1
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Yip CW, Hon CC, Yasuzawa K, Sivaraman DM, Ramilowski JA, Shibayama Y, Agrawal S, Prabhu AV, Parr C, Severin J, Lan YJ, Dostie J, Petri A, Nishiyori-Sueki H, Tagami M, Itoh M, López-Redondo F, Kouno T, Chang JC, Luginbühl J, Kato M, Murata M, Yip WH, Shu X, Abugessaisa I, Hasegawa A, Suzuki H, Kauppinen S, Yagi K, Okazaki Y, Kasukawa T, de Hoon M, Carninci P, Shin JW. Antisense-oligonucleotide-mediated perturbation of long non-coding RNA reveals functional features in stem cells and across cell types. Cell Rep 2022; 41:111893. [PMID: 36577377 DOI: 10.1016/j.celrep.2022.111893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 08/30/2022] [Accepted: 12/07/2022] [Indexed: 12/28/2022] Open
Abstract
Within the scope of the FANTOM6 consortium, we perform a large-scale knockdown of 200 long non-coding RNAs (lncRNAs) in human induced pluripotent stem cells (iPSCs) and systematically characterize their roles in self-renewal and pluripotency. We find 36 lncRNAs (18%) exhibiting cell growth inhibition. From the knockdown of 123 lncRNAs with transcriptome profiling, 36 lncRNAs (29.3%) show molecular phenotypes. Integrating the molecular phenotypes with chromatin-interaction assays further reveals cis- and trans-interacting partners as potential primary targets. Additionally, cell-type enrichment analysis identifies lncRNAs associated with pluripotency, while the knockdown of LINC02595, CATG00000090305.1, and RP11-148B6.2 modulates colony formation of iPSCs. We compare our results with previously published fibroblasts phenotyping data and find that 2.9% of the lncRNAs exhibit a consistent cell growth phenotype, whereas we observe 58.3% agreement in molecular phenotypes. This highlights that molecular phenotyping is more comprehensive in revealing affected pathways.
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Affiliation(s)
- Chi Wai Yip
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan
| | - Chung-Chau Hon
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan
| | - Kayoko Yasuzawa
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan
| | - Divya M Sivaraman
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan; Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, Kerala 695 011, India
| | - Jordan A Ramilowski
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan; Advanced Medical Research Center, Yokohama City University, Yokohama, Kanagawa 236-0004, Japan
| | - Youtaro Shibayama
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan
| | - Saumya Agrawal
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan
| | - Anika V Prabhu
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan
| | - Callum Parr
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan
| | - Jessica Severin
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan
| | - Yan Jun Lan
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan
| | - Josée Dostie
- Department of Biochemistry, Rosalind and Morris Goodman Cancer Research Center, McGill University, Montréal, QC, Canada
| | - Andreas Petri
- Center for RNA Medicine, Department of Clinical Medicine, Aalborg University, Copenhagen 2450, Denmark
| | | | - Michihira Tagami
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan
| | - Masayoshi Itoh
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan
| | | | - Tsukasa Kouno
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan
| | - Jen-Chien Chang
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan
| | - Joachim Luginbühl
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan
| | - Masaki Kato
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan
| | - Mitsuyoshi Murata
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan
| | - Wing Hin Yip
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan
| | - Xufeng Shu
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan; Department of Computational Biology and Medical Sciences, The University of Tokyo, Kashiwa, Chiba, 277-8562, Japan
| | - Imad Abugessaisa
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan
| | - Akira Hasegawa
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan
| | - Harukazu Suzuki
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan
| | - Sakari Kauppinen
- Center for RNA Medicine, Department of Clinical Medicine, Aalborg University, Copenhagen 2450, Denmark
| | - Ken Yagi
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan
| | - Yasushi Okazaki
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan
| | - Takeya Kasukawa
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan
| | - Michiel de Hoon
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan
| | - Piero Carninci
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan; Human Technopole, via Rita Levi Montalcini 1, Milan, Italy
| | - Jay W Shin
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan; Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore 138672, Singapore.
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2
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de Hoon M, Bonetti A, Plessy C, Ando Y, Hon CC, Ishizu Y, Itoh M, Kato S, Lin D, Maekawa S, Murata M, Nishiyori H, Shin JW, Stolte J, Suzuki AM, Tagami M, Takahashi H, Thongjuea S, Forrest ARR, Hayashizaki Y, Kere J, Carninci P. Deep sequencing of short capped RNAs reveals novel families of noncoding RNAs. Genome Res 2022; 32:gr.276647.122. [PMID: 35961773 PMCID: PMC9528987 DOI: 10.1101/gr.276647.122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 08/09/2022] [Indexed: 12/03/2022]
Abstract
In eukaryotes, capped RNAs include long transcripts such as messenger RNAs and long noncoding RNAs, as well as shorter transcripts such as spliceosomal RNAs, small nucleolar RNAs, and enhancer RNAs. Long capped transcripts can be profiled using cap analysis gene expression (CAGE) sequencing and other methods. Here, we describe a sequencing library preparation protocol for short capped RNAs, apply it to a differentiation time course of the human cell line THP-1, and systematically compare the landscape of short capped RNAs to that of long capped RNAs. Transcription initiation peaks associated with genes in the sense direction have a strong preference to produce either long or short capped RNAs, with one out of six peaks detected in the short capped RNA libraries only. Gene-associated short capped RNAs have highly specific 3' ends, typically overlapping splice sites. Enhancers also preferentially generate either short or long capped RNAs, with 10% of enhancers observed in the short capped RNA libraries only. Enhancers producing either short or long capped RNAs show enrichment for GWAS-associated disease SNPs. We conclude that deep sequencing of short capped RNAs reveals new families of noncoding RNAs and elucidates the diversity of transcripts generated at known and novel promoters and enhancers.
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Affiliation(s)
- Michiel de Hoon
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan
| | - Alessandro Bonetti
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan
| | - Charles Plessy
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan
| | - Yoshinari Ando
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan
| | - Chung-Chau Hon
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan
| | - Yuri Ishizu
- RIKEN Center for Life Science Technologies, Division of Genomic Technologies, Yokohama, Kanagawa 230-0045, Japan
| | - Masayoshi Itoh
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan
- RIKEN Preventive Medicine and Diagnosis Innovation Program, Wako, Saitama 351-0198, Japan
| | - Sachi Kato
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan
| | - Dongyan Lin
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan
- Integrated Program in Neuroscience, McGill University, Montreal, Quebec H3A 1A1, Canada
- Mila, Montreal, Quebec H2S 3H1, Canada
| | - Sho Maekawa
- RIKEN Omics Science Center (OSC), Yokohama, Kanagawa 230-0045, Japan
| | - Mitsuyoshi Murata
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan
| | - Hiromi Nishiyori
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan
| | - Jay W Shin
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan
- Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), Singapore, 138632, Singapore
| | - Jens Stolte
- RIKEN Center for Life Science Technologies, Division of Genomic Technologies, Yokohama, Kanagawa 230-0045, Japan
| | - Ana Maria Suzuki
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan
| | - Michihira Tagami
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan
| | - Hazuki Takahashi
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan
| | - Supat Thongjuea
- RIKEN Center for Life Science Technologies, Division of Genomic Technologies, Yokohama, Kanagawa 230-0045, Japan
| | - Alistair R R Forrest
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan
- Harry Perkins Institute of Medical Research, QEII Medical Centre and Centre for Medical Research, The University of Western Australia, Nedlands, Perth, Western Australia 6009, Australia
| | - Yoshihide Hayashizaki
- RIKEN Preventive Medicine and Diagnosis Innovation Program, Wako, Saitama 351-0198, Japan
- RIKEN Omics Science Center (OSC), Yokohama, Kanagawa 230-0045, Japan
| | - Juha Kere
- Department of Biosciences and Nutrition, Karolinska Institutet, Huddinge 14157, Sweden
- Stem Cells and Metabolism Research Program, University of Helsinki and Folkhälsan Research Center, Helsinki 00290, Finland
| | - Piero Carninci
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan
- Human Technopole, Milan 20157, Italy
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3
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Way GP, Greene CS, Carninci P, Carvalho BS, de Hoon M, Finley SD, Gosline SJC, Lȇ Cao KA, Lee JSH, Marchionni L, Robine N, Sindi SS, Theis FJ, Yang JYH, Carpenter AE, Fertig EJ. A field guide to cultivating computational biology. PLoS Biol 2021; 19:e3001419. [PMID: 34618807 PMCID: PMC8525744 DOI: 10.1371/journal.pbio.3001419] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Revised: 10/19/2021] [Indexed: 11/18/2022] Open
Abstract
Evolving in sync with the computation revolution over the past 30 years, computational biology has emerged as a mature scientific field. While the field has made major contributions toward improving scientific knowledge and human health, individual computational biology practitioners at various institutions often languish in career development. As optimistic biologists passionate about the future of our field, we propose solutions for both eager and reluctant individual scientists, institutions, publishers, funding agencies, and educators to fully embrace computational biology. We believe that in order to pave the way for the next generation of discoveries, we need to improve recognition for computational biologists and better align pathways of career success with pathways of scientific progress. With 10 outlined steps, we call on all adjacent fields to move away from the traditional individual, single-discipline investigator research model and embrace multidisciplinary, data-driven, team science.
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Affiliation(s)
- Gregory P. Way
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
- Center for Health AI, University of Colorado School of Medicine, Aurora, Colorado, United States of America
| | - Casey S. Greene
- Center for Health AI, University of Colorado School of Medicine, Aurora, Colorado, United States of America
| | - Piero Carninci
- RIKEN Center for Integrative Medical Sciences Yokohama, Kanagawa, Japan
- Human Technopole, Milan, Italy
| | - Benilton S. Carvalho
- Department of Statistics, Institute of Mathematics, Statistics and Scientific Computing, University of Campinas, Campinas, Brazil
| | - Michiel de Hoon
- RIKEN Center for Integrative Medical Sciences Yokohama, Kanagawa, Japan
| | - Stacey D. Finley
- Department of Biomedical Engineering, Quantitative and Computational Biology, and Chemical Engineering & Materials Science, University of Southern California, Los Angeles, California, United States of America
| | - Sara J. C. Gosline
- Pacific Northwest National Laboratory, Seattle, Washington, United States of America
| | - Kim-Anh Lȇ Cao
- Melbourne Integrative Genomics, School of Mathematics and Statistics, The University of Melbourne, Melbourne, Australia
| | - Jerry S. H. Lee
- Ellison Institute and Departments of Medicine/Oncology, Chemical Engineering, and Material Sciences, University of Southern California, Los Angeles, California, United States of America
| | - Luigi Marchionni
- Department of Pathology and Laboratory Medicine, Weill-Cornell Medicine, New York, New York, United States of America
| | - Nicolas Robine
- Computational Biology Lab, New York Genome Center, New York, New York, United States of America
| | - Suzanne S. Sindi
- Department of Applied Mathematics, University of California Merced, Merced, California, United States of America
| | - Fabian J. Theis
- Institute of Computational Biology, Helmholtz Center Munich and Department of Mathematics, Technical University of Munich, Munich, Germany
| | - Jean Y. H. Yang
- Charles Perkins Centre and School of Mathematics and Statistics, The University of Sydney, Australia
| | - Anne E. Carpenter
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
| | - Elana J. Fertig
- Convergence Institute, Departments of Oncology, Biomedical Engineering, and Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, Maryland, United States of America
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4
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Bonetti A, Agostini F, Suzuki AM, Hashimoto K, Pascarella G, Gimenez J, Roos L, Nash AJ, Ghilotti M, Cameron CJF, Valentine M, Medvedeva YA, Noguchi S, Agirre E, Kashi K, Samudyata, Luginbühl J, Cazzoli R, Agrawal S, Luscombe NM, Blanchette M, Kasukawa T, de Hoon M, Arner E, Lenhard B, Plessy C, Castelo-Branco G, Orlando V, Carninci P. Author Correction: RADICL-seq identifies general and cell type–specific principles of genome-wide RNA-chromatin interactions. Nat Commun 2021; 12:3128. [PMID: 34011993 PMCID: PMC8134558 DOI: 10.1038/s41467-021-23542-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
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5
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Hashimoto M, Saito Y, Nakagawa R, Ogahara I, Takagi S, Takata S, Amitani H, Endo M, Yuki H, Ramilowski JA, Severin J, Manabe RI, Watanabe T, Ozaki K, Kaneko A, Kajita H, Fujiki S, Sato K, Honma T, Uchida N, Fukami T, Okazaki Y, Ohara O, Shultz LD, Yamada M, Taniguchi S, Vyas P, de Hoon M, Momozawa Y, Ishikawa F. Combined inhibition of XIAP and BCL2 drives maximal therapeutic efficacy in genetically diverse aggressive acute myeloid leukemia. ACTA ACUST UNITED AC 2021; 2:340-356. [DOI: 10.1038/s43018-021-00177-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Accepted: 01/22/2021] [Indexed: 01/18/2023]
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6
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Gažová I, Lefevre L, Bush SJ, Clohisey S, Arner E, de Hoon M, Severin J, van Duin L, Andersson R, Lengeling A, Hume DA, Summers KM. The Transcriptional Network That Controls Growth Arrest and Macrophage Differentiation in the Human Myeloid Leukemia Cell Line THP-1. Front Cell Dev Biol 2020; 8:498. [PMID: 32719792 PMCID: PMC7347797 DOI: 10.3389/fcell.2020.00498] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2019] [Accepted: 05/25/2020] [Indexed: 12/12/2022] Open
Abstract
The response of the human acute myeloid leukemia cell line THP-1 to phorbol esters has been widely studied to test candidate leukemia therapies and as a model of cell cycle arrest and monocyte-macrophage differentiation. Here we have employed Cap Analysis of Gene Expression (CAGE) to analyze a dense time course of transcriptional regulation in THP-1 cells treated with phorbol myristate acetate (PMA) over 96 h. PMA treatment greatly reduced the numbers of cells entering S phase and also blocked cells exiting G2/M. The PMA-treated cells became adherent and expression of mature macrophage-specific genes increased progressively over the duration of the time course. Within 1–2 h PMA induced known targets of tumor protein p53 (TP53), notably CDKN1A, followed by gradual down-regulation of cell-cycle associated genes. Also within the first 2 h, PMA induced immediate early genes including transcription factor genes encoding proteins implicated in macrophage differentiation (EGR2, JUN, MAFB) and down-regulated genes for transcription factors involved in immature myeloid cell proliferation (MYB, IRF8, GFI1). The dense time course revealed that the response to PMA was not linear and progressive. Rather, network-based clustering of the time course data highlighted a sequential cascade of transient up- and down-regulated expression of genes encoding feedback regulators, as well as transcription factors associated with macrophage differentiation and their inferred target genes. CAGE also identified known and candidate novel enhancers expressed in THP-1 cells and many novel inducible genes that currently lack functional annotation and/or had no previously known function in macrophages. The time course is available on the ZENBU platform allowing comparison to FANTOM4 and FANTOM5 data.
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Affiliation(s)
- Iveta Gažová
- The Roslin Institute, The University of Edinburgh, Edinburgh, United Kingdom
| | - Lucas Lefevre
- The Roslin Institute, The University of Edinburgh, Edinburgh, United Kingdom
| | - Stephen J Bush
- The Roslin Institute, The University of Edinburgh, Edinburgh, United Kingdom
| | - Sara Clohisey
- The Roslin Institute, The University of Edinburgh, Edinburgh, United Kingdom
| | - Erik Arner
- RIKEN Center for Integrative Medical Sciences, Kanagawa, Yokohama, Japan
| | - Michiel de Hoon
- RIKEN Center for Integrative Medical Sciences, Kanagawa, Yokohama, Japan
| | - Jessica Severin
- RIKEN Center for Integrative Medical Sciences, Kanagawa, Yokohama, Japan
| | - Lucas van Duin
- Bioinformatics Centre, University of Copenhagen, Copenhagen, Denmark
| | - Robin Andersson
- Bioinformatics Centre, University of Copenhagen, Copenhagen, Denmark
| | | | - David A Hume
- Mater Research Institute - University of Queensland, Translational Research Institute, Brisbane, QLD, Australia
| | - Kim M Summers
- The Roslin Institute, The University of Edinburgh, Edinburgh, United Kingdom.,Mater Research Institute - University of Queensland, Translational Research Institute, Brisbane, QLD, Australia
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7
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Bonetti A, Agostini F, Suzuki AM, Hashimoto K, Pascarella G, Gimenez J, Roos L, Nash AJ, Ghilotti M, Cameron CJF, Valentine M, Medvedeva YA, Noguchi S, Agirre E, Kashi K, Samudyata, Luginbühl J, Cazzoli R, Agrawal S, Luscombe NM, Blanchette M, Kasukawa T, Hoon MD, Arner E, Lenhard B, Plessy C, Castelo-Branco G, Orlando V, Carninci P. RADICL-seq identifies general and cell type-specific principles of genome-wide RNA-chromatin interactions. Nat Commun 2020; 11:1018. [PMID: 32094342 PMCID: PMC7039879 DOI: 10.1038/s41467-020-14337-6] [Citation(s) in RCA: 74] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Accepted: 12/18/2019] [Indexed: 12/18/2022] Open
Abstract
Mammalian genomes encode tens of thousands of noncoding RNAs. Most noncoding transcripts exhibit nuclear localization and several have been shown to play a role in the regulation of gene expression and chromatin remodeling. To investigate the function of such RNAs, methods to massively map the genomic interacting sites of multiple transcripts have been developed; however, these methods have some limitations. Here, we introduce RNA And DNA Interacting Complexes Ligated and sequenced (RADICL-seq), a technology that maps genome-wide RNA–chromatin interactions in intact nuclei. RADICL-seq is a proximity ligation-based methodology that reduces the bias for nascent transcription, while increasing genomic coverage and unique mapping rate efficiency compared with existing methods. RADICL-seq identifies distinct patterns of genome occupancy for different classes of transcripts as well as cell type–specific RNA-chromatin interactions, and highlights the role of transcription in the establishment of chromatin structure. Mammalian genomes encode tens of thousands of ncRNAs that have important roles in regulation of gene expression and chromatin organization. Here, the authors present RADICLseq to map RNA-chromatin interactions in intact nuclei to shed light on these fine-tuned processes.
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Affiliation(s)
- Alessandro Bonetti
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, 230-0045, Japan. .,Laboratory of Molecular Neurobiology, Department Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden.
| | | | - Ana Maria Suzuki
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, 230-0045, Japan.,Department of Medicine (H7), Karolinska Institutet, Stockholm, 141 86, Sweden
| | - Kosuke Hashimoto
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, 230-0045, Japan
| | - Giovanni Pascarella
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, 230-0045, Japan
| | - Juliette Gimenez
- Epigenetics and Genome Reprogramming Laboratory, IRCCS Fondazione Santa Lucia, Rome, Italy
| | - Leonie Roos
- Faculty of Medicine, Imperial College London, Institute of Clinical Sciences, London, W12 0NN, UK.,MRC London Institute of Medical Sciences, London, W12 0NN, UK
| | - Alex J Nash
- Faculty of Medicine, Imperial College London, Institute of Clinical Sciences, London, W12 0NN, UK.,MRC London Institute of Medical Sciences, London, W12 0NN, UK
| | - Marco Ghilotti
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, 230-0045, Japan
| | - Christopher J F Cameron
- School of Computer Science, McGill University, Montréal, QC, Canada.,Department of Biochemistry and Goodman Cancer Research Centre, McGill University, Montréal, QC, Canada
| | - Matthew Valentine
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, 230-0045, Japan
| | - Yulia A Medvedeva
- Institute of Bioengineering, Research Centre of Biotechnology, Russian Academy of Science, 117312, Moscow, Russia.,Department of Computational Biology, Vavilov Institute of General Genetics, Russian Academy of Science, 119991, Moscow, Russia.,Department of Biological and Medical Physics, Moscow Institute of Physics and Technology, 141701, Dolgoprudny, Moscow Region, Russia
| | - Shuhei Noguchi
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, 230-0045, Japan
| | - Eneritz Agirre
- Laboratory of Molecular Neurobiology, Department Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Kaori Kashi
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, 230-0045, Japan
| | - Samudyata
- Laboratory of Molecular Neurobiology, Department Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Joachim Luginbühl
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, 230-0045, Japan
| | - Riccardo Cazzoli
- Department of Experimental Oncology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Saumya Agrawal
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, 230-0045, Japan
| | - Nicholas M Luscombe
- The Francis Crick Institute, 1 Midland Road, London, NW1 1AT, UK.,UCL Genetics Institute, University College London, London, WC1E 6BT, UK.,Okinawa Institute of Science and Technology, Graduate University, 1919-1 Tancha, Onna-son, Kunigami-gun, Okinawa, 904-0495, Japan
| | | | - Takeya Kasukawa
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, 230-0045, Japan
| | - Michiel de Hoon
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, 230-0045, Japan
| | - Erik Arner
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, 230-0045, Japan
| | - Boris Lenhard
- Faculty of Medicine, Imperial College London, Institute of Clinical Sciences, London, W12 0NN, UK.,MRC London Institute of Medical Sciences, London, W12 0NN, UK.,Sars International Centre for Marine Molecular Biology, University of Bergen, 5008, Bergen, Norway
| | - Charles Plessy
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, 230-0045, Japan
| | - Gonçalo Castelo-Branco
- Laboratory of Molecular Neurobiology, Department Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Valerio Orlando
- Epigenetics and Genome Reprogramming Laboratory, IRCCS Fondazione Santa Lucia, Rome, Italy. .,KAUST Environmental Epigenetics Program, King Abdullah University of Science and Technology (KAUST), Division of Biological Environmental Sciences and Engineering, 23955-6900, Thuwal, Saudi Arabia.
| | - Piero Carninci
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, 230-0045, Japan.
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8
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Huang Y, Furuno M, Arakawa T, Takizawa S, de Hoon M, Suzuki H, Arner E. A framework for identification of on- and off-target transcriptional responses to drug treatment. Sci Rep 2019; 9:17603. [PMID: 31772269 PMCID: PMC6879629 DOI: 10.1038/s41598-019-54180-4] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Accepted: 11/09/2019] [Indexed: 12/26/2022] Open
Abstract
Owing to safety concerns or insufficient efficacy, few drug candidates are approved for marketing. Drugs already on the market may be withdrawn due to adverse effects (AEs) discovered after market introduction. Comprehensively investigating the on-/off-target effects of drugs can help expose AEs during the drug development process. We have developed an integrative framework for systematic identification of on-/off-target pathways and elucidation of the underlying regulatory mechanisms, by combining promoter expression profiling after drug treatment with gene perturbation of the primary drug target. Expression profiles from statin-treated cells and HMG-CoA reductase knockdowns were analyzed using the framework, allowing for identification of not only reported adverse effects but also novel candidates of off-target effects from statin treatment, including key regulatory elements of on- and off-targets. Our findings may provide new insights for finding new usages or potential side effects of drug treatment.
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Affiliation(s)
- Yi Huang
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, 230-0045, Japan
| | - Masaaki Furuno
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, 230-0045, Japan
| | - Takahiro Arakawa
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, 230-0045, Japan
| | - Satoshi Takizawa
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, 230-0045, Japan
| | - Michiel de Hoon
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, 230-0045, Japan
| | - Harukazu Suzuki
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, 230-0045, Japan
| | - Erik Arner
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, 230-0045, Japan.
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9
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Kulyté A, Kwok KHM, de Hoon M, Carninci P, Hayashizaki Y, Arner P, Arner E. MicroRNA-27a/b-3p and PPARG regulate SCAMP3 through a feed-forward loop during adipogenesis. Sci Rep 2019; 9:13891. [PMID: 31554889 PMCID: PMC6761119 DOI: 10.1038/s41598-019-50210-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Accepted: 09/06/2019] [Indexed: 12/12/2022] Open
Abstract
MicroRNAs (miRNA) modulate gene expression through feed-back and forward loops. Previous studies identified miRNAs that regulate transcription factors, including Peroxisome Proliferator Activated Receptor Gamma (PPARG), in adipocytes, but whether they influence adipogenesis via such regulatory loops remain elusive. Here we predicted and validated a novel feed-forward loop regulating adipogenesis and involved miR-27a/b-3p, PPARG and Secretory Carrier Membrane Protein 3 (SCAMP3). In this loop, expression of both PPARG and SCAMP3 was independently suppressed by miR-27a/b-3p overexpression. Knockdown of PPARG downregulated SCAMP3 expression at the late phase of adipogenesis, whereas reduction of SCAMP3 mRNA levels increased PPARG expression at early phase in differentiation. The latter was accompanied with upregulation of adipocyte-enriched genes, including ADIPOQ and FABP4, suggesting an anti-adipogenic role for SCAMP3. PPARG and SCAMP3 exhibited opposite behaviors regarding correlations with clinical phenotypes, including body mass index, body fat mass, adipocyte size, lipolytic and lipogenic capacity, and secretion of pro-inflammatory cytokines. While adipose PPARG expression was associated with more favorable metabolic phenotypes, SCAMP3 expression was linked to increased fat mass and insulin resistance. Together, we identified a feed-forward loop through which miR-27a/b-3p, PPARG and SCAMP3 cooperatively fine tune the regulation of adipogenesis, which potentially may impact whole body metabolism.
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Affiliation(s)
- Agné Kulyté
- Lipid laboratory, Department of Medicine H7, Karolinska Institutet, Huddinge, Sweden.
| | - Kelvin Ho Man Kwok
- Lipid laboratory, Department of Medicine H7, Karolinska Institutet, Huddinge, Sweden.,Department of Biosciences and Nutrition, Karolinska Institutet, Huddinge, Sweden
| | - Michiel de Hoon
- RIKEN Center for Integrative Medical Sciences (IMS), Yokohama, Kanagawa, 230-0045, Japan
| | - Piero Carninci
- RIKEN Center for Integrative Medical Sciences (IMS), Yokohama, Kanagawa, 230-0045, Japan
| | - Yoshihide Hayashizaki
- RIKEN Preventive Medicine and Diagnosis Innovation Program, Yokohama, Kanagawa, 230-0045, Japan
| | - Peter Arner
- Lipid laboratory, Department of Medicine H7, Karolinska Institutet, Huddinge, Sweden
| | - Erik Arner
- RIKEN Center for Integrative Medical Sciences (IMS), Yokohama, Kanagawa, 230-0045, Japan.
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10
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Sakaue S, Hirata J, Maeda Y, Kawakami E, Nii T, Kishikawa T, Ishigaki K, Terao C, Suzuki K, Akiyama M, Suita N, Masuda T, Ogawa K, Yamamoto K, Saeki Y, Matsushita M, Yoshimura M, Matsuoka H, Ikari K, Taniguchi A, Yamanaka H, Kawaji H, Lassmann T, Itoh M, Yoshitomi H, Ito H, Ohmura K, R Forrest AR, Hayashizaki Y, Carninci P, Kumanogoh A, Kamatani Y, de Hoon M, Yamamoto K, Okada Y. Integration of genetics and miRNA-target gene network identified disease biology implicated in tissue specificity. Nucleic Acids Res 2019; 46:11898-11909. [PMID: 30407537 PMCID: PMC6294505 DOI: 10.1093/nar/gky1066] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2018] [Accepted: 10/22/2018] [Indexed: 12/20/2022] Open
Abstract
MicroRNAs (miRNAs) modulate the post-transcriptional regulation of target genes and are related to biology of complex human traits, but genetic landscape of miRNAs remains largely unknown. Given the strikingly tissue-specific miRNA expression profiles, we here expand a previous method to quantitatively evaluate enrichment of genome-wide association study (GWAS) signals on miRNA-target gene networks (MIGWAS) to further estimate tissue-specific enrichment. Our approach integrates tissue-specific expression profiles of miRNAs (∼1800 miRNAs in 179 cells) with GWAS to test whether polygenic signals enrich in miRNA-target gene networks and whether they fall within specific tissues. We applied MIGWAS to 49 GWASs (nTotal = 3 520 246), and successfully identified biologically relevant tissues. Further, MIGWAS could point miRNAs as candidate biomarkers of the trait. As an illustrative example, we performed differentially expressed miRNA analysis between rheumatoid arthritis (RA) patients and healthy controls (n = 63). We identified novel biomarker miRNAs (e.g. hsa-miR-762) by integrating differentially expressed miRNAs with MIGWAS results for RA, as well as novel associated loci with significant genetic risk (rs56656810 at MIR762 at 16q11; n = 91 482, P = 3.6 × 10-8). Our result highlighted that miRNA-target gene network contributes to human disease genetics in a cell type-specific manner, which could yield an efficient screening of miRNAs as promising biomarkers.
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Affiliation(s)
- Saori Sakaue
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita 565-0871, Japan.,Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan.,Department of Allergy and Rheumatology, Graduate School of Medicine, the University of Tokyo, Tokyo 113-8655, Japan
| | - Jun Hirata
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita 565-0871, Japan.,Pharmaceutical Discovery Research Laboratories, TEIJIN PHARMA LIMITED, Hino 191-8512, Japan
| | - Yuichi Maeda
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, WPI Immunology Frontier Research Center, Osaka University, Suita 565-0871, Japan.,Laboratory of Immune Regulation, Department of Microbiology and Immunology, Graduate School of Medicine, WPI Immunology Frontier Research Center, Osaka University, Suita 565-0871, Japan.,Japan Agency for Medical Research and Development-Core Research for Evolutional Science and Technology, Tokyo 100-0004, Japan
| | - Eiryo Kawakami
- Healthcare and Medical Data Driven AI based Predictive Reasoning Development Unit, Medical Sciences Innovation Hub Program (MIH), RIKEN, Yokohama 230-0045, Japan.,Laboratory for Developmental Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan
| | - Takuro Nii
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, WPI Immunology Frontier Research Center, Osaka University, Suita 565-0871, Japan.,Laboratory of Immune Regulation, Department of Microbiology and Immunology, Graduate School of Medicine, WPI Immunology Frontier Research Center, Osaka University, Suita 565-0871, Japan.,Japan Agency for Medical Research and Development-Core Research for Evolutional Science and Technology, Tokyo 100-0004, Japan
| | - Toshihiro Kishikawa
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita 565-0871, Japan.,Department of Otorhinolaryngology - Head and Neck Surgery, Osaka University Graduate School of Medicine, Suita 565-0871, Japan
| | - Kazuyoshi Ishigaki
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan
| | - Chikashi Terao
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan.,Clinical Research Center, Shizuoka General Hospital, Shizuoka 420-0881, Japan.,The School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka 422-8526, Japan.,Department of Rheumatology and Clinical Immunology, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan
| | - Ken Suzuki
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita 565-0871, Japan.,Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan
| | - Masato Akiyama
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan.,Department of Ophthalmology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Fukuoka 812-8582, Japan
| | - Naomasa Suita
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita 565-0871, Japan.,Discovery Technology Research Laboratories, Tsukuba Research Institute, Ono Pharmaceutical Co., Ltd., Tsukuba 300-4247, Japan
| | - Tatsuo Masuda
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita 565-0871, Japan.,Department of Obstetrics and Gynecology, Osaka University Graduate School of Medicine, Suita 565-0871, Japan
| | - Kotaro Ogawa
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita 565-0871, Japan.,Department of Neurology, Osaka University Graduate School of Medicine, Suita 565-0871, Japan
| | - Kenichi Yamamoto
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita 565-0871, Japan.,Department of Pediatrics, Osaka University Graduate School of Medicine, Suita 565-0871, Japan
| | - Yukihiko Saeki
- Department of Clinical Research, National Hospital Organization (NHO) Osaka Minami Medical Center, Japan
| | - Masato Matsushita
- Department of Rheumatology and Allergology, Saiseikai Senri Hospital, Suita 565-0862, Japan.,Department of Rheumatology and Allergology, National Hospital Organization (NHO) Osaka Minami Medical Center, Japan
| | - Maiko Yoshimura
- Department of Rheumatology and Allergology, National Hospital Organization (NHO) Osaka Minami Medical Center, Japan
| | - Hidetoshi Matsuoka
- Department of Rheumatology and Allergology, National Hospital Organization (NHO) Osaka Minami Medical Center, Japan
| | - Katsunori Ikari
- Institute of Rheumatology, Tokyo Women's Medical University, Tokyo 162-0054, Japan.,Core Research for Evolutional Science and Technology (CREST), Japan Science and Technology Agency (JST), Tokyo 162-0054, Japan
| | - Atsuo Taniguchi
- Institute of Rheumatology, Tokyo Women's Medical University, Tokyo 162-0054, Japan
| | - Hisashi Yamanaka
- Institute of Rheumatology, Tokyo Women's Medical University, Tokyo 162-0054, Japan
| | - Hideya Kawaji
- Division of Genomic Technologies, RIKEN Center for Life Science Technologies, Yokohama 230-0045, Japan.,RIKEN Omics Science Center (OSC), Yokohama 230-0045, Japan.,RIKEN Preventive Medicine and Diagnosis Innovation Program (PMI), Saitama 351-0198, Japan.,Preventive Medicine and Applied Genomics Unit, RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan
| | - Timo Lassmann
- Division of Genomic Technologies, RIKEN Center for Life Science Technologies, Yokohama 230-0045, Japan.,RIKEN Omics Science Center (OSC), Yokohama 230-0045, Japan.,Telethon Kids Institute, the University of Western Australia, Western Australia 6008, Australia
| | - Masayoshi Itoh
- Division of Genomic Technologies, RIKEN Center for Life Science Technologies, Yokohama 230-0045, Japan.,RIKEN Omics Science Center (OSC), Yokohama 230-0045, Japan.,RIKEN Preventive Medicine and Diagnosis Innovation Program (PMI), Saitama 351-0198, Japan
| | - Hiroyuki Yoshitomi
- Laboratory of Tissue Regeneration, Department of Regeneration Science and Engineering, Institute for Frontier Life and Medical Sciences, Kyoto University, Kyoto 606-8507, Japan.,Department of Orthopaedic Surgery, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan
| | - Hiromu Ito
- Department of Orthopaedic Surgery, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan
| | - Koichiro Ohmura
- Department of Rheumatology and Clinical Immunology, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan
| | - Alistair R R Forrest
- Division of Genomic Technologies, RIKEN Center for Life Science Technologies, Yokohama 230-0045, Japan.,RIKEN Omics Science Center (OSC), Yokohama 230-0045, Japan.,Harry Perkins Institute of Medical Research, QEII Medical Centre and Centre for Medical Research, the University of Western Australia, Western Australia 6009, Australia
| | - Yoshihide Hayashizaki
- RIKEN Omics Science Center (OSC), Yokohama 230-0045, Japan.,RIKEN Preventive Medicine and Diagnosis Innovation Program (PMI), Saitama 351-0198, Japan
| | - Piero Carninci
- Division of Genomic Technologies, RIKEN Center for Life Science Technologies, Yokohama 230-0045, Japan.,RIKEN Omics Science Center (OSC), Yokohama 230-0045, Japan.,Laboratory for Transcriptome Technology, RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045 Japan
| | - Atsushi Kumanogoh
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, WPI Immunology Frontier Research Center, Osaka University, Suita 565-0871, Japan
| | - Yoichiro Kamatani
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan.,Kyoto-McGill International Collaborative School in Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto 606-8507, Japan
| | - Michiel de Hoon
- Division of Genomic Technologies, RIKEN Center for Life Science Technologies, Yokohama 230-0045, Japan.,RIKEN Omics Science Center (OSC), Yokohama 230-0045, Japan.,Laboratory for Applied Computational Genomics, RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045 Japan
| | - Kazuhiko Yamamoto
- Laboratory for Autoimmune Diseases, RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita 565-0871, Japan.,Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan.,Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita 565-0871, Japan
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11
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Abstract
At the beginning of this century, the Human Genome Project produced the first drafts of the human genome sequence. Following this, large-scale functional genomics studies were initiated to understand the molecular basis underlying the translation of the instructions encoded in the genome into the biological traits of organisms. Instrumental in the ensuing revolution in functional genomics were the rapid advances in massively parallel sequencing technologies as well as the development of a wide diversity of protocols that make use of these technologies to understand cellular behavior at the molecular level. Here, we review recent advances in functional genomic methods, discuss some of their current capabilities and limitations, and briefly sketch future directions within the field.
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Affiliation(s)
- Roderic Guigo
- Centre for Genomic Regulation, The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
| | - Michiel de Hoon
- RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
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12
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Alhendi AMN, Patrikakis M, Daub CO, Kawaji H, Itoh M, de Hoon M, Carninci P, Hayashizaki Y, Arner E, Khachigian LM. Promoter Usage and Dynamics in Vascular Smooth Muscle Cells Exposed to Fibroblast Growth Factor-2 or Interleukin-1β. Sci Rep 2018; 8:13164. [PMID: 30177712 PMCID: PMC6120868 DOI: 10.1038/s41598-018-30702-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2018] [Accepted: 08/03/2018] [Indexed: 01/22/2023] Open
Abstract
Smooth muscle cells (SMC) in blood vessels are normally growth quiescent and transcriptionally inactive. Our objective was to understand promoter usage and dynamics in SMC acutely exposed to a prototypic growth factor or pro-inflammatory cytokine. Using cap analysis gene expression (FANTOM5 project) we report differences in promoter dynamics for immediate-early genes (IEG) and other genes when SMC are exposed to fibroblast growth factor-2 or interleukin-1β. Of the 1871 promoters responding to FGF2 or IL-1β considerably more responded to FGF2 (68.4%) than IL-1β (18.5%) and 13.2% responded to both. Expression clustering reveals sets of genes induced, repressed or unchanged. Among IEG responding rapidly to FGF2 or IL-1β were FOS, FOSB and EGR-1, which mediates human SMC migration. Motif activity response analysis (MARA) indicates most transcription factor binding motifs in response to FGF2 were associated with a sharp induction at 1 h, whereas in response to IL-1β, most motifs were associated with a biphasic change peaking generally later. MARA revealed motifs for FOS_FOS{B,L1}_JUN{B,D} and EGR-1..3 in the cluster peaking 1 h after FGF2 exposure whereas these motifs were in clusters peaking 1 h or later in response to IL-1β. Our findings interrogating CAGE data demonstrate important differences in promoter usage and dynamics in SMC exposed to FGF2 or IL-1β.
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Affiliation(s)
- Ahmad M N Alhendi
- Vascular Biology and Translational Research, School of Medical Sciences, University of New South Wales, Sydney, 2052, Australia
| | - Margaret Patrikakis
- Vascular Biology and Translational Research, School of Medical Sciences, University of New South Wales, Sydney, 2052, Australia
| | - Carsten O Daub
- RIKEN Center for Life Science Technologies (Division of Genomic Technologies) (CLST DGT), 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan
- Department of Biosciences and Nutrition and Science for Life Laboratory, Karolinska Institutet, SE-141 86, Stockholm, Sweden
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, 230-0045, Japan
| | - Hideya Kawaji
- RIKEN Center for Life Science Technologies (Division of Genomic Technologies) (CLST DGT), 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan
- RIKEN Omics Science Center (OSC), 1-7-22 Suehiro-cho, Tsurumi-ku Yokohama, 230-0045, Japan
- RIKEN Preventive Medicine and Diagnosis Innovation Program (PMI), 2-1 Hirosawa, Wako-shi, Saitama, 351-0198, Japan
- Preventive Medicine and Applied Genomics Unit, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, 230-0045, Japan
| | - Masayoshi Itoh
- RIKEN Center for Life Science Technologies (Division of Genomic Technologies) (CLST DGT), 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan
- RIKEN Omics Science Center (OSC), 1-7-22 Suehiro-cho, Tsurumi-ku Yokohama, 230-0045, Japan
- RIKEN Preventive Medicine and Diagnosis Innovation Program (PMI), 2-1 Hirosawa, Wako-shi, Saitama, 351-0198, Japan
| | - Michiel de Hoon
- RIKEN Center for Life Science Technologies (Division of Genomic Technologies) (CLST DGT), 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan
- RIKEN Omics Science Center (OSC), 1-7-22 Suehiro-cho, Tsurumi-ku Yokohama, 230-0045, Japan
- Laboratory for Applied Computational Genomics, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, 230-0045, Japan
| | - Piero Carninci
- RIKEN Center for Life Science Technologies (Division of Genomic Technologies) (CLST DGT), 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan
- RIKEN Omics Science Center (OSC), 1-7-22 Suehiro-cho, Tsurumi-ku Yokohama, 230-0045, Japan
- Laboratory for Transcriptome Technology, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, 230-0045, Japan
| | - Yoshihide Hayashizaki
- RIKEN Omics Science Center (OSC), 1-7-22 Suehiro-cho, Tsurumi-ku Yokohama, 230-0045, Japan
- RIKEN Preventive Medicine and Diagnosis Innovation Program (PMI), 2-1 Hirosawa, Wako-shi, Saitama, 351-0198, Japan
| | - Erik Arner
- RIKEN Center for Life Science Technologies (Division of Genomic Technologies) (CLST DGT), 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan
- RIKEN Omics Science Center (OSC), 1-7-22 Suehiro-cho, Tsurumi-ku Yokohama, 230-0045, Japan
- Laboratory for Applied Regulatory Genomics Network Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, 230-0045, Japan
| | - Levon M Khachigian
- Vascular Biology and Translational Research, School of Medical Sciences, University of New South Wales, Sydney, 2052, Australia.
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13
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Rapakoulia T, Gao X, Huang Y, de Hoon M, Okada-Hatakeyama M, Suzuki H, Arner E. Genome-scale regression analysis reveals a linear relationship for promoters and enhancers after combinatorial drug treatment. Bioinformatics 2018; 33:3696-3700. [PMID: 28961713 PMCID: PMC5860321 DOI: 10.1093/bioinformatics/btx503] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2017] [Accepted: 08/07/2017] [Indexed: 11/12/2022] Open
Abstract
Motivation Drug combination therapy for treatment of cancers and other multifactorial diseases has the potential of increasing the therapeutic effect, while reducing the likelihood of drug resistance. In order to reduce time and cost spent in comprehensive screens, methods are needed which can model additive effects of possible drug combinations. Results We here show that the transcriptional response to combinatorial drug treatment at promoters, as measured by single molecule CAGE technology, is accurately described by a linear combination of the responses of the individual drugs at a genome wide scale. We also find that the same linear relationship holds for transcription at enhancer elements. We conclude that the described approach is promising for eliciting the transcriptional response to multidrug treatment at promoters and enhancers in an unbiased genome wide way, which may minimize the need for exhaustive combinatorial screens. Availability and implementation The CAGE sequence data used in this study is available in the DDBJ Sequence Read Archive (http://trace.ddbj.nig.ac.jp/index_e.html), accession number DRP001113. Contact xin.gao@kaust.edu.sa or erik.arner@riken.jp. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Trisevgeni Rapakoulia
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia.,Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
| | - Xin Gao
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia.,Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
| | - Yi Huang
- Molecular Network Control Genomics Unit, RIKEN Center for Life Science Technologies, Yokohama, Kanagawa 230-0045, Japan
| | - Michiel de Hoon
- RIKEN Center for Life Science Technologies (Division of Genomic Technologies) (CLST (DGT)), Yokohama, Kanagawa 230-0045, Japan
| | - Mariko Okada-Hatakeyama
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan.,Institute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan
| | - Harukazu Suzuki
- RIKEN Center for Life Science Technologies (Division of Genomic Technologies) (CLST (DGT)), Yokohama, Kanagawa 230-0045, Japan
| | - Erik Arner
- Molecular Network Control Genomics Unit, RIKEN Center for Life Science Technologies, Yokohama, Kanagawa 230-0045, Japan
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14
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Newie I, Søkilde R, Persson H, Jacomasso T, Gorbatenko A, Borg Å, de Hoon M, Pedersen SF, Rovira C. HER2-encoded mir-4728 forms a receptor-independent circuit with miR-21-5p through the non-canonical poly(A) polymerase PAPD5. Sci Rep 2016; 6:35664. [PMID: 27752128 PMCID: PMC5067774 DOI: 10.1038/srep35664] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2016] [Accepted: 10/04/2016] [Indexed: 12/24/2022] Open
Abstract
We previously reported that the human HER2 gene encodes the intronic microRNA mir-4728, which is overexpressed together with its oncogenic host gene and may act independently of the HER2 receptor. More recently, we also reported that the oncogenic miR-21-5p is regulated by 3' tailing and trimming by the non-canonical poly(A) polymerase PAPD5 and the ribonuclease PARN. Here we demonstrate a dual function for the HER2 locus in upregulation of miR-21-5p; while HER2 signalling activates transcription of mir-21, miR-4728-3p specifically stabilises miR-21-5p through inhibition of PAPD5. Our results establish a new and unexpected oncogenic role for the HER2 locus that is not currently being targeted by any anti-HER2 therapy.
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Affiliation(s)
- Inga Newie
- Department of Clinical Sciences, Lund, Division of Oncology and Pathology, Lund University Cancer Center, Lund, Sweden.,BioCARE, Strategic Cancer Research Program, Lund, Sweden
| | - Rolf Søkilde
- Department of Clinical Sciences, Lund, Division of Oncology and Pathology, Lund University Cancer Center, Lund, Sweden.,BioCARE, Strategic Cancer Research Program, Lund, Sweden
| | - Helena Persson
- Department of Clinical Sciences, Lund, Division of Oncology and Pathology, Lund University Cancer Center, Lund, Sweden
| | - Thiago Jacomasso
- Department of Clinical Sciences, Lund, Division of Oncology and Pathology, Lund University Cancer Center, Lund, Sweden
| | - Andrej Gorbatenko
- Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Åke Borg
- Department of Clinical Sciences, Lund, Division of Oncology and Pathology, Lund University Cancer Center, Lund, Sweden.,BioCARE, Strategic Cancer Research Program, Lund, Sweden.,CREATE Health, Strategic Centre for Translational Cancer Research, Lund, Sweden
| | - Michiel de Hoon
- Division of Genomic Technologies, RIKEN Center for Life Science Technologies, Yokohama, Japan
| | - Stine F Pedersen
- Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Carlos Rovira
- Department of Clinical Sciences, Lund, Division of Oncology and Pathology, Lund University Cancer Center, Lund, Sweden.,BioCARE, Strategic Cancer Research Program, Lund, Sweden.,CREATE Health, Strategic Centre for Translational Cancer Research, Lund, Sweden
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15
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Abstract
Big leaps in science happen when scientists from different backgrounds interact. In the past 15 years, the FANTOM Consortium has brought together scientists from different fields to analyze and interpret genomic data produced with novel technologies, including mouse full-length cDNAs and, more recently, expression profiling at single-nucleotide resolution by cap-analysis gene expression. The FANTOM Consortium has provided the most comprehensive mouse cDNA collection for functional studies and extensive maps of the human and mouse transcriptome comprising promoters, enhancers, as well as the network of their regulatory interactions. More importantly, serendipitous observations of the FANTOM dataset led us to realize that the mammalian genome is pervasively transcribed, even from retrotransposon elements, which were previously considered junk DNA. The majority of products from the mammalian genome are long non-coding RNAs (lncRNAs), including sense-antisense, intergenic, and enhancer RNAs. While the biological function has been elucidated for some lncRNAs, more than 98 % of them remain without a known function. We argue that large-scale studies are urgently needed to address the functional role of lncRNAs.
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Affiliation(s)
- Michiel de Hoon
- Division of Genomic Technologies, RIKEN Center for Life Science Technologies, Yokohama, 230-0045, Japan.
| | - Jay W Shin
- Division of Genomic Technologies, RIKEN Center for Life Science Technologies, Yokohama, 230-0045, Japan.
| | - Piero Carninci
- Division of Genomic Technologies, RIKEN Center for Life Science Technologies, Yokohama, 230-0045, Japan.
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16
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Kouno T, de Hoon M, Mar JC, Tomaru Y, Kawano M, Carninci P, Suzuki H, Hayashizaki Y, Shin JW. Temporal dynamics and transcriptional control using single-cell gene expression analysis. Genome Biol 2014; 14:R118. [PMID: 24156252 PMCID: PMC4015031 DOI: 10.1186/gb-2013-14-10-r118] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2013] [Accepted: 10/24/2013] [Indexed: 01/30/2023] Open
Abstract
Background Changes in environmental conditions lead to expression variation that manifest at the level of gene regulatory networks. Despite a strong understanding of the role noise plays in synthetic biological systems, it remains unclear how propagation of expression heterogeneity in an endogenous regulatory network is distributed and utilized by cells transitioning through a key developmental event. Results Here we investigate the temporal dynamics of a single-cell transcriptional network of 45 transcription factors in THP-1 human myeloid monocytic leukemia cells undergoing differentiation to macrophages. We systematically measure temporal regulation of expression and variation by profiling 120 single cells at eight distinct time points, and infer highly controlled regulatory modules through which signaling operates with stochastic effects. This reveals dynamic and specific rewiring as a cellular strategy for differentiation. The integration of both positive and negative co-expression networks further identifies the proto-oncogene MYB as a network hinge to modulate both the pro- and anti-differentiation pathways. Conclusions Compared to averaged cell populations, temporal single-cell expression profiling provides a much more powerful technique to probe for mechanistic insights underlying cellular differentiation. We believe that our approach will form the basis of novel strategies to study the regulation of transcription at a single-cell level.
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Davis MR, Andersson R, Severin J, de Hoon M, Bertin N, Baillie JK, Kawaji H, Sandelin A, Forrest ARR, Summers KM. Transcriptional profiling of the human fibrillin/LTBP gene family, key regulators of mesenchymal cell functions. Mol Genet Metab 2014; 112:73-83. [PMID: 24703491 PMCID: PMC4019825 DOI: 10.1016/j.ymgme.2013.12.006] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/29/2013] [Revised: 12/06/2013] [Accepted: 12/06/2013] [Indexed: 01/23/2023]
Abstract
The fibrillins and latent transforming growth factor binding proteins (LTBPs) form a superfamily of extracellular matrix (ECM) proteins characterized by the presence of a unique domain, the 8-cysteine transforming growth factor beta (TGFβ) binding domain. These proteins are involved in the structure of the extracellular matrix and controlling the bioavailability of TGFβ family members. Genes encoding these proteins show differential expression in mesenchymal cell types which synthesize the extracellular matrix. We have investigated the promoter regions of the seven gene family members using the FANTOM5 CAGE database for human. While the protein and nucleotide sequences show considerable sequence similarity, the promoter regions were quite diverse. Most genes had a single predominant transcription start site region but LTBP1 and LTBP4 had two regions initiating different transcripts. Most of the family members were expressed in a range of mesenchymal and other cell types, often associated with use of alternative promoters or transcription start sites within a promoter in different cell types. FBN3 was the lowest expressed gene, and was found only in embryonic and fetal tissues. The different promoters for one gene were more similar to each other in expression than to promoters of the other family members. Notably expression of all 22 LTBP2 promoters was tightly correlated and quite distinct from all other family members. We located candidate enhancer regions likely to be involved in expression of the genes. Each gene was associated with a unique subset of transcription factors across multiple promoters although several motifs including MAZ, SP1, GTF2I and KLF4 showed overrepresentation across the gene family. FBN1 and FBN2, which had similar expression patterns, were regulated by different transcription factors. This study highlights the role of alternative transcription start sites in regulating the tissue specificity of closely related genes and suggests that this important class of extracellular matrix proteins is subject to subtle regulatory variations that explain the differential roles of members of this gene family.
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Affiliation(s)
- Margaret R Davis
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush EH25 9RG, UK.
| | - Robin Andersson
- The Bioinformatics Centre, Department of Biology and Biotech Research and Innovation Centre, University of Copenhagen, Ole Maaloes Vej 5, 2200 Copenhagen N, Denmark.
| | - Jessica Severin
- RIKEN Omics Science Center, Yokohama, Kanagawa 230-0045, Japan(1); RIKEN Center for Life Science Technologies, Division of Genomic Technologies, Yokohama, Kanagawa 230-0045, Japan.
| | - Michiel de Hoon
- RIKEN Omics Science Center, Yokohama, Kanagawa 230-0045, Japan(1); RIKEN Center for Life Science Technologies, Division of Genomic Technologies, Yokohama, Kanagawa 230-0045, Japan.
| | - Nicolas Bertin
- RIKEN Omics Science Center, Yokohama, Kanagawa 230-0045, Japan(1); RIKEN Center for Life Science Technologies, Division of Genomic Technologies, Yokohama, Kanagawa 230-0045, Japan.
| | - J Kenneth Baillie
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush EH25 9RG, UK.
| | - Hideya Kawaji
- RIKEN Omics Science Center, Yokohama, Kanagawa 230-0045, Japan(1); RIKEN Center for Life Science Technologies, Division of Genomic Technologies, Yokohama, Kanagawa 230-0045, Japan; RIKEN Preventive Medicine and Diagnosis Innovation Program, Wako, Saitama 351-0198, Japan.
| | - Albin Sandelin
- The Bioinformatics Centre, Department of Biology and Biotech Research and Innovation Centre, University of Copenhagen, Ole Maaloes Vej 5, 2200 Copenhagen N, Denmark.
| | - Alistair R R Forrest
- RIKEN Omics Science Center, Yokohama, Kanagawa 230-0045, Japan(1); RIKEN Center for Life Science Technologies, Division of Genomic Technologies, Yokohama, Kanagawa 230-0045, Japan.
| | - Kim M Summers
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush EH25 9RG, UK; The University of Queensland Northside Clinical School, Prince Charles Hospital, Chermside 4032, Australia.
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Harbers M, Kato S, de Hoon M, Hayashizaki Y, Carninci P, Plessy C. Comparison of RNA- or LNA-hybrid oligonucleotides in template-switching reactions for high-speed sequencing library preparation. BMC Genomics 2013; 14:665. [PMID: 24079827 PMCID: PMC3853366 DOI: 10.1186/1471-2164-14-665] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2013] [Accepted: 09/24/2013] [Indexed: 11/24/2022] Open
Abstract
Background Analyzing the RNA pool or transcription start sites requires effective means to convert RNA into cDNA libraries for digital expression counting. With current high-speed sequencers, it is necessary to flank the cDNAs with specific adapters. Adding template-switching oligonucleotides to reverse transcription reactions is the most commonly used approach when working with very small quantities of RNA even from single cells. Results Here we compared the performance of DNA-RNA, DNA-LNA and DNA oligonucleotides in template-switching during nanoCAGE library preparation. Test libraries from rat muscle and HeLa cell RNA were prepared in technical triplicates and sequenced for comparison of the gene coverage and distribution of the reads within transcripts. The DNA-RNA oligonucleotide showed the highest specificity for capped 5′ ends of mRNA, whereas the DNA-LNA provided similar gene coverage with more reads falling within exons. Conclusions While confirming the cap-specific preference of DNA-RNA oligonucleotides in template-switching reactions, our data indicate that DNA-LNA hybrid oligonucleotides could potentially find other applications in random RNA sequencing.
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Affiliation(s)
- Matthias Harbers
- RIKEN Center for Life Science Technologies, Division of Genomics Technologies, Yokohama, Kanagawa 230-0045, Japan.
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Hasegawa R, Tomaru Y, de Hoon M, Suzuki H, Hayashizaki Y, Shin JW. Identification of ZNF395 as a novel modulator of adipogenesis. Exp Cell Res 2012; 319:68-76. [PMID: 23142027 DOI: 10.1016/j.yexcr.2012.11.003] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2012] [Revised: 10/01/2012] [Accepted: 11/01/2012] [Indexed: 12/20/2022]
Abstract
Adipogenesis is the process of cell differentiation by which mesenchymal stem cells (MSC) become adipocytes. Investigating the transcriptional regulatory process during adipogenesis may provide strategies to prevent obesity and other metabolic disorders. In recent years, numerous zinc finger proteins (ZFPs) have been implicated in regulating differentiation and cell fate determination. To investigate the regulatory role of ZFPs involved in adipogenesis, we performed genome-wide microarray expression profiling of an adipogenesis time series. Particularly focusing on the transiently responsive ZFPs, we identified and characterized the functional role of ZNF395 in adipogenesis. A systematic ablation of the ZNF395 transcript during adipogenesis revealed 40% reduction of adipocytes when compared to control. Furthermore, the number of adipocytes as well as the expression of key adipocyte markers were greatly induced when MSC were co-transduced with ZNF395 and PPARG2. To further elucidate the functional role of ZNF395 during adipogenesis, we attempted to trans-differentiate human dermal fibroblasts with PPARG2. The test remarkably revealed that ZNF395 in conjunction with PPARG2 greatly induced adipogenesis from dermal fibroblasts when compared to PPARG2 alone. These loss and gain of function experiments firmly establish that ZNF395 coordinate the transcriptional regulatory pathway with PPARG2, which may be necessary for the genesis of adipocytes.
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Affiliation(s)
- Ryota Hasegawa
- Omics Science Center (OSC), RIKEN Yokohama Institute, Tsurumi-ku, Yokohama, Kanagawa, Japan
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20
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Suzuki T, Nakano-Ikegaya M, Yabukami-Okuda H, de Hoon M, Severin J, Saga-Hatano S, Shin JW, Kubosaki A, Simon C, Hasegawa Y, Hayashizaki Y, Suzuki H. Reconstruction of monocyte transcriptional regulatory network accompanies monocytic functions in human fibroblasts. PLoS One 2012; 7:e33474. [PMID: 22428058 PMCID: PMC3302774 DOI: 10.1371/journal.pone.0033474] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2011] [Accepted: 02/15/2012] [Indexed: 02/02/2023] Open
Abstract
Transcriptional regulatory networks (TRN) control the underlying mechanisms behind cellular functions and they are defined by a set of core transcription factors regulating cascades of peripheral genes. Here we report SPI1, CEBPA, MNDA and IRF8 as core transcription factors of monocyte TRN and demonstrate functional inductions of phagocytosis, inflammatory response and chemotaxis activities in human dermal fibroblasts. The Gene Ontology and KEGG pathway analyses also revealed notable representation of genes involved in immune response and endocytosis in fibroblasts. Moreover, monocyte TRN-inducers triggered multiple monocyte-specific genes based on the transcription factor motif response analysis and suggest that complex cellular TRNs are uniquely amenable to elicit cell-specific functions in unrelated cell types.
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Affiliation(s)
- Takahiro Suzuki
- Omics Science Center (OSC), RIKEN Yokohama Institute, Yokohama, Kanagawa, Japan
| | - Mika Nakano-Ikegaya
- Omics Science Center (OSC), RIKEN Yokohama Institute, Yokohama, Kanagawa, Japan
| | | | - Michiel de Hoon
- Omics Science Center (OSC), RIKEN Yokohama Institute, Yokohama, Kanagawa, Japan
| | - Jessica Severin
- Omics Science Center (OSC), RIKEN Yokohama Institute, Yokohama, Kanagawa, Japan
| | - Satomi Saga-Hatano
- Omics Science Center (OSC), RIKEN Yokohama Institute, Yokohama, Kanagawa, Japan
| | - Jay W. Shin
- Omics Science Center (OSC), RIKEN Yokohama Institute, Yokohama, Kanagawa, Japan
| | - Atsutaka Kubosaki
- Omics Science Center (OSC), RIKEN Yokohama Institute, Yokohama, Kanagawa, Japan
| | - Christophe Simon
- Omics Science Center (OSC), RIKEN Yokohama Institute, Yokohama, Kanagawa, Japan
| | - Yuki Hasegawa
- Omics Science Center (OSC), RIKEN Yokohama Institute, Yokohama, Kanagawa, Japan
| | - Yoshihide Hayashizaki
- Omics Science Center (OSC), RIKEN Yokohama Institute, Yokohama, Kanagawa, Japan
- Division of Genomic Information Resources, Supramolecular Biology, International Graduate School of Arts and Sciences, Yokohama City University, Yokohama, Kanagawa, Japan
| | - Harukazu Suzuki
- Omics Science Center (OSC), RIKEN Yokohama Institute, Yokohama, Kanagawa, Japan
- * E-mail:
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Sierro N, Makita Y, de Hoon M, Nakai K. DBTBS: a database of transcriptional regulation in Bacillus subtilis containing upstream intergenic conservation information. Nucleic Acids Res 2007; 36:D93-6. [PMID: 17962296 PMCID: PMC2247474 DOI: 10.1093/nar/gkm910] [Citation(s) in RCA: 284] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
DBTBS, first released in 1999, is a reference database on transcriptional regulation in Bacillus subtilis, summarizing the experimentally characterized transcription factors, their recognition sequences and the genes they regulate. Since the previous release, the original content was extended by the addition of the data contained in 569 new publications, the total of which now reaches 947. The number of B. subtilis promoters annotated in the database was more than doubled to 1475. In addition, 463 experimentally validated B. subtilis operons and their terminators have been included. Given the increase in the number of fully sequenced bacterial genomes, we decided to extend the usability of DBTBS in comparative regulatory genomics. We therefore created a new section on the conservation of the upstream regulatory sequences between homologous genes in 40 Gram-positive bacterial species, as well as on the presence of overrepresented hexameric motifs that may have regulatory functions. DBTBS can be accessed at: http://dbtbs.hgc.jp.
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Affiliation(s)
- Nicolas Sierro
- Human Genome Center, The Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, Japan
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de Hoon M, Imoto S, Miyano S. Inferring Gene Regulatory Networks from Time-Ordered Gene Expression Data Using Differential Equations. Discovery Science 2002. [DOI: 10.1007/3-540-36182-0_24] [Citation(s) in RCA: 20] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
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