction is a universal feature of cells and has emerged as a global regulator of gene expression. Compaction is maintained by a multitude of architectural proteins, long non-coding RNAs (lncRNAs), and regulatory DNA. Each component comprises interlinked regulatory circuits that organize the genome in three-dimensional (3D) space to manage gene expression. In this review, we update the current state of 3D genome catalogues and focus on how recent technological advances in 3D genomics are leading to an enhanced understanding of disease mechanisms. We highlight the use of genome-wide chromatin conformation capture (Hi-C) coupled with oligonucleotide capture technology (capture Hi-C) to map interactions between gene promoters and distal regulatory elements such as enhancers that are enriched for disease variants from genome-wide association studies (GWASs). We discuss how aberrations in architectural units are associated with various pathological outcomes, and explore how recent advances in genome and epigenome editing show great promise for a systematic understanding of complex genetic disorders. Our growing understanding of 3D genome architecture—coupled with the ability to engineer changes in it—may create novel therapeutic opportunities.
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Affiliation(s)
- Arpit Mishra
- Division of Medical Genetics, Department of Medicine, Department of Genome Sciences, Institute for Stem Cell and Regenerative Medicine, University of Washington School of Medicine, Seattle, WA, 98195-5065, USA
| | - R David Hawkins
- Division of Medical Genetics, Department of Medicine, Department of Genome Sciences, Institute for Stem Cell and Regenerative Medicine, University of Washington School of Medicine, Seattle, WA, 98195-5065, USA.
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128
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Burren OS, Rubio García A, Javierre BM, Rainbow DB, Cairns J, Cooper NJ, Lambourne JJ, Schofield E, Castro Dopico X, Ferreira RC, Coulson R, Burden F, Rowlston SP, Downes K, Wingett SW, Frontini M, Ouwehand WH, Fraser P, Spivakov M, Todd JA, Wicker LS, Cutler AJ, Wallace C. Chromosome contacts in activated T cells identify autoimmune disease candidate genes. Genome Biol 2017; 18:165. [PMID: 28870212 PMCID: PMC5584004 DOI: 10.1186/s13059-017-1285-0] [Citation(s) in RCA: 54] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2017] [Accepted: 07/21/2017] [Indexed: 12/19/2022] Open
Abstract
Background Autoimmune disease-associated variants are preferentially found in regulatory regions in immune cells, particularly CD4+ T cells. Linking such regulatory regions to gene promoters in disease-relevant cell contexts facilitates identification of candidate disease genes. Results Within 4 h, activation of CD4+ T cells invokes changes in histone modifications and enhancer RNA transcription that correspond to altered expression of the interacting genes identified by promoter capture Hi-C. By integrating promoter capture Hi-C data with genetic associations for five autoimmune diseases, we prioritised 245 candidate genes with a median distance from peak signal to prioritised gene of 153 kb. Just under half (108/245) prioritised genes related to activation-sensitive interactions. This included IL2RA, where allele-specific expression analyses were consistent with its interaction-mediated regulation, illustrating the utility of the approach. Conclusions Our systematic experimental framework offers an alternative approach to candidate causal gene identification for variants with cell state-specific functional effects, with achievable sample sizes. Electronic supplementary material The online version of this article (doi:10.1186/s13059-017-1285-0) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Oliver S Burren
- Department of Medicine, University of Cambridge, Addenbrooke's Hospital, Cambridge, CB2 0SP, UK.,JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, Department of Medical Genetics, NIHR Cambridge Biomedical Research Centre, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, CB2 0XY, UK
| | - Arcadio Rubio García
- JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, Department of Medical Genetics, NIHR Cambridge Biomedical Research Centre, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, CB2 0XY, UK.,Present address: JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, NIHR Oxford Biomedical Research Centre, University of Oxford, Roosevelt Drive, Oxford, OX3 7BN, UK
| | - Biola-Maria Javierre
- Nuclear Dynamics Programme, The Babraham Institute, Babraham Research Campus, Cambridge, CB22 3AT, UK
| | - Daniel B Rainbow
- JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, Department of Medical Genetics, NIHR Cambridge Biomedical Research Centre, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, CB2 0XY, UK.,Present address: JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, NIHR Oxford Biomedical Research Centre, University of Oxford, Roosevelt Drive, Oxford, OX3 7BN, UK
| | - Jonathan Cairns
- Nuclear Dynamics Programme, The Babraham Institute, Babraham Research Campus, Cambridge, CB22 3AT, UK
| | - Nicholas J Cooper
- JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, Department of Medical Genetics, NIHR Cambridge Biomedical Research Centre, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, CB2 0XY, UK
| | - John J Lambourne
- Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Long Road, Cambridge, CB2 0PT, UK
| | - Ellen Schofield
- JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, Department of Medical Genetics, NIHR Cambridge Biomedical Research Centre, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, CB2 0XY, UK
| | - Xaquin Castro Dopico
- JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, Department of Medical Genetics, NIHR Cambridge Biomedical Research Centre, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, CB2 0XY, UK
| | - Ricardo C Ferreira
- JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, Department of Medical Genetics, NIHR Cambridge Biomedical Research Centre, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, CB2 0XY, UK.,Present address: JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, NIHR Oxford Biomedical Research Centre, University of Oxford, Roosevelt Drive, Oxford, OX3 7BN, UK
| | - Richard Coulson
- JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, Department of Medical Genetics, NIHR Cambridge Biomedical Research Centre, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, CB2 0XY, UK
| | - Frances Burden
- Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Long Road, Cambridge, CB2 0PT, UK.,National Health Service Blood and Transplant, Cambridge Biomedical Campus, Long Road, Cambridge, CB2 0PT, UK
| | - Sophia P Rowlston
- Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Long Road, Cambridge, CB2 0PT, UK.,National Health Service Blood and Transplant, Cambridge Biomedical Campus, Long Road, Cambridge, CB2 0PT, UK
| | - Kate Downes
- Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Long Road, Cambridge, CB2 0PT, UK.,National Health Service Blood and Transplant, Cambridge Biomedical Campus, Long Road, Cambridge, CB2 0PT, UK
| | - Steven W Wingett
- Nuclear Dynamics Programme, The Babraham Institute, Babraham Research Campus, Cambridge, CB22 3AT, UK
| | - Mattia Frontini
- Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Long Road, Cambridge, CB2 0PT, UK.,National Health Service Blood and Transplant, Cambridge Biomedical Campus, Long Road, Cambridge, CB2 0PT, UK.,British Heart Foundation Centre of Excellence, Division of Cardiovascular Medicine, Addenbrooke's Hospital, Hills Road, Cambridge, CB2 0QQ, UK
| | - Willem H Ouwehand
- Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Long Road, Cambridge, CB2 0PT, UK.,National Health Service Blood and Transplant, Cambridge Biomedical Campus, Long Road, Cambridge, CB2 0PT, UK.,British Heart Foundation Centre of Excellence, Division of Cardiovascular Medicine, Addenbrooke's Hospital, Hills Road, Cambridge, CB2 0QQ, UK.,Department of Human Genetics, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1HH, UK
| | - Peter Fraser
- Nuclear Dynamics Programme, The Babraham Institute, Babraham Research Campus, Cambridge, CB22 3AT, UK
| | - Mikhail Spivakov
- Nuclear Dynamics Programme, The Babraham Institute, Babraham Research Campus, Cambridge, CB22 3AT, UK
| | - John A Todd
- JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, Department of Medical Genetics, NIHR Cambridge Biomedical Research Centre, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, CB2 0XY, UK.,Present address: JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, NIHR Oxford Biomedical Research Centre, University of Oxford, Roosevelt Drive, Oxford, OX3 7BN, UK
| | - Linda S Wicker
- JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, Department of Medical Genetics, NIHR Cambridge Biomedical Research Centre, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, CB2 0XY, UK.,Present address: JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, NIHR Oxford Biomedical Research Centre, University of Oxford, Roosevelt Drive, Oxford, OX3 7BN, UK
| | - Antony J Cutler
- JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, Department of Medical Genetics, NIHR Cambridge Biomedical Research Centre, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, CB2 0XY, UK.,Present address: JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, NIHR Oxford Biomedical Research Centre, University of Oxford, Roosevelt Drive, Oxford, OX3 7BN, UK
| | - Chris Wallace
- Department of Medicine, University of Cambridge, Addenbrooke's Hospital, Cambridge, CB2 0SP, UK. .,JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, Department of Medical Genetics, NIHR Cambridge Biomedical Research Centre, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, CB2 0XY, UK. .,MRC Biostatistics Unit, University of Cambridge, Cambridge Institute of Public Health, Cambridge Biomedical Campus, Cambridge, CB2 0SR, UK.
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129
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Genome organization: connecting the developmental origins of disease and genetic variation. J Dev Orig Health Dis 2017; 9:260-265. [PMID: 28847340 DOI: 10.1017/s2040174417000678] [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: 11/07/2022]
Abstract
An adverse early life environment can increase the risk of metabolic and other disorders later in life. Genetic variation can modify an individual's susceptibility to these environmental challenges. These gene by environment interactions are important, but difficult, to dissect. The nucleus is the primary organelle where environmental responses impact directly on the genetic variants within the genome, resulting in changes to the biology of the genome and ultimately the phenotype. Understanding genome biology requires the integration of the linear DNA sequence, epigenetic modifications and nuclear proteins that are present within the nucleus. The interactions between these layers of information may be captured in the emergent spatial genome organization. As such genome organization represents a key research area for decoding the role of genetic variation in the Developmental Origins of Health and Disease.
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130
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Elkon R, Agami R. Characterization of noncoding regulatory DNA in the human genome. Nat Biotechnol 2017; 35:732-746. [DOI: 10.1038/nbt.3863] [Citation(s) in RCA: 63] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2016] [Accepted: 03/31/2017] [Indexed: 12/22/2022]
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131
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Ulianov SV, Galitsyna AA, Flyamer IM, Golov AK, Khrameeva EE, Imakaev MV, Abdennur NA, Gelfand MS, Gavrilov AA, Razin SV. Activation of the alpha-globin gene expression correlates with dramatic upregulation of nearby non-globin genes and changes in local and large-scale chromatin spatial structure. Epigenetics Chromatin 2017; 10:35. [PMID: 28693562 PMCID: PMC5504709 DOI: 10.1186/s13072-017-0142-4] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2016] [Accepted: 07/03/2017] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND In homeotherms, the alpha-globin gene clusters are located within permanently open genome regions enriched in housekeeping genes. Terminal erythroid differentiation results in dramatic upregulation of alpha-globin genes making their expression comparable to the rRNA transcriptional output. Little is known about the influence of the erythroid-specific alpha-globin gene transcription outburst on adjacent, widely expressed genes and large-scale chromatin organization. Here, we have analyzed the total transcription output, the overall chromatin contact profile, and CTCF binding within the 2.7 Mb segment of chicken chromosome 14 harboring the alpha-globin gene cluster in cultured lymphoid cells and cultured erythroid cells before and after induction of terminal erythroid differentiation. RESULTS We found that, similarly to mammalian genome, the chicken genomes is organized in TADs and compartments. Full activation of the alpha-globin gene transcription in differentiated erythroid cells is correlated with upregulation of several adjacent housekeeping genes and the emergence of abundant intergenic transcription. An extended chromosome region encompassing the alpha-globin cluster becomes significantly decompacted in differentiated erythroid cells, and depleted in CTCF binding and CTCF-anchored chromatin loops, while the sub-TAD harboring alpha-globin gene cluster and the upstream major regulatory element (MRE) becomes highly enriched with chromatin interactions as compared to lymphoid and proliferating erythroid cells. The alpha-globin gene domain and the neighboring loci reside within the A-like chromatin compartment in both lymphoid and erythroid cells and become further segregated from the upstream gene desert upon terminal erythroid differentiation. CONCLUSIONS Our findings demonstrate that the effects of tissue-specific transcription activation are not restricted to the host genomic locus but affect the overall chromatin structure and transcriptional output of the encompassing topologically associating domain.
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Affiliation(s)
- Sergey V Ulianov
- Institute of Gene Biology of the Russian Academy of Sciences, Moscow, Russia 119334.,Faculty of Biology, M.V. Lomonosov Moscow State University, Moscow, Russia 119992
| | - Aleksandra A Galitsyna
- Institute of Gene Biology of the Russian Academy of Sciences, Moscow, Russia 119334.,Faculty of Bioengineering and Bioinformatics, M.V. Lomonosov Moscow State University, Moscow, Russia 119992.,Institute for Information Transmission Problems (the Kharkevich Institute) of the Russian Academy of Sciences, Moscow, Russia 127051
| | - Ilya M Flyamer
- Institute of Gene Biology of the Russian Academy of Sciences, Moscow, Russia 119334.,Faculty of Biology, M.V. Lomonosov Moscow State University, Moscow, Russia 119992.,MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Arkadiy K Golov
- Institute of Gene Biology of the Russian Academy of Sciences, Moscow, Russia 119334
| | - Ekaterina E Khrameeva
- Skolkovo Institute of Science and Technology, Skolkovo, Russia 143026.,Institute for Information Transmission Problems (the Kharkevich Institute) of the Russian Academy of Sciences, Moscow, Russia 127051
| | - Maxim V Imakaev
- Department of Physics, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
| | - Nezar A Abdennur
- Computational and Systems Biology Graduate Program, Massachusetts Institute of Technology, Cambridge, MA USA
| | - Mikhail S Gelfand
- Faculty of Bioengineering and Bioinformatics, M.V. Lomonosov Moscow State University, Moscow, Russia 119992.,Skolkovo Institute of Science and Technology, Skolkovo, Russia 143026.,Institute for Information Transmission Problems (the Kharkevich Institute) of the Russian Academy of Sciences, Moscow, Russia 127051.,Faculty of Computer Science, Higher School of Economics, Moscow, Russia 125319
| | - Alexey A Gavrilov
- Institute of Gene Biology of the Russian Academy of Sciences, Moscow, Russia 119334
| | - Sergey V Razin
- Institute of Gene Biology of the Russian Academy of Sciences, Moscow, Russia 119334.,Faculty of Biology, M.V. Lomonosov Moscow State University, Moscow, Russia 119992
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132
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Zhu Y, Tazearslan C, Suh Y. Challenges and progress in interpretation of non-coding genetic variants associated with human disease. Exp Biol Med (Maywood) 2017; 242:1325-1334. [PMID: 28581336 DOI: 10.1177/1535370217713750] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
Abstract
Genome-wide association studies have shown that the far majority of disease-associated variants reside in the non-coding regions of the genome, suggesting that gene regulatory changes contribute to disease risk. To identify truly causal non-coding variants and their affected target genes remains challenging but is a critical step to translate the genetic associations to molecular mechanisms and ultimately clinical applications. Here we review genomic/epigenomic resources and in silico tools that can be used to identify causal non-coding variants and experimental strategies to validate their functionalities. Impact statement Most signals from genome-wide association studies (GWASs) map to the non-coding genome, and functional interpretation of these associations remained challenging. We reviewed recent progress in methodologies of studying the non-coding genome and argued that no single approach allows one to effectively identify the causal regulatory variants from GWAS results. By illustrating the advantages and limitations of each method, our review potentially provided a guideline for taking a combinatorial approach to accurately predict, prioritize, and eventually experimentally validate the causal variants.
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Affiliation(s)
- Yizhou Zhu
- 1 Department of Genetics, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Cagdas Tazearslan
- 1 Department of Genetics, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Yousin Suh
- 1 Department of Genetics, Albert Einstein College of Medicine, Bronx, NY 10461, USA.,2 Department of Ophthalmology & Visual Sciences, Albert Einstein College of Medicine, Bronx, NY 10461, USA.,3 Department of Medicine, Albert Einstein College of Medicine, Bronx, NY 10461, USA
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133
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Crawford TD, Vartanian S, Côté A, Bellon S, Duplessis M, Flynn EM, Hewitt M, Huang HR, Kiefer JR, Murray J, Nasveschuk CG, Pardo E, Romero FA, Sandy P, Tang Y, Taylor AM, Tsui V, Wang J, Wang S, Zawadzke L, Albrecht BK, Magnuson SR, Cochran AG, Stokoe D. Inhibition of bromodomain-containing protein 9 for the prevention of epigenetically-defined drug resistance. Bioorg Med Chem Lett 2017; 27:3534-3541. [PMID: 28606761 DOI: 10.1016/j.bmcl.2017.05.063] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2017] [Revised: 05/19/2017] [Accepted: 05/20/2017] [Indexed: 01/29/2023]
Abstract
Bromodomain-containing protein 9 (BRD9), an epigenetic "reader" of acetylated lysines on post-translationally modified histone proteins, is upregulated in multiple cancer cell lines. To assess the functional role of BRD9 in cancer cell lines, we identified a small-molecule inhibitor of the BRD9 bromodomain. Starting from a pyrrolopyridone lead, we used structure-based drug design to identify a potent and highly selective in vitro tool compound 11, (GNE-375). While this compound showed minimal effects in cell viability or gene expression assays, it showed remarkable potency in preventing the emergence of a drug tolerant population in EGFR mutant PC9 cells treated with EGFR inhibitors. Such tolerance has been linked to an altered epigenetic state, and 11 decreased BRD9 binding to chromatin, and this was associated with decreased expression of ALDH1A1, a gene previously shown to be important in drug tolerance. BRD9 inhibitors may therefore show utility in preventing epigenetically-defined drug resistance.
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Affiliation(s)
- Terry D Crawford
- Genentech Inc., 1 DNA Way, South San Francisco, CA 94080, United States.
| | - Steffan Vartanian
- Genentech Inc., 1 DNA Way, South San Francisco, CA 94080, United States
| | - Alexandre Côté
- Constellation Pharmaceuticals, 215 First Street, Suite 200, Cambridge, MA 02142, United States
| | - Steve Bellon
- Constellation Pharmaceuticals, 215 First Street, Suite 200, Cambridge, MA 02142, United States
| | - Martin Duplessis
- Constellation Pharmaceuticals, 215 First Street, Suite 200, Cambridge, MA 02142, United States
| | - E Megan Flynn
- Genentech Inc., 1 DNA Way, South San Francisco, CA 94080, United States
| | - Michael Hewitt
- Constellation Pharmaceuticals, 215 First Street, Suite 200, Cambridge, MA 02142, United States
| | - Hon-Ren Huang
- Constellation Pharmaceuticals, 215 First Street, Suite 200, Cambridge, MA 02142, United States
| | - James R Kiefer
- Genentech Inc., 1 DNA Way, South San Francisco, CA 94080, United States
| | - Jeremy Murray
- Genentech Inc., 1 DNA Way, South San Francisco, CA 94080, United States
| | | | - Eneida Pardo
- Constellation Pharmaceuticals, 215 First Street, Suite 200, Cambridge, MA 02142, United States
| | - F Anthony Romero
- Genentech Inc., 1 DNA Way, South San Francisco, CA 94080, United States
| | - Peter Sandy
- Constellation Pharmaceuticals, 215 First Street, Suite 200, Cambridge, MA 02142, United States
| | - Yong Tang
- Constellation Pharmaceuticals, 215 First Street, Suite 200, Cambridge, MA 02142, United States
| | - Alexander M Taylor
- Constellation Pharmaceuticals, 215 First Street, Suite 200, Cambridge, MA 02142, United States
| | - Vickie Tsui
- Genentech Inc., 1 DNA Way, South San Francisco, CA 94080, United States
| | - Jian Wang
- Wuxi Apptec Co., Ltd., 288 Fute Zhong Road, Waigaoqiao Free Trade Zone, Shanghai 200131, PR China
| | - Shumei Wang
- Genentech Inc., 1 DNA Way, South San Francisco, CA 94080, United States
| | - Laura Zawadzke
- Constellation Pharmaceuticals, 215 First Street, Suite 200, Cambridge, MA 02142, United States
| | - Brian K Albrecht
- Constellation Pharmaceuticals, 215 First Street, Suite 200, Cambridge, MA 02142, United States
| | - Steven R Magnuson
- Genentech Inc., 1 DNA Way, South San Francisco, CA 94080, United States
| | - Andrea G Cochran
- Genentech Inc., 1 DNA Way, South San Francisco, CA 94080, United States
| | - David Stokoe
- Genentech Inc., 1 DNA Way, South San Francisco, CA 94080, United States.
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134
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Espinal-Enríquez J, Fresno C, Anda-Jáuregui G, Hernández-Lemus E. RNA-Seq based genome-wide analysis reveals loss of inter-chromosomal regulation in breast cancer. Sci Rep 2017; 7:1760. [PMID: 28496157 PMCID: PMC5431987 DOI: 10.1038/s41598-017-01314-1] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2016] [Accepted: 03/27/2017] [Indexed: 12/21/2022] Open
Abstract
Breast cancer is a complex heterogeneous disease. Common hallmark features of cancer can be found. Their origin may be traced back to their intricate relationships governing regulatory programs during the development of this disease. To unveil distinctive features of the transcriptional regulation program in breast cancer, a pipeline for RNA-seq analysis in 780 breast cancer and 101 healthy breast samples, at gene expression and network level, was implemented. Inter-chromosomal relationships between genes resulted strikingly scarce in a cancer network, in comparison to its healthy counterpart. We suggest that inter-chromosomal regulation loss may be a novel feature in breast cancer. Additional evidence was obtained by independent validation in microarray and Hi-C data as well as supplementary computational analyses. Functional analysis showed upregulation in processes related to cell cycle and division; while migration, adhesion and cell-to-cell communication, were downregulated. Both the BRCA1 DNA repairing signalling and the Estrogen-mediated G1/S phase entry pathways were found upregulated. In addition, a synergistic underexpression of the γ-protocadherin complex, located at Chr5q31 is also shown. This region has previously been reported to be hypermethylated in breast cancer. These findings altogether provide further evidence for the central role of transcriptional regulatory programs in shaping malignant phenotypes.
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Affiliation(s)
- Jesús Espinal-Enríquez
- Computational Genomics Division, National Institute of Genomic Medicine (INMEGEN), 14610, Mexico City, Mexico
- Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México (UNAM), 04510, Mexico City, Mexico
| | - Cristóbal Fresno
- Computational Genomics Division, National Institute of Genomic Medicine (INMEGEN), 14610, Mexico City, Mexico
- UA AREA CS. AGR. ING. BIO Y S, CONICET - Universidad Católica de Córdoba, Córdoba, Argentina
| | - Guillermo Anda-Jáuregui
- Computational Genomics Division, National Institute of Genomic Medicine (INMEGEN), 14610, Mexico City, Mexico
- Department of Biomedical Sciences, School of Medicine and Health Sciences, University of North Dakota, 501 North Columbia Rd Stop 9061, Grand Forks, ND, 58203, USA
| | - Enrique Hernández-Lemus
- Computational Genomics Division, National Institute of Genomic Medicine (INMEGEN), 14610, Mexico City, Mexico.
- Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México (UNAM), 04510, Mexico City, Mexico.
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135
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Osborne CS, Mifsud B. Capturing genomic relationships that matter. Chromosome Res 2017; 25:15-24. [PMID: 28078515 PMCID: PMC5346121 DOI: 10.1007/s10577-016-9546-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2016] [Revised: 12/08/2016] [Accepted: 12/19/2016] [Indexed: 12/16/2022]
Abstract
There is a strong interrelationship within the cell nucleus between form and function of the genome. This connection is exhibited across multiple hierarchies, ranging from grand-scale positioning of chromosomes and their intersection with specific nuclear functional activities, the segregation of chromosome structure into distinct domains and long-range regulatory contacts that drive spatial and temporal expression patterns of genes. Fifteen years ago, the development of the chromosome conformation capture method placed the nature of specific, long-range regulatory interactions under scrutiny. However, its development and integration with next-generation sequencing technologies has greatly expanded the breadth and scope of what is detected. The sheer scale of data offered by these important advances has come with new and challenging bottlenecks that are both experimental and bioinformatical. Here, we discuss the recent and prospective development and implementation of new methodologies and analytical tools that are allowing an in-depth, yet focussed characterisation of genomic contacts that are associated with functional activities in the nucleus.
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Affiliation(s)
- Cameron S Osborne
- Department of Medical and Molecular Genetics, King's College London, London, UK.
| | - Borbála Mifsud
- William Harvey Research Institute, Queen Mary University London, London, UK
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136
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Abstract
Recent advances in chromosome conformation capture technologies are improving the current appreciation of how 3D genome architecture affects its function in different cell types and disease. Long-range chromatin interactions are organized into topologically associated domains, which are known to play a role in constraining gene expression patterns. However, in cancer cells there are alterations in the 3D genome structure, which impacts on gene regulation. Disruption of topologically associated domains architecture can result in alterations in chromatin interactions that bring new regulatory elements and genes together, leading to altered expression of oncogenes and tumor suppressor genes. Here, we discuss the impact of genetic and epigenetic changes in cancer and how this affects the spatial organization of chromatin. Understanding how disruptions to the 3D architecture contribute to the cancer genome will provide novel insights into the principles of epigenetic gene regulation in cancer and mechanisms responsible for cancer associated mutations and rearrangements.
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Affiliation(s)
- Joanna Achinger-Kawecka
- Epigenetics Research Laboratory, Genomics & Epigenetics Division, Garvan Institute of Medical Research, 384 Victoria Street, Darlinghurst, NSW 2010, Australia
- St. Vincent's Clinical School, Faculty of Medicine, University of New South Wales, Darlinghurst, NSW 2010, Australia
| | - Susan J Clark
- Epigenetics Research Laboratory, Genomics & Epigenetics Division, Garvan Institute of Medical Research, 384 Victoria Street, Darlinghurst, NSW 2010, Australia
- St. Vincent's Clinical School, Faculty of Medicine, University of New South Wales, Darlinghurst, NSW 2010, Australia
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137
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Karathia H, Kingsford C, Girvan M, Hannenhalli S. A pathway-centric view of spatial proximity in the 3D nucleome across cell lines. Sci Rep 2016; 6:39279. [PMID: 27976707 PMCID: PMC5157015 DOI: 10.1038/srep39279] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2016] [Accepted: 11/22/2016] [Indexed: 12/29/2022] Open
Abstract
In various contexts, spatially proximal genes have been shown to be functionally related. However, the extent to which spatial proximity of genes in a pathway contributes to the pathway's context-specific activity is not known. Leveraging Hi-C data in six human cell-lines, we show that spatial proximity of genes in a pathway is highly correlated with the pathway's context-specific expression and function. Furthermore, spatial proximity of pathway genes correlates with interactions of their protein products, and the specific pathway genes that are proximal to one another tend to occupy higher levels in the regulatory hierarchy. In addition to intra-pathway proximity, related pathways are spatially proximal to one another and housekeeping-genes tend to be proximal to several other pathways suggesting their coordinating role. Substantially extending previous works, our study reveals a pathway-centric organization of 3D-nucleome, whereby, functionally related interacting driver genes tend to be in spatial-proximity in a context-specific manner.
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Affiliation(s)
- Hiren Karathia
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD, USA
| | - Carl Kingsford
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Michelle Girvan
- Department of Physics, University of Maryland, College Park, MD, USA
| | - Sridhar Hannenhalli
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD, USA
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138
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Abstract
Chromosomes of eukaryotes adopt highly dynamic and complex hierarchical structures in the nucleus. The three-dimensional (3D) organization of chromosomes profoundly affects DNA replication, transcription and the repair of DNA damage. Thus, a thorough understanding of nuclear architecture is fundamental to the study of nuclear processes in eukaryotic cells. Recent years have seen rapid proliferation of technologies to investigate genome organization and function. Here, we review experimental and computational methodologies for 3D genome analysis, with special focus on recent advances in high-throughput chromatin conformation capture (3C) techniques and data analysis.
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Affiliation(s)
- Anthony D Schmitt
- Ludwig Institute for Cancer Research and the University of California, San Diego (UCSD) Biomedical Sciences Graduate Program, 9500 Gilman Drive, La Jolla, California 92093, USA
| | - Ming Hu
- Department of Population Health, Division of Biostatistics, New York University School of Medicine, 650 First Avenue, Room 540, New York, New York 10016, USA
- Present address: Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation, 9500 Euclid Avenue, Cleveland, Ohio 44195, USA
| | - Bing Ren
- Ludwig Institute for Cancer Research, Department of Cellular and Molecular Medicine, Moores Cancer Center and Institute of Genomic Medicine, University of California, San Diego (UCSD) School of Medicine, 9500 Gilman Drive, La Jolla, California 92093, USA
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139
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Javierre BM, Burren OS, Wilder SP, Kreuzhuber R, Hill SM, Sewitz S, Cairns J, Wingett SW, Várnai C, Thiecke MJ, Burden F, Farrow S, Cutler AJ, Rehnström K, Downes K, Grassi L, Kostadima M, Freire-Pritchett P, Wang F, Stunnenberg HG, Todd JA, Zerbino DR, Stegle O, Ouwehand WH, Frontini M, Wallace C, Spivakov M, Fraser P. Lineage-Specific Genome Architecture Links Enhancers and Non-coding Disease Variants to Target Gene Promoters. Cell 2016; 167:1369-1384.e19. [PMID: 27863249 PMCID: PMC5123897 DOI: 10.1016/j.cell.2016.09.037] [Citation(s) in RCA: 633] [Impact Index Per Article: 79.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2016] [Revised: 09/06/2016] [Accepted: 09/22/2016] [Indexed: 12/20/2022]
Abstract
Long-range interactions between regulatory elements and gene promoters play key roles in transcriptional regulation. The vast majority of interactions are uncharted, constituting a major missing link in understanding genome control. Here, we use promoter capture Hi-C to identify interacting regions of 31,253 promoters in 17 human primary hematopoietic cell types. We show that promoter interactions are highly cell type specific and enriched for links between active promoters and epigenetically marked enhancers. Promoter interactomes reflect lineage relationships of the hematopoietic tree, consistent with dynamic remodeling of nuclear architecture during differentiation. Interacting regions are enriched in genetic variants linked with altered expression of genes they contact, highlighting their functional role. We exploit this rich resource to connect non-coding disease variants to putative target promoters, prioritizing thousands of disease-candidate genes and implicating disease pathways. Our results demonstrate the power of primary cell promoter interactomes to reveal insights into genomic regulatory mechanisms underlying common diseases.
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Affiliation(s)
- Biola M Javierre
- Nuclear Dynamics Programme, The Babraham Institute, Babraham Research Campus, Cambridge CB22 3AT, UK
| | - Oliver S Burren
- JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, Department of Medical Genetics, NIHR Cambridge Biomedical Research Centre, Cambridge Institute for Medical Research, University of Cambridge, Cambridge CB2 0XY, UK
| | - Steven P Wilder
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge CB10 1SD, UK
| | - Roman Kreuzhuber
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge CB10 1SD, UK; Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Long Road, Cambridge CB2 0PT, UK; National Health Service Blood and Transplant, Cambridge Biomedical Campus, Long Road, Cambridge CB2 0PT, UK
| | - Steven M Hill
- MRC Biostatistics Unit, Cambridge Institute of Public Health, Cambridge Biomedical Campus, Cambridge CB2 0SR, UK
| | - Sven Sewitz
- Nuclear Dynamics Programme, The Babraham Institute, Babraham Research Campus, Cambridge CB22 3AT, UK
| | - Jonathan Cairns
- Nuclear Dynamics Programme, The Babraham Institute, Babraham Research Campus, Cambridge CB22 3AT, UK
| | - Steven W Wingett
- Nuclear Dynamics Programme, The Babraham Institute, Babraham Research Campus, Cambridge CB22 3AT, UK
| | - Csilla Várnai
- Nuclear Dynamics Programme, The Babraham Institute, Babraham Research Campus, Cambridge CB22 3AT, UK
| | - Michiel J Thiecke
- Nuclear Dynamics Programme, The Babraham Institute, Babraham Research Campus, Cambridge CB22 3AT, UK
| | - Frances Burden
- Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Long Road, Cambridge CB2 0PT, UK; National Health Service Blood and Transplant, Cambridge Biomedical Campus, Long Road, Cambridge CB2 0PT, UK
| | - Samantha Farrow
- Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Long Road, Cambridge CB2 0PT, UK; National Health Service Blood and Transplant, Cambridge Biomedical Campus, Long Road, Cambridge CB2 0PT, UK
| | - Antony J Cutler
- JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, Department of Medical Genetics, NIHR Cambridge Biomedical Research Centre, Cambridge Institute for Medical Research, University of Cambridge, Cambridge CB2 0XY, UK
| | - Karola Rehnström
- Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Long Road, Cambridge CB2 0PT, UK; National Health Service Blood and Transplant, Cambridge Biomedical Campus, Long Road, Cambridge CB2 0PT, UK
| | - Kate Downes
- Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Long Road, Cambridge CB2 0PT, UK; National Health Service Blood and Transplant, Cambridge Biomedical Campus, Long Road, Cambridge CB2 0PT, UK
| | - Luigi Grassi
- Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Long Road, Cambridge CB2 0PT, UK; National Health Service Blood and Transplant, Cambridge Biomedical Campus, Long Road, Cambridge CB2 0PT, UK
| | - Myrto Kostadima
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge CB10 1SD, UK; Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Long Road, Cambridge CB2 0PT, UK; National Health Service Blood and Transplant, Cambridge Biomedical Campus, Long Road, Cambridge CB2 0PT, UK
| | - Paula Freire-Pritchett
- Nuclear Dynamics Programme, The Babraham Institute, Babraham Research Campus, Cambridge CB22 3AT, UK
| | - Fan Wang
- MRC Biostatistics Unit, Cambridge Institute of Public Health, Cambridge Biomedical Campus, Cambridge CB2 0SR, UK
| | - Hendrik G Stunnenberg
- Department of Molecular Biology, Faculty of Science, Radboud Institute for Molecular Life Sciences, Radboud University Nijmegen, Geert Grooteplein Zuid 30, 6525 GA Nijmegen, the Netherlands
| | - John A Todd
- JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, Department of Medical Genetics, NIHR Cambridge Biomedical Research Centre, Cambridge Institute for Medical Research, University of Cambridge, Cambridge CB2 0XY, UK
| | - Daniel R Zerbino
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge CB10 1SD, UK
| | - Oliver Stegle
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge CB10 1SD, UK
| | - Willem H Ouwehand
- Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Long Road, Cambridge CB2 0PT, UK; National Health Service Blood and Transplant, Cambridge Biomedical Campus, Long Road, Cambridge CB2 0PT, UK; British Heart Foundation Centre of Excellence, Division of Cardiovascular Medicine, Addenbrooke's Hospital, Hills Road, Cambridge CB2 0QQ, UK; Department of Human Genetics, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1HH, UK
| | - Mattia Frontini
- Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Long Road, Cambridge CB2 0PT, UK; National Health Service Blood and Transplant, Cambridge Biomedical Campus, Long Road, Cambridge CB2 0PT, UK; British Heart Foundation Centre of Excellence, Division of Cardiovascular Medicine, Addenbrooke's Hospital, Hills Road, Cambridge CB2 0QQ, UK.
| | - Chris Wallace
- JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, Department of Medical Genetics, NIHR Cambridge Biomedical Research Centre, Cambridge Institute for Medical Research, University of Cambridge, Cambridge CB2 0XY, UK; MRC Biostatistics Unit, Cambridge Institute of Public Health, Cambridge Biomedical Campus, Cambridge CB2 0SR, UK; Department of Medicine, University of Cambridge, Addenbrooke's Hospital, Cambridge CB2 0SP, UK.
| | - Mikhail Spivakov
- Nuclear Dynamics Programme, The Babraham Institute, Babraham Research Campus, Cambridge CB22 3AT, UK.
| | - Peter Fraser
- Nuclear Dynamics Programme, The Babraham Institute, Babraham Research Campus, Cambridge CB22 3AT, UK.
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140
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Schmitt AD, Hu M, Jung I, Xu Z, Qiu Y, Tan CL, Li Y, Lin S, Lin Y, Barr CL, Ren B. A Compendium of Chromatin Contact Maps Reveals Spatially Active Regions in the Human Genome. Cell Rep 2016; 17:2042-2059. [PMID: 27851967 PMCID: PMC5478386 DOI: 10.1016/j.celrep.2016.10.061] [Citation(s) in RCA: 516] [Impact Index Per Article: 64.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2016] [Revised: 09/02/2016] [Accepted: 10/18/2016] [Indexed: 01/19/2023] Open
Abstract
The three-dimensional configuration of DNA is integral to all nuclear processes in eukaryotes, yet our knowledge of the chromosome architecture is still limited. Genome-wide chromosome conformation capture studies have uncovered features of chromatin organization in cultured cells, but genome architecture in human tissues has yet to be explored. Here, we report the most comprehensive survey to date of chromatin organization in human tissues. Through integrative analysis of chromatin contact maps in 21 primary human tissues and cell types, we find topologically associating domains highly conserved in different tissues. We also discover genomic regions that exhibit unusually high levels of local chromatin interactions. These frequently interacting regions (FIREs) are enriched for super-enhancers and are near tissue-specifically expressed genes. They display strong tissue-specificity in local chromatin interactions. Additionally, FIRE formation is partially dependent on CTCF and the Cohesin complex. We further show that FIREs can help annotate the function of non-coding sequence variants.
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Affiliation(s)
- Anthony D Schmitt
- Ludwig Institute for Cancer Research, La Jolla, CA 92093, USA; UCSD Biomedical Sciences Graduate Program, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - Ming Hu
- Division of Biostatistics, Department of Population Health, New York University School of Medicine, 650 First Avenue, New York, NY 10016, USA.
| | - Inkyung Jung
- Ludwig Institute for Cancer Research, La Jolla, CA 92093, USA
| | - Zheng Xu
- Departments of Genetics, Biostatistics, and Computer Science, University of North Carolina, Chapel Hill, NC 27599, USA; Quantitative Life Sciences Initiative, University of Nebraska, Lincoln, NE 68583, USA; Department of Statistics, University of Nebraska, Lincoln, NE 68583, USA
| | - Yunjiang Qiu
- Ludwig Institute for Cancer Research, La Jolla, CA 92093, USA; USCD Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - Catherine L Tan
- Ludwig Institute for Cancer Research, La Jolla, CA 92093, USA
| | - Yun Li
- Departments of Genetics, Biostatistics, and Computer Science, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Shin Lin
- Division of Cardiology, Department of Medicine, University of Washington, 850 Republican Street, Seattle, WA 98108, USA
| | - Yiing Lin
- Department of Surgery, Washington University School of Medicine, 660 S Euclid Ave., Campus Box 8109, St. Louis, MO 63110, USA
| | - Cathy L Barr
- Krembil Research Institute University Health Network, The Hospital for Sick Children, The University of Toronto, Krembil Discovery Tower, 60 Leonard Ave. 8KD-412, Toronto, ON M5T 2S8, Canada
| | - Bing Ren
- Ludwig Institute for Cancer Research, La Jolla, CA 92093, USA; Department of Cellular and Molecular Medicine, Moores Cancer Center and Institute of Genome Medicine, UCSD School of Medicine, 9500 Gilman Drive, La Jolla, CA 92093, USA.
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141
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Capture Hi-C identifies a novel causal gene, IL20RA, in the pan-autoimmune genetic susceptibility region 6q23. Genome Biol 2016; 17:212. [PMID: 27799070 PMCID: PMC5088679 DOI: 10.1186/s13059-016-1078-x] [Citation(s) in RCA: 67] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2016] [Accepted: 10/05/2016] [Indexed: 01/15/2023] Open
Abstract
BACKGROUND The identification of causal genes from genome-wide association studies (GWAS) is the next important step for the translation of genetic findings into biologically meaningful mechanisms of disease and potential therapeutic targets. Using novel chromatin interaction detection techniques and allele specific assays in T and B cell lines, we provide compelling evidence that redefines causal genes at the 6q23 locus, one of the most important loci that confers autoimmunity risk. RESULTS Although the function of disease-associated non-coding single nucleotide polymorphisms (SNPs) at 6q23 is unknown, the association is generally assigned to TNFAIP3, the closest gene. However, the DNA fragment containing the associated SNPs interacts through chromatin looping not only with TNFAIP3, but also with IL20RA, located 680 kb upstream. The risk allele of the most likely causal SNP, rs6927172, is correlated with both a higher frequency of interactions and increased expression of IL20RA, along with a stronger binding of both the NFκB transcription factor and chromatin marks characteristic of active enhancers in T-cells. CONCLUSIONS Our results highlight the importance of gene assignment for translating GWAS findings into biologically meaningful mechanisms of disease and potential therapeutic targets; indeed, monoclonal antibody therapy targeting IL-20 is effective in the treatment of rheumatoid arthritis and psoriasis, both with strong GWAS associations to this region.
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142
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Zhang M, Wang Z, Obazee O, Jia J, Childs EJ, Hoskins J, Figlioli G, Mocci E, Collins I, Chung CC, Hautman C, Arslan AA, Beane-Freeman L, Bracci PM, Buring J, Duell EJ, Gallinger S, Giles GG, Goodman GE, Goodman PJ, Kamineni A, Kolonel LN, Kulke MH, Malats N, Olson SH, Sesso HD, Visvanathan K, White E, Zheng W, Abnet CC, Albanes D, Andreotti G, Brais L, Bueno-de-Mesquita HB, Basso D, Berndt SI, Boutron-Ruault MC, Bijlsma MF, Brenner H, Burdette L, Campa D, Caporaso NE, Capurso G, Cavestro GM, Cotterchio M, Costello E, Elena J, Boggi U, Gaziano JM, Gazouli M, Giovannucci EL, Goggins M, Gross M, Haiman CA, Hassan M, Helzlsouer KJ, Hu N, Hunter DJ, Iskierka-Jazdzewska E, Jenab M, Kaaks R, Key TJ, Khaw KT, Klein EA, Kogevinas M, Krogh V, Kupcinskas J, Kurtz RC, Landi MT, Landi S, Marchand LL, Mambrini A, Mannisto S, Milne RL, Neale RE, Oberg AL, Panico S, Patel AV, Peeters PHM, Peters U, Pezzilli R, Porta M, Purdue M, Quiros JR, Riboli E, Rothman N, Scarpa A, Scelo G, Shu XO, Silverman DT, Soucek P, Strobel O, Sund M, Małecka-Panas E, Taylor PR, Tavano F, Travis RC, Thornquist M, Tjønneland A, Tobias GS, Trichopoulos D, Vashist Y, Vodicka P, Wactawski-Wende J, Wentzensen N, Yu H, Yu K, Zeleniuch-Jacquotte A, Kooperberg C, Risch HA, Jacobs EJ, Li D, Fuchs C, Hoover R, Hartge P, Chanock SJ, Petersen GM, Stolzenberg-Solomon RS, Wolpin BM, Kraft P, Klein AP, Canzian F, Amundadottir LT. Three new pancreatic cancer susceptibility signals identified on chromosomes 1q32.1, 5p15.33 and 8q24.21. Oncotarget 2016; 7:66328-66343. [PMID: 27579533 PMCID: PMC5340084 DOI: 10.18632/oncotarget.11041] [Citation(s) in RCA: 78] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/1969] [Accepted: 12/31/1969] [Indexed: 12/20/2022] Open
Abstract
Genome-wide association studies (GWAS) have identified common pancreatic cancer susceptibility variants at 13 chromosomal loci in individuals of European descent. To identify new susceptibility variants, we performed imputation based on 1000 Genomes (1000G) Project data and association analysis using 5,107 case and 8,845 control subjects from 27 cohort and case-control studies that participated in the PanScan I-III GWAS. This analysis, in combination with a two-staged replication in an additional 6,076 case and 7,555 control subjects from the PANcreatic Disease ReseArch (PANDoRA) and Pancreatic Cancer Case-Control (PanC4) Consortia uncovered 3 new pancreatic cancer risk signals marked by single nucleotide polymorphisms (SNPs) rs2816938 at chromosome 1q32.1 (per allele odds ratio (OR) = 1.20, P = 4.88x10 -15), rs10094872 at 8q24.21 (OR = 1.15, P = 3.22x10 -9) and rs35226131 at 5p15.33 (OR = 0.71, P = 1.70x10 -8). These SNPs represent independent risk variants at previously identified pancreatic cancer risk loci on chr1q32.1 ( NR5A2), chr8q24.21 ( MYC) and chr5p15.33 ( CLPTM1L- TERT) as per analyses conditioned on previously reported susceptibility variants. We assessed expression of candidate genes at the three risk loci in histologically normal ( n = 10) and tumor ( n = 8) derived pancreatic tissue samples and observed a marked reduction of NR5A2 expression (chr1q32.1) in the tumors (fold change -7.6, P = 5.7x10 -8). This finding was validated in a second set of paired ( n = 20) histologically normal and tumor derived pancreatic tissue samples (average fold change for three NR5A2 isoforms -31.3 to -95.7, P = 7.5x10 -4-2.0x10 -3). Our study has identified new susceptibility variants independently conferring pancreatic cancer risk that merit functional follow-up to identify target genes and explain the underlying biology.
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Affiliation(s)
- Mingfeng Zhang
- Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Zhaoming Wang
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
- Cancer Genomics Research Laboratory, National Cancer Institute, Division of Cancer Epidemiology and Genetics, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, Frederick, Maryland, USA
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - Ofure Obazee
- Genomic Epidemiology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jinping Jia
- Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Erica J. Childs
- Department of Oncology, the Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Jason Hoskins
- Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Gisella Figlioli
- Genomic Epidemiology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Evelina Mocci
- Department of Oncology, the Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Irene Collins
- Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Charles C. Chung
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
- Cancer Genomics Research Laboratory, National Cancer Institute, Division of Cancer Epidemiology and Genetics, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, Frederick, Maryland, USA
| | - Christopher Hautman
- Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Alan A. Arslan
- Department of Obstetrics and Gynecology, New York University School of Medicine, New York, New York, USA
- Department of Environmental Medicine, New York University School of Medicine, New York, New York, USA
- New York University Cancer Institute, New York, New York, USA
| | - Laura Beane-Freeman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Paige M. Bracci
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California, USA
| | - Julie Buring
- Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
- Division of Aging, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Eric J. Duell
- Unit of Nutrition and Cancer, Cancer Epidemiology Research Program, Bellvitge Biomedical Research Institute (IDIBELL), Catalan Institute of Oncology (ICO), Barcelona, Spain
| | - Steven Gallinger
- Lunenfeld Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada
| | - Graham G. Giles
- Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria, Australia
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Gary E. Goodman
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Phyllis J. Goodman
- Southwest Oncology Group Statistical Center, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Aruna Kamineni
- Group Health Research Institute, Seattle, Washington, USA
| | - Laurence N. Kolonel
- Cancer Epidemiology Program, University of Hawaii Cancer Center, Honolulu, Hawaii, USA
| | - Matthew H. Kulke
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - Núria Malats
- Genetic and Molecular Epidemiology Group, CNIO-Spanish National Cancer Research Centre, Madrid, Spain
| | - Sara H. Olson
- Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, New York, USA
| | - Howard D. Sesso
- Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
- Division of Aging, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
- Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA
| | - Kala Visvanathan
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Emily White
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
- Department of Epidemiology, University of Washington, Seattle, Washington, USA
| | - Wei Zheng
- Division of Epidemiology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Christian C. Abnet
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Demetrius Albanes
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Gabriella Andreotti
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Lauren Brais
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - H. Bas Bueno-de-Mesquita
- Department for Determinants of Chronic Diseases (DCD), National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
- Department of Social & Preventive Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Daniela Basso
- Department of Laboratory Medicine, University Hospital of Padova, Padua, Italy
| | - Sonja I. Berndt
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Marie-Christine Boutron-Ruault
- Inserm, Centre for Research in Epidemiology and Population Health (CESP), U1018, Nutrition, Hormones and Women's Health Team, F-94805, Villejuif, France
- University Paris Sud, UMRS 1018, F-94805, Villejuif, France
- IGR, F-94805, Villejuif, France
| | - Maarten F. Bijlsma
- Laboratory for Experimental Oncology and Radiobiology, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Laurie Burdette
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
- Cancer Genomics Research Laboratory, National Cancer Institute, Division of Cancer Epidemiology and Genetics, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, Frederick, Maryland, USA
| | - Daniele Campa
- Department of Biology, University of Pisa, Pisa, Italy
| | - Neil E. Caporaso
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Gabriele Capurso
- Digestive and Liver Disease Unit, ‘Sapienza’ University of Rome, Rome, Italy
| | - Giulia Martina Cavestro
- Gastroenterology and Gastrointestinal Endoscopy Unit, Vita-Salute San Raffaele University, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Michelle Cotterchio
- Prevention and Cancer Control, Cancer Care Ontario, Toronto, Ontario, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Eithne Costello
- National Institute for Health Research Liverpool Pancreas Biomedical Research Unit, University of Liverpool, Liverpool, United Kingdom
| | - Joanne Elena
- Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Ugo Boggi
- Department of Surgery, Unit of Experimental Surgical Pathology, University Hospital of Pisa, Pisa, Italy
| | - J. Michael Gaziano
- Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
- Division of Aging, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
- Massachusetts Veteran's Epidemiology, Research, and Information Center, Geriatric Research Education and Clinical Center, Veterans Affairs Boston Healthcare System, Boston, Massachusetts, USA
| | - Maria Gazouli
- Department of Basic Medical Sciences, Laboratory of Biology, Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Edward L. Giovannucci
- Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, and Harvard Medical School, Boston, Massachusetts, USA
- Department of Nutrition, Harvard School of Public Health, Boston, Massachusetts, USA
| | - Michael Goggins
- Department of Pathology, Sidney Kimmel Cancer Center and Johns Hopkins University, Baltimore, Maryland, USA
- Department of Medicine, Sidney Kimmel Cancer Center and Johns Hopkins University, Baltimore, Maryland, USA
- Department of Oncology, Sidney Kimmel Cancer Center and Johns Hopkins University, Baltimore, Maryland, USA
| | - Myron Gross
- Laboratory of Medicine and Pathology, University of Minnesota, Minneapolis, Minnesota, USA
| | - Christopher A. Haiman
- Preventive Medicine, University of Southern California, Los Angeles, California, USA
| | - Manal Hassan
- Department of Gastrointestinal Medical Oncology, University of Texas M.D. Anderson Cancer Center, Houston, Texas, USA
| | - Kathy J. Helzlsouer
- Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Nan Hu
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - David J. Hunter
- Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
- Harvard School of Public Health, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | | | - Mazda Jenab
- International Agency for Research on Cancer (IARC), Lyon, France
| | - Rudolf Kaaks
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Timothy J. Key
- Cancer Epidemiology Unit, University of Oxford, Oxford, United Kingdom
| | - Kay-Tee Khaw
- School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Eric A. Klein
- Glickman Urological and Kidney Institute, Cleveland Clinic, Cleveland, Ohio, USA
| | - Manolis Kogevinas
- Centre de Recerca en Epidemiologia Ambiental (CREAL), CIBER Epidemiología y Salud Pública (CIBERESP), Spain
- Hospital del Mar Institute of Medical Research (IMIM), Barcelona, Spain
- National School of Public Health, Athens, Greece
| | - Vittorio Krogh
- Epidemiology and Prevention Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Juozas Kupcinskas
- Department of Gastroenterology, Lithuanian University of Health Sciences, Kaunas, Lithuania
| | - Robert C. Kurtz
- Department of Medicine, Memorial Sloan-Kettering Cancer Center, New York, New York, USA
| | - Maria T. Landi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Stefano Landi
- Department of Biology, University of Pisa, Pisa, Italy
| | - Le Loic Marchand
- Cancer Epidemiology Program, University of Hawaii Cancer Center, Honolulu, Hawaii, USA
| | - Andrea Mambrini
- Oncology Department, ASL1 Massa Carrara, Massa Carrara, Italy
| | - Satu Mannisto
- National Institute for Health and Welfare, Department of Chronic Disease Prevention, Helsinki, Finland
| | - Roger L. Milne
- Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria, Australia
| | - Rachel E. Neale
- Department of Population Health, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Ann L. Oberg
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Salvatore Panico
- Dipartimento di Medicina Clinica E Chirurgia, Federico II Univeristy, Naples, Italy
| | - Alpa V. Patel
- Epidemiology Research Program, American Cancer Society, Atlanta, Georgia, USA
| | - Petra H. M. Peeters
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Ulrike Peters
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
- Department of Epidemiology, University of Washington, Seattle, Washington, USA
| | - Raffaele Pezzilli
- Pancreas Unit, Department of Digestive Diseases and Internal Medicine, Sant'Orsola-Malpighi Hospital, Bologna, Italy
| | - Miquel Porta
- Hospital del Mar Institute of Medical Research (IMIM), Barcelona, Spain
- School of Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain
- CIBER de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Mark Purdue
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - J. Ramón Quiros
- Public Health and Participation Directorate, Asturias, Spain
| | - Elio Riboli
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
| | - Nathaniel Rothman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Aldo Scarpa
- ARC-NET: Centre for Applied Research on Cancer, University and Hospital Trust of Verona, Verona, Italy
| | - Ghislaine Scelo
- International Agency for Research on Cancer (IARC), Lyon, France
| | - Xiao-Ou Shu
- Division of Epidemiology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Debra T. Silverman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Pavel Soucek
- Laboratory of Pharmacogenomics, Biomedical Center, Faculty of Medicine in Pilsen, Charles University in Prague, Pilsen, Czech Republic
| | - Oliver Strobel
- Department of General Surgery, University Hospital Heidelberg, Heidelberg, Germany
| | - Malin Sund
- Department of Surgical and Peroperative Sciences, Umeå University, Umeå, Sweden
| | - Ewa Małecka-Panas
- Department of Digestive Tract Diseases, Medical University of Łodz, Łodz, Poland
| | - Philip R. Taylor
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Francesca Tavano
- Division of Gastroenterology and Research Laboratory, IRCCS Scientific Institute and Regional General Hospital “Casa Sollievo della Sofferenza”, San Giovanni Rotondo, Italy
| | - Ruth C. Travis
- Cancer Epidemiology Unit, University of Oxford, Oxford, United Kingdom
| | - Mark Thornquist
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Anne Tjønneland
- Institute of Cancer Epidemiology, Danish Cancer Society, Copenhagen, Denmark
| | - Geoffrey S. Tobias
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Dimitrios Trichopoulos
- Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA
- Bureau of Epidemiologic Research, Academy of Athens, Athens, Greece
- Hellenic Health Foundation, Athens, Greece
| | - Yogesh Vashist
- Department of General, Visceral and Thoracic Surgery, University Hamburg-Eppendorf, Hamburg, Germany
| | - Pavel Vodicka
- Department of Molecular Biology of Cancer, Institute of Experimental Medicine, Academy of Sciences of the Czech Republic, Prague, Czech Republic
| | - Jean Wactawski-Wende
- Department of Social and Preventive Medicine, University at Buffalo, Buffalo, New York, USA
| | - Nicolas Wentzensen
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Herbert Yu
- Cancer Epidemiology Program, University of Hawaii Cancer Center, Honolulu, Hawaii, USA
| | - Kai Yu
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Anne Zeleniuch-Jacquotte
- Department of Environmental Medicine, New York University School of Medicine, New York, New York, USA
- New York University Cancer Institute, New York, New York, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Harvey A. Risch
- Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, Connecticut, USA
| | - Eric J. Jacobs
- Epidemiology Research Program, American Cancer Society, Atlanta, Georgia, USA
| | - Donghui Li
- Department of Gastrointestinal Medical Oncology, University of Texas M.D. Anderson Cancer Center, Houston, Texas, USA
| | - Charles Fuchs
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, and Harvard Medical School, Boston, Massachusetts, USA
| | - Robert Hoover
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Patricia Hartge
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Stephen J. Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Gloria M. Petersen
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Rachael S. Stolzenberg-Solomon
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Brian M. Wolpin
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - Peter Kraft
- Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA
- Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, USA
| | - Alison P. Klein
- Department of Oncology, the Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- Department of Epidemiology, the Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Federico Canzian
- Genomic Epidemiology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Laufey T. Amundadottir
- Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
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Wyszynski A, Hong CC, Lam K, Michailidou K, Lytle C, Yao S, Zhang Y, Bolla MK, Wang Q, Dennis J, Hopper JL, Southey MC, Schmidt MK, Broeks A, Muir K, Lophatananon A, Fasching PA, Beckmann MW, Peto J, Dos-Santos-Silva I, Sawyer EJ, Tomlinson I, Burwinkel B, Marme F, Guénel P, Truong T, Bojesen SE, Nordestgaard BG, González-Neira A, Benitez J, Neuhausen SL, Brenner H, Dieffenbach AK, Meindl A, Schmutzler RK, Brauch H, Nevanlinna H, Khan S, Matsuo K, Ito H, Dörk T, Bogdanova NV, Lindblom A, Margolin S, Mannermaa A, Kosma VM, Wu AH, Van Den Berg D, Lambrechts D, Wildiers H, Chang-Claude J, Rudolph A, Radice P, Peterlongo P, Couch FJ, Olson JE, Giles GG, Milne RL, Haiman CA, Henderson BE, Dumont M, Teo SH, Wong TY, Kristensen V, Zheng W, Long J, Winqvist R, Pylkäs K, Andrulis IL, Knight JA, Devilee P, Seynaeve C, García-Closas M, Figueroa J, Klevebring D, Czene K, Hooning MJ, van den Ouweland AMW, Darabi H, Shu XO, Gao YT, Cox A, Blot W, Signorello LB, Shah M, Kang D, Choi JY, Hartman M, Miao H, Hamann U, Jakubowska A, Lubinski J, Sangrajrang S, McKay J, Toland AE, Yannoukakos D, Shen CY, Wu PE, Swerdlow A, Orr N, Simard J, Pharoah PDP, Dunning AM, Chenevix-Trench G, Hall P, Bandera E, Amos C, Ambrosone C, Easton DF, Cole MD. An intergenic risk locus containing an enhancer deletion in 2q35 modulates breast cancer risk by deregulating IGFBP5 expression. Hum Mol Genet 2016; 25:3863-3876. [PMID: 27402876 PMCID: PMC5216618 DOI: 10.1093/hmg/ddw223] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2016] [Revised: 06/11/2016] [Accepted: 07/04/2016] [Indexed: 12/20/2022] Open
Abstract
Breast cancer is the most diagnosed malignancy and the second leading cause of cancer mortality in females. Previous association studies have identified variants on 2q35 associated with the risk of breast cancer. To identify functional susceptibility loci for breast cancer, we interrogated the 2q35 gene desert for chromatin architecture and functional variation correlated with gene expression. We report a novel intergenic breast cancer risk locus containing an enhancer copy number variation (enCNV; deletion) located approximately 400Kb upstream to IGFBP5, which overlaps an intergenic ERα-bound enhancer that loops to the IGFBP5 promoter. The enCNV is correlated with modified ERα binding and monoallelic-repression of IGFBP5 following oestrogen treatment. We investigated the association of enCNV genotype with breast cancer in 1,182 cases and 1,362 controls, and replicate our findings in an independent set of 62,533 cases and 60,966 controls from 41 case control studies and 11 GWAS. We report a dose-dependent inverse association of 2q35 enCNV genotype (percopy OR = 0.68 95%CI 0.55-0.83, P = 0.0002; replication OR = 0.77 95% CI 0.73-0.82, P = 2.1 × 10-19) and identify 13 additional linked variants (r2 > 0.8) in the 20Kb linkage block containing the enCNV (P = 3.2 × 10-15 - 5.6 × 10-17). These associations were independent of previously reported 2q35 variants, rs13387042/rs4442975 and rs16857609, and were stronger for ER-positive than ER-negative disease. Together, these results suggest that 2q35 breast cancer risk loci may be mediating their effect through IGFBP5.
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Affiliation(s)
- Asaf Wyszynski
- Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA
| | - Chi-Chen Hong
- Department of Cancer Prevention and Control, Roswell Park Cancer Institute, Buffalo, NY 14263, USA
| | | | - Kyriaki Michailidou
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, UK
| | - Christian Lytle
- Molecular Biology Core Facility, Dartmouth College, Hanover, NH 03755 USA
| | - Song Yao
- Department of Cancer Prevention and Control, Roswell Park Cancer Institute, Buffalo, NY 14263, USA
| | - Yali Zhang
- Department of Cancer Prevention and Control, Roswell Park Cancer Institute, Buffalo, NY 14263, USA
| | - Manjeet K Bolla
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, UK
| | - Qin Wang
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, UK
| | - Joe Dennis
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, UK
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria 3010, Australia
| | - Melissa C Southey
- Department of Pathology, The University of Melbourne, Melbourne, Victoria 3010, Australia
| | - Marjanka K Schmidt
- Netherlands Cancer Institute, Antoni van Leeuwenhoek hospital, 1066 CX Amsterdam, The Netherlands
| | - Annegien Broeks
- Wellcome Trust Centre for Human Genetics and Oxford Biomedical Research Centre, University of Oxford, OX3 7BN, UK
| | - Kenneth Muir
- Division of Health Sciences, Warwick Medical school, Warwick University, Coventry, CV4 7AL, UK
- Institute of Population Health, University of Manchester, Manchester, M13 9PL, UK
| | - Artitaya Lophatananon
- Department of Obstetrics and Gynecology, University of Heidelberg, 69120 Heidelberg, Germany
| | - Peter A Fasching
- University Breast Center Franconia, Department of Gynecology and Obstetrics, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen-EMN, 91054 Erlangen, Germany
- David Geffen School of Medicine, Department of Medicine Division of Hematology and Oncology, University of California at Los Angeles, Los Angeles, CA 90095, USA
| | - Matthias W Beckmann
- University Breast Center Franconia, Department of Gynecology and Obstetrics, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen-EMN, 91054 Erlangen, Germany
| | - Julian Peto
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK
| | - Isabel Dos-Santos-Silva
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK
| | - Elinor J Sawyer
- Research Oncology, Division of Cancer Studies, King's College London, Guy's Hospital, London, SE1 9RT, UK
| | - Ian Tomlinson
- Wellcome Trust Centre for Human Genetics and Oxford Biomedical Research Centre, University of Oxford, OX3 7BN, UK
| | - Barbara Burwinkel
- Department of Obstetrics and Gynecology, University of Heidelberg, 69120 Heidelberg, Germany
- Molecular Epidemiology Group, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Frederik Marme
- Department of Obstetrics and Gynecology, University of Heidelberg, 69120 Heidelberg, Germany
- National Center for Tumor Diseases, University of Heidelberg, 69120 Heidelberg, Germany
| | - Pascal Guénel
- Inserm (National Institute of Health and Medical Research), CESP (Center for Research in Epidemiology and Population Health), U1018, Environmental Epidemiology of Cancer, 94807 Villejuif, France
- University Paris-Sud, UMRS 1018, 94807 Villejuif, France
| | - Thérèse Truong
- Inserm (National Institute of Health and Medical Research), CESP (Center for Research in Epidemiology and Population Health), U1018, Environmental Epidemiology of Cancer, 94807 Villejuif, France
- University Paris-Sud, UMRS 1018, 94807 Villejuif, France
| | - Stig E Bojesen
- Copenhagen General Population Study, Herlev Hospital, Copenhagen University Hospital, 2730 Herlev, Denmark
- Department of Clinical Biochemistry, Herlev Hospital, Copenhagen University Hospital, 2730 Herlev, Denmark
| | - Børge G Nordestgaard
- Copenhagen General Population Study, Herlev Hospital, Copenhagen University Hospital, 2730 Herlev, Denmark
- Department of Clinical Biochemistry, Herlev Hospital, Copenhagen University Hospital, 2730 Herlev, Denmark
| | - Anna González-Neira
- Human Genotyping-CEGEN Unit, Human Cancer Genetics Program, Spanish National Cancer Research Centre (CNIO), 28029 Madrid, Spain
| | - Javier Benitez
- Human Genetics Group, Human Cancer Genetics Program, Spanish National Cancer Research Centre (CNIO), 28029 Madrid, Spain
- Centro de Investigación en Red de Enfermedades Raras (CIBERER), 46010 Valencia, Spain
| | | | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
- German Cancer Consortium (DKTK), 69120 Heidelberg, Germany
| | - Aida Karina Dieffenbach
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
- German Cancer Consortium (DKTK), 69120 Heidelberg, Germany
| | - Alfons Meindl
- Division of Gynaecology and Obstetrics, Technische Universität München, 81675 Munich, Germany
| | - Rita K Schmutzler
- Division of Molecular Gyneco-Oncology, Department of Gynaecology and Obstetrics, University Hospital of Cologne, 50931 Cologne, Germany
- Center of Familial Breast and Ovarian Cancer, University Hospital of Cologne, 50931 Cologne, Germany
- Center for Integrated Oncology (CIO), University Hospital of Cologne, 50931 Cologne, Germany
- Center for Molecular Medicine Cologne (CMMC), University of Cologne, 50931 Cologne, Germany
| | - Hiltrud Brauch
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, 70376 Stuttgart, Germany
- University of Tübingen, 72074 Tübingen, Germany
| | - Heli Nevanlinna
- Department of Obstetrics and Gynecology, University of Helsinki and Helsinki University Central Hospital, Helsinki, FI-00029 HUS, Finland
| | - Sofia Khan
- Department of Obstetrics and Gynecology, University of Helsinki and Helsinki University Central Hospital, Helsinki, FI-00029 HUS, Finland
| | - Keitaro Matsuo
- Department of Preventive Medicine, Kyushu University Faculty of Medical Sciences, Fukuoka, Japan
| | - Hidemi Ito
- Division of Epidemiology and Prevention, Aichi Cancer Center Research Institute, Nagoya, Aichi, 464-8681, Japan
| | - Thilo Dörk
- Department of Obstetrics and Gynaecology, Hannover Medical School, 30625 Hannover, Germany
| | - Natalia V Bogdanova
- Department of Radiation Oncology, Hannover Medical School, 30625 Hannover, Germany
| | - Annika Lindblom
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm SE-17177, Sweden
| | - Sara Margolin
- Department of Oncology - Pathology, Karolinska Institutet, Stockholm SE-17177, Sweden
| | - Arto Mannermaa
- School of Medicine, Institute of Clinical Medicine, Pathology and Forensic Medicine, University of Eastern Finland, FI-70211 Kuopio, Finland
- Cancer Center of Eastern Finland, University of Eastern Finland, FI-70211 Kuopio, Finland
- Imaging Center, Department of Clinical Pathology, Kuopio University Hospital, 70210 Kuopio, Finland
| | - Veli-Matti Kosma
- School of Medicine, Institute of Clinical Medicine, Pathology and Forensic Medicine, University of Eastern Finland, FI-70211 Kuopio, Finland
- Cancer Center of Eastern Finland, University of Eastern Finland, FI-70211 Kuopio, Finland
- Imaging Center, Department of Clinical Pathology, Kuopio University Hospital, 70210 Kuopio, Finland
| | - Anna H Wu
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - David Van Den Berg
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Diether Lambrechts
- Vesalius Research Center (VRC), VIB, 3000 Leuven, Belgium
- Laboratory for Translational Genetics, Department of Oncology, University of Leuven, 3000 Leuven, Belgium
| | - Hans Wildiers
- Multidisciplinary Breast Center, Department of General Medical Oncology, University Hospitals Leuven, B-3000 Leuven, Belgium
| | - Jenny Chang-Claude
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
- University Cancer Center Hamburg (UCCH), University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Anja Rudolph
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Paolo Radice
- Unit of Molecular Bases of Genetic Risk and Genetic Testing, Department of Preventive and Predictive Medicine, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), 20133 Milan, Italy
| | - Paolo Peterlongo
- IFOM, Fondazione Istituto FIRC di Oncologia Molecolare, 20139 Milan, Italy
| | - Fergus J Couch
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905, USA
| | - Janet E Olson
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA
| | - Graham G Giles
- Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Victoria 3004, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria 3010, Australia
| | - Roger L Milne
- Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Victoria 3004, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria 3010, Australia
| | - Christopher A Haiman
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Brian E Henderson
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Martine Dumont
- Centre Hospitalier Universitaire de Québec Research Center and Laval University, QC, G1V 4G2, Canada
| | - Soo Hwang Teo
- Cancer Research Initiatives Foundation, Sime Darby Medical Centre, 47500 Subang Jaya, Selangor, Malaysia
- Breast Cancer Research Unit, University Malaya Cancer Research Institute, University Malaya Medical Centre (UMMC), 59100 Kuala Lumpur, Malaysia
| | - Tien Y Wong
- Singapore Eye Research Institute, National University of Singapore, Singapore 168751
| | - Vessela Kristensen
- Department of Genetics, Institute for Cancer Research, Oslo University Hospital, Radiumhospitalet, N-0310 Oslo, Norway
- Institute of Clinical Medicine, University of Oslo (UiO), 0450 Oslo, Norway
- Department of Clinical Molecular Biology (EpiGen), University of Oslo (UiO), 0450 Oslo, Norway
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN 37203, USA
| | - Jirong Long
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN 37203, USA
| | - Robert Winqvist
- Laboratory of Cancer Genetics and Tumor Biology, Department of Clinical Chemistry and Biocenter Oulu, University of Oulu, NordLab Oulu/Oulu University Hospital, FI-90220 Oulu, Finland
| | - Katri Pylkäs
- Laboratory of Cancer Genetics and Tumor Biology, Department of Clinical Chemistry and Biocenter Oulu, University of Oulu, NordLab Oulu/Oulu University Hospital, FI-90220 Oulu, Finland
| | - Irene L Andrulis
- Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital, Toronto, ON, M5G 1X5, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, M5S 1A8, Canada
| | - Julia A Knight
- Prosserman Centre for Health Research, Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital, Toronto, ON, M5G 1X5, Canada
- Division of Epidemiology, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, M5S 1A8, Canada
| | - Peter Devilee
- Department of Human Genetics & Department of Pathology, Leiden University Medical Center, 2333 ZC Leiden, The Netherlands
| | - Caroline Seynaeve
- Family Cancer Clinic, Department of Medical Oncology, Erasmus MC-Daniel den Hoed Cancer Center, 3075 EA Rotterdam, The Netherlands
| | - Montserrat García-Closas
- Division of Genetics and Epidemiology, Institute of Cancer Research, Sutton, SM2 5NG, UK
- Breakthrough Breast Cancer Research Centre, Division of Breast Cancer Research, The Institute of Cancer Research, London, SW3 6JB, UK
| | - Jonine Figueroa
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD 20850, USA
| | - Daniel Klevebring
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm SE-17177, Sweden
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm SE-17177, Sweden
| | - Maartje J Hooning
- Department of Medical Oncology, Erasmus University Medical Center, 3075 EA Rotterdam, The Netherlands
| | - Ans M W van den Ouweland
- Department of Clinical Genetics, Erasmus University Medical Center, 3075 EA Rotterdam, The Netherlands
| | - Hatef Darabi
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm SE-17177, Sweden
| | - Xiao-Ou Shu
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN 37203, USA
| | - Yu-Tang Gao
- Department of Epidemiology, Shanghai Cancer Institute, Xuhui, Shanghai, China
| | - Angela Cox
- CRUK/YCR Sheffield Cancer Research Centre, Department of Oncology, University of Sheffield, Sheffield, S10 2RX, UK
| | - William Blot
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN 37203, USA
- International Epidemiology Institute, Rockville, MD 20850, USA
| | - Lisa B Signorello
- Division of Cancer Prevention, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Mitul Shah
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, CB1 8RN, UK
| | - Daehee Kang
- Department of Preventive Medicine, Seoul National University College of Medicine, Seoul 110-799, Korea
- Department of Biomedical Sciences, Seoul National University Graduate School, Seoul 151-742, Korea
- Cancer Research Institute, Seoul National University College of Medicine, Seoul 110-799, Korea
| | - Ji-Yeob Choi
- Department of Biomedical Sciences, Seoul National University Graduate School, Seoul 151-742, Korea
- Cancer Research Institute, Seoul National University College of Medicine, Seoul 110-799, Korea
| | - Mikael Hartman
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore 117597
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore 117597
| | - Hui Miao
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore 117597
| | - Ute Hamann
- Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Anna Jakubowska
- Department of Genetics and Pathology, Pomeranian Medical University, 70-115 Szczecin, Poland
| | - Jan Lubinski
- Department of Genetics and Pathology, Pomeranian Medical University, 70-115 Szczecin, Poland
| | | | - James McKay
- International Agency for Research on Cancer, 69372 Lyon, CEDEX 08, France
| | - Amanda E Toland
- Department of Molecular Virology, Immunology and Medical Genetics, Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210, USA
| | - Drakoulis Yannoukakos
- Molecular Diagnostics Laboratory, IRRP, National Centre for Scientific Research "Demokritos", Aghia Paraskevi Attikis, 153 10 Athens, Greece
| | - Chen-Yang Shen
- Taiwan Biobank, Institute of Biomedical Sciences, Academia Sinica, Taipei 115, Taiwan
- Institute of Biomedical Sciences, Academia Sinica, Taipei 115, Taiwan
- School of Public Health, China Medical University, Taichung 404, Taiwan
| | - Pei-Ei Wu
- Taiwan Biobank, Institute of Biomedical Sciences, Academia Sinica, Taipei 115, Taiwan
- Institute of Biomedical Sciences, Academia Sinica, Taipei 115, Taiwan
| | - Anthony Swerdlow
- Division of Genetics and Epidemiology, Institute of Cancer Research, Sutton, SM2 5NG, UK
- Division of Breast Cancer Research, Institute of Cancer Research, Sutton, SM2 5NG, UK
| | - Nick Orr
- Breakthrough Breast Cancer Research Centre, The Institute of Cancer Research, London, SW3 6JB, UK
| | - Jacques Simard
- Centre Hospitalier Universitaire de Québec Research Center and Laval University, QC, G1V 4G2, Canada
| | - Paul D P Pharoah
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, UK
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, CB1 8RN, UK
| | - Alison M Dunning
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, CB1 8RN, UK
| | - Georgia Chenevix-Trench
- Department of Genetics, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm SE-17177, Sweden
| | - Elisa Bandera
- Rutgers Cancer Institute of New Jersey, New Brunswick, NJ 08901 USA
| | - Chris Amos
- Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA
| | - Christine Ambrosone
- Department of Cancer Prevention and Control, Roswell Park Cancer Institute, Buffalo, NY 14263, USA
| | - Douglas F Easton
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Michael D Cole
- Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA
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Pancaldi V, Carrillo-de-Santa-Pau E, Javierre BM, Juan D, Fraser P, Spivakov M, Valencia A, Rico D. Integrating epigenomic data and 3D genomic structure with a new measure of chromatin assortativity. Genome Biol 2016; 17:152. [PMID: 27391817 PMCID: PMC4939006 DOI: 10.1186/s13059-016-1003-3] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2016] [Accepted: 06/07/2016] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND Network analysis is a powerful way of modeling chromatin interactions. Assortativity is a network property used in social sciences to identify factors affecting how people establish social ties. We propose a new approach, using chromatin assortativity, to integrate the epigenomic landscape of a specific cell type with its chromatin interaction network and thus investigate which proteins or chromatin marks mediate genomic contacts. RESULTS We use high-resolution promoter capture Hi-C and Hi-Cap data as well as ChIA-PET data from mouse embryonic stem cells to investigate promoter-centered chromatin interaction networks and calculate the presence of specific epigenomic features in the chromatin fragments constituting the nodes of the network. We estimate the association of these features with the topology of four chromatin interaction networks and identify features localized in connected areas of the network. Polycomb group proteins and associated histone marks are the features with the highest chromatin assortativity in promoter-centered networks. We then ask which features distinguish contacts amongst promoters from contacts between promoters and other genomic elements. We observe higher chromatin assortativity of the actively elongating form of RNA polymerase 2 (RNAPII) compared with inactive forms only in interactions between promoters and other elements. CONCLUSIONS Contacts among promoters and between promoters and other elements have different characteristic epigenomic features. We identify a possible role for the elongating form of RNAPII in mediating interactions among promoters, enhancers, and transcribed gene bodies. Our approach facilitates the study of multiple genome-wide epigenomic profiles, considering network topology and allowing the comparison of chromatin interaction networks.
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Affiliation(s)
- Vera Pancaldi
- Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain.
| | | | | | - David Juan
- Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain
| | - Peter Fraser
- Nuclear Dynamics Programme, The Babraham Institute, Cambridge, UK
| | - Mikhail Spivakov
- Nuclear Dynamics Programme, The Babraham Institute, Cambridge, UK
| | - Alfonso Valencia
- Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain
| | - Daniel Rico
- Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain.
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145
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Cairns J, Freire-Pritchett P, Wingett SW, Várnai C, Dimond A, Plagnol V, Zerbino D, Schoenfelder S, Javierre BM, Osborne C, Fraser P, Spivakov M. CHiCAGO: robust detection of DNA looping interactions in Capture Hi-C data. Genome Biol 2016; 17:127. [PMID: 27306882 PMCID: PMC4908757 DOI: 10.1186/s13059-016-0992-2] [Citation(s) in RCA: 252] [Impact Index Per Article: 31.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2016] [Accepted: 05/25/2016] [Indexed: 12/14/2022] Open
Abstract
Capture Hi-C (CHi-C) is a method for profiling chromosomal interactions involving targeted regions of interest, such as gene promoters, globally and at high resolution. Signal detection in CHi-C data involves a number of statistical challenges that are not observed when using other Hi-C-like techniques. We present a background model and algorithms for normalisation and multiple testing that are specifically adapted to CHi-C experiments. We implement these procedures in CHiCAGO ( http://regulatorygenomicsgroup.org/chicago ), an open-source package for robust interaction detection in CHi-C. We validate CHiCAGO by showing that promoter-interacting regions detected with this method are enriched for regulatory features and disease-associated SNPs.
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Affiliation(s)
- Jonathan Cairns
- Nuclear Dynamics Programme, Babraham Institute, Cambridge, UK
| | | | - Steven W Wingett
- Nuclear Dynamics Programme, Babraham Institute, Cambridge, UK
- Bioinformatics Group, Babraham Institute, Cambridge, UK
| | - Csilla Várnai
- Nuclear Dynamics Programme, Babraham Institute, Cambridge, UK
| | - Andrew Dimond
- Nuclear Dynamics Programme, Babraham Institute, Cambridge, UK
| | | | - Daniel Zerbino
- European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK
| | | | | | - Cameron Osborne
- Department of Medical and Molecular Genetics, King's College, London, UK
| | - Peter Fraser
- Nuclear Dynamics Programme, Babraham Institute, Cambridge, UK
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146
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IDH-mutant glioma specific association of rs55705857 located at 8q24.21 involves MYC deregulation. Sci Rep 2016; 6:27569. [PMID: 27282637 PMCID: PMC4901315 DOI: 10.1038/srep27569] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2015] [Accepted: 05/16/2016] [Indexed: 12/20/2022] Open
Abstract
The single nucleotide polymorphism rs55705857, located in a non-coding but evolutionarily conserved region at 8q24.21, is strongly associated with IDH-mutant glioma development and was suggested to be a causal variant. However, the molecular mechanism underlying this association has remained unknown. With a case control study in 285 gliomas, 316 healthy controls, 380 systemic cancers, 31 other CNS-tumors, and 120 IDH-mutant cartilaginous tumors, we identified that the association was specific to IDH-mutant gliomas. Odds-ratios were 9.25 (5.17–16.52; 95% CI) for IDH-mutated gliomas and 12.85 (5.94–27.83; 95% CI) for IDH-mutated, 1p/19q co-deleted gliomas. Decreasing strength with increasing anaplasia implied a modulatory effect. No somatic mutations were noted at this locus in 114 blood-tumor pairs, nor was there a copy number difference between risk-allele and only-ancestral allele carriers. CCDC26 RNA-expression was rare and not different between the two groups. There were only minor subtype-specific differences in common glioma driver genes. RNA sequencing and LC-MS/MS comparisons pointed to significantly altered MYC-signaling. Baseline enhancer activity of the conserved region specifically on the MYC promoter and its further positive modulation by the SNP risk-allele was shown in vitro. Our findings implicate MYC deregulation as the underlying cause of the observed association.
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147
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Proudhon C, Snetkova V, Raviram R, Lobry C, Badri S, Jiang T, Hao B, Trimarchi T, Kluger Y, Aifantis I, Bonneau R, Skok JA. Active and Inactive Enhancers Cooperate to Exert Localized and Long-Range Control of Gene Regulation. Cell Rep 2016; 15:2159-2169. [PMID: 27239026 PMCID: PMC4899175 DOI: 10.1016/j.celrep.2016.04.087] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2015] [Revised: 04/06/2016] [Accepted: 04/20/2016] [Indexed: 01/24/2023] Open
Abstract
V(D)J recombination relies on the presence of proximal enhancers that activate the antigen receptor (AgR) loci in a lineage- and stage-specific manner. Unexpectedly, we find that both active and inactive AgR enhancers cooperate to disseminate their effects in a localized and long-range manner. Here, we demonstrate the importance of short-range contacts between active enhancers that constitute an Igk super-enhancer in B cells. Deletion of one element reduces the interaction frequency between other enhancers in the hub, which compromises the transcriptional output of each component. Furthermore, we establish that, in T cells, long-range contact and cooperation between the inactive Igk enhancer MiEκ and the active Tcrb enhancer Eβ alters enrichment of CBFβ binding in a manner that impacts Tcrb recombination. These findings underline the complexities of enhancer regulation and point to a role for localized and long-range enhancer-sharing between active and inactive elements in lineage- and stage-specific control.
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MESH Headings
- Animals
- Enhancer Elements, Genetic
- Gene Expression Regulation
- Gene Rearrangement, beta-Chain T-Cell Antigen Receptor
- Mice, Inbred C57BL
- Protein Binding/genetics
- RNA, Messenger/genetics
- RNA, Messenger/metabolism
- Receptors, Antigen, T-Cell, alpha-beta/genetics
- Receptors, Antigen, T-Cell, alpha-beta/metabolism
- T-Lymphocytes/immunology
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Affiliation(s)
- Charlotte Proudhon
- Department of Pathology, NYU School of Medicine, New York, NY 10016, USA
| | - Valentina Snetkova
- Department of Pathology, NYU School of Medicine, New York, NY 10016, USA
| | - Ramya Raviram
- Department of Pathology, NYU School of Medicine, New York, NY 10016, USA; Department of Biology, NYU, New York, NY 10003, USA
| | - Camille Lobry
- Department of Pathology, NYU School of Medicine, New York, NY 10016, USA
| | - Sana Badri
- Department of Pathology, NYU School of Medicine, New York, NY 10016, USA; Department of Biology, NYU, New York, NY 10003, USA; Center for Health Informatics and Bioinformatics, NYU School of Medicine, New York, NY 10016, USA
| | - Tingting Jiang
- Department of Pathology, Yale University, New Haven, CT 06510, USA
| | - Bingtao Hao
- Department of Pathology, NYU School of Medicine, New York, NY 10016, USA
| | - Thomas Trimarchi
- Department of Pathology, NYU School of Medicine, New York, NY 10016, USA
| | - Yuval Kluger
- Department of Pathology, Yale University, New Haven, CT 06510, USA
| | - Iannis Aifantis
- Department of Pathology, NYU School of Medicine, New York, NY 10016, USA
| | - Richard Bonneau
- Department of Biology, NYU, New York, NY 10003, USA; Department of Computer Science, Courant Institute of Mathematical Sciences, NYU, New York, NY 10003, USA; Simons Center for Data Analysis, New York, NY 10010, USA
| | - Jane A Skok
- Department of Pathology, NYU School of Medicine, New York, NY 10016, USA.
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148
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Yarwood A, Eyre S, Worthington J. Genetic susceptibility to rheumatoid arthritis and its implications for novel drug discovery. Expert Opin Drug Discov 2016; 11:805-13. [PMID: 27267163 DOI: 10.1080/17460441.2016.1195366] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
INTRODUCTION Over 100 susceptibility loci have now been identified for rheumatoid arthritis (RA), several of which are already the targets of approved RA therapies providing proof of concept for the use of genetics in novel drug development for RA. Determining how these loci contribute to disease will be key to elucidating the mechanisms driving disease development, which has the potential for major impact on therapeutic development. AREAS COVERED Here the authors review the use of genetics in drug discovery, including the use of 'omics' data to prioritise potential drug targets at susceptibility loci using RA as an exemplar. They discuss the current state of RA genetics its impact on stratified medicine, and how the findings from RA genetics studies can be used to inform drug discovery. EXPERT OPINION It is anticipated that functional characterisation of disease variants will provide biological validation of a gene as a drug target, providing safer targets, with an increased likelihood of efficacy. In the future, techniques such as genome editing may represent a plausible option for RA therapy. Technologies such as genome-wide chromatin conformation capture Hi-C and CRISPR will be crucial to inform our understanding of how diseases develop and in developing new treatments.
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Affiliation(s)
- Annie Yarwood
- a Arthritis Research UK Centre for Genetics and Genomics, Centre for Musculoskeletal Research, Institute of Inflammation and Repair, Faculty of Medical and Human Sciences, Manchester Academic Health Science Centre , The University of Manchester , Manchester , UK
| | - Steve Eyre
- a Arthritis Research UK Centre for Genetics and Genomics, Centre for Musculoskeletal Research, Institute of Inflammation and Repair, Faculty of Medical and Human Sciences, Manchester Academic Health Science Centre , The University of Manchester , Manchester , UK
| | - Jane Worthington
- a Arthritis Research UK Centre for Genetics and Genomics, Centre for Musculoskeletal Research, Institute of Inflammation and Repair, Faculty of Medical and Human Sciences, Manchester Academic Health Science Centre , The University of Manchester , Manchester , UK.,b NIHR Manchester Musculoskeletal Biomedical Research Unit, Central Manchester NHS Foundation Trust , Manchester Academic Health Science Centre , Manchester , UK
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149
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Chromosome conformation capture technologies and their impact in understanding genome function. Chromosoma 2016; 126:33-44. [DOI: 10.1007/s00412-016-0593-6] [Citation(s) in RCA: 92] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2015] [Revised: 04/10/2016] [Accepted: 04/12/2016] [Indexed: 12/17/2022]
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150
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Sekelja M, Paulsen J, Collas P. 4D nucleomes in single cells: what can computational modeling reveal about spatial chromatin conformation? Genome Biol 2016; 17:54. [PMID: 27052789 PMCID: PMC4823877 DOI: 10.1186/s13059-016-0923-2] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
Genome-wide sequencing technologies enable investigations of the structural properties of the genome in various spatial dimensions. Here, we review computational techniques developed to model the three-dimensional genome in single cells versus ensembles of cells and assess their underlying assumptions. We further address approaches to study the spatio-temporal aspects of genome organization from single-cell data.
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Affiliation(s)
- Monika Sekelja
- Department of Molecular Medicine, Faculty of Medicine, University of Oslo, PO Box 1112, Blindern, 0317, Oslo, Norway
| | - Jonas Paulsen
- Department of Molecular Medicine, Faculty of Medicine, University of Oslo, PO Box 1112, Blindern, 0317, Oslo, Norway
| | - Philippe Collas
- Department of Molecular Medicine, Faculty of Medicine, University of Oslo, PO Box 1112, Blindern, 0317, Oslo, Norway.
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