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Zhou X, Zhou L, Qian F, Chen J, Zhang Y, Yu Z, Zhang J, Yang Y, Li Y, Song C, Wang Y, Shang D, Dong L, Zhu J, Li C, Wang Q. TFTG: A comprehensive database for human transcription factors and their targets. Comput Struct Biotechnol J 2024; 23:1877-1885. [PMID: 38707542 PMCID: PMC11068477 DOI: 10.1016/j.csbj.2024.04.036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Revised: 04/15/2024] [Accepted: 04/15/2024] [Indexed: 05/07/2024] Open
Abstract
Transcription factors (TFs) are major contributors to gene transcription, especially in controlling cell-specific gene expression and disease occurrence and development. Uncovering the relationship between TFs and their target genes is critical to understanding the mechanism of action of TFs. With the development of high-throughput sequencing techniques, a large amount of TF-related data has accumulated, which can be used to identify their target genes. In this study, we developed TFTG (Transcription Factor and Target Genes) database (http://tf.liclab.net/TFTG), which aimed to provide a large number of available human TF-target gene resources by multiple strategies, besides performing a comprehensive functional and epigenetic annotations and regulatory analyses of TFs. We identified extensive available TF-target genes by collecting and processing TF-associated ChIP-seq datasets, perturbation RNA-seq datasets and motifs. We also obtained experimentally confirmed relationships between TF and target genes from available resources. Overall, the target genes of TFs were obtained through integrating the relevant data of various TFs as well as fourteen identification strategies. Meanwhile, TFTG was embedded with user-friendly search, analysis, browsing, downloading and visualization functions. TFTG is designed to be a convenient resource for exploring human TF-target gene regulations, which will be useful for most users in the TF and gene expression regulation research.
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Affiliation(s)
- Xinyuan Zhou
- The First Affiliated Hospital & Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- College of Artificial Intelligence and Big Data For Medical Sciences, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, China
| | - Liwei Zhou
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, China
| | - Fengcui Qian
- The First Affiliated Hospital & Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences & MOE Key Lab of Rare Pediatric Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
| | - Jiaxin Chen
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Yuexin Zhang
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
| | - Zhengmin Yu
- School of Computer, University of South China, Hengyang, Hunan 421001, China
| | - Jian Zhang
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Yongsan Yang
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Yanyu Li
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Chao Song
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
| | - Yuezhu Wang
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Desi Shang
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
| | - Longlong Dong
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Jiang Zhu
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Chunquan Li
- The First Affiliated Hospital & Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Hunan Provincial Maternal and Child Health Care Hospital, National Health Commission Key Laboratory of Birth Defect Research and Prevention, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences & MOE Key Lab of Rare Pediatric Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- School of Computer, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, China
| | - Qiuyu Wang
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
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Shen X, Li X, Wu T, Guo T, Lv J, He Z, Luo M, Zhu X, Tian Y, Lai W, Dong C, Hu X, Wu L. TRIM33 plays a critical role in regulating dendritic cell differentiation and homeostasis by modulating Irf8 and Bcl2l11 transcription. Cell Mol Immunol 2024; 21:752-769. [PMID: 38822080 PMCID: PMC11214632 DOI: 10.1038/s41423-024-01179-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Accepted: 04/25/2024] [Indexed: 06/02/2024] Open
Abstract
The development of distinct dendritic cell (DC) subsets, namely, plasmacytoid DCs (pDCs) and conventional DC subsets (cDC1s and cDC2s), is controlled by specific transcription factors. IRF8 is essential for the fate specification of cDC1s. However, how the expression of Irf8 is regulated is not fully understood. In this study, we identified TRIM33 as a critical regulator of DC differentiation and maintenance. TRIM33 deletion in Trim33fl/fl Cre-ERT2 mice significantly impaired DC differentiation from hematopoietic progenitors at different developmental stages. TRIM33 deficiency downregulated the expression of multiple genes associated with DC differentiation in these progenitors. TRIM33 promoted the transcription of Irf8 to facilitate the differentiation of cDC1s by maintaining adequate CDK9 and Ser2 phosphorylated RNA polymerase II (S2 Pol II) levels at Irf8 gene sites. Moreover, TRIM33 prevented the apoptosis of DCs and progenitors by directly suppressing the PU.1-mediated transcription of Bcl2l11, thereby maintaining DC homeostasis. Taken together, our findings identified TRIM33 as a novel and crucial regulator of DC differentiation and maintenance through the modulation of Irf8 and Bcl2l11 expression. The finding that TRIM33 functions as a critical regulator of both DC differentiation and survival provides potential benefits for devising DC-based immune interventions and therapies.
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Affiliation(s)
- Xiangyi Shen
- Institute for Immunology, School of Basic Medical Sciences, Tsinghua University, 100084, Beijing, China
| | - Xiaoguang Li
- Institute for Immunology, School of Basic Medical Sciences, Tsinghua University, 100084, Beijing, China
- Joint Graduate Program of Peking-Tsinghua-National Institute of Biological Sciences, School of Life Sciences, Tsinghua University, 100084, Beijing, China
| | - Tao Wu
- Institute for Immunology, School of Basic Medical Sciences, Tsinghua University, 100084, Beijing, China
- Tsinghua-Peking Center for Life Sciences, Tsinghua University, 100084, Beijing, China
| | - Tingting Guo
- Institute for Immunology, School of Basic Medical Sciences, Tsinghua University, 100084, Beijing, China
- Tsinghua-Peking Center for Life Sciences, Tsinghua University, 100084, Beijing, China
| | - Jiaoyan Lv
- Institute for Immunology, School of Basic Medical Sciences, Tsinghua University, 100084, Beijing, China
- Tsinghua-Peking Center for Life Sciences, Tsinghua University, 100084, Beijing, China
| | - Zhimin He
- Institute for Immunology, School of Basic Medical Sciences, Tsinghua University, 100084, Beijing, China
- Tsinghua-Peking Center for Life Sciences, Tsinghua University, 100084, Beijing, China
| | - Maocai Luo
- Institute for Immunology, School of Basic Medical Sciences, Tsinghua University, 100084, Beijing, China
| | - Xinyi Zhu
- Institute for Immunology, School of Basic Medical Sciences, Tsinghua University, 100084, Beijing, China
- Tsinghua-Peking Center for Life Sciences, Tsinghua University, 100084, Beijing, China
| | - Yujie Tian
- Institute for Immunology, School of Basic Medical Sciences, Tsinghua University, 100084, Beijing, China
- Joint Graduate Program of Peking-Tsinghua-National Institute of Biological Sciences, School of Life Sciences, Tsinghua University, 100084, Beijing, China
| | - Wenlong Lai
- Institute for Immunology, School of Basic Medical Sciences, Tsinghua University, 100084, Beijing, China
| | - Chen Dong
- Institute for Immunology, School of Basic Medical Sciences, Tsinghua University, 100084, Beijing, China
- Tsinghua-Peking Center for Life Sciences, Tsinghua University, 100084, Beijing, China
- Beijing Key Laboratory for Immunological Research on Chronic Diseases, 100084, Beijing, China
- Westlake University School of Medicine, Hangzhou, 310024, China
| | - Xiaoyu Hu
- Institute for Immunology, School of Basic Medical Sciences, Tsinghua University, 100084, Beijing, China
- Tsinghua-Peking Center for Life Sciences, Tsinghua University, 100084, Beijing, China
- Beijing Key Laboratory for Immunological Research on Chronic Diseases, 100084, Beijing, China
| | - Li Wu
- Institute for Immunology, School of Basic Medical Sciences, Tsinghua University, 100084, Beijing, China.
- Tsinghua-Peking Center for Life Sciences, Tsinghua University, 100084, Beijing, China.
- Beijing Key Laboratory for Immunological Research on Chronic Diseases, 100084, Beijing, China.
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3
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Du Y, Jiang X, Zhang Y, Ying J, Yi Q. Epigenetic mechanism of SET7/9-mediated histone methylation modification in high glucose-induced ferroptosis in retinal pigment epithelial cells. J Bioenerg Biomembr 2024; 56:297-309. [PMID: 38602631 DOI: 10.1007/s10863-024-10016-z] [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: 12/26/2023] [Accepted: 03/19/2024] [Indexed: 04/12/2024]
Abstract
Ferroptosis of the retinal pigment epithelial (RPE) cells leads to retinal neuron injury and even visual loss. Our study aims to investigate the role of the SET domain with lysine methyltransferase 7/9 (SET7/9) in regulating high glucose (HG)-induced ferroptosis in RPE cells. The cell model was established by HG treatment. The levels of SET7/9 and Sirtuin 6 (SIRT6) were inhibited and Runt-related transcription factor 1 (RUNX1) was overexpressed through cell transfection, and then their levels in ARPE-19 cells were detected. Cell viability and apoptosis was detected. The levels of reactive oxygen species, malondialdehyde, glutathione, ferrous ion, glutathione peroxidase 4, and acyl-CoA synthetase long-chain family member 4 were detected. SET7/9 and trimethylation of histone H3 at lysine 4 (H3K4me3) levels in the RUNX1 promoter region and RUNX1 level in the SIRT6 promoter region were measured. The relationship between RUNX1 and SIRT6 was verified. SET7/9 and RUNX1 were highly expressed while SIRT6 was poorly expressed in HG-induced ARPE-19 cells. SET7/9 inhibition increased cell viability and inhibited cell apoptosis and ferroptosis. Mechanistically, SET7/9 increased H3K4me3 on the RUNX1 promoter to promote RUNX1, and RUNX1 repressed SIRT6 expression. Overexpression of RUNX1 or silencing SIRT6 partially reversed the inhibitory effect of SET7/9 silencing on HG-induced ferroptosis. In conclusion, SET7/9 promoted ferroptosis of RPE cells through the SIRT6/RUNX1 pathway.
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Affiliation(s)
- Yue Du
- Pharmacy Department of Ningbo Eye Hospital, Wenzhou Medical University, Ningbo, China
| | - Xue Jiang
- Ophthalmology Department of Ningbo Eye Hospital, Wenzhou Medical University, No. 599 Beimingcheng Road, 315042, Ningbo, Zhejiang Province, China
| | - Yanyan Zhang
- Ophthalmology Department of Ningbo Eye Hospital, Wenzhou Medical University, No. 599 Beimingcheng Road, 315042, Ningbo, Zhejiang Province, China
| | - Jianing Ying
- Ophthalmology Department of Ningbo Eye Hospital, Wenzhou Medical University, No. 599 Beimingcheng Road, 315042, Ningbo, Zhejiang Province, China
- Health Science Center, Ningbo University, Ningbo, China
| | - Quanyong Yi
- Ophthalmology Department of Ningbo Eye Hospital, Wenzhou Medical University, No. 599 Beimingcheng Road, 315042, Ningbo, Zhejiang Province, China.
- Health Science Center, Ningbo University, Ningbo, China.
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Saldana-Guerrero IM, Montano-Gutierrez LF, Boswell K, Hafemeister C, Poon E, Shaw LE, Stavish D, Lea RA, Wernig-Zorc S, Bozsaky E, Fetahu IS, Zoescher P, Pötschger U, Bernkopf M, Wenninger-Weinzierl A, Sturtzel C, Souilhol C, Tarelli S, Shoeb MR, Bozatzi P, Rados M, Guarini M, Buri MC, Weninger W, Putz EM, Huang M, Ladenstein R, Andrews PW, Barbaric I, Cresswell GD, Bryant HE, Distel M, Chesler L, Taschner-Mandl S, Farlik M, Tsakiridis A, Halbritter F. A human neural crest model reveals the developmental impact of neuroblastoma-associated chromosomal aberrations. Nat Commun 2024; 15:3745. [PMID: 38702304 PMCID: PMC11068915 DOI: 10.1038/s41467-024-47945-7] [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] [Received: 01/06/2023] [Accepted: 04/15/2024] [Indexed: 05/06/2024] Open
Abstract
Early childhood tumours arise from transformed embryonic cells, which often carry large copy number alterations (CNA). However, it remains unclear how CNAs contribute to embryonic tumourigenesis due to a lack of suitable models. Here we employ female human embryonic stem cell (hESC) differentiation and single-cell transcriptome and epigenome analysis to assess the effects of chromosome 17q/1q gains, which are prevalent in the embryonal tumour neuroblastoma (NB). We show that CNAs impair the specification of trunk neural crest (NC) cells and their sympathoadrenal derivatives, the putative cells-of-origin of NB. This effect is exacerbated upon overexpression of MYCN, whose amplification co-occurs with CNAs in NB. Moreover, CNAs potentiate the pro-tumourigenic effects of MYCN and mutant NC cells resemble NB cells in tumours. These changes correlate with a stepwise aberration of developmental transcription factor networks. Together, our results sketch a mechanistic framework for the CNA-driven initiation of embryonal tumours.
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Affiliation(s)
- Ingrid M Saldana-Guerrero
- Centre for Stem Cell Biology, School of Biosciences, The University of Sheffield, Sheffield, UK
- Neuroscience Institute, The University of Sheffield, Sheffield, UK
- Sheffield Institute for Nucleic Acids (SInFoNiA), School of Medicine and Population Health, The University of Sheffield, Sheffield, UK
| | | | - Katy Boswell
- Centre for Stem Cell Biology, School of Biosciences, The University of Sheffield, Sheffield, UK
- Neuroscience Institute, The University of Sheffield, Sheffield, UK
| | | | - Evon Poon
- Division of Clinical Studies, The Institute of Cancer Research (ICR) & Royal Marsden NHS Trust, London, UK
| | - Lisa E Shaw
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | - Dylan Stavish
- Centre for Stem Cell Biology, School of Biosciences, The University of Sheffield, Sheffield, UK
- Neuroscience Institute, The University of Sheffield, Sheffield, UK
| | - Rebecca A Lea
- Centre for Stem Cell Biology, School of Biosciences, The University of Sheffield, Sheffield, UK
- Neuroscience Institute, The University of Sheffield, Sheffield, UK
| | - Sara Wernig-Zorc
- St. Anna Children's Cancer Research Institute (CCRI), Vienna, Austria
| | - Eva Bozsaky
- St. Anna Children's Cancer Research Institute (CCRI), Vienna, Austria
| | - Irfete S Fetahu
- St. Anna Children's Cancer Research Institute (CCRI), Vienna, Austria
- Medical University of Vienna, Department of Neurology, Division of Neuropathology and Neurochemistry, Vienna, Austria
| | - Peter Zoescher
- St. Anna Children's Cancer Research Institute (CCRI), Vienna, Austria
| | - Ulrike Pötschger
- St. Anna Children's Cancer Research Institute (CCRI), Vienna, Austria
| | - Marie Bernkopf
- St. Anna Children's Cancer Research Institute (CCRI), Vienna, Austria
- Labdia Labordiagnostik GmbH, Vienna, Austria
| | | | - Caterina Sturtzel
- St. Anna Children's Cancer Research Institute (CCRI), Vienna, Austria
| | - Celine Souilhol
- Centre for Stem Cell Biology, School of Biosciences, The University of Sheffield, Sheffield, UK
- Neuroscience Institute, The University of Sheffield, Sheffield, UK
- Biomolecular Sciences Research Centre, Department of Biosciences and Chemistry, Sheffield Hallam University, Sheffield, UK
| | - Sophia Tarelli
- Centre for Stem Cell Biology, School of Biosciences, The University of Sheffield, Sheffield, UK
- Neuroscience Institute, The University of Sheffield, Sheffield, UK
| | - Mohamed R Shoeb
- St. Anna Children's Cancer Research Institute (CCRI), Vienna, Austria
| | - Polyxeni Bozatzi
- St. Anna Children's Cancer Research Institute (CCRI), Vienna, Austria
| | - Magdalena Rados
- St. Anna Children's Cancer Research Institute (CCRI), Vienna, Austria
| | - Maria Guarini
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
| | - Michelle C Buri
- St. Anna Children's Cancer Research Institute (CCRI), Vienna, Austria
| | - Wolfgang Weninger
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | - Eva M Putz
- St. Anna Children's Cancer Research Institute (CCRI), Vienna, Austria
| | - Miller Huang
- Children's Hospital Los Angeles, Cancer and Blood Disease Institutes, and The Saban Research Institute, Los Angeles, CA, USA
- Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Ruth Ladenstein
- St. Anna Children's Cancer Research Institute (CCRI), Vienna, Austria
| | - Peter W Andrews
- Centre for Stem Cell Biology, School of Biosciences, The University of Sheffield, Sheffield, UK
| | - Ivana Barbaric
- Centre for Stem Cell Biology, School of Biosciences, The University of Sheffield, Sheffield, UK
- Neuroscience Institute, The University of Sheffield, Sheffield, UK
| | | | - Helen E Bryant
- Sheffield Institute for Nucleic Acids (SInFoNiA), School of Medicine and Population Health, The University of Sheffield, Sheffield, UK
| | - Martin Distel
- St. Anna Children's Cancer Research Institute (CCRI), Vienna, Austria
| | - Louis Chesler
- Division of Clinical Studies, The Institute of Cancer Research (ICR) & Royal Marsden NHS Trust, London, UK
| | | | - Matthias Farlik
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | - Anestis Tsakiridis
- Centre for Stem Cell Biology, School of Biosciences, The University of Sheffield, Sheffield, UK.
- Neuroscience Institute, The University of Sheffield, Sheffield, UK.
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Trimbour R, Deutschmann IM, Cantini L. Molecular mechanisms reconstruction from single-cell multi-omics data with HuMMuS. Bioinformatics 2024; 40:btae143. [PMID: 38460192 PMCID: PMC11065476 DOI: 10.1093/bioinformatics/btae143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 12/20/2023] [Accepted: 03/07/2024] [Indexed: 03/11/2024] Open
Abstract
MOTIVATION The molecular identity of a cell results from a complex interplay between heterogeneous molecular layers. Recent advances in single-cell sequencing technologies have opened the possibility to measure such molecular layers of regulation. RESULTS Here, we present HuMMuS, a new method for inferring regulatory mechanisms from single-cell multi-omics data. Differently from the state-of-the-art, HuMMuS captures cooperation between biological macromolecules and can easily include additional layers of molecular regulation. We benchmarked HuMMuS with respect to the state-of-the-art on both paired and unpaired multi-omics datasets. Our results proved the improvements provided by HuMMuS in terms of transcription factor (TF) targets, TF binding motifs and regulatory regions prediction. Finally, once applied to snmC-seq, scATAC-seq and scRNA-seq data from mouse brain cortex, HuMMuS enabled to accurately cluster scRNA profiles and to identify potential driver TFs. AVAILABILITY AND IMPLEMENTATION HuMMuS is available at https://github.com/cantinilab/HuMMuS.
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Affiliation(s)
- Remi Trimbour
- Institut Pasteur, Université Paris Cité, CNRS UMR 3738, Machine Learning for Integrative Genomics Group, F-75015 Paris, France
- Institut de Biologie de l’Ecole Normale Supérieure, CNRS, INSERM, Ecole Normale Supérieure, Université PSL, 75005 Paris, France
| | - Ina Maria Deutschmann
- Institut de Biologie de l’Ecole Normale Supérieure, CNRS, INSERM, Ecole Normale Supérieure, Université PSL, 75005 Paris, France
| | - Laura Cantini
- Institut Pasteur, Université Paris Cité, CNRS UMR 3738, Machine Learning for Integrative Genomics Group, F-75015 Paris, France
- Institut de Biologie de l’Ecole Normale Supérieure, CNRS, INSERM, Ecole Normale Supérieure, Université PSL, 75005 Paris, France
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6
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Chen Z, Guo X, Tao R, Huyghe JR, Law PJ, Fernandez-Rozadilla C, Ping J, Jia G, Long J, Li C, Shen Q, Xie Y, Timofeeva MN, Thomas M, Schmit SL, Díez-Obrero V, Devall M, Moratalla-Navarro F, Fernandez-Tajes J, Palles C, Sherwood K, Briggs SEW, Svinti V, Donnelly K, Farrington SM, Blackmur J, Vaughan-Shaw PG, Shu XO, Lu Y, Broderick P, Studd J, Harrison TA, Conti DV, Schumacher FR, Melas M, Rennert G, Obón-Santacana M, Martín-Sánchez V, Oh JH, Kim J, Jee SH, Jung KJ, Kweon SS, Shin MH, Shin A, Ahn YO, Kim DH, Oze I, Wen W, Matsuo K, Matsuda K, Tanikawa C, Ren Z, Gao YT, Jia WH, Hopper JL, Jenkins MA, Win AK, Pai RK, Figueiredo JC, Haile RW, Gallinger S, Woods MO, Newcomb PA, Duggan D, Cheadle JP, Kaplan R, Kerr R, Kerr D, Kirac I, Böhm J, Mecklin JP, Jousilahti P, Knekt P, Aaltonen LA, Rissanen H, Pukkala E, Eriksson JG, Cajuso T, Hänninen U, Kondelin J, Palin K, Tanskanen T, Renkonen-Sinisalo L, Männistö S, Albanes D, Weinstein SJ, Ruiz-Narvaez E, Palmer JR, Buchanan DD, Platz EA, Visvanathan K, Ulrich CM, Siegel E, Brezina S, Gsur A, Campbell PT, Chang-Claude J, Hoffmeister M, Brenner H, Slattery ML, Potter JD, Tsilidis KK, Schulze MB, Gunter MJ, Murphy N, Castells A, Castellví-Bel S, Moreira L, Arndt V, Shcherbina A, Bishop DT, Giles GG, Southey MC, Idos GE, McDonnell KJ, Abu-Ful Z, Greenson JK, Shulman K, Lejbkowicz F, Offit K, Su YR, Steinfelder R, Keku TO, van Guelpen B, Hudson TJ, Hampel H, Pearlman R, Berndt SI, Hayes RB, Martinez ME, Thomas SS, Pharoah PDP, Larsson SC, Yen Y, Lenz HJ, White E, Li L, Doheny KF, Pugh E, Shelford T, Chan AT, Cruz-Correa M, Lindblom A, Hunter DJ, Joshi AD, Schafmayer C, Scacheri PC, Kundaje A, Schoen RE, Hampe J, Stadler ZK, Vodicka P, Vodickova L, Vymetalkova V, Edlund CK, Gauderman WJ, Shibata D, Toland A, Markowitz S, Kim A, Chanock SJ, van Duijnhoven F, Feskens EJM, Sakoda LC, Gago-Dominguez M, Wolk A, Pardini B, FitzGerald LM, Lee SC, Ogino S, Bien SA, Kooperberg C, Li CI, Lin Y, Prentice R, Qu C, Bézieau S, Yamaji T, Sawada N, Iwasaki M, Le Marchand L, Wu AH, Qu C, McNeil CE, Coetzee G, Hayward C, Deary IJ, Harris SE, Theodoratou E, Reid S, Walker M, Ooi LY, Lau KS, Zhao H, Hsu L, Cai Q, Dunlop MG, Gruber SB, Houlston RS, Moreno V, Casey G, Peters U, Tomlinson I, Zheng W. Fine-mapping analysis including over 254,000 East Asian and European descendants identifies 136 putative colorectal cancer susceptibility genes. Nat Commun 2024; 15:3557. [PMID: 38670944 PMCID: PMC11053150 DOI: 10.1038/s41467-024-47399-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 03/26/2024] [Indexed: 04/28/2024] Open
Abstract
Genome-wide association studies (GWAS) have identified more than 200 common genetic variants independently associated with colorectal cancer (CRC) risk, but the causal variants and target genes are mostly unknown. We sought to fine-map all known CRC risk loci using GWAS data from 100,204 cases and 154,587 controls of East Asian and European ancestry. Our stepwise conditional analyses revealed 238 independent association signals of CRC risk, each with a set of credible causal variants (CCVs), of which 28 signals had a single CCV. Our cis-eQTL/mQTL and colocalization analyses using colorectal tissue-specific transcriptome and methylome data separately from 1299 and 321 individuals, along with functional genomic investigation, uncovered 136 putative CRC susceptibility genes, including 56 genes not previously reported. Analyses of single-cell RNA-seq data from colorectal tissues revealed 17 putative CRC susceptibility genes with distinct expression patterns in specific cell types. Analyses of whole exome sequencing data provided additional support for several target genes identified in this study as CRC susceptibility genes. Enrichment analyses of the 136 genes uncover pathways not previously linked to CRC risk. Our study substantially expanded association signals for CRC and provided additional insight into the biological mechanisms underlying CRC development.
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Affiliation(s)
- Zhishan Chen
- Division of Epidemiology, Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Xingyi Guo
- Division of Epidemiology, Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Ran Tao
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, 37232, TN, USA
| | - Jeroen R Huyghe
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Philip J Law
- Division of Genetics and Epidemiology, Institute of Cancer Research, London, UK
| | - Ceres Fernandez-Rozadilla
- Edinburgh Cancer Research Centre, Institute of Genomics and Cancer, University of Edinburgh, Edinburgh, UK
- Genomic Medicine Group, Instituto de Investigacion Sanitaria de Santiago, Santiago de Compostela, Spain
| | - Jie Ping
- Division of Epidemiology, Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Guochong Jia
- Division of Epidemiology, Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jirong Long
- Division of Epidemiology, Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Chao Li
- Division of Epidemiology, Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Quanhu Shen
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yuhan Xie
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
| | - Maria N Timofeeva
- Colon Cancer Genetics Group, Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
- Danish Institute for Advanced Study, Department of Public Health, University of Southern Denmark, Odense, Denmark
| | - Minta Thomas
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Stephanie L Schmit
- Genomic Medicine Institute, Cleveland Clinic, Cleveland, OH, USA
- Population and Cancer Prevention Program, Case Comprehensive Cancer Center, Cleveland, OH, USA
| | - Virginia Díez-Obrero
- Colorectal Cancer Group, ONCOBELL Program, Bellvitge Biomedical Research Institute, Barcelona, Spain
- Consortium for Biomedical Research in Epidemiology and Public Health, Madrid, Spain
- Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona, Spain
- Oncology Data Analytics Program, Catalan Institute of Oncology, Barcelona, Spain
| | - Matthew Devall
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | - Ferran Moratalla-Navarro
- Colorectal Cancer Group, ONCOBELL Program, Bellvitge Biomedical Research Institute, Barcelona, Spain
- Consortium for Biomedical Research in Epidemiology and Public Health, Madrid, Spain
- Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona, Spain
- Oncology Data Analytics Program, Catalan Institute of Oncology, Barcelona, Spain
| | - Juan Fernandez-Tajes
- Edinburgh Cancer Research Centre, Institute of Genomics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Claire Palles
- Institute of Cancer and Genomic Sciences, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
| | - Kitty Sherwood
- Edinburgh Cancer Research Centre, Institute of Genomics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Sarah E W Briggs
- Department of Public Health, Richard Doll Building, University of Oxford, Oxford, UK
| | - Victoria Svinti
- Colon Cancer Genetics Group, Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Kevin Donnelly
- Colon Cancer Genetics Group, Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Susan M Farrington
- Colon Cancer Genetics Group, Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - James Blackmur
- Colon Cancer Genetics Group, Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Peter G Vaughan-Shaw
- Colon Cancer Genetics Group, Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Xiao-Ou Shu
- Division of Epidemiology, Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yingchang Lu
- Division of Epidemiology, Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Peter Broderick
- Division of Genetics and Epidemiology, Institute of Cancer Research, London, UK
| | - James Studd
- Division of Genetics and Epidemiology, Institute of Cancer Research, London, UK
| | - Tabitha A Harrison
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - David V Conti
- Department of Preventive Medicine, USC Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Fredrick R Schumacher
- Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, OH, USA
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Marilena Melas
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, OH, USA
| | - Gad Rennert
- Clalit National Cancer Control Center, Haifa, Israel
- Department of Community Medicine and Epidemiology, Lady Davis Carmel Medical Center, Haifa, Israel
- Ruth and Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel
| | - Mireia Obón-Santacana
- Colorectal Cancer Group, ONCOBELL Program, Bellvitge Biomedical Research Institute, Barcelona, Spain
- Consortium for Biomedical Research in Epidemiology and Public Health, Madrid, Spain
- Oncology Data Analytics Program, Catalan Institute of Oncology, Barcelona, Spain
| | - Vicente Martín-Sánchez
- Consortium for Biomedical Research in Epidemiology and Public Health, Madrid, Spain
- Biomedicine Institute, University of León, León, Spain
| | - Jae Hwan Oh
- Center for Colorectal Cancer, National Cancer Center Hospital, National Cancer Center, Gyeonggi-do, South Korea
| | - Jeongseon Kim
- Department of Cancer Biomedical Science, Graduate School of Cancer Science and Policy, National Cancer Center, Gyeonggi-do, South Korea
| | - Sun Ha Jee
- Department of Epidemiology and Health Promotion, Graduate School of Public Health, Yonsei University, Seoul, South Korea
| | - Keum Ji Jung
- Department of Epidemiology and Health Promotion, Graduate School of Public Health, Yonsei University, Seoul, South Korea
| | - Sun-Seog Kweon
- Department of Preventive Medicine, Chonnam National University Medical School, Gwangju, South Korea
| | - Min-Ho Shin
- Department of Preventive Medicine, Chonnam National University Medical School, Gwangju, South Korea
| | - Aesun Shin
- Cancer Research Institute, Seoul National University, Seoul, South Korea
- Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, South Korea
| | - Yoon-Ok Ahn
- Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, South Korea
| | - Dong-Hyun Kim
- Department of Social and Preventive Medicine, Hallym University College of Medicine, Okcheon-dong, South Korea
| | - Isao Oze
- Division of Cancer Epidemiology and Prevention, Aichi Cancer Center Research Institute, Nagoya, Japan
| | - Wanqing Wen
- Division of Epidemiology, Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Keitaro Matsuo
- Department of Epidemiology, Nagoya University Graduate School of Medicine, Nagoya, Japan
- Division of Molecular and Clinical Epidemiology, Aichi Cancer Center Research Institute, Nagoya, Japan
| | - Koichi Matsuda
- Laboratory of Clinical Genome Sequencing, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of Tokyo, Tokyo, Japan
| | - Chizu Tanikawa
- Laboratory of Genome Technology, Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo, Japan
| | - Zefang Ren
- School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Yu-Tang Gao
- State Key Laboratory of Oncogenes and Related Genes and Department of Epidemiology, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Wei-Hua Jia
- State Key Laboratory of Oncology in South China, Cancer Center, Sun Yat-sen University, Guangzhou, China
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
- Department of Epidemiology, School of Public Health and Institute of Health and Environment, Seoul National University, Seoul, South Korea
| | - Mark A Jenkins
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
| | - Aung Ko Win
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
| | - Rish K Pai
- Department of Laboratory Medicine and Pathology, Mayo Clinic Arizona, Scottsdale, AZ, USA
| | - Jane C Figueiredo
- Department of Preventive Medicine, USC Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Medicine, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Robert W Haile
- Division of Oncology, Department of Medicine, Cedars-Sinai Cancer Research Center for Health Equity, Los Angeles, CA, USA
| | - Steven Gallinger
- Lunenfeld Tanenbaum Research Institute, Mount Sinai Hospital, University of Toronto, Toronto, ON, Canada
| | - Michael O Woods
- Division of Biomedical Sciences, Memorial University of Newfoundland, St. John, ON, Canada
| | - Polly A Newcomb
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
- School of Public Health, University of Washington, Seattle, WA, USA
| | - David Duggan
- City of Hope National Medical Center, Translational Genomics Research Institute, Phoenix, AZ, USA
| | | | - Richard Kaplan
- MRC Clinical Trials Unit, Medical Research Council, Cardiff, UK
| | - Rachel Kerr
- Department of Oncology, University of Oxford, Oxford, UK
| | - David Kerr
- Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Iva Kirac
- Department of Surgical Oncology, University Hospital for Tumors, Sestre milosrdnice University Hospital Center, Zagreb, Croatia
| | - Jan Böhm
- Department of Pathology, Central Finland Health Care District, Jyväskylä, Finland
| | | | - Pekka Jousilahti
- Department of Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Paul Knekt
- Department of Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Lauri A Aaltonen
- Department of Medical and Clinical Genetics, University of Helsinki, Helsinki, Finland
- Genome-Scale Biology Research Program, University of Helsinki, Helsinki, Finland
| | - Harri Rissanen
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Eero Pukkala
- Faculty of Social Sciences, Tampere University, Tampere, Finland
- Finnish Cancer Registry, Institute for Statistical and Epidemiological Cancer Research, Helsinki, Finland
| | - Johan G Eriksson
- Folkhälsan Research Centre, University of Helsinki, Helsinki, Finland
- Human Potential Translational Research Programme, National University of Singapore, Singapore, Singapore
- Unit of General Practice and Primary Health Care, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Tatiana Cajuso
- Department of Medical and Clinical Genetics, University of Helsinki, Helsinki, Finland
- Genome-Scale Biology Research Program, University of Helsinki, Helsinki, Finland
| | - Ulrika Hänninen
- Department of Medical and Clinical Genetics, University of Helsinki, Helsinki, Finland
- Genome-Scale Biology Research Program, University of Helsinki, Helsinki, Finland
| | - Johanna Kondelin
- Department of Medical and Clinical Genetics, University of Helsinki, Helsinki, Finland
- Genome-Scale Biology Research Program, University of Helsinki, Helsinki, Finland
| | - Kimmo Palin
- Department of Medical and Clinical Genetics, University of Helsinki, Helsinki, Finland
- Genome-Scale Biology Research Program, University of Helsinki, Helsinki, Finland
| | - Tomas Tanskanen
- Department of Medical and Clinical Genetics, University of Helsinki, Helsinki, Finland
- Genome-Scale Biology Research Program, University of Helsinki, Helsinki, Finland
| | | | - Satu Männistö
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Demetrius Albanes
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Stephanie J Weinstein
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Edward Ruiz-Narvaez
- Department of Nutritional Sciences, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Julie R Palmer
- Department of Medicine, Boston University School of Medicine, Boston, MA, USA
- Slone Epidemiology Center at Boston University, Boston, MA, USA
| | - Daniel D Buchanan
- Colorectal Oncogenomics Group, Department of Clinical Pathology, University of Melbourne, Parkville, VIC, Australia
- Genomic Medicine and Family Cancer Clinic, Royal Melbourne Hospital, Parkville, VIC, Australia
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Parkville, VIC, Australia
| | - Elizabeth A Platz
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Kala Visvanathan
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Cornelia M Ulrich
- Huntsman Cancer Institute and Department of Population Health Sciences, University of Utah, Salt Lake City, UT, USA
| | - Erin Siegel
- Cancer Epidemiology Program, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Stefanie Brezina
- Institute of Cancer Research, Department of Medicine I, Medical University Vienna, Vienna, Austria
| | - Andrea Gsur
- Institute of Cancer Research, Department of Medicine I, Medical University Vienna, Vienna, Austria
| | - Peter T Campbell
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, New York, NY, USA
| | - Jenny Chang-Claude
- Division of Cancer Epidemiology, German Cancer Research Center, Heidelberg, Germany
- University Medical Centre Hamburg-Eppendorf, University Cancer Centre Hamburg, Hamburg, Germany
| | - Michael Hoffmeister
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center, Heidelberg, Germany
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center, Heidelberg, Germany
- Division of Preventive Oncology, German Cancer Research Center and National Center for Tumor Diseases, Heidelberg, Germany
- German Cancer Consortium, German Cancer Research Center, Heidelberg, Germany
| | - Martha L Slattery
- Department of Internal Medicine, University of Utah, Salt Lake City, UT, USA
| | - John D Potter
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Research Centre for Hauora and Health, Massey University, Wellington, New Zealand
| | - Kostas K Tsilidis
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece
| | - Matthias B Schulze
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
- Institute of Nutritional Science, University of Potsdam, Potsdam, Germany
| | - Marc J Gunter
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - Neil Murphy
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - Antoni Castells
- Gastroenterology Department, Hospital Clínic, Institut d'Investigacions Biomèdiques August Pi i Sunyer, Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas, University of Barcelona, Barcelona, Spain
| | - Sergi Castellví-Bel
- Gastroenterology Department, Hospital Clínic, Institut d'Investigacions Biomèdiques August Pi i Sunyer, Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas, University of Barcelona, Barcelona, Spain
| | - Leticia Moreira
- Gastroenterology Department, Hospital Clínic, Institut d'Investigacions Biomèdiques August Pi i Sunyer, Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas, University of Barcelona, Barcelona, Spain
| | - Volker Arndt
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center, Heidelberg, Germany
| | - Anna Shcherbina
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - D Timothy Bishop
- Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
| | - Graham G Giles
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
| | - Melissa C Southey
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
- Department of Clinical Pathology, University of Melbourne, Melbourne, VIC, Australia
| | - Gregory E Idos
- Department of Medical Oncology and Center For Precision Medicine, City of Hope National Medical Center, Duarte, CA, USA
| | - Kevin J McDonnell
- Clalit National Cancer Control Center, Haifa, Israel
- Ruth and Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel
- Department of Medical Oncology and Center For Precision Medicine, City of Hope National Medical Center, Duarte, CA, USA
| | - Zomoroda Abu-Ful
- Department of Community Medicine and Epidemiology, Lady Davis Carmel Medical Center, Haifa, Israel
| | - Joel K Greenson
- Clalit National Cancer Control Center, Haifa, Israel
- Ruth and Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Katerina Shulman
- Department of Community Medicine and Epidemiology, Lady Davis Carmel Medical Center, Haifa, Israel
| | - Flavio Lejbkowicz
- Clalit National Cancer Control Center, Haifa, Israel
- Department of Community Medicine and Epidemiology, Lady Davis Carmel Medical Center, Haifa, Israel
- Clalit Health Services, Personalized Genomic Service, Lady Davis Carmel Medical Center, Haifa, Israel
| | - Kenneth Offit
- Clinical Genetics Service, Department of Medicine, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
- Department of Medicine, Weill Cornell Medical College, New York, NY, USA
| | - Yu-Ru Su
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Robert Steinfelder
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Temitope O Keku
- Center for Gastrointestinal Biology and Disease, University of North Carolina, Chapel Hill, NC, USA
| | - Bethany van Guelpen
- Department of Radiation Sciences, Oncology Unit, Umeå University, Umeå, Sweden
- Wallenberg Centre for Molecular Medicine, Umeå University, Umeå, Sweden
| | | | - Heather Hampel
- Division of Human Genetics, Department of Internal Medicine, Ohio State University Comprehensive Cancer Center, Columbus, OH, USA
| | - Rachel Pearlman
- Division of Human Genetics, Department of Internal Medicine, Ohio State University Comprehensive Cancer Center, Columbus, OH, USA
| | - Sonja I Berndt
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Richard B Hayes
- Division of Epidemiology, Department of Population Health, New York University School of Medicine, New York, NY, USA
| | - Marie Elena Martinez
- Department of Family Medicine and Public Health, University of California San Diego, La Jolla, CA, USA
- Population Sciences, Disparities and Community Engagement, University of California San Diego Moores Cancer Center, La Jolla, CA, USA
| | | | - Paul D P Pharoah
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Susanna C Larsson
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Yun Yen
- Taipei Medical University, Taipei, Taiwan
| | - Heinz-Josef Lenz
- Department of Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Emily White
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Department of Epidemiology, University of Washington School of Public Health, Seattle, WA, USA
| | - Li Li
- Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, OH, USA
| | - Kimberly F Doheny
- Center for Inherited Disease Research, Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Elizabeth Pugh
- Center for Inherited Disease Research, Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Tameka Shelford
- Center for Inherited Disease Research, Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Andrew T Chan
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
- Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Marcia Cruz-Correa
- Comprehensive Cancer Center, University of Puerto Rico, San Juan, Puerto Rico
| | - Annika Lindblom
- Department of Clinical Genetics, Karolinska University Hospital, Stockholm, Sweden
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
| | - David J Hunter
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Amit D Joshi
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Clemens Schafmayer
- Department of General Surgery, University Hospital Rostock, Rostock, Germany
| | - Peter C Scacheri
- Department of Genetics and Genome Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Anshul Kundaje
- Department of Genetics, Stanford University, Stanford, CA, USA
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Robert E Schoen
- Department of Medicine and Epidemiology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Jochen Hampe
- Department of Medicine I, University Hospital Dresden, Technische Universität Dresden, Dresden, Germany
| | - Zsofia K Stadler
- Department of Medicine, Weill Cornell Medical College, New York, NY, USA
- Department of Medicine, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - Pavel Vodicka
- Department of Molecular Biology of Cancer, Institute of Experimental Medicine of the Czech Academy of Sciences, Prague, Czech Republic
- Faculty of Medicine and Biomedical Center in Pilsen, Charles University, Pilsen, Czech Republic
- Institute of Biology and Medical Genetics, First Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Ludmila Vodickova
- Department of Molecular Biology of Cancer, Institute of Experimental Medicine of the Czech Academy of Sciences, Prague, Czech Republic
- Faculty of Medicine and Biomedical Center in Pilsen, Charles University, Pilsen, Czech Republic
- Institute of Biology and Medical Genetics, First Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Veronika Vymetalkova
- Department of Molecular Biology of Cancer, Institute of Experimental Medicine of the Czech Academy of Sciences, Prague, Czech Republic
- Faculty of Medicine and Biomedical Center in Pilsen, Charles University, Pilsen, Czech Republic
- Institute of Biology and Medical Genetics, First Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Christopher K Edlund
- Department of Preventive Medicine, USC Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - W James Gauderman
- Department of Preventive Medicine, USC Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - David Shibata
- Department of Surgery, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Amanda Toland
- Departments of Cancer Biology and Genetics and Internal Medicine, Comprehensive Cancer Center, Ohio State University, Columbus, OH, USA
| | - Sanford Markowitz
- Departments of Medicine and Genetics, Case Comprehensive Cancer Center, Case Western Reserve University and University Hospitals of Cleveland, Cleveland, OH, USA
| | - Andre Kim
- Department of Preventive Medicine, USC Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Stephen J Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Franzel van Duijnhoven
- Division of Human Nutrition and Health, Wageningen University and Research, Wageningen, The Netherlands
| | - Edith J M Feskens
- Division of Human Nutrition, Wageningen University and Research, Wageningen, The Netherlands
| | - Lori C Sakoda
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Manuela Gago-Dominguez
- Genomic Medicine Group, Galician Public Foundation of Genomic Medicine, Servicio Galego de Saude, Santiago de Compostela, Spain
- Instituto de Investigación Sanitaria de Santiago de Compostela, Santiago de Compostela, Spain
| | - Alicja Wolk
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Barbara Pardini
- Candiolo Cancer Institute FPO-IRCCS, Candiolo, (TO), Italy
- Italian Institute for Genomic Medicine, Candiolo Cancer Institute FPO-IRCCS, Candiolo, (TO), Italy
| | - Liesel M FitzGerald
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
- Menzies Institute for Medical Research, University of Tasmania, Hobart, TAS, Australia
| | - Soo Chin Lee
- National University Cancer Institute, Singapore, Cancer Science Institute of Singapore, National University of Singapore, Singapore, Singapore
| | - Shuji Ogino
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cancer Immunology Program, Dana-Farber Harvard Cancer Center, Boston, MA, USA
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Stephanie A Bien
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Charles Kooperberg
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Christopher I Li
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Yi Lin
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Ross Prentice
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Conghui Qu
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Stéphane Bézieau
- Service de Génétique Médicale, Centre Hospitalier Universitaire Nantes, Nantes, France
| | - Taiki Yamaji
- Division of Epidemiology, National Cancer Center Institute for Cancer Control, National Cancer Center, Tokyo, Japan
| | - Norie Sawada
- Division of Cohort Research, National Cancer Center Institute for Cancer Control, National Cancer Center, Tokyo, Japan
| | - Motoki Iwasaki
- Division of Epidemiology, National Cancer Center Institute for Cancer Control, National Cancer Center, Tokyo, Japan
- Division of Cohort Research, National Cancer Center Institute for Cancer Control, National Cancer Center, Tokyo, Japan
| | | | - Anna H Wu
- Preventative Medicine, University of Southern California, Los Angeles, CA, USA
| | - Chenxu Qu
- USC Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Caroline E McNeil
- USC Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | | | - Caroline Hayward
- MRC Human Genetics Unit, Institute of Genomics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Ian J Deary
- Lothian Birth Cohorts group, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Sarah E Harris
- Lothian Birth Cohorts group, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Evropi Theodoratou
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Stuart Reid
- Colon Cancer Genetics Group, Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Marion Walker
- Colon Cancer Genetics Group, Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Li Yin Ooi
- Colon Cancer Genetics Group, Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
- Department of Pathology, National University Hospital, National University Health System, Singapore, Singapore
| | - Ken S Lau
- Epithelial Biology Center and Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Hongyu Zhao
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
- Department of Genetics, Yale School of Medicine, New Haven, CT, USA
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
| | - Li Hsu
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Department of Biostatistics, School of Public Health, University of Washington, Seattle, WA, USA
| | - Qiuyin Cai
- Division of Epidemiology, Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Malcolm G Dunlop
- Colon Cancer Genetics Group, Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Stephen B Gruber
- Department of Medical Oncology and Center For Precision Medicine, City of Hope National Medical Center, Duarte, CA, USA
| | - Richard S Houlston
- Division of Genetics and Epidemiology, Institute of Cancer Research, London, UK
| | - Victor Moreno
- Colorectal Cancer Group, ONCOBELL Program, Bellvitge Biomedical Research Institute, Barcelona, Spain
- Consortium for Biomedical Research in Epidemiology and Public Health, Madrid, Spain
- Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona, Spain
- Oncology Data Analytics Program, Catalan Institute of Oncology, Barcelona, Spain
| | - Graham Casey
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | - Ulrike Peters
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Ian Tomlinson
- Edinburgh Cancer Research Centre, Institute of Genomics and Cancer, University of Edinburgh, Edinburgh, UK
- Institute of Cancer and Genomic Sciences, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA.
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Mavridou E, Lema Fernandez AG, Nardelli C, Pierini V, Quintini M, Arniani S, Di Giacomo D, Crescenzi B, Matteucci C, Sambani C, Mecucci C. A novel t(X;21)(p11.4;q22.12) translocation adds to the role of BCOR and RUNX1 in myelodysplastic syndromes and acute myeloid leukemias. Genes Chromosomes Cancer 2024; 63:e23235. [PMID: 38656651 DOI: 10.1002/gcc.23235] [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: 12/28/2023] [Revised: 03/20/2024] [Accepted: 03/28/2024] [Indexed: 04/26/2024] Open
Abstract
In myeloid neoplasms, both fusion genes and gene mutations are well-established events identifying clinicopathological entities. In this study, we present a thus far undescribed t(X;21)(p11.4;q22.12) in five cases with myelodysplastic syndrome (MDS) or acute myeloid leukemia (AML). The translocation was isolated or accompanied by additional changes. It did not generate any fusion gene or gene deregulation by aberrant juxtaposition with regulatory sequences. Molecular analysis by targeted next-generation sequencing showed that the translocation was accompanied by at least one somatic mutation in TET2, EZH2, RUNX1, ASXL1, SRSF2, ZRSR2, DNMT3A, and NRAS genes. Co-occurrence of deletion of RUNX1 in 21q22 and of BCOR in Xp11 was associated with t(X;21). BCOR haploinsufficiency corresponded to a significant hypo-expression in t(X;21) cases, compared to normal controls and to normal karyotype AML. By contrast, RUNX1 expression was not altered, suggesting a compensatory effect by the remaining allele. Whole transcriptome analysis showed that overexpression of HOXA9 differentiated t(X;21) from both controls and t(8;21)-positive AML. In conclusion, we characterized a new recurrent reciprocal t(X;21)(p11.4;q22.12) chromosome translocation in MDS and AML, generating simultaneous BCOR and RUNX1 deletions rather than a fusion gene at the genomic level.
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Affiliation(s)
- Elena Mavridou
- Hematology and Bone Marrow Transplantation Unit, Department of Medicine and Surgery, University of Perugia, Perugia, Italy
| | - Anair Graciela Lema Fernandez
- Hematology and Bone Marrow Transplantation Unit, Department of Medicine and Surgery, University of Perugia, Perugia, Italy
| | - Carlotta Nardelli
- Hematology and Bone Marrow Transplantation Unit, Department of Medicine and Surgery, University of Perugia, Perugia, Italy
| | - Valentina Pierini
- Hematology and Bone Marrow Transplantation Unit, Department of Medicine and Surgery, University of Perugia, Perugia, Italy
| | - Martina Quintini
- Hematology and Bone Marrow Transplantation Unit, Department of Medicine and Surgery, University of Perugia, Perugia, Italy
| | - Silvia Arniani
- Hematology and Bone Marrow Transplantation Unit, Department of Medicine and Surgery, University of Perugia, Perugia, Italy
| | - Danika Di Giacomo
- Hematology and Bone Marrow Transplantation Unit, Department of Medicine and Surgery, University of Perugia, Perugia, Italy
| | - Barbara Crescenzi
- Hematology and Bone Marrow Transplantation Unit, Department of Medicine and Surgery, University of Perugia, Perugia, Italy
| | - Caterina Matteucci
- Hematology and Bone Marrow Transplantation Unit, Department of Medicine and Surgery, University of Perugia, Perugia, Italy
| | - Constantina Sambani
- Laboratory of Health Physics, Radiobiology & Cytogenetics, National Center for Scientific Research (NCSR) "Demokritos", Athens, Greece
| | - Cristina Mecucci
- Hematology and Bone Marrow Transplantation Unit, Department of Medicine and Surgery, University of Perugia, Perugia, Italy
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8
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Natri HM, Del Azodi CB, Peter L, Taylor CJ, Chugh S, Kendle R, Chung MI, Flaherty DK, Matlock BK, Calvi CL, Blackwell TS, Ware LB, Bacchetta M, Walia R, Shaver CM, Kropski JA, McCarthy DJ, Banovich NE. Cell-type-specific and disease-associated expression quantitative trait loci in the human lung. Nat Genet 2024; 56:595-604. [PMID: 38548990 PMCID: PMC11018522 DOI: 10.1038/s41588-024-01702-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 02/28/2024] [Indexed: 04/04/2024]
Abstract
Common genetic variants confer substantial risk for chronic lung diseases, including pulmonary fibrosis. Defining the genetic control of gene expression in a cell-type-specific and context-dependent manner is critical for understanding the mechanisms through which genetic variation influences complex traits and disease pathobiology. To this end, we performed single-cell RNA sequencing of lung tissue from 66 individuals with pulmonary fibrosis and 48 unaffected donors. Using a pseudobulk approach, we mapped expression quantitative trait loci (eQTLs) across 38 cell types, observing both shared and cell-type-specific regulatory effects. Furthermore, we identified disease interaction eQTLs and demonstrated that this class of associations is more likely to be cell-type-specific and linked to cellular dysregulation in pulmonary fibrosis. Finally, we connected lung disease risk variants to their regulatory targets in disease-relevant cell types. These results indicate that cellular context determines the impact of genetic variation on gene expression and implicates context-specific eQTLs as key regulators of lung homeostasis and disease.
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Affiliation(s)
- Heini M Natri
- Translational Genomics Research Institute, Phoenix, AZ, USA
| | - Christina B Del Azodi
- St. Vincent's Institute of Medical Research, Melbourne, Victoria, Australia
- Melbourne Integrative Genomics, University of Melbourne, Melbourne, Victoria, Australia
| | - Lance Peter
- Translational Genomics Research Institute, Phoenix, AZ, USA
| | - Chase J Taylor
- Division of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Sagrika Chugh
- St. Vincent's Institute of Medical Research, Melbourne, Victoria, Australia
- Melbourne Integrative Genomics, University of Melbourne, Melbourne, Victoria, Australia
- School of Mathematics and Statistics, Faculty of Science, University of Melbourne, Melbourne, Victoria, Australia
| | - Robert Kendle
- Translational Genomics Research Institute, Phoenix, AZ, USA
| | - Mei-I Chung
- Translational Genomics Research Institute, Phoenix, AZ, USA
| | - David K Flaherty
- Flow Cytometry Shared Resource, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Brittany K Matlock
- Flow Cytometry Shared Resource, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Carla L Calvi
- Division of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Timothy S Blackwell
- Division of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Cell and Developmental Biology, Vanderbilt University, Nashville, TN, USA
- Department of Veterans Affairs Medical Center, Nashville, TN, USA
| | - Lorraine B Ware
- Division of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Matthew Bacchetta
- Department of Cardiac Surgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Rajat Walia
- Department of Thoracic Disease and Transplantation, Norton Thoracic Institute, Phoenix, AZ, USA
| | - Ciara M Shaver
- Division of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jonathan A Kropski
- Division of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Cell and Developmental Biology, Vanderbilt University, Nashville, TN, USA
- Department of Veterans Affairs Medical Center, Nashville, TN, USA
| | - Davis J McCarthy
- St. Vincent's Institute of Medical Research, Melbourne, Victoria, Australia
- Melbourne Integrative Genomics, University of Melbourne, Melbourne, Victoria, Australia
- School of Mathematics and Statistics, Faculty of Science, University of Melbourne, Melbourne, Victoria, Australia
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9
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Jensen TD, Ni B, Reuter CM, Gorzynski JE, Fazal S, Bonner D, Ungar RA, Goddard PC, Raja A, Ashley EA, Bernstein JA, Zuchner S, Greicius MD, Montgomery SB, Schatz MC, Wheeler MT, Battle A. Integration of transcriptomics and long-read genomics prioritizes structural variants in rare disease. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.22.24304565. [PMID: 38585781 PMCID: PMC10996727 DOI: 10.1101/2024.03.22.24304565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Rare structural variants (SVs) - insertions, deletions, and complex rearrangements - can cause Mendelian disease, yet they remain difficult to accurately detect and interpret. We sequenced and analyzed Oxford Nanopore long-read genomes of 68 individuals from the Undiagnosed Disease Network (UDN) with no previously identified diagnostic mutations from short-read sequencing. Using our optimized SV detection pipelines and 571 control long-read genomes, we detected 716 long-read rare (MAF < 0.01) SV alleles per genome on average, achieving a 2.4x increase from short-reads. To characterize the functional effects of rare SVs, we assessed their relationship with gene expression from blood or fibroblasts from the same individuals, and found that rare SVs overlapping enhancers were enriched (LOR = 0.46) near expression outliers. We also evaluated tandem repeat expansions (TREs) and found 14 rare TREs per genome; notably these TREs were also enriched near overexpression outliers. To prioritize candidate functional SVs, we developed Watershed-SV, a probabilistic model that integrates expression data with SV-specific genomic annotations, which significantly outperforms baseline models that don't incorporate expression data. Watershed-SV identified a median of eight high-confidence functional SVs per UDN genome. Notably, this included compound heterozygous deletions in FAM177A1 shared by two siblings, which were likely causal for a rare neurodevelopmental disorder. Our observations demonstrate the promise of integrating long-read sequencing with gene expression towards improving the prioritization of functional SVs and TREs in rare disease patients.
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10
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Chen JP, Diekmann C, Wu H, Chen C, Della Chiara G, Berrino E, Georgiadis KL, Bouwman BAM, Virdi M, Harbers L, Bellomo SE, Marchiò C, Bienko M, Crosetto N. scCircle-seq unveils the diversity and complexity of extrachromosomal circular DNAs in single cells. Nat Commun 2024; 15:1768. [PMID: 38409079 PMCID: PMC10897160 DOI: 10.1038/s41467-024-45972-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 02/08/2024] [Indexed: 02/28/2024] Open
Abstract
Extrachromosomal circular DNAs (eccDNAs) have emerged as important intra-cellular mobile genetic elements that affect gene copy number and exert in trans regulatory roles within the cell nucleus. Here, we describe scCircle-seq, a method for profiling eccDNAs and unraveling their diversity and complexity in single cells. We implement and validate scCircle-seq in normal and cancer cell lines, demonstrating that most eccDNAs vary largely between cells and are stochastically inherited during cell division, although their genomic landscape is cell type-specific and can be used to accurately cluster cells of the same origin. eccDNAs are preferentially produced from chromatin regions enriched in H3K9me3 and H3K27me3 histone marks and are induced during replication stress conditions. Concomitant sequencing of eccDNAs and RNA from the same cell uncovers the absence of correlation between eccDNA copy number and gene expression levels, except for a few oncogenes, including MYC, contained within a large eccDNA in colorectal cancer cells. Lastly, we apply scCircle-seq to one prostate cancer and two breast cancer specimens, revealing cancer-specific eccDNA landscapes and a higher propensity of eccDNAs to form in amplified genomic regions. scCircle-seq is a scalable tool that can be used to dissect the complexity of eccDNAs across different cell and tissue types, and further expands the potential of eccDNAs for cancer diagnostics.
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Affiliation(s)
- Jinxin Phaedo Chen
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, 17177, Sweden.
- Science for Life Laboratory, Tomtebodavägen 23A, Solna, 17165, Sweden.
| | - Constantin Diekmann
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, 17177, Sweden
- Science for Life Laboratory, Tomtebodavägen 23A, Solna, 17165, Sweden
| | - Honggui Wu
- Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing, PR China
- School of Life Sciences, Peking University, Beijing, PR China
| | - Chong Chen
- State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, PR China
| | | | - Enrico Berrino
- Candiolo Cancer Institute, FPO - IRCCS, Candiolo, SP142, km 3,95, 10060, Turin, Italy
- Department of Medical Sciences, University of Turin, Turin, Italy
| | - Konstantinos L Georgiadis
- Science for Life Laboratory, Tomtebodavägen 23A, Solna, 17165, Sweden
- Department of Oncology and Pathology, Karolinska Institutet, Stockholm, 17177, Sweden
| | - Britta A M Bouwman
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, 17177, Sweden
- Science for Life Laboratory, Tomtebodavägen 23A, Solna, 17165, Sweden
| | - Mohit Virdi
- Human Technopole, Viale Rita Levi-Montalcini 1, 22157, Milan, Italy
| | - Luuk Harbers
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, 17177, Sweden
- Science for Life Laboratory, Tomtebodavägen 23A, Solna, 17165, Sweden
| | - Sara Erika Bellomo
- Candiolo Cancer Institute, FPO - IRCCS, Candiolo, SP142, km 3,95, 10060, Turin, Italy
| | - Caterina Marchiò
- Candiolo Cancer Institute, FPO - IRCCS, Candiolo, SP142, km 3,95, 10060, Turin, Italy
- Department of Medical Sciences, University of Turin, Turin, Italy
| | - Magda Bienko
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, 17177, Sweden.
- Science for Life Laboratory, Tomtebodavägen 23A, Solna, 17165, Sweden.
- Human Technopole, Viale Rita Levi-Montalcini 1, 22157, Milan, Italy.
| | - Nicola Crosetto
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, 17177, Sweden.
- Science for Life Laboratory, Tomtebodavägen 23A, Solna, 17165, Sweden.
- Human Technopole, Viale Rita Levi-Montalcini 1, 22157, Milan, Italy.
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11
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Gu J, Li Y, Tian Y, Zhang Y, Cheng Y, Tang Y. Noncanonical functions of microRNAs in the nucleus. Acta Biochim Biophys Sin (Shanghai) 2024; 56:151-161. [PMID: 38167929 PMCID: PMC10984876 DOI: 10.3724/abbs.2023268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2023] [Accepted: 11/03/2023] [Indexed: 01/05/2024] Open
Abstract
MicroRNAs (miRNAs) are small noncoding RNAs (ncRNAs) that play their roles in the regulation of physiological and pathological processes. Originally, it was assumed that miRNAs only modulate gene expression posttranscriptionally in the cytoplasm by inducing target mRNA degradation. However, with further research, evidence shows that mature miRNAs also exist in the cell nucleus, where they can impact gene transcription and ncRNA maturation in several ways. This review provides an overview of novel models of nuclear miRNA functions. Some of the models remain to be verified by experimental evidence, and more details of the miRNA regulation network remain to be discovered in the future.
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Affiliation(s)
- Jiayi Gu
- College of Basic Medical SciencesShanghai Jiao Tong University School of MedicineShanghai200001China
| | - Yuanan Li
- College of Basic Medical SciencesShanghai Jiao Tong University School of MedicineShanghai200001China
| | - Youtong Tian
- College of Basic Medical SciencesShanghai Jiao Tong University School of MedicineShanghai200001China
| | - Yehao Zhang
- College of Basic Medical SciencesShanghai Jiao Tong University School of MedicineShanghai200001China
| | - Yongjun Cheng
- Department of Rheumatologythe First People’s Hospital of WenlingWenling317500China
| | - Yuanjia Tang
- Shanghai Institute of Rheumatology/Department of RheumatologyRenji HospitalShanghai Jiao Tong University School of MedicineShanghai200001China
- State Key Laboratory of Oncogenes and Related GenesShanghai Cancer InstituteRenji HospitalShanghai200031China
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12
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Yang Z, Shi M, Liang Y, Zhang F, Li C, Lu Y, Yin T, Wang Z, Li Y, Hao M, Guo R, Yang H, Lei G, Sun F, Zhang Y, Deng Z, Tian Y, Yu L, Bai C, Wang L, Wan C, Wang H, Yang P. Three-dimensional chromatin landscapes in hepatocellular carcinoma associated with hepatitis B virus. J Gastroenterol 2024; 59:119-137. [PMID: 37925679 DOI: 10.1007/s00535-023-02053-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 10/11/2023] [Indexed: 11/07/2023]
Abstract
BACKGROUND Three-dimensional (3D) chromatin architecture frequently altered in cancer. However, its changes during the pathogenesis of hepatocellular carcinoma (HCC) remained elusive. METHODS Hi-C and RNA-seq were applied to study the 3D chromatin landscapes and gene expression of HCC and ANHT. Hi-C Pro was used to generate genome-wide raw interaction matrices, which were normalized via iterative correction (ICE). Moreover, the chromosomes were divided into different compartments according to the first principal component (E1). Furthermore, topologically associated domains (TADs) were visualized via WashU Epigenome Browser. Furthermore, differential expression analysis of ANHT and HCC was performed using the DESeq2 R package. Additionally, dysregulated genes associated with 3D genome architecture altered were confirmed using TCGA, qRT-PCR, immunohistochemistry (IHC), etc. RESULTS: First, the intrachromosomal interactions of chr1, chr2, chr5, and chr11 were significantly different, and the interchromosomal interactions of chr4-chr10, chr13-chr21, chr15-chr22, and chr16-chr19 are remarkably different between ANHT and HCC, which resulted in the up-regulation of TP53I3 and ZNF738 and the down-regulation of APOC3 and APOA5 in HCC. Second, 49 compartment regions on 18 chromosomes have significantly switched (A-B or B-A) during HCC tumorigenesis, contributing to up-regulation of RAP2A. Finally, a tumor-specific TAD boundary located on chr5: 6271000-6478000 and enhancer hijacking were identified in HCC tissues, potentially associated with the elevated expression of MED10, whose expression were associated with poor prognosis of HCC patients. CONCLUSION This study demonstrates the crucial role of chromosomal structure variation in HCC oncogenesis and potential novel biomarkers of HCC, laying a foundation for cancer precision medicine development.
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Affiliation(s)
- Zhao Yang
- College of Life Science and Technology, Innovation Center of Molecular Diagnostics, Beijing University of Chemical Technology, Beijing, 100029, China.
- The First Medical Center, Chinese PLA General Hospital, Beijing, 100853, China.
- College of Life Science and Technology, Key Laboratory of Protection and Utilization of Biological Resources in Tarim Basin of Xinjiang Production and Construction Corps, Tarim University, Alar, 843300, Xinjiang, China.
| | - Mengran Shi
- College of Life Science and Technology, Innovation Center of Molecular Diagnostics, Beijing University of Chemical Technology, Beijing, 100029, China
| | - Youfeng Liang
- College of Life Science and Technology, Innovation Center of Molecular Diagnostics, Beijing University of Chemical Technology, Beijing, 100029, China
| | - Fuhan Zhang
- College of Life Science and Technology, Innovation Center of Molecular Diagnostics, Beijing University of Chemical Technology, Beijing, 100029, China
| | - Cong Li
- The First Medical Center, Chinese PLA General Hospital, Beijing, 100853, China
| | - Yinying Lu
- The Fifth Medical Center, Chinese PLA General Hospital, Beijing, 100039, China
| | - Taian Yin
- College of Life Science and Technology, Innovation Center of Molecular Diagnostics, Beijing University of Chemical Technology, Beijing, 100029, China
| | - Zhaohai Wang
- The First Medical Center, Chinese PLA General Hospital, Beijing, 100853, China
| | - Yongchao Li
- College of Life Science and Technology, Innovation Center of Molecular Diagnostics, Beijing University of Chemical Technology, Beijing, 100029, China
- College of Life Science and Technology, Key Laboratory of Protection and Utilization of Biological Resources in Tarim Basin of Xinjiang Production and Construction Corps, Tarim University, Alar, 843300, Xinjiang, China
| | - Mingxuan Hao
- College of Life Science and Technology, Innovation Center of Molecular Diagnostics, Beijing University of Chemical Technology, Beijing, 100029, China
| | - Rui Guo
- College of Life Science and Technology, Innovation Center of Molecular Diagnostics, Beijing University of Chemical Technology, Beijing, 100029, China
| | - Hao Yang
- The First Medical Center, Chinese PLA General Hospital, Beijing, 100853, China
| | - Guanglin Lei
- The Fifth Medical Center, Chinese PLA General Hospital, Beijing, 100039, China
| | - Fang Sun
- The Fifth Medical Center, Chinese PLA General Hospital, Beijing, 100039, China
| | - Yu Zhang
- The Fifth Medical Center, Chinese PLA General Hospital, Beijing, 100039, China
| | - Zhuoya Deng
- The First Medical Center, Chinese PLA General Hospital, Beijing, 100853, China
| | - Yuying Tian
- The First Medical Center, Chinese PLA General Hospital, Beijing, 100853, China
| | - Linxiang Yu
- The Fifth Medical Center, Chinese PLA General Hospital, Beijing, 100039, China
| | - Changqing Bai
- Department of Respiratory, Shenzhen University General Hospital, Shenzhen, 518055, China
| | - Lei Wang
- College of Life Science and Technology, Innovation Center of Molecular Diagnostics, Beijing University of Chemical Technology, Beijing, 100029, China
| | - Chuanxing Wan
- College of Life Science and Technology, Key Laboratory of Protection and Utilization of Biological Resources in Tarim Basin of Xinjiang Production and Construction Corps, Tarim University, Alar, 843300, Xinjiang, China
| | - Haifeng Wang
- Department of Urology, Second Affiliated Hospital of Kunming Medical University, Kunming, 650504, China.
| | - Penghui Yang
- The First Medical Center, Chinese PLA General Hospital, Beijing, 100853, China.
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13
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Marroquín-Rivera A, Zhao C, Pessoni AM, Bherer J, Mansouri S, Droit A, Labonté B. Immune-related transcriptomic and epigenetic reconfiguration in BV2 cells after lipopolysaccharide exposure: an in vitro omics integrative study. Inflamm Res 2024; 73:211-225. [PMID: 38216730 DOI: 10.1007/s00011-023-01830-z] [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: 10/19/2023] [Revised: 11/27/2023] [Accepted: 11/29/2023] [Indexed: 01/14/2024] Open
Abstract
BACKGROUND Molecular alterations affecting microglia have been consistently associated with the inflammatory response. These cells can have pro- or anti-inflammatory activity, phenotypes thought to be regulated by epigenetic mechanisms. Still, little is known about the details on how epigenetic marks regulate the expression of genes in the context of an inflammatory response. METHODS Through CUT&RUN, we profiled four genome-wide histone marks (HM) (H3K4me1, H3K4me3, H3K27ac, and H3K27me3) in lipopolysaccharide-exposed cells and compared their distributions to control cells. Transcriptomic profiles were determined through RNA-seq and differentially expressed genes were identified and contrasted with the epigenetic landscapes. Other downstream analyses were also included in this study. RESULTS Our results illustrate an effectively induced M1 phenotype in microglial cells derived from LPS exposure. We observed differential bound regions associated with the genes classically involved in the inflammatory response in the expected direction according to each histone modification. Consistently, our transcriptomic analysis yielded a conspicuous illustration of the LPS-induced immune activity showing the up-regulation of Nf-κB-induced mRNAs (TNF-α, nfκbiz, nfκbia) and other important genes (Marco, Il-6, etc.). Furthermore, we integrated both omics profiles and identified an important reconfiguration of the genome induced by LPS. The latter was depicted by 8 different chromatin states that changed between conditions and that associated with unique clusters of differentially expressed genes, which not only represented regulatory elements, but also underlined distinct biological functions (inhibition of morphogenesis; changes in metabolism, homeostasis, and cytokine regulation; activation of the inflammatory response). CONCLUSION This study exhibits important differences in the distribution of histone modifications in treated and control BV2 cells, constituting an epigenetic reconfiguration that leads to the inflammatory response. Also, it highlights the importance of these marks' regulatory role in gene expression and provides possible targets for further studies in the context of inflammation.
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Affiliation(s)
- Arturo Marroquín-Rivera
- CERVO Brain Research Center, Québec City, QC, Canada
- Department of Psychiatry and Neurosciences, Faculty of Medicine, Université Laval, Québec City, QC, Canada
| | - Chenqi Zhao
- CERVO Brain Research Center, Québec City, QC, Canada
| | - André Moreira Pessoni
- CERVO Brain Research Center, Québec City, QC, Canada
- Department of Psychiatry and Neurosciences, Faculty of Medicine, Université Laval, Québec City, QC, Canada
| | | | - Samaneh Mansouri
- CERVO Brain Research Center, Québec City, QC, Canada
- Department of Social and Preventive Medicine, Faculty of Medicine, Université Laval, Québec City, QC, Canada
| | - Arnaud Droit
- Genomics Center, Centre Hospitalier Universitaire de Québec-Université Laval Research Center, Québec City, QC, Canada
- Department of Molecular Medicine, Faculty of Medicine, Université Laval, Québec City, QC, Canada
| | - Benoit Labonté
- CERVO Brain Research Center, Québec City, QC, Canada.
- Department of Psychiatry and Neurosciences, Faculty of Medicine, Université Laval, Québec City, QC, Canada.
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14
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Zhang Y, Zhang P, Wu H. Enhancer-MDLF: a novel deep learning framework for identifying cell-specific enhancers. Brief Bioinform 2024; 25:bbae083. [PMID: 38485768 PMCID: PMC10938904 DOI: 10.1093/bib/bbae083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Revised: 01/27/2024] [Accepted: 02/07/2024] [Indexed: 03/18/2024] Open
Abstract
Enhancers, noncoding DNA fragments, play a pivotal role in gene regulation, facilitating gene transcription. Identifying enhancers is crucial for understanding genomic regulatory mechanisms, pinpointing key elements and investigating networks governing gene expression and disease-related mechanisms. Existing enhancer identification methods exhibit limitations, prompting the development of our novel multi-input deep learning framework, termed Enhancer-MDLF. Experimental results illustrate that Enhancer-MDLF outperforms the previous method, Enhancer-IF, across eight distinct human cell lines and exhibits superior performance on generic enhancer datasets and enhancer-promoter datasets, affirming the robustness of Enhancer-MDLF. Additionally, we introduce transfer learning to provide an effective and potential solution to address the prediction challenges posed by enhancer specificity. Furthermore, we utilize model interpretation to identify transcription factor binding site motifs that may be associated with enhancer regions, with important implications for facilitating the study of enhancer regulatory mechanisms. The source code is openly accessible at https://github.com/HaoWuLab-Bioinformatics/Enhancer-MDLF.
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Affiliation(s)
- Yao Zhang
- School of Software, Shandong University, Jinan, 250100, Shandong, China
| | - Pengyu Zhang
- College of Information Engineering, Northwest A&F University, Yangling, 712100, Shaanxi, China
| | - Hao Wu
- School of Software, Shandong University, Jinan, 250100, Shandong, China
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15
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Abbas A, Chandratre K, Gao Y, Yuan J, Zhang MQ, Mani RS. ChIPr: accurate prediction of cohesin-mediated 3D genome organization from 2D chromatin features. Genome Biol 2024; 25:15. [PMID: 38217027 PMCID: PMC10785520 DOI: 10.1186/s13059-023-03158-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Accepted: 12/22/2023] [Indexed: 01/14/2024] Open
Abstract
The three-dimensional genome organization influences diverse nuclear processes. Here we present Chromatin Interaction Predictor (ChIPr), a suite of regression models based on deep neural networks, random forest, and gradient boosting to predict cohesin-mediated chromatin interaction strength between any two loci in the genome. The predictions of ChIPr correlate well with ChIA-PET data in four cell lines. The standard ChIPr model requires three experimental inputs: ChIP-Seq signals for RAD21, H3K27ac, and H3K27me3 but works well with just RAD21 signal. Integrative analysis reveals novel insights into the role of CTCF motif, its orientation, and CTCF binding on cohesin-mediated chromatin interactions.
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Affiliation(s)
- Ahmed Abbas
- Department of Pathology, UT Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Khyati Chandratre
- Department of Biological Sciences, Center for Systems Biology, The University of Texas at Dallas, Richardson, TX, 75080, USA
| | - Yunpeng Gao
- Department of Pathology, UT Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Jiapei Yuan
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, 300020, China
| | - Michael Q Zhang
- Department of Biological Sciences, Center for Systems Biology, The University of Texas at Dallas, Richardson, TX, 75080, USA.
| | - Ram S Mani
- Department of Pathology, UT Southwestern Medical Center, Dallas, TX, 75390, USA.
- Department of Urology, UT Southwestern Medical Center, Dallas, TX, 75390, USA.
- Harold C. Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, TX, 75390, USA.
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16
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Lismer A, Shao X, Dumargne MC, Lafleur C, Lambrot R, Chan D, Toft G, Bonde JP, MacFarlane AJ, Bornman R, Aneck-Hahn N, Patrick S, Bailey JM, de Jager C, Dumeaux V, Trasler JM, Kimmins S. The Association between Long-Term DDT or DDE Exposures and an Altered Sperm Epigenome-a Cross-Sectional Study of Greenlandic Inuit and South African VhaVenda Men. ENVIRONMENTAL HEALTH PERSPECTIVES 2024; 132:17008. [PMID: 38294233 PMCID: PMC10829569 DOI: 10.1289/ehp12013] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 09/26/2023] [Accepted: 12/20/2023] [Indexed: 02/01/2024]
Abstract
BACKGROUND The organochlorine dichlorodiphenyltrichloroethane (DDT) is banned worldwide owing to its negative health effects. It is exceptionally used as an insecticide for malaria control. Exposure occurs in regions where DDT is applied, as well as in the Arctic, where its endocrine disrupting metabolite, p , p ' -dichlorodiphenyldichloroethylene (p , p ' -DDE) accumulates in marine mammals and fish. DDT and p , p ' -DDE exposures are linked to birth defects, infertility, cancer, and neurodevelopmental delays. Of particular concern is the potential of DDT use to impact the health of generations to come via the heritable sperm epigenome. OBJECTIVES The objective of this study was to assess the sperm epigenome in relation to p , p ' -DDE serum levels between geographically diverse populations. METHODS In the Limpopo Province of South Africa, we recruited 247 VhaVenda South African men and selected 50 paired blood serum and semen samples, and 47 Greenlandic Inuit blood and semen paired samples were selected from a total of 193 samples from the biobank of the INUENDO cohort, an EU Fifth Framework Programme Research and Development project. Sample selection was based on obtaining a range of p , p ' -DDE serum levels (mean = 870.734 ± 134.030 ng / mL ). We assessed the sperm epigenome in relation to serum p , p ' -DDE levels using MethylC-Capture-sequencing (MCC-seq) and chromatin immunoprecipitation followed by sequencing (ChIP-seq). We identified genomic regions with altered DNA methylation (DNAme) and differential enrichment of histone H3 lysine 4 trimethylation (H3K4me3) in sperm. RESULTS Differences in DNAme and H3K4me3 enrichment were identified at transposable elements and regulatory regions involved in fertility, disease, development, and neurofunction. A subset of regions with sperm DNAme and H3K4me3 that differed between exposure groups was predicted to persist in the preimplantation embryo and to be associated with embryonic gene expression. DISCUSSION These findings suggest that DDT and p , p ' -DDE exposure impacts the sperm epigenome in a dose-response-like manner and may negatively impact the health of future generations through epigenetic mechanisms. Confounding factors, such as other environmental exposures, genetic diversity, and selection bias, cannot be ruled out. https://doi.org/10.1289/EHP12013.
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Affiliation(s)
- Ariane Lismer
- Department of Pharmacology and Therapeutics, Faculty of Medicine and Health Sciences, McGill University, Montreal, Quebec, Canada
| | - Xiaojian Shao
- Digital Technologies Research Centre, National Research Council Canada, Ottawa, Ontario, Canada
- Department of Biochemistry, Microbiology and Immunology, University of Ottawa, Ottawa, Ontario, Canada
| | - Marie-Charlotte Dumargne
- Department of Animal Science, Faculty of Agricultural and Environmental Sciences, McGill University, Montreal, Quebec, Canada
| | - Christine Lafleur
- University of Montreal Hospital Research Centre, Montreal, Quebec, Canada
| | - Romain Lambrot
- University of Montreal Hospital Research Centre, Montreal, Quebec, Canada
| | - Donovan Chan
- Child Health and Human Development Program, Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada
| | - Gunnar Toft
- Steno Diabetes Center Aarhus, Aarhus University Hospital, Aarhus, Denmark
| | - Jens Peter Bonde
- Department of Occupational and Environmental Medicine, Bispebjerg University Hospital, Copenhagen, Denmark
- Institute of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Amanda J. MacFarlane
- Agriculture Food and Nutrition Evidence Center, Texas A&M University, Fort Worth, Texas, USA
| | - Riana Bornman
- Environmental Chemical Pollution and Health Research Unit, School of Health Systems and Public Health, Faculty of Health Sciences, University of Pretoria, Pretoria, South Africa
- University of Pretoria Institute for Sustainable Malaria Control, School of Health Systems and Public Health, Faculty of Health Sciences, University of Pretoria, South Africa
| | - Natalie Aneck-Hahn
- University of Pretoria Institute for Sustainable Malaria Control, School of Health Systems and Public Health, Faculty of Health Sciences, University of Pretoria, South Africa
| | - Sean Patrick
- University of Pretoria Institute for Sustainable Malaria Control, School of Health Systems and Public Health, Faculty of Health Sciences, University of Pretoria, South Africa
| | - Janice M. Bailey
- Research Centre on Reproduction and Intergenerational Health, Department of Animal Sciences, Université Laval, Quebec, Quebec, Canada
| | - Christiaan de Jager
- Environmental Chemical Pollution and Health Research Unit, School of Health Systems and Public Health, Faculty of Health Sciences, University of Pretoria, Pretoria, South Africa
- University of Pretoria Institute for Sustainable Malaria Control, School of Health Systems and Public Health, Faculty of Health Sciences, University of Pretoria, South Africa
| | - Vanessa Dumeaux
- Department of Anatomy and Cell Biology, Western University, London, Ontario, Canada
- Department of Oncology, Western University, London, Ontario, Canada
| | - Jacquetta M. Trasler
- Department of Pharmacology and Therapeutics, Faculty of Medicine and Health Sciences, McGill University, Montreal, Quebec, Canada
- Child Health and Human Development Program, Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada
- Department of Human Genetics, Faculty of Medicine, McGill University, Montreal, Quebec, Canada
- Department of Pediatrics, Faculty of Medicine, McGill University, Montreal, Quebec, Canada
| | - Sarah Kimmins
- Department of Pharmacology and Therapeutics, Faculty of Medicine and Health Sciences, McGill University, Montreal, Quebec, Canada
- University of Montreal Hospital Research Centre, Montreal, Quebec, Canada
- Department of Pathology and Cell Biology, Faculty of Medicine, University of Montreal, Quebec, Canada
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17
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Miao Z, Kim J. Uniform quantification of single-nucleus ATAC-seq data with Paired-Insertion Counting (PIC) and a model-based insertion rate estimator. Nat Methods 2024; 21:32-36. [PMID: 38049698 PMCID: PMC10776405 DOI: 10.1038/s41592-023-02103-7] [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] [Received: 05/13/2022] [Accepted: 10/25/2023] [Indexed: 12/06/2023]
Abstract
Existing approaches to scoring single-nucleus assay for transposase-accessible chromatin with sequencing (snATAC-seq) feature matrices from sequencing reads are inconsistent, affecting downstream analyses and displaying artifacts. We show that, even with sparse single-cell data, quantitative counts are informative for estimating the regulatory state of a cell, which calls for a consistent treatment. We propose Paired-Insertion Counting as a uniform method for snATAC-seq feature characterization and provide a probability model for inferring latent insertion dynamics from snATAC-seq count matrices.
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Affiliation(s)
- Zhen Miao
- Graduate Group in Genomics and Computational Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Biology, University of Pennsylvania, Philadelphia, PA, USA
| | - Junhyong Kim
- Graduate Group in Genomics and Computational Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Biology, University of Pennsylvania, Philadelphia, PA, USA.
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18
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Sun Y, Wiese M, Hmadi R, Karayol R, Seyfferth J, Martinez Greene JA, Erdogdu NU, Deboutte W, Arrigoni L, Holz H, Renschler G, Hirsch N, Foertsch A, Basilicata MF, Stehle T, Shvedunova M, Bella C, Pessoa Rodrigues C, Schwalb B, Cramer P, Manke T, Akhtar A. MSL2 ensures biallelic gene expression in mammals. Nature 2023; 624:173-181. [PMID: 38030723 PMCID: PMC10700137 DOI: 10.1038/s41586-023-06781-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Accepted: 10/24/2023] [Indexed: 12/01/2023]
Abstract
In diploid organisms, biallelic gene expression enables the production of adequate levels of mRNA1,2. This is essential for haploinsufficient genes, which require biallelic expression for optimal function to prevent the onset of developmental disorders1,3. Whether and how a biallelic or monoallelic state is determined in a cell-type-specific manner at individual loci remains unclear. MSL2 is known for dosage compensation of the male X chromosome in flies. Here we identify a role of MSL2 in regulating allelic expression in mammals. Allele-specific bulk and single-cell analyses in mouse neural progenitor cells revealed that, in addition to the targets showing biallelic downregulation, a class of genes transitions from biallelic to monoallelic expression after MSL2 loss. Many of these genes are haploinsufficient. In the absence of MSL2, one allele remains active, retaining active histone modifications and transcription factor binding, whereas the other allele is silenced, exhibiting loss of promoter-enhancer contacts and the acquisition of DNA methylation. Msl2-knockout mice show perinatal lethality and heterogeneous phenotypes during embryonic development, supporting a role for MSL2 in regulating gene dosage. The role of MSL2 in preserving biallelic expression of specific dosage-sensitive genes sets the stage for further investigation of other factors that are involved in allelic dosage compensation in mammalian cells, with considerable implications for human disease.
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Affiliation(s)
- Yidan Sun
- Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany
| | - Meike Wiese
- Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany
| | - Raed Hmadi
- Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany
| | - Remzi Karayol
- Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany
| | - Janine Seyfferth
- Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany
| | - Juan Alfonso Martinez Greene
- Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany
- Faculty of Biology, University of Freiburg, Freiburg, Germany
| | - Niyazi Umut Erdogdu
- Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany
- Faculty of Biology, University of Freiburg, Freiburg, Germany
| | - Ward Deboutte
- Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany
| | - Laura Arrigoni
- Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany
| | - Herbert Holz
- Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany
| | - Gina Renschler
- Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany
| | - Naama Hirsch
- Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany
| | - Arion Foertsch
- Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany
- Faculty of Biology, University of Freiburg, Freiburg, Germany
| | | | - Thomas Stehle
- Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany
| | - Maria Shvedunova
- Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany
| | - Chiara Bella
- Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany
| | | | - Bjoern Schwalb
- Max Planck Institute for Multidisciplinary Sciences, Goettingen, Germany
| | - Patrick Cramer
- Max Planck Institute for Multidisciplinary Sciences, Goettingen, Germany
| | - Thomas Manke
- Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany
| | - Asifa Akhtar
- Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany.
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19
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Guo Y, Tian J, Song C, Han W, Zhu C, Li H, Zhang S, Chen K, Li N, Carlborg Ö, Hu X. Mapping and Functional Dissection of the Rumpless Trait in Piao Chicken Identifies a Causal Loss of Function Mutation in the Novel Gene Rum. Mol Biol Evol 2023; 40:msad273. [PMID: 38069902 PMCID: PMC10735294 DOI: 10.1093/molbev/msad273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 10/21/2023] [Accepted: 11/20/2023] [Indexed: 12/23/2023] Open
Abstract
Rumpless chickens exhibit an abnormality in their tail development. The genetics and biology of this trait has been studied for decades to illustrate a broad variation in both the types of inheritance and the severity in the developmental defects of the tail. In this study, we created a backcross pedigree by intercrossing Piao (rumpless) with Xianju (normal) to investigate the genetic mechanisms and molecular basis of the rumpless trait in Piao chicken. Through genome-wide association and linkage analyses, the candidate region was fine-mapped to 798.5 kb (chromosome 2: 86.9 to 87.7 Mb). Whole-genome sequencing analyses identified a single variant, a 4.2 kb deletion, which was completely associated with the rumpless phenotype. Explorations of the expression data identified a novel causative gene, Rum, that produced a long, intronless transcript across the deletion. The expression of Rum is embryo-specific, and it regulates the expression of MSGN1, a key factor in regulating T-box transcription factors required for mesoderm formation and differentiation. These results provide genetic and molecular experimental evidence for a novel mechanism regulating tail development in chicken and report the likely causal mutation for the tail abnormity in the Piao chicken. The novel regulatory gene, Rum, will, due to its role in fundamental embryo development, be of interest for further explorations of a potential role in tail and skeletal development also in other vertebrates.
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Affiliation(s)
- Ying Guo
- State Key Laboratory of Animal Biotech Breeding, China Agricultural University, Beijing CN-100193, China
- National Engineering Laboratory for Animal Breeding, China Agricultural University, Beijing CN-100193, China
- Yazhouwan National Laboratory, Sanya CN-572024, China
- Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala SE-751 23, Sweden
| | - Jing Tian
- State Key Laboratory of Animal Biotech Breeding, China Agricultural University, Beijing CN-100193, China
- National Engineering Laboratory for Animal Breeding, China Agricultural University, Beijing CN-100193, China
- Inner Mongolia Academy of Agricultural & Animal Husbandry Sciences, Hohhot CN-010031, China
| | - Chi Song
- National Chickens Genetic Resources, Jiangsu Institute of Poultry Science, Yangzhou CN-225125, Jiangsu, China
| | - Wei Han
- National Chickens Genetic Resources, Jiangsu Institute of Poultry Science, Yangzhou CN-225125, Jiangsu, China
| | - Chunhong Zhu
- National Chickens Genetic Resources, Jiangsu Institute of Poultry Science, Yangzhou CN-225125, Jiangsu, China
| | - Huifang Li
- National Chickens Genetic Resources, Jiangsu Institute of Poultry Science, Yangzhou CN-225125, Jiangsu, China
| | - Shuangjie Zhang
- National Chickens Genetic Resources, Jiangsu Institute of Poultry Science, Yangzhou CN-225125, Jiangsu, China
| | - Kuanwei Chen
- National Chickens Genetic Resources, Jiangsu Institute of Poultry Science, Yangzhou CN-225125, Jiangsu, China
| | - Ning Li
- State Key Laboratory of Animal Biotech Breeding, China Agricultural University, Beijing CN-100193, China
- National Engineering Laboratory for Animal Breeding, China Agricultural University, Beijing CN-100193, China
| | - Örjan Carlborg
- Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala SE-751 23, Sweden
| | - Xiaoxiang Hu
- State Key Laboratory of Animal Biotech Breeding, China Agricultural University, Beijing CN-100193, China
- National Engineering Laboratory for Animal Breeding, China Agricultural University, Beijing CN-100193, China
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20
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Rossini R, Oshaghi M, Nekrasov M, Bellanger A, Domaschenz R, Dijkwel Y, Abdelhalim M, Collas P, Tremethick D, Paulsen J. Multi-level 3D genome organization deteriorates during breast cancer progression. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.26.568711. [PMID: 38076897 PMCID: PMC10705249 DOI: 10.1101/2023.11.26.568711] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
Breast cancer entails intricate alterations in genome organization and expression. However, how three-dimensional (3D) chromatin structure changes in the progression from a normal to a breast cancer malignant state remains unknown. To address this, we conducted an analysis combining Hi-C data with lamina-associated domains (LADs), epigenomic marks, and gene expression in an in vitro model of breast cancer progression. Our results reveal that while the fundamental properties of topologically associating domains (TADs) remain largely stable, significant changes occur in the organization of compartments and subcompartments. These changes are closely correlated with alterations in the expression of oncogenic genes. We also observe a restructuring of TAD-TAD interactions, coinciding with a loss of spatial compartmentalization and radial positioning of the 3D genome. Notably, we identify a previously unrecognized interchromosomal insertion event, wherein a locus on chromosome 8 housing the MYC oncogene is inserted into a highly active subcompartment on chromosome 10. This insertion leads to the formation of de novo enhancer contacts and activation of the oncogene, illustrating how structural variants can interact with the 3D genome to drive oncogenic states. In summary, our findings provide evidence for the degradation of genome organization at multiple scales during breast cancer progression revealing novel relationships between genome 3D structure and oncogenic processes.
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Affiliation(s)
- Roberto Rossini
- Department of Biosciences, Faculty of Mathematics and Natural Sciences, University of Oslo, 0316 Oslo, Norway
| | - Mohammadsaleh Oshaghi
- Department of Biosciences, Faculty of Mathematics and Natural Sciences, University of Oslo, 0316 Oslo, Norway
| | - Maxim Nekrasov
- Department of Genome Sciences, The John Curtin School of Medical Research, The Australian National University, Canberra, Australian Capital Territory, Australia
| | - Aurélie Bellanger
- Department of Molecular Medicine, Institute of Basic Medical Sciences, Faculty of Medicine, University of Oslo, 0317 Oslo, Norway
| | - Renae Domaschenz
- Department of Genome Sciences, The John Curtin School of Medical Research, The Australian National University, Canberra, Australian Capital Territory, Australia
| | - Yasmin Dijkwel
- Department of Genome Sciences, The John Curtin School of Medical Research, The Australian National University, Canberra, Australian Capital Territory, Australia
| | - Mohamed Abdelhalim
- Department of Molecular Medicine, Institute of Basic Medical Sciences, Faculty of Medicine, University of Oslo, 0317 Oslo, Norway
| | - Philippe Collas
- Department of Molecular Medicine, Institute of Basic Medical Sciences, Faculty of Medicine, University of Oslo, 0317 Oslo, Norway
- Department of Immunology and Transfusion Medicine, Oslo University Hospital, 0424 Oslo, Norway
| | - David Tremethick
- Department of Genome Sciences, The John Curtin School of Medical Research, The Australian National University, Canberra, Australian Capital Territory, Australia
| | - Jonas Paulsen
- Department of Biosciences, Faculty of Mathematics and Natural Sciences, University of Oslo, 0316 Oslo, Norway
- Centre for Bioinformatics, Department of Informatics, University of Oslo, 0316 Oslo, Norway
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21
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Hwang J, Kang X, Wolf C, Touma M. Mapping Chromatin Occupancy of Ppp1r1b-lncRNA Genome-Wide Using Chromatin Isolation by RNA Purification (ChIRP)-seq. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.04.565657. [PMID: 37961291 PMCID: PMC10635152 DOI: 10.1101/2023.11.04.565657] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Long non-coding RNA (lncRNA) mediated transcriptional regulation is increasingly recognized as an important gene regulatory mechanism during development and disease. LncRNAs are emerging as critical regulators of chromatin state; yet the nature and the extent of their interactions with chromatin remain to be fully revealed. We have previously identified Ppp1r1b-lncRNA as an essential epigenetic regulator of myogenic differentiation in cardiac and skeletal myocytes in mice and humans. We further demonstrated that Ppp1r1b-lncRNA function is mediated by the interaction with the chromatin-modifying complex polycomb repressive complex 2 (PRC2) at the promoter of myogenic differentiation transcription factors, TBX5 and MyoD1. Herein, we employed an unbiased chromatin isolation by RNA purification (ChIRP) and high throughput sequencing to map the repertoire of Ppp1r1b-lncRNA chromatin occupancy genome-wide in the mouse muscle myoblast cell line. We uncovered a total of 99732 true peaks corresponding to Ppp1r1b-lncRNA binding sites at high confidence (P-value < 1e-5 and enrichment score ≥ 10). The Ppp1r1b-lncRNA-binding sites averaged 558 bp in length and were distributed widely within the coding and non-coding regions of the genome. Approximately 46% of these true peaks were mapped to gene elements, of which 1180 were mapped to experimentally validated promoter sequences. Importantly, the promoter-mapped binding sites were enriched in myogenic transcription factors and heart development while exhibiting focal interactions with known motifs of proximal promoters and transcription initiation by RNA polII, including TATA, transcription initiator, CCAAT-box, and GC-box, supporting Ppp1r1b-lncRNA role in transcription initiation of myogenic regulators. Remarkably, nearly 40% of Ppp1r1b-lncRNA-binding sites mapped to gene introns, were enriched with the Homeobox family of transcription factors, and exhibited TA-rich motif sequences, suggesting potential motif specific Ppp1r1b-lncRNA-bound introns. Lastly, more than 136521enhancer sequences were detected in Ppp1r1b-lncRNA-occupancy sites at high confidence. Among these enhancers,12% exhibited cell type/tissue-specific enrichment in fetal heart and muscles. Together, our findings provide further insights into the genome-wide Ppp1r1b-lncRNA: Chromatin interactome that may potentially dictate its function in myogenic differentiation and potentially other cellular and biological processes.
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Affiliation(s)
- John Hwang
- Neonatal/Congenital Heart Laboratory, Cardiovascular Research Laboratory, University of California Los Angeles, Los Angeles, CA
- Department of Pediatrics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA
- Children’s Discovery and Innovation Institute, Department of Pediatrics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA
| | - Xuedong Kang
- Neonatal/Congenital Heart Laboratory, Cardiovascular Research Laboratory, University of California Los Angeles, Los Angeles, CA
- Department of Pediatrics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA
| | - Charlotte Wolf
- Neonatal/Congenital Heart Laboratory, Cardiovascular Research Laboratory, University of California Los Angeles, Los Angeles, CA
- Medical and Life Science, College of Life Science, University of California Los Angeles, Los Angeles, CA
| | - Marlin Touma
- Neonatal/Congenital Heart Laboratory, Cardiovascular Research Laboratory, University of California Los Angeles, Los Angeles, CA
- Department of Pediatrics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA
- Children’s Discovery and Innovation Institute, Department of Pediatrics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA
- Molecular Biology Institute, College of Life Science, University of California Los Angeles, Los Angeles, CA
- Eli and Edythe Broad Stem Cell Research Center, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA
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22
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Gonçalves TM, Stewart CL, Baxley SD, Xu J, Li D, Gabel HW, Wang T, Avraham O, Zhao G. Towards a comprehensive regulatory map of Mammalian Genomes. RESEARCH SQUARE 2023:rs.3.rs-3294408. [PMID: 37841836 PMCID: PMC10571623 DOI: 10.21203/rs.3.rs-3294408/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/17/2023]
Abstract
Genome mapping studies have generated a nearly complete collection of genes for the human genome, but we still lack an equivalently vetted inventory of human regulatory sequences. Cis-regulatory modules (CRMs) play important roles in controlling when, where, and how much a gene is expressed. We developed a training data-free CRM-prediction algorithm, the Mammalian Regulatory MOdule Detector (MrMOD) for accurate CRM prediction in mammalian genomes. MrMOD provides genome position-fixed CRM models similar to the fixed gene models for the mouse and human genomes using only genomic sequences as the inputs with one adjustable parameter - the significance p-value. Importantly, MrMOD predicts a comprehensive set of high-resolution CRMs in the mouse and human genomes including all types of regulatory modules not limited to any tissue, cell type, developmental stage, or condition. We computationally validated MrMOD predictions used a compendium of 21 orthogonal experimental data sets including thousands of experimentally defined CRMs and millions of putative regulatory elements derived from hundreds of different tissues, cell types, and stimulus conditions obtained from multiple databases. In ovo transgenic reporter assay demonstrates the power of our prediction in guiding experimental design. We analyzed CRMs located in the chromosome 17 using unsupervised machine learning and identified groups of CRMs with multiple lines of evidence supporting their functionality, linking CRMs with upstream binding transcription factors and downstream target genes. Our work provides a comprehensive base pair resolution annotation of the functional regulatory elements and non-functional regions in the mammalian genomes.
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Affiliation(s)
| | | | | | - Jason Xu
- Missouri University of Science & Technology
| | - Daofeng Li
- Washington University School of Medicine
| | | | - Ting Wang
- Washington University School of Medicine
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23
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Zhu W, Huang H, Ming W, Zhang R, Gu Y, Bai Y, Liu X, Liu H, Liu Y, Gu W, Sun X. Delineating highly transcribed noncoding elements landscape in breast cancer. Comput Struct Biotechnol J 2023; 21:4432-4445. [PMID: 37731598 PMCID: PMC10507584 DOI: 10.1016/j.csbj.2023.09.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 08/27/2023] [Accepted: 09/10/2023] [Indexed: 09/22/2023] Open
Abstract
Highly transcribed noncoding elements (HTNEs) are critical noncoding elements with high levels of transcriptional capacity in particular cohorts involved in multiple cellular biological processes. Investigation of HTNEs with persistent aberrant expression in abnormal tissues could be of benefit in exploring their roles in disease occurrence and progression. Breast cancer is a highly heterogeneous disease for which early screening and prognosis are exceedingly crucial. In this study, we developed a HTNE identification framework to systematically investigate HTNE landscapes in breast cancer patients and identified over ten thousand HTNEs. The robustness and rationality of our framework were demonstrated via public datasets. We revealed that HTNEs had significant chromatin characteristics of enhancers and long noncoding RNAs (lncRNAs) and were significantly enriched with RNA-binding proteins as well as targeted by miRNAs. Further, HTNE-associated genes were significantly overexpressed and exhibited strong correlations with breast cancer. Ultimately, we explored the subtype-specific transcriptional processes associated with HTNEs and uncovered the HTNE signatures that could classify breast cancer subtypes based on the properties of hormone receptors. Our results highlight that the identified HTNEs as well as their associated genes play crucial roles in breast cancer progression and correlate with subtype-specific transcriptional processes of breast cancer.
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Affiliation(s)
- Wenyong Zhu
- State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Hao Huang
- State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Wenlong Ming
- State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Rongxin Zhang
- State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Yu Gu
- State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Yunfei Bai
- State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Xiaoan Liu
- Department of Breast Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Hongde Liu
- State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Yun Liu
- Department of Information, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Wanjun Gu
- State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
- Collaborative Innovation Center of Jiangsu Province of Cancer Prevention and Treatment of Chinese Medicine, School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing, China
| | - Xiao Sun
- State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
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24
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Song C, Zhang Y, Huang H, Wang Y, Zhao X, Zhang G, Yin M, Feng C, Wang Q, Qian F, Shang D, Zhang J, Liu J, Li C, Tang H. Cis-Cardio: A comprehensive analysis platform for cardiovascular-relavant cis-regulation in human and mouse. MOLECULAR THERAPY. NUCLEIC ACIDS 2023; 33:655-667. [PMID: 37637211 PMCID: PMC10458290 DOI: 10.1016/j.omtn.2023.07.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 07/25/2023] [Indexed: 08/29/2023]
Abstract
Cis-regulatory elements are important molecular switches in controlling gene expression and are regarded as determinant hubs in the transcriptional regulatory network. Collection and processing of large-scale cis-regulatory data are urgent to decipher the potential mechanisms of cardiovascular diseases from a cis-regulatory element aspect. Here, we developed a novel web server, Cis-Cardio, which aims to document a large number of available cardiovascular-related cis-regulatory data and to provide analysis for unveiling the comprehensive mechanisms at a cis-regulation level. The current version of Cis-Cardio catalogs a total of 45,382,361 genomic regions from 1,013 human and mouse epigenetic datasets, including ATAC-seq, DNase-seq, Histone ChIP-seq, TF/TcoF ChIP-seq, RNA polymerase ChIP-seq, and Cohesin ChIP-seq. Importantly, Cis-Cardio provides six analysis tools, including region overlap analysis, element upstream/downstream analysis, transcription regulator enrichment analysis, variant interpretation, and protein-protein interaction-based co-regulatory analysis. Additionally, Cis-Cardio provides detailed and abundant (epi-) genetic annotations in cis-regulatory regions, such as super-enhancers, enhancers, transcription factor binding sites (TFBSs), methylation sites, common SNPs, risk SNPs, expression quantitative trait loci (eQTLs), motifs, DNase I hypersensitive sites (DHSs), and 3D chromatin interactions. In summary, Cis-Cardio is a valuable resource for elucidating and analyzing regulatory cues of cardiovascular-specific cis-regulatory elements. The platform is freely available at http://www.licpathway.net/Cis-Cardio/index.html.
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Affiliation(s)
- Chao Song
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Hunan Provincial Key Laboratory of Multi-omics and Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan 421001, China
- School of Computer, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, China
| | - Yuexin Zhang
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Hunan Provincial Key Laboratory of Multi-omics and Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan 421001, China
- School of Computer, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, China
| | - Hong Huang
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Hunan Provincial Key Laboratory of Multi-omics and Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, China
- Clinical Research Center for Myocardial Injury in Hunan Province, Hengyang, Hunan 421001, China
| | - Yuezhu Wang
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Xilong Zhao
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Guorui Zhang
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Hunan Provincial Key Laboratory of Multi-omics and Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan 421001, China
| | - Mingxue Yin
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Hunan Provincial Key Laboratory of Multi-omics and Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan 421001, China
| | - Chenchen Feng
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Qiuyu Wang
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Hunan Provincial Key Laboratory of Multi-omics and Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan 421001, China
- School of Computer, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
| | - Fengcui Qian
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Hunan Provincial Key Laboratory of Multi-omics and Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan 421001, China
- School of Computer, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, China
| | - Desi Shang
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Hunan Provincial Key Laboratory of Multi-omics and Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan 421001, China
- School of Computer, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
| | - Jian Zhang
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Jiaqi Liu
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Hunan Provincial Key Laboratory of Multi-omics and Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan 421001, China
- School of Computer, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, China
| | - Chunquan Li
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Hunan Provincial Key Laboratory of Multi-omics and Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan 421001, China
- School of Computer, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- National Health Commission Key Laboratory of Birth Defect Research and Prevention, Hunan Provincial Maternal and Child Health Care Hospital, Changsha, Hunan 410008, China
- Key Laboratory of Rare Pediatric Diseases, Ministry of Education, University of South China, Hengyang, Hunan 421001, China
| | - Huifang Tang
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Hunan Provincial Key Laboratory of Multi-omics and Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan 421001, China
- School of Computer, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, China
- Clinical Research Center for Myocardial Injury in Hunan Province, Hengyang, Hunan 421001, China
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Yang M, Ali O, Bjørås M, Wang J. Identifying functional regulatory mutation blocks by integrating genome sequencing and transcriptome data. iScience 2023; 26:107266. [PMID: 37520692 PMCID: PMC10371843 DOI: 10.1016/j.isci.2023.107266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 04/05/2023] [Accepted: 06/28/2023] [Indexed: 08/01/2023] Open
Abstract
Millions of single nucleotide variants (SNVs) exist in the human genome; however, it remains challenging to identify functional SNVs associated with diseases. We propose a non-encoding SNVs analysis tool bpb3, BayesPI-BAR version 3, aiming to identify the functional mutation blocks (FMBs) by integrating genome sequencing and transcriptome data. The identified FMBs display high frequency SNVs, significant changes in transcription factors (TFs) binding affinity and are nearby the regulatory regions of differentially expressed genes. A two-level Bayesian approach with a biophysical model for protein-DNA interactions is implemented, to compute TF-DNA binding affinity changes based on clustered position weight matrices (PWMs) from over 1700 TF-motifs. The epigenetic data, such as the DNA methylome can also be integrated to scan FMBs. By testing the datasets from follicular lymphoma and melanoma, bpb3 automatically and robustly identifies FMBs, demonstrating that bpb3 can provide insight into patho-mechanisms, and therapeutic targets from transcriptomic and genomic data.
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Affiliation(s)
- Mingyi Yang
- Department of Microbiology, Oslo University Hospital and University of Oslo, Oslo, Norway
- Department of Medical Biochemistry, Oslo University Hospital and University of Oslo, Oslo, Norway
| | - Omer Ali
- Department of Pathology, Oslo University Hospital - Norwegian Radium Hospital, Oslo, Norway
- Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Magnar Bjørås
- Department of Microbiology, Oslo University Hospital and University of Oslo, Oslo, Norway
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
| | - Junbai Wang
- Department of Clinical Molecular Biology (EpiGen), Akershus University Hospital and University of Oslo, Lørenskog, Norway
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26
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Kupkova K, Shetty SJ, Pray-Grant MG, Grant PA, Haque R, Petri WA, Auble DT. Globally elevated levels of histone H3 lysine 9 trimethylation in early infancy are associated with poor growth trajectory in Bangladeshi children. Clin Epigenetics 2023; 15:129. [PMID: 37568218 PMCID: PMC10422758 DOI: 10.1186/s13148-023-01548-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 08/06/2023] [Indexed: 08/13/2023] Open
Abstract
BACKGROUND Stunting is a global health problem affecting hundreds of millions of children worldwide and contributing to 45% of deaths in children under the age of five. Current therapeutic interventions have limited efficacy. Understanding the epigenetic changes underlying stunting will elucidate molecular mechanisms and likely lead to new therapies. RESULTS We profiled the repressive mark histone H3 lysine 9 trimethylation (H3K9me3) genome-wide in peripheral blood mononuclear cells (PBMCs) from 18-week-old infants (n = 15) and mothers (n = 14) enrolled in the PROVIDE study established in an urban slum in Bangladesh. We associated H3K9me3 levels within individual loci as well as genome-wide with anthropometric measurements and other biomarkers of stunting and performed functional annotation of differentially affected regions. Despite the relatively small number of samples from this vulnerable population, we observed globally elevated H3K9me3 levels were associated with poor linear growth between birth and one year of age. A large proportion of the differentially methylated genes code for proteins targeting viral mRNA and highly significant regions were enriched in transposon elements with potential regulatory roles in immune system activation and cytokine production. Maternal data show a similar trend with child's anthropometry; however, these trends lack statistical significance to infer an intergenerational relationship. CONCLUSIONS We speculate that high H3K9me3 levels may result in poor linear growth by repressing genes involved in immune system activation. Importantly, changes to H3K9me3 were detectable before the overt manifestation of stunting and therefore may be valuable as new biomarkers of stunting.
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Affiliation(s)
- Kristyna Kupkova
- Department of Biochemistry and Molecular Genetics, University of Virginia Health System, Charlottesville, VA, 22908, USA
- Center for Public Health Genomics, University of Virginia Health System, Charlottesville, VA, 22908, USA
| | - Savera J Shetty
- Department of Biochemistry and Molecular Genetics, University of Virginia Health System, Charlottesville, VA, 22908, USA
| | - Marilyn G Pray-Grant
- Department of Biomedical Science, Charles E. Schmidt College of Medicine, Florida Atlantic University, Boca Raton, FL, 33431, USA
| | - Patrick A Grant
- Department of Biomedical Science, Charles E. Schmidt College of Medicine, Florida Atlantic University, Boca Raton, FL, 33431, USA
| | - Rashidul Haque
- Infectious Disease Division, International Centre for Diarrhoeal Disease Research, Dhaka, 1000, Bangladesh
| | - William A Petri
- Division of Infectious Diseases and International Health, University of Virginia Health System, Charlottesville, VA, 22908, USA
| | - David T Auble
- Department of Biochemistry and Molecular Genetics, University of Virginia Health System, Charlottesville, VA, 22908, USA.
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27
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Jung YH, Wang HLV, Ali S, Corces VG, Kremsky I. Characterization of a strain-specific CD-1 reference genome reveals potential inter- and intra-strain functional variability. BMC Genomics 2023; 24:437. [PMID: 37537522 PMCID: PMC10401811 DOI: 10.1186/s12864-023-09523-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 07/19/2023] [Indexed: 08/05/2023] Open
Abstract
BACKGROUND CD-1 is an outbred mouse stock that is frequently used in toxicology, pharmacology, and fundamental biomedical research. Although inbred strains are typically better suited for such studies due to minimal genetic variability, outbred stocks confer practical advantages over inbred strains, such as improved breeding performance and low cost. Knowledge of the full genetic variability of CD-1 would make it more useful in toxicology, pharmacology, and fundamental biomedical research. RESULTS We performed deep genomic DNA sequencing of CD-1 mice and used the data to identify genome-wide SNPs, indels, and germline transposable elements relative to the mm10 reference genome. We used multiple genome-wide sequencing data types and previously published CD-1 SNPs to validate our called variants. We used the called variants to construct a strain-specific CD-1 reference genome, which we show can improve mappability and reduce experimental biases from genome-wide sequencing data derived from CD-1 mice. Based on previously published ChIP-seq and ATAC-seq data, we find evidence that genetic variation between CD-1 mice can lead to alterations in transcription factor binding. We also identified a number of variants in the coding region of genes which could have effects on translation of genes. CONCLUSIONS We have identified millions of previously unidentified CD-1 variants with the potential to confound studies involving CD-1. We used the identified variants to construct a CD-1-specific reference genome, which can improve accuracy and reduce bias when aligning genomics data derived from CD-1 mice.
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Affiliation(s)
- Yoon Hee Jung
- Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, USA
| | - Hsiao-Lin V Wang
- Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, USA
| | - Samir Ali
- Department of Basic Sciences, Loma Linda University School of Medicine, Loma Linda, CA, 92350, USA
| | - Victor G Corces
- Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, USA
| | - Isaac Kremsky
- Department of Basic Sciences, Loma Linda University School of Medicine, Loma Linda, CA, 92350, USA.
- Center for Genomics, Loma Linda University School of Medicine, Loma Linda, CA, USA.
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28
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He Y, Xue Y, Wang J, Huang Y, Liu L, Huang Y, Gao YQ. Diffusion-enhanced characterization of 3D chromatin structure reveals its linkage to gene regulatory networks and the interactome. Genome Res 2023; 33:1354-1368. [PMID: 37491077 PMCID: PMC10547250 DOI: 10.1101/gr.277737.123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 07/21/2023] [Indexed: 07/27/2023]
Abstract
The interactome networks at the DNA, RNA, and protein levels are crucial for cellular functions, and the diverse variations of these networks are heavily involved in the establishment of different cell states. We have developed a diffusion-based method, Hi-C to geometry (CTG), to obtain reliable geometric information on the chromatin from Hi-C data. CTG produces a consistent and reproducible framework for the 3D genomic structure and provides a reliable and quantitative understanding of the alterations of genomic structures under different cellular conditions. The genomic structure yielded by CTG serves as an architectural blueprint of the dynamic gene regulatory network, based on which cell-specific correspondence between gene-gene and corresponding protein-protein physical interactions, as well as transcription correlation, is revealed. We also find that gene fusion events are significantly enriched between genes of short CTG distances and are thus close in 3D space. These findings indicate that 3D chromatin structure is at least partially correlated with downstream processes such as transcription, gene regulation, and even regulatory networking through affecting protein-protein interactions.
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Affiliation(s)
- Yueying He
- Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Yue Xue
- Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Jingyao Wang
- Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Yupeng Huang
- Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Lu Liu
- Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing 100871, China
- School of Life Sciences, Peking University, Beijing 100871, China
| | - Yanyi Huang
- Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China;
- Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing 100871, China
| | - Yi Qin Gao
- Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China;
- Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing 100871, China
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29
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Li D, Zhao XY, Zhou S, Hu Q, Wu F, Lee HY. Multidimensional profiling reveals GATA1-modulated stage-specific chromatin states and functional associations during human erythropoiesis. Nucleic Acids Res 2023; 51:6634-6653. [PMID: 37254808 PMCID: PMC10359633 DOI: 10.1093/nar/gkad468] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2023] [Revised: 04/18/2023] [Accepted: 05/13/2023] [Indexed: 06/01/2023] Open
Abstract
Mammalian erythroid development can be divided into three stages: hematopoietic stem and progenitor cell (HSPC), erythroid progenitor (Ery-Pro), and erythroid precursor (Ery-Pre). However, the mechanisms by which the 3D genome changes to establish the stage-specific transcription programs that are critical for erythropoiesis remain unclear. Here, we analyze the chromatin landscape at multiple levels in defined populations from primary human erythroid culture. While compartments and topologically associating domains remain largely unchanged, ∼50% of H3K27Ac-marked enhancers are dynamic in HSPC versus Ery-Pre. The enhancer anchors of enhancer-promoter loops are enriched for occupancy of respective stage-specific transcription factors (TFs), indicating these TFs orchestrate the enhancer connectome rewiring. The master TF of erythropoiesis, GATA1, is found to occupy most erythroid gene promoters at the Ery-Pro stage, and mediate conspicuous local rewiring through acquiring binding at the distal regions in Ery-Pre, promoting productive erythroid transcription output. Knocking out GATA1 binding sites precisely abrogates local rewiring and corresponding gene expression. Interestingly, knocking down GATA1 can transiently revert the cell state to an earlier stage and prolong the window of progenitor state. This study reveals mechanistic insights underlying chromatin rearrangements during development by integrating multidimensional chromatin landscape analyses to associate with transcription output and cellular states.
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Affiliation(s)
- Dong Li
- Ministry of Education Key Laboratory of Cell Proliferation and Differentiation, School of Life Sciences, Peking University, Beijing 100871, China
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Xin-Ying Zhao
- Ministry of Education Key Laboratory of Cell Proliferation and Differentiation, School of Life Sciences, Peking University, Beijing 100871, China
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Shuo Zhou
- Ministry of Education Key Laboratory of Cell Proliferation and Differentiation, School of Life Sciences, Peking University, Beijing 100871, China
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Qi Hu
- Ministry of Education Key Laboratory of Cell Proliferation and Differentiation, School of Life Sciences, Peking University, Beijing 100871, China
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Fan Wu
- Ministry of Education Key Laboratory of Cell Proliferation and Differentiation, School of Life Sciences, Peking University, Beijing 100871, China
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Hsiang-Ying Lee
- Ministry of Education Key Laboratory of Cell Proliferation and Differentiation, School of Life Sciences, Peking University, Beijing 100871, China
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
- Peking University People's Hospital, Peking University Institute of Hematology, National Clinical Research Center for Hematologic Disease, Beijing 100871, China
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30
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Wang W, Wu Q, Li C. iEnhancer-DCSA: identifying enhancers via dual-scale convolution and spatial attention. BMC Genomics 2023; 24:393. [PMID: 37442977 DOI: 10.1186/s12864-023-09468-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 06/20/2023] [Indexed: 07/15/2023] Open
Abstract
BACKGROUND Due to the dynamic nature of enhancers, identifying enhancers and their strength are major bioinformatics challenges. With the development of deep learning, several models have facilitated enhancers detection in recent years. However, existing studies either neglect different length motifs information or treat the features at all spatial locations equally. How to effectively use multi-scale motifs information while ignoring irrelevant information is a question worthy of serious consideration. In this paper, we propose an accurate and stable predictor iEnhancer-DCSA, mainly composed of dual-scale fusion and spatial attention, automatically extracting features of different length motifs and selectively focusing on the important features. RESULTS Our experimental results demonstrate that iEnhancer-DCSA is remarkably superior to existing state-of-the-art methods on the test dataset. Especially, the accuracy and MCC of enhancer identification are improved by 3.45% and 9.41%, respectively. Meanwhile, the accuracy and MCC of enhancer classification are improved by 7.65% and 18.1%, respectively. Furthermore, we conduct ablation studies to demonstrate the effectiveness of dual-scale fusion and spatial attention. CONCLUSIONS iEnhancer-DCSA will be a valuable computational tool in identifying and classifying enhancers, especially for those not included in the training dataset.
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Affiliation(s)
- Wenjun Wang
- School of Software Engineering, South China University of Technology, Guangzhou, China
- School of Data Science and Information Engineering, Guizhou Minzu University, Guiyang, China
- Key Laboratory of Big Data and Intelligent Robot, Ministry of Education, Guangzhou, China
| | - Qingyao Wu
- School of Software Engineering, South China University of Technology, Guangzhou, China.
- Pazhou Lab, Guangzhou, China.
- Peng Cheng Laboratory, Shenzhen, China.
| | - Chunshan Li
- Department of Computer Science and Technology, Harbin Institute of Technology, Weihai, China.
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31
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Lin M, Chen Y, Xia S, He Z, Yu X, Huang L, Lin S, Liang B, Huang Z, Mei S, Liu D, Zheng L, Luo Y. Integrative profiling of extrachromosomal circular DNA in placenta and maternal plasma provides insights into the biology of fetal growth restriction and reveals potential biomarkers. Front Genet 2023; 14:1128082. [PMID: 37476414 PMCID: PMC10354665 DOI: 10.3389/fgene.2023.1128082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 02/24/2023] [Indexed: 07/22/2023] Open
Abstract
Introduction: Fetal growth restriction (FGR) is a placenta-mediated pregnancy complication that predisposes fetuses to perinatal complications. Maternal plasma cell-free DNA harbors DNA originating from placental trophoblasts, which is promising for the prenatal diagnosis and prediction of pregnancy complications. Extrachromosomal circular DNA (eccDNA) is emerging as an ideal biomarker and target for several diseases. Methods: We utilized eccDNA sequencing and bioinformatic pipeline to investigate the characteristics and associations of eccDNA in placenta and maternal plasma, the role of placental eccDNA in the pathogenesis of FGR, and potential plasma eccDNA biomarkers of FGR. Results: Using our bioinformatics pipelines, we identified multi-chromosomal-fragment and single-fragment eccDNA in placenta, but almost exclusively single-fragment eccDNA in maternal plasma. Relative to that in plasma, eccDNA in placenta was larger and substantially more abundant in exons, untranslated regions, promoters, repetitive elements [short interspersed nuclear elements (SINEs)/Alu, SINEs/mammalian-wide interspersed repeats, long terminal repeats/endogenous retrovirus-like elements, and single recognition particle RNA], and transcription factor binding motifs. Placental multi-chromosomal-fragment eccDNA was enriched in confident enhancer regions predicted to pertain to genes in apoptosis, energy, cell growth, and autophagy pathways. Placental eccDNA-associated genes whose abundance differed between the FGR and control groups were associated with immunity-related gene ontology (GO) terms. The combined analysis of plasma and placental eccDNA-associated genes in the FGR and control groups led to the identification of potential biomarkers that were assigned to the GO terms of the epigenetic regulation of gene expression and nutrient-related processes, respectively. Conclusion: Together, our results highlight links between placenta functions and multi-chromosomal-fragment and single-fragment eccDNA. The integrative analysis of placental and plasma eccDNA confirmed the potential of these molecules as disease-specific biomarkers of FGR.
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Affiliation(s)
- Minhuan Lin
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Yiqing Chen
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Shuting Xia
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Zhiming He
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Xuegao Yu
- Clinical Laboratory, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Linhuan Huang
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Shaobin Lin
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Binrun Liang
- MOE Key Laboratory of Gene Function and Regulation, State Key Laboratory for Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
| | - Ziliang Huang
- MOE Key Laboratory of Gene Function and Regulation, State Key Laboratory for Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
| | - Shiqiang Mei
- MOE Key Laboratory of Gene Function and Regulation, State Key Laboratory for Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
| | - Dong Liu
- Clinical Laboratory, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Lingling Zheng
- MOE Key Laboratory of Gene Function and Regulation, State Key Laboratory for Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
| | - Yanmin Luo
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
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Davidson RK, Kanojia S, Wu W, Kono T, Xu J, Osmulski M, Bone RN, Casey N, Evans-Molina C, Sims EK, Spaeth JM. The Chd4 Helicase Regulates Chromatin Accessibility and Gene Expression Critical for β-Cell Function In Vivo. Diabetes 2023; 72:746-757. [PMID: 36913741 PMCID: PMC10202766 DOI: 10.2337/db22-0939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 03/01/2023] [Indexed: 03/15/2023]
Abstract
The transcriptional activity of Pdx1 is modulated by a diverse array of coregulatory factors that govern chromatin accessibility, histone modifications, and nucleosome distribution. We previously identified the Chd4 subunit of the nucleosome remodeling and deacetylase complex as a Pdx1-interacting factor. To identify how loss of Chd4 impacts glucose homeostasis and gene expression programs in β-cells in vivo, we generated an inducible β-cell-specific Chd4 knockout mouse model. Removal of Chd4 from mature islet β-cells rendered mutant animals glucose intolerant, in part due to defects in insulin secretion. We observed an increased ratio of immature-to-mature insulin granules in Chd4-deficient β-cells that correlated with elevated levels of proinsulin both within isolated islets and from plasma following glucose stimulation in vivo. RNA sequencing and assay for transposase-accessible chromatin with sequencing showed that lineage-labeled Chd4-deficient β-cells have alterations in chromatin accessibility and altered expression of genes critical for β-cell function, including MafA, Slc2a2, Chga, and Chgb. Knockdown of CHD4 from a human β-cell line revealed similar defects in insulin secretion and alterations in several β-cell-enriched gene targets. These results illustrate how critical Chd4 activities are in controlling genes essential for maintaining β-cell function. ARTICLE HIGHLIGHTS Pdx1-Chd4 interactions were previously shown to be compromised in β-cells from human donors with type 2 diabetes. β-Cell-specific removal of Chd4 impairs insulin secretion and leads to glucose intolerance in mice. Expression of key β-cell functional genes and chromatin accessibility are compromised in Chd4-deficient β-cells. Chromatin remodeling activities enacted by Chd4 are essential for β-cell function under normal physiological conditions.
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Affiliation(s)
- Rebecca K. Davidson
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN
- Center for Diabetes and Metabolic Diseases, Indiana University School of Medicine, Indianapolis, IN
- Herman B Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN
| | - Sukrati Kanojia
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN
- Center for Diabetes and Metabolic Diseases, Indiana University School of Medicine, Indianapolis, IN
- Herman B Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN
| | - Wenting Wu
- Center for Diabetes and Metabolic Diseases, Indiana University School of Medicine, Indianapolis, IN
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN
| | - Tatsuyoshi Kono
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN
- Center for Diabetes and Metabolic Diseases, Indiana University School of Medicine, Indianapolis, IN
- Herman B Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN
| | - Jerry Xu
- Center for Diabetes and Metabolic Diseases, Indiana University School of Medicine, Indianapolis, IN
- Herman B Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN
| | - Meredith Osmulski
- Center for Diabetes and Metabolic Diseases, Indiana University School of Medicine, Indianapolis, IN
- Herman B Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN
| | - Robert N. Bone
- Center for Diabetes and Metabolic Diseases, Indiana University School of Medicine, Indianapolis, IN
- Herman B Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN
| | - Nolan Casey
- Center for Diabetes and Metabolic Diseases, Indiana University School of Medicine, Indianapolis, IN
- Herman B Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN
| | - Carmella Evans-Molina
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN
- Center for Diabetes and Metabolic Diseases, Indiana University School of Medicine, Indianapolis, IN
- Herman B Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN
- Department of Anatomy, Cell Biology, and Physiology, Indiana University School of Medicine, Indianapolis, IN
- Richard L. Roudebush Veterans’ Administration Medical Center, Indianapolis, IN
| | - Emily K. Sims
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN
- Center for Diabetes and Metabolic Diseases, Indiana University School of Medicine, Indianapolis, IN
- Herman B Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN
| | - Jason M. Spaeth
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN
- Center for Diabetes and Metabolic Diseases, Indiana University School of Medicine, Indianapolis, IN
- Herman B Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN
- Department of Anatomy, Cell Biology, and Physiology, Indiana University School of Medicine, Indianapolis, IN
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33
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Lubatti G, Stock M, Iturbide A, Ruiz Tejada Segura ML, Riepl M, Tyser RCV, Danese A, Colomé-Tatché M, Theis FJ, Srinivas S, Torres-Padilla ME, Scialdone A. CIARA: a cluster-independent algorithm for identifying markers of rare cell types from single-cell sequencing data. Development 2023; 150:dev201264. [PMID: 37294170 DOI: 10.1242/dev.201264] [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: 09/05/2022] [Accepted: 04/25/2023] [Indexed: 05/18/2023]
Abstract
A powerful feature of single-cell genomics is the possibility of identifying cell types from their molecular profiles. In particular, identifying novel rare cell types and their marker genes is a key potential of single-cell RNA sequencing. Standard clustering approaches perform well in identifying relatively abundant cell types, but tend to miss rarer cell types. Here, we have developed CIARA (Cluster Independent Algorithm for the identification of markers of RAre cell types), a cluster-independent computational tool designed to select genes that are likely to be markers of rare cell types. Genes selected by CIARA are subsequently integrated with common clustering algorithms to single out groups of rare cell types. CIARA outperforms existing methods for rare cell type detection, and we use it to find previously uncharacterized rare populations of cells in a human gastrula and among mouse embryonic stem cells treated with retinoic acid. Moreover, CIARA can be applied more generally to any type of single-cell omic data, thus allowing the identification of rare cells across multiple data modalities. We provide implementations of CIARA in user-friendly packages available in R and Python.
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Affiliation(s)
- Gabriele Lubatti
- Institute of Epigenetics and Stem Cells, Helmholtz Munich, D-81377 Munich, Germany
- Institute of Functional Epigenetics, Helmholtz Munich, D-85764 Neuherberg, Germany
- Institute of Computational Biology, Helmholtz Munich, D-85764 Neuherberg, Germany
| | - Marco Stock
- Institute of Epigenetics and Stem Cells, Helmholtz Munich, D-81377 Munich, Germany
- Institute of Functional Epigenetics, Helmholtz Munich, D-85764 Neuherberg, Germany
- Institute of Computational Biology, Helmholtz Munich, D-85764 Neuherberg, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, D-85354 Freising, Germany
| | - Ane Iturbide
- Institute of Epigenetics and Stem Cells, Helmholtz Munich, D-81377 Munich, Germany
| | - Mayra L Ruiz Tejada Segura
- Institute of Epigenetics and Stem Cells, Helmholtz Munich, D-81377 Munich, Germany
- Institute of Functional Epigenetics, Helmholtz Munich, D-85764 Neuherberg, Germany
- Institute of Computational Biology, Helmholtz Munich, D-85764 Neuherberg, Germany
| | - Melina Riepl
- Institute of Epigenetics and Stem Cells, Helmholtz Munich, D-81377 Munich, Germany
- Institute of Functional Epigenetics, Helmholtz Munich, D-85764 Neuherberg, Germany
- Institute of Computational Biology, Helmholtz Munich, D-85764 Neuherberg, Germany
| | - Richard C V Tyser
- Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge CB2 0AW, UK
| | - Anna Danese
- Biomedical Center Munich (BMC), Physiological Genomics, Faculty of Medicine, Ludwig Maximilians University, D-82152 Munich, Germany
| | - Maria Colomé-Tatché
- Institute of Computational Biology, Helmholtz Munich, D-85764 Neuherberg, Germany
- Biomedical Center (BMC), Physiological Chemistry, Faculty of Medicine, Ludwig Maximilians University, D-82152 Munich, Germany
| | - Fabian J Theis
- Institute of Computational Biology, Helmholtz Munich, D-85764 Neuherberg, Germany
- Department of Mathematics, Technical University of Munich, D-85748 Munich, Germany
| | - Shankar Srinivas
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford OX1 3PT, UK
| | - Maria-Elena Torres-Padilla
- Institute of Epigenetics and Stem Cells, Helmholtz Munich, D-81377 Munich, Germany
- Faculty of Biology, Ludwig-Maximilians University, D-82152 Munich, Germany
| | - Antonio Scialdone
- Institute of Epigenetics and Stem Cells, Helmholtz Munich, D-81377 Munich, Germany
- Institute of Functional Epigenetics, Helmholtz Munich, D-85764 Neuherberg, Germany
- Institute of Computational Biology, Helmholtz Munich, D-85764 Neuherberg, Germany
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Htet M, Lei S, Bajpayi S, Zoitou A, Chamakioti M, Tampakakis E. The role of noncoding genetic variants in cardiomyopathy. Front Cardiovasc Med 2023; 10:1116925. [PMID: 37283586 PMCID: PMC10239966 DOI: 10.3389/fcvm.2023.1116925] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 05/04/2023] [Indexed: 06/08/2023] Open
Abstract
Cardiomyopathies remain one of the leading causes of morbidity and mortality worldwide. Environmental risk factors and genetic predisposition account for most cardiomyopathy cases. As with all complex diseases, there are significant challenges in the interpretation of the molecular mechanisms underlying cardiomyopathy-associated genetic variants. Given the technical improvements and reduced costs of DNA sequence technologies, an increasing number of patients are now undergoing genetic testing, resulting in a continuously expanding list of novel mutations. However, many patients carry noncoding genetic variants, and although emerging evidence supports their contribution to cardiac disease, their role in cardiomyopathies remains largely understudied. In this review, we summarize published studies reporting on the association of different types of noncoding variants with various types of cardiomyopathies. We focus on variants within transcriptional enhancers, promoters, intronic sites, and untranslated regions that are likely associated with cardiac disease. Given the broad nature of this topic, we provide an overview of studies that are relatively recent and have sufficient evidence to support a significant degree of causality. We believe that more research with additional validation of noncoding genetic variants will provide further mechanistic insights on the development of cardiac disease, and noncoding variants will be increasingly incorporated in future genetic screening tests.
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Affiliation(s)
- Myo Htet
- Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD, United States
| | - Shunyao Lei
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Sheetal Bajpayi
- Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD, United States
| | - Asimina Zoitou
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
| | | | - Emmanouil Tampakakis
- Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD, United States
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
- Department of Genetic Medicine, Johns Hopkins University, Baltimore, MD, United States
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35
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Nepal C, Andersen JB. Alternative promoters in CpG depleted regions are prevalently associated with epigenetic misregulation of liver cancer transcriptomes. Nat Commun 2023; 14:2712. [PMID: 37169774 PMCID: PMC10175279 DOI: 10.1038/s41467-023-38272-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 04/24/2023] [Indexed: 05/13/2023] Open
Abstract
Transcriptional regulation is commonly governed by alternative promoters. However, the regulatory architecture in alternative and reference promoters, and how they differ, remains elusive. In 100 CAGE-seq libraries from hepatocellular carcinoma patients, here we annotate 4083 alternative promoters in 2926 multi-promoter genes, which are largely undetected in normal livers. These genes are enriched in oncogenic processes and predominantly show association with overall survival. Alternative promoters are narrow nucleosome depleted regions, CpG island depleted, and enriched for tissue-specific transcription factors. Globally tumors lose DNA methylation. We show hierarchical retention of intragenic DNA methylation with CG-poor regions rapidly losing methylation, while CG-rich regions retain it, a process mediated by differential SETD2, H3K36me3, DNMT3B, and TET1 binding. This mechanism is validated in SETD2 knockdown cells and SETD2-mutated patients. Selective DNA methylation loss in CG-poor regions makes the chromatin accessible for alternative transcription. We show alternative promoters can control tumor transcriptomes and their regulatory architecture.
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Affiliation(s)
- Chirag Nepal
- Biotech Research and Innovation Centre (BRIC), Department of Health and Medical Sciences, University of Copenhagen, Ole Maaløes Vej 5, Copenhagen N, DK-2200, Denmark.
- Center for Genomics, School of Medicine, Loma Linda University, Loma Linda, CA, 92350, USA.
| | - Jesper B Andersen
- Biotech Research and Innovation Centre (BRIC), Department of Health and Medical Sciences, University of Copenhagen, Ole Maaløes Vej 5, Copenhagen N, DK-2200, Denmark.
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36
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Suen HC, Rao S, Luk ACS, Zhang R, Yang L, Qi H, So HC, Hobbs RM, Lee TL, Liao J. The single-cell chromatin accessibility landscape in mouse perinatal testis development. eLife 2023; 12:e75624. [PMID: 37096870 PMCID: PMC10174692 DOI: 10.7554/elife.75624] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 04/24/2023] [Indexed: 04/26/2023] Open
Abstract
Spermatogenesis depends on an orchestrated series of developing events in germ cells and full maturation of the somatic microenvironment. To date, the majority of efforts to study cellular heterogeneity in testis has been focused on single-cell gene expression rather than the chromatin landscape shaping gene expression. To advance our understanding of the regulatory programs underlying testicular cell types, we analyzed single-cell chromatin accessibility profiles in more than 25,000 cells from mouse developing testis. We showed that single-cell sequencing assay for transposase-accessible chromatin (scATAC-Seq) allowed us to deconvolve distinct cell populations and identify cis-regulatory elements (CREs) underlying cell-type specification. We identified sets of transcription factors associated with cell type-specific accessibility, revealing novel regulators of cell fate specification and maintenance. Pseudotime reconstruction revealed detailed regulatory dynamics coordinating the sequential developmental progressions of germ cells and somatic cells. This high-resolution dataset also unveiled previously unreported subpopulations within both the Sertoli and Leydig cell groups. Further, we defined candidate target cell types and genes of several genome-wide association study (GWAS) signals, including those associated with testosterone levels and coronary artery disease. Collectively, our data provide a blueprint of the 'regulon' of the mouse male germline and supporting somatic cells.
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Affiliation(s)
- Hoi Ching Suen
- Developmental and Regenerative Biology Program, School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, ShatinHong KongHong Kong
| | - Shitao Rao
- School of Medical Technology and Engineering, Fujian Medical UniversityFujianChina
- Cancer Biology and Experimental Therapeutics Program, School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, ShatinHong KongChina
| | - Alfred Chun Shui Luk
- Developmental and Regenerative Biology Program, School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, ShatinHong KongHong Kong
| | - Ruoyu Zhang
- Cancer Biology and Experimental Therapeutics Program, School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, ShatinHong KongChina
| | - Lele Yang
- Guangzhou Regenerative Medicine and Health Bioland Laboratory, Guangzhou Institutes of Biomedicine and HealthGuangzhouChina
| | - Huayu Qi
- Guangzhou Regenerative Medicine and Health Bioland Laboratory, Guangzhou Institutes of Biomedicine and HealthGuangzhouChina
| | - Hon Cheong So
- Cancer Biology and Experimental Therapeutics Program, School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, ShatinHong KongChina
| | - Robin M Hobbs
- Germline Stem Cell Biology Laboratory, Centre for Reproductive Health, Hudson Institute of Medical ResearchMelbourneAustralia
| | - Tin-lap Lee
- Developmental and Regenerative Biology Program, School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, ShatinHong KongHong Kong
| | - Jinyue Liao
- Developmental and Regenerative Biology Program, School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, ShatinHong KongHong Kong
- Department of Chemical Pathology, The Chinese University of Hong Kong, Shatin, New TerritoriesHong KongChina
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37
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Sun Y, Xu X, Lin L, Xu K, Zheng Y, Ren C, Tao H, Wang X, Zhao H, Tu W, Bai X, Wang J, Huang Q, Li Y, Chen H, Li H, Bo X. A graph neural network-based interpretable framework reveals a novel DNA fragility-associated chromatin structural unit. Genome Biol 2023; 24:90. [PMID: 37095580 PMCID: PMC10124043 DOI: 10.1186/s13059-023-02916-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 03/22/2023] [Indexed: 04/26/2023] Open
Abstract
BACKGROUND DNA double-strand breaks (DSBs) are among the most deleterious DNA lesions, and they can cause cancer if improperly repaired. Recent chromosome conformation capture techniques, such as Hi-C, have enabled the identification of relationships between the 3D chromatin structure and DSBs, but little is known about how to explain these relationships, especially from global contact maps, or their contributions to DSB formation. RESULTS Here, we propose a framework that integrates graph neural network (GNN) to unravel the relationship between 3D chromatin structure and DSBs using an advanced interpretable technique GNNExplainer. We identify a new chromatin structural unit named the DNA fragility-associated chromatin interaction network (FaCIN). FaCIN is a bottleneck-like structure, and it helps to reveal a universal form of how the fragility of a piece of DNA might be affected by the whole genome through chromatin interactions. Moreover, we demonstrate that neck interactions in FaCIN can serve as chromatin structural determinants of DSB formation. CONCLUSIONS Our study provides a more systematic and refined view enabling a better understanding of the mechanisms of DSB formation under the context of the 3D genome.
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Affiliation(s)
- Yu Sun
- Institute of Health Service and Transfusion Medicine, Beijing, 100850, China
| | - Xiang Xu
- Institute of Health Service and Transfusion Medicine, Beijing, 100850, China
| | - Lin Lin
- Institute of Health Service and Transfusion Medicine, Beijing, 100850, China
| | - Kang Xu
- Institute of Health Service and Transfusion Medicine, Beijing, 100850, China
| | - Yang Zheng
- Institute of Health Service and Transfusion Medicine, Beijing, 100850, China
| | - Chao Ren
- Institute of Health Service and Transfusion Medicine, Beijing, 100850, China
| | - Huan Tao
- Institute of Health Service and Transfusion Medicine, Beijing, 100850, China
| | - Xu Wang
- 4Paradigm Inc, Beijing, China
| | | | | | - Xuemei Bai
- The First Affiliated Hospital of Harbin Medical University, Harbin, 150001, China
| | - Junting Wang
- The First Affiliated Hospital of Harbin Medical University, Harbin, 150001, China
| | - Qiya Huang
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yaru Li
- Institute of Health Service and Transfusion Medicine, Beijing, 100850, China
| | - Hebing Chen
- Institute of Health Service and Transfusion Medicine, Beijing, 100850, China.
| | - Hao Li
- Institute of Health Service and Transfusion Medicine, Beijing, 100850, China.
| | - Xiaochen Bo
- Institute of Health Service and Transfusion Medicine, Beijing, 100850, China.
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38
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Serrano JB, Tabeling NC, de Winter-Korver CM, van Daalen SKM, van Pelt AMM, Mulder CL. Sperm DNA methylation is predominantly stable in mice offspring born after transplantation of long-term cultured spermatogonial stem cells. Clin Epigenetics 2023; 15:58. [PMID: 37029425 PMCID: PMC10080964 DOI: 10.1186/s13148-023-01469-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 03/21/2023] [Indexed: 04/09/2023] Open
Abstract
BACKGROUND Spermatogonial stem cell transplantation (SSCT) is proposed as a fertility therapy for childhood cancer survivors. SSCT starts with cryopreserving a testicular biopsy prior to gonadotoxic treatments such as cancer treatments. When the childhood cancer survivor reaches adulthood and desires biological children, the biopsy is thawed and SSCs are propagated in vitro and subsequently auto-transplanted back into their testis. However, culturing stress during long-term propagation can result in epigenetic changes in the SSCs, such as DNA methylation alterations, and might be inherited by future generations born after SSCT. Therefore, SSCT requires a detailed preclinical epigenetic assessment of the derived offspring before this novel cell therapy is clinically implemented. With this aim, the DNA methylation status of sperm from SSCT-derived offspring, with in vitro propagated SSCs, was investigated in a multi-generational mouse model using reduced-representation bisulfite sequencing. RESULTS Although there were some methylation differences, they represent less than 0.5% of the total CpGs and methylated regions, in all generations. Unsupervised clustering of all samples showed no distinct grouping based on their pattern of methylation differences. After selecting the few single genes that are significantly altered in multiple generations of SSCT offspring compared to control, we validated the results with quantitative Bisulfite Sanger sequencing and RT-qPCRin various organs. Differential methylation was confirmed only for Tal2, being hypomethylated in sperm of SSCT offspring and presenting higher gene expression in ovaries of SSCT F1 offspring compared to control F1. CONCLUSIONS We found no major differences in DNA methylation between SSCT-derived offspring and control, both in F1 and F2 sperm. The reassuring outcomes from our study are a prerequisite for promising translation of SSCT to the human situation.
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Affiliation(s)
- Joana B Serrano
- Reproductive Biology Laboratory, Center for Reproductive Medicine, Amsterdam UMC, University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands
- Amsterdam Reproduction and Development Research Institute, Amsterdam, The Netherlands
| | - Nils C Tabeling
- Reproductive Biology Laboratory, Center for Reproductive Medicine, Amsterdam UMC, University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands
- Amsterdam Reproduction and Development Research Institute, Amsterdam, The Netherlands
| | - Cindy M de Winter-Korver
- Reproductive Biology Laboratory, Center for Reproductive Medicine, Amsterdam UMC, University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands
- Amsterdam Reproduction and Development Research Institute, Amsterdam, The Netherlands
| | - Saskia K M van Daalen
- Reproductive Biology Laboratory, Center for Reproductive Medicine, Amsterdam UMC, University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands
- Amsterdam Reproduction and Development Research Institute, Amsterdam, The Netherlands
| | - Ans M M van Pelt
- Reproductive Biology Laboratory, Center for Reproductive Medicine, Amsterdam UMC, University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands
- Amsterdam Reproduction and Development Research Institute, Amsterdam, The Netherlands
| | - Callista L Mulder
- Reproductive Biology Laboratory, Center for Reproductive Medicine, Amsterdam UMC, University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands.
- Amsterdam Reproduction and Development Research Institute, Amsterdam, The Netherlands.
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Mills C, Marconett CN, Lewinger JP, Mi H. PEACOCK: a machine learning approach to assess the validity of cell type-specific enhancer-gene regulatory relationships. NPJ Syst Biol Appl 2023; 9:9. [PMID: 37012250 PMCID: PMC10070356 DOI: 10.1038/s41540-023-00270-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 02/20/2023] [Indexed: 04/05/2023] Open
Abstract
The vast majority of disease-associated variants identified in genome-wide association studies map to enhancers, powerful regulatory elements which orchestrate the recruitment of transcriptional complexes to their target genes' promoters to upregulate transcription in a cell type- and timing-dependent manner. These variants have implicated thousands of enhancers in many common genetic diseases, including nearly all cancers. However, the etiology of most of these diseases remains unknown because the regulatory target genes of the vast majority of enhancers are unknown. Thus, identifying the target genes of as many enhancers as possible is crucial for learning how enhancer regulatory activities function and contribute to disease. Based on experimental results curated from scientific publications coupled with machine learning methods, we developed a cell type-specific score predictive of an enhancer targeting a gene. We computed the score genome-wide for every possible cis enhancer-gene pair and validated its predictive ability in four widely used cell lines. Using a pooled final model trained across multiple cell types, all possible gene-enhancer regulatory links in cis (~17 M) were scored and added to the publicly available PEREGRINE database ( www.peregrineproj.org ). These scores provide a quantitative framework for the enhancer-gene regulatory prediction that can be incorporated into downstream statistical analyses.
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Affiliation(s)
- Caitlin Mills
- Division of Bioinformatics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90089, USA
| | - Crystal N Marconett
- Department of Surgery, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90089, USA
- Department of Biochemistry and Molecular Medicine, Keck School of Medicine USC, Los Angeles, CA, USA
- Norris Cancer Center, Keck School of Medicine USC, Los Angeles, CA, USA
| | - Juan Pablo Lewinger
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90089, USA.
| | - Huaiyu Mi
- Division of Bioinformatics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90089, USA.
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40
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Chen J, Fuhler NA, Noguchi KK, Dougherty JD. MYT1L is required for suppressing earlier neuronal development programs in the adult mouse brain. Genome Res 2023; 33:541-556. [PMID: 37100461 PMCID: PMC10234307 DOI: 10.1101/gr.277413.122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 03/09/2023] [Indexed: 04/28/2023]
Abstract
In vitro studies indicate the neurodevelopmental disorder gene myelin transcription factor 1-like (MYT1L) suppresses non-neuronal lineage genes during fibroblast-to-neuron direct differentiation. However, MYT1L's molecular and cellular functions in the adult mammalian brain have not been fully characterized. Here, we found that MYT1L loss leads to up-regulated deep layer (DL) gene expression, corresponding to an increased ratio of DL/UL neurons in the adult mouse cortex. To define potential mechanisms, we conducted Cleavage Under Targets & Release Using Nuclease (CUT&RUN) to map MYT1L binding targets and epigenetic changes following MYT1L loss in mouse developing cortex and adult prefrontal cortex (PFC). We found MYT1L mainly binds to open chromatin, but with different transcription factor co-occupancies between promoters and enhancers. Likewise, multiomic data set integration revealed that, at promoters, MYT1L loss does not change chromatin accessibility but increases H3K4me3 and H3K27ac, activating both a subset of earlier neuronal development genes as well as Bcl11b, a key regulator for DL neuron development. Meanwhile, we discovered that MYT1L normally represses the activity of neurogenic enhancers associated with neuronal migration and neuronal projection development by closing chromatin structures and promoting removal of active histone marks. Further, we showed that MYT1L interacts with HDAC2 and transcriptional repressor SIN3B in vivo, providing potential mechanisms underlying repressive effects on histone acetylation and gene expression. Overall, our findings provide a comprehensive map of MYT1L binding in vivo and mechanistic insights into how MYT1L loss leads to aberrant activation of earlier neuronal development programs in the adult mouse brain.
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Affiliation(s)
- Jiayang Chen
- Department of Genetics, Washington University School of Medicine, St. Louis, Missouri 63110, USA
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri 63110, USA
| | - Nicole A Fuhler
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri 63110, USA
- Intellectual and Developmental Disabilities Research Center, Washington University School of Medicine, St. Louis, Missouri 63108, USA
| | - Kevin K Noguchi
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri 63110, USA
- Intellectual and Developmental Disabilities Research Center, Washington University School of Medicine, St. Louis, Missouri 63108, USA
| | - Joseph D Dougherty
- Department of Genetics, Washington University School of Medicine, St. Louis, Missouri 63110, USA;
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri 63110, USA
- Intellectual and Developmental Disabilities Research Center, Washington University School of Medicine, St. Louis, Missouri 63108, USA
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41
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Whole Genome Analysis of Dizygotic Twins With Autism Reveals Prevalent Transposon Insertion Within Neuronal Regulatory Elements: Potential Implications for Disease Etiology and Clinical Assessment. J Autism Dev Disord 2023; 53:1091-1106. [PMID: 35759154 DOI: 10.1007/s10803-022-05636-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/03/2022] [Indexed: 10/17/2022]
Abstract
Transposable elements (TEs) have been implicated in autism spectrum disorder (ASD). However, our understanding of their roles is far from complete. Herein, we explored de novo TE insertions (dnTEIs) and de novo variants (DNVs) across the genomes of dizygotic twins with ASD and their parents. The neuronal regulatory elements had a tendency to harbor dnTEIs that were shared between twins, but ASD-risk genes had dnTEIs that were unique to each twin. The dnTEIs were 4.6-fold enriched in enhancers that are active in embryonic stem cell (ESC)-neurons (p < 0.001), but DNVs were 1.5-fold enriched in active enhancers of astrocytes (p = 0.0051). Our findings suggest that dnTEIs and DNVs play a role in ASD etiology by disrupting enhancers of neurons and astrocytes.
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42
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Issa H, Swart LE, Rasouli M, Ashtiani M, Nakjang S, Jyotsana N, Schuschel K, Heuser M, Blair H, Heidenreich O. Nanoparticle-mediated targeting of the fusion gene RUNX1/ETO in t(8;21)-positive acute myeloid leukaemia. Leukemia 2023; 37:820-834. [PMID: 36823395 PMCID: PMC10079536 DOI: 10.1038/s41375-023-01854-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 02/10/2023] [Accepted: 02/14/2023] [Indexed: 02/25/2023]
Abstract
A hallmark of acute myeloid leukaemias (AMLs) are chromosomal rearrangements that give rise to novel leukaemia-specific fusion genes. Most of these fusion genes are both initiating and driving events in AML and therefore constitute ideal therapeutic targets but are challenging to target by conventional drug development. siRNAs are frequently used for the specific suppression of fusion gene expression but require special formulations for efficient in vivo delivery. Here we describe the use of siRNA-loaded lipid nanoparticles for the specific therapeutic targeting of the leukaemic fusion gene RUNX1/ETO. Transient knockdown of RUNX1/ETO reduces its binding to its target genes and alters the binding of RUNX1 and its co-factor CBFβ. Transcriptomic changes in vivo were associated with substantially increased median survival of a t(8;21)-AML mouse model. Importantly, transient knockdown in vivo causes long-lasting inhibition of leukaemic proliferation and clonogenicity, induction of myeloid differentiation and a markedly impaired re-engraftment potential in vivo. These data strongly suggest that temporary inhibition of RUNX1/ETO results in long-term restriction of leukaemic self-renewal. Our results provide proof for the feasibility of targeting RUNX1/ETO in a pre-clinical setting and support the further development of siRNA-LNPs for the treatment of fusion gene-driven malignancies.
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Affiliation(s)
- Hasan Issa
- Wolfson Childhood Cancer Research Centre, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK.,Department of Pediatrics, Goethe University Frankfurt, Frankfurt, Germany
| | - Laura E Swart
- Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands
| | - Milad Rasouli
- Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands.,Department of Pediatric Oncology, Erasmus MC-Sophia Children's Hospital, Rotterdam, The Netherlands
| | - Minoo Ashtiani
- Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands
| | - Sirintra Nakjang
- Wolfson Childhood Cancer Research Centre, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Nidhi Jyotsana
- Department of Hematology, Hemostasis, Oncology and Stem Cell Transplantation, Hannover Medical School, Hannover, Germany
| | | | - Michael Heuser
- Department of Hematology, Hemostasis, Oncology and Stem Cell Transplantation, Hannover Medical School, Hannover, Germany
| | - Helen Blair
- Wolfson Childhood Cancer Research Centre, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Olaf Heidenreich
- Wolfson Childhood Cancer Research Centre, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK. .,Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands.
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43
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Barsoum M, Stenzel AT, Bochyńska A, Kuo CC, Tsompanidis A, Sayadi-Boroujeni R, Bussmann P, Lüscher-Firzlaff J, Costa IG, Lüscher B. Loss of the Ash2l subunit of histone H3K4 methyltransferase complexes reduces chromatin accessibility at promoters. Sci Rep 2022; 12:21506. [PMID: 36513698 PMCID: PMC9747801 DOI: 10.1038/s41598-022-25881-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Accepted: 12/06/2022] [Indexed: 12/15/2022] Open
Abstract
Changes in gene expression programs are intimately linked to cell fate decisions. Post-translational modifications of core histones contribute to control gene expression. Methylation of lysine 4 of histone H3 (H3K4) correlates with active promoters and gene transcription. This modification is catalyzed by KMT2 methyltransferases, which require interaction with 4 core subunits, WDR5, RBBP5, ASH2L and DPY30, for catalytic activity. Ash2l is necessary for organismal development and for tissue homeostasis. In mouse embryo fibroblasts (MEFs), Ash2l loss results in gene repression, provoking a senescence phenotype. We now find that upon knockout of Ash2l both H3K4 mono- and tri-methylation (H3K4me1 and me3, respectively) were deregulated. In particular, loss of H3K4me3 at promoters correlated with gene repression, especially at CpG island promoters. Ash2l loss resulted in increased loading of histone H3 and reduced chromatin accessibility at promoters, accompanied by an increase of repressing and a decrease of activating histone marks. Moreover, we observed altered binding of CTCF upon Ash2l loss. Lost and gained binding was noticed at promoter-associated and intergenic sites, respectively. Thus, Ash2l loss and reduction of H3K4me3 correlate with altered chromatin accessibility and transcription factor binding. These findings contribute to a more detailed understanding of mechanistic consequences of H3K4me3 loss and associated repression of gene transcription and thus of the observed cellular consequences.
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Affiliation(s)
- Mirna Barsoum
- grid.1957.a0000 0001 0728 696XInstitute of Biochemistry and Molecular Biology, Faculty of Medicine, RWTH Aachen University, Pauwelsstrasse 30, 52074 Aachen, Germany
| | - Alexander T. Stenzel
- grid.1957.a0000 0001 0728 696XInstitute of Biochemistry and Molecular Biology, Faculty of Medicine, RWTH Aachen University, Pauwelsstrasse 30, 52074 Aachen, Germany
| | - Agnieszka Bochyńska
- grid.1957.a0000 0001 0728 696XInstitute of Biochemistry and Molecular Biology, Faculty of Medicine, RWTH Aachen University, Pauwelsstrasse 30, 52074 Aachen, Germany
| | - Chao-Chung Kuo
- grid.1957.a0000 0001 0728 696XInstitute for Computational Genomics, Faculty of Medicine, RWTH Aachen University, Pauwelsstrasse 30, 52074 Aachen, Germany ,grid.1957.a0000 0001 0728 696XInterdisciplinary Center for Clinical Research (IZKF), Faculty of Medicine, RWTH Aachen University, Pauwelsstrasse 30, 52074 Aachen, Germany
| | - Alexander Tsompanidis
- grid.1957.a0000 0001 0728 696XInstitute of Biochemistry and Molecular Biology, Faculty of Medicine, RWTH Aachen University, Pauwelsstrasse 30, 52074 Aachen, Germany
| | - Roksaneh Sayadi-Boroujeni
- grid.1957.a0000 0001 0728 696XInstitute of Biochemistry and Molecular Biology, Faculty of Medicine, RWTH Aachen University, Pauwelsstrasse 30, 52074 Aachen, Germany
| | - Philip Bussmann
- grid.1957.a0000 0001 0728 696XInstitute of Biochemistry and Molecular Biology, Faculty of Medicine, RWTH Aachen University, Pauwelsstrasse 30, 52074 Aachen, Germany
| | - Juliane Lüscher-Firzlaff
- grid.1957.a0000 0001 0728 696XInstitute of Biochemistry and Molecular Biology, Faculty of Medicine, RWTH Aachen University, Pauwelsstrasse 30, 52074 Aachen, Germany
| | - Ivan G. Costa
- grid.1957.a0000 0001 0728 696XInstitute for Computational Genomics, Faculty of Medicine, RWTH Aachen University, Pauwelsstrasse 30, 52074 Aachen, Germany
| | - Bernhard Lüscher
- Institute of Biochemistry and Molecular Biology, Faculty of Medicine, RWTH Aachen University, Pauwelsstrasse 30, 52074, Aachen, Germany.
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44
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Integrating extrusion complex-associated pattern to predict cell type-specific long-range chromatin loops. iScience 2022; 25:105687. [PMID: 36567710 PMCID: PMC9768375 DOI: 10.1016/j.isci.2022.105687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2022] [Revised: 11/10/2022] [Accepted: 11/25/2022] [Indexed: 12/07/2022] Open
Abstract
The chromatin loop plays a critical role in the study of gene expression and disease. Supervised learning-based algorithms to predict the chromatin loops require large priori information to satisfy the model construction, while the prediction sensitivity of unsupervised learning-based algorithms is still unsatisfactory. Therefore, we propose an unsupervised algorithm, Ecomap-loop. It takes advantage of extrusion complex-associated patterns, including CTCF, RAD21, and SMC enrichments, as well as the orientation distribution of CTCF motif of loops to build feature matrices; then the eigen decomposition model is employed to obtain the cell type-specific loops. We compare the performance of Ecomap-loop with the state-of-the-art unsupervised algorithm using Hi-C, ChIA-PET, expression quantitative trait locus (eQTL), and CRISPR interference (CRISPRi) screen data; the results show that Ecomap-loop achieves the best in four cell types. In addition, the functional analysis reveals the ability of Ecomap-loop to predict active functionality-related and cell type-specific loops.
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45
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Tang Y, Aryal S, Geng X, Zhou X, Fast VG, Zhang J, Lu R, Zhou Y. TBX20 Improves Contractility and Mitochondrial Function During Direct Human Cardiac Reprogramming. Circulation 2022; 146:1518-1536. [PMID: 36102189 PMCID: PMC9662826 DOI: 10.1161/circulationaha.122.059713] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Direct cardiac reprogramming of fibroblasts into cardiomyocytes has emerged as a promising strategy to remuscularize injured myocardium. However, it is insufficient to generate functional induced cardiomyocytes from human fibroblasts using conventional reprogramming cocktails, and the underlying molecular mechanisms are not well studied. METHODS To discover potential missing factors for human direct reprogramming, we performed transcriptomic comparison between human induced cardiomyocytes and functional cardiomyocytes. RESULTS We identified TBX20 (T-box transcription factor 20) as the top cardiac gene that is unable to be activated by the MGT133 reprogramming cocktail (MEF2C, GATA4, TBX5, and miR-133). TBX20 is required for normal heart development and cardiac function in adult cardiomyocytes, yet its role in cardiac reprogramming remains undefined. We show that the addition of TBX20 to the MGT133 cocktail (MGT+TBX20) promotes cardiac reprogramming and activates genes associated with cardiac contractility, maturation, and ventricular heart. Human induced cardiomyocytes produced with MGT+TBX20 demonstrated more frequent beating, calcium oscillation, and higher energy metabolism as evidenced by increased mitochondria numbers and mitochondrial respiration. Mechanistically, comprehensive transcriptomic, chromatin occupancy, and epigenomic studies revealed that TBX20 colocalizes with MGT reprogramming factors at cardiac gene enhancers associated with heart contraction, promotes chromatin binding and co-occupancy of MGT factors at these loci, and synergizes with MGT for more robust activation of target gene transcription. CONCLUSIONS TBX20 consolidates MGT cardiac reprogramming factors to activate cardiac enhancers to promote cardiac cell fate conversion. Human induced cardiomyocytes generated with TBX20 showed enhanced cardiac function in contractility and mitochondrial respiration.
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Affiliation(s)
- Yawen Tang
- Department of Biomedical Engineering (Y.T., X.G., V.G.F., J.Z., Y.Z.), Heersink School of Medicine, School of Engineering, University of Alabama at Birmingham
| | - Sajesan Aryal
- Department of Medicine, Division of Hematology and Oncology (S.A., X.Z., R.L.), Heersink School of Medicine, School of Engineering, University of Alabama at Birmingham.,O’Neal Comprehensive Cancer Center (S.A., X.Z., R.L.), Heersink School of Medicine, School of Engineering, University of Alabama at Birmingham
| | - Xiaoxiao Geng
- Department of Biomedical Engineering (Y.T., X.G., V.G.F., J.Z., Y.Z.), Heersink School of Medicine, School of Engineering, University of Alabama at Birmingham
| | - Xinyue Zhou
- Department of Medicine, Division of Hematology and Oncology (S.A., X.Z., R.L.), Heersink School of Medicine, School of Engineering, University of Alabama at Birmingham.,O’Neal Comprehensive Cancer Center (S.A., X.Z., R.L.), Heersink School of Medicine, School of Engineering, University of Alabama at Birmingham
| | - Vladimir G. Fast
- Department of Biomedical Engineering (Y.T., X.G., V.G.F., J.Z., Y.Z.), Heersink School of Medicine, School of Engineering, University of Alabama at Birmingham
| | - Jianyi Zhang
- Department of Biomedical Engineering (Y.T., X.G., V.G.F., J.Z., Y.Z.), Heersink School of Medicine, School of Engineering, University of Alabama at Birmingham
| | - Rui Lu
- Department of Medicine, Division of Hematology and Oncology (S.A., X.Z., R.L.), Heersink School of Medicine, School of Engineering, University of Alabama at Birmingham.,O’Neal Comprehensive Cancer Center (S.A., X.Z., R.L.), Heersink School of Medicine, School of Engineering, University of Alabama at Birmingham
| | - Yang Zhou
- Department of Biomedical Engineering (Y.T., X.G., V.G.F., J.Z., Y.Z.), Heersink School of Medicine, School of Engineering, University of Alabama at Birmingham
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46
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Ni P, Wilson D, Su Z. A map of cis-regulatory modules and constituent transcription factor binding sites in 80% of the mouse genome. BMC Genomics 2022; 23:714. [PMID: 36261804 PMCID: PMC9583556 DOI: 10.1186/s12864-022-08933-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2022] [Accepted: 10/11/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Mouse is probably the most important model organism to study mammal biology and human diseases. A better understanding of the mouse genome will help understand the human genome, biology and diseases. However, despite the recent progress, the characterization of the regulatory sequences in the mouse genome is still far from complete, limiting its use to understand the regulatory sequences in the human genome. RESULTS Here, by integrating binding peaks in ~ 9,000 transcription factor (TF) ChIP-seq datasets that cover 79.9% of the mouse mappable genome using an efficient pipeline, we were able to partition these binding peak-covered genome regions into a cis-regulatory module (CRM) candidate (CRMC) set and a non-CRMC set. The CRMCs contain 912,197 putative CRMs and 38,554,729 TF binding sites (TFBSs) islands, covering 55.5% and 24.4% of the mappable genome, respectively. The CRMCs tend to be under strong evolutionary constraints, indicating that they are likely cis-regulatory; while the non-CRMCs are largely selectively neutral, indicating that they are unlikely cis-regulatory. Based on evolutionary profiles of the genome positions, we further estimated that 63.8% and 27.4% of the mouse genome might code for CRMs and TFBSs, respectively. CONCLUSIONS Validation using experimental data suggests that at least most of the CRMCs are authentic. Thus, this unprecedentedly comprehensive map of CRMs and TFBSs can be a good resource to guide experimental studies of regulatory genomes in mice and humans.
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Affiliation(s)
- Pengyu Ni
- Department of Bioinformatics and Genomics, the University of North Carolina at Charlotte, Charlotte, NC, 28223, USA
| | - David Wilson
- Department of Bioinformatics and Genomics, the University of North Carolina at Charlotte, Charlotte, NC, 28223, USA
| | - Zhengchang Su
- Department of Bioinformatics and Genomics, the University of North Carolina at Charlotte, Charlotte, NC, 28223, USA.
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47
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Ni P, Moe J, Su Z. Accurate prediction of functional states of cis-regulatory modules reveals common epigenetic rules in humans and mice. BMC Biol 2022; 20:221. [PMID: 36199141 PMCID: PMC9535988 DOI: 10.1186/s12915-022-01426-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 09/29/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Predicting cis-regulatory modules (CRMs) in a genome and their functional states in various cell/tissue types of the organism are two related challenging computational tasks. Most current methods attempt to simultaneously achieve both using data of multiple epigenetic marks in a cell/tissue type. Though conceptually attractive, they suffer high false discovery rates and limited applications. To fill the gaps, we proposed a two-step strategy to first predict a map of CRMs in the genome, and then predict functional states of all the CRMs in various cell/tissue types of the organism. We have recently developed an algorithm for the first step that was able to more accurately and completely predict CRMs in a genome than existing methods by integrating numerous transcription factor ChIP-seq datasets in the organism. Here, we presented machine-learning methods for the second step. RESULTS We showed that functional states in a cell/tissue type of all the CRMs in the genome could be accurately predicted using data of only 1~4 epigenetic marks by a variety of machine-learning classifiers. Our predictions are substantially more accurate than the best achieved so far. Interestingly, a model trained on a cell/tissue type in humans can accurately predict functional states of CRMs in different cell/tissue types of humans as well as of mice, and vice versa. Therefore, epigenetic code that defines functional states of CRMs in various cell/tissue types is universal at least in humans and mice. Moreover, we found that from tens to hundreds of thousands of CRMs were active in a human and mouse cell/tissue type, and up to 99.98% of them were reutilized in different cell/tissue types, while as small as 0.02% of them were unique to a cell/tissue type that might define the cell/tissue type. CONCLUSIONS Our two-step approach can accurately predict functional states in any cell/tissue type of all the CRMs in the genome using data of only 1~4 epigenetic marks. Our approach is also more cost-effective than existing methods that typically use data of more epigenetic marks. Our results suggest common epigenetic rules for defining functional states of CRMs in various cell/tissue types in humans and mice.
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Affiliation(s)
- Pengyu Ni
- Department of Bioinformatics and Genomics, the University of North Carolina at Charlotte, Charlotte, NC, 28223, USA
| | - Joshua Moe
- Department of Bioinformatics and Genomics, the University of North Carolina at Charlotte, Charlotte, NC, 28223, USA
| | - Zhengchang Su
- Department of Bioinformatics and Genomics, the University of North Carolina at Charlotte, Charlotte, NC, 28223, USA.
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48
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Gandhi S, Mitterhoff R, Rapoport R, Farago M, Greenberg A, Hodge L, Eden S, Benner C, Goren A, Simon I. Mitotic H3K9ac is controlled by phase-specific activity of HDAC2, HDAC3, and SIRT1. Life Sci Alliance 2022; 5:5/10/e202201433. [PMID: 35981887 PMCID: PMC9389593 DOI: 10.26508/lsa.202201433] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 07/29/2022] [Accepted: 08/01/2022] [Indexed: 11/24/2022] Open
Abstract
Combination of immunofluorescence, Western blot, and ChIP-seq revealed the interplay between HDAC2, HDAC3, and SIRT1 in H3K9 deacetylation during mitosis of mammalian cells. Histone acetylation levels are reduced during mitosis. To study the mitotic regulation of H3K9ac, we used an array of inhibitors targeting specific histone deacetylases. We evaluated the involvement of the targeted enzymes in regulating H3K9ac during all mitotic stages by immunofluorescence and immunoblots. We identified HDAC2, HDAC3, and SIRT1 as modulators of H3K9ac mitotic levels. HDAC2 inhibition increased H3K9ac levels in prophase, whereas HDAC3 or SIRT1 inhibition increased H3K9ac levels in metaphase. Next, we performed ChIP-seq on mitotic-arrested cells following targeted inhibition of these histone deacetylases. We found that both HDAC2 and HDAC3 have a similar impact on H3K9ac, and inhibiting either of these two HDACs substantially increases the levels of this histone acetylation in promoters, enhancers, and insulators. Altogether, our results support a model in which H3K9 deacetylation is a stepwise process—at prophase, HDAC2 modulates most transcription-associated H3K9ac-marked loci, and at metaphase, HDAC3 maintains the reduced acetylation, whereas SIRT1 potentially regulates H3K9ac by impacting HAT activity.
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Affiliation(s)
- Shashi Gandhi
- Department of Microbiology and Molecular Genetics, Institute of Medical Research Israel-Canada, Faculty of Medicine, The Hebrew University, Jerusalem, Israel
| | - Raizy Mitterhoff
- Department of Microbiology and Molecular Genetics, Institute of Medical Research Israel-Canada, Faculty of Medicine, The Hebrew University, Jerusalem, Israel
| | - Rachel Rapoport
- Department of Microbiology and Molecular Genetics, Institute of Medical Research Israel-Canada, Faculty of Medicine, The Hebrew University, Jerusalem, Israel
| | - Marganit Farago
- Department of Microbiology and Molecular Genetics, Institute of Medical Research Israel-Canada, Faculty of Medicine, The Hebrew University, Jerusalem, Israel
| | - Avraham Greenberg
- Department of Microbiology and Molecular Genetics, Institute of Medical Research Israel-Canada, Faculty of Medicine, The Hebrew University, Jerusalem, Israel
| | - Lauren Hodge
- Department of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Sharon Eden
- Department of Microbiology and Molecular Genetics, Institute of Medical Research Israel-Canada, Faculty of Medicine, The Hebrew University, Jerusalem, Israel
| | - Christopher Benner
- Department of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Alon Goren
- Department of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Itamar Simon
- Department of Microbiology and Molecular Genetics, Institute of Medical Research Israel-Canada, Faculty of Medicine, The Hebrew University, Jerusalem, Israel
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49
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Tran JR, Zheng X, Adam SA, Goldman RD, Zheng Y. High quality mapping of chromatin at or near the nuclear lamina from small numbers of cells reveals cell cycle and developmental changes of chromatin at the nuclear periphery. Nucleic Acids Res 2022; 50:e117. [PMID: 36130229 PMCID: PMC9723609 DOI: 10.1093/nar/gkac762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 07/28/2022] [Accepted: 09/16/2022] [Indexed: 12/24/2022] Open
Abstract
The chromatin associated with the nuclear lamina (NL) is referred to as lamina-associated domains (LADs). Here, we present an adaptation of the tyramide-signal amplification sequencing (TSA-seq) protocol, which we call chromatin pull down-based TSA-seq (cTSA-seq), that can be used to map chromatin regions at or near the NL from as little as 50 000 cells. The cTSA-seq mapped regions are composed of previously defined LADs and smaller chromatin regions that fall within the Hi-C defined B-compartment containing nuclear peripheral heterochromatin. We used cTSA-seq to map chromatin at or near the assembling NL in cultured cells progressing through early G1. cTSA-seq revealed that the distal ends of chromosomes are near or at the reassembling NL during early G1, a feature similar to those found in senescent cells. We expand the use of cTSA-seq to the mapping of chromatin at or near the NL from fixed-frozen mouse cerebellar tissue sections. This mapping reveals a general conservation of NL-associated chromatin and identifies global and local changes during cerebellar development. The cTSA-seq method reported here is useful for analyzing chromatin at or near the NL from small numbers of cells derived from both in vitro and in vivo sources.
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Affiliation(s)
- Joseph R Tran
- Correspondence may also be addressed to Joseph R. Tran. Tel: +1 410 246 3032; Fax: +1 410 243 6311;
| | - Xiaobin Zheng
- Department of Embryology, Carnegie Institution for Science, 3520 San Martin Drive, Baltimore, MD 21218, USA
| | - Stephen A Adam
- Department of Cell and Developmental Biology, Northwestern University, Feinberg School of Medicine, Ward Building 11-145, 303 E. Chicago Ave. Chicago, IL 60611, USA
| | - Robert D Goldman
- Department of Cell and Developmental Biology, Northwestern University, Feinberg School of Medicine, Ward Building 11-145, 303 E. Chicago Ave. Chicago, IL 60611, USA
| | - Yixian Zheng
- To whom correspondence should be addressed. Tel: +1 410 246 3032; Fax: +1 410 243 6311;
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50
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Abstract
Chromatin architecture, a key regulator of gene expression, can be inferred using chromatin contact data from chromosome conformation capture, or Hi-C. However, classical Hi-C does not preserve multi-way contacts. Here we use long sequencing reads to map genome-wide multi-way contacts and investigate higher order chromatin organization in the human genome. We use hypergraph theory for data representation and analysis, and quantify higher order structures in neonatal fibroblasts, biopsied adult fibroblasts, and B lymphocytes. By integrating multi-way contacts with chromatin accessibility, gene expression, and transcription factor binding, we introduce a data-driven method to identify cell type-specific transcription clusters. We provide transcription factor-mediated functional building blocks for cell identity that serve as a global signature for cell types. Mapping higher order chromatin architecture is important. Here the authors use long sequencing reads to map genome-wide multi-way contacts and investigate higher order chromatin organisation; they use hypergraph theory for data representation and analysis, and apply this to different cell types.
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