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Soleymani F, Paquet E, Viktor HL, Michalowski W. Structure-based protein and small molecule generation using EGNN and diffusion models: A comprehensive review. Comput Struct Biotechnol J 2024; 23:2779-2797. [PMID: 39050782 PMCID: PMC11268121 DOI: 10.1016/j.csbj.2024.06.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Revised: 06/13/2024] [Accepted: 06/18/2024] [Indexed: 07/27/2024] Open
Abstract
Recent breakthroughs in deep learning have revolutionized protein sequence and structure prediction. These advancements are built on decades of protein design efforts, and are overcoming traditional time and cost limitations. Diffusion models, at the forefront of these innovations, significantly enhance design efficiency by automating knowledge acquisition. In the field of de novo protein design, the goal is to create entirely novel proteins with predetermined structures. Given the arbitrary positions of proteins in 3-D space, graph representations and their properties are widely used in protein generation studies. A critical requirement in protein modelling is maintaining spatial relationships under transformations (rotations, translations, and reflections). This property, known as equivariance, ensures that predicted protein characteristics adapt seamlessly to changes in orientation or position. Equivariant graph neural networks offer a solution to this challenge. By incorporating equivariant graph neural networks to learn the score of the probability density function in diffusion models, one can generate proteins with robust 3-D structural representations. This review examines the latest deep learning advancements, specifically focusing on frameworks that combine diffusion models with equivariant graph neural networks for protein generation.
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Affiliation(s)
- Farzan Soleymani
- Telfer School of Management, University of Ottawa, ON, K1N 6N5, Canada
| | - Eric Paquet
- National Research Council, 1200 Montreal Road, Ottawa, ON, K1A 0R6, Canada
- School of Electrical Engineering and Computer Science, University of Ottawa, ON, K1N 6N5, Canada
| | - Herna Lydia Viktor
- School of Electrical Engineering and Computer Science, University of Ottawa, ON, K1N 6N5, Canada
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2
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Otto M, Zheng Y, Grablowitz P, Wiehe T. Detecting adaptive changes in gene copy number distribution accompanying the human out-of-Africa expansion. Hum Genome Var 2024; 11:37. [PMID: 39313504 PMCID: PMC11420239 DOI: 10.1038/s41439-024-00293-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Revised: 07/05/2024] [Accepted: 07/22/2024] [Indexed: 09/25/2024] Open
Abstract
Genes with multiple copies are likely to be maintained by stabilizing selection, which puts a bound to unlimited expansion of copy number. We designed a model in which copy number variation is generated by unequal recombination, which fits well with several genes surveyed in three human populations. Based on this theoretical model and computer simulations, we were interested in determining whether the gene copy number distribution in the derived European and Asian populations can be explained by a purely demographic scenario or whether shifts in the distribution are signatures of adaptation. Although the copy number distribution in most of the analyzed gene clusters can be explained by a bottleneck, such as in the out-of-Africa expansion of Homo sapiens 60-10 kyrs ago, we identified several candidate genes, such as AMY1A and PGA3, whose copy numbers are likely to differ among African, Asian, and European populations.
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Affiliation(s)
- Moritz Otto
- Institue for Genetics, University of Cologne, Cologne, Germany
| | - Yichen Zheng
- Institue for Genetics, University of Cologne, Cologne, Germany
| | - Paul Grablowitz
- Department of Computer Science, University of Tübingen, Tübingen, Germany
| | - Thomas Wiehe
- Institue for Genetics, University of Cologne, Cologne, Germany.
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3
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Kwon S, Safer J, Nguyen DT, Hoksza D, May P, Arbesfeld JA, Rubin AF, Campbell AJ, Burgin A, Iqbal S. Genomics 2 Proteins portal: a resource and discovery tool for linking genetic screening outputs to protein sequences and structures. Nat Methods 2024:10.1038/s41592-024-02409-0. [PMID: 39294369 DOI: 10.1038/s41592-024-02409-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Accepted: 08/09/2024] [Indexed: 09/20/2024]
Abstract
Recent advances in AI-based methods have revolutionized the field of structural biology. Concomitantly, high-throughput sequencing and functional genomics have generated genetic variants at an unprecedented scale. However, efficient tools and resources are needed to link disparate data types-to 'map' variants onto protein structures, to better understand how the variation causes disease, and thereby design therapeutics. Here we present the Genomics 2 Proteins portal ( https://g2p.broadinstitute.org/ ): a human proteome-wide resource that maps 20,076,998 genetic variants onto 42,413 protein sequences and 77,923 structures, with a comprehensive set of structural and functional features. Additionally, the Genomics 2 Proteins portal allows users to interactively upload protein residue-wise annotations (for example, variants and scores) as well as the protein structure beyond databases to establish the connection between genomics to proteins. The portal serves as an easy-to-use discovery tool for researchers and scientists to hypothesize the structure-function relationship between natural or synthetic variations and their molecular phenotypes.
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Affiliation(s)
- Seulki Kwon
- Center for the Development of Therapeutics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jordan Safer
- Center for the Development of Therapeutics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Duyen T Nguyen
- PATTERN, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - David Hoksza
- Department of Software Engineering, Faculty of Mathematics and Physics, Charles University, Prague, Czech Republic
| | - Patrick May
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Jeremy A Arbesfeld
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, OH, USA
| | - Alan F Rubin
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia
- Department of Medical Biology, University of Melbourne, Parkville, Victoria, Australia
| | - Arthur J Campbell
- Center for the Development of Therapeutics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Alex Burgin
- Center for the Development of Therapeutics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Sumaiya Iqbal
- Center for the Development of Therapeutics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
- Cancer Data Sciences, Dana-Farber/Harvard Cancer Center, Boston, MA, USA.
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4
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Olvera-León R, Zhang F, Offord V, Zhao Y, Tan HK, Gupta P, Pal T, Robles-Espinoza CD, Arriaga-González FG, Matsuyama LSAS, Delage E, Dicks E, Ezquina S, Rowlands CF, Turnbull C, Pharoah P, Perry JRB, Jasin M, Waters AJ, Adams DJ. High-resolution functional mapping of RAD51C by saturation genome editing. Cell 2024:S0092-8674(24)00968-1. [PMID: 39299233 DOI: 10.1016/j.cell.2024.08.039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 02/29/2024] [Accepted: 08/20/2024] [Indexed: 09/22/2024]
Abstract
Pathogenic variants in RAD51C confer an elevated risk of breast and ovarian cancer, while individuals homozygous for specific RAD51C alleles may develop Fanconi anemia. Using saturation genome editing (SGE), we functionally assess 9,188 unique variants, including >99.5% of all possible coding sequence single-nucleotide alterations. By computing changes in variant abundance and Gaussian mixture modeling (GMM), we functionally classify 3,094 variants to be disruptive and use clinical truth sets to reveal an accuracy/concordance of variant classification >99.9%. Cell fitness was the primary assay readout allowing us to observe a phenomenon where specific missense variants exhibit distinct depletion kinetics potentially suggesting that they represent hypomorphic alleles. We further explored our exhaustive functional map, revealing critical residues on the RAD51C structure and resolving variants found in cancer-segregating kindred. Furthermore, through interrogation of UK Biobank and a large multi-center ovarian cancer cohort, we find significant associations between SGE-depleted variants and cancer diagnoses.
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Affiliation(s)
- Rebeca Olvera-León
- Wellcome Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK; Laboratorio Internacional de Investigación sobre el Genoma Humano, Universidad Nacional Autónoma de México, Campus Juriquilla, Querétaro, Querétaro, Mexico
| | - Fang Zhang
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY, USA; Developmental Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Victoria Offord
- Wellcome Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | - Yajie Zhao
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK
| | - Hong Kee Tan
- Wellcome Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | - Prashant Gupta
- Wellcome Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | - Tuya Pal
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center (VUMC)/Vanderbilt-Ingram Cancer Center (VICC), Nashville, TN, USA
| | - Carla Daniela Robles-Espinoza
- Wellcome Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK; Laboratorio Internacional de Investigación sobre el Genoma Humano, Universidad Nacional Autónoma de México, Campus Juriquilla, Querétaro, Querétaro, Mexico
| | - Fernanda G Arriaga-González
- Wellcome Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK; Laboratorio Internacional de Investigación sobre el Genoma Humano, Universidad Nacional Autónoma de México, Campus Juriquilla, Querétaro, Querétaro, Mexico
| | | | - Erwan Delage
- Wellcome Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | - Ed Dicks
- Department of Public Health and Primary Care, University of Cambridge, Robinson Way, Cambridge, UK
| | - Suzana Ezquina
- Department of Public Health and Primary Care, University of Cambridge, Robinson Way, Cambridge, UK
| | - Charlie F Rowlands
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK
| | - Clare Turnbull
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK; National Cancer Registration and Analysis Service, National Health Service (NHS) England, London, UK; Cancer Genetics Unit, The Royal Marsden NHS Foundation Trust, London, UK
| | - Paul Pharoah
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - John R B Perry
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK
| | - Maria Jasin
- Developmental Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Andrew J Waters
- Wellcome Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK.
| | - David J Adams
- Wellcome Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK.
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5
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Chao KH, Mao A, Salzberg SL, Pertea M. Splam: a deep-learning-based splice site predictor that improves spliced alignments. Genome Biol 2024; 25:243. [PMID: 39285451 PMCID: PMC11406845 DOI: 10.1186/s13059-024-03379-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 08/28/2024] [Indexed: 09/19/2024] Open
Abstract
The process of splicing messenger RNA to remove introns plays a central role in creating genes and gene variants. We describe Splam, a novel method for predicting splice junctions in DNA using deep residual convolutional neural networks. Unlike previous models, Splam looks at a 400-base-pair window flanking each splice site, reflecting the biological splicing process that relies primarily on signals within this window. Splam also trains on donor and acceptor pairs together, mirroring how the splicing machinery recognizes both ends of each intron. Compared to SpliceAI, Splam is consistently more accurate, achieving 96% accuracy in predicting human splice junctions.
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Affiliation(s)
- Kuan-Hao Chao
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, 21218, USA.
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, 21211, USA.
| | - Alan Mao
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, 21218, USA
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, 21211, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Steven L Salzberg
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, 21218, USA
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, 21211, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
- Department of Biostatistics, Johns Hopkins University, Baltimore, MD, 21205, USA
| | - Mihaela Pertea
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, 21218, USA.
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, 21211, USA.
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA.
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Gohlke J, Lindqvist J, Hourani Z, Heintzman S, Tonino P, Elsheikh B, Morales A, Vatta M, Burghes A, Granzier H, Roggenbuck J. Pathomechanisms of Monoallelic variants in TTN causing skeletal muscle disease. Hum Mol Genet 2024:ddae136. [PMID: 39277846 DOI: 10.1093/hmg/ddae136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Revised: 07/01/2024] [Accepted: 09/06/2024] [Indexed: 09/17/2024] Open
Abstract
Pathogenic variants in the titin gene (TTN) are known to cause a wide range of cardiac and musculoskeletal disorders, with skeletal myopathy mostly attributed to biallelic variants. We identified monoallelic truncating variants (TTNtv), splice site or internal deletions in TTN in probands with mild, progressive axial and proximal weakness, with dilated cardiomyopathy frequently developing with age. These variants segregated in an autosomal dominant pattern in 7 out of 8 studied families. We investigated the impact of these variants on mRNA, protein levels, and skeletal muscle structure and function. Results reveal that nonsense-mediated decay likely prevents accumulation of harmful truncated protein in skeletal muscle in patients with TTNtvs. Splice variants and an out-of-frame deletion induce aberrant exon skipping, while an in-frame deletion produces shortened titin with intact N- and C-termini, resulting in disrupted sarcomeric structure. All variant types were associated with genome-wide changes in splicing patterns, which represent a hallmark of disease progression. Lastly, RNA-seq studies revealed that GDF11, a member of the TGF-β superfamily, is upregulated in diseased tissue, indicating that it might be a useful therapeutic target in skeletal muscle titinopathies.
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Affiliation(s)
- Jochen Gohlke
- Department of Cellular and Molecular Medicine, University of Arizona, 1656 E. Mabel St., Tucson, AZ 85724, United States
| | - Johan Lindqvist
- Department of Cellular and Molecular Medicine, University of Arizona, 1656 E. Mabel St., Tucson, AZ 85724, United States
| | - Zaynab Hourani
- Department of Cellular and Molecular Medicine, University of Arizona, 1656 E. Mabel St., Tucson, AZ 85724, United States
| | - Sarah Heintzman
- Department of Neurology, The Ohio State University Wexner Medical Center, 395 W. 12th Ave, Columbus, OH 43210, United States
| | - Paola Tonino
- Research, Innovation and Impact Core Facilities Department, University of Arizona, 1333 N. Martin Ave, Tucson, AZ 85719, United States
| | - Bakri Elsheikh
- Department of Neurology, The Ohio State University Wexner Medical Center, 395 W. 12th Ave, Columbus, OH 43210, United States
| | - Ana Morales
- Invitae Corporation, 1400 16th St., San Francisco, CA 94103, United States
| | - Matteo Vatta
- Invitae Corporation, 1400 16th St., San Francisco, CA 94103, United States
| | - Arthur Burghes
- Department of Biological Chemistry and Pharmacology, The Ohio State University Wexner Medical Center, 370 W 9th Ave, Columbus, OH 43210, United States
| | - Henk Granzier
- Department of Cellular and Molecular Medicine, University of Arizona, 1656 E. Mabel St., Tucson, AZ 85724, United States
| | - Jennifer Roggenbuck
- Department of Neurology, The Ohio State University Wexner Medical Center, 395 W. 12th Ave, Columbus, OH 43210, United States
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Plummer L, Balasubramanian R, Stamou M, Campbell M, Dewan P, Bryant N, Salnikov K, Lippincott M, Seminara S. Lack of a genetic risk continuum between pubertal timing in the general population and idiopathic hypogonadotropic hypogonadism. J Neuroendocrinol 2024:e13445. [PMID: 39256164 DOI: 10.1111/jne.13445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Revised: 08/23/2024] [Accepted: 08/27/2024] [Indexed: 09/12/2024]
Abstract
Pubertal timing is a highly heritable trait in the general population. Recently, a large-scale exome-wide association study has implicated rare variants in six genes (KDM4C, MC3R, MKRN3, PDE10A, TACR3, and ZNF483) as genetic determinants of pubertal timing within the general population. Two of the genes (TACR3, MKRN3) are already implicated in extreme disorders of pubertal timing. This observation suggests that there may be a pervasive "genetic risk continuum" wherein genes that govern pubertal timing in the general population, by extension, may also be causal for rare Mendelian disorders of pubertal timing. Hence, we hypothesized that the four novel genes linked to pubertal timing in the population will also contribute to idiopathic hypogonadotropic hypogonadism (IHH), a genetic disorder characterized by absent puberty. Exome sequencing data from 1322 unrelated IHH probands were reviewed for rare sequence variants (RSVs) (minor allele frequency bins: <1%; <0.1%; <0.01%) in the six genes linked to puberty in the general population. A gene-based rare variant association testing (RVAT) was performed between the IHH cohort and a reference public genomic sequences repository-the Genome Aggregation Database (gnomAD). As expected, RVAT analysis showed that RSVs in TACR3, a known IHH gene, were significantly enriched in the IHH cohort compared to gnomAD cohort across all three MAF bins. However, RVAT analysis of the remaining five genes failed to show any RSV enrichment in the IHH cohort across all MAF bins. Our findings argue strongly against a pervasive genetic risk continuum between pubertal timing in the general population and extreme pubertal phenotypes. The biologic basis of such distinct genetic architectures' merits further evaluation.
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Affiliation(s)
- Lacey Plummer
- Center for Reproductive Medicine, Reproductive Endocrine Unit and The Endocrine Unit of the Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Ravikumar Balasubramanian
- Center for Reproductive Medicine, Reproductive Endocrine Unit and The Endocrine Unit of the Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Maria Stamou
- Center for Reproductive Medicine, Reproductive Endocrine Unit and The Endocrine Unit of the Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Mark Campbell
- Center for Reproductive Medicine, Reproductive Endocrine Unit and The Endocrine Unit of the Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Pranav Dewan
- Center for Reproductive Medicine, Reproductive Endocrine Unit and The Endocrine Unit of the Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Nora Bryant
- Center for Reproductive Medicine, Reproductive Endocrine Unit and The Endocrine Unit of the Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Kathryn Salnikov
- Center for Reproductive Medicine, Reproductive Endocrine Unit and The Endocrine Unit of the Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Margaret Lippincott
- Center for Reproductive Medicine, Reproductive Endocrine Unit and The Endocrine Unit of the Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Stephanie Seminara
- Center for Reproductive Medicine, Reproductive Endocrine Unit and The Endocrine Unit of the Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
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8
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Chen X, Fansler MM, Janjoš U, Ule J, Mayr C. The FXR1 network acts as a signaling scaffold for actomyosin remodeling. Cell 2024; 187:5048-5063.e25. [PMID: 39106863 PMCID: PMC11380585 DOI: 10.1016/j.cell.2024.07.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 05/24/2024] [Accepted: 07/08/2024] [Indexed: 08/09/2024]
Abstract
It is currently not known whether mRNAs fulfill structural roles in the cytoplasm. Here, we report the fragile X-related protein 1 (FXR1) network, an mRNA-protein (mRNP) network present throughout the cytoplasm, formed by FXR1-mediated packaging of exceptionally long mRNAs. These mRNAs serve as an underlying condensate scaffold and concentrate FXR1 molecules. The FXR1 network contains multiple protein binding sites and functions as a signaling scaffold for interacting proteins. We show that it is necessary for RhoA signaling-induced actomyosin reorganization to provide spatial proximity between kinases and their substrates. Point mutations in FXR1, found in its homolog FMR1, where they cause fragile X syndrome, disrupt the network. FXR1 network disruption prevents actomyosin remodeling-an essential and ubiquitous process for the regulation of cell shape, migration, and synaptic function. Our findings uncover a structural role for cytoplasmic mRNA and show how the FXR1 RNA-binding protein as part of the FXR1 network acts as an organizer of signaling reactions.
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Affiliation(s)
- Xiuzhen Chen
- Cancer Biology and Genetics Program, Sloan Kettering Institute, New York, NY 10065, USA
| | - Mervin M Fansler
- Cancer Biology and Genetics Program, Sloan Kettering Institute, New York, NY 10065, USA
| | - Urška Janjoš
- National Institute of Chemistry, Hajdrihova 19, 1001 Ljubljana, Slovenia; Biosciences PhD Program, Biotechnical Faculty, University of Ljubljana, Ljubljana, Slovenia
| | - Jernej Ule
- National Institute of Chemistry, Hajdrihova 19, 1001 Ljubljana, Slovenia; UK Dementia Research Institute at King's College London, London SE5 9NU, UK
| | - Christine Mayr
- Cancer Biology and Genetics Program, Sloan Kettering Institute, New York, NY 10065, USA.
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9
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Kiser D, Elhanan G, Bolze A, Neveux I, Schlauch KA, Metcalf WJ, Cirulli ET, McCarthy C, Greenberg LA, Grime S, Blitstein JMS, Plauth W, Grzymski JJ. Screening Familial Risk for Hereditary Breast and Ovarian Cancer. JAMA Netw Open 2024; 7:e2435901. [PMID: 39320887 DOI: 10.1001/jamanetworkopen.2024.35901] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/26/2024] Open
Abstract
Importance Most patients with pathogenic or likely pathogenic (P/LP) variants for breast cancer have not undergone genetic testing. Objective To identify patients meeting family history criteria for genetic testing in the electronic health record (EHR). Design, Setting, and Participants This study included both cross-sectional (observation date, February 1, 2024) and retrospective cohort (observation period, January 1, 2018, to February 1, 2024) analyses. Participants included patients aged 18 to 79 years enrolled in Renown Health, a large health system in Northern Nevada. Genotype was known for 38 003 patients enrolled in Healthy Nevada Project (HNP), a population genomics study. Exposure An EHR indicating that a patient is positive for criteria according to the Seven-Question Family History Questionnaire (hereafter, FHS7 positive) assessing familial risk for hereditary breast and ovarian cancer (HBOC). Main Outcomes and Measures The primary outcomes were the presence of P/LP variants in the ATM, BRCA1, BRCA2, CHEK2, or PALB2 genes (cross-sectional analysis) or a diagnosis of cancer (cohort analysis). Age-adjusted cancer incidence rates per 100 000 patients per year were calculated using the 2020 US population as the standard. Hazard ratios (HRs) for cancer attributable to FHS7-positive status were estimated using cause-specific hazard models. Results Among 835 727 patients, 423 393 (50.7%) were female and 29 913 (3.6%) were FHS7 positive. Among those who were FHS7 positive, 24 535 (82.0%) had no evidence of prior genetic testing for HBOC in their EHR. Being FHS7 positive was associated with increased prevalence of P/LP variants in BRCA1/BRCA2 (odds ratio [OR], 3.34; 95% CI, 2.48-4.47), CHEK2 (OR, 1.62; 95% CI, 1.05-2.43), and PALB2 (OR, 2.84; 95% CI, 1.23-6.16) among HNP female individuals, and in BRCA1/BRCA2 (OR, 3.35; 95% CI, 1.93-5.56) among HNP male individuals. Being FHS7 positive was also associated with significantly increased risk of cancer among 131 622 non-HNP female individuals (HR, 1.44; 95% CI, 1.22-1.70) but not among 114 982 non-HNP male individuals (HR, 1.11; 95% CI, 0.87-1.42). Among 1527 HNP survey respondents, 352 of 383 EHR-FHS7 positive patients (91.9%) were survey-FHS7 positive, but only 352 of 883 survey-FHS7 positive patients (39.9%) were EHR-FHS7 positive. Of the 29 913 FHS7-positive patients, 19 764 (66.1%) were identified only after parsing free-text family history comments. Socioeconomic differences were also observed between EHR-FHS7-negative and EHR-FHS7-positive patients, suggesting disparities in recording family history. Conclusions and Relevance In this cross-sectional study, EHR-derived FHS7 identified thousands of patients with familial risk for breast cancer, indicating a substantial gap in genetic testing. However, limitations in EHR family history data suggested that other identification methods, such as direct-to-patient questionnaires, are required to fully address this gap.
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Affiliation(s)
- Daniel Kiser
- University of Nevada Reno School of Medicine, Reno
| | - Gai Elhanan
- University of Nevada Reno School of Medicine, Reno
| | | | - Iva Neveux
- University of Nevada Reno School of Medicine, Reno
| | | | | | | | | | | | | | | | | | - Joseph J Grzymski
- University of Nevada Reno School of Medicine, Reno
- Renown Health, Reno, Nevada
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10
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Sarygina EV, Kozlova AS, Ponomarenko EA, Ilgisonis EV. The human proteome size as a technological development function. BIOMEDITSINSKAIA KHIMIIA 2024; 70:364-373. [PMID: 39324201 DOI: 10.18097/pbmc20247005364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/27/2024]
Abstract
Changes in information on the number of human proteoforms, post-translational modification (PTM) events, alternative splicing (AS), single-amino acid polymorphisms (SAP) associated with protein-coding genes in the neXtProt database have been retrospectively analyzed. In 2016, our group proposed three mathematical models for predicting the number of different proteins (proteoforms) in the human proteome. Eight years later, we compared the original data of the information resources and their contribution to the prediction results, correlating the differences with new approaches to experimental and bioinformatic analysis of protein modifications. The aim of this work is to update information on the status of records in the databases of identified proteoforms since 2016, as well as to identify trends in changes in the quantities of these records. According to various information models, modern experimental methods may identify from 5 to 125 million different proteoforms: the proteins formed due to alternative splicing, the implementation of single nucleotide polymorphisms at the proteomic level, and post-translational modifications in various combinations. This result reflects an increase in the size of the human proteome by 20 or more times over the past 8 years.
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Affiliation(s)
- E V Sarygina
- Institute of Biomedical Chemistry, Moscow, Russia
| | - A S Kozlova
- Institute of Biomedical Chemistry, Moscow, Russia
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11
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Kiseleva OI, Arzumanian VA, Kurbatov IY, Poverennaya EV. In silico and in cellulo approaches for functional annotation of human protein splice variants. BIOMEDITSINSKAIA KHIMIIA 2024; 70:315-328. [PMID: 39324196 DOI: 10.18097/pbmc20247005315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/27/2024]
Abstract
The elegance of pre-mRNA splicing mechanisms continues to interest scientists even after over a half century, since the discovery of the fact that coding regions in genes are interrupted by non-coding sequences. The vast majority of human genes have several mRNA variants, coding structurally and functionally different protein isoforms in a tissue-specific manner and with a linkage to specific developmental stages of the organism. Alteration of splicing patterns shifts the balance of functionally distinct proteins in living systems, distorts normal molecular pathways, and may trigger the onset and progression of various pathologies. Over the past two decades, numerous studies have been conducted in various life sciences disciplines to deepen our understanding of splicing mechanisms and the extent of their impact on the functioning of living systems. This review aims to summarize experimental and computational approaches used to elucidate the functions of splice variants of a single gene based on our experience accumulated in the laboratory of interactomics of proteoforms at the Institute of Biomedical Chemistry (IBMC) and best global practices.
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Affiliation(s)
- O I Kiseleva
- Institute of Biomedical Chemistry, Moscow, Russia
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12
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Wang C, Huang W, Zhong Y, Zou X, Liu S, Li J, Sun Y, Zhou K, Chen X, Li Z, Wang S, Huang Y, Bai Y, Yin J, Jin X, Liu S, Yuan Y, Deng Q, Jiang M, Liu C, Liu L, Xu X, Wu L. Single-cell multi-modal chromatin profiles revealing epigenetic regulations of cells in hepatocellular carcinoma. Clin Transl Med 2024; 14:e70000. [PMID: 39210544 PMCID: PMC11362026 DOI: 10.1002/ctm2.70000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2024] [Revised: 07/12/2024] [Accepted: 08/05/2024] [Indexed: 09/04/2024] Open
Abstract
BACKGROUND Various epigenetic regulations systematically govern gene expression in cells involving various biological processes. Dysregulation of the epigenome leads to aberrant transcriptional programs and subsequently results in diseases, such as cancer. Therefore, comprehensive profiling epigenomics is essential for exploring the mechanisms underlying gene expression regulation during development and disease. METHODS In this study, we developed single-cell chromatin proteins and accessibility tagmentation (scCPA-Tag), a multi-modal single-cell epigenetic profile capturing technique based on barcoded Tn5 transposases and a droplet microfluidics platform. scCPA-Tag enables the simultaneous capture of DNA profiles of histone modification and chromatin accessibility in the same cell. RESULTS By applying scCPA-Tag to K562 cells and a hepatocellular carcinoma (HCC) sample, we found that the silence of several chromatin-accessible genes can be attributed to lysine-27-trimethylation of the histone H3 tail (H3K27me3) modification. We characterized the epigenetic features of the tumour cells and different immune cell types in the HCC tumour tissue by scCPA-Tag. Besides, a tumour cell subtype (C2) with more aggressive features was identified and characterized by high chromatin accessibility and a lower abundance of H3K27me3 on tumour-promoting genes. CONCLUSIONS Our multi-modal scCPA-Tag provides a comprehensive approach for exploring the epigenetic landscapes of heterogeneous cell types and revealing the mechanisms of gene expression regulation during developmental and pathological processes at the single-cell level. HIGHLIGHTS scCPA-Tag offers a highly efficient and high throughput technique to simultaneously profile histone modification and chromatin accessibility within a single cell. scCPA-Tag enables to uncover multiple epigenetic modification features of cellular compositions within tumor tissues. scCPA-Tag facilitates the exploration of the epigenetic landscapes of heterogeneous cell types and provides the mechanisms governing gene expression regulation.
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Affiliation(s)
- Chunqing Wang
- College of Life SciencesUniversity of Chinese Academy of SciencesBeijingChina
- BGI ResearchChongqingChina
- BGI ResearchShenzhenChina
| | - Waidong Huang
- College of Life SciencesUniversity of Chinese Academy of SciencesBeijingChina
- BGI ResearchChongqingChina
| | | | - Xuanxuan Zou
- BGI ResearchChongqingChina
- BGI ResearchShenzhenChina
- Department of Medical LaboratoryHubei Provincial Clinical Research Center for Parkinson's DiseaseXiangyang No.1 People's Hospital, Hubei University of MedicineXiangyangChina
| | - Shang Liu
- BGI ResearchChongqingChina
- BGI ResearchShenzhenChina
| | - Jie Li
- BGI ResearchShenzhenChina
| | - Yunfan Sun
- Department of Liver Surgery & Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, and Key Laboratory of Carcinogenesis and Cancer InvasionMinistry of EducationShanghaiChina
- Zhongshan‐BGI Precision Medical CenterZhongshan Hospital, Fudan UniversityShanghaiChina
| | - Kaiqian Zhou
- Department of Liver Surgery & Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, and Key Laboratory of Carcinogenesis and Cancer InvasionMinistry of EducationShanghaiChina
- Zhongshan‐BGI Precision Medical CenterZhongshan Hospital, Fudan UniversityShanghaiChina
| | - Xi Chen
- BGI ResearchChongqingChina
- BGI ResearchShenzhenChina
| | - Zihao Li
- BGI ResearchShenzhenChina
- School of Biology and Biological EngineeringSouth China University of TechnologyGuangzhouChina
| | | | | | | | | | | | | | - Yue Yuan
- Department of Liver Surgery & Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, and Key Laboratory of Carcinogenesis and Cancer InvasionMinistry of EducationShanghaiChina
| | - Qiuting Deng
- College of Life SciencesUniversity of Chinese Academy of SciencesBeijingChina
- Department of Liver Surgery & Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, and Key Laboratory of Carcinogenesis and Cancer InvasionMinistry of EducationShanghaiChina
| | | | - Chuanyu Liu
- BGI ResearchShenzhenChina
- Shanxi Medical University‐BGI Collaborative Center for Future MedicineShanxi Medical UniversityTaiyuanChina
| | - Longqi Liu
- Department of Liver Surgery & Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, and Key Laboratory of Carcinogenesis and Cancer InvasionMinistry of EducationShanghaiChina
| | - Xun Xu
- BGI ResearchShenzhenChina
| | - Liang Wu
- BGI ResearchChongqingChina
- BGI ResearchShenzhenChina
- Zhongshan‐BGI Precision Medical CenterZhongshan Hospital, Fudan UniversityShanghaiChina
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13
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Degalez F, Bardou P, Lagarrigue S. GEGA (Gallus Enriched Gene Annotation): an online tool providing genomics and functional information across 47 tissues for a chicken gene-enriched atlas gathering Ensembl and Refseq genome annotations. NAR Genom Bioinform 2024; 6:lqae101. [PMID: 39157583 PMCID: PMC11327871 DOI: 10.1093/nargab/lqae101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 06/21/2024] [Accepted: 08/14/2024] [Indexed: 08/20/2024] Open
Abstract
GEGA is a user-friendly tool designed to navigate through various genomic and functional information related to an enriched gene atlas in chicken that integrates the gene catalogues from the two reference databases, NCBI-RefSeq and EMBL-Ensembl/GENCODE, along with four additional rich resources such as FAANG and NONCODE. Using the latest GRCg7b genome assembly, GEGA encompasses a total of 78 323 genes, including 24 102 protein-coding genes (PCGs) and 44 428 long non-coding RNAs (lncRNAs), significantly increasing the number of genes provided by each resource independently. However, GEGA is more than just a gene database. It offers a range of features that allow us to go deeper into the functional aspects of these genes. Users can explore gene expression and co-expression profiles across 47 tissues from 36 datasets and 1400 samples, discover tissue-specific variations and their expression as a function of sex or age and extract orthologous genes or their genomic configuration relative to the closest gene. For the communities interested in a specific gene, a list of genes or a quantitative trait locus region in chicken, GEGA's user-friendly interface facilitates efficient gene analysis, easy downloading of results and a multitude of graphical representations, from genomic information to detailed visualization of expression levels.
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Affiliation(s)
- Fabien Degalez
- PEGASE, INRAE, Institut Agro, 35590 Saint Gilles, France
| | - Philippe Bardou
- Sigenae, GenPhySE, Université de Toulouse, INRAE, ENVT, F-31326 Castanet Tolosan, France
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14
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Wright CF, Sharp LN, Jackson L, Murray A, Ware JS, MacArthur DG, Rehm HL, Patel KA, Weedon MN. Guidance for estimating penetrance of monogenic disease-causing variants in population cohorts. Nat Genet 2024; 56:1772-1779. [PMID: 39075210 DOI: 10.1038/s41588-024-01842-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 06/24/2024] [Indexed: 07/31/2024]
Abstract
Penetrance is the probability that an individual with a pathogenic genetic variant develops a specific disease. Knowing the penetrance of variants for monogenic disorders is important for counseling of individuals. Until recently, estimates of penetrance have largely relied on affected individuals and their at-risk family members being clinically referred for genetic testing, a 'phenotype-first' approach. This approach substantially overestimates the penetrance of variants because of ascertainment bias. The recent availability of whole-genome sequencing data in individuals from very-large-scale population-based cohorts now allows 'genotype-first' estimates of penetrance for many conditions. Although this type of population-based study can underestimate penetrance owing to recruitment biases, it provides more accurate estimates of penetrance for secondary or incidental findings. Here, we provide guidance for the conduct of penetrance studies to ensure that robust genotypes and phenotypes are used to accurately estimate penetrance of variants and groups of similarly annotated variants from population-based studies.
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Affiliation(s)
- Caroline F Wright
- Department of Clinical and Biomedical Sciences, Medical School, University of Exeter, Exeter, UK.
| | - Luke N Sharp
- Department of Clinical and Biomedical Sciences, Medical School, University of Exeter, Exeter, UK
| | - Leigh Jackson
- Department of Clinical and Biomedical Sciences, Medical School, University of Exeter, Exeter, UK
| | - Anna Murray
- Department of Clinical and Biomedical Sciences, Medical School, University of Exeter, Exeter, UK
| | - James S Ware
- National Heart and Lung Institute and MRC Laboratory of Medical Sciences, Imperial College London, London, UK
- Hammersmith Hospital, Imperial College Healthcare NHS Trust, London, UK
- Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Daniel G MacArthur
- Centre for Population Genomics, Garvan Institute of Medical Research and UNSW Sydney, Sydney, New South Wales, Australia
- Centre for Population Genomics, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
| | - Heidi L Rehm
- Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Kashyap A Patel
- Department of Clinical and Biomedical Sciences, Medical School, University of Exeter, Exeter, UK
| | - Michael N Weedon
- Department of Clinical and Biomedical Sciences, Medical School, University of Exeter, Exeter, UK.
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15
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Ma W, Chaisson MJ. Genotyping sequence-resolved copy-number variation using pangenomes reveals paralog-specific global diversity and expression divergence of duplicated genes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.11.607269. [PMID: 39149335 PMCID: PMC11326217 DOI: 10.1101/2024.08.11.607269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
Human pangenomes contain assemblies of non-reference copy-number variable (CNV) genes. We developed a new method, ctyper, to identify the copy-number of specific alleles of CNV genes cataloged in pangenomes with NGS datasets. Applying ctyper to the 1000-genomes samples revealed population stratification of paralogs and two classes of CNVs: recent CNVs due to ongoing duplications, and polymorphic CNVs from non-reference ancient paralogs. Expression quantitative trait locus analysis determined allele-specific expression within gene families, revealing that 7.94% of paralogs and 3.28% orthologs had significantly divergent expression. Case studies of individual genes include finding lower expression on SMN-1 copies that arose from conversion from SMN-2, and increased expression on a form of AMY2B that has undergone a translocation. Moreover, 4.7% of paralogs and 1.2% of orthologs had different most-expressed tissues. Furthermore, the genotypes explain more expression variance than known eQTL variants. Overall, ctyper enables biobank-scale genotyping of sequence-resolved CNVs.
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Affiliation(s)
- Walfred Ma
- Quantitative and Computational Biology, University of Southern California, CA, USA
| | - Mark Jp Chaisson
- Quantitative and Computational Biology, University of Southern California, CA, USA
- The Genomic and Epigenomic Regulation Program, USC Norris Cancer Center, University of Southern California, Los Angeles, California 90033, USA
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16
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Robinson JR, Denny JC, Zeng C. Replicating the Association of Variants in BSN and APBA1 with Obesity in Diverse Populations. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.08.21.24312322. [PMID: 39228706 PMCID: PMC11370512 DOI: 10.1101/2024.08.21.24312322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2024]
Abstract
In a recent study by Zhao et al., rare protein-truncating variants (PTVs) in the BSN and APBA1 genes showed effects on obesity that exceeded those of well-known genes such as MC4R in a UK cohort. In this study, we leveraged the All of Us Research Program, to investigate the association of predicted LoF (pLoF) PTVs in BSN and APBA1 with body mass index (BMI) across a population of diverse ancestry. Our analysis revealed that the impact of pLoF variants in BSN and APBA1 on BMI was notably greater in this cohort, especially among individuals of European ancestry. Additionally, a phenome-wide association study (PheWAS) using the extensive phenotypic data available in the All of Us Research Program uncovered novel associations of BSN and APBA1 heterozygous pLoF carriers with various phenotypes. Specifically, BSN pLoF variants were associated with pulmonary hypertension, atrial fibrillation, and anticoagulant use, while APBA1 pLoF variants were linked to disorders of the temporomandibular joint. These findings underscore the potential of large-scale biobanks in advancing genetic discovery.
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17
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Lazareva TE, Barbitoff YA, Nasykhova YA, Glotov AS. Major Causes of Conflicting Interpretations of Variant Pathogenicity in Rare Disease: A Systematic Analysis. J Pers Med 2024; 14:864. [PMID: 39202055 PMCID: PMC11355203 DOI: 10.3390/jpm14080864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2024] [Revised: 07/31/2024] [Accepted: 08/12/2024] [Indexed: 09/03/2024] Open
Abstract
The identification of the genetic causes of inherited disorders from next-generation sequencing (NGS) data remains a complicated process, in particular due to challenges in interpretation of the vast amount of generated data and hundreds of candidate variants identified. Inconsistencies in variant classification, where genetic centers classify the same variant differently, can hinder accurate diagnoses for rare diseases. Publicly available databases that collect data on human genetic variations and their association with diseases provide ample opportunities to discover conflicts in variant interpretation worldwide. In this study, we explored patterns of variant classification discrepancies using data from ClinVar, a public archive of variant interpretations. We found that 5.7% of variants have conflicting interpretations (COIs) reported, and the vast majority of interpretation conflicts arise for variants of uncertain significance (VUS). As many as 78% of clinically relevant genes harbor variants with COIs, and genes with high COI rates tended to have more exons and longer transcripts, with a greater proportion of genes linked to several distinct conditions. The enrichment analysis of COI-enriched genes revealed that the products of these genes are involved in cardiac disorders, muscle development, and function. To improve diagnoses, we believe that specific variant interpretation rules could be developed for such genes. Additionally, our findings underscore the need for the publication of variant pathogenicity evidence and the importance of considering every variant as VUS unless proven otherwise.
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Affiliation(s)
- Tatyana E. Lazareva
- Department of Genomic Medicine, D.O. Ott Research Institute of Obstetrics, Gynaecology, and Reproductology, Mendeleevskaya Line 3, 199034 St. Petersburg, Russia
| | - Yury A. Barbitoff
- Department of Genomic Medicine, D.O. Ott Research Institute of Obstetrics, Gynaecology, and Reproductology, Mendeleevskaya Line 3, 199034 St. Petersburg, Russia
- Bioinformatics Institute, Kantemirovskaya St. 2A, 197342 St. Petersburg, Russia
| | - Yulia A. Nasykhova
- Department of Genomic Medicine, D.O. Ott Research Institute of Obstetrics, Gynaecology, and Reproductology, Mendeleevskaya Line 3, 199034 St. Petersburg, Russia
| | - Andrey S. Glotov
- Department of Genomic Medicine, D.O. Ott Research Institute of Obstetrics, Gynaecology, and Reproductology, Mendeleevskaya Line 3, 199034 St. Petersburg, Russia
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18
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Rodriguez JM, Abascal F, Cerdán-Vélez D, Gómez LM, Vázquez J, Tress ML. Evidence for widespread translation of 5' untranslated regions. Nucleic Acids Res 2024; 52:8112-8126. [PMID: 38953162 DOI: 10.1093/nar/gkae571] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 06/07/2024] [Accepted: 06/19/2024] [Indexed: 07/03/2024] Open
Abstract
Ribosome profiling experiments support the translation of a range of novel human open reading frames. By contrast, most peptides from large-scale proteomics experiments derive from just one source, 5' untranslated regions. Across the human genome we find evidence for 192 translated upstream regions, most of which would produce protein isoforms with extended N-terminal ends. Almost all of these N-terminal extensions are from highly abundant genes, which suggests that the novel regions we detect are just the tip of the iceberg. These upstream regions have characteristics that are not typical of coding exons. Their GC-content is remarkably high, even higher than 5' regions in other genes, and a large majority have non-canonical start codons. Although some novel upstream regions have cross-species conservation - five have orthologues in invertebrates for example - the reading frames of two thirds are not conserved beyond simians. These non-conserved regions also have no evidence of purifying selection, which suggests that much of this translation is not functional. In addition, non-conserved upstream regions have significantly more peptides in cancer cell lines than would be expected, a strong indication that an aberrant or noisy translation initiation process may play an important role in translation from upstream regions.
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Affiliation(s)
- Jose Manuel Rodriguez
- Cardiovascular Proteomics Laboratory, Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), 28029 Madrid, Spain
- CIBER de Enfermedades Cardiovasculares (CIBERCV), 28029 Madrid, Spain
| | - Federico Abascal
- Somatic Evolution Group, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SA. UK
| | - Daniel Cerdán-Vélez
- Bioinformatics Unit, Spanish National Cancer Research Centre (CNIO), 28029 Madrid, Spain
| | - Laura Martínez Gómez
- Bioinformatics Unit, Spanish National Cancer Research Centre (CNIO), 28029 Madrid, Spain
| | - Jesús Vázquez
- Cardiovascular Proteomics Laboratory, Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), 28029 Madrid, Spain
- CIBER de Enfermedades Cardiovasculares (CIBERCV), 28029 Madrid, Spain
| | - Michael L Tress
- Bioinformatics Unit, Spanish National Cancer Research Centre (CNIO), 28029 Madrid, Spain
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19
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Rots D, Choufani S, Faundes V, Dingemans AJM, Joss S, Foulds N, Jones EA, Stewart S, Vasudevan P, Dabir T, Park SM, Jewell R, Brown N, Pais L, Jacquemont S, Jizi K, Ravenswaaij-Arts CMAV, Kroes HY, Stumpel CTRM, Ockeloen CW, Diets IJ, Nizon M, Vincent M, Cogné B, Besnard T, Kambouris M, Anderson E, Zackai EH, McDougall C, Donoghue S, O'Donnell-Luria A, Valivullah Z, O'Leary M, Srivastava S, Byers H, Leslie N, Mazzola S, Tiller GE, Vera M, Shen JJ, Boles R, Jain V, Brischoux-Boucher E, Kinning E, Simpson BN, Giltay JC, Harris J, Keren B, Guimier A, Marijon P, Vries BBAD, Motter CS, Mendelsohn BA, Coffino S, Gerkes EH, Afenjar A, Visconti P, Bacchelli E, Maestrini E, Delahaye-Duriez A, Gooch C, Hendriks Y, Adams H, Thauvin-Robinet C, Josephi-Taylor S, Bertoli M, Parker MJ, Rutten JW, Caluseriu O, Vernon HJ, Kaziyev J, Zhu J, Kremen J, Frazier Z, Osika H, Breault D, Nair S, Lewis SME, Ceroni F, Viggiano M, Posar A, Brittain H, Giovanna T, Giulia G, Quteineh L, Ha-Vinh Leuchter R, Zonneveld-Huijssoon E, Mellado C, Marey I, Coudert A, Aracena Alvarez MI, Kennis MGP, Bouman A, Roifman M, Amorós Rodríguez MI, Ortigoza-Escobar JD, Vernimmen V, Sinnema M, Pfundt R, Brunner HG, Vissers LELM, Kleefstra T, Weksberg R, Banka S. Pathogenic variants in KMT2C result in a neurodevelopmental disorder distinct from Kleefstra and Kabuki syndromes. Am J Hum Genet 2024; 111:1626-1642. [PMID: 39013459 PMCID: PMC11339626 DOI: 10.1016/j.ajhg.2024.06.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 06/08/2024] [Accepted: 06/19/2024] [Indexed: 07/18/2024] Open
Abstract
Trithorax-related H3K4 methyltransferases, KMT2C and KMT2D, are critical epigenetic modifiers. Haploinsufficiency of KMT2C was only recently recognized as a cause of neurodevelopmental disorder (NDD), so the clinical and molecular spectrums of the KMT2C-related NDD (now designated as Kleefstra syndrome 2) are largely unknown. We ascertained 98 individuals with rare KMT2C variants, including 75 with protein-truncating variants (PTVs). Notably, ∼15% of KMT2C PTVs were inherited. Although the most highly expressed KMT2C transcript consists of only the last four exons, pathogenic PTVs were found in almost all the exons of this large gene. KMT2C variant interpretation can be challenging due to segmental duplications and clonal hematopoesis-induced artifacts. Using samples from 27 affected individuals, divided into discovery and validation cohorts, we generated a moderate strength disorder-specific KMT2C DNA methylation (DNAm) signature and demonstrate its utility in classifying non-truncating variants. Based on 81 individuals with pathogenic/likely pathogenic variants, we demonstrate that the KMT2C-related NDD is characterized by developmental delay, intellectual disability, behavioral and psychiatric problems, hypotonia, seizures, short stature, and other comorbidities. The facial module of PhenoScore, applied to photographs of 34 affected individuals, reveals that the KMT2C-related facial gestalt is significantly different from the general NDD population. Finally, using PhenoScore and DNAm signatures, we demonstrate that the KMT2C-related NDD is clinically and epigenetically distinct from Kleefstra and Kabuki syndromes. Overall, we define the clinical features, molecular spectrum, and DNAm signature of the KMT2C-related NDD and demonstrate they are distinct from Kleefstra and Kabuki syndromes highlighting the need to rename this condition.
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Affiliation(s)
- Dmitrijs Rots
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, the Netherlands; Department of Clinical Genetics, Erasmus MC, Rotterdam, the Netherlands; Genetics Laboratory, Children's Clinical University Hospital, Riga, Latvia
| | - Sanaa Choufani
- Genetics and Genome Biology Program, Research Institute, the Hospital for Sick Children, Toronto, ON M5G 1X8, Canada
| | - Victor Faundes
- Laboratorio de Genética y Enfermedades Metabólicas, Instituto de Nutrición y Tecnología de Los Alimentos (INTA), Universidad de Chile, Santiago, Chile; Manchester Centre for Genomic Medicine, Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | | | - Shelagh Joss
- West of Scotland Centre for Genomic Medicine, Queen Elizabeth University Hospital, Glasgow, UK
| | - Nicola Foulds
- Wessex Clinical Genetics Services, University Hospital Southampton NHS Foundation Trust, Southampton SO16 5YA, UK
| | - Elizabeth A Jones
- Manchester Centre for Genomic Medicine, St Mary's Hospital, Manchester University NHS Foundation Trust, Health Innovation Manchester, Manchester, UK; Division of Evolution, Infection and Genomics, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Sarah Stewart
- Manchester Centre for Genomic Medicine, St Mary's Hospital, Manchester University NHS Foundation Trust, Health Innovation Manchester, Manchester, UK
| | - Pradeep Vasudevan
- Department of Clinical Genetics, University Hospitals of Leicester, Leicester Royal Infirmary, Leicester LE1 7RH, UK
| | - Tabib Dabir
- Northern Ireland Regional Genetics Centre, Belfast City Hospital, Belfast, UK
| | - Soo-Mi Park
- Department of Clinical Genetics, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Rosalyn Jewell
- Yorkshire Regional Genetics Service, Chapel Allerton Hospital, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Natasha Brown
- Victorian Clinical Genetics Service, Murdoch Children's Research Institute, Melbourne, VIC, Australia; Department of Paediatrics, Royal Children's Hospital, The University of Melbourne, Melbourne, VIC, Australia
| | - Lynn Pais
- Center for Mendelian Genomics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Khadijé Jizi
- Service de Génétique Médicale, CHU Ste-Justine, Montréal, QC, Canada
| | | | - Hester Y Kroes
- Division Laboratories, Pharmacy and Biomedical Genetics, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Constance T R M Stumpel
- Department of Clinical Genetics, Maastricht University Medical Center, Maastricht, the Netherlands; GROW-School for Oncology and Reproduction, Maastricht, the Netherlands
| | - Charlotte W Ockeloen
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Illja J Diets
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Mathilde Nizon
- Service de Génétique Médicale, Centre Hospitalier Universitaire de Nantes, Nantes, France
| | - Marie Vincent
- Service de Génétique Médicale, Centre Hospitalier Universitaire de Nantes, Nantes, France
| | - Benjamin Cogné
- Service de Génétique Médicale, Centre Hospitalier Universitaire de Nantes, Nantes, France
| | - Thomas Besnard
- Service de Génétique Médicale, Centre Hospitalier Universitaire de Nantes, Nantes, France
| | - Marios Kambouris
- Division of Genetics, Department of Pathology and Laboratory Medicine Department, Sidra Medicine, Doha, Qatar
| | - Emily Anderson
- Liverpool Centre for Genomic Medicine, Liverpool Women's NHS Foundation Trust, Liverpool, UK
| | - Elaine H Zackai
- Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Carey McDougall
- Division of Human Genetics, Department of Pediatrics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Sarah Donoghue
- Division of Human Genetics, Department of Pediatrics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Anne O'Donnell-Luria
- Center for Mendelian Genomics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Zaheer Valivullah
- Center for Mendelian Genomics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Melanie O'Leary
- Center for Mendelian Genomics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA
| | | | - Heather Byers
- Department of Pediatrics, Stanford University, Stanford, CA, USA
| | - Nancy Leslie
- Division of Human Genetics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Sarah Mazzola
- Center for Personalized Genetic Healthcare, Cleveland Clinic, Cleveland, OH, USA
| | - George E Tiller
- Department of Genetics, Kaiser Permanente, Los Angeles, CA, USA
| | - Moin Vera
- Department of Genetics, Kaiser Permanente, Los Angeles, CA, USA
| | - Joseph J Shen
- Division of Genetics, Department of Pediatrics, UCSF Fresno, Fresno, CA, USA; Division of Genomic Medicine, Department of Pediatrics, University of California Davis, Sacramento, CA, USA
| | | | - Vani Jain
- All Wales Medical Genomics Service, Wales Genomic Health Centre, Cardiff Edge Business Park, Longwood Drive, Whitchurch, Cardiff CF14 7YU, UK
| | | | - Esther Kinning
- Clinical Genetics, Birmingham Women's and Children's, Birmingham, UK
| | - Brittany N Simpson
- Division of Human Genetics, Cincinnati Children's Hospital Medical Center, Department of Pediatrics, University of Cincinnati, Cincinnati, OH, USA
| | - Jacques C Giltay
- Division Laboratories, Pharmacy and Biomedical Genetics, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Jacqueline Harris
- Kennedy Krieger Institute, Baltimore, MD, USA; Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Boris Keren
- Department of Genetics, APHP Sorbonne University, Paris, France
| | - Anne Guimier
- Service de Médecine Genomique des Maladies Rares, CRMR Anomalies Du Développement, Hôpital Necker-Enfants Malades, Assistance Publique des Hôpitaux de Paris, Paris, France
| | - Pierre Marijon
- Laboratoire de Biologie Médicale Multisites Seqoia FMG2025, 75014 Paris, France
| | - Bert B A de Vries
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, the Netherlands
| | | | | | - Samantha Coffino
- Department of Pediatric Neurology, Kaiser Permanente, Oakland, CA, USA
| | - Erica H Gerkes
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Alexandra Afenjar
- APHP Sorbonne Université, Centre de Référence Malformations et Maladies Congénitales Du Cervelet et Déficiences Intellectuelles de Causes Rares, Département de Génétique et Embryologie Médicale, Hôpital Trousseau, Paris, France
| | - Paola Visconti
- IRCCS Istituto Delle Scienze Neurologiche di Bologna, UOSI Disturbi Dello Spettro Autistico, Bologna, Italy
| | - Elena Bacchelli
- Pharmacy and Biotechnology Department, University of Bologna, Bologna, Italy
| | - Elena Maestrini
- Pharmacy and Biotechnology Department, University of Bologna, Bologna, Italy
| | | | - Catherine Gooch
- Division of Genetics and Genomic Medicine, Department of Pediatrics, Washington University School of Medicine, St Louis, MO, USA
| | - Yvonne Hendriks
- Department of Clinical Genetics, Leiden University Medical Center, Leiden, the Netherlands
| | - Hieab Adams
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, the Netherlands; Department of Clinical Genetics, Erasmus MC, Rotterdam, the Netherlands
| | - Christel Thauvin-Robinet
- Unité Fonctionnelle Innovation en Diagnostic Génomique des Maladies Rares, Dijon, France; Inserm, UMR1231, Equipe GAD, Bâtiment B3, Université de Bourgogne Franche Comté, Dijon Cedex, France; Centre de Référence Déficiences Intellectuelles de Causes Rares, FHU-TRANSLAD, CHU Dijon Bourgogne, Dijon, France
| | - Sarah Josephi-Taylor
- Department of Clinical Genetics, The Children's Hospital at Westmead, Sydney, NSW, Australia; Discipline of Genomic Medicine, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia
| | - Marta Bertoli
- Northern Genetics Service, Newcastle Upon Tyne NHS Foundation Trust, Newcastle Upon Tyne, UK
| | - Michael J Parker
- Department of Clinical Genetics, Sheffield Children's Hospital, Sheffield, UK
| | - Julie W Rutten
- Department of Clinical Genetics, Leiden University Medical Center, Leiden, the Netherlands
| | - Oana Caluseriu
- Department of Medical Genetics, University of Alberta, Edmonton, Canada
| | - Hilary J Vernon
- Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Jonah Kaziyev
- Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Jia Zhu
- Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Jessica Kremen
- Division of Endocrinology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Zoe Frazier
- Rosamund Stone Zander Translational Neuroscience Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Hailey Osika
- Rosamund Stone Zander Translational Neuroscience Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - David Breault
- Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Sreelata Nair
- Department of Fetal Medicine, Lifeline Super Specialty Hospital, Kerala, India
| | - Suzanne M E Lewis
- Department of Medical Genetics, BC Children's Hospital Research Institute, The University of British Columbia, Vancouver, BC, Canada
| | - Fabiola Ceroni
- Pharmacy and Biotechnology Department, University of Bologna, Bologna, Italy; Faculty of Health and Life Sciences, Oxford Brookes University, Oxford, UK
| | - Marta Viggiano
- Pharmacy and Biotechnology Department, University of Bologna, Bologna, Italy
| | - Annio Posar
- IRCCS Istituto Delle Scienze Neurologiche di Bologna, UOSI Disturbi Dello Spettro Autistico, Bologna, Italy; Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Helen Brittain
- Department of Clinical Genetics, Birmingham Women's & Children's NHS Trust, Birmingham, UK
| | - Traficante Giovanna
- Medical Genetics Unit, Meyer Children's Hospital IRCCS Florence, Florence, Italy
| | - Gori Giulia
- Medical Genetics Unit,Meyer Children's Hospital IRCCS, Florence, Italy
| | - Lina Quteineh
- Division of Genetic Medicine, Geneva University Hospitals, 1205 Geneva, Switzerland
| | - Russia Ha-Vinh Leuchter
- Division of Development and Growth, Department of Pediatrics, University of Geneva, Geneva, Switzerland
| | - Evelien Zonneveld-Huijssoon
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Cecilia Mellado
- Sección de Genética y Errores Congénitos Del Metabolismo, División de Pediatría, Pontificia Universidad Católica de Chile, Santiago, Chile
| | | | | | - Mariana Inés Aracena Alvarez
- Unit of Genetics and Metabolic Diseases, Division of Pediatrics, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Milou G P Kennis
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Arianne Bouman
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Maian Roifman
- The Prenatal Diagnosis and Medical Genetics Program, Division of Maternal Fetal Medicine, Department of Obstetrics and Gynaecology, University of Toronto, Toronto, Canada
| | | | - Juan Dario Ortigoza-Escobar
- Movement Disorders Unit, Institut de Recerca Sant Joan de Déu, CIBERER-ISCIII and European Reference Network for Rare Neurological Diseases (ERN-RND), Barcelona, Spain
| | - Vivian Vernimmen
- Department of Clinical Genetics, Maastricht University Medical Center, Maastricht, the Netherlands; GROW-School for Oncology and Reproduction, Maastricht, the Netherlands
| | - Margje Sinnema
- Department of Clinical Genetics, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Rolph Pfundt
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Han G Brunner
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, the Netherlands; Department of Clinical Genetics, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Lisenka E L M Vissers
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, the Netherlands; Research Institute for Medical Innovation, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Tjitske Kleefstra
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, the Netherlands; Department of Clinical Genetics, Erasmus MC, Rotterdam, the Netherlands; Center of Excellence for Neuropsychiatry, Vincent van Gogh Institute for Psychiatry, Venray, the Netherlands.
| | - Rosanna Weksberg
- Genetics and Genome Biology Program, Research Institute, the Hospital for Sick Children, Toronto, ON M5G 1X8, Canada; Division of Clinical and Metabolic Genetics, Department of Pediatrics, the Hospital for Sick Children, University of Toronto, Toronto, ON M5G 1X8, Canada.
| | - Siddharth Banka
- Manchester Centre for Genomic Medicine, St Mary's Hospital, Manchester University NHS Foundation Trust, Health Innovation Manchester, Manchester, UK; Manchester Centre for Genomic Medicine, Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
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20
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Moth CW, Sheehan JH, Mamun AA, Sivley RM, Gulsevin A, Rinker D, Capra JA, Meiler J. VUStruct: a compute pipeline for high throughput and personalized structural biology. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.06.606224. [PMID: 39149406 PMCID: PMC11326201 DOI: 10.1101/2024.08.06.606224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
Effective diagnosis and treatment of rare genetic disorders requires the interpretation of a patient's genetic variants of unknown significance (VUSs). Today, clinical decision-making is primarily guided by gene-phenotype association databases and DNA-based scoring methods. Our web-accessible variant analysis pipeline, VUStruct, supplements these established approaches by deeply analyzing the downstream molecular impact of variation in context of 3D protein structure. VUStruct's growing impact is fueled by the co-proliferation of protein 3D structural models, gene sequencing, compute power, and artificial intelligence. Contextualizing VUSs in protein 3D structural models also illuminates longitudinal genomics studies and biochemical bench research focused on VUS, and we created VUStruct for clinicians and researchers alike. We now introduce VUStruct to the broad scientific community as a mature, web-facing, extensible, High Performance Computing (HPC) software pipeline. VUStruct maps missense variants onto automatically selected protein structures and launches a broad range of analyses. These include energy-based assessments of protein folding and stability, pathogenicity prediction through spatial clustering analysis, and machine learning (ML) predictors of binding surface disruptions and nearby post-translational modification sites. The pipeline also considers the entire input set of VUS and identifies genes potentially involved in digenic disease. VUStruct's utility in clinical rare disease genome interpretation has been demonstrated through its analysis of over 175 Undiagnosed Disease Network (UDN) Patient cases. VUStruct-leveraged hypotheses have often informed clinicians in their consideration of additional patient testing, and we report here details from two cases where VUStruct was key to their solution. We also note successes with academic research collaborators, for whom VUStruct has informed research directions in both computational genomics and wet lab studies.
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Affiliation(s)
- Christopher W. Moth
- Departments of Chemistry, Pharmacology, and Biomedical Informatics; Center for Structural Biology and Institute of Chemical Biology; Vanderbilt Univ., Nashville, TN 37232, USA
| | - Jonathan H. Sheehan
- Division of Infection Diseases, Milliken Dept. of Internal Medicine, Washington Univ. of Medicine in St. Louis, MO 63110, USA
| | - Abdullah Al Mamun
- Departments of Chemistry, Pharmacology, and Biomedical Informatics; Center for Structural Biology and Institute of Chemical Biology; Vanderbilt Univ., Nashville, TN 37232, USA
| | | | - Alican Gulsevin
- Department of Pharmaceutical Sciences, College of Pharmacy and Health Sciences, Butler University, Indianapolis, IN 46208, USA
| | - David Rinker
- Department of Biological Sciences, Evolutionary Studies Initiative; Vanderbilt Univ., Nashville, TN 37232, USA
| | - John A. Capra
- Bakar Computational Health Science Institute and Department of Epidemiology and Biostatistics, Univ. of California San Francisco, CA 94143, USA
| | - Jens Meiler
- Departments of Chemistry, Pharmacology, and Biomedical Informatics; Center for Structural Biology and Institute of Chemical Biology; Vanderbilt Univ., Nashville, TN 37232, USA
- Leipzig University Medical School, Institute for Drug Discovery, Brüderstraße 34, 04103 Leipzig, Germany
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21
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Kerkhofs K, Guydosh NR, Bayfield MA. Respiratory Syncytial Virus (RSV) optimizes the translational landscape during infection. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.02.606199. [PMID: 39131278 PMCID: PMC11312563 DOI: 10.1101/2024.08.02.606199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
Viral infection often triggers eukaryotic initiator factor 2α (eIF2α) phosphorylation, leading to global 5'-cap-dependent translation inhibition. RSV encodes messenger RNAs (mRNAs) mimicking 5'-cap structures of host mRNAs and thus inhibition of cap-dependent translation initiation would likely also reduce viral translation. We confirmed that RSV limits widespread translation initiation inhibition and unexpectedly found that the fraction of ribosomes within polysomes increases during infection, indicating higher ribosome loading on mRNAs during infection. We found that AU-rich host transcripts that are less efficiently translated under normal conditions become more efficient at recruiting ribosomes, similar to RSV transcripts. Viral transcripts are transcribed in cytoplasmic inclusion bodies, where the viral AU-rich binding protein M2-1 has been shown to bind viral transcripts and shuttle them into the cytoplasm. We further demonstrated that M2-1 is found on polysomes, and that M2-1 might deliver host AU-rich transcripts for translation.
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Affiliation(s)
- Kyra Kerkhofs
- Department of Biology, Faculty of Science, York University, Toronto, Ontario N3J 1P3, Canada
| | - Nicholas R. Guydosh
- Section on mRNA Regulation and Translation, Laboratory of Biochemistry & Genetics. National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Mark A. Bayfield
- Department of Biology, Faculty of Science, York University, Toronto, Ontario N3J 1P3, Canada
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22
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Wright MW, Thaxton CL, Nelson T, DiStefano MT, Savatt JM, Brush MH, Cheung G, Mandell ME, Wulf B, Ward TJ, Goehringer S, O'Neill T, Weller P, Preston CG, Keseler IM, Goldstein JL, Strande NT, McGlaughon J, Azzariti DR, Cordova I, Dziadzio H, Babb L, Riehle K, Milosavljevic A, Martin CL, Rehm HL, Plon SE, Berg JS, Riggs ER, Klein TE. Generating Clinical-Grade Gene-Disease Validity Classifications Through the ClinGen Data Platforms. Annu Rev Biomed Data Sci 2024; 7:31-50. [PMID: 38663031 DOI: 10.1146/annurev-biodatasci-102423-112456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/25/2024]
Abstract
Clinical genetic laboratories must have access to clinically validated biomedical data for precision medicine. A lack of accessibility, normalized structure, and consistency in evaluation complicates interpretation of disease causality, resulting in confusion in assessing the clinical validity of genes and genetic variants for diagnosis. A key goal of the Clinical Genome Resource (ClinGen) is to fill the knowledge gap concerning the strength of evidence supporting the role of a gene in a monogenic disease, which is achieved through a process known as Gene-Disease Validity curation. Here we review the work of ClinGen in developing a curation infrastructure that supports the standardization, harmonization, and dissemination of Gene-Disease Validity data through the creation of frameworks and the utilization of common data standards. This infrastructure is based on several applications, including the ClinGen GeneTracker, Gene Curation Interface, Data Exchange, GeneGraph, and website.
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Affiliation(s)
- Matt W Wright
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, California, USA; ,
| | - Courtney L Thaxton
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, USA;
| | | | - Marina T DiStefano
- Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | | | - Matthew H Brush
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Gloria Cheung
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, California, USA; ,
| | - Mark E Mandell
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, California, USA; ,
| | - Bryan Wulf
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, California, USA; ,
| | - T J Ward
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, USA;
| | | | - Terry O'Neill
- Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | | | - Christine G Preston
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, California, USA; ,
| | - Ingrid M Keseler
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, California, USA; ,
| | - Jennifer L Goldstein
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, USA;
| | | | - Jennifer McGlaughon
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, USA;
| | - Danielle R Azzariti
- Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | | | - Hannah Dziadzio
- Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Lawrence Babb
- Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Kevin Riehle
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA
| | | | | | - Heidi L Rehm
- Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Sharon E Plon
- Department of Pediatrics, Division of Hematology-Oncology, Baylor College of Medicine, Houston, Texas, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA
| | - Jonathan S Berg
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, USA;
| | | | - Teri E Klein
- Departments of Medicine (Biomedical Informatics Research) and Genetics, Stanford University School of Medicine, Stanford, California, USA
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, California, USA; ,
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23
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Seifert BA, Reddi HV, Kang BE, Bean LJH, Shealy A, Rose NC. Myotonic dystrophy type 1 testing, 2024 revision: A technical standard of the American College of Medical Genetics and Genomics (ACMG). Genet Med 2024; 26:101145. [PMID: 38836869 PMCID: PMC11298302 DOI: 10.1016/j.gim.2024.101145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Accepted: 04/11/2024] [Indexed: 06/06/2024] Open
Abstract
Myotonic dystrophy type 1 (DM1) is a form of muscular dystrophy causing progressive muscle loss and weakness. Although clinical features can manifest at any age, it is the most common form of muscular dystrophy with onset in adulthood. DM1 is an autosomal dominant condition, resulting from an unstable CTG expansion in the 3'-untranslated region of the myotonic dystrophy protein kinase (DMPK) gene. The age of onset and the severity of the phenotype are roughly correlated with the size of the CTG expansion. Multiple methodologies can be used to diagnose affected individuals with DM1, including polymerase chain reaction, Southern blot, and triplet repeat-primed polymerase chain reaction. Recently, triplet repeat interruptions have been described, which may affect clinical outcomes of a fully-variable allele in DMPK. This document supersedes the Technical Standards and Guidelines for Myotonic Dystrophy originally published in 2009 and reaffirmed in 2015. It is designed for genetic testing professionals who are already familiar with the disease and the methods of analysis.
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Affiliation(s)
- Bryce A Seifert
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD
| | - Honey V Reddi
- Department of Pathology and Laboratory Medicine, Medical College of Wisconsin, Milwaukee, WI
| | - Benjamin E Kang
- Department of Pathology and Pediatrics, University of Michigan Medical School, Ann Arbor, MI; Vanderbilt University Medical Center, Nashville, TN
| | | | - Amy Shealy
- Cleveland Clinic Center for Personalized Genetic Healthcare, Cleveland, OH
| | - Nancy C Rose
- Division of Maternal Fetal Medicine, Department of Obstetrics and Gynecology, University of Utah, Salt Lake City, UT
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24
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Chen Y, Dawes R, Kim HC, Ljungdahl A, Stenton SL, Walker S, Lord J, Lemire G, Martin-Geary AC, Ganesh VS, Ma J, Ellingford JM, Delage E, D'Souza EN, Dong S, Adams DR, Allan K, Bakshi M, Baldwin EE, Berger SI, Bernstein JA, Bhatnagar I, Blair E, Brown NJ, Burrage LC, Chapman K, Coman DJ, Compton AG, Cunningham CA, D'Souza P, Danecek P, Délot EC, Dias KR, Elias ER, Elmslie F, Evans CA, Ewans L, Ezell K, Fraser JL, Gallacher L, Genetti CA, Goriely A, Grant CL, Haack T, Higgs JE, Hinch AG, Hurles ME, Kuechler A, Lachlan KL, Lalani SR, Lecoquierre F, Leitão E, Fevre AL, Leventer RJ, Liebelt JE, Lindsay S, Lockhart PJ, Ma AS, Macnamara EF, Mansour S, Maurer TM, Mendez HR, Metcalfe K, Montgomery SB, Moosajee M, Nassogne MC, Neumann S, O'Donoghue M, O'Leary M, Palmer EE, Pattani N, Phillips J, Pitsava G, Pysar R, Rehm HL, Reuter CM, Revencu N, Riess A, Rius R, Rodan L, Roscioli T, Rosenfeld JA, Sachdev R, Shaw-Smith CJ, Simons C, Sisodiya SM, Snell P, St Clair L, Stark Z, Stewart HS, Tan TY, Tan NB, Temple SEL, Thorburn DR, Tifft CJ, Uebergang E, VanNoy GE, Vasudevan P, Vilain E, Viskochil DH, Wedd L, Wheeler MT, White SM, Wojcik M, Wolfe LA, Wolfenson Z, Wright CF, Xiao C, Zocche D, Rubenstein JL, Markenscoff-Papadimitriou E, Fica SM, Baralle D, Depienne C, MacArthur DG, Howson JMM, Sanders SJ, O'Donnell-Luria A, Whiffin N. De novo variants in the RNU4-2 snRNA cause a frequent neurodevelopmental syndrome. Nature 2024; 632:832-840. [PMID: 38991538 PMCID: PMC11338827 DOI: 10.1038/s41586-024-07773-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2024] [Accepted: 07/02/2024] [Indexed: 07/13/2024]
Abstract
Around 60% of individuals with neurodevelopmental disorders (NDD) remain undiagnosed after comprehensive genetic testing, primarily of protein-coding genes1. Large genome-sequenced cohorts are improving our ability to discover new diagnoses in the non-coding genome. Here we identify the non-coding RNA RNU4-2 as a syndromic NDD gene. RNU4-2 encodes the U4 small nuclear RNA (snRNA), which is a critical component of the U4/U6.U5 tri-snRNP complex of the major spliceosome2. We identify an 18 base pair region of RNU4-2 mapping to two structural elements in the U4/U6 snRNA duplex (the T-loop and stem III) that is severely depleted of variation in the general population, but in which we identify heterozygous variants in 115 individuals with NDD. Most individuals (77.4%) have the same highly recurrent single base insertion (n.64_65insT). In 54 individuals in whom it could be determined, the de novo variants were all on the maternal allele. We demonstrate that RNU4-2 is highly expressed in the developing human brain, in contrast to RNU4-1 and other U4 homologues. Using RNA sequencing, we show how 5' splice-site use is systematically disrupted in individuals with RNU4-2 variants, consistent with the known role of this region during spliceosome activation. Finally, we estimate that variants in this 18 base pair region explain 0.4% of individuals with NDD. This work underscores the importance of non-coding genes in rare disorders and will provide a diagnosis to thousands of individuals with NDD worldwide.
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Affiliation(s)
- Yuyang Chen
- Big Data Institute, University of Oxford, Oxford, UK
- Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Ruebena Dawes
- Big Data Institute, University of Oxford, Oxford, UK
- Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Hyung Chul Kim
- Big Data Institute, University of Oxford, Oxford, UK
- Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Alicia Ljungdahl
- Institute of Developmental and Regenerative Medicine, Department of Paediatrics, University of Oxford, Oxford, UK
- Department of Psychiatry and Behavioral Sciences, UCSF Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
| | - Sarah L Stenton
- Broad Center for Mendelian Genomics, Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Jenny Lord
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Gabrielle Lemire
- Broad Center for Mendelian Genomics, Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Alexandra C Martin-Geary
- Big Data Institute, University of Oxford, Oxford, UK
- Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Vijay S Ganesh
- Broad Center for Mendelian Genomics, Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Jialan Ma
- Broad Center for Mendelian Genomics, Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jamie M Ellingford
- Genomics England, London, UK
- Manchester Centre for Genomic Medicine, Manchester University NHS Foundation Trust, Manchester, UK
- Division of Evolution, Infection and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicines and Health, University of Manchester, Manchester, UK
| | - Erwan Delage
- Human Genetics, Wellcome Sanger Institute, Hinxton, UK
| | - Elston N D'Souza
- Big Data Institute, University of Oxford, Oxford, UK
- Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Shan Dong
- Institute of Developmental and Regenerative Medicine, Department of Paediatrics, University of Oxford, Oxford, UK
- Department of Psychiatry and Behavioral Sciences, UCSF Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
| | - David R Adams
- Undiagnosed Disesases Program, National Human Genome Research Institute, Bethesda, MD, USA
| | - Kirsten Allan
- Victorian Clinical Genetics Services, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
| | - Madhura Bakshi
- Department of Clinical Genetics, Liverpool Hospital, Sydney, New South Wales, Australia
| | - Erin E Baldwin
- Division of Medical Genetics, Department of Pediatrics, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Seth I Berger
- Center for Genetic Medicine Research, Children's National Research Institute, Washington, DC, USA
- Division of Genetics and Metabolism, Children's National Hospital, Washington, DC, USA
| | - Jonathan A Bernstein
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
- GREGoR Stanford Site, Stanford University School of Medicine, Stanford, CA, USA
- Center for Undiagnosed Diseases, Stanford University School of Medicine, Stanford, CA, USA
| | - Ishita Bhatnagar
- Oxford Centre for Genomic Medicine, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Ed Blair
- Oxford Centre for Genomic Medicine, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Natasha J Brown
- Victorian Clinical Genetics Services, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
- Department of Paediatrics, University of Melbourne, Melbourne, Victoria, Australia
| | - Lindsay C Burrage
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Kimberly Chapman
- Division of Genetics and Metabolism, Children's National Hospital, Washington, DC, USA
| | - David J Coman
- Department of Metabolic Medicine, Queensland Children's Hospital, Brisbane, Queensland, Australia
- Faculty of Medicine, University of Queensland, Brisbane, Queensland, Australia
- School of Medicine, Griffith university, Gold Coast, Queensland, Australia
| | - Alison G Compton
- Victorian Clinical Genetics Services, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
- Department of Paediatrics, University of Melbourne, Melbourne, Victoria, Australia
- Murdoch Children's Research Institute, Melbourne, Victoria, Australia
| | - Chloe A Cunningham
- Victorian Clinical Genetics Services, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
- Department of Paediatrics, University of Melbourne, Melbourne, Victoria, Australia
| | - Precilla D'Souza
- Undiagnosed Disesases Program, National Human Genome Research Institute, Bethesda, MD, USA
| | - Petr Danecek
- Human Genetics, Wellcome Sanger Institute, Hinxton, UK
| | - Emmanuèle C Délot
- Center for Genetic Medicine Research, Children's National Research Institute, Washington, DC, USA
| | - Kerith-Rae Dias
- Neuroscience Research Australia, Sydney, New South Wales, Australia
- Prince of Wales Clinical School, Faculty of Medicine, University of New South Wales, Sydney, New South Wales, Australia
| | - Ellen R Elias
- Department of Pediatrics, Children's Hospital Colorado, Aurora, CO, USA
- University of Colorado School of Medicine, University of Colorado, Aurora, CO, USA
| | - Frances Elmslie
- South West Thames Centre for Genomics, St George's University Hospitals NHS Foundation Trust, London, UK
| | - Care-Anne Evans
- Neuroscience Research Australia, Sydney, New South Wales, Australia
- New South Wales Health Pathology Randwick Genomics, Prince of Wales Hospital, Sydney, New South Wales, Australia
| | - Lisa Ewans
- Discipline of Paediatrics and Child Health, Faculty of Medicine and Health, University of New South Wales, Sydney, New South Wales, Australia
- Centre for Clinical Genetics, Sydney Children's Hospitals Network, Randwick, New South Wales, Australia
- Genomics and Inherited Disease Program, Garvan Institute of Medical Research, Darlinghurst, North South Wales, Australia
| | - Kimberly Ezell
- Division of Medical Genetics and Genomic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jamie L Fraser
- Center for Genetic Medicine Research, Children's National Research Institute, Washington, DC, USA
- Division of Genetics and Metabolism, Children's National Hospital, Washington, DC, USA
| | - Lyndon Gallacher
- Victorian Clinical Genetics Services, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
- Department of Paediatrics, University of Melbourne, Melbourne, Victoria, Australia
| | - Casie A Genetti
- Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Manton Center for Orphan Disease Research, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Anne Goriely
- MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- NIHR Biomedical Research Centre, Oxford, UK
| | - Christina L Grant
- Division of Genetics and Metabolism, Children's National Hospital, Washington, DC, USA
| | - Tobias Haack
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
- Center for Rare Diseases Tübingen, University of Tübingen, Tübingen, Germany
| | - Jenny E Higgs
- Liverpool Centre for Genomic Medicine, Liverpool Women's Hospital, Liverpool, UK
| | - Anjali G Hinch
- Centre for Human Genetics, University of Oxford, Oxford, UK
| | | | - Alma Kuechler
- Institute of Human Genetics, University Hospital Essen, University Duisburg-Essen, Essen, Germany
| | - Katherine L Lachlan
- Wessex Clinical Genetics Service, University Hospital Southampton NHS Trust, Southampton, UK
- Department of Human Genetics and Genomic Medicine, Southampton University, Southampton, UK
| | - Seema R Lalani
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - François Lecoquierre
- University of Rouen Normandie, Inserm U1245 and CHU Rouen, Department of Genetics and Reference Center for Developmental Disorders, Rouen, France
| | - Elsa Leitão
- Institute of Human Genetics, University Hospital Essen, University Duisburg-Essen, Essen, Germany
| | - Anna Le Fevre
- Victorian Clinical Genetics Services, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
| | - Richard J Leventer
- Department of Paediatrics, University of Melbourne, Melbourne, Victoria, Australia
- Murdoch Children's Research Institute, Melbourne, Victoria, Australia
- Royal Children's Hospital, Melbourne, Victoria, Australia
| | - Jan E Liebelt
- Paediatric and Reproductive Genetics Unit, South Australian Clinical Genetics Service, Women's and Children's Hospital, North Adelaide, South Australia, Australia
- Repromed, Dulwich, South Australia, Australia
| | - Sarah Lindsay
- Human Genetics, Wellcome Sanger Institute, Hinxton, UK
| | - Paul J Lockhart
- Department of Paediatrics, University of Melbourne, Melbourne, Victoria, Australia
- Bruce Lefroy Centre, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
| | - Alan S Ma
- Department of Clinical Genetics, Sydney Children's Hospitals Network Westmead, Sydney, New South Wales, Australia
- Specialty of Genomic Medicine, University of Sydney, Sydney, New South Wales, Australia
| | - Ellen F Macnamara
- Undiagnosed Disesases Program, National Human Genome Research Institute, Bethesda, MD, USA
| | - Sahar Mansour
- South West Thames Centre for Genomics, St George's University Hospitals NHS Foundation Trust, London, UK
| | - Taylor M Maurer
- GREGoR Stanford Site, Stanford University School of Medicine, Stanford, CA, USA
- Center for Undiagnosed Diseases, Stanford University School of Medicine, Stanford, CA, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Hector R Mendez
- GREGoR Stanford Site, Stanford University School of Medicine, Stanford, CA, USA
- Center for Undiagnosed Diseases, Stanford University School of Medicine, Stanford, CA, USA
- Department of Medicine - Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Kay Metcalfe
- Manchester Centre for Genomic Medicine, St. Mary's Hospital, Manchester University NHS Foundation Trust, Health Innovation Manchester, Manchester, UK
| | - Stephen B Montgomery
- GREGoR Stanford Site, Stanford University School of Medicine, Stanford, CA, USA
- Center for Undiagnosed Diseases, Stanford University School of Medicine, Stanford, CA, USA
- Department of Pathology, Department of Genetics, Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA
| | - Mariya Moosajee
- UCL Institute of Ophthalmology, London, UK
- The Francis Crick Institute, London, UK
- Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | - Marie-Cécile Nassogne
- Service de Neurologie Pédiatrique, Cliniques Universitaires Saint-Luc, UCLouvain, Brussels, Belgium
- Institut des Maladies Rares, Cliniques Universitaires Saint-Luc, UCLouvain, Brussels, Belgium
| | - Serena Neumann
- Division of Medical Genetics and Genomic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | | | - Melanie O'Leary
- Broad Center for Mendelian Genomics, Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Elizabeth E Palmer
- Discipline of Paediatrics and Child Health, Faculty of Medicine and Health, University of New South Wales, Sydney, New South Wales, Australia
- Centre for Clinical Genetics, Sydney Children's Hospitals Network, Randwick, New South Wales, Australia
| | - Nikhil Pattani
- South West Thames Centre for Genomics, St George's University Hospitals NHS Foundation Trust, London, UK
| | - John Phillips
- Division of Medical Genetics and Genomic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Georgia Pitsava
- Institute for Clinical and Translational Research, University of California Irvine, Irvine, CA, USA
| | - Ryan Pysar
- Discipline of Paediatrics and Child Health, Faculty of Medicine and Health, University of New South Wales, Sydney, New South Wales, Australia
- Centre for Clinical Genetics, Sydney Children's Hospitals Network, Randwick, New South Wales, Australia
- Department of Clinical Genetics, The Children's Hospital at Westmead, Westmead, New South Wales, Australia
| | - Heidi L Rehm
- Broad Center for Mendelian Genomics, Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Chloe M Reuter
- GREGoR Stanford Site, Stanford University School of Medicine, Stanford, CA, USA
- Center for Undiagnosed Diseases, Stanford University School of Medicine, Stanford, CA, USA
- Department of Medicine - Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Nicole Revencu
- Center for Human Genetics, Cliniques Universitaires Saint-Luc, Université Catholique de Louvain, Brussels, Belgium
| | - Angelika Riess
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
| | - Rocio Rius
- Department of Paediatrics, University of Melbourne, Melbourne, Victoria, Australia
- Centre for Population Genomics, Garvan Institute of Medical Research and UNSW Sydney, Sydney, New South Wales, Australia
- Centre for Population Genomics, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
| | - Lance Rodan
- Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Tony Roscioli
- Neuroscience Research Australia, Sydney, New South Wales, Australia
- Prince of Wales Clinical School, Faculty of Medicine, University of New South Wales, Sydney, New South Wales, Australia
- New South Wales Health Pathology Randwick Genomics, Prince of Wales Hospital, Sydney, New South Wales, Australia
| | - Jill A Rosenfeld
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Rani Sachdev
- Discipline of Paediatrics and Child Health, Faculty of Medicine and Health, University of New South Wales, Sydney, New South Wales, Australia
- Centre for Clinical Genetics, Sydney Children's Hospitals Network, Randwick, New South Wales, Australia
| | - Charles J Shaw-Smith
- Department of Clinical Genetics, Peninsula Regional Clinical Genetics Service, Royal Devon University Hospital, Exeter, UK
| | - Cas Simons
- Centre for Population Genomics, Garvan Institute of Medical Research and UNSW Sydney, Sydney, New South Wales, Australia
- Centre for Population Genomics, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
| | - Sanjay M Sisodiya
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK
- UK and Chalfont Centre for Epilepsy, Chalfont St Peter, UK
| | - Penny Snell
- Bruce Lefroy Centre, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
| | - Laura St Clair
- Department of Clinical Genetics, Sydney Children's Hospitals Network Westmead, Sydney, New South Wales, Australia
| | - Zornitza Stark
- Victorian Clinical Genetics Services, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
- Department of Paediatrics, University of Melbourne, Melbourne, Victoria, Australia
| | - Helen S Stewart
- Oxford Centre for Genomic Medicine, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Tiong Yang Tan
- Victorian Clinical Genetics Services, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
- Department of Paediatrics, University of Melbourne, Melbourne, Victoria, Australia
| | - Natalie B Tan
- Victorian Clinical Genetics Services, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
| | - Suzanna E L Temple
- Department of Clinical Genetics, Liverpool Hospital, Sydney, New South Wales, Australia
- School of Women's and Children's Health, University of New South Wales, Sydney, New South Wales, Australia
| | - David R Thorburn
- Victorian Clinical Genetics Services, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
- Department of Paediatrics, University of Melbourne, Melbourne, Victoria, Australia
- Murdoch Children's Research Institute, Melbourne, Victoria, Australia
| | - Cynthia J Tifft
- Undiagnosed Disesases Program, National Human Genome Research Institute, Bethesda, MD, USA
| | - Eloise Uebergang
- Murdoch Children's Research Institute, Melbourne, Victoria, Australia
| | - Grace E VanNoy
- Broad Center for Mendelian Genomics, Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Pradeep Vasudevan
- Medical Genetics, University of Leicester, Leicester Royal Infirmary, Leicester, UK
| | - Eric Vilain
- Institute for Clinical and Translational Science, University of California Irvine, Irvine, CA, USA
| | - David H Viskochil
- Division of Medical Genetics, Department of Pediatrics, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Laura Wedd
- Centre for Population Genomics, Garvan Institute of Medical Research and UNSW Sydney, Sydney, New South Wales, Australia
- Centre for Population Genomics, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
| | - Matthew T Wheeler
- GREGoR Stanford Site, Stanford University School of Medicine, Stanford, CA, USA
- Center for Undiagnosed Diseases, Stanford University School of Medicine, Stanford, CA, USA
- Department of Medicine - Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Susan M White
- Victorian Clinical Genetics Services, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
- Department of Paediatrics, University of Melbourne, Melbourne, Victoria, Australia
| | - Monica Wojcik
- Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Manton Center for Orphan Disease Research, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Lynne A Wolfe
- Undiagnosed Disesases Program, National Human Genome Research Institute, Bethesda, MD, USA
| | - Zoe Wolfenson
- Undiagnosed Disesases Program, National Human Genome Research Institute, Bethesda, MD, USA
| | - Caroline F Wright
- Department of Clinical and Biomedical Sciences, University of Exeter, Exeter, UK
| | - Changrui Xiao
- Department of Neurology, University of California Irvine, Irvine, CA, USA
| | - David Zocche
- North West Thames Regional Genetics Service, Northwick Park and St Mark's Hospitals, London, UK
| | - John L Rubenstein
- Department of Psychiatry and Behavioral Sciences, UCSF Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
| | - Eirene Markenscoff-Papadimitriou
- Department of Psychiatry, Langley Porter Psychiatric Institute, UCSF Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
| | | | - Diana Baralle
- School of Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, UK
- National Institute for Health Research (NIHR) Southampton Biomedical Research Centre, University Hospital Southampton NHS Foundation Trust, Southampton, UK
| | - Christel Depienne
- Institute of Human Genetics, University Hospital Essen, University Duisburg-Essen, Essen, Germany
| | - Daniel G MacArthur
- Centre for Population Genomics, Garvan Institute of Medical Research and UNSW Sydney, Sydney, New South Wales, Australia
- Centre for Population Genomics, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
| | - Joanna M M Howson
- Human Genetics Centre of Excellence, Novo Nordisk Research Centre, Oxford, UK
| | - Stephan J Sanders
- Institute of Developmental and Regenerative Medicine, Department of Paediatrics, University of Oxford, Oxford, UK
- Department of Psychiatry and Behavioral Sciences, UCSF Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
| | - Anne O'Donnell-Luria
- Broad Center for Mendelian Genomics, Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Nicola Whiffin
- Big Data Institute, University of Oxford, Oxford, UK.
- Centre for Human Genetics, University of Oxford, Oxford, UK.
- Broad Center for Mendelian Genomics, Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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25
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Ma JG, O’Neill MJ, Richardson E, Thomson KL, Ingles J, Muhammad A, Solus JF, Davogustto G, Anderson KC, Benjamin Shoemaker M, Stergachis AB, Floyd BJ, Dunn K, Parikh VN, Chubb H, Perrin MJ, Roden DM, Vandenberg JI, Ng CA, Glazer AM. Multisite Validation of a Functional Assay to Adjudicate SCN5A Brugada Syndrome-Associated Variants. CIRCULATION. GENOMIC AND PRECISION MEDICINE 2024; 17:e004569. [PMID: 38953211 PMCID: PMC11335442 DOI: 10.1161/circgen.124.004569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Accepted: 05/17/2024] [Indexed: 07/03/2024]
Abstract
BACKGROUND Brugada syndrome is an inheritable arrhythmia condition that is associated with rare, loss-of-function variants in SCN5A. Interpreting the pathogenicity of SCN5A missense variants is challenging, and ≈79% of SCN5A missense variants in ClinVar are currently classified as variants of uncertain significance. Automated patch clamp technology enables high-throughput functional studies of ion channel variants and can provide evidence for variant reclassification. METHODS An in vitro SCN5A-Brugada syndrome automated patch clamp assay was independently performed at Vanderbilt University Medical Center and Victor Chang Cardiac Research Institute. The assay was calibrated according to ClinGen Sequence Variant Interpretation recommendations using high-confidence variant controls (n=49). Normal and abnormal ranges of function were established based on the distribution of benign variant assay results. Odds of pathogenicity values were derived from the experimental results according to ClinGen Sequence Variant Interpretation recommendations. The calibrated assay was then used to study SCN5A variants of uncertain significance observed in 4 families with Brugada syndrome and other arrhythmia phenotypes associated with SCN5A loss-of-function. RESULTS Variant channel parameters generated independently at the 2 research sites showed strong correlations, including peak INa density (R2=0.86). The assay accurately distinguished benign controls (24/25 concordant variants) from pathogenic controls (23/24 concordant variants). Odds of pathogenicity values were 0.042 for normal function and 24.0 for abnormal function, corresponding to strong evidence for both American College of Medical Genetics and Genomics/Association for Molecular Pathology benign and pathogenic functional criteria (BS3 and PS3, respectively). Application of the assay to 4 clinical SCN5A variants of uncertain significance revealed loss-of-function for 3/4 variants, enabling reclassification to likely pathogenic. CONCLUSIONS This validated high-throughput assay provides clinical-grade functional evidence to aid the classification of current and future SCN5A-Brugada syndrome variants of uncertain significance.
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Affiliation(s)
- Joanne G. Ma
- Mark Cowley Lidwill Research Program in Cardiac Electrophysiology, Victor Chang Cardiac Research Inst
- School of Clinical Medicine, UNSW Sydney, Darlinghurst, NSW, Australia
| | | | - Ebony Richardson
- Clinical Genomics Laboratory, Ctr for Population Genomics, Garvan Inst of Medical Rsrch, Darlinghurst, NSW & Australia & Murdoch Children’s Research Inst, Melbourne, Australia
| | - Kate L. Thomson
- Oxford Genetics Laboratories, Churchill Hospital, Oxford, UK
| | - Jodie Ingles
- Clinical Genomics Laboratory, Ctr for Population Genomics, Garvan Inst of Medical Rsrch, Darlinghurst, NSW & Australia & Murdoch Children’s Research Inst, Melbourne, Australia
| | | | - Joseph F. Solus
- Vanderbilt Ctr for Arrhythmia Research & Therapeutics (VanCART), Division of Clinical Pharmacology, Dept of Medicine, Nashville, TN
| | | | | | | | | | - Brendan J. Floyd
- Stanford Ctr for Inherited Cardiovascular Disease, Stanford Univ School of Medicine, Stanford, CA
| | - Kyla Dunn
- Stanford Ctr for Inherited Cardiovascular Disease, Stanford Univ School of Medicine, Stanford, CA
| | - Victoria N. Parikh
- Stanford Ctr for Inherited Cardiovascular Disease, Stanford Univ School of Medicine, Stanford, CA
| | - Henry Chubb
- Stanford Ctr for Inherited Cardiovascular Disease, Stanford Univ School of Medicine, Stanford, CA
| | - Mark J. Perrin
- Dept of Genomic Medicine, Royal Melbourne Hospital, Victoria, Australia
| | - Dan M. Roden
- Depts of Pharmacology, and Biomedical Informatics, Vanderbilt Univ Medical Ctr, Nashville, TN
| | - Jamie I. Vandenberg
- Mark Cowley Lidwill Research Program in Cardiac Electrophysiology, Victor Chang Cardiac Research Inst
- School of Clinical Medicine, UNSW Sydney, Darlinghurst, NSW, Australia
| | - Chai-Ann Ng
- Mark Cowley Lidwill Research Program in Cardiac Electrophysiology, Victor Chang Cardiac Research Inst
- School of Clinical Medicine, UNSW Sydney, Darlinghurst, NSW, Australia
| | - Andrew M. Glazer
- Vanderbilt Ctr for Arrhythmia Research & Therapeutics (VanCART), Division of Clinical Pharmacology, Dept of Medicine, Nashville, TN
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26
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Ormond C, Ryan NM, Byerley W, Heron EA, Corvin A. Investigating copy number variants in schizophrenia pedigrees using a new consensus pipeline called PECAN. Sci Rep 2024; 14:17518. [PMID: 39080331 PMCID: PMC11289470 DOI: 10.1038/s41598-024-66021-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Accepted: 06/26/2024] [Indexed: 08/02/2024] Open
Abstract
Copy number variants (CNVs) have been implicated in many human diseases, including psychiatric disorders. Whole genome sequencing offers advantages in CNV calling compared to previous array-based methods. Here we present a robust and transparent CNV calling pipeline, PECAN (PEdigree Copy number vAriaNt calling), for short-read, whole genome sequencing data, comprised of a novel combination of four calling methods and structural variant genotyping. This method is scalable and can incorporate pedigree information to retain lower-confidence CNVs that would otherwise be discarded. We have robustly benchmarked PECAN using gold-standard CNV calls for two well-established evaluation samples, NA12878 and HG002, showing that PECAN performs with high precision and recall on both datasets, outperforming another pedigree-based CNV calling pipeline. As part of this work, we provide a list of high-confidence gold standard CNVs for the NA12878 reference sample, curated from multiple studies. We applied PECAN to a collection of pedigrees multiply affected with schizophrenia and identified a rare deletion that perfectly co-segregates with schizophrenia in one of the pedigrees. The CNV overlaps the gene PITRM1, which has been implicated in a complex phenotype including ataxia, developmental delay, and schizophrenia-like episodes in affected adults.
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Affiliation(s)
- Cathal Ormond
- Neuropsychiatric Genetics Research Group, Department of Psychiatry, Trinity Centre for Health Sciences, Trinity College Dublin, James' Street, Dublin 8, Ireland
| | - Niamh M Ryan
- Neuropsychiatric Genetics Research Group, Department of Psychiatry, Trinity Centre for Health Sciences, Trinity College Dublin, James' Street, Dublin 8, Ireland
| | - William Byerley
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA, USA
| | - Elizabeth A Heron
- Neuropsychiatric Genetics Research Group, Department of Psychiatry, Trinity Centre for Health Sciences, Trinity College Dublin, James' Street, Dublin 8, Ireland
| | - Aiden Corvin
- Neuropsychiatric Genetics Research Group, Department of Psychiatry, Trinity Centre for Health Sciences, Trinity College Dublin, James' Street, Dublin 8, Ireland.
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27
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Hara A, Lu E, Johnstone L, Wei M, Sun S, Hallmark B, Watkins JC, Zhang HH, Yao G, Chilton FH. Identification of an Allele-Specific Transcription Factor Binding Interaction that May Regulate PLA2G2A Gene Expression. Bioinform Biol Insights 2024; 18:11779322241261427. [PMID: 39081667 PMCID: PMC11287738 DOI: 10.1177/11779322241261427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Accepted: 05/24/2024] [Indexed: 08/02/2024] Open
Abstract
The secreted phospholipase A2 (sPLA2) isoform, sPLA2-IIA, has been implicated in a variety of diseases and conditions, including bacteremia, cardiovascular disease, COVID-19, sepsis, adult respiratory distress syndrome, and certain cancers. Given its significant role in these conditions, understanding the regulatory mechanisms impacting its levels is crucial. Genome-wide association studies (GWAS) have identified several single nucleotide polymorphisms (SNPs), including rs11573156, that are associated with circulating levels of sPLA2-IIA. The work in the manuscript leveraged 4 publicly available datasets to investigate the mechanism by which rs11573156 influences sPLA2-IIA levels via bioinformatics and modeling analysis. Through genotype-tissue expression (GTEx), 234 expression quantitative trait loci (eQTLs) were identified for the gene that encodes for sPLA2-IIA, PLA2G2A. SNP2TFBS was used to ascertain the binding affinities between transcription factors (TFs) to both the reference and alternative alleles of identified eQTL SNPs. Subsequently, candidate TF-SNP interactions were cross-referenced with the ChIP-seq results in matched tissues from ENCODE. SP1-rs11573156 emerged as the significant TF-SNP pair in the liver. Further analysis revealed that the upregulation of PLA2G2A transcript levels through the rs11573156 variant was likely affected by tissue SP1 protein levels. Using an ordinary differential equation based on Michaelis-Menten kinetic assumptions, we modeled the dependence of PLA2G2A transcription on SP1 protein levels, incorporating the SNP influence. Collectively, our analysis strongly suggests that the difference in the binding dynamics of SP1 to different rs11573156 alleles may underlie the allele-specific PLA2G2A expression in different tissues, a mechanistic model that awaits future direct experimental validation. This mechanism likely contributes to the variation in circulating sPLA2-IIA protein levels in the human population, with implications for a wide range of human diseases.
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Affiliation(s)
- Aki Hara
- School of Nutritional Sciences and Wellness, College of Agriculture and Life Sciences, The University of Arizona, Tucson, AZ, USA
| | - Eric Lu
- Department of Molecular and Cellular Biology, The University of Arizona, Tucson, AZ, USA
| | - Laurel Johnstone
- School of Nutritional Sciences and Wellness, College of Agriculture and Life Sciences, The University of Arizona, Tucson, AZ, USA
| | - Michelle Wei
- Department of Molecular and Cellular Biology, The University of Arizona, Tucson, AZ, USA
| | - Shudong Sun
- Department of Mathematics, The University of Arizona, Tucson, AZ, USA
- Statistics Interdisciplinary Program, The University of Arizona, Tucson, AZ, USA
| | - Brian Hallmark
- BIO5 Institute, The University of Arizona, Tucson, AZ, USA
| | - Joseph C Watkins
- Department of Mathematics, The University of Arizona, Tucson, AZ, USA
- Statistics Interdisciplinary Program, The University of Arizona, Tucson, AZ, USA
| | - Hao Helen Zhang
- Department of Mathematics, The University of Arizona, Tucson, AZ, USA
- Statistics Interdisciplinary Program, The University of Arizona, Tucson, AZ, USA
| | - Guang Yao
- Department of Molecular and Cellular Biology, The University of Arizona, Tucson, AZ, USA
| | - Floyd H Chilton
- School of Nutritional Sciences and Wellness, College of Agriculture and Life Sciences, The University of Arizona, Tucson, AZ, USA
- BIO5 Institute, The University of Arizona, Tucson, AZ, USA
- Center for Precision Nutrition and Wellness, The University of Arizona, Tucson, AZ, USA
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28
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Stenton SL, Pejaver V, Bergquist T, Biesecker LG, Byrne AB, Nadeau EAW, Greenblatt MS, Harrison SM, Tavtigian SV, Radivojac P, Brenner SE, O'Donnell-Luria A. Assessment of the evidence yield for the calibrated PP3/BP4 computational recommendations. Genet Med 2024; 26:101213. [PMID: 39030733 DOI: 10.1016/j.gim.2024.101213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Revised: 07/10/2024] [Accepted: 07/11/2024] [Indexed: 07/21/2024] Open
Abstract
PURPOSE To investigate the number of rare missense variants observed in human genome sequences by ACMG/AMP PP3/BP4 evidence strength, following the ClinGen-calibrated PP3/BP4 computational recommendations. METHODS Missense variants from the genome sequences of 300 probands from the Rare Genomes Project with suspected rare disease were analyzed using computational prediction tools that were able to reach PP3_Strong and BP4_Moderate evidence strengths (BayesDel, MutPred2, REVEL, and VEST4). The numbers of variants at each evidence strength were analyzed across disease-associated genes and genome-wide. RESULTS From a median of 75.5 rare (≤1% allele frequency) missense variants in disease-associated genes per proband, a median of one reached PP3_Strong, 3-5 PP3_Moderate, and 3-5 PP3_Supporting. Most were allocated BP4 evidence (median 41-49 per proband) or were indeterminate (median 17.5-19 per proband). Extending the analysis to all protein-coding genes genome-wide, the number of variants reaching PP3_Strong score thresholds increased approximately 2.6-fold compared with disease-associated genes, with a median per proband of 1-3 PP3_Strong, 8-16 PP3_Moderate, and 10-17 PP3_Supporting. CONCLUSION A small number of variants per proband reached PP3_Strong and PP3_Moderate in 3424 disease-associated genes. Although not the intended use of the recommendations, this was also observed genome-wide. Use of PP3/BP4 evidence as recommended from calibrated computational prediction tools in the clinical diagnostic laboratory is unlikely to inappropriately contribute to the classification of an excessive number of variants as pathogenic or likely pathogenic by ACMG/AMP rules.
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Affiliation(s)
- Sarah L Stenton
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA; Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA
| | - Vikas Pejaver
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Timothy Bergquist
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Leslie G Biesecker
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD
| | - Alicia B Byrne
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA
| | - Emily A W Nadeau
- Department of Medicine and University of Vermont Cancer Center, University of Vermont, Larner College of Medicine, Burlington, VT
| | - Marc S Greenblatt
- Department of Medicine and University of Vermont Cancer Center, University of Vermont, Larner College of Medicine, Burlington, VT
| | - Steven M Harrison
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA; Ambry Genetics, Aliso Viejo, CA
| | - Sean V Tavtigian
- Department of Oncological Sciences, Huntsman Cancer Institute, University of Utah School of Medicine, Salt Lake City, UT
| | - Predrag Radivojac
- Khoury College of Computer Sciences, Northeastern University, Boston, MA
| | - Steven E Brenner
- Department of Plant and Microbial Biology and Center for Computational Biology, University of California, (redundant), Berkeley, CA
| | - Anne O'Donnell-Luria
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA; Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA.
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29
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Tilliole P, Fix S, Godin JD. hnRNPs: roles in neurodevelopment and implication for brain disorders. Front Mol Neurosci 2024; 17:1411639. [PMID: 39086926 PMCID: PMC11288931 DOI: 10.3389/fnmol.2024.1411639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2024] [Accepted: 06/17/2024] [Indexed: 08/02/2024] Open
Abstract
Heterogeneous nuclear ribonucleoproteins (hnRNPs) constitute a family of multifunctional RNA-binding proteins able to process nuclear pre-mRNAs into mature mRNAs and regulate gene expression in multiple ways. They comprise at least 20 different members in mammals, named from A (HNRNP A1) to U (HNRNP U). Many of these proteins are components of the spliceosome complex and can modulate alternative splicing in a tissue-specific manner. Notably, while genes encoding hnRNPs exhibit ubiquitous expression, increasing evidence associate these proteins to various neurodevelopmental and neurodegenerative disorders, such as intellectual disability, epilepsy, microcephaly, amyotrophic lateral sclerosis, or dementias, highlighting their crucial role in the central nervous system. This review explores the evolution of the hnRNPs family, highlighting the emergence of numerous new members within this family, and sheds light on their implications for brain development.
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Affiliation(s)
- Pierre Tilliole
- Institut de Génétique et de Biologie Moléculaire et Cellulaire, IGBMC, Illkirch, France
- Centre National de la Recherche Scientifique, CNRS, UMR7104, Illkirch, France
- Institut National de la Santé et de la Recherche Médicale, INSERM, U1258, Illkirch, France
- Université de Strasbourg, Strasbourg, France
| | - Simon Fix
- Institut de Génétique et de Biologie Moléculaire et Cellulaire, IGBMC, Illkirch, France
- Centre National de la Recherche Scientifique, CNRS, UMR7104, Illkirch, France
- Institut National de la Santé et de la Recherche Médicale, INSERM, U1258, Illkirch, France
- Université de Strasbourg, Strasbourg, France
| | - Juliette D. Godin
- Institut de Génétique et de Biologie Moléculaire et Cellulaire, IGBMC, Illkirch, France
- Centre National de la Recherche Scientifique, CNRS, UMR7104, Illkirch, France
- Institut National de la Santé et de la Recherche Médicale, INSERM, U1258, Illkirch, France
- Université de Strasbourg, Strasbourg, France
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30
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Villani RM, McKenzie ME, Davidson AL, Spurdle AB. Regional-specific calibration enables application of computational evidence for clinical classification of 5' cis-regulatory variants in Mendelian disease. Am J Hum Genet 2024; 111:1301-1315. [PMID: 38815586 PMCID: PMC11267523 DOI: 10.1016/j.ajhg.2024.05.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 05/02/2024] [Accepted: 05/03/2024] [Indexed: 06/01/2024] Open
Abstract
To date, clinical genetic testing for Mendelian disease variants has focused heavily on exonic coding and intronic gene regions. This multi-step study was undertaken to provide an evidence base for selecting and applying computational approaches for use in clinical classification of 5' cis-regulatory region variants. Curated datasets of clinically reported disease-causing 5' cis-regulatory region variants and variants from matched genomic regions in population controls were used to calibrate six bioinformatic tools as predictors of variant pathogenicity. Likelihood ratio estimates were aligned to code weights following ClinGen recommendations for application of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) classification scheme. Considering code assignment across all reference dataset variants, performance was best for CADD (81.2%) and REMM (81.5%). Optimized thresholds provided moderate evidence toward pathogenicity (CADD, REMM) and moderate (CADD) or supporting (REMM) evidence against pathogenicity. Both sensitivity and specificity of prediction were improved when further categorizing variants based on location in an EPDnew-defined promoter region. Combining predictions (CADD, REMM, and location in a promoter region) increased specificity at the expense of sensitivity. Importantly, the optimal CADD thresholds for assigning ACMG/AMP codes PP3 (≥10) and BP4 (≤8) were vastly different from recommendations for protein-coding variants (PP3 ≥25.3; BP4 ≤22.7); CADD <22.7 would incorrectly assign BP4 for >90% of reported disease-causing cis-regulatory region variants. Our results demonstrate the need to consider a tiered approach and tailored score thresholds to optimize bioinformatic impact prediction for clinical classification of 5' cis-regulatory region variants.
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Affiliation(s)
- Rehan M Villani
- Population Health Program, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Maddison E McKenzie
- Population Health Program, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Aimee L Davidson
- Population Health Program, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Amanda B Spurdle
- Population Health Program, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia; University of Queensland, Brisbane, Queensland, Australia.
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31
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Lin TY, Kleemann L, Jeżowski J, Dobosz D, Rawski M, Indyka P, Ważny G, Mehta R, Chramiec-Głąbik A, Koziej Ł, Ranff T, Fufezan C, Wawro M, Kochan J, Bereta J, Leidel SA, Glatt S. The molecular basis of tRNA selectivity by human pseudouridine synthase 3. Mol Cell 2024; 84:2472-2489.e8. [PMID: 38996458 PMCID: PMC11258540 DOI: 10.1016/j.molcel.2024.06.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 03/14/2024] [Accepted: 06/13/2024] [Indexed: 07/14/2024]
Abstract
Pseudouridine (Ψ), the isomer of uridine, is ubiquitously found in RNA, including tRNA, rRNA, and mRNA. Human pseudouridine synthase 3 (PUS3) catalyzes pseudouridylation of position 38/39 in tRNAs. However, the molecular mechanisms by which it recognizes its RNA targets and achieves site specificity remain elusive. Here, we determine single-particle cryo-EM structures of PUS3 in its apo form and bound to three tRNAs, showing how the symmetric PUS3 homodimer recognizes tRNAs and positions the target uridine next to its active site. Structure-guided and patient-derived mutations validate our structural findings in complementary biochemical assays. Furthermore, we deleted PUS1 and PUS3 in HEK293 cells and mapped transcriptome-wide Ψ sites by Pseudo-seq. Although PUS1-dependent sites were detectable in tRNA and mRNA, we found no evidence that human PUS3 modifies mRNAs. Our work provides the molecular basis for PUS3-mediated tRNA modification in humans and explains how its tRNA modification activity is linked to intellectual disabilities.
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Affiliation(s)
- Ting-Yu Lin
- Małopolska Centre of Biotechnology, Jagiellonian University, 30-387 Kraków, Poland.
| | - Leon Kleemann
- Department of Chemistry, Biochemistry and Pharmaceutical Sciences, University of Bern, 3012 Bern, Switzerland; Graduate School for Cellular and Biomedical Sciences, University of Bern, 3012 Bern, Switzerland
| | - Jakub Jeżowski
- Małopolska Centre of Biotechnology, Jagiellonian University, 30-387 Kraków, Poland; Department of Cell Biochemistry, Faculty of Biochemistry, Biophysics and Biotechnology, Jagiellonian University, 30-387 Kraków, Poland
| | - Dominika Dobosz
- Małopolska Centre of Biotechnology, Jagiellonian University, 30-387 Kraków, Poland
| | - Michał Rawski
- Małopolska Centre of Biotechnology, Jagiellonian University, 30-387 Kraków, Poland; SOLARIS National Synchrotron Radiation Centre, Jagiellonian University, 30-392 Kraków, Poland
| | - Paulina Indyka
- Małopolska Centre of Biotechnology, Jagiellonian University, 30-387 Kraków, Poland; SOLARIS National Synchrotron Radiation Centre, Jagiellonian University, 30-392 Kraków, Poland
| | - Grzegorz Ważny
- SOLARIS National Synchrotron Radiation Centre, Jagiellonian University, 30-392 Kraków, Poland; Doctoral School of Exact and Natural Sciences, Jagiellonian University, 30-348 Kraków, Poland
| | - Rahul Mehta
- Małopolska Centre of Biotechnology, Jagiellonian University, 30-387 Kraków, Poland; Doctoral School of Exact and Natural Sciences, Jagiellonian University, 30-348 Kraków, Poland
| | | | - Łukasz Koziej
- Małopolska Centre of Biotechnology, Jagiellonian University, 30-387 Kraków, Poland
| | - Tristan Ranff
- Institute of Pharmacy and Molecular Biotechnology, Heidelberg University, 69120 Heidelberg, Germany
| | - Christian Fufezan
- Institute of Pharmacy and Molecular Biotechnology, Heidelberg University, 69120 Heidelberg, Germany
| | - Mateusz Wawro
- Department of Cell Biochemistry, Faculty of Biochemistry, Biophysics and Biotechnology, Jagiellonian University, 30-387 Kraków, Poland
| | - Jakub Kochan
- Department of Cell Biochemistry, Faculty of Biochemistry, Biophysics and Biotechnology, Jagiellonian University, 30-387 Kraków, Poland
| | - Joanna Bereta
- Department of Cell Biochemistry, Faculty of Biochemistry, Biophysics and Biotechnology, Jagiellonian University, 30-387 Kraków, Poland
| | - Sebastian A Leidel
- Department of Chemistry, Biochemistry and Pharmaceutical Sciences, University of Bern, 3012 Bern, Switzerland; Graduate School for Cellular and Biomedical Sciences, University of Bern, 3012 Bern, Switzerland.
| | - Sebastian Glatt
- Małopolska Centre of Biotechnology, Jagiellonian University, 30-387 Kraków, Poland.
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32
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Kornrumpf K, Kurz N, Drofenik K, Krauß L, Schneider C, Koch R, Beißbarth T, Dönitz J. SeqCAT: Sequence Conversion and Analysis Toolbox. Nucleic Acids Res 2024; 52:W116-W120. [PMID: 38801081 PMCID: PMC11223787 DOI: 10.1093/nar/gkae422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2024] [Revised: 04/30/2024] [Accepted: 05/07/2024] [Indexed: 05/29/2024] Open
Abstract
Dealing with sequence coordinates in different formats and reference genomes is challenging in genetic research. This complexity arises from the need to convert and harmonize datasets of different sources using alternating nomenclatures. Since manual processing is time-consuming and requires specialized knowledge, the Sequence Conversion and Analysis Toolbox (SeqCAT) was developed for daily work with genetic datasets. Our tool provides a range of functions designed to standardize and convert gene variant coordinates based on various sequence types. Its user-friendly web interface provides easy access to all functionalities, while the Application Programming Interface (API) enables automation within pipelines. SeqCAT provides access to human genomic, protein and transcript data, utilizing various data resources and packages and extending them with its own unique features. The platform covers a wide range of genetic research needs with its 14 different applications and 3 info points, including search for transcript and gene information, transition between reference genomes, variant mapping, and genetic event review. Notable examples are 'Convert Protein to DNA Position' for translation of amino acid changes into genomic single nucleotide variants, or 'Fusion Check' for frameshift determination in gene fusions. SeqCAT is an excellent resource for converting sequence coordinate data into the required formats and is available at: https://mtb.bioinf.med.uni-goettingen.de/SeqCAT/.
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Affiliation(s)
- Kevin Kornrumpf
- Department of Medical Bioinformatics, University Medical Center Göttingen, Goldschmidtstr. 1, 37077 Göttingen, Germany
| | - Nadine S Kurz
- Department of Medical Bioinformatics, University Medical Center Göttingen, Goldschmidtstr. 1, 37077 Göttingen, Germany
- Göttingen Comprehensive Cancer Center (G-CCC), 37075 Göttingen, Germany
| | - Klara Drofenik
- Department of Medical Bioinformatics, University Medical Center Göttingen, Goldschmidtstr. 1, 37077 Göttingen, Germany
| | - Lukas Krauß
- Department of General, Visceral and Pediatric Surgery, University Medical Center Göttingen, Robert-Koch Str. 40, 37075 Göttingen, Germany
| | - Carolin Schneider
- Department of General, Visceral and Pediatric Surgery, University Medical Center Göttingen, Robert-Koch Str. 40, 37075 Göttingen, Germany
| | - Raphael Koch
- Department of Hematology and Medical Oncology, University Medical Center Göttingen, Robert-Koch Str. 40, 37075 Göttingen, Germany
| | - Tim Beißbarth
- Department of Medical Bioinformatics, University Medical Center Göttingen, Goldschmidtstr. 1, 37077 Göttingen, Germany
- Campus Institute Data Science (CIDAS), Göttingen, Germany
| | - Jürgen Dönitz
- Department of Medical Bioinformatics, University Medical Center Göttingen, Goldschmidtstr. 1, 37077 Göttingen, Germany
- Göttingen Comprehensive Cancer Center (G-CCC), 37075 Göttingen, Germany
- Campus Institute Data Science (CIDAS), Göttingen, Germany
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33
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Malcikova J, Pavlova S, Baliakas P, Chatzikonstantinou T, Tausch E, Catherwood M, Rossi D, Soussi T, Tichy B, Kater AP, Niemann CU, Davi F, Gaidano G, Stilgenbauer S, Rosenquist R, Stamatopoulos K, Ghia P, Pospisilova S. ERIC recommendations for TP53 mutation analysis in chronic lymphocytic leukemia-2024 update. Leukemia 2024; 38:1455-1468. [PMID: 38755420 PMCID: PMC11217004 DOI: 10.1038/s41375-024-02267-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 04/24/2024] [Accepted: 04/25/2024] [Indexed: 05/18/2024]
Abstract
In chronic lymphocytic leukemia (CLL), analysis of TP53 aberrations (deletion and/or mutation) is a crucial part of treatment decision-making algorithms. Technological and treatment advances have resulted in the need for an update of the last recommendations for TP53 analysis in CLL, published by ERIC, the European Research Initiative on CLL, in 2018. Based on the current knowledge of the relevance of low-burden TP53-mutated clones, a specific variant allele frequency (VAF) cut-off for reporting TP53 mutations is no longer recommended, but instead, the need for thorough method validation by the reporting laboratory is emphasized. The result of TP53 analyses should always be interpreted within the context of available laboratory and clinical information, treatment indication, and therapeutic options. Methodological aspects of introducing next-generation sequencing (NGS) in routine practice are discussed with a focus on reliable detection of low-burden clones. Furthermore, potential interpretation challenges are presented, and a simplified algorithm for the classification of TP53 variants in CLL is provided, representing a consensus based on previously published guidelines. Finally, the reporting requirements are highlighted, including a template for clinical reports of TP53 aberrations. These recommendations are intended to assist diagnosticians in the correct assessment of TP53 mutation status, but also physicians in the appropriate understanding of the lab reports, thus decreasing the risk of misinterpretation and incorrect management of patients in routine practice whilst also leading to improved stratification of patients with CLL in clinical trials.
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Affiliation(s)
- Jitka Malcikova
- Department of Internal Medicine, Hematology and Oncology, and Institute of Medical Genetics and Genomics, University Hospital Brno and Medical Faculty, Masaryk University, Brno, Czech Republic
- Central European Institute of Technology, Masaryk University, Brno, Czech Republic
| | - Sarka Pavlova
- Department of Internal Medicine, Hematology and Oncology, and Institute of Medical Genetics and Genomics, University Hospital Brno and Medical Faculty, Masaryk University, Brno, Czech Republic
- Central European Institute of Technology, Masaryk University, Brno, Czech Republic
| | - Panagiotis Baliakas
- Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | | | - Eugen Tausch
- Division of CLL, Department of Internal Medicine III, Ulm University, Ulm, Germany
| | - Mark Catherwood
- Haematology Department, Belfast Health and Social Care Trust, Belfast, United Kingdom
| | - Davide Rossi
- Hematology, Oncology Institute of Southern Switzerland and Institute of Oncology Research, Università della Svizzera Italiana, Bellinzona, Switzerland
| | - Thierry Soussi
- Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
- Hematopoietic and Leukemic Development, UMRS_938, Sorbonne University, Paris, France
| | - Boris Tichy
- Central European Institute of Technology, Masaryk University, Brno, Czech Republic
| | - Arnon P Kater
- Department of Hematology, Cancer Center Amsterdam, Amsterdam University Medical Centers, Amsterdam, the Netherlands
| | | | - Frederic Davi
- Sorbonne Université, Paris, France
- Department of Hematology, Hôpital Pitié-Salpêtière, AP-HP, Paris, France
| | - Gianluca Gaidano
- Division of Haematology, Department of Translational Medicine, University of Eastern Piedmont, Novara, Italy
| | - Stephan Stilgenbauer
- Division of CLL, Department of Internal Medicine III, Ulm University, Ulm, Germany
| | - Richard Rosenquist
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
- Clinical Genetics and Genomics, Karolinska University Hospital, Stockholm, Sweden
| | - Kostas Stamatopoulos
- Institute of Applied Biosciences, Centre for Research and Technology Hellas, Thessaloniki, Greece
| | - Paolo Ghia
- Università Vita-Salute San Raffaele, Milan, Italy.
- Strategic Research Program on CLL, Division of Experimental Oncology, IRCCS Ospedale San Raffaele, Milan, Italy.
| | - Sarka Pospisilova
- Department of Internal Medicine, Hematology and Oncology, and Institute of Medical Genetics and Genomics, University Hospital Brno and Medical Faculty, Masaryk University, Brno, Czech Republic.
- Central European Institute of Technology, Masaryk University, Brno, Czech Republic.
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34
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Pardo-Palacios FJ, Wang D, Reese F, Diekhans M, Carbonell-Sala S, Williams B, Loveland JE, De María M, Adams MS, Balderrama-Gutierrez G, Behera AK, Gonzalez Martinez JM, Hunt T, Lagarde J, Liang CE, Li H, Meade MJ, Moraga Amador DA, Prjibelski AD, Birol I, Bostan H, Brooks AM, Çelik MH, Chen Y, Du MRM, Felton C, Göke J, Hafezqorani S, Herwig R, Kawaji H, Lee J, Li JL, Lienhard M, Mikheenko A, Mulligan D, Nip KM, Pertea M, Ritchie ME, Sim AD, Tang AD, Wan YK, Wang C, Wong BY, Yang C, Barnes I, Berry AE, Capella-Gutierrez S, Cousineau A, Dhillon N, Fernandez-Gonzalez JM, Ferrández-Peral L, Garcia-Reyero N, Götz S, Hernández-Ferrer C, Kondratova L, Liu T, Martinez-Martin A, Menor C, Mestre-Tomás J, Mudge JM, Panayotova NG, Paniagua A, Repchevsky D, Ren X, Rouchka E, Saint-John B, Sapena E, Sheynkman L, Smith ML, Suner MM, Takahashi H, Youngworth IA, Carninci P, Denslow ND, Guigó R, Hunter ME, Maehr R, Shen Y, Tilgner HU, Wold BJ, Vollmers C, Frankish A, Au KF, Sheynkman GM, Mortazavi A, Conesa A, Brooks AN. Systematic assessment of long-read RNA-seq methods for transcript identification and quantification. Nat Methods 2024; 21:1349-1363. [PMID: 38849569 DOI: 10.1038/s41592-024-02298-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 05/03/2024] [Indexed: 06/09/2024]
Abstract
The Long-read RNA-Seq Genome Annotation Assessment Project Consortium was formed to evaluate the effectiveness of long-read approaches for transcriptome analysis. Using different protocols and sequencing platforms, the consortium generated over 427 million long-read sequences from complementary DNA and direct RNA datasets, encompassing human, mouse and manatee species. Developers utilized these data to address challenges in transcript isoform detection, quantification and de novo transcript detection. The study revealed that libraries with longer, more accurate sequences produce more accurate transcripts than those with increased read depth, whereas greater read depth improved quantification accuracy. In well-annotated genomes, tools based on reference sequences demonstrated the best performance. Incorporating additional orthogonal data and replicate samples is advised when aiming to detect rare and novel transcripts or using reference-free approaches. This collaborative study offers a benchmark for current practices and provides direction for future method development in transcriptome analysis.
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Affiliation(s)
| | - Dingjie Wang
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Fairlie Reese
- Department of Developmental and Cell Biology, University of California, Irvine, Irvine, CA, USA
- Center for Complex Biological Systems, University of California, Irvine, Irvine, CA, USA
| | - Mark Diekhans
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Sílvia Carbonell-Sala
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Brian Williams
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Jane E Loveland
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus Hinxton, Cambridge, UK
| | - Maite De María
- Department of Physiological Sciences, College of Veterinary Medicine, Gainesville, FL, USA
- Cherokee Nation System Solutions, contractor to the US Geological Survey-Wetland and Aquatic Research Center, Gainesville, FL, USA
| | - Matthew S Adams
- Department of Molecular Cell and Developmental Biology, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Gabriela Balderrama-Gutierrez
- Department of Developmental and Cell Biology, University of California, Irvine, Irvine, CA, USA
- Center for Complex Biological Systems, University of California, Irvine, Irvine, CA, USA
| | - Amit K Behera
- Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Jose M Gonzalez Martinez
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus Hinxton, Cambridge, UK
| | - Toby Hunt
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus Hinxton, Cambridge, UK
| | - Julien Lagarde
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
- Flomics Biotech, SL, Barcelona, Spain
| | - Cindy E Liang
- Department of Molecular Cell and Developmental Biology, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Haoran Li
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Marcus Jerryd Meade
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA, USA
| | - David A Moraga Amador
- Interdisciplinary Center for Biotechnology Research, University of Florida, Gainesville, FL, USA
| | - Andrey D Prjibelski
- Department of Computer Science, University of Helsinki, Helsinki, Finland
- Center for Bioinformatics and Algorithmic Biotechnology, Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg, Russia
| | - Inanc Birol
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, British Columbia, Canada
| | - Hamed Bostan
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Ashley M Brooks
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Muhammed Hasan Çelik
- Department of Developmental and Cell Biology, University of California, Irvine, Irvine, CA, USA
- Center for Complex Biological Systems, University of California, Irvine, Irvine, CA, USA
| | - Ying Chen
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
| | - Mei R M Du
- Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia
| | - Colette Felton
- Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Jonathan Göke
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
- Department of Statistics and Data Science, National University of Singapore, Singapore, Singapore
| | - Saber Hafezqorani
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, British Columbia, Canada
| | - Ralf Herwig
- Department Computational Molecular Biology, Max-Planck-Institute for Molecular Genetics, Berlin, Germany
| | - Hideya Kawaji
- Research Center for Genome & Medical Sciences, Tokyo Metropolitan Institute of Medical Science, Tokyo, Japan
| | - Joseph Lee
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
| | - Jian-Liang Li
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Matthias Lienhard
- Department Computational Molecular Biology, Max-Planck-Institute for Molecular Genetics, Berlin, Germany
| | - Alla Mikheenko
- Department of Neuromuscular Diseases, UCL Queen Square Institute of Neurology, London, UK
| | - Dennis Mulligan
- Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Ka Ming Nip
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, British Columbia, Canada
| | - Mihaela Pertea
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA
| | - Matthew E Ritchie
- Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia
- Department of Medical Biology, The University of Melbourne, Parkville, Victoria, Australia
| | - Andre D Sim
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
| | - Alison D Tang
- Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Yuk Kei Wan
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Changqing Wang
- Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia
| | - Brandon Y Wong
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA
| | - Chen Yang
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, British Columbia, Canada
| | - If Barnes
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus Hinxton, Cambridge, UK
| | - Andrew E Berry
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus Hinxton, Cambridge, UK
| | | | - Alyssa Cousineau
- Program in Molecular Medicine, Diabetes Center of Excellence, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Namrita Dhillon
- Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA, USA
| | | | - Luis Ferrández-Peral
- Institute for Integrative Systems Biology, Spanish National Research Council (CSIC), Paterna, Spain
| | - Natàlia Garcia-Reyero
- Energy, Installations & Environment, Office of the Assistant Secretary of Defense, Washington, DC, USA
| | | | | | | | | | | | | | - Jorge Mestre-Tomás
- Institute for Integrative Systems Biology, Spanish National Research Council (CSIC), Paterna, Spain
| | - Jonathan M Mudge
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus Hinxton, Cambridge, UK
| | - Nedka G Panayotova
- Interdisciplinary Center for Biotechnology Research, University of Florida, Gainesville, FL, USA
| | - Alejandro Paniagua
- Institute for Integrative Systems Biology, Spanish National Research Council (CSIC), Paterna, Spain
| | | | - Xingjie Ren
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA
| | - Eric Rouchka
- Department of Biochemistry & Molecular Genetics, University of Louisville, Louisville, KY, USA
| | - Brandon Saint-John
- Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Enrique Sapena
- European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Leon Sheynkman
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA, USA
| | - Melissa Laird Smith
- Department of Biochemistry & Molecular Genetics, University of Louisville, Louisville, KY, USA
| | - Marie-Marthe Suner
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus Hinxton, Cambridge, UK
| | - Hazuki Takahashi
- Center for Integrative Medical Sciences, Laboratory for Transcriptome Technology, RIKEN, Yokohama, Japan
| | | | - Piero Carninci
- Center for Integrative Medical Sciences, Laboratory for Transcriptome Technology, RIKEN, Yokohama, Japan
- Human Technopole, Milano, Italy
| | - Nancy D Denslow
- Department of Physiological Sciences, College of Veterinary Medicine, Gainesville, FL, USA
- Center for Environmental and Human Toxicology, Department of Physiological Sciences, University of Florida, Gainesville, FL, USA
| | - Roderic Guigó
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Margaret E Hunter
- US Geological Survey, Wetland and Aquatic Research Center, Gainesville, FL, USA
| | - Rene Maehr
- Program in Molecular Medicine, Diabetes Center of Excellence, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Yin Shen
- Institute for Human Genetics, Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Hagen U Tilgner
- Brain and Mind Research Institute and Center for Neurogenetics, Weill Cornell Medicine, New York City, NY, USA
| | - Barbara J Wold
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Christopher Vollmers
- Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA, USA.
| | - Adam Frankish
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus Hinxton, Cambridge, UK.
| | - Kin Fai Au
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA.
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
| | - Gloria M Sheynkman
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA, USA.
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA.
- UVA Cancer Center, University of Virginia, Charlottesville, VA, USA.
| | - Ali Mortazavi
- Department of Developmental and Cell Biology, University of California, Irvine, Irvine, CA, USA.
- Center for Complex Biological Systems, University of California, Irvine, Irvine, CA, USA.
| | - Ana Conesa
- Institute for Integrative Systems Biology, Spanish National Research Council (CSIC), Paterna, Spain.
- Microbiology and Cell Science Department, Institute for Food and Agricultural Sciences, University of Florida, Gainesville, FL, USA.
| | - Angela N Brooks
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, USA.
- Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA, USA.
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Lim LQJ, Adler L, Hajaj E, Soria LR, Perry RBT, Darzi N, Brody R, Furth N, Lichtenstein M, Bab-Dinitz E, Porat Z, Melman T, Brandis A, Malitsky S, Itkin M, Aylon Y, Ben-Dor S, Orr I, Pri-Or A, Seger R, Shaul Y, Ruppin E, Oren M, Perez M, Meier J, Brunetti-Pierri N, Shema E, Ulitsky I, Erez A. ASS1 metabolically contributes to the nuclear and cytosolic p53-mediated DNA damage response. Nat Metab 2024; 6:1294-1309. [PMID: 38858597 PMCID: PMC11272581 DOI: 10.1038/s42255-024-01060-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/23/2023] [Accepted: 04/30/2024] [Indexed: 06/12/2024]
Abstract
Downregulation of the urea cycle enzyme argininosuccinate synthase (ASS1) in multiple tumors is associated with a poor prognosis partly because of the metabolic diversion of cytosolic aspartate for pyrimidine synthesis, supporting proliferation and mutagenesis owing to nucleotide imbalance. Here, we find that prolonged loss of ASS1 promotes DNA damage in colon cancer cells and fibroblasts from subjects with citrullinemia type I. Following acute induction of DNA damage with doxorubicin, ASS1 expression is elevated in the cytosol and the nucleus with at least a partial dependency on p53; ASS1 metabolically restrains cell cycle progression in the cytosol by restricting nucleotide synthesis. In the nucleus, ASS1 and ASL generate fumarate for the succination of SMARCC1, destabilizing the chromatin-remodeling complex SMARCC1-SNF5 to decrease gene transcription, specifically in a subset of the p53-regulated cell cycle genes. Thus, following DNA damage, ASS1 is part of the p53 network that pauses cell cycle progression, enabling genome maintenance and survival. Loss of ASS1 contributes to DNA damage and promotes cell cycle progression, likely contributing to cancer mutagenesis and, hence, adaptability potential.
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Affiliation(s)
- Lisha Qiu Jin Lim
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Lital Adler
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Emma Hajaj
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
- Department of Medicine D, Beilinson Hospital, Petah Tikva, Israel
| | - Leandro R Soria
- Telethon Institute of Genetics and Medicine, Pozzuoli, Italy
| | - Rotem Ben-Tov Perry
- Department of Immunology and Regenerative Biology, Weizmann Institute of Science, Rehovot, Israel
- Department of Molecular Neuroscience, Weizmann Institute of Science, Rehovot, Israel
| | - Naama Darzi
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Ruchama Brody
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Noa Furth
- Department of Immunology and Regenerative Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Michal Lichtenstein
- Department of Biochemistry and Molecular Biology, The Institute for Medical Research Israel-Canada, Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Elizabeta Bab-Dinitz
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Ziv Porat
- Department of Life Sciences Core Facilities, Weizmann Institute of Science, Rehovot, Israel
| | - Tevie Melman
- Department of Life Sciences Core Facilities, Weizmann Institute of Science, Rehovot, Israel
| | - Alexander Brandis
- Department of Life Sciences Core Facilities, Weizmann Institute of Science, Rehovot, Israel
| | - Sergey Malitsky
- Department of Life Sciences Core Facilities, Weizmann Institute of Science, Rehovot, Israel
| | - Maxim Itkin
- Department of Life Sciences Core Facilities, Weizmann Institute of Science, Rehovot, Israel
| | - Yael Aylon
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Shifra Ben-Dor
- Department of Life Sciences Core Facilities, Weizmann Institute of Science, Rehovot, Israel
| | - Irit Orr
- Department of Life Sciences Core Facilities, Weizmann Institute of Science, Rehovot, Israel
| | - Amir Pri-Or
- The De Botton Protein Profiling Institute of the Nancy and Stephen Grand Israel National Center for Personalized Medicine, Weizmann Institute of Science, Rehovot, Israel
| | - Rony Seger
- Department of Immunology and Regenerative Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Yoav Shaul
- Department of Biochemistry and Molecular Biology, The Institute for Medical Research Israel-Canada, Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Eytan Ruppin
- Cancer Data Science Lab, Center for Cancer Research, National Cancer Institute, National Institute of Health, Bethesda, MD, USA
| | - Moshe Oren
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Minervo Perez
- Cancer Data Science Lab, Center for Cancer Research, National Cancer Institute, National Institute of Health, Bethesda, MD, USA
| | - Jordan Meier
- Cancer Data Science Lab, Center for Cancer Research, National Cancer Institute, National Institute of Health, Bethesda, MD, USA
| | - Nicola Brunetti-Pierri
- Telethon Institute of Genetics and Medicine, Pozzuoli, Italy
- Department of Translational Medicine, Medical Genetics, University of Naples Federico II, Naples, Italy
- Scuola Superiore Meridionale (SSM, School of Advanced Studies), Genomics and Experimental Medicine Program, University of Naples Federico II, Naples, Italy
| | - Efrat Shema
- Department of Immunology and Regenerative Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Igor Ulitsky
- Department of Immunology and Regenerative Biology, Weizmann Institute of Science, Rehovot, Israel
- Department of Molecular Neuroscience, Weizmann Institute of Science, Rehovot, Israel
| | - Ayelet Erez
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel.
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36
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Guha S, Reddi HV, Aarabi M, DiStefano M, Wakeling E, Dungan JS, Gregg AR. Laboratory testing for preconception/prenatal carrier screening: A technical standard of the American College of Medical Genetics and Genomics (ACMG). Genet Med 2024; 26:101137. [PMID: 38814327 DOI: 10.1016/j.gim.2024.101137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Accepted: 04/01/2024] [Indexed: 05/31/2024] Open
Abstract
Carrier screening has historically assessed a relatively small number of autosomal recessive and X-linked conditions selected based on frequency in a specific subpopulation and association with severe morbidity or mortality. Advances in genomic technologies enable simultaneous screening of individuals for several conditions. The American College of Medical Genetics and Genomics recently published a clinical practice resource that presents a framework when offering screening for autosomal recessive and X-linked conditions during pregnancy and preconception and recommends a tier-based approach when considering the number of conditions to screen for and their frequency within the US population in general. This laboratory technical standard aims to complement the practice resource and to put forth considerations for clinical laboratories and clinicians who offer preconception/prenatal carrier screening.
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Affiliation(s)
| | - Honey V Reddi
- Department of Pathology and Laboratory Medicine, Medical College of Wisconsin, Milwaukee, WI
| | - Mahmoud Aarabi
- UPMC Medical Genetics and Genomics Laboratories, UPMC Magee-Womens Hospital, Pittsburgh, PA; Departments of Pathology and Obstetrics, Gynecology, and Reproductive Sciences, University of Pittsburgh School of Medicine, Pittsburgh, PA
| | | | | | - Jeffrey S Dungan
- Department of Obstetrics and Gynecology, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Anthony R Gregg
- Department of Obstetrics and Gynecology, Prisma Health, Columbia, SC
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Waters AJ, Brendler-Spaeth T, Smith D, Offord V, Tan HK, Zhao Y, Obolenski S, Nielsen M, van Doorn R, Murphy JE, Gupta P, Rowlands CF, Hanson H, Delage E, Thomas M, Radford EJ, Gerety SS, Turnbull C, Perry JRB, Hurles ME, Adams DJ. Saturation genome editing of BAP1 functionally classifies somatic and germline variants. Nat Genet 2024; 56:1434-1445. [PMID: 38969833 PMCID: PMC11250367 DOI: 10.1038/s41588-024-01799-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 05/14/2024] [Indexed: 07/07/2024]
Abstract
Many variants that we inherit from our parents or acquire de novo or somatically are rare, limiting the precision with which we can associate them with disease. We performed exhaustive saturation genome editing (SGE) of BAP1, the disruption of which is linked to tumorigenesis and altered neurodevelopment. We experimentally characterized 18,108 unique variants, of which 6,196 were found to have abnormal functions, and then used these data to evaluate phenotypic associations in the UK Biobank. We also characterized variants in a large population-ascertained tumor collection, in cancer pedigrees and ClinVar, and explored the behavior of cancer-associated variants compared to that of variants linked to neurodevelopmental phenotypes. Our analyses demonstrated that disruptive germline BAP1 variants were significantly associated with higher circulating levels of the mitogen IGF-1, suggesting a possible pathological mechanism and therapeutic target. Furthermore, we built a variant classifier with >98% sensitivity and specificity and quantify evidence strengths to aid precision variant interpretation.
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Affiliation(s)
| | | | | | | | | | - Yajie Zhao
- Metabolic Research Laboratory, Wellcome-MRC Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | | | - Maartje Nielsen
- Department of Clinical Genetics, Leiden University Medical Center, Leiden, the Netherlands
| | - Remco van Doorn
- Department of Dermatology, Leiden University Medical Center, Leiden, the Netherlands
| | | | | | - Charlie F Rowlands
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK
| | - Helen Hanson
- Department of Clinical Genetics, Royal Devon University Healthcare NHS Foundation Trust, Exeter, UK
- Faculty of Health and Life Sciences, University of Exeter, Exeter, UK
| | | | | | - Elizabeth J Radford
- Wellcome Sanger Institute, Hinxton, UK
- Department of Paediatrics, University of Cambridge, Cambridge, UK
| | | | - Clare Turnbull
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK
- National Cancer Registration and Analysis Service, NHS England, London, UK
- Cancer Genetics Unit, The Royal Marsden NHS Foundation Trust, London, UK
| | - John R B Perry
- Metabolic Research Laboratory, Wellcome-MRC Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
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Gelmi MC, de Ru AH, van Veelen PA, Tjokrodirijo RTN, Stern MH, Houy A, Verdijk RM, Vu THK, Ksander BR, Vaarwater J, Kilic E, Brosens E, Jager MJ. Protein and mRNA Expression in Uveal Melanoma Cell Lines Are Related to GNA and BAP1 Mutation Status. Invest Ophthalmol Vis Sci 2024; 65:37. [PMID: 39042403 PMCID: PMC11268447 DOI: 10.1167/iovs.65.8.37] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2024] [Accepted: 06/26/2024] [Indexed: 07/24/2024] Open
Abstract
Purpose Cell lines are being used in preclinical uveal melanoma (UM) research. Because not all cell lines harbor typical GNAQ or GNA11 hotspot mutations, we aimed at better classifying them and determining whether we could find genetic causes to explain the protein and mRNA expression profiles of the cell lines. Methods We studied protein and mRNA expression of 14 UM cell lines and determined the presence of single nucleotide variants and small insertions and deletions with next-generation sequencing and copy number alterations with a single nucleotide polymorphism array. The lists of differentially expressed proteins and genes were merged, and shared lists were created, keeping only terms with concordant mRNA and protein expression. Enrichment analyses were performed on the shared lists. Results Cell lines Mel285 and Mel290 are separate from GNA-mutated cell lines and show downregulation of melanosome-related markers. Both lack typical UM mutations but each harbors four putatively deleterious variants in CTNNB1, PPP1R10, LIMCH1, and APC in Mel285 and ARID1A, PPP1R10, SPG11, and RNF43 in Mel290. The upregulated terms in Mel285 and Mel290 did not point to a convincing alternative origin. Mel285 shows loss of chromosomes 1p, 3p, partial 3q, 6, and partial 8p, whereas Mel290 shows loss of 1p and 6. Expression in the other 12 cell lines was related to BAP1 expression. Conclusions Although Mel285 and Mel290 have copy number alterations that fit UM, multi-omics analyses show that they belong to a separate group compared to the other analyzed UM cell lines. Therefore, they may not be representative models to test potential therapeutic targets for UM.
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Affiliation(s)
- Maria Chiara Gelmi
- Department of Ophthalmology, Leiden University Medical Center, Leiden, The Netherlands
| | - Arnoud H. de Ru
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, The Netherlands
| | - Peter A. van Veelen
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, The Netherlands
| | | | - Marc-Henri Stern
- Inserm U830, DNA Repair and Uveal Melanoma (D.R.U.M.), Institut Curie, PSL Research University, Paris, France
| | - Alexandre Houy
- Inserm U830, DNA Repair and Uveal Melanoma (D.R.U.M.), Institut Curie, PSL Research University, Paris, France
| | - Robert M. Verdijk
- Department of Pathology, Ophthalmic Pathology Section, Erasmus MC, Rotterdam, The Netherlands
- Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands
| | - T. H. Khanh Vu
- Department of Ophthalmology, Leiden University Medical Center, Leiden, The Netherlands
| | - Bruce R. Ksander
- Schepens Eye Research Institute, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts, United States
| | - Jolanda Vaarwater
- Department of Clinical Genetics, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
- Department of Ophthalmology, Erasmus MC, Rotterdam, The Netherlands
| | - Emine Kilic
- Department of Ophthalmology, Erasmus MC, Rotterdam, The Netherlands
| | - Erwin Brosens
- Department of Clinical Genetics, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Martine J. Jager
- Department of Ophthalmology, Leiden University Medical Center, Leiden, The Netherlands
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Dvorak P, Hlavac V, Hanicinec V, Rao BH, Soucek P. Genes divided according to the relative position of the longest intron show increased representation in different KEGG pathways. BMC Genomics 2024; 25:649. [PMID: 38943073 PMCID: PMC11214234 DOI: 10.1186/s12864-024-10558-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 06/24/2024] [Indexed: 07/01/2024] Open
Abstract
Despite the fact that introns mean an energy and time burden for eukaryotic cells, they play an irreplaceable role in the diversification and regulation of protein production. As a common feature of eukaryotic genomes, it has been reported that in protein-coding genes, the longest intron is usually one of the first introns. The goal of our work was to find a possible difference in the biological function of genes that fulfill this common feature compared to genes that do not. Data on the lengths of all introns in genes were extracted from the genomes of six vertebrates (human, mouse, koala, chicken, zebrafish and fugu) and two other model organisms (nematode worm and arabidopsis). We showed that more than 40% of protein-coding genes have the relative position of the longest intron located in the second or third tertile of all introns. Genes divided according to the relative position of the longest intron were found to be significantly increased in different KEGG pathways. Genes with the longest intron in the first tertile predominate in a range of pathways for amino acid and lipid metabolism, various signaling, cell junctions or ABC transporters. Genes with the longest intron in the second or third tertile show increased representation in pathways associated with the formation and function of the spliceosome and ribosomes. In the two groups of genes defined in this way, we further demonstrated the difference in the length of the longest introns and the distribution of their absolute positions. We also pointed out other characteristics, namely the positive correlation between the length of the longest intron and the sum of the lengths of all other introns in the gene and the preservation of the exact same absolute and relative position of the longest intron between orthologous genes.
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Affiliation(s)
- Pavel Dvorak
- Department of Biology, Faculty of Medicine in Pilsen, Charles University, Alej Svobody 76, 32300, Pilsen, Czech Republic.
- Biomedical Center, Faculty of Medicine in Pilsen, Charles University, Alej Svobody 76, 32300, Pilsen, Czech Republic.
- Institute of Medical Genetics, University Hospital Pilsen, Dr. Edvarda Benese 13, 30599, Pilsen, Czech Republic.
| | - Viktor Hlavac
- Biomedical Center, Faculty of Medicine in Pilsen, Charles University, Alej Svobody 76, 32300, Pilsen, Czech Republic
- Toxicogenomics Unit, National Institute of Public Health, Srobarova 48, 10042, Prague, Czech Republic
| | - Vojtech Hanicinec
- Biomedical Center, Faculty of Medicine in Pilsen, Charles University, Alej Svobody 76, 32300, Pilsen, Czech Republic
| | - Bhavana Hemantha Rao
- Biomedical Center, Faculty of Medicine in Pilsen, Charles University, Alej Svobody 76, 32300, Pilsen, Czech Republic
| | - Pavel Soucek
- Biomedical Center, Faculty of Medicine in Pilsen, Charles University, Alej Svobody 76, 32300, Pilsen, Czech Republic
- Toxicogenomics Unit, National Institute of Public Health, Srobarova 48, 10042, Prague, Czech Republic
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40
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Deguchi Y, Kikutake C, Suyama M. Subtype-specific alternative splicing events in breast cancer identified by large-scale data analysis. Sci Rep 2024; 14:14158. [PMID: 38898123 PMCID: PMC11187070 DOI: 10.1038/s41598-024-65035-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Accepted: 06/17/2024] [Indexed: 06/21/2024] Open
Abstract
Genome analysis in cancer has focused mainly on elucidating the function and regulatory mechanisms of genes that exhibit differential expression or mutation in cancer samples compared to normal samples. Recently, transcriptome analysis revealed that abnormal splicing events in cancer samples could contribute to cancer pathogenesis. Moreover, splicing variants in cancer reportedly generate diverse cancer antigens. Although abnormal splicing events are expected to be potential targets in cancer immunotherapy, the exploration of such targets and their biological significance in cancer have not been fully understood. In this study, to explore subtype-specific alternative splicing events, we conducted a comprehensive analysis of splicing events for each breast cancer subtype using large-scale splicing data derived from The Cancer Genome Atlas and found subtype-specific alternative splicing patterns. Analyses indicated that genes that produce subtype-specific alternative splicing events are potential novel targets for immunotherapy against breast cancer. The subtype-specific alternative splicing events identified in this study, which were not identified by mutation or differential expression analysis, bring new significance to previously overlooked splicing events.
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Affiliation(s)
- Yui Deguchi
- Division of Bioinformatics, Medical Institute of Bioregulation, Kyushu University, Fukuoka, 812-8582, Japan
| | - Chie Kikutake
- Division of Bioinformatics, Medical Institute of Bioregulation, Kyushu University, Fukuoka, 812-8582, Japan
| | - Mikita Suyama
- Division of Bioinformatics, Medical Institute of Bioregulation, Kyushu University, Fukuoka, 812-8582, Japan.
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Insana G, Martin MJ, Pearson WR. Improved selection of canonical proteins for reference proteomes. NAR Genom Bioinform 2024; 6:lqae066. [PMID: 38863529 PMCID: PMC11165316 DOI: 10.1093/nargab/lqae066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Revised: 05/04/2024] [Accepted: 05/23/2024] [Indexed: 06/13/2024] Open
Abstract
The 'canonical' protein sets distributed by UniProt are widely used for similarity searching, and functional and structural annotation. For many investigators, canonical sequences are the only version of a protein examined. However, higher eukaryotes often encode multiple isoforms of a protein from a single gene. For unreviewed (UniProtKB/TrEMBL) protein sequences, the longest sequence in a Gene-Centric group is chosen as canonical. This choice can create inconsistencies, selecting >95% identical orthologs with dramatically different lengths, which is biologically unlikely. We describe the ortho2tree pipeline, which examines Reference Proteome canonical and isoform sequences from sets of orthologous proteins, builds multiple alignments, constructs gap-distance trees, and identifies low-cost clades of isoforms with similar lengths. After examining 140 000 proteins from eight mammals in UniProtKB release 2022_05, ortho2tree proposed 7804 canonical changes for release 2023_01, while confirming 53 434 canonicals. Gap distributions for isoforms selected by ortho2tree are similar to those in bacterial and yeast alignments, organisms unaffected by isoform selection, suggesting ortho2tree canonicals more accurately reflect genuine biological variation. 82% of ortho2tree proposed-changes agreed with MANE; for confirmed canonicals, 92% agreed with MANE. Ortho2tree can improve canonical assignment among orthologous sequences that are >60% identical, a group that includes vertebrates and higher plants.
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Affiliation(s)
- Giuseppe Insana
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, UK
| | - Maria J Martin
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, UK
| | - William R Pearson
- Department of Biochemistry and Molecular Genetics, University of Virginia School of Medicine, Charlottesville, VA 22908, USA
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Chen X, Fansler MM, Janjoš U, Ule J, Mayr C. The FXR1 network acts as signaling scaffold for actomyosin remodeling. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.11.05.565677. [PMID: 37961296 PMCID: PMC10635158 DOI: 10.1101/2023.11.05.565677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
It is currently not known whether mRNAs fulfill structural roles in the cytoplasm. Here, we report the FXR1 network, an mRNA-protein (mRNP) network present throughout the cytoplasm, formed by FXR1-mediated packaging of exceptionally long mRNAs. These mRNAs serve as underlying condensate scaffold and concentrate FXR1 molecules. The FXR1 network contains multiple protein binding sites and functions as a signaling scaffold for interacting proteins. We show that it is necessary for RhoA signaling-induced actomyosin reorganization to provide spatial proximity between kinases and their substrates. Point mutations in FXR1, found in its homolog FMR1, where they cause Fragile X syndrome, disrupt the network. FXR1 network disruption prevents actomyosin remodeling-an essential and ubiquitous process for the regulation of cell shape, migration, and synaptic function. These findings uncover a structural role for cytoplasmic mRNA and show how the FXR1 RNA-binding protein as part of the FXR1 network acts as organizer of signaling reactions.
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Affiliation(s)
- Xiuzhen Chen
- Cancer Biology and Genetics Program, Sloan Kettering Institute, New York, NY 10065, USA
| | - Mervin M. Fansler
- Cancer Biology and Genetics Program, Sloan Kettering Institute, New York, NY 10065, USA
| | - Urška Janjoš
- National Institute of Chemistry, Hajdrihova 19, 1001 Ljubljana, Slovenia
- Biosciences PhD Program, Biotechnical Faculty, University of Ljubljana, Ljubljana, Slovenia
| | - Jernej Ule
- National Institute of Chemistry, Hajdrihova 19, 1001 Ljubljana, Slovenia
- UK Dementia Research Institute at King’s College London, London, SE5 9NU, UK
| | - Christine Mayr
- Cancer Biology and Genetics Program, Sloan Kettering Institute, New York, NY 10065, USA
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43
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Hazell Pickering S, Abdelhalim M, Collas P, Briand N. Alternative isoform expression of key thermogenic genes in human beige adipocytes. Front Endocrinol (Lausanne) 2024; 15:1395750. [PMID: 38859907 PMCID: PMC11163967 DOI: 10.3389/fendo.2024.1395750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Accepted: 05/07/2024] [Indexed: 06/12/2024] Open
Abstract
Background The beneficial effect of thermogenic adipocytes in maintaining body weight and protecting against metabolic disorders has raised interest in understanding the regulatory mechanisms defining white and beige adipocyte identity. Although alternative splicing has been shown to propagate adipose browning signals in mice, this has yet to be thoroughly investigated in human adipocytes. Methods We performed parallel white and beige adipogenic differentiation using primary adipose stem cells from 6 unrelated healthy subjects and assessed differential gene and isoform expression in mature adipocytes by RNA sequencing. Results We find 777 exon junctions with robust differential usage between white and beige adipocytes in all 6 subjects, mapping to 562 genes. Importantly, only 10% of these differentially spliced genes are also differentially expressed, indicating that alternative splicing constitutes an additional layer of gene expression regulation during beige adipocyte differentiation. Functional classification of alternative isoforms points to a gain of function for key thermogenic transcription factors such as PPARG and CITED1, and enzymes such as PEMT, or LPIN1. We find that a large majority of the splice variants arise from differential TSS usage, with beige-specific TSSs being enriched for PPARγ and MED1 binding compared to white-specific TSSs. Finally, we validate beige specific isoform expression at the protein level for two thermogenic regulators, PPARγ and PEMT. Discussion These results suggest that differential isoform expression through alternative TSS usage is an important regulatory mechanism for human adipocyte thermogenic specification.
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Affiliation(s)
- Sarah Hazell Pickering
- Department of Molecular Medicine, Institute of Basic Medical Sciences, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Mohamed Abdelhalim
- Department of Molecular Medicine, Institute of Basic Medical Sciences, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Philippe Collas
- Department of Molecular Medicine, Institute of Basic Medical Sciences, Faculty of Medicine, University of Oslo, Oslo, Norway
- Department of Immunology and Transfusion Medicine, Oslo University Hospital, Oslo, Norway
| | - Nolwenn Briand
- Department of Molecular Medicine, Institute of Basic Medical Sciences, Faculty of Medicine, University of Oslo, Oslo, Norway
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Li G, Wu J, Wang X. Predicting functional UTR variants by integrating region-specific features. Brief Bioinform 2024; 25:bbae248. [PMID: 38783704 PMCID: PMC11116830 DOI: 10.1093/bib/bbae248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Revised: 03/30/2024] [Accepted: 05/08/2024] [Indexed: 05/25/2024] Open
Abstract
The untranslated region (UTR) of messenger ribonucleic acid (mRNA), including the 5'UTR and 3'UTR, plays a critical role in regulating gene expression and translation. Variants within the UTR can lead to changes associated with human traits and diseases; however, computational prediction of UTR variant effect is challenging. Current noncoding variant prediction mainly focuses on the promoters and enhancers, neglecting the unique sequence of the UTR and thereby limiting their predictive accuracy. In this study, using consolidated datasets of UTR variants from disease databases and large-scale experimental data, we systematically analyzed more than 50 region-specific features of UTR, including functional elements, secondary structure, sequence composition and site conservation. Our analysis reveals that certain features, such as C/G-related sequence composition in 5'UTR and A/T-related sequence composition in 3'UTR, effectively differentiate between nonfunctional and functional variant sets, unveiling potential sequence determinants of functional UTR variants. Leveraging these insights, we developed two classification models to predict functional UTR variants using machine learning, achieving an area under the curve (AUC) value of 0.94 for 5'UTR and 0.85 for 3'UTR, outperforming all existing methods. Our models will be valuable for enhancing clinical interpretation of genetic variants, facilitating the prediction and management of disease risk.
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Affiliation(s)
- Guangyu Li
- State Key Laboratory of Common Mechanism Research for Major Diseases; Center for bioinformatics, National Infrastructures for Translational Medicine, Institute of Clinical Medicine and Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 1 Shuai Fu Yuan, Dongcheng District, Beijing 100005, China
| | - Jiayu Wu
- State Key Laboratory of Common Mechanism Research for Major Diseases; Center for bioinformatics, National Infrastructures for Translational Medicine, Institute of Clinical Medicine and Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 1 Shuai Fu Yuan, Dongcheng District, Beijing 100005, China
| | - Xiaoyue Wang
- State Key Laboratory of Common Mechanism Research for Major Diseases; Center for bioinformatics, National Infrastructures for Translational Medicine, Institute of Clinical Medicine and Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 1 Shuai Fu Yuan, Dongcheng District, Beijing 100005, China
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45
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Abbott M, Angione K, Forbes E, Stoecker M, Saenz M, Neul JL, Marsh ED, Skinner SA, Percy AK, Benke TA. Rett syndrome diagnostic odyssey: Limitations of NextGen sequencing. Am J Med Genet A 2024:e63725. [PMID: 38775384 DOI: 10.1002/ajmg.a.63725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 05/09/2024] [Indexed: 05/30/2024]
Abstract
Typical (or classic) Rett syndrome (RTT) is an X-linked neurodevelopmental disorder characterized by a period of regression, partial or complete loss of purposeful hand movements, and acquired speech, impaired gait, and stereotyped hand movements. In over 95% of typical RTT, a pathogenic variant is found in the methyl-CPG binding protein 2 gene (MECP2). Here, we describe a young woman with clinically diagnosed typical RTT syndrome who lacked a genetic diagnosis despite 20 years of investigation and multiple rounds of sequencing the MECP2 gene. Recently, additional genetic testing using next-generation sequencing was completed, which revealed a partial insertion of the BCL11A gene within exon 4 of MECP2, resulting in a small deletion in MECP2, causing likely disruption of MeCP2 function due to a frameshift. This case demonstrates the ever-changing limitations of genetic testing, as well as the importance of continual pursuit of a diagnosis as technologies improve and are more widely utilized.
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Affiliation(s)
- Megan Abbott
- Department of Child Neurology, Children's Hospital Colorado, Aurora, Colorado, USA
- School of Medicine, University of Colorado Denver | Anschutz Medical Campus, Aurora, Colorado, USA
| | - Katie Angione
- Department of Child Neurology, Children's Hospital Colorado, Aurora, Colorado, USA
- School of Medicine, University of Colorado Denver | Anschutz Medical Campus, Aurora, Colorado, USA
| | - Emily Forbes
- School of Medicine, University of Colorado Denver | Anschutz Medical Campus, Aurora, Colorado, USA
| | | | - Margarita Saenz
- Department of Child Neurology, Children's Hospital Colorado, Aurora, Colorado, USA
- School of Medicine, University of Colorado Denver | Anschutz Medical Campus, Aurora, Colorado, USA
| | - Jeffrey L Neul
- Departments of Pediatrics and Pharmacology, Vanderbilt Kennedy Center, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Eric D Marsh
- Division of Neurology, Children's Hospital of Philadelphia, Departments of Neurology and Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | | | - Alan K Percy
- University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Tim A Benke
- Department of Child Neurology, Children's Hospital Colorado, Aurora, Colorado, USA
- School of Medicine, University of Colorado Denver | Anschutz Medical Campus, Aurora, Colorado, USA
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46
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Forrest IS, Duffy Á, Park JK, Vy HMT, Pasquale LR, Nadkarni GN, Cho JH, Do R. Genome-first evaluation with exome sequence and clinical data uncovers underdiagnosed genetic disorders in a large healthcare system. Cell Rep Med 2024; 5:101518. [PMID: 38642551 PMCID: PMC11148562 DOI: 10.1016/j.xcrm.2024.101518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 05/01/2023] [Accepted: 03/26/2024] [Indexed: 04/22/2024]
Abstract
Population-based genomic screening may help diagnose individuals with disease-risk variants. Here, we perform a genome-first evaluation for nine disorders in 29,039 participants with linked exome sequences and electronic health records (EHRs). We identify 614 individuals with 303 pathogenic/likely pathogenic or predicted loss-of-function (P/LP/LoF) variants, yielding 644 observations; 487 observations (76%) lack a corresponding clinical diagnosis in the EHR. Upon further investigation, 75 clinically undiagnosed observations (15%) have evidence of symptomatic untreated disease, including familial hypercholesterolemia (3 of 6 [50%] undiagnosed observations with disease evidence) and breast cancer (23 of 106 [22%]). These genetic findings enable targeted phenotyping that reveals new diagnoses in previously undiagnosed individuals. Disease yield is greater with variants in penetrant genes for which disease is observed in carriers in an independent cohort. The prevalence of P/LP/LoF variants exceeds that of clinical diagnoses, and some clinically undiagnosed carriers are discovered to have disease. These results highlight the potential of population-based genomic screening.
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Affiliation(s)
- Iain S Forrest
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Medical Scientist Training Program, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Áine Duffy
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Joshua K Park
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Medical Scientist Training Program, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Ha My T Vy
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Center for Genomic Data Analytics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Louis R Pasquale
- Department of Ophthalmology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Eye and Vision Research Institute, New York Eye and Ear Infirmary of Mount Sinai, New York, NY 10003, USA
| | - Girish N Nadkarni
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Division of Data-driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Judy H Cho
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Ron Do
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Center for Genomic Data Analytics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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47
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Chao KH, Heinz JM, Hoh C, Mao A, Shumate A, Pertea M, Salzberg SL. Combining DNA and protein alignments to improve genome annotation with LiftOn. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.16.593026. [PMID: 38798552 PMCID: PMC11118573 DOI: 10.1101/2024.05.16.593026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
As the number and variety of assembled genomes continues to grow, the number of annotated genomes is falling behind, particularly for eukaryotes. DNA-based mapping tools help to address this challenge, but they are only able to transfer annotation between closely-related species. Here we introduce LiftOn, a homology-based software tool that integrates DNA and protein alignments to enhance the accuracy of genome-scale annotation and to allow mapping between relatively distant species. LiftOn's protein-centric algorithm considers both types of alignments, chooses optimal open reading frames, resolves overlapping gene loci, and finds additional gene copies where they exist. LiftOn can reliably transfer annotation between genomes representing members of the same species, as we demonstrate on human, mouse, honey bee, rice, and Arabidopsis thaliana. It can further map annotation effectively across species pairs as far apart as mouse and rat or Drosophila melanogaster and D. erecta.
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Affiliation(s)
- Kuan-Hao Chao
- Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Jakob M. Heinz
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
| | - Celine Hoh
- Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Alan Mao
- Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Alaina Shumate
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Mihaela Pertea
- Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Steven L Salzberg
- Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Biostatistics, Johns Hopkins University, Baltimore, MD 21211, USA
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48
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Minkin I, Salzberg SL. CONSERVATION ASSESSMENT OF HUMAN SPLICE SITE ANNOTATION BASED ON A 470-GENOME ALIGNMENT. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.12.01.569581. [PMID: 38076842 PMCID: PMC10705407 DOI: 10.1101/2023.12.01.569581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/17/2023]
Abstract
Despite many improvements over the years, the annotation of the human genome remains imperfect, and different annotations of the human reference genome sometimes contradict one another. The use of evolutionarily conserved sequences provides a strategy for selecting a high-confidence subset of the annotation that is more likely to be related to biological functions, and the rapidly growing number of genomes from other species increases its power. Using the latest whole genome alignment, we found that splice sites from protein-coding genes in the high-quality MANE annotation are consistently conserved across more than 400 species. We also studied splice sites from the RefSeq, GENCODE, and CHESS databases that are not present in MANE. We trained a logistic regression classifier to distinguish between the conservation exhibited by sites from MANE versus sites chosen randomly from neutrally evolving sequence. We found that splice sites classified by our model as conserved have lower SNP rates and better transcriptomic support. We then computed a subset of transcripts only using either "conserved" splice sites or ones from MANE. This subset is enriched in high-confidence transcripts of the major gene catalogs that appear to be under purifying selection and are more likely to be correct and functionally relevant.
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Affiliation(s)
- Ilia Minkin
- Department of Biomedical Engineering, Center for Computational Biology, Johns Hopkins University, Baltimore, MD 21211, USA
| | - Steven L Salzberg
- Department of Biomedical Engineering, Center for Computational Biology, Department of Computer Science, Department of Biostatistics, Johns Hopkins University, Baltimore, MD 21211, USA
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49
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Hoh C, Salzberg SL. Discovering Intron Gain Events in Humans through Large-Scale Evolutionary Comparisons. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.02.592247. [PMID: 38746259 PMCID: PMC11092651 DOI: 10.1101/2024.05.02.592247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
The rapid growth in the number of sequenced genomes makes it possible to search for the appearance of entirely new introns in the human lineage. In this study, we compared the genomic sequences for 19,120 human protein-coding genes to a collection of 3493 vertebrate genomes, mapping the patterns of intron alignments onto a phylogenetic tree. This mapping allowed us to trace many intron gain events to precise locations in the tree, corresponding to distinct points in evolutionary history. We discovered 584 intron gain events, all of them relatively recent, in 514 distinct human genes. Among these events, we explored the hypothesis that intronization was the mechanism responsible for intron gain. Intronization events were identified by locating instances where human introns correspond to exonic sequences in homologous vertebrate genes. Although apparently rare, we found three compelling cases of intronization, and for each of those we compared the human protein sequence and structure to homologous genes that lack the introns.
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Affiliation(s)
- Celine Hoh
- Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD 21211, USA
| | - Steven L Salzberg
- Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD 21211, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21211, USA
- Department of Biostatistics, Johns Hopkins University, Baltimore, MD 21205, USA
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50
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Ryu J, Barkal S, Yu T, Jankowiak M, Zhou Y, Francoeur M, Phan QV, Li Z, Tognon M, Brown L, Love MI, Bhat V, Lettre G, Ascher DB, Cassa CA, Sherwood RI, Pinello L. Joint genotypic and phenotypic outcome modeling improves base editing variant effect quantification. Nat Genet 2024; 56:925-937. [PMID: 38658794 DOI: 10.1038/s41588-024-01726-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 03/21/2024] [Indexed: 04/26/2024]
Abstract
CRISPR base editing screens enable analysis of disease-associated variants at scale; however, variable efficiency and precision confounds the assessment of variant-induced phenotypes. Here, we provide an integrated experimental and computational pipeline that improves estimation of variant effects in base editing screens. We use a reporter construct to measure guide RNA (gRNA) editing outcomes alongside their phenotypic consequences and introduce base editor screen analysis with activity normalization (BEAN), a Bayesian network that uses per-guide editing outcomes provided by the reporter and target site chromatin accessibility to estimate variant impacts. BEAN outperforms existing tools in variant effect quantification. We use BEAN to pinpoint common regulatory variants that alter low-density lipoprotein (LDL) uptake, implicating previously unreported genes. Additionally, through saturation base editing of LDLR, we accurately quantify missense variant pathogenicity that is consistent with measurements in UK Biobank patients and identify underlying structural mechanisms. This work provides a widely applicable approach to improve the power of base editing screens for disease-associated variant characterization.
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Affiliation(s)
- Jayoung Ryu
- Molecular Pathology Unit, Krantz Family Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Gene Regulation Observatory, The Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Sam Barkal
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Tian Yu
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Martin Jankowiak
- Gene Regulation Observatory, The Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Yunzhuo Zhou
- School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane, Queensland, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Matthew Francoeur
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Quang Vinh Phan
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Zhijian Li
- Molecular Pathology Unit, Krantz Family Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA
- Gene Regulation Observatory, The Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Manuel Tognon
- Molecular Pathology Unit, Krantz Family Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA
- Gene Regulation Observatory, The Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Computer Science Department, University of Verona, Verona, Italy
| | - Lara Brown
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Michael I Love
- Department of Genetics, Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Vineel Bhat
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Guillaume Lettre
- Montreal Heart Institute, Montréal, Quebec, Canada
- Faculté de Médecine, Université de Montréal, Montréal, Quebec, Canada
| | - David B Ascher
- School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane, Queensland, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Christopher A Cassa
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
| | - Richard I Sherwood
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
| | - Luca Pinello
- Molecular Pathology Unit, Krantz Family Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA.
- Gene Regulation Observatory, The Broad Institute of Harvard and MIT, Cambridge, MA, USA.
- Department of Pathology, Harvard Medical School, Boston, MA, USA.
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