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Chowdhury HMAM, Boult T, Oluwadare O. Comparative study on chromatin loop callers using Hi-C data reveals their effectiveness. BMC Bioinformatics 2024; 25:123. [PMID: 38515011 PMCID: PMC10958853 DOI: 10.1186/s12859-024-05713-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 02/19/2024] [Indexed: 03/23/2024] Open
Abstract
BACKGROUND Chromosome is one of the most fundamental part of cell biology where DNA holds the hierarchical information. DNA compacts its size by forming loops, and these regions house various protein particles, including CTCF, SMC3, H3 histone. Numerous sequencing methods, such as Hi-C, ChIP-seq, and Micro-C, have been developed to investigate these properties. Utilizing these data, scientists have developed a variety of loop prediction techniques that have greatly improved their methods for characterizing loop prediction and related aspects. RESULTS In this study, we categorized 22 loop calling methods and conducted a comprehensive study of 11 of them. Additionally, we have provided detailed insights into the methodologies underlying these algorithms for loop detection, categorizing them into five distinct groups based on their fundamental approaches. Furthermore, we have included critical information such as resolution, input and output formats, and parameters. For this analysis, we utilized the GM12878 Hi-C datasets at 5 KB, 10 KB, 100 KB and 250 KB resolutions. Our evaluation criteria encompassed various factors, including memory usages, running time, sequencing depth, and recovery of protein-specific sites such as CTCF, H3K27ac, and RNAPII. CONCLUSION This analysis offers insights into the loop detection processes of each method, along with the strengths and weaknesses of each, enabling readers to effectively choose suitable methods for their datasets. We evaluate the capabilities of these tools and introduce a novel Biological, Consistency, and Computational robustness score ( B C C score ) to measure their overall robustness ensuring a comprehensive evaluation of their performance.
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Affiliation(s)
- H M A Mohit Chowdhury
- Department of Computer Science, University of Colorado at Colorado Springs, 1420 Austin Bluffs Pkwy, Colorado Springs, CO, 80918, USA
| | - Terrance Boult
- Department of Computer Science, University of Colorado at Colorado Springs, 1420 Austin Bluffs Pkwy, Colorado Springs, CO, 80918, USA
| | - Oluwatosin Oluwadare
- Department of Computer Science, University of Colorado at Colorado Springs, 1420 Austin Bluffs Pkwy, Colorado Springs, CO, 80918, USA.
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
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2
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Wahl N, Espeso-Gil S, Chietera P, Nagel A, Laighneach A, Morris DW, Rajarajan P, Akbarian S, Dechant G, Apostolova G. SATB2 organizes the 3D genome architecture of cognition in cortical neurons. Mol Cell 2024; 84:621-639.e9. [PMID: 38244545 PMCID: PMC10923151 DOI: 10.1016/j.molcel.2023.12.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 10/02/2023] [Accepted: 12/15/2023] [Indexed: 01/22/2024]
Abstract
The DNA-binding protein SATB2 is genetically linked to human intelligence. We studied its influence on the three-dimensional (3D) epigenome by mapping chromatin interactions and accessibility in control versus SATB2-deficient cortical neurons. We find that SATB2 affects the chromatin looping between enhancers and promoters of neuronal-activity-regulated genes, thus influencing their expression. It also alters A/B compartments, topologically associating domains, and frequently interacting regions. Genes linked to SATB2-dependent 3D genome changes are implicated in highly specialized neuronal functions and contribute to cognitive ability and risk for neuropsychiatric and neurodevelopmental disorders. Non-coding DNA regions with a SATB2-dependent structure are enriched for common variants associated with educational attainment, intelligence, and schizophrenia. Our data establish SATB2 as a cell-type-specific 3D genome modulator, which operates both independently and in cooperation with CCCTC-binding factor (CTCF) to set up the chromatin landscape of pyramidal neurons for cognitive processes.
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Affiliation(s)
- Nico Wahl
- Institute for Neuroscience, Medical University of Innsbruck, Innsbruck 6020, Austria.
| | - Sergio Espeso-Gil
- Institute for Neuroscience, Medical University of Innsbruck, Innsbruck 6020, Austria; Department of Psychiatry, Friedman Brain Institute, Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Paola Chietera
- Institute for Neuroscience, Medical University of Innsbruck, Innsbruck 6020, Austria
| | - Amelie Nagel
- Institute for Neuroscience, Medical University of Innsbruck, Innsbruck 6020, Austria
| | - Aodán Laighneach
- Centre for Neuroimaging, Cognition and Genomics (NICOG), School of Biological and Chemical Sciences, University of Galway, Galway, H91 TK33, Ireland
| | - Derek W Morris
- Centre for Neuroimaging, Cognition and Genomics (NICOG), School of Biological and Chemical Sciences, University of Galway, Galway, H91 TK33, Ireland
| | - Prashanth Rajarajan
- Department of Psychiatry, Friedman Brain Institute, Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Schahram Akbarian
- Department of Psychiatry, Friedman Brain Institute, Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Georg Dechant
- Institute for Neuroscience, Medical University of Innsbruck, Innsbruck 6020, Austria.
| | - Galina Apostolova
- Institute for Neuroscience, Medical University of Innsbruck, Innsbruck 6020, Austria.
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3
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Wheeler MM, Stilp AM, Rao S, Halldórsson BV, Beyter D, Wen J, Mihkaylova AV, McHugh CP, Lane J, Jiang MZ, Raffield LM, Jun G, Sedlazeck FJ, Metcalf G, Yao Y, Bis JB, Chami N, de Vries PS, Desai P, Floyd JS, Gao Y, Kammers K, Kim W, Moon JY, Ratan A, Yanek LR, Almasy L, Becker LC, Blangero J, Cho MH, Curran JE, Fornage M, Kaplan RC, Lewis JP, Loos RJF, Mitchell BD, Morrison AC, Preuss M, Psaty BM, Rich SS, Rotter JI, Tang H, Tracy RP, Boerwinkle E, Abecasis GR, Blackwell TW, Smith AV, Johnson AD, Mathias RA, Nickerson DA, Conomos MP, Li Y, Þorsteinsdóttir U, Magnússon MK, Stefansson K, Pankratz ND, Bauer DE, Auer PL, Reiner AP. Whole genome sequencing identifies structural variants contributing to hematologic traits in the NHLBI TOPMed program. Nat Commun 2022; 13:7592. [PMID: 36481753 PMCID: PMC9732337 DOI: 10.1038/s41467-022-35354-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 11/29/2022] [Indexed: 12/13/2022] Open
Abstract
Genome-wide association studies have identified thousands of single nucleotide variants and small indels that contribute to variation in hematologic traits. While structural variants are known to cause rare blood or hematopoietic disorders, the genome-wide contribution of structural variants to quantitative blood cell trait variation is unknown. Here we utilized whole genome sequencing data in ancestrally diverse participants of the NHLBI Trans Omics for Precision Medicine program (N = 50,675) to detect structural variants associated with hematologic traits. Using single variant tests, we assessed the association of common and rare structural variants with red cell-, white cell-, and platelet-related quantitative traits and observed 21 independent signals (12 common and 9 rare) reaching genome-wide significance. The majority of these associations (N = 18) replicated in independent datasets. In genome-editing experiments, we provide evidence that a deletion associated with lower monocyte counts leads to disruption of an S1PR3 monocyte enhancer and decreased S1PR3 expression.
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Affiliation(s)
- Marsha M Wheeler
- Department of Genome Sciences, University of Washington, Seattle, WA, 98105, USA
| | - Adrienne M Stilp
- Department of Biostatistics, University of Washington, Seattle, WA, 98105, USA
| | - Shuquan Rao
- Division of Hematology/Oncology, Boston Children's Hospital, Boston, MA, 02115, USA
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA, 02115, USA
- Harvard Stem Cell Institute, Boston, MA, 02138, USA
- Broad Institute, Cambridge, MA, 02142, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, 02115, USA
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300020, China
| | - Bjarni V Halldórsson
- deCODE genetics/Amgen Inc., Reykjavik, Iceland
- School of Technology, Reykjavik University, Reykjavík, Iceland
| | | | - Jia Wen
- Departments of Biostatistics, Genetics, Computer Science, Applied Physical Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Anna V Mihkaylova
- Department of Biostatistics, University of Washington, Seattle, WA, 98105, USA
| | - Caitlin P McHugh
- Department of Biostatistics, University of Washington, Seattle, WA, 98105, USA
| | - John Lane
- Department of Laboratory Medicine and Pathology, University of Minnesota Medical School, Minneapolis, MN, 55455, USA
| | - Min-Zhi Jiang
- Departments of Biostatistics, Genetics, Computer Science, Applied Physical Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Laura M Raffield
- Department of Genetics, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Goo Jun
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Fritz J Sedlazeck
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Ginger Metcalf
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Yao Yao
- Division of Hematology/Oncology, Boston Children's Hospital, Boston, MA, 02115, USA
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA, 02115, USA
- Harvard Stem Cell Institute, Boston, MA, 02138, USA
- Broad Institute, Cambridge, MA, 02142, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, 02115, USA
| | - Joshua B Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, 98101, USA
| | - Nathalie Chami
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Paul S de Vries
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
- Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Pinkal Desai
- Division of Hematology and Oncology, Weill Cornell Medical College, New York, NY, 10065, USA
| | - James S Floyd
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, 98101, USA
| | - Yan Gao
- Jackson Heart Study, Department of Medicine, University of Mississippi, Jackson, MS, 39216, USA
| | - Kai Kammers
- GeneSTAR Research Program, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Wonji Kim
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, 2115, USA
| | - Jee-Young Moon
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - Aakrosh Ratan
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, 22908, USA
| | - Lisa R Yanek
- GeneSTAR Research Program, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Laura Almasy
- Children's Hospital of Philadelphia and University of Pennsylvania School of Medicine, Philadelphia, PA, 19104, USA
| | - Lewis C Becker
- GeneSTAR Research Program, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - John Blangero
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, 78520, USA
| | - Michael H Cho
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, 2115, USA
| | - Joanne E Curran
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, 78520, USA
| | - Myriam Fornage
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Robert C Kaplan
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - Joshua P Lewis
- Department of Medicine, Division of Endocrinology, Diabetes, and Nutrition, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Ruth J F Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Braxton D Mitchell
- Department of Medicine, Division of Endocrinology, Diabetes, and Nutrition, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Alanna C Morrison
- Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Michael Preuss
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, 98101, USA
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, 22908, USA
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, 90502, USA
| | - Hua Tang
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Russell P Tracy
- Departments of Pathology & Laboratory Medicine and Biochemistry, Larner College of Medicine at the University of Vermont, Colchester, VT, 5446, USA
| | - Eric Boerwinkle
- Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Goncalo R Abecasis
- TOPMed Informatics Research Center, University of Michigan, Department of Biostatistics, Ann Arbor, MI, 48109, USA
| | - Thomas W Blackwell
- TOPMed Informatics Research Center, University of Michigan, Department of Biostatistics, Ann Arbor, MI, 48109, USA
| | - Albert V Smith
- TOPMed Informatics Research Center, University of Michigan, Department of Biostatistics, Ann Arbor, MI, 48109, USA
| | - Andrew D Johnson
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung and Blood Institute, Framingham, MA, 1702, USA
| | - Rasika A Mathias
- GeneSTAR Research Program, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Deborah A Nickerson
- Department of Genome Sciences, University of Washington, Seattle, WA, 98105, USA
| | - Matthew P Conomos
- Department of Biostatistics, University of Washington, Seattle, WA, 98105, USA
| | - Yun Li
- Departments of Biostatistics, Genetics, Computer Science, Applied Physical Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Unnur Þorsteinsdóttir
- deCODE genetics/Amgen Inc., Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, 101, Reykjavik, Iceland
| | - Magnús K Magnússon
- deCODE genetics/Amgen Inc., Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, 101, Reykjavik, Iceland
| | - Kari Stefansson
- deCODE genetics/Amgen Inc., Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, 101, Reykjavik, Iceland
| | - Nathan D Pankratz
- Department of Laboratory Medicine and Pathology, University of Minnesota Medical School, Minneapolis, MN, 55455, USA
| | - Daniel E Bauer
- Division of Hematology/Oncology, Boston Children's Hospital, Boston, MA, 02115, USA
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA, 02115, USA
- Harvard Stem Cell Institute, Boston, MA, 02138, USA
- Broad Institute, Cambridge, MA, 02142, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, 02115, USA
| | - Paul L Auer
- Division of Biostatistics, Institute for Health and Equity, and Cancer Center, Medical College of Wisconsin, Milwaukee, WI, 53226, USA.
| | - Alex P Reiner
- Department of Epidemiology, University of Washington, Seattle, WA, 98105, USA.
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4
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Zhong W, Liu W, Chen J, Sun Q, Hu M, Li Y. Understanding the function of regulatory DNA interactions in the interpretation of non-coding GWAS variants. Front Cell Dev Biol 2022; 10:957292. [PMID: 36060805 PMCID: PMC9437546 DOI: 10.3389/fcell.2022.957292] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 07/21/2022] [Indexed: 01/11/2023] Open
Abstract
Genome-wide association studies (GWAS) have identified a vast number of variants associated with various complex human diseases and traits. However, most of these GWAS variants reside in non-coding regions producing no proteins, making the interpretation of these variants a daunting challenge. Prior evidence indicates that a subset of non-coding variants detected within or near cis-regulatory elements (e.g., promoters, enhancers, silencers, and insulators) might play a key role in disease etiology by regulating gene expression. Advanced sequencing- and imaging-based technologies, together with powerful computational methods, enabling comprehensive characterization of regulatory DNA interactions, have substantially improved our understanding of the three-dimensional (3D) genome architecture. Recent literature witnesses plenty of examples where using chromosome conformation capture (3C)-based technologies successfully links non-coding variants to their target genes and prioritizes relevant tissues or cell types. These examples illustrate the critical capability of 3D genome organization in annotating non-coding GWAS variants. This review discusses how 3D genome organization information contributes to elucidating the potential roles of non-coding GWAS variants in disease etiology.
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Affiliation(s)
- Wujuan Zhong
- Biostatistics and Research Decision Sciences, Merck & Co, Inc, Rahway, NJ, United States
| | - Weifang Liu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Jiawen Chen
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Quan Sun
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Ming Hu
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH, United States
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
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5
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Liu W, Zhong W, Chen J, Huang B, Hu M, Li Y. Understanding Regulatory Mechanisms of Brain Function and Disease through 3D Genome Organization. Genes (Basel) 2022; 13:genes13040586. [PMID: 35456393 PMCID: PMC9027261 DOI: 10.3390/genes13040586] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 03/17/2022] [Accepted: 03/23/2022] [Indexed: 02/01/2023] Open
Abstract
The human genome has a complex and dynamic three-dimensional (3D) organization, which plays a critical role for gene regulation and genome function. The importance of 3D genome organization in brain development and function has been well characterized in a region- and cell-type-specific fashion. Recent technological advances in chromosome conformation capture (3C)-based techniques, imaging approaches, and ligation-free methods, along with computational methods to analyze the data generated, have revealed 3D genome features at different scales in the brain that contribute to our understanding of genetic mechanisms underlying neuropsychiatric diseases and other brain-related traits. In this review, we discuss how these advances aid in the genetic dissection of brain-related traits.
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Affiliation(s)
- Weifang Liu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; (W.L.); (J.C.)
| | - Wujuan Zhong
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., Rahway, NJ 07065, USA;
| | - Jiawen Chen
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; (W.L.); (J.C.)
| | - Bo Huang
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94143, USA;
- Chan Zuckerberg Biohub, San Francisco, CA 94158, USA
- Department of Biochemistry and Biophysics, University of California, San Francisco, CA 94143, USA
| | - Ming Hu
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH 44195, USA
- Correspondence: (M.H.); (Y.L.)
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; (W.L.); (J.C.)
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Correspondence: (M.H.); (Y.L.)
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6
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Liu W, Sun Q, Huang L, Bhattacharya A, Wang GW, Tan X, Kuban KCK, Joseph RM, O'Shea TM, Fry RC, Li Y, Santos HP. Innovative computational approaches shed light on genetic mechanisms underlying cognitive impairment among children born extremely preterm. J Neurodev Disord 2022; 14:16. [PMID: 35240980 PMCID: PMC8903548 DOI: 10.1186/s11689-022-09429-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 02/22/2022] [Indexed: 11/27/2022] Open
Abstract
BACKGROUND Although survival rates for infants born extremely preterm (gestation < 28 weeks) have improved significantly in recent decades, neurodevelopmental impairment remains a major concern. Children born extremely preterm remain at high risk for cognitive impairment from early childhood to adulthood. However, there is limited evidence on genetic factors associated with cognitive impairment in this population. METHODS First, we used a latent profile analysis (LPA) approach to characterize neurocognitive function at age 10 for children born extremely preterm. Children were classified into two groups: (1) no or low cognitive impairment, and (2) moderate-to-severe cognitive impairment. Second, we performed TOPMed-based genotype imputation on samples with genotype array data (n = 528). Third, we then conducted a genome-wide association study (GWAS) for LPA-inferred cognitive impairment. Finally, computational analysis was conducted to explore potential mechanisms underlying the variant x LPA association. RESULTS We identified two loci reaching genome-wide significance (p value < 5e-8): TEA domain transcription factor 4 (TEAD4 at rs11829294, p value = 2.40e-8) and syntaxin 18 (STX18 at rs79453226, p value = 1.91e-8). Integrative analysis with brain expression quantitative trait loci (eQTL), chromatin conformation, and epigenomic annotations suggests tetraspanin 9 (TSPAN9) and protein arginine methyltransferase 8 (PRMT8) as potential functional genes underlying the GWAS signal at the TEAD4 locus. CONCLUSIONS We conducted a novel computational analysis by utilizing an LPA-inferred phenotype with genetics data for the first time. This study suggests that rs11829294 and its LD buddies have potential regulatory roles on genes that could impact neurocognitive impairment for extreme preterm born children.
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Affiliation(s)
- Weifang Liu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Quan Sun
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Le Huang
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Arjun Bhattacharya
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Geoffery W Wang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Xianming Tan
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Karl C K Kuban
- Department of Pediatrics, Boston University, Boston, MA, USA
| | - Robert M Joseph
- Department of Anatomy & Neurobiology, Boston University, Boston, MA, USA
| | - T Michael O'Shea
- Department of Pediatrics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Rebecca C Fry
- Department of Environmental Sciences and Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
| | - Hudson P Santos
- School of Nursing, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
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Mourad R. TADreg: a versatile regression framework for TAD identification, differential analysis and rearranged 3D genome prediction. BMC Bioinformatics 2022; 23:82. [PMID: 35236295 PMCID: PMC8892791 DOI: 10.1186/s12859-022-04614-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 02/16/2022] [Indexed: 11/10/2022] Open
Abstract
Background/Aim In higher eukaryotes, the three-dimensional (3D) organization of the genome is intimately related to numerous key biological functions including gene expression, DNA repair and DNA replication regulations. Alteration of 3D organization, in particular topologically associating domains (TADs), is detrimental to the organism and can give rise to a broad range of diseases such as cancers. Methods Here, we propose a versatile regression framework which not only identifies TADs in a fast and accurate manner, but also detects differential TAD borders across conditions for which few methods exist, and predicts 3D genome reorganization after chromosomal rearrangement. Moreover, the framework is biologically meaningful, has an intuitive interpretation and is easy to visualize. Result and conclusion The novel regression ranks among top TAD callers. Moreover, it identifies new features of the genome we called TAD facilitators, and that are enriched with specific transcription factors. It also unveils the importance of cell-type specific transcription factors in establishing novel TAD borders during neuronal differentiation. Lastly, it compares favorably with the state-of-the-art method for predicting rearranged 3D genome. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04614-0.
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Affiliation(s)
- Raphaël Mourad
- CNRS, UPS, MCD, Centre de Biologie Intégrative (CBI), University of Toulouse, 31062, Toulouse, France.
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8
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Rowland B, Huh R, Hou Z, Crowley C, Wen J, Shen Y, Hu M, Giusti-Rodríguez P, Sullivan PF, Li Y. THUNDER: A reference-free deconvolution method to infer cell type proportions from bulk Hi-C data. PLoS Genet 2022; 18:e1010102. [PMID: 35259165 PMCID: PMC8932604 DOI: 10.1371/journal.pgen.1010102] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 03/18/2022] [Accepted: 02/14/2022] [Indexed: 11/30/2022] Open
Abstract
Hi-C data provide population averaged estimates of three-dimensional chromatin contacts across cell types and states in bulk samples. Effective analysis of Hi-C data entails controlling for the potential confounding factor of differential cell type proportions across heterogeneous bulk samples. We propose a novel unsupervised deconvolution method for inferring cell type composition from bulk Hi-C data, the Two-step Hi-c UNsupervised DEconvolution appRoach (THUNDER). We conducted extensive simulations to test THUNDER based on combining two published single-cell Hi-C (scHi-C) datasets. THUNDER more accurately estimates the underlying cell type proportions compared to reference-free methods (e.g., TOAST, and NMF) and is more robust than reference-dependent methods (e.g. MuSiC). We further demonstrate the practical utility of THUNDER to estimate cell type proportions and identify cell-type-specific interactions in Hi-C data from adult human cortex tissue samples. THUNDER will be a useful tool in adjusting for varying cell type composition in population samples, facilitating valid and more powerful downstream analysis such as differential chromatin organization studies. Additionally, THUNDER estimated contact profiles provide a useful exploratory framework to investigate cell-type-specificity of the chromatin interactome while experimental data is still rare.
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Affiliation(s)
- Bryce Rowland
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Ruth Huh
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Zoey Hou
- Department of Engineering Sciences and Applied Mathematics, Northwestern University, Evanston, Illinois, United States of America
| | - Cheynna Crowley
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Jia Wen
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Yin Shen
- Institute for Human Genetics, University of California San Francisco, San Francisco, California, United States of America
- Department of Neurology, University of California San Francisco, San Francisco, California, United States of America
| | - Ming Hu
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, Ohio, United States of America
| | - Paola Giusti-Rodríguez
- Department of Psychiatry, University of Florida College of Medicine, Gainesville, Florida, United States of America
| | - Patrick F. Sullivan
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
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9
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Parallel analysis of transcription, integration, and sequence of single HIV-1 proviruses. Cell 2022; 185:266-282.e15. [PMID: 35026153 PMCID: PMC8809251 DOI: 10.1016/j.cell.2021.12.011] [Citation(s) in RCA: 123] [Impact Index Per Article: 61.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 10/17/2021] [Accepted: 12/10/2021] [Indexed: 01/09/2023]
Abstract
HIV-1-infected cells that persist despite antiretroviral therapy (ART) are frequently considered "transcriptionally silent," but active viral gene expression may occur in some cells, challenging the concept of viral latency. Applying an assay for profiling the transcriptional activity and the chromosomal locations of individual proviruses, we describe a global genomic and epigenetic map of transcriptionally active and silent proviral species and evaluate their longitudinal evolution in persons receiving suppressive ART. Using genome-wide epigenetic reference data, we show that proviral transcriptional activity is associated with activating epigenetic chromatin features in linear proximity of integration sites and in their inter- and intrachromosomal contact regions. Transcriptionally active proviruses were actively selected against during prolonged ART; however, this pattern was violated by large clones of virally infected cells that may outcompete negative selection forces through elevated intrinsic proliferative activity. Our results suggest that transcriptionally active proviruses are dynamically evolving under selection pressure by host factors.
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10
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Di Giammartino DC, Polyzos A, Apostolou E. Assessing Specific Networks of Chromatin Interactions with HiChIP. Methods Mol Biol 2022; 2532:113-141. [PMID: 35867248 DOI: 10.1007/978-1-0716-2497-5_7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The introduction of chromosome conformation capture (3C)-based technologies coupled with next-generation sequencing have significantly advanced our understanding of how the genetic material is organized within the eukaryotic nucleus. Three-dimensional (3D) genomic organization occurs at hierarchical levels, ranging from chromosome territories and subnuclear compartments to smaller self-associated domains and fine-scale chromatin interactions. The latter can be further categorized into different subtypes, such as structural or regulatory, based either on their presumed functionality and/or the factors that mediate their formation. Various enrichment strategies coupled with 3C-based technologies have been developed to prospectively isolate and quantify chromatin interactions around regions occupied by specific proteins or marks of interest. These approaches not only enable high-resolution characterization of the selected chromatin contacts at a cost-effective manner, but also offer important biological insights into their organizational principles and regulatory function. In this chapter, we will focus on the recently developed HiChIP technology with an emphasis on the discovery of putative active enhancers and promoter interactions in cell types of interest. We will describe the specific steps for designing, performing and analyzing successful HiChIP experiments as well as important limitations and considerations.
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Affiliation(s)
- Dafne Campigli Di Giammartino
- Sanford I. Weill Department of Medicine, Division of Hematology/Oncology, Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
| | - Alexander Polyzos
- Sanford I. Weill Department of Medicine, Division of Hematology/Oncology, Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
| | - Effie Apostolou
- Sanford I. Weill Department of Medicine, Division of Hematology/Oncology, Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA.
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11
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Pratt BM, Won H. Advances in profiling chromatin architecture shed light on the regulatory dynamics underlying brain disorders. Semin Cell Dev Biol 2022; 121:153-160. [PMID: 34483043 PMCID: PMC8761161 DOI: 10.1016/j.semcdb.2021.08.013] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 08/18/2021] [Accepted: 08/23/2021] [Indexed: 01/03/2023]
Abstract
Understanding the exquisitely complex nature of the three-dimensional organization of the genome and how it affects gene regulation remains a central question in biology. Recent advances in sequencing- and imaging-based approaches in decoding the three-dimensional chromatin landscape have enabled a systematic characterization of gene regulatory architecture. In this review, we outline how chromatin architecture provides a reference atlas to predict the functional consequences of non-coding variants associated with human traits and disease. High-throughput perturbation assays such as massively parallel reporter assays (MPRA) and CRISPR-based genome engineering in combination with a reference atlas opened an avenue for going beyond observational studies to experimentally validating the regulatory principles of the genome. We conclude by providing a suggested path forward by calling attention to barriers that can be addressed for a more complete understanding of the regulatory landscape of the human brain.
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Affiliation(s)
- Brandon M Pratt
- Department of Pharmacology, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Hyejung Won
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA; UNC Neuroscience Center, University of North Carolina, Chapel Hill, NC 27599, USA.
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12
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From GWAS variant to function: A study of ∼148,000 variants for blood cell traits. HGG ADVANCES 2021; 3:100063. [PMID: 35047852 PMCID: PMC8756514 DOI: 10.1016/j.xhgg.2021.100063] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Accepted: 09/30/2021] [Indexed: 12/15/2022] Open
Abstract
Genome-wide association studies (GWASs) have identified hundreds of thousands of genetic variants associated with complex diseases and traits. However, most variants are noncoding and not clearly linked to genes, making it challenging to interpret these GWAS signals. We present a systematic variant-to-function study, prioritizing the most likely functional elements of the genome for experimental follow-up, for >148,000 variants identified for hematological traits. Specifically, we developed VAMPIRE: Variant Annotation Method Pointing to Interesting Regulatory Effects, an interactive web application implemented in R Shiny. This tool efficiently integrates and displays information from multiple complementary sources, including epigenomic signatures from blood-cell-relevant tissues or cells, functional and conservation summary scores, variant impact on protein and gene expression, chromatin conformation information, as well as publicly available GWAS and phenome-wide association study (PheWAS) results. Leveraging data generated from independently performed functional validation experiments, we demonstrate that our prioritized variants, genes, or variant-gene links are significantly more likely to be experimentally validated. This study not only has important implications for systematic and efficient revelation of functional mechanisms underlying GWAS variants for hematological traits but also provides a prototype that can be adapted to many other complex traits, paving the path for efficient variant-to-function (V2F) analyses.
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13
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Animesh S, Choudhary R, Wong BJH, Koh CTJ, Ng XY, Tay JKX, Chong WQ, Jian H, Chen L, Goh BC, Fullwood MJ. Profiling of 3D Genome Organization in Nasopharyngeal Cancer Needle Biopsy Patient Samples by a Modified Hi-C Approach. Front Genet 2021; 12:673530. [PMID: 34539729 PMCID: PMC8446523 DOI: 10.3389/fgene.2021.673530] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Accepted: 07/31/2021] [Indexed: 11/16/2022] Open
Abstract
Nasopharyngeal cancer (NPC), a cancer derived from epithelial cells in the nasopharynx, is a cancer common in China, Southeast Asia, and Africa. The three-dimensional (3D) genome organization of nasopharyngeal cancer is poorly understood. A major challenge in understanding the 3D genome organization of cancer samples is the lack of a method for the characterization of chromatin interactions in solid cancer needle biopsy samples. Here, we developed Biop-C, a modified in situ Hi-C method using solid cancer needle biopsy samples. We applied Biop-C to characterize three nasopharyngeal cancer solid cancer needle biopsy patient samples. We identified topologically associated domains (TADs), chromatin interaction loops, and frequently interacting regions (FIREs) at key oncogenes in nasopharyngeal cancer from the Biop-C heatmaps. We observed that the genomic features are shared at some important oncogenes, but the patients also display extensive heterogeneity at certain genomic loci. On analyzing the super enhancer landscape in nasopharyngeal cancer cell lines, we found that the super enhancers are associated with FIREs and can be linked to distal genes via chromatin loops in NPC. Taken together, our results demonstrate the utility of our Biop-C method in investigating 3D genome organization in solid cancers.
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Affiliation(s)
- Sambhavi Animesh
- Cancer Science Institute of Singapore, Centre for Translational Medicine, National University of Singapore, Singapore, Singapore
| | - Ruchi Choudhary
- Cancer Science Institute of Singapore, Centre for Translational Medicine, National University of Singapore, Singapore, Singapore.,School of Biological Sciences, Nanyang Technological University, Singapore, Singapore
| | | | - Charlotte Tze Jia Koh
- School of Biological Sciences, Nanyang Technological University, Singapore, Singapore
| | - Xin Yi Ng
- Department of Haematology-Oncology, National University Cancer Institute, National University Health System, Singapore, Singapore
| | - Joshua Kai Xun Tay
- Department of Otolaryngology - Head and Neck Surgery, National University of Singapore, Singapore, Singapore
| | - Wan-Qin Chong
- Department of Haematology-Oncology, National University Cancer Institute, National University Health System, Singapore, Singapore
| | - Han Jian
- Cancer Science Institute of Singapore, Centre for Translational Medicine, National University of Singapore, Singapore, Singapore
| | - Leilei Chen
- Cancer Science Institute of Singapore, Centre for Translational Medicine, National University of Singapore, Singapore, Singapore.,Department of Anatomy, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Boon Cher Goh
- Cancer Science Institute of Singapore, Centre for Translational Medicine, National University of Singapore, Singapore, Singapore.,Department of Haematology-Oncology, National University Cancer Institute, National University Health System, Singapore, Singapore.,Department of Pharmacology, Yong Loo Lin School of Medicine, National University Health System, Singapore, Singapore
| | - Melissa Jane Fullwood
- Cancer Science Institute of Singapore, Centre for Translational Medicine, National University of Singapore, Singapore, Singapore.,School of Biological Sciences, Nanyang Technological University, Singapore, Singapore
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14
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Kai Y, Li BE, Zhu M, Li GY, Chen F, Han Y, Cha HJ, Orkin SH, Cai W, Huang J, Yuan GC. Mapping the evolving landscape of super-enhancers during cell differentiation. Genome Biol 2021; 22:269. [PMID: 34526084 PMCID: PMC8442463 DOI: 10.1186/s13059-021-02485-x] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Accepted: 09/02/2021] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND Super-enhancers are clusters of enhancer elements that play critical roles in the maintenance of cell identity. Current investigations on super-enhancers are centered on the established ones in static cell types. How super-enhancers are established during cell differentiation remains obscure. RESULTS Here, by developing an unbiased approach to systematically analyze the evolving landscape of super-enhancers during cell differentiation in multiple lineages, we discover a general trend where super-enhancers emerge through three distinct temporal patterns: conserved, temporally hierarchical, and de novo. The three types of super-enhancers differ further in association patterns in target gene expression, functional enrichment, and 3D chromatin organization, suggesting they may represent distinct structural and functional subtypes. Furthermore, we dissect the enhancer repertoire within temporally hierarchical super-enhancers, and find enhancers that emerge at early and late stages are enriched with distinct transcription factors, suggesting that the temporal order of establishment of elements within super-enhancers may be directed by underlying DNA sequence. CRISPR-mediated deletion of individual enhancers in differentiated cells shows that both the early- and late-emerged enhancers are indispensable for target gene expression, while in undifferentiated cells early enhancers are involved in the regulation of target genes. CONCLUSIONS In summary, our analysis highlights the heterogeneity of the super-enhancer population and provides new insights to enhancer functions within super-enhancers.
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Affiliation(s)
- Yan Kai
- Department of Pediatric Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, 02115, USA
| | - Bin E Li
- Cancer and Blood Disorders Center, Boston Children's Hospital and Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, 02115, USA
| | - Ming Zhu
- State Key Laboratory of Cellular Stress Biology, Innovation Center for Cell Signaling Network, School of Life Sciences, Xiamen University, Xiamen, 361102, Fujian, China
| | - Grace Y Li
- Cancer and Blood Disorders Center, Boston Children's Hospital and Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, 02115, USA
| | - Fei Chen
- State Key Laboratory of Cellular Stress Biology, Innovation Center for Cell Signaling Network, School of Life Sciences, Xiamen University, Xiamen, 361102, Fujian, China
| | - Yingli Han
- State Key Laboratory of Cellular Stress Biology, Innovation Center for Cell Signaling Network, School of Life Sciences, Xiamen University, Xiamen, 361102, Fujian, China
| | - Hye Ji Cha
- Cancer and Blood Disorders Center, Boston Children's Hospital and Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, 02115, USA
| | - Stuart H Orkin
- Cancer and Blood Disorders Center, Boston Children's Hospital and Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, 02115, USA
- Howard Hughes Medical Institute, Boston, MA, 02115, USA
| | - Wenqing Cai
- Cancer and Blood Disorders Center, Boston Children's Hospital and Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, 02115, USA.
| | - Jialiang Huang
- State Key Laboratory of Cellular Stress Biology, Innovation Center for Cell Signaling Network, School of Life Sciences, Xiamen University, Xiamen, 361102, Fujian, China.
| | - Guo-Cheng Yuan
- Department of Pediatric Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, 02115, USA.
- Department of Genetics and Genomic Sciences, Charles Bronfman Institute for Precision Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
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15
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Jin X, Fudenberg G, Pollard KS. Genome-wide variability in recombination activity is associated with meiotic chromatin organization. Genome Res 2021; 31:1561-1572. [PMID: 34301629 PMCID: PMC8415379 DOI: 10.1101/gr.275358.121] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Accepted: 07/22/2021] [Indexed: 11/24/2022]
Abstract
Recombination enables reciprocal exchange of genomic information between parental chromosomes and successful segregation of homologous chromosomes during meiosis. Errors in this process lead to negative health outcomes, whereas variability in recombination rate affects genome evolution. In mammals, most crossovers occur in hotspots defined by PRDM9 motifs, although PRDM9 binding peaks are not all equally hot. We hypothesize that dynamic patterns of meiotic genome folding are linked to recombination activity. We apply an integrative bioinformatics approach to analyze how three-dimensional (3D) chromosomal organization during meiosis relates to rates of double-strand-break (DSB) and crossover (CO) formation at PRDM9 binding peaks. We show that active, spatially accessible genomic regions during meiotic prophase are associated with DSB-favored loci, which further adopt a transient locally active configuration in early prophase. Conversely, crossover formation is depleted among DSBs in spatially accessible regions during meiotic prophase, particularly within gene bodies. We also find evidence that active chromatin regions have smaller average loop sizes in mammalian meiosis. Collectively, these findings establish that differences in chromatin architecture along chromosomal axes are associated with variable recombination activity. We propose an updated framework describing how 3D organization of brush-loop chromosomes during meiosis may modulate recombination.
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Affiliation(s)
- Xiaofan Jin
- Gladstone Institutes, San Francisco, California 94158, USA
| | - Geoff Fudenberg
- Gladstone Institutes, San Francisco, California 94158, USA.,Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, California 90089, USA
| | - Katherine S Pollard
- Gladstone Institutes, San Francisco, California 94158, USA.,University of California San Francisco, San Francisco, California 94143, USA.,Chan-Zuckerberg Biohub, San Francisco, California 94158, USA
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16
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Hu B, Won H, Mah W, Park RB, Kassim B, Spiess K, Kozlenkov A, Crowley CA, Pochareddy S, Li Y, Dracheva S, Sestan N, Akbarian S, Geschwind DH. Neuronal and glial 3D chromatin architecture informs the cellular etiology of brain disorders. Nat Commun 2021; 12:3968. [PMID: 34172755 PMCID: PMC8233376 DOI: 10.1038/s41467-021-24243-0] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Accepted: 05/26/2021] [Indexed: 02/06/2023] Open
Abstract
Cellular heterogeneity in the human brain obscures the identification of robust cellular regulatory networks, which is necessary to understand the function of non-coding elements and the impact of non-coding genetic variation. Here we integrate genome-wide chromosome conformation data from purified neurons and glia with transcriptomic and enhancer profiles, to characterize the gene regulatory landscape of two major cell classes in the human brain. We then leverage cell-type-specific regulatory landscapes to gain insight into the cellular etiology of several brain disorders. We find that Alzheimer's disease (AD)-associated epigenetic dysregulation is linked to neurons and oligodendrocytes, whereas genetic risk factors for AD highlighted microglia, suggesting that different cell types may contribute to disease risk, via different mechanisms. Moreover, integration of glutamatergic and GABAergic regulatory maps with genetic risk factors for schizophrenia (SCZ) and bipolar disorder (BD) identifies shared (parvalbumin-expressing interneurons) and distinct cellular etiologies (upper layer neurons for BD, and deeper layer projection neurons for SCZ). Collectively, these findings shed new light on cell-type-specific gene regulatory networks in brain disorders.
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Affiliation(s)
- Benxia Hu
- grid.410711.20000 0001 1034 1720UNC Neuroscience Center, University of North Carolina, Chapel Hill, NC USA ,grid.410711.20000 0001 1034 1720Department of Genetics, University of North Carolina, Chapel Hill, NC USA
| | - Hyejung Won
- grid.410711.20000 0001 1034 1720UNC Neuroscience Center, University of North Carolina, Chapel Hill, NC USA ,grid.410711.20000 0001 1034 1720Department of Genetics, University of North Carolina, Chapel Hill, NC USA
| | - Won Mah
- grid.410711.20000 0001 1034 1720UNC Neuroscience Center, University of North Carolina, Chapel Hill, NC USA ,grid.410711.20000 0001 1034 1720Department of Genetics, University of North Carolina, Chapel Hill, NC USA
| | - Royce B. Park
- grid.59734.3c0000 0001 0670 2351Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY USA ,grid.59734.3c0000 0001 0670 2351Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Bibi Kassim
- grid.59734.3c0000 0001 0670 2351Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY USA ,grid.59734.3c0000 0001 0670 2351Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Keeley Spiess
- grid.410711.20000 0001 1034 1720UNC Neuroscience Center, University of North Carolina, Chapel Hill, NC USA
| | - Alexey Kozlenkov
- grid.59734.3c0000 0001 0670 2351Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY USA ,grid.59734.3c0000 0001 0670 2351Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY USA ,grid.274295.f0000 0004 0420 1184James J. Peters VA Medical Center, Bronx, NY USA
| | - Cheynna A. Crowley
- grid.410711.20000 0001 1034 1720Department of Genetics, University of North Carolina, Chapel Hill, NC USA
| | - Sirisha Pochareddy
- grid.47100.320000000419368710Department of Neuroscience and Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, CT USA
| | | | - Yun Li
- grid.410711.20000 0001 1034 1720Department of Genetics, University of North Carolina, Chapel Hill, NC USA ,grid.410711.20000 0001 1034 1720Biostatistics, University of North Carolina, Chapel Hill, NC USA ,grid.410711.20000 0001 1034 1720Computer Science, University of North Carolina, Chapel Hill, NC USA
| | - Stella Dracheva
- grid.59734.3c0000 0001 0670 2351Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY USA ,grid.59734.3c0000 0001 0670 2351Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY USA ,grid.274295.f0000 0004 0420 1184James J. Peters VA Medical Center, Bronx, NY USA
| | - Nenad Sestan
- grid.47100.320000000419368710Department of Psychiatry, Yale School of Medicine, New Haven, CT USA ,grid.47100.320000000419368710Department of Genetics, Yale School of Medicine, New Haven, CT USA ,grid.47100.320000000419368710Department of Comparative Medicine, Program in Integrative Cell Signaling and Neurobiology of Metabolism, Yale School of Medicine, New Haven, CT USA ,grid.47100.320000000419368710Program in Cellular Neuroscience, Neurodegeneration, and Repair and Yale Child Study Center, Yale School of Medicine, New Haven, CT USA
| | - Schahram Akbarian
- grid.59734.3c0000 0001 0670 2351Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY USA ,grid.59734.3c0000 0001 0670 2351Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Daniel H. Geschwind
- grid.19006.3e0000 0000 9632 6718Neurogenetics Program, Department of Neurology, David Geffen School of Medicine University of California, Los Angeles, CA USA ,grid.19006.3e0000 0000 9632 6718Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine University of California, Los Angeles, CA 90095 USA ,grid.19006.3e0000 0000 9632 6718Department of Human Genetics, David Geffen School of Medicine University of California, Los Angeles, CA USA ,grid.19006.3e0000 0000 9632 6718Department of Psychiatry and Biobehavioral Sciences, Semel Institute, David Geffen School of Medicine University of California, Los Angeles, CA USA
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17
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Jiang Y, Li W, Lindsey-Boltz LA, Yang Y, Li Y, Sancar A. Super hotspots and super coldspots in the repair of UV-induced DNA damage in the human genome. J Biol Chem 2021; 296:100581. [PMID: 33771559 PMCID: PMC8081918 DOI: 10.1016/j.jbc.2021.100581] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 03/18/2021] [Accepted: 03/22/2021] [Indexed: 02/07/2023] Open
Abstract
The formation of UV-induced DNA damage and its repair are influenced by many factors that modulate lesion formation and the accessibility of repair machinery. However, it remains unknown which genomic sites are prioritized for immediate repair after UV damage induction, and whether these prioritized sites overlap with hotspots of UV damage. We identified the super hotspots subject to the earliest repair for (6-4) pyrimidine-pyrimidone photoproduct by using the eXcision Repair-sequencing (XR-seq) method. We further identified super coldspots for (6-4) pyrimidine-pyrimidone photoproduct repair and super hotspots for cyclobutane pyrimidine dimer repair by analyzing available XR-seq time-course data. By integrating datasets of XR-seq, Damage-seq, adductSeq, and cyclobutane pyrimidine dimer-seq, we show that neither repair super hotspots nor repair super coldspots overlap hotspots of UV damage. Furthermore, we demonstrate that repair super hotspots are significantly enriched in frequently interacting regions and superenhancers. Finally, we report our discovery of an enrichment of cytosine in repair super hotspots and super coldspots. These findings suggest that local DNA features together with large-scale chromatin features contribute to the orders of magnitude variability in the rates of UV damage repair.
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Affiliation(s)
- Yuchao Jiang
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina, USA; Department of Genetics, School of Medicine, University of North Carolina, Chapel Hill, North Carolina, USA; Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, North Carolina, USA.
| | - Wentao Li
- Department of Biochemistry and Biophysics, School of Medicine, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Laura A Lindsey-Boltz
- Department of Biochemistry and Biophysics, School of Medicine, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Yuchen Yang
- Department of Genetics, School of Medicine, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Yun Li
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina, USA; Department of Genetics, School of Medicine, University of North Carolina, Chapel Hill, North Carolina, USA; Department of Computer Science, College of Arts and Sciences, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Aziz Sancar
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, North Carolina, USA; Department of Biochemistry and Biophysics, School of Medicine, University of North Carolina, Chapel Hill, North Carolina, USA.
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