1
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Guerrero Zuniga A, Aikin TJ, McKenney C, Lendner Y, Phung A, Hook PW, Meltzer A, Timp W, Regot S. Sustained ERK signaling promotes G2 cell cycle exit and primes cells for whole-genome duplication. Dev Cell 2024:S1534-5807(24)00200-4. [PMID: 38640927 DOI: 10.1016/j.devcel.2024.03.032] [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: 10/28/2022] [Revised: 01/30/2024] [Accepted: 03/25/2024] [Indexed: 04/21/2024]
Abstract
Whole-genome duplication (WGD) is a frequent event in cancer evolution that fuels chromosomal instability. WGD can result from mitotic errors or endoreduplication, yet the molecular mechanisms that drive WGD remain unclear. Here, we use live single-cell analysis to characterize cell-cycle dynamics upon aberrant Ras-ERK signaling. We find that sustained ERK signaling in human cells leads to reactivation of the APC/C in G2, resulting in tetraploid G0-like cells that are primed for WGD. This process is independent of DNA damage or p53 but dependent on p21. Transcriptomics analysis and live-cell imaging showed that constitutive ERK activity promotes p21 expression, which is necessary and sufficient to inhibit CDK activity and which prematurely activates the anaphase-promoting complex (APC/C). Finally, either loss of p53 or reduced ERK signaling allowed for endoreduplication, completing a WGD event. Thus, sustained ERK signaling-induced G2 cell cycle exit represents an alternative path to WGD.
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Affiliation(s)
- Adler Guerrero Zuniga
- Department of Molecular Biology and Genetics, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Department of Oncology, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; The Biochemistry, Cellular and Molecular Biology Graduate Program, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Timothy J Aikin
- Department of Molecular Biology and Genetics, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Department of Oncology, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; The Biochemistry, Cellular and Molecular Biology Graduate Program, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Connor McKenney
- Department of Molecular Biology and Genetics, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Department of Oncology, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; The Biochemistry, Cellular and Molecular Biology Graduate Program, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Yovel Lendner
- Department of Molecular Biology and Genetics, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Department of Oncology, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Alain Phung
- Department of Molecular Biology and Genetics, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Department of Oncology, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Paul W Hook
- Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, MD 21205, USA
| | - Amy Meltzer
- Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, MD 21205, USA
| | - Winston Timp
- Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, MD 21205, USA
| | - Sergi Regot
- Department of Molecular Biology and Genetics, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Department of Oncology, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
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2
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Kovaka S, Hook PW, Jenike KM, Shivakumar V, Morina LB, Razaghi R, Timp W, Schatz MC. Uncalled4 improves nanopore DNA and RNA modification detection via fast and accurate signal alignment. bioRxiv 2024:2024.03.05.583511. [PMID: 38496646 PMCID: PMC10942365 DOI: 10.1101/2024.03.05.583511] [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] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Nanopore signal analysis enables detection of nucleotide modifications from native DNA and RNA sequencing, providing both accurate genetic/transcriptomic and epigenetic information without additional library preparation. Presently, only a limited set of modifications can be directly basecalled (e.g. 5-methylcytosine), while most others require exploratory methods that often begin with alignment of nanopore signal to a nucleotide reference. We present Uncalled4, a toolkit for nanopore signal alignment, analysis, and visualization. Uncalled4 features an efficient banded signal alignment algorithm, BAM signal alignment file format, statistics for comparing signal alignment methods, and a reproducible de novo training method for k-mer-based pore models, revealing potential errors in ONT's state-of-the-art DNA model. We apply Uncalled4 to RNA 6-methyladenine (m6A) detection in seven human cell lines, identifying 26% more modifications than Nanopolish using m6Anet, including in several genes where m6A has known implications in cancer. Uncalled4 is available open-source at github.com/skovaka/uncalled4.
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3
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Volkel KD, Lin KN, Hook PW, Timp W, Keung AJ, Tuck JM. FrameD: framework for DNA-based data storage design, verification, and validation. Bioinformatics 2023; 39:btad572. [PMID: 37713474 PMCID: PMC10563143 DOI: 10.1093/bioinformatics/btad572] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 07/27/2023] [Accepted: 09/13/2023] [Indexed: 09/17/2023] Open
Abstract
MOTIVATION DNA-based data storage is a quickly growing field that hopes to harness the massive theoretical information density of DNA molecules to produce a competitive next-generation storage medium suitable for archival data. In recent years, many DNA-based storage system designs have been proposed. Given that no common infrastructure exists for simulating these storage systems, comparing many different designs along with many different error models is increasingly difficult. To address this challenge, we introduce FrameD, a simulation infrastructure for DNA storage systems that leverages the underlying modularity of DNA storage system designs to provide a framework to express different designs while being able to reuse common components. RESULTS We demonstrate the utility of FrameD and the need for a common simulation platform using a case study. Our case study compares designs that utilize strand copies differently, some that align strand copies using multiple sequence alignment algorithms and others that do not. We found that the choice to include multiple sequence alignment in the pipeline is dependent on the error rate and the type of errors being injected and is not always beneficial. In addition to supporting a wide range of designs, FrameD provides the user with transparent parallelism to deal with a large number of reads from sequencing and the need for many fault injection iterations. We believe that FrameD fills a void in the tools publicly available to the DNA storage community by providing a modular and extensible framework with support for massive parallelism. As a result, it will help accelerate the design process of future DNA-based storage systems. AVAILABILITY AND IMPLEMENTATION The source code for FrameD along with the data generated during the demonstration of FrameD is available in a public Github repository at https://github.com/dna-storage/framed, (https://dx.doi.org/10.5281/zenodo.7757762).
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Affiliation(s)
- Kevin D Volkel
- Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC, 27606, United States
| | - Kevin N Lin
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC, 27695, United States
| | - Paul W Hook
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21218, United States
| | - Winston Timp
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21218, United States
| | - Albert J Keung
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC, 27695, United States
| | - James M Tuck
- Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC, 27606, United States
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4
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Rhie A, Nurk S, Cechova M, Hoyt SJ, Taylor DJ, Altemose N, Hook PW, Koren S, Rautiainen M, Alexandrov IA, Allen J, Asri M, Bzikadze AV, Chen NC, Chin CS, Diekhans M, Flicek P, Formenti G, Fungtammasan A, Garcia Giron C, Garrison E, Gershman A, Gerton JL, Grady PGS, Guarracino A, Haggerty L, Halabian R, Hansen NF, Harris R, Hartley GA, Harvey WT, Haukness M, Heinz J, Hourlier T, Hubley RM, Hunt SE, Hwang S, Jain M, Kesharwani RK, Lewis AP, Li H, Logsdon GA, Lucas JK, Makalowski W, Markovic C, Martin FJ, Mc Cartney AM, McCoy RC, McDaniel J, McNulty BM, Medvedev P, Mikheenko A, Munson KM, Murphy TD, Olsen HE, Olson ND, Paulin LF, Porubsky D, Potapova T, Ryabov F, Salzberg SL, Sauria MEG, Sedlazeck FJ, Shafin K, Shepelev VA, Shumate A, Storer JM, Surapaneni L, Taravella Oill AM, Thibaud-Nissen F, Timp W, Tomaszkiewicz M, Vollger MR, Walenz BP, Watwood AC, Weissensteiner MH, Wenger AM, Wilson MA, Zarate S, Zhu Y, Zook JM, Eichler EE, O'Neill RJ, Schatz MC, Miga KH, Makova KD, Phillippy AM. The complete sequence of a human Y chromosome. Nature 2023; 621:344-354. [PMID: 37612512 PMCID: PMC10752217 DOI: 10.1038/s41586-023-06457-y] [Citation(s) in RCA: 41] [Impact Index Per Article: 41.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Accepted: 07/19/2023] [Indexed: 08/25/2023]
Abstract
The human Y chromosome has been notoriously difficult to sequence and assemble because of its complex repeat structure that includes long palindromes, tandem repeats and segmental duplications1-3. As a result, more than half of the Y chromosome is missing from the GRCh38 reference sequence and it remains the last human chromosome to be finished4,5. Here, the Telomere-to-Telomere (T2T) consortium presents the complete 62,460,029-base-pair sequence of a human Y chromosome from the HG002 genome (T2T-Y) that corrects multiple errors in GRCh38-Y and adds over 30 million base pairs of sequence to the reference, showing the complete ampliconic structures of gene families TSPY, DAZ and RBMY; 41 additional protein-coding genes, mostly from the TSPY family; and an alternating pattern of human satellite 1 and 3 blocks in the heterochromatic Yq12 region. We have combined T2T-Y with a previous assembly of the CHM13 genome4 and mapped available population variation, clinical variants and functional genomics data to produce a complete and comprehensive reference sequence for all 24 human chromosomes.
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Affiliation(s)
- Arang Rhie
- Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Sergey Nurk
- Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
- Oxford Nanopore Technologies Inc., Oxford, UK
| | - Monika Cechova
- Faculty of Informatics, Masaryk University, Brno, Czech Republic
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA, USA
| | - Savannah J Hoyt
- Department of Molecular and Cell Biology, University of Connecticut, Storrs, CT, USA
| | - Dylan J Taylor
- Department of Biology, Johns Hopkins University, Baltimore, MD, USA
| | - Nicolas Altemose
- Department of Molecular and Cell Biology, University of California, Berkeley, CA, USA
| | - Paul W Hook
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Sergey Koren
- Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Mikko Rautiainen
- Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Ivan A Alexandrov
- Federal Research Center of Biotechnology of the Russian Academy of Sciences, Moscow, Russia
- Center for Algorithmic Biotechnology, Saint Petersburg State University, St Petersburg, Russia
- Department of Anatomy and Anthropology and Department of Human Molecular Genetics and Biochemistry, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv-Yafo, Israel
| | - Jamie Allen
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Mobin Asri
- UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, CA, USA
| | - Andrey V Bzikadze
- Graduate Program in Bioinformatics and Systems Biology, University of California, San Diego, CA, USA
| | - Nae-Chyun Chen
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Chen-Shan Chin
- GeneDX Holdings Corp, Stamford, CT, USA
- Foundation of Biological Data Science, Belmont, CA, USA
| | - Mark Diekhans
- UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, CA, USA
| | - Paul Flicek
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
- Department of Genetics, University of Cambridge, Cambridge, UK
| | | | | | - Carlos Garcia Giron
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Erik Garrison
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Ariel Gershman
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Jennifer L Gerton
- Stowers Institute for Medical Research, Kansas City, MO, USA
- University of Kansas Medical Center, Kansas City, MO, USA
| | - Patrick G S Grady
- Department of Molecular and Cell Biology, University of Connecticut, Storrs, CT, USA
| | - Andrea Guarracino
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA
- Genomics Research Centre, Human Technopole, Milan, Italy
| | - Leanne Haggerty
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Reza Halabian
- Institute of Bioinformatics, Faculty of Medicine, University of Münster, Münster, Germany
| | - Nancy F Hansen
- Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
- Cancer Genetics and Comparative Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Robert Harris
- Department of Biology, Pennsylvania State University, University Park, PA, USA
| | - Gabrielle A Hartley
- Department of Molecular and Cell Biology, University of Connecticut, Storrs, CT, USA
| | - William T Harvey
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Marina Haukness
- UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, CA, USA
| | - Jakob Heinz
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Thibaut Hourlier
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | | | - Sarah E Hunt
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Stephen Hwang
- XDBio Program, Johns Hopkins University, Baltimore, MD, USA
| | - Miten Jain
- Department of Bioengineering, Department of Physics, Northeastern University, Boston, MA, USA
| | - Rupesh K Kesharwani
- Human Genome Sequencing Center, Baylor College of Medicine, One Baylor Plaza, Houston, TX, USA
| | - Alexandra P Lewis
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Heng Li
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Glennis A Logsdon
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Julian K Lucas
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA, USA
- UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, CA, USA
| | - Wojciech Makalowski
- Institute of Bioinformatics, Faculty of Medicine, University of Münster, Münster, Germany
| | - Christopher Markovic
- Genome Technology Access Center at the McDonnell Genome Institute, Washington University, St. Louis, MO, USA
| | - Fergal J Martin
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Ann M Mc Cartney
- Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Rajiv C McCoy
- Department of Biology, Johns Hopkins University, Baltimore, MD, USA
| | - Jennifer McDaniel
- Biosystems and Biomaterials Division, National Institute of Standards and Technology, Gaithersburg, MD, USA
| | - Brandy M McNulty
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA, USA
- UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, CA, USA
| | - Paul Medvedev
- Department of Computer Science and Engineering, Pennsylvania State University, University Park, PA, USA
- Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, PA, USA
- Center for Computational Biology and Bioinformatics, Pennsylvania State University, University Park, PA, USA
| | - Alla Mikheenko
- Center for Algorithmic Biotechnology, Saint Petersburg State University, St Petersburg, Russia
- UCL Queen Square Institute of Neurology, UCL, London, UK
| | - Katherine M Munson
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Terence D Murphy
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Hugh E Olsen
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA, USA
- UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, CA, USA
| | - Nathan D Olson
- Biosystems and Biomaterials Division, National Institute of Standards and Technology, Gaithersburg, MD, USA
| | - Luis F Paulin
- Human Genome Sequencing Center, Baylor College of Medicine, One Baylor Plaza, Houston, TX, USA
| | - David Porubsky
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Tamara Potapova
- Stowers Institute for Medical Research, Kansas City, MO, USA
| | - Fedor Ryabov
- Masters Program in National Research University Higher School of Economics, Moscow, Russia
| | - Steven L Salzberg
- Departments of Biomedical Engineering, Computer Science, and Biostatistics, Johns Hopkins University, Baltimore, MD, USA
| | | | - Fritz J Sedlazeck
- Human Genome Sequencing Center, Baylor College of Medicine, One Baylor Plaza, Houston, TX, USA
- Department of Computer Science, Rice University, Houston, TX, USA
| | | | | | - Alaina Shumate
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | | | - Likhitha Surapaneni
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Angela M Taravella Oill
- Center for Evolution and Medicine, School of Life Sciences, Arizona State University, Tempe, AZ, USA
| | - Françoise Thibaud-Nissen
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Winston Timp
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Marta Tomaszkiewicz
- Department of Biology, Pennsylvania State University, University Park, PA, USA
- Department of Biomedical Engineering, Pennsylvania State University, State College, PA, USA
| | - Mitchell R Vollger
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Brian P Walenz
- Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Allison C Watwood
- Department of Biology, Pennsylvania State University, University Park, PA, USA
| | | | | | - Melissa A Wilson
- Center for Evolution and Medicine, School of Life Sciences, Arizona State University, Tempe, AZ, USA
| | - Samantha Zarate
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Yiming Zhu
- Human Genome Sequencing Center, Baylor College of Medicine, One Baylor Plaza, Houston, TX, USA
| | - Justin M Zook
- Biosystems and Biomaterials Division, National Institute of Standards and Technology, Gaithersburg, MD, USA
| | - Evan E Eichler
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
- Investigator, Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA
| | - Rachel J O'Neill
- Department of Molecular and Cell Biology, University of Connecticut, Storrs, CT, USA
- Institute for Systems Genomics, University of Connecticut, Storrs, CT, USA
- Department of Genetics and Genome Sciences, UConn Health, Farmington, CT, USA
| | - Michael C Schatz
- Department of Biology, Johns Hopkins University, Baltimore, MD, USA
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Karen H Miga
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA, USA
- UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, CA, USA
| | - Kateryna D Makova
- Department of Biology, Pennsylvania State University, University Park, PA, USA
| | - Adam M Phillippy
- Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA.
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5
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Abstract
The maturation of high-throughput short-read sequencing technology over the past two decades has shaped the way genomes are studied. Recently, single-molecule, long-read sequencing has emerged as an essential tool in deciphering genome structure and function, including filling gaps in the human reference genome, measuring the epigenome and characterizing splicing variants in the transcriptome. With recent technological developments, these single-molecule technologies have moved beyond genome assembly and are being used in a variety of ways, including to selectively sequence specific loci with long reads, measure chromatin state and protein-DNA binding in order to investigate the dynamics of gene regulation, and rapidly determine copy number variation. These increasingly flexible uses of single-molecule technologies highlight a young and fast-moving part of the field that is leading to a more accessible era of nucleic acid sequencing.
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Affiliation(s)
- Paul W Hook
- Department of Biomedical Engineering, Molecular Biology and Genetics, and Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Winston Timp
- Department of Biomedical Engineering, Molecular Biology and Genetics, and Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA.
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6
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Boyd RJ, McClymont SA, Barrientos NB, Hook PW, Law WD, Rose RJ, Waite EL, Rathinavelu J, Avramopoulos D, McCallion AS. Evaluating the mouse neural precursor line, SN4741, as a suitable proxy for midbrain dopaminergic neurons. BMC Genomics 2023; 24:306. [PMID: 37286935 PMCID: PMC10245633 DOI: 10.1186/s12864-023-09398-y] [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] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 05/23/2023] [Indexed: 06/09/2023] Open
Abstract
To overcome the ethical and technical limitations of in vivo human disease models, the broader scientific community frequently employs model organism-derived cell lines to investigate disease mechanisms, pathways, and therapeutic strategies. Despite the widespread use of certain in vitro models, many still lack contemporary genomic analysis supporting their use as a proxy for the affected human cells and tissues. Consequently, it is imperative to determine how accurately and effectively any proposed biological surrogate may reflect the biological processes it is assumed to model. One such cellular surrogate of human disease is the established mouse neural precursor cell line, SN4741, which has been used to elucidate mechanisms of neurotoxicity in Parkinson disease for over 25 years. Here, we are using a combination of classic and contemporary genomic techniques - karyotyping, RT-qPCR, single cell RNA-seq, bulk RNA-seq, and ATAC-seq - to characterize the transcriptional landscape, chromatin landscape, and genomic architecture of this cell line, and evaluate its suitability as a proxy for midbrain dopaminergic neurons in the study of Parkinson disease. We find that SN4741 cells possess an unstable triploidy and consistently exhibits low expression of dopaminergic neuron markers across assays, even when the cell line is shifted to the non-permissive temperature that drives differentiation. The transcriptional signatures of SN4741 cells suggest that they are maintained in an undifferentiated state at the permissive temperature and differentiate into immature neurons at the non-permissive temperature; however, they may not be dopaminergic neuron precursors, as previously suggested. Additionally, the chromatin landscapes of SN4741 cells, in both the differentiated and undifferentiated states, are not concordant with the open chromatin profiles of ex vivo, mouse E15.5 forebrain- or midbrain-derived dopaminergic neurons. Overall, our data suggest that SN4741 cells may reflect early aspects of neuronal differentiation but are likely not a suitable proxy for dopaminergic neurons as previously thought. The implications of this study extend broadly, illuminating the need for robust biological and genomic rationale underpinning the use of in vitro models of molecular processes.
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Affiliation(s)
- Rachel J. Boyd
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205 USA
| | - Sarah A. McClymont
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205 USA
| | - Nelson B. Barrientos
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205 USA
| | - Paul W. Hook
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205 USA
| | - William D. Law
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205 USA
| | - Rebecca J. Rose
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205 USA
| | - Eric L. Waite
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205 USA
| | - Jay Rathinavelu
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205 USA
| | - Dimitrios Avramopoulos
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205 USA
| | - Andrew S. McCallion
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205 USA
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21287 USA
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7
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Boyd RJ, McClymont SA, Barrientos NB, Hook PW, Law WD, Rose RJ, Waite EL, Rathinavelu J, Avramopoulos D, McCallion AS. Evaluating the mouse neural precursor line, SN4741, as a suitable proxy for midbrain dopaminergic neurons. Res Sq 2023:rs.3.rs-2520557. [PMID: 36824793 PMCID: PMC9949168 DOI: 10.21203/rs.3.rs-2520557/v1] [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] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
Abstract
To overcome the ethical and technical limitations of in vivo human disease models, the broader scientific community frequently employs model organism-derived cell lines to investigate of disease mechanisms, pathways, and therapeutic strategies. Despite the widespread use of certain in vitro models, many still lack contemporary genomic analysis supporting their use as a proxy for the affected human cells and tissues. Consequently, it is imperative to determine how accurately and effectively any proposed biological surrogate may reflect the biological processes it is assumed to model. One such cellular surrogate of human disease is the established mouse neural precursor cell line, SN4741, which has been used to elucidate mechanisms of neurotoxicity in Parkinson disease for over 25 years. Here, we are using a combination of classic and contemporary genomic techniques - karyotyping, RT-qPCR, single cell RNA-seq, bulk RNA-seq, and ATAC-seq - to characterize the transcriptional landscape, chromatin landscape, and genomic architecture of this cell line, and evaluate its suitability as a proxy for midbrain dopaminergic neurons in the study of Parkinson disease. We find that SN4741 cells possess an unstable triploidy and consistently exhibits low expression of dopaminergic neuron markers across assays, even when the cell line is shifted to the non-permissive temperature that drives differentiation. The transcriptional signatures of SN4741 cells suggest that they are maintained in an undifferentiated state at the permissive temperature and differentiate into immature neurons at the non-permissive temperature; however, they may not be dopaminergic neuron precursors, as previously suggested. Additionally, the chromatin landscapes of SN4741 cells, in both the differentiated and undifferentiated states, are not concordant with the open chromatin profiles of ex vivo , mouse E15.5 forebrain- or midbrain-derived dopaminergic neurons. Overall, our data suggest that SN4741 cells may reflect early aspects of neuronal differentiation but are likely not a suitable a proxy for dopaminergic neurons as previously thought. The implications of this study extend broadly, illuminating the need for robust biological and genomic rationale underpinning the use of in vitro models of molecular processes.
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8
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Boyd RJ, McClymont SA, Barrientos NB, Hook PW, Law WD, Rose RJ, Waite EL, Avramopoulos D, McCallion AS. Evaluating the mouse neural precursor line, SN4741, as a suitable proxy for midbrain dopaminergic neurons. bioRxiv 2023:2023.01.23.525270. [PMID: 36747739 PMCID: PMC9900784 DOI: 10.1101/2023.01.23.525270] [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] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
To overcome the ethical and technical limitations of in vivo human disease models, the broader scientific community frequently employs model organism-derived cell lines to investigate of disease mechanisms, pathways, and therapeutic strategies. Despite the widespread use of certain in vitro models, many still lack contemporary genomic analysis supporting their use as a proxy for the affected human cells and tissues. Consequently, it is imperative to determine how accurately and effectively any proposed biological surrogate may reflect the biological processes it is assumed to model. One such cellular surrogate of human disease is the established mouse neural precursor cell line, SN4741, which has been used to elucidate mechanisms of neurotoxicity in Parkinson disease for over 25 years. Here, we are using a combination of classic and contemporary genomic techniques - karyotyping, RT-qPCR, single cell RNA-seq, bulk RNA-seq, and ATAC-seq - to characterize the transcriptional landscape, chromatin landscape, and genomic architecture of this cell line, and evaluate its suitability as a proxy for midbrain dopaminergic neurons in the study of Parkinson disease. We find that SN4741 cells possess an unstable triploidy and consistently exhibits low expression of dopaminergic neuron markers across assays, even when the cell line is shifted to the non-permissive temperature that drives differentiation. The transcriptional signatures of SN4741 cells suggest that they are maintained in an undifferentiated state at the permissive temperature and differentiate into immature neurons at the non-permissive temperature; however, they may not be dopaminergic neuron precursors, as previously suggested. Additionally, the chromatin landscapes of SN4741 cells, in both the differentiated and undifferentiated states, are not concordant with the open chromatin profiles of ex vivo , mouse E15.5 forebrain- or midbrain-derived dopaminergic neurons. Overall, our data suggest that SN4741 cells may reflect early aspects of neuronal differentiation but are likely not a suitable a proxy for dopaminergic neurons as previously thought. The implications of this study extend broadly, illuminating the need for robust biological and genomic rationale underpinning the use of in vitro models of molecular processes.
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Affiliation(s)
- Rachel J. Boyd
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Sarah A. McClymont
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Nelson B. Barrientos
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Paul W. Hook
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - William D. Law
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Rebecca J. Rose
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Eric L. Waite
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Dimitrios Avramopoulos
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Andrew S. McCallion
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.,Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA.,To whom correspondence should be addressed Andrew S. McCallion -
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9
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Gershman A, Sauria MEG, Guitart X, Vollger MR, Hook PW, Hoyt SJ, Jain M, Shumate A, Razaghi R, Koren S, Altemose N, Caldas GV, Logsdon GA, Rhie A, Eichler EE, Schatz MC, O'Neill RJ, Phillippy AM, Miga KH, Timp W. Epigenetic patterns in a complete human genome. Science 2022; 376:eabj5089. [PMID: 35357915 PMCID: PMC9170183 DOI: 10.1126/science.abj5089] [Citation(s) in RCA: 97] [Impact Index Per Article: 48.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
The completion of a telomere-to-telomere human reference genome, T2T-CHM13, has resolved complex regions of the genome, including repetitive and homologous regions. Here, we present a high-resolution epigenetic study of previously unresolved sequences, representing entire acrocentric chromosome short arms, gene family expansions, and a diverse collection of repeat classes. This resource precisely maps CpG methylation (32.28 million CpGs), DNA accessibility, and short-read datasets (166,058 previously unresolved chromatin immunoprecipitation sequencing peaks) to provide evidence of activity across previously unidentified or corrected genes and reveals clinically relevant paralog-specific regulation. Probing CpG methylation across human centromeres from six diverse individuals generated an estimate of variability in kinetochore localization. This analysis provides a framework with which to investigate the most elusive regions of the human genome, granting insights into epigenetic regulation.
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Affiliation(s)
- Ariel Gershman
- Department of Molecular Biology and Genetics, Johns Hopkins University, Baltimore, MD, USA
| | - Michael E G Sauria
- Department of Biology and Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Xavi Guitart
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Mitchell R Vollger
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Paul W Hook
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Savannah J Hoyt
- Institute for Systems Genomics, University of Connecticut, Storrs, CT, USA
- Department of Molecular and Cell Biology, University of Connecticut, Storrs, CT, USA
| | - Miten Jain
- UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, CA, USA
| | - Alaina Shumate
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Roham Razaghi
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Sergey Koren
- Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Nicolas Altemose
- Department of Bioengineering, University of California Berkeley, Berkeley, CA, USA
| | - Gina V Caldas
- Department of Molecular and Cell Biology, University of California Berkeley, Berkeley CA, USA
| | - Glennis A Logsdon
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Arang Rhie
- Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Evan E Eichler
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA
| | - Michael C Schatz
- Department of Biology and Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Rachel J O'Neill
- Institute for Systems Genomics, University of Connecticut, Storrs, CT, USA
- Department of Molecular and Cell Biology, University of Connecticut, Storrs, CT, USA
| | - Adam M Phillippy
- Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Karen H Miga
- UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, CA, USA
| | - Winston Timp
- Department of Molecular Biology and Genetics, Johns Hopkins University, Baltimore, MD, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
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10
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Soto-Beasley AI, Walton RL, Valentino RR, Hook PW, Labbé C, Heckman MG, Johnson PW, Goff LA, Uitti RJ, McLean PJ, Springer W, McCallion AS, Wszolek ZK, Ross OA. Screening non-MAPT genes of the Chr17q21 H1 haplotype in Parkinson's disease. Parkinsonism Relat Disord 2020; 78:138-144. [PMID: 32829096 PMCID: PMC7686230 DOI: 10.1016/j.parkreldis.2020.07.022] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/01/2020] [Revised: 07/04/2020] [Accepted: 07/24/2020] [Indexed: 01/07/2023]
Abstract
INTRODUCTION The microtubule-associated protein tau (MAPT) gene is considered a strong genetic risk factor for Parkinson's disease (PD) in Caucasians. MAPT is located within an inversion region of high linkage disequilibrium designated as H1 and H2 haplotype, and contains eight other genes which have been implicated in neurodegeneration. The aim of the current study was to identify common coding variants in strong linkage disequilibrium (LD) within the associated loci on chr17q21 harboring MAPT. METHODS Sanger sequencing of coding exons in 90 Caucasian late-onset PD (LOPD) patients was performed. Specific gene sequencing for LRRC37A, LRRC37A2, ARL17A and ARL17B was not possible given the high homology, presence of pseudogenes and copy number variants that are in the region, and therefore four genes (NSF, KANSL1, SPPL2C, and CRHR1) were included in the analysis. Coding variants from these four genes that did not perfectly tag (r2 = 1) the MAPT H1/H2 haplotype were genotyped in an independent replication series of Caucasian PD cases (N = 851) and controls (N = 730). RESULTS In the 90 LOPD cases we identified 30 coding variants. Eleven non-synonymous variants tagged the MAPT H1/H2 haplotype, including two SPPL2C variants (rs12185233 and rs12373123) that had high pathogenic combined annotation dependent depletion (CADD) scores of >20. In the replication series, the non-synonymous KANSL1 rs17585974 variant was in very strong LD with MAPT H1/H2 and had a high CADD score of 24.7. CONCLUSION We have identified several non-synonymous variants across neighboring genes of MAPT that may warrant further genetic and functional investigation within the biological etiology of PD.
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Affiliation(s)
| | - Ronald L. Walton
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL 32224, USA
| | | | - Paul W. Hook
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Catherine Labbé
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Michael G. Heckman
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Patrick W. Johnson
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Loyal A. Goff
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA,Solomon H. Snyder Department of Neuroscience and Kavli Neurodiscovery Institute, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Ryan J. Uitti
- Department of Neurology, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Pamela J. McLean
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL 32224, USA,Neuroscience PhD Program, Mayo Clinic Graduate School of Biomedical Sciences
| | - Wolfdieter Springer
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL 32224, USA,Neuroscience PhD Program, Mayo Clinic Graduate School of Biomedical Sciences
| | - Andrew S. McCallion
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA,Department of Molecular and Comparative Pathobiology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA,Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | | | - Owen A. Ross
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL 32224, USA,Department of Clinical Genomics, Mayo Clinic, Jacksonville, FL 32224, USA,School of Medicine and Medical Science, University College Dublin, Dublin, Ireland,Neuroscience PhD Program, Mayo Clinic Graduate School of Biomedical Sciences,Corresponding author’s contact information: Owen A. Ross, Ph.D., Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224, Tel: +1 (904)-953-6280, Fax: +1 (904)-953-7370,
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11
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Hook PW, McCallion AS. Leveraging mouse chromatin data for heritability enrichment informs common disease architecture and reveals cortical layer contributions to schizophrenia. Genome Res 2020; 30:528-539. [PMID: 32303558 PMCID: PMC7197474 DOI: 10.1101/gr.256578.119] [Citation(s) in RCA: 11] [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: 08/29/2019] [Accepted: 03/30/2020] [Indexed: 12/21/2022]
Abstract
Genome-wide association studies have implicated thousands of noncoding variants across common human phenotypes. However, they cannot directly inform the cellular context in which disease-associated variants act. Here, we use open chromatin profiles from discrete mouse cell populations to address this challenge. We applied stratified linkage disequilibrium score regression and evaluated heritability enrichment in 64 genome-wide association studies, emphasizing schizophrenia. We provide evidence that mouse-derived human open chromatin profiles can serve as powerful proxies for difficult to obtain human cell populations, facilitating the illumination of common disease heritability enrichment across an array of human phenotypes. We demonstrate that signatures from discrete subpopulations of cortical excitatory and inhibitory neurons are significantly enriched for schizophrenia heritability with maximal enrichment in cortical layer V excitatory neurons. We also show that differences between schizophrenia and bipolar disorder are concentrated in excitatory neurons in cortical layers II-III, IV, and V, as well as the dentate gyrus. Finally, we leverage these data to fine-map variants in 177 schizophrenia loci nominating variants in 104/177. We integrate these data with transcription factor binding site, chromatin interaction, and validated enhancer data, placing variants in the cellular context where they may modulate risk.
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Affiliation(s)
- Paul W Hook
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA
| | - Andrew S McCallion
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA.,Department of Comparative and Molecular Pathobiology, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA.,Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA
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12
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McClymont SA, Hook PW, Soto AI, Reed X, Law WD, Kerans SJ, Waite EL, Briceno NJ, Thole JF, Heckman MG, Diehl NN, Wszolek ZK, Moore CD, Zhu H, Akiyama JA, Dickel DE, Visel A, Pennacchio LA, Ross OA, Beer MA, McCallion AS. Parkinson-Associated SNCA Enhancer Variants Revealed by Open Chromatin in Mouse Dopamine Neurons. Am J Hum Genet 2018; 103:874-892. [PMID: 30503521 DOI: 10.1016/j.ajhg.2018.10.018] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2018] [Accepted: 10/17/2018] [Indexed: 12/31/2022] Open
Abstract
The progressive loss of midbrain (MB) dopaminergic (DA) neurons defines the motor features of Parkinson disease (PD), and modulation of risk by common variants in PD has been well established through genome-wide association studies (GWASs). We acquired open chromatin signatures of purified embryonic mouse MB DA neurons because we anticipated that a fraction of PD-associated genetic variation might mediate the variants' effects within this neuronal population. Correlation with >2,300 putative enhancers assayed in mice revealed enrichment for MB cis-regulatory elements (CREs), and these data were reinforced by transgenic analyses of six additional sequences in zebrafish and mice. One CRE, within intron 4 of the familial PD gene SNCA, directed reporter expression in catecholaminergic neurons from transgenic mice and zebrafish. Sequencing of this CRE in 986 individuals with PD and 992 controls revealed two common variants associated with elevated PD risk. To assess potential mechanisms of action, we screened >16,000 proteins for DNA binding capacity and identified a subset whose binding is impacted by these enhancer variants. Additional genotyping across the SNCA locus identified a single PD-associated haplotype, containing the minor alleles of both of the aforementioned PD-risk variants. Our work posits a model for how common variation at SNCA might modulate PD risk and highlights the value of cell-context-dependent guided searches for functional non-coding variation.
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Affiliation(s)
- Sarah A McClymont
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Paul W Hook
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Alexandra I Soto
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Xylena Reed
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - William D Law
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Samuel J Kerans
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Eric L Waite
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Nicole J Briceno
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Joey F Thole
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Michael G Heckman
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Nancy N Diehl
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Jacksonville, FL 32224, USA
| | | | - Cedric D Moore
- Department of Pharmacology and Molecular Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Heng Zhu
- Department of Pharmacology and Molecular Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Jennifer A Akiyama
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Diane E Dickel
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Axel Visel
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA; US Department of Energy Joint Genome Institute, Walnut Creek, CA 94598, USA; School of Natural Sciences, University of California, Merced, CA 95343, USA
| | - Len A Pennacchio
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA; US Department of Energy Joint Genome Institute, Walnut Creek, CA 94598, USA; Comparative Biochemistry Program, University of California, Berkeley, CA 94720, USA
| | - Owen A Ross
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL 32224, USA; Neuroscience Track, Mayo Graduate School, Jacksonville, FL 32224, USA; Department of Clinical Genomics, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Michael A Beer
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Andrew S McCallion
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Department of Molecular and Comparative Pathobiology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA.
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13
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Abstract
We recently demonstrated that the gene encoding the RNA binding motif protein 24 (RBM24) is expressed during mouse cardiogenesis, and determined the developmental requirement for its zebrafish homologs Rbm24a and Rbm24b during cardiac development. We demonstrate here that both Rbm24a and Rbm24b are also required for normal somite and craniofacial development. Diminution of rbm24a or rbm24b gene products by morpholino knockdown resulted in significant disruption of somite formation. Detailed in situ hybridization-based analyses of a spectrum of somitogenesis-associated transcripts revealed reduced expression of the cyclic muscle pattering genes dlc and dld encoding Notch ligands, as well as their respective target genes her7, her1. By contrast expression of the Notch receptors notch1a and notch3 appears unchanged. Some RBM-family members have been implicated in pre-mRNA processing. Analysis of affected Notch-pathway mRNAs in rbm24a and rbm24b morpholino-injected embryos revealed aberrant transcript fragments of dlc and dld, but not her1 or her7, suggesting the reduction in transcription levels of Notch pathway components may result from aberrant processing of its ligands. These data imply a previously unknown requirement for Rbm24a and Rbm24b in somite and craniofacial development. Although we anticipate the influence of disrupting RBM24 homologs likely extends beyond the Notch pathway, our results suggest their perturbation may directly, or indirectly, compromise post-transcriptional processing, exemplified by imprecise processing of dlc and dld.
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Affiliation(s)
- Samantha Maragh
- Biochemical Science Division, National Institute of Standards and Technology, Gaithersburg, Maryland, United States of America
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Ronald A. Miller
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Seneca L. Bessling
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Guangliang Wang
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Paul W. Hook
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Andrew S. McCallion
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
- Department of Molecular and Comparative Pathobiology, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
- * E-mail:
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