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Giovannetti A, Lazzari S, Mangoni M, Traversa A, Mazza T, Parisi C, Caputo V. Exploring non-coding genetic variability in ACE2: Functional annotation and in vitro validation of regulatory variants. Gene 2024; 915:148422. [PMID: 38570058 DOI: 10.1016/j.gene.2024.148422] [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: 01/22/2024] [Revised: 02/23/2024] [Accepted: 03/13/2024] [Indexed: 04/05/2024]
Abstract
The surge in human whole-genome sequencing data has facilitated the study of non-coding region variations, yet understanding their biological significance remains a challenge. We used a computational workflow to assess the regulatory potential of non-coding variants, with a particular focus on the Angiotensin Converting Enzyme 2 (ACE2) gene. This gene is crucial in physiological processes and serves as the entry point for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus causing coronavirus disease 19 (COVID-19). In our analysis, using data from the gnomAD population database and functional annotation, we identified 17 significant Single Nucleotide Variants (SNVs) in ACE2, particularly in its enhancers, promoters, and 3' untranslated regions (UTRs). We found preliminary evidence supporting the regulatory impact of some of these variants on ACE2 expression. Our detailed examination of two SNVs, rs147718775 and rs140394675, in the ACE2 promoter revealed that these co-occurring SNVs, when mutated, significantly enhance promoter activity, suggesting a possible increase in specific ACE2 isoform expression. This method proves effective in identifying and interpreting impactful non-coding variants, aiding in further studies and enhancing understanding of molecular bases of monogenic and complex traits.
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Affiliation(s)
- Agnese Giovannetti
- Clinical Genomics Laboratory, Fondazione IRCCS Casa Sollievo della Sofferenza, Viale Cappuccini, snc, 71013 S. Giovanni Rotondo (FG), Italy.
| | - Sara Lazzari
- Department of Experimental Medicine, Sapienza University of Rome, Viale Regina Elena, 324, 00161 Rome, Italy.
| | - Manuel Mangoni
- Department of Experimental Medicine, Sapienza University of Rome, Viale Regina Elena, 324, 00161 Rome, Italy; Bioinformatics Laboratory, Fondazione IRCCS Casa Sollievo della Sofferenza, Viale Cappuccini, snc, 71013 S. Giovanni Rotondo (FG), Italy.
| | - Alice Traversa
- Department of Experimental Medicine, Sapienza University of Rome, Viale Regina Elena, 324, 00161 Rome, Italy; Dipartimento di Scienze della Vita, della Salute e delle Professioni Sanitarie, Università degli Studi "Link Campus University", Via del Casale di San Pio V 44, 00165 Roma, Italy.
| | - Tommaso Mazza
- Bioinformatics Laboratory, Fondazione IRCCS Casa Sollievo della Sofferenza, Viale Cappuccini, snc, 71013 S. Giovanni Rotondo (FG), Italy.
| | - Chiara Parisi
- Institute of Biochemistry and Cell Biology, CNR-National Research Council, Via Ercole Ramarini, 32, 00015 Monterotondo Scalo (RM), Italy.
| | - Viviana Caputo
- Department of Experimental Medicine, Sapienza University of Rome, Viale Regina Elena, 324, 00161 Rome, Italy.
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2
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Carvalho E, Dias A, Coelho T, Sousa A, Alves-Ferreira M, Santos M, Lemos C. Hereditary transthyretin amyloidosis: a myriad of factors that influence phenotypic variability. J Neurol 2024:10.1007/s00415-024-12509-8. [PMID: 38907862 DOI: 10.1007/s00415-024-12509-8] [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: 04/30/2024] [Revised: 06/02/2024] [Accepted: 06/07/2024] [Indexed: 06/24/2024]
Abstract
Hereditary transthyretin-related amyloidosis (ATTRv amyloidosis) is a rare and progressively debilitating disease characterized by the deposition of transthyretin (TTR) amyloid fibrils in various organs and tissues, most commonly in the heart and peripheral nerves. This pathological deposition can lead to significant organ dysfunction and, ultimately, organ failure. ATTRv amyloidosis exhibits a broad range of clinical presentations, from purely neurological symptoms to purely cardiac manifestations, as well as mixed phenotypes which result from both neurological and cardiac implications. This wide phenotypical spectrum realistically challenges disease diagnosis and prognosis, especially in individuals without or with an unknown family history. Multiple factors are thought to contribute to this variability, including genetic, epigenetic, and even environmental influences. Understanding these factors is crucial, as they can significantly affect disease expression and progression. This review aims to summarize each of these contributing factors, to help elucidate the current knowledge on the phenotypical variability of ATTRv amyloidosis.
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Affiliation(s)
- Estefânia Carvalho
- Instituto de Investigação e Inovação Em Saúde (i3S), University of Porto, Porto, Portugal
- Instituto de Ciências Biomédicas Abel Salazar (ICBAS), University of Porto, Porto, Portugal
| | - Andreia Dias
- Instituto de Investigação e Inovação Em Saúde (i3S), University of Porto, Porto, Portugal
- Instituto de Ciências Biomédicas Abel Salazar (ICBAS), University of Porto, Porto, Portugal
| | - Teresa Coelho
- Unidade Corino de Andrade (UCA), Centro Hospitalar Universitário de Santo António (CHUdSA), Porto, Portugal
| | - Alda Sousa
- Instituto de Investigação e Inovação Em Saúde (i3S), University of Porto, Porto, Portugal
- Instituto de Ciências Biomédicas Abel Salazar (ICBAS), University of Porto, Porto, Portugal
| | - Miguel Alves-Ferreira
- Instituto de Investigação e Inovação Em Saúde (i3S), University of Porto, Porto, Portugal
- Instituto de Ciências Biomédicas Abel Salazar (ICBAS), University of Porto, Porto, Portugal
- Center for Preditive and Preventive Genetics (CGPP), Institute for Molecular and Cell Biology (IBMC), Instituto de Investigação e Inovação Em Saúde (i3S), University of Porto, Porto, Portugal
| | - Mariana Santos
- Instituto de Investigação e Inovação Em Saúde (i3S), University of Porto, Porto, Portugal
- Instituto de Ciências Biomédicas Abel Salazar (ICBAS), University of Porto, Porto, Portugal
- Institute for Molecular and Cell Biology (IBMC), Instituto de Investigação e Inovação Em Saúde (i3S), University of Porto, Porto, Portugal
| | - Carolina Lemos
- Instituto de Investigação e Inovação Em Saúde (i3S), University of Porto, Porto, Portugal.
- Instituto de Ciências Biomédicas Abel Salazar (ICBAS), University of Porto, Porto, Portugal.
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3
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Parmar JM, Laing NG, Kennerson ML, Ravenscroft G. Genetics of inherited peripheral neuropathies and the next frontier: looking backwards to progress forwards. J Neurol Neurosurg Psychiatry 2024:jnnp-2024-333436. [PMID: 38744462 DOI: 10.1136/jnnp-2024-333436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Accepted: 04/10/2024] [Indexed: 05/16/2024]
Abstract
Inherited peripheral neuropathies (IPNs) encompass a clinically and genetically heterogeneous group of disorders causing length-dependent degeneration of peripheral autonomic, motor and/or sensory nerves. Despite gold-standard diagnostic testing for pathogenic variants in over 100 known associated genes, many patients with IPN remain genetically unsolved. Providing patients with a diagnosis is critical for reducing their 'diagnostic odyssey', improving clinical care, and for informed genetic counselling. The last decade of massively parallel sequencing technologies has seen a rapid increase in the number of newly described IPN-associated gene variants contributing to IPN pathogenesis. However, the scarcity of additional families and functional data supporting variants in potential novel genes is prolonging patient diagnostic uncertainty and contributing to the missing heritability of IPNs. We review the last decade of IPN disease gene discovery to highlight novel genes, structural variation and short tandem repeat expansions contributing to IPN pathogenesis. From the lessons learnt, we provide our vision for IPN research as we anticipate the future, providing examples of emerging technologies, resources and tools that we propose that will expedite the genetic diagnosis of unsolved IPN families.
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Affiliation(s)
- Jevin M Parmar
- Rare Disease Genetics and Functional Genomics, Harry Perkins Institute of Medical Research, Perth, Western Australia, Australia
- Centre for Medical Research, Faculty of Health and Medical Sciences, The University of Western Australia, Perth, Western Australia, Australia
| | - Nigel G Laing
- Centre for Medical Research, Faculty of Health and Medical Sciences, The University of Western Australia, Perth, Western Australia, Australia
- Preventive Genetics, Harry Perkins Institute of Medical Research, Perth, Western Australia, Australia
| | - Marina L Kennerson
- Northcott Neuroscience Laboratory, ANZAC Research Institute, Concord, New South Wales, Australia
- Molecular Medicine Laboratory, Concord Hospital, Concord, New South Wales, Australia
| | - Gianina Ravenscroft
- Rare Disease Genetics and Functional Genomics, Harry Perkins Institute of Medical Research, Perth, Western Australia, Australia
- Centre for Medical Research, Faculty of Health and Medical Sciences, The University of Western Australia, Perth, Western Australia, Australia
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4
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Cheng YHH, Bohaczuk SC, Stergachis AB. Functional categorization of gene regulatory variants that cause Mendelian conditions. Hum Genet 2024; 143:559-605. [PMID: 38436667 PMCID: PMC11078748 DOI: 10.1007/s00439-023-02639-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 12/30/2023] [Indexed: 03/05/2024]
Abstract
Much of our current understanding of rare human diseases is driven by coding genetic variants. However, non-coding genetic variants play a pivotal role in numerous rare human diseases, resulting in diverse functional impacts ranging from altered gene regulation, splicing, and/or transcript stability. With the increasing use of genome sequencing in clinical practice, it is paramount to have a clear framework for understanding how non-coding genetic variants cause disease. To this end, we have synthesized the literature on hundreds of non-coding genetic variants that cause rare Mendelian conditions via the disruption of gene regulatory patterns and propose a functional classification system. Specifically, we have adapted the functional classification framework used for coding variants (i.e., loss-of-function, gain-of-function, and dominant-negative) to account for features unique to non-coding gene regulatory variants. We identify that non-coding gene regulatory variants can be split into three distinct categories by functional impact: (1) non-modular loss-of-expression (LOE) variants; (2) modular loss-of-expression (mLOE) variants; and (3) gain-of-ectopic-expression (GOE) variants. Whereas LOE variants have a direct corollary with coding loss-of-function variants, mLOE and GOE variants represent disease mechanisms that are largely unique to non-coding variants. These functional classifications aim to provide a unified terminology for categorizing the functional impact of non-coding variants that disrupt gene regulatory patterns in Mendelian conditions.
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Affiliation(s)
- Y H Hank Cheng
- Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA, USA
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Stephanie C Bohaczuk
- Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Andrew B Stergachis
- Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA, USA.
- Department of Genome Sciences, University of Washington, Seattle, WA, USA.
- Brotman Baty Institute for Precision Medicine, Seattle, WA, USA.
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5
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Houzelstein D, Eozenou C, Lagos CF, Elzaiat M, Bignon-Topalovic J, Gonzalez I, Laville V, Schlick L, Wankanit S, Madon P, Kirtane J, Athalye A, Buonocore F, Bigou S, Conway GS, Bohl D, Achermann JC, Bashamboo A, McElreavey K. A conserved NR5A1-responsive enhancer regulates SRY in testis-determination. Nat Commun 2024; 15:2796. [PMID: 38555298 PMCID: PMC10981742 DOI: 10.1038/s41467-024-47162-2] [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: 07/10/2022] [Accepted: 03/21/2024] [Indexed: 04/02/2024] Open
Abstract
The Y-linked SRY gene initiates mammalian testis-determination. However, how the expression of SRY is regulated remains elusive. Here, we demonstrate that a conserved steroidogenic factor-1 (SF-1)/NR5A1 binding enhancer is required for appropriate SRY expression to initiate testis-determination in humans. Comparative sequence analysis of SRY 5' regions in mammals identified an evolutionary conserved SF-1/NR5A1-binding motif within a 250 bp region of open chromatin located 5 kilobases upstream of the SRY transcription start site. Genomic analysis of 46,XY individuals with disrupted testis-determination, including a large multigenerational family, identified unique single-base substitutions of highly conserved residues within the SF-1/NR5A1-binding element. In silico modelling and in vitro assays demonstrate the enhancer properties of the NR5A1 motif. Deletion of this hemizygous element by genome-editing, in a novel in vitro cellular model recapitulating human Sertoli cell formation, resulted in a significant reduction in expression of SRY. Therefore, human NR5A1 acts as a regulatory switch between testis and ovary development by upregulating SRY expression, a role that may predate the eutherian radiation. We show that disruption of an enhancer can phenocopy variants in the coding regions of SRY that cause human testis dysgenesis. Since disease causing variants in enhancers are currently rare, the regulation of gene expression in testis-determination offers a paradigm to define enhancer activity in a key developmental process.
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Affiliation(s)
- Denis Houzelstein
- Institut Pasteur, Université Paris Cité, Human Developmental Genetics Unit, F-75015, Paris, France.
- Centre National de la Recherche Scientifique, CNRS, UMR 3738, Paris, France.
| | - Caroline Eozenou
- Institut Pasteur, Université Paris Cité, Human Developmental Genetics Unit, F-75015, Paris, France
- Centre National de la Recherche Scientifique, CNRS, UMR 3738, Paris, France
- Institut Cochin, Université Paris Cité, INSERM, CNRS, Paris, France
| | - Carlos F Lagos
- Chemical Biology & Drug Discovery Lab, Escuela de Química y Farmacia, Facultad de Medicina y Ciencia, Universidad San Sebastián, Campus Los Leones, Lota 2465 Providencia, 7510157, Santiago, Chile
- Centro Ciencia & Vida, Fundación Ciencia & Vida, Av. del Valle Norte 725, Huechuraba, 8580702, Santiago, Chile
| | - Maëva Elzaiat
- Institut Pasteur, Université Paris Cité, Human Developmental Genetics Unit, F-75015, Paris, France
- Centre National de la Recherche Scientifique, CNRS, UMR 3738, Paris, France
| | - Joelle Bignon-Topalovic
- Institut Pasteur, Université Paris Cité, Human Developmental Genetics Unit, F-75015, Paris, France
- Centre National de la Recherche Scientifique, CNRS, UMR 3738, Paris, France
| | - Inma Gonzalez
- Centre National de la Recherche Scientifique, CNRS, UMR 3738, Paris, France
- Institut Pasteur, Université Paris Cité, Epigenomics, Proliferation, and the Identity of Cells Unit, F-75015, Paris, France
| | - Vincent Laville
- Centre National de la Recherche Scientifique, CNRS, UMR 3738, Paris, France
- Institut Pasteur, Université Paris Cité, Stem Cells and Development Unit, F-75015, Paris, France
- Institut Pasteur, Université Paris Cité, Bioinformatics and Biostatistics Hub, F-75015, Paris, France
| | - Laurène Schlick
- Institut Pasteur, Université Paris Cité, Human Developmental Genetics Unit, F-75015, Paris, France
- Centre National de la Recherche Scientifique, CNRS, UMR 3738, Paris, France
| | - Somboon Wankanit
- Institut Pasteur, Université Paris Cité, Human Developmental Genetics Unit, F-75015, Paris, France
- Centre National de la Recherche Scientifique, CNRS, UMR 3738, Paris, France
- Department of Pediatrics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Prochi Madon
- Department of Assisted Reproduction and Genetics, Jaslok Hospital and Research Centre, Mumbai, India
| | - Jyotsna Kirtane
- Department of Pediatric Surgery, Jaslok Hospital and Research Centre, Mumbai, India
| | - Arundhati Athalye
- Department of Assisted Reproduction and Genetics, Jaslok Hospital and Research Centre, Mumbai, India
| | - Federica Buonocore
- Genetics and Genomic Medicine Research & Teaching Department, UCL GOS Institute of Child Health, University College London, London, United Kingdom
| | - Stéphanie Bigou
- ICV-iPS core facility, Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, Inserm, CNRS, APHP, Hôpital de la Pitié Salpêtrière, Paris, France
| | - Gerard S Conway
- Institute for Women's Health, University College London, London, United Kingdom
| | - Delphine Bohl
- ICV-iPS core facility, Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, Inserm, CNRS, APHP, Hôpital de la Pitié Salpêtrière, Paris, France
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, Inserm, CNRS, APHP, Hôpital de la Pitié Salpêtrière, Paris, France
| | - John C Achermann
- Genetics and Genomic Medicine Research & Teaching Department, UCL GOS Institute of Child Health, University College London, London, United Kingdom
| | - Anu Bashamboo
- Institut Pasteur, Université Paris Cité, Human Developmental Genetics Unit, F-75015, Paris, France
- Centre National de la Recherche Scientifique, CNRS, UMR 3738, Paris, France
| | - Ken McElreavey
- Institut Pasteur, Université Paris Cité, Human Developmental Genetics Unit, F-75015, Paris, France.
- Centre National de la Recherche Scientifique, CNRS, UMR 3738, Paris, France.
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6
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Alda-Catalinas C, Ibarra-Soria X, Flouri C, Gordillo JE, Cousminer D, Hutchinson A, Sun B, Pembroke W, Ullrich S, Krejci A, Cortes A, Acevedo A, Malla S, Fishwick C, Drewes G, Rapiteanu R. Mapping the functional impact of non-coding regulatory elements in primary T cells through single-cell CRISPR screens. Genome Biol 2024; 25:42. [PMID: 38308274 PMCID: PMC10835965 DOI: 10.1186/s13059-024-03176-z] [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: 06/18/2023] [Accepted: 01/18/2024] [Indexed: 02/04/2024] Open
Abstract
BACKGROUND Drug targets with genetic evidence are expected to increase clinical success by at least twofold. Yet, translating disease-associated genetic variants into functional knowledge remains a fundamental challenge of drug discovery. A key issue is that the vast majority of complex disease associations cannot be cleanly mapped to a gene. Immune disease-associated variants are enriched within regulatory elements found in T-cell-specific open chromatin regions. RESULTS To identify genes and molecular programs modulated by these regulatory elements, we develop a CRISPRi-based single-cell functional screening approach in primary human T cells. Our pipeline enables the interrogation of transcriptomic changes induced by the perturbation of regulatory elements at scale. We first optimize an efficient CRISPRi protocol in primary CD4+ T cells via CROPseq vectors. Subsequently, we perform a screen targeting 45 non-coding regulatory elements and 35 transcription start sites and profile approximately 250,000 T -cell single-cell transcriptomes. We develop a bespoke analytical pipeline for element-to-gene (E2G) mapping and demonstrate that our method can identify both previously annotated and novel E2G links. Lastly, we integrate genetic association data for immune-related traits and demonstrate how our platform can aid in the identification of effector genes for GWAS loci. CONCLUSIONS We describe "primary T cell crisprQTL" - a scalable, single-cell functional genomics approach for mapping regulatory elements to genes in primary human T cells. We show how this framework can facilitate the interrogation of immune disease GWAS hits and propose that the combination of experimental and QTL-based techniques is likely to address the variant-to-function problem.
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Affiliation(s)
| | | | | | | | | | | | - Bin Sun
- Genomic Sciences, GSK, Stevenage, UK
| | | | | | | | | | | | | | | | - Gerard Drewes
- Genomic Sciences, GSK, Stevenage, UK
- Genomic Sciences, GSK, Collegeville, PA, USA
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7
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Ünal P, Lu Y, Bueno-de-Mesquita B, van Eijck CHJ, Talar-Wojnarowska R, Szentesi A, Gazouli M, Kreivenaite E, Tavano F, Małecka-Wojciesko E, Erőss B, Oliverius M, Bunduc S, Nóbrega Aoki M, Vodickova L, Boggi U, Giaccherini M, Kondrackiene J, Chammas R, Palmieri O, Theodoropoulos GE, Bijlsma MF, Basso D, Mohelnikova-Duchonova B, Soucek P, Izbicki JR, Kiudelis V, Vanella G, Arcidiacono PG, Włodarczyk B, Hackert T, Schöttker B, Uzunoglu FG, Bambi F, Goetz M, Hlavac V, Brenner H, Perri F, Carrara S, Landi S, Hegyi P, Dijk F, Maiello E, Capretti G, Testoni SGG, Petrone MC, Stocker H, Ermini S, Archibugi L, Gentiluomo M, Cavestro GM, Pezzilli R, Di Franco G, Milanetto AC, Sperti C, Neoptolemos JP, Morelli L, Vokacova K, Pasquali C, Lawlor RT, Bazzocchi F, Kupcinskas J, Capurso G, Campa D, Canzian F. Polymorphisms in transcription factor binding sites and enhancer regions and pancreatic ductal adenocarcinoma risk. Hum Genomics 2024; 18:12. [PMID: 38308339 PMCID: PMC10837899 DOI: 10.1186/s40246-024-00576-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: 11/09/2023] [Accepted: 01/23/2024] [Indexed: 02/04/2024] Open
Abstract
Genome-wide association studies (GWAS) are a powerful tool for detecting variants associated with complex traits and can help risk stratification and prevention strategies against pancreatic ductal adenocarcinoma (PDAC). However, the strict significance threshold commonly used makes it likely that many true risk loci are missed. Functional annotation of GWAS polymorphisms is a proven strategy to identify additional risk loci. We aimed to investigate single-nucleotide polymorphisms (SNP) in regulatory regions [transcription factor binding sites (TFBSs) and enhancers] that could change the expression profile of multiple genes they act upon and thereby modify PDAC risk. We analyzed a total of 12,636 PDAC cases and 43,443 controls from PanScan/PanC4 and the East Asian GWAS (discovery populations), and the PANDoRA consortium (replication population). We identified four associations that reached study-wide statistical significance in the overall meta-analysis: rs2472632(A) (enhancer variant, OR 1.10, 95%CI 1.06,1.13, p = 5.5 × 10-8), rs17358295(G) (enhancer variant, OR 1.16, 95%CI 1.10,1.22, p = 6.1 × 10-7), rs2232079(T) (TFBS variant, OR 0.88, 95%CI 0.83,0.93, p = 6.4 × 10-6) and rs10025845(A) (TFBS variant, OR 1.88, 95%CI 1.50,1.12, p = 1.32 × 10-5). The SNP with the most significant association, rs2472632, is located in an enhancer predicted to target the coiled-coil domain containing 34 oncogene. Our results provide new insights into genetic risk factors for PDAC by a focused analysis of polymorphisms in regulatory regions and demonstrating the usefulness of functional prioritization to identify loci associated with PDAC risk.
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Affiliation(s)
- Pelin Ünal
- Genomic Epidemiology Group, German Cancer Research Center, In Neuenheimer Feld 280, 69120, Heidelberg, Germany
| | - Ye Lu
- Genomic Epidemiology Group, German Cancer Research Center, In Neuenheimer Feld 280, 69120, Heidelberg, Germany
| | - Bas Bueno-de-Mesquita
- Department for Determinants of Chronic Diseases, National Institute for Public Health and the Environment, Bilthoven, The Netherlands
| | - Casper H J van Eijck
- Department of Surgery, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | | | - Andrea Szentesi
- Institute for Translational Medicine, Medical School, University of Pécs, Pécs, Hungary
| | - Maria Gazouli
- Laboratory of Biology, Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Edita Kreivenaite
- Gastroenterology Department and Institute for Digestive Research, Lithuanian University of Health Sciences, Kaunas, Lithuania
| | - Francesca Tavano
- Division of Gastroenterology and Research Laboratory, Fondazione IRCCS "Casa Sollievo della Sofferenza" Hospital, San Giovanni Rotondo, FG, Italy
| | | | - Bálint Erőss
- Institute for Translational Medicine, Medical School, University of Pécs, Pécs, Hungary
- Center for Translational Medicine, Semmelweis University, Budapest, Hungary
- Division of Pancreatic Diseases, Heart and Vascular Center, Semmelweis University, Budapest, Hungary
| | - Martin Oliverius
- Department of Surgery, University Hospital Kralovske Vinohrady, Third Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Stefania Bunduc
- Center for Translational Medicine, Semmelweis University, Budapest, Hungary
- Division of Pancreatic Diseases, Heart and Vascular Center, Semmelweis University, Budapest, Hungary
- Carol Davila University of Medicine and Pharmacy, Bucharest, Romania
| | - Mateus Nóbrega Aoki
- Laboratory for Applied Science and Technology in Health, Carlos Chagas Institute, Curitiba, PR, Brazil
| | - Ludmila Vodickova
- Biomedical Center, Faculty of Medicine in Pilsen, Charles University, Plzeň, Czech Republic
- Department of Molecular Biology of Cancer, Institute of Experimental Medicine of the Czech Academy of Sciences, Prague, Czech Republic
- Institute of Biology and Medical Genetics, Institute of Physiology, 1st Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Ugo Boggi
- Division of General and Transplant Surgery, Pisa University Hospital, Pisa, Italy
| | | | - Jurate Kondrackiene
- Gastroenterology Department and Institute for Digestive Research, Lithuanian University of Health Sciences, Kaunas, Lithuania
| | - Roger Chammas
- Department of Radiology and Oncology, Institute of Cancer of São Paulo, São Paulo, Brazil
| | - Orazio Palmieri
- Division of Gastroenterology and Research Laboratory, Fondazione IRCCS "Casa Sollievo della Sofferenza" Hospital, San Giovanni Rotondo, FG, Italy
| | - George E Theodoropoulos
- First Propaedeutic University Surgery Clinic, Hippocratio General Hospital, Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Maarten F Bijlsma
- Laboratory for Experimental Oncology and Radiobiology, Center of Experimental Molecular Medicine, Amsterdam UMC Location University of Amsterdam, Amsterdam, the Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, the Netherlands
| | - Daniela Basso
- Department of Medicine, Laboratory Medicine, University of Padova, Padua, Italy
| | | | - Pavel Soucek
- Biomedical Center, Faculty of Medicine in Pilsen, Charles University, Plzeň, Czech Republic
| | - Jakob R Izbicki
- Department of General Visceral and Thoracic Surgery, University of Hamburg Medical Institutions, Hamburg, Germany
| | - Vytautas Kiudelis
- Gastroenterology Department and Institute for Digestive Research, Lithuanian University of Health Sciences, Kaunas, Lithuania
| | - Giuseppe Vanella
- PancreatoBiliary Endoscopy and Endosonography Division, Pancreas Translational and Clinical Research Center, San Raffaele Scientific Institute, Milan, Italy
- Digestive and Liver Disease Unit, S. Andrea Hospital, Rome, Italy
| | - Paolo Giorgio Arcidiacono
- PancreatoBiliary Endoscopy and Endosonography Division, Pancreas Translational and Clinical Research Center, San Raffaele Scientific Institute, Milan, Italy
| | - Barbara Włodarczyk
- Department of Digestive Tract Diseases, Medical University of Lodz, Lodz, Poland
| | - Thilo Hackert
- Department of General, Visceral and Transplant Surgery, Heidelberg University Hospital, Heidelberg, Germany
| | - Ben Schöttker
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center, Heidelberg, Germany
- Network Aging Research (NAR), Heidelberg University, Heidelberg, Germany
| | - Faik G Uzunoglu
- Department of General Visceral and Thoracic Surgery, University of Hamburg Medical Institutions, Hamburg, Germany
| | - Franco Bambi
- Blood Transfusion Service, Meyer Children's Hospital, Florence, Italy
| | - Mara Goetz
- Department of General Visceral and Thoracic Surgery, University of Hamburg Medical Institutions, Hamburg, Germany
| | - Viktor Hlavac
- Biomedical Center, Faculty of Medicine in Pilsen, Charles University, Plzeň, Czech Republic
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center, Heidelberg, Germany
- Network Aging Research (NAR), Heidelberg University, Heidelberg, Germany
- Division of Preventive Oncology, German Cancer Research Center and National Center for Tumor Diseases, Heidelberg, Germany
- German Cancer Consortium (DKTK), German Cancer Research Center, Heidelberg, Germany
| | - Francesco Perri
- Division of Gastroenterology and Research Laboratory, Fondazione IRCCS "Casa Sollievo della Sofferenza" Hospital, San Giovanni Rotondo, FG, Italy
| | - Silvia Carrara
- Endoscopic Unit, Department of Gastroenterology, IRCCS Humanitas Research Hospital, Milan, Italy
| | - Stefano Landi
- Department of Biology, University of Pisa, Pisa, Italy
| | - Péter Hegyi
- Institute for Translational Medicine, Medical School, University of Pécs, Pécs, Hungary
- János Szentágothai Research Center, University of Pécs, Pécs, Hungary
- Center for Translational Medicine, Semmelweis University, Budapest, Hungary
- Division of Pancreatic Diseases, Heart and Vascular Center, Semmelweis University, Budapest, Hungary
| | - Frederike Dijk
- Department of Pathology, Cancer Center Amsterdam, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Evaristo Maiello
- Department of Oncology, Fondazione IRCCS "Casa Sollievo della Sofferenza" Hospital, San Giovanni Rotondo, FG, Italy
| | - Giovanni Capretti
- Pancreatic Unit, IRCCS Humanitas Research Hospital, Milan, Italy
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
| | - Sabrina Gloria Giulia Testoni
- PancreatoBiliary Endoscopy and Endosonography Division, Pancreas Translational and Clinical Research Center, San Raffaele Scientific Institute, Milan, Italy
| | - Maria Chiara Petrone
- PancreatoBiliary Endoscopy and Endosonography Division, Pancreas Translational and Clinical Research Center, San Raffaele Scientific Institute, Milan, Italy
| | - Hannah Stocker
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center, Heidelberg, Germany
- Network Aging Research (NAR), Heidelberg University, Heidelberg, Germany
| | - Stefano Ermini
- Blood Transfusion Service, Meyer Children's Hospital, Florence, Italy
| | - Livia Archibugi
- PancreatoBiliary Endoscopy and Endosonography Division, Pancreas Translational and Clinical Research Center, San Raffaele Scientific Institute, Milan, Italy
- Digestive and Liver Disease Unit, S. Andrea Hospital, Rome, Italy
| | | | - Giulia Martina Cavestro
- Gastroenterology and Gastrointestinal Endoscopy Unit, IRCCS San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | | | - Gregorio Di Franco
- General Surgery Unit, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | | | - Cosimo Sperti
- Department of Surgery, Oncology and Gastroenterology, University of Padova, Padua, Italy
| | - John P Neoptolemos
- Department of General, Visceral and Transplant Surgery, Heidelberg University Hospital, Heidelberg, Germany
| | - Luca Morelli
- General Surgery Unit, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Klara Vokacova
- Department of Molecular Biology of Cancer, Institute of Experimental Medicine of the Czech Academy of Sciences, Prague, Czech Republic
- Institute of Biology and Medical Genetics, Institute of Physiology, 1st Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Claudio Pasquali
- Department of Surgery, Oncology and Gastroenterology, University of Padova, Padua, Italy
| | - Rita T Lawlor
- Department of Diagnostics and Public Health, ARC-Net Centre for Applied Research on Cancer, University of Verona, Verona, Italy
| | - Francesca Bazzocchi
- Department of Surgery, Fondazione IRCCS "Casa Sollievo della Sofferenza" Hospital, San Giovanni Rotondo, FG, Italy
| | - Juozas Kupcinskas
- Gastroenterology Department and Institute for Digestive Research, Lithuanian University of Health Sciences, Kaunas, Lithuania
| | - Gabriele Capurso
- PancreatoBiliary Endoscopy and Endosonography Division, Pancreas Translational and Clinical Research Center, San Raffaele Scientific Institute, Milan, Italy
- Digestive and Liver Disease Unit, S. Andrea Hospital, Rome, Italy
| | - Daniele Campa
- Department of Biology, University of Pisa, Pisa, Italy
| | - Federico Canzian
- Genomic Epidemiology Group, German Cancer Research Center, In Neuenheimer Feld 280, 69120, Heidelberg, Germany.
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8
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Feng X, Liu S, Li K, Bu F, Yuan H. NCAD v1.0: a database for non-coding variant annotation and interpretation. J Genet Genomics 2024; 51:230-242. [PMID: 38142743 DOI: 10.1016/j.jgg.2023.12.005] [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: 08/30/2023] [Revised: 12/15/2023] [Accepted: 12/18/2023] [Indexed: 12/26/2023]
Abstract
The application of whole genome sequencing is expanding in clinical diagnostics across various genetic disorders, and the significance of non-coding variants in penetrant diseases is increasingly being demonstrated. Therefore, it is urgent to improve the diagnostic yield by exploring the pathogenic mechanisms of variants in non-coding regions. However, the interpretation of non-coding variants remains a significant challenge, due to the complex functional regulatory mechanisms of non-coding regions and the current limitations of available databases and tools. Hence, we develop the non-coding variant annotation database (NCAD, http://www.ncawdb.net/), encompassing comprehensive insights into 665,679,194 variants, regulatory elements, and element interaction details. Integrating data from 96 sources, spanning both GRCh37 and GRCh38 versions, NCAD v1.0 provides vital information to support the genetic diagnosis of non-coding variants, including allele frequencies of 12 diverse populations, with a particular focus on the population frequency information for 230,235,698 variants in 20,964 Chinese individuals. Moreover, it offers prediction scores for variant functionality, five categories of regulatory elements, and four types of non-coding RNAs. With its rich data and comprehensive coverage, NCAD serves as a valuable platform, empowering researchers and clinicians with profound insights into non-coding regulatory mechanisms while facilitating the interpretation of non-coding variants.
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Affiliation(s)
- Xiaoshu Feng
- Institute of Rare Diseases, West China Hospital, Sichuan University, Chengdu, Sichuan 610044, China
| | - Sihan Liu
- Institute of Rare Diseases, West China Hospital, Sichuan University, Chengdu, Sichuan 610044, China
| | - Ke Li
- Institute of Rare Diseases, West China Hospital, Sichuan University, Chengdu, Sichuan 610044, China
| | - Fengxiao Bu
- Institute of Rare Diseases, West China Hospital, Sichuan University, Chengdu, Sichuan 610044, China.
| | - Huijun Yuan
- Institute of Rare Diseases, West China Hospital, Sichuan University, Chengdu, Sichuan 610044, China.
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9
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Wang Z, Zhao G, Zhu Z, Wang Y, Xiang X, Zhang S, Luo T, Zhou Q, Qiu J, Tang B, Xia K, Li B, Li J. VarCards2: an integrated genetic and clinical database for ACMG-AMP variant-interpretation guidelines in the human whole genome. Nucleic Acids Res 2024; 52:D1478-D1489. [PMID: 37956311 PMCID: PMC10767961 DOI: 10.1093/nar/gkad1061] [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: 09/15/2023] [Revised: 10/21/2023] [Accepted: 10/25/2023] [Indexed: 11/15/2023] Open
Abstract
VarCards, an online database, combines comprehensive variant- and gene-level annotation data to streamline genetic counselling for coding variants. Recognising the increasing clinical relevance of non-coding variations, there has been an accelerated development of bioinformatics tools dedicated to interpreting non-coding variations, including single-nucleotide variants and copy number variations. Regrettably, most tools remain as either locally installed databases or command-line tools dispersed across diverse online platforms. Such a landscape poses inconveniences and challenges for genetic counsellors seeking to utilise these resources without advanced bioinformatics expertise. Consequently, we developed VarCards2, which incorporates nearly nine billion artificially generated single-nucleotide variants (including those from mitochondrial DNA) and compiles vital annotation information for genetic counselling based on ACMG-AMP variant-interpretation guidelines. These annotations include (I) functional effects; (II) minor allele frequencies; (III) comprehensive function and pathogenicity predictions covering all potential variants, such as non-synonymous substitutions, non-canonical splicing variants, and non-coding variations and (IV) gene-level information. Furthermore, VarCards2 incorporates 368 820 266 documented short insertions and deletions and 2 773 555 documented copy number variations, complemented by their corresponding annotation and prediction tools. In conclusion, VarCards2, by integrating over 150 variant- and gene-level annotation sources, significantly enhances the efficiency of genetic counselling and can be freely accessed at http://www.genemed.tech/varcards2/.
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Affiliation(s)
- Zheng Wang
- National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Hunan Key Laboratory of Molecular Precision Medicine, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
| | - Guihu Zhao
- National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Bioinformatics Center, Furong Laboratory & Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
| | - Zhaopo Zhu
- Center for Medical Genetics & Hunan Key Laboratory, School of Life Sciences, Central South University, Changsha, Hunan 410008, China
| | - Yijing Wang
- National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Bioinformatics Center, Furong Laboratory & Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
| | - Xudong Xiang
- National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
| | - Shiyu Zhang
- Xiangya School of Medicine, Central South University, Changsha, Hunan 410013, China
| | - Tengfei Luo
- Center for Medical Genetics & Hunan Key Laboratory, School of Life Sciences, Central South University, Changsha, Hunan 410008, China
| | - Qiao Zhou
- National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Bioinformatics Center, Furong Laboratory & Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
| | - Jian Qiu
- National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Hunan Key Laboratory of Molecular Precision Medicine, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
| | - Beisha Tang
- National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Department of Neurology, & Multi-Omics Research Center for Brain Disorders, The First Affiliated Hospital, University of South China, Hengyang, Hunan, China
| | - Kun Xia
- Center for Medical Genetics & Hunan Key Laboratory, School of Life Sciences, Central South University, Changsha, Hunan 410008, China
| | - Bin Li
- National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Bioinformatics Center, Furong Laboratory & Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
| | - Jinchen Li
- National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Center for Medical Genetics & Hunan Key Laboratory, School of Life Sciences, Central South University, Changsha, Hunan 410008, China
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Bioinformatics Center, Furong Laboratory & Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
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10
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Calame DG, Emrick LT. Functional genomics and small molecules in mitochondrial neurodevelopmental disorders. Neurotherapeutics 2024; 21:e00316. [PMID: 38244259 PMCID: PMC10903096 DOI: 10.1016/j.neurot.2024.e00316] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 12/16/2023] [Accepted: 01/02/2024] [Indexed: 01/22/2024] Open
Abstract
Mitochondria are critical for brain development and homeostasis. Therefore, pathogenic variation in the mitochondrial or nuclear genome which disrupts mitochondrial function frequently results in developmental disorders and neurodegeneration at the organismal level. Large-scale application of genome-wide technologies to individuals with mitochondrial diseases has dramatically accelerated identification of mitochondrial disease-gene associations in humans. Multi-omic and high-throughput studies involving transcriptomics, proteomics, metabolomics, and saturation genome editing are providing deeper insights into the functional consequence of mitochondrial genomic variation. Integration of deep phenotypic and genomic data through allelic series continues to uncover novel mitochondrial functions and permit mitochondrial gene function dissection on an unprecedented scale. Finally, mitochondrial disease-gene associations illuminate disease mechanisms and thereby direct therapeutic strategies involving small molecules and RNA-DNA therapeutics. This review summarizes progress in functional genomics and small molecule therapeutics in mitochondrial neurodevelopmental disorders.
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Affiliation(s)
- Daniel G Calame
- Section of Pediatric Neurology and Developmental Neuroscience, Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA; Texas Children's Hospital, Houston, TX, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA.
| | - Lisa T Emrick
- Section of Pediatric Neurology and Developmental Neuroscience, Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA; Texas Children's Hospital, Houston, TX, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
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11
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Ren X, Yang H, Nierenberg JL, Sun Y, Chen J, Beaman C, Pham T, Nobuhara M, Takagi MA, Narayan V, Li Y, Ziv E, Shen Y. High-throughput PRIME-editing screens identify functional DNA variants in the human genome. Mol Cell 2023; 83:4633-4645.e9. [PMID: 38134886 PMCID: PMC10766087 DOI: 10.1016/j.molcel.2023.11.021] [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: 05/12/2023] [Revised: 10/07/2023] [Accepted: 11/16/2023] [Indexed: 12/24/2023]
Abstract
Despite tremendous progress in detecting DNA variants associated with human disease, interpreting their functional impact in a high-throughput and single-base resolution manner remains challenging. Here, we develop a pooled prime-editing screen method, PRIME, that can be applied to characterize thousands of coding and non-coding variants in a single experiment with high reproducibility. To showcase its applications, we first identified essential nucleotides for a 716 bp MYC enhancer via PRIME-mediated single-base resolution analysis. Next, we applied PRIME to functionally characterize 1,304 genome-wide association study (GWAS)-identified non-coding variants associated with breast cancer and 3,699 variants from ClinVar. We discovered that 103 non-coding variants and 156 variants of uncertain significance are functional via affecting cell fitness. Collectively, we demonstrate that PRIME is capable of characterizing genetic variants at single-base resolution and scale, advancing accurate genome annotation for disease risk prediction, diagnosis, and therapeutic target identification.
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Affiliation(s)
- Xingjie Ren
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA
| | - Han Yang
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA
| | - Jovia L Nierenberg
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | - Yifan Sun
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA
| | - Jiawen Chen
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Cooper Beaman
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA
| | - Thu Pham
- Pharmaceutical Sciences and Pharmacogenomics Graduate Program, University of California, San Francisco, San Francisco, CA, USA
| | - Mai Nobuhara
- Pharmaceutical Sciences and Pharmacogenomics Graduate Program, University of California, San Francisco, San Francisco, CA, USA
| | - Maya Asami Takagi
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA
| | - Vivek Narayan
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Genetics, University of North Carolina, Chapel Hill, NC, USA; Department of Computer Science, University of North Carolina, Chapel Hill, NC, USA
| | - Elad Ziv
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA; Division of General Internal Medicine, Department of Medicine, and Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
| | - Yin Shen
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA; Department of Neurology, University of California, San Francisco, San Francisco, CA, USA; Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA.
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12
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Filonov SV, Podkolodnyy NL, Podkolodnaya OA, Tverdokhleb NN, Ponomarenko PM, Rasskazov DA, Bogomolov AG, Ponomarenko MP. Human_SNP_TATAdb: a database of SNPs that statistically significantly change the affinity of the TATA-binding protein to human gene promoters: genome-wide analysis and use cases. Vavilovskii Zhurnal Genet Selektsii 2023; 27:728-736. [PMID: 38213714 PMCID: PMC10777301 DOI: 10.18699/vjgb-23-85] [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: 08/22/2023] [Revised: 09/15/2023] [Accepted: 09/19/2023] [Indexed: 01/13/2024] Open
Abstract
It was previously shown that the expression levels of human genes positively correlate with TBP affinity for the promoters of these genes. In turn, single nucleotide polymorphisms (SNPs) in human gene promoters can affect TBP affinity for DNA and, as a consequence, gene expression. The Institute of Cytology and Genetics SB RAS (ICG) has developed a method for predicting TBP affinity for gene promoters based on a three-step binding mecha- nism: (1) TBP slides along DNA, (2) TBP stops at the binding site, and (3) the TBP-promoter complex is fixed due to DNA helix bending. The method showed a high correlation of theoretical predictions with measured values during repeated experimental testing by independent groups of researchers. This model served as a base for other ICG web services, SNP_TATA_Z-tester and SNP_TATA_Comparator, which make a statistical assessment of the SNP-induced change in the affinity of TBP binding to the human gene promoter and help predict changes in expression that may be associated with a genetic predisposition to diseases or phenotypic features of the organism. In this work, we integrated into a single database information about SNPs in human gene promoters obtained by automatic extrac- tion from various heterogeneous data sources, as well as the estimates of TBP affinity for the promoter obtained using the three-step binding model and predicting their effect on gene expression for wild-type promoters and promoters with SNPs. We have shown that Human_SNP_TATAdb can be used for annotation and identification of candidate SNP markers of diseases. The results of a genome-wide data analysis are presented, including the distri- bution of genes with respect to the number of transcripts, the distribution of SNPs affecting TBP-DNA affinity with respect to positions within promoters, as well as patterns linking TBP affinity for the promoter, the specificity of the TBP binding site for the promoter and other characteristics of promoters. The results of the genome-wide analysis showed that the affinity of TBP for the promoter and the specificity of its binding site are statistically related to other characteristics of promoters important for the functional classification of promoters and the study of the features of differential gene expression.
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Affiliation(s)
- S V Filonov
- Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia Novosibirsk State University, Novosibirsk, Russia
| | - N L Podkolodnyy
- Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia Institute of Computational Mathematics and Mathematical Geophysics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
| | - O A Podkolodnaya
- Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
| | - N N Tverdokhleb
- Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
| | - P M Ponomarenko
- Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
| | - D A Rasskazov
- Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
| | - A G Bogomolov
- Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
| | - M P Ponomarenko
- Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
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13
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Yuan M, Liu X, Wang M, Li Z, Li H, Leng L, Wang S. A Functional Variant Alters the Binding of Bone morphogenetic protein 2 to the Transcription Factor NF-κB to Regulate Bone morphogenetic protein 2 Gene Expression and Chicken Abdominal Fat Deposition. Animals (Basel) 2023; 13:3401. [PMID: 37958155 PMCID: PMC10650395 DOI: 10.3390/ani13213401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Revised: 10/27/2023] [Accepted: 10/31/2023] [Indexed: 11/15/2023] Open
Abstract
In this study, we employed a dual-luciferase reporter assay and electrophoretic mobility shift analysis (EMSA) in vitro to explore whether a 12-base pair (bp) insertion/deletion (InDel) variant (namely g.14798187_14798188insTCCCTGCCCCCT) within intron 2 of the chicken BMP2 gene, which was significantly associated with chicken abdominal fat weight and abdominal fat percentage, is a functional marker and its potential regulatory mechanism. The reporter analysis demonstrated that the luciferase activity of the deletion allele was extremely significantly higher than that of the insertion allele (p < 0.01). A bioinformatics analysis revealed that compared to the deletion allele, the insertion allele created a transcription factor binding site of nuclear factor-kappa B (NF-κB), which exhibited an inhibitory effect on fat deposition. A dual-luciferase reporter assay demonstrated that the inhibitory effect of NF-κB on the deletion allele was stronger than that on the insertion allele. EMSA indicated that the binding affinity of NF-κB for the insertion allele was stronger than that for the deletion allele. In conclusion, the 12-bp InDel chicken BMP2 gene variant is a functional variant affecting fat deposition in chickens, which may partially regulate BMP2 gene expression by affecting the binding of transcription factor NF-κB to the BMP2 gene.
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Affiliation(s)
- Meng Yuan
- Key Laboratory of Chicken Genetics and Breeding, Ministry of Agriculture and Rural Affairs, Harbin 150030, China; (M.Y.); (X.L.); (M.W.); (Z.L.); (H.L.)
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Education Department of Heilongjiang Province, Harbin 150030, China
- College of Animal Science and Technology, Northeast Agricultural University, Harbin 150030, China
| | - Xin Liu
- Key Laboratory of Chicken Genetics and Breeding, Ministry of Agriculture and Rural Affairs, Harbin 150030, China; (M.Y.); (X.L.); (M.W.); (Z.L.); (H.L.)
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Education Department of Heilongjiang Province, Harbin 150030, China
- College of Animal Science and Technology, Northeast Agricultural University, Harbin 150030, China
| | - Mengdie Wang
- Key Laboratory of Chicken Genetics and Breeding, Ministry of Agriculture and Rural Affairs, Harbin 150030, China; (M.Y.); (X.L.); (M.W.); (Z.L.); (H.L.)
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Education Department of Heilongjiang Province, Harbin 150030, China
- College of Animal Science and Technology, Northeast Agricultural University, Harbin 150030, China
| | - Ziwei Li
- Key Laboratory of Chicken Genetics and Breeding, Ministry of Agriculture and Rural Affairs, Harbin 150030, China; (M.Y.); (X.L.); (M.W.); (Z.L.); (H.L.)
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Education Department of Heilongjiang Province, Harbin 150030, China
- College of Animal Science and Technology, Northeast Agricultural University, Harbin 150030, China
| | - Hui Li
- Key Laboratory of Chicken Genetics and Breeding, Ministry of Agriculture and Rural Affairs, Harbin 150030, China; (M.Y.); (X.L.); (M.W.); (Z.L.); (H.L.)
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Education Department of Heilongjiang Province, Harbin 150030, China
- College of Animal Science and Technology, Northeast Agricultural University, Harbin 150030, China
| | - Li Leng
- Key Laboratory of Chicken Genetics and Breeding, Ministry of Agriculture and Rural Affairs, Harbin 150030, China; (M.Y.); (X.L.); (M.W.); (Z.L.); (H.L.)
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Education Department of Heilongjiang Province, Harbin 150030, China
- College of Animal Science and Technology, Northeast Agricultural University, Harbin 150030, China
| | - Shouzhi Wang
- Key Laboratory of Chicken Genetics and Breeding, Ministry of Agriculture and Rural Affairs, Harbin 150030, China; (M.Y.); (X.L.); (M.W.); (Z.L.); (H.L.)
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Education Department of Heilongjiang Province, Harbin 150030, China
- College of Animal Science and Technology, Northeast Agricultural University, Harbin 150030, China
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14
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Bohn E, Lau TTY, Wagih O, Masud T, Merico D. A curated census of pathogenic and likely pathogenic UTR variants and evaluation of deep learning models for variant effect prediction. Front Mol Biosci 2023; 10:1257550. [PMID: 37745687 PMCID: PMC10517338 DOI: 10.3389/fmolb.2023.1257550] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Accepted: 08/28/2023] [Indexed: 09/26/2023] Open
Abstract
Introduction: Variants in 5' and 3' untranslated regions (UTR) contribute to rare disease. While predictive algorithms to assist in classifying pathogenicity can potentially be highly valuable, the utility of these tools is often unclear, as it depends on carefully selected training and validation conditions. To address this, we developed a high confidence set of pathogenic (P) and likely pathogenic (LP) variants and assessed deep learning (DL) models for predicting their molecular effects. Methods: 3' and 5' UTR variants documented as P or LP (P/LP) were obtained from ClinVar and refined by reviewing the annotated variant effect and reassessing evidence of pathogenicity following published guidelines. Prediction scores from sequence-based DL models were compared between three groups: P/LP variants acting though the mechanism for which the model was designed (model-matched), those operating through other mechanisms (model-mismatched), and putative benign variants. PhyloP was used to compare conservation scores between P/LP and putative benign variants. Results: 295 3' and 188 5' UTR variants were obtained from ClinVar, of which 26 3' and 68 5' UTR variants were classified as P/LP. Predictions by DL models achieved statistically significant differences when comparing modelmatched P/LP variants to both putative benign variants and modelmismatched P/LP variants, as well as when comparing all P/LP variants to putative benign variants. PhyloP conservation scores were significantly higher among P/LP compared to putative benign variants for both the 3' and 5' UTR. Discussion: In conclusion, we present a high-confidence set of P/LP 3' and 5' UTR variants spanning a range of mechanisms and supported by detailed pathogenicity and molecular mechanism evidence curation. Predictions from DL models further substantiate these classifications. These datasets will support further development and validation of DL algorithms designed to predict the functional impact of variants that may be implicated in rare disease.
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Affiliation(s)
- Emma Bohn
- Deep Genomics Inc., Toronto, ON, Canada
| | | | | | | | - Daniele Merico
- Deep Genomics Inc., Toronto, ON, Canada
- The Centre for Applied Genomics, Hospital for Sick Children, Toronto, ON, Canada
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15
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Wright SN, Leger BS, Rosenthal SB, Liu SN, Jia T, Chitre AS, Polesskaya O, Holl K, Gao J, Cheng R, Garcia Martinez A, George A, Gileta AF, Han W, Netzley AH, King CP, Lamparelli A, Martin C, St Pierre CL, Wang T, Bimschleger H, Richards J, Ishiwari K, Chen H, Flagel SB, Meyer P, Robinson TE, Solberg Woods LC, Kreisberg JF, Ideker T, Palmer AA. Genome-wide association studies of human and rat BMI converge on synapse, epigenome, and hormone signaling networks. Cell Rep 2023; 42:112873. [PMID: 37527041 PMCID: PMC10546330 DOI: 10.1016/j.celrep.2023.112873] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 07/05/2023] [Accepted: 07/11/2023] [Indexed: 08/03/2023] Open
Abstract
A vexing observation in genome-wide association studies (GWASs) is that parallel analyses in different species may not identify orthologous genes. Here, we demonstrate that cross-species translation of GWASs can be greatly improved by an analysis of co-localization within molecular networks. Using body mass index (BMI) as an example, we show that the genes associated with BMI in humans lack significant agreement with those identified in rats. However, the networks interconnecting these genes show substantial overlap, highlighting common mechanisms including synaptic signaling, epigenetic modification, and hormonal regulation. Genetic perturbations within these networks cause abnormal BMI phenotypes in mice, too, supporting their broad conservation across mammals. Other mechanisms appear species specific, including carbohydrate biosynthesis (humans) and glycerolipid metabolism (rodents). Finally, network co-localization also identifies cross-species convergence for height/body length. This study advances a general paradigm for determining whether and how phenotypes measured in model species recapitulate human biology.
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Affiliation(s)
- Sarah N Wright
- Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA; Program in Bioinformatics and Systems Biology, University of California San Diego, La Jolla, CA 92093, USA
| | - Brittany S Leger
- Department of Psychiatry, University of California San Diego, La Jolla, CA 93093, USA; Program in Biomedical Sciences, University of California San Diego, La Jolla, CA 93093, USA
| | - Sara Brin Rosenthal
- Center for Computational Biology & Bioinformatics, Department of Medicine, University of California, San Diego, La Jolla, CA 92093, USA
| | - Sophie N Liu
- Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA
| | - Tongqiu Jia
- Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA
| | - Apurva S Chitre
- Department of Psychiatry, University of California San Diego, La Jolla, CA 93093, USA
| | - Oksana Polesskaya
- Department of Psychiatry, University of California San Diego, La Jolla, CA 93093, USA
| | - Katie Holl
- Department of Physiology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Jianjun Gao
- Department of Psychiatry, University of California San Diego, La Jolla, CA 93093, USA
| | - Riyan Cheng
- Department of Psychiatry, University of California San Diego, La Jolla, CA 93093, USA
| | - Angel Garcia Martinez
- Department of Pharmacology, University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Anthony George
- Clinical and Research Institute on Addictions, University at Buffalo, Buffalo, NY 14203, USA
| | - Alexander F Gileta
- Department of Psychiatry, University of California San Diego, La Jolla, CA 93093, USA; Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA
| | - Wenyan Han
- Department of Pharmacology, University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Alesa H Netzley
- Department of Psychiatry, University of Michigan, Ann Arbor, MI 48109, USA
| | - Christopher P King
- Clinical and Research Institute on Addictions, University at Buffalo, Buffalo, NY 14203, USA; Department of Psychology, University at Buffalo, Buffalo, NY 14260, USA
| | | | - Connor Martin
- Clinical and Research Institute on Addictions, University at Buffalo, Buffalo, NY 14203, USA; Department of Psychology, University at Buffalo, Buffalo, NY 14260, USA
| | | | - Tengfei Wang
- Department of Pharmacology, University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Hannah Bimschleger
- Department of Psychiatry, University of California San Diego, La Jolla, CA 93093, USA
| | - Jerry Richards
- Clinical and Research Institute on Addictions, University at Buffalo, Buffalo, NY 14203, USA
| | - Keita Ishiwari
- Clinical and Research Institute on Addictions, University at Buffalo, Buffalo, NY 14203, USA; Department of Pharmacology and Toxicology, University at Buffalo, Buffalo, NY 14203, USA
| | - Hao Chen
- Department of Pharmacology, University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Shelly B Flagel
- Department of Psychiatry, University of Michigan, Ann Arbor, MI 48109, USA; Michigan Neuroscience Institute, University of Michigan, Ann Arbor, MI 48109, USA
| | - Paul Meyer
- Department of Psychology, University at Buffalo, Buffalo, NY 14260, USA
| | - Terry E Robinson
- Department of Psychology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Leah C Solberg Woods
- Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
| | - Jason F Kreisberg
- Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA
| | - Trey Ideker
- Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA; Institute for Genomic Medicine, University of California San Diego, La Jolla, CA 92093, USA.
| | - Abraham A Palmer
- Department of Psychiatry, University of California San Diego, La Jolla, CA 93093, USA; Institute for Genomic Medicine, University of California San Diego, La Jolla, CA 92093, USA.
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16
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Lyons EL, Watson D, Alodadi MS, Haugabook SJ, Tawa GJ, Hannah-Shmouni F, Porter FD, Collins JR, Ottinger EA, Mudunuri US. Rare disease variant curation from literature: assessing gaps with creatine transport deficiency in focus. BMC Genomics 2023; 24:460. [PMID: 37587458 PMCID: PMC10433598 DOI: 10.1186/s12864-023-09561-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 08/08/2023] [Indexed: 08/18/2023] Open
Abstract
BACKGROUND Approximately 4-8% of the world suffers from a rare disease. Rare diseases are often difficult to diagnose, and many do not have approved therapies. Genetic sequencing has the potential to shorten the current diagnostic process, increase mechanistic understanding, and facilitate research on therapeutic approaches but is limited by the difficulty of novel variant pathogenicity interpretation and the communication of known causative variants. It is unknown how many published rare disease variants are currently accessible in the public domain. RESULTS This study investigated the translation of knowledge of variants reported in published manuscripts to publicly accessible variant databases. Variants, symptoms, biochemical assay results, and protein function from literature on the SLC6A8 gene associated with X-linked Creatine Transporter Deficiency (CTD) were curated and reported as a highly annotated dataset of variants with clinical context and functional details. Variants were harmonized, their availability in existing variant databases was analyzed and pathogenicity assignments were compared with impact algorithm predictions. 24% of the pathogenic variants found in PubMed articles were not captured in any database used in this analysis while only 65% of the published variants received an accurate pathogenicity prediction from at least one impact prediction algorithm. CONCLUSIONS Despite being published in the literature, pathogenicity data on patient variants may remain inaccessible for genetic diagnosis, therapeutic target identification, mechanistic understanding, or hypothesis generation. Clinical and functional details presented in the literature are important to make pathogenicity assessments. Impact predictions remain imperfect but are improving, especially for single nucleotide exonic variants, however such predictions are less accurate or unavailable for intronic and multi-nucleotide variants. Developing text mining workflows that use natural language processing for identifying diseases, genes and variants, along with impact prediction algorithms and integrating with details on clinical phenotypes and functional assessments might be a promising approach to scale literature mining of variants and assigning correct pathogenicity. The curated variants list created by this effort includes context details to improve any such efforts on variant curation for rare diseases.
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Affiliation(s)
- Erica L Lyons
- Advanced Biomedical Computational Science, Frederick National Laboratory for Cancer Research, Frederick, MD, 21702, USA
| | - Daniel Watson
- Advanced Biomedical Computational Science, Frederick National Laboratory for Cancer Research, Frederick, MD, 21702, USA
| | - Mohammad S Alodadi
- Advanced Biomedical Computational Science, Frederick National Laboratory for Cancer Research, Frederick, MD, 21702, USA
| | - Sharie J Haugabook
- Division of Preclinical Innovation, Therapeutic Development Branch, Therapeutics for Rare and Neglected Diseases (TRND) Program, National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Gregory J Tawa
- Division of Preclinical Innovation, Therapeutic Development Branch, Therapeutics for Rare and Neglected Diseases (TRND) Program, National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Fady Hannah-Shmouni
- Division of Translational Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Forbes D Porter
- Division of Translational Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Jack R Collins
- Advanced Biomedical Computational Science, Frederick National Laboratory for Cancer Research, Frederick, MD, 21702, USA
| | - Elizabeth A Ottinger
- Division of Preclinical Innovation, Therapeutic Development Branch, Therapeutics for Rare and Neglected Diseases (TRND) Program, National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, MD, 20892, USA.
| | - Uma S Mudunuri
- Advanced Biomedical Computational Science, Frederick National Laboratory for Cancer Research, Frederick, MD, 21702, USA.
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17
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Fan K, Pfister E, Weng Z. Toward a comprehensive catalog of regulatory elements. Hum Genet 2023; 142:1091-1111. [PMID: 36935423 DOI: 10.1007/s00439-023-02519-3] [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: 11/06/2022] [Accepted: 01/03/2023] [Indexed: 03/21/2023]
Abstract
Regulatory elements are the genomic regions that interact with transcription factors to control cell-type-specific gene expression in different cellular environments. A precise and complete catalog of functional elements encoded by the human genome is key to understanding mammalian gene regulation. Here, we review the current state of regulatory element annotation. We first provide an overview of assays for characterizing functional elements, including genome, epigenome, transcriptome, three-dimensional chromatin interaction, and functional validation assays. We then discuss computational methods for defining regulatory elements, including peak-calling and other statistical modeling methods. Finally, we introduce several high-quality lists of regulatory element annotations and suggest potential future directions.
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Affiliation(s)
- Kaili Fan
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Chan Medical School, 368 Plantation Street, ASC5-1069, Worcester, MA, 01605, USA
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, 02138, USA
| | - Edith Pfister
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Chan Medical School, 368 Plantation Street, ASC5-1069, Worcester, MA, 01605, USA
| | - Zhiping Weng
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Chan Medical School, 368 Plantation Street, ASC5-1069, Worcester, MA, 01605, USA.
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18
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Yang ZH, Cai X, Ding ZL, Li W, Zhang CY, Huo JH, Zhang Y, Wang L, Zhang LM, Li SW, Li M, Zhang C, Chang H, Xiao X. Identification of a psychiatric risk gene NISCH at 3p21.1 GWAS locus mediating dendritic spine morphogenesis and cognitive function. BMC Med 2023; 21:254. [PMID: 37443018 PMCID: PMC10347724 DOI: 10.1186/s12916-023-02931-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 06/08/2023] [Indexed: 07/15/2023] Open
Abstract
BACKGROUND Schizophrenia and bipolar disorder (BD) are believed to share clinical symptoms, genetic risk, etiological factors, and pathogenic mechanisms. We previously reported that single nucleotide polymorphisms spanning chromosome 3p21.1 showed significant associations with both schizophrenia and BD, and a risk SNP rs2251219 was in linkage disequilibrium with a human specific Alu polymorphism rs71052682, which showed enhancer effects on transcriptional activities using luciferase reporter assays in U251 and U87MG cells. METHODS CRISPR/Cas9-directed genome editing, real-time quantitative PCR, and public Hi-C data were utilized to investigate the correlation between the Alu polymorphism rs71052682 and NISCH. Primary neuronal culture, immunofluorescence staining, co-immunoprecipitation, lentiviral vector production, intracranial stereotaxic injection, behavioral assessment, and drug treatment were used to examine the physiological impacts of Nischarin (encoded by NISCH). RESULTS Deleting the Alu sequence in U251 and U87MG cells reduced mRNA expression of NISCH, the gene locates 180 kb from rs71052682, and Hi-C data in brain tissues confirmed the extensive chromatin contacts. These data suggested that the genetic risk of schizophrenia and BD predicted elevated NISCH expression, which was also consistent with the observed higher NISCH mRNA levels in the brain tissues from psychiatric patients compared with controls. We then found that overexpression of NISCH resulted in a significantly decreased density of mushroom dendritic spines with a simultaneously increased density of thin dendritic spines in primary cultured neurons. Intriguingly, elevated expression of this gene in mice also led to impaired spatial working memory in the Y-maze. Given that Nischarin is the target of anti-hypertensive agents clonidine and tizanidine, which have shown therapeutic effects in patients with schizophrenia and patients with BD in preliminary clinical trials, we demonstrated that treatment with those antihypertensive drugs could reduce NISCH mRNA expression and rescue the impaired working memory in mice. CONCLUSIONS We identify a psychiatric risk gene NISCH at 3p21.1 GWAS locus influencing dendritic spine morphogenesis and cognitive function, and Nischarin may have potentials for future therapeutic development.
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Affiliation(s)
- Zhi-Hui Yang
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences and Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
- Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, Yunnan, China
| | - Xin Cai
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences and Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
- Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, Yunnan, China
| | - Zhong-Li Ding
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences and Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
- Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, Yunnan, China
| | - Wei Li
- Department of Blood Transfusion, The Second Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Chu-Yi Zhang
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences and Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
- Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, Yunnan, China
| | - Jin-Hua Huo
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences and Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
- Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, Yunnan, China
| | - Yue Zhang
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences and Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
- Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, Yunnan, China
| | - Lu Wang
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences and Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
| | - Lin-Ming Zhang
- Department of Neurology, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Shi-Wu Li
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences and Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
| | - Ming Li
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences and Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
| | - Chen Zhang
- Clinical Research Center & Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai, China.
| | - Hong Chang
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences and Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China.
| | - Xiao Xiao
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences and Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China.
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19
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Bocher O, Willer CJ, Zeggini E. Unravelling the genetic architecture of human complex traits through whole genome sequencing. Nat Commun 2023; 14:3520. [PMID: 37316478 DOI: 10.1038/s41467-023-39259-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 06/06/2023] [Indexed: 06/16/2023] Open
Affiliation(s)
- Ozvan Bocher
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764, Neuherberg, Germany
| | - Cristen J Willer
- Department of Internal Medicine, Division of Cardiology, University of Michigan, Ann Arbor, MI, 48109, USA
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, 48109, USA
- Department of Computational Medicine and Biostatistics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Eleftheria Zeggini
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764, Neuherberg, Germany.
- Technical University of Munich (TUM) and Klinikum Rechts der Isar, TUM School of Medicine, Ismaninger Str. 22, 81675, Munich, Germany.
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20
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Wang Z, Zhao G, Li B, Fang Z, Chen Q, Wang X, Luo T, Wang Y, Zhou Q, Li K, Xia L, Zhang Y, Zhou X, Pan H, Zhao Y, Wang Y, Wang L, Guo J, Tang B, Xia K, Li J. Performance Comparison of Computational Methods for the Prediction of the Function and Pathogenicity of Non-coding Variants. GENOMICS, PROTEOMICS & BIOINFORMATICS 2023; 21:649-661. [PMID: 35272052 PMCID: PMC10787016 DOI: 10.1016/j.gpb.2022.02.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Revised: 12/28/2021] [Accepted: 02/27/2022] [Indexed: 06/14/2023]
Abstract
Non-coding variants in the human genome significantly influence human traits and complex diseases via their regulation and modification effects. Hence, an increasing number of computational methods are developed to predict the effects of variants in human non-coding sequences. However, it is difficult for inexperienced users to select appropriate computational methods from dozens of available methods. To solve this issue, we assessed 12 performance metrics of 24 methods on four independent non-coding variant benchmark datasets: (1) rare germline variants from clinical relevant sequence variants (ClinVar), (2) rare somatic variants from Catalogue Of Somatic Mutations In Cancer (COSMIC), (3) common regulatory variants from curated expression quantitative trait locus (eQTL) data, and (4) disease-associated common variants from curated genome-wide association studies (GWAS). All 24 tested methods performed differently under various conditions, indicating varying strengths and weaknesses under different scenarios. Importantly, the performance of existing methods was acceptable for rare germline variants from ClinVar with the area under the receiver operating characteristic curve (AUROC) of 0.4481-0.8033 and poor for rare somatic variants from COSMIC (AUROC = 0.4984-0.7131), common regulatory variants from curated eQTL data (AUROC = 0.4837-0.6472), and disease-associated common variants from curated GWAS (AUROC = 0.4766-0.5188). We also compared the prediction performance of 24 methods for non-coding de novo mutations in autism spectrum disorder, and found that the combined annotation-dependent depletion (CADD) and context-dependent tolerance score (CDTS) methods showed better performance. Summarily, we assessed the performance of 24 computational methods under diverse scenarios, providing preliminary advice for proper tool selection and guiding the development of new techniques in interpreting non-coding variants.
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Affiliation(s)
- Zheng Wang
- National Clinical Research Centre for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Guihu Zhao
- National Clinical Research Centre for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha 410008, China; Department of Neurology, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Bin Li
- National Clinical Research Centre for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha 410008, China; Department of Neurology, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Zhenghuan Fang
- Centre for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha 410008, China
| | - Qian Chen
- National Clinical Research Centre for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Xiaomeng Wang
- Centre for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha 410008, China
| | - Tengfei Luo
- Centre for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha 410008, China
| | - Yijing Wang
- Centre for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha 410008, China
| | - Qiao Zhou
- National Clinical Research Centre for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Kuokuo Li
- Centre for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha 410008, China
| | - Lu Xia
- Centre for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha 410008, China
| | - Yi Zhang
- National Clinical Research Centre for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Xun Zhou
- National Clinical Research Centre for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Hongxu Pan
- Department of Neurology, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Yuwen Zhao
- Department of Neurology, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Yige Wang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Lin Wang
- Centre for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha 410008, China; Reproductive Medicine Center, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Jifeng Guo
- National Clinical Research Centre for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha 410008, China; Department of Neurology, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Beisha Tang
- National Clinical Research Centre for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha 410008, China; Department of Neurology, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Kun Xia
- Centre for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha 410008, China
| | - Jinchen Li
- National Clinical Research Centre for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha 410008, China; Department of Neurology, Xiangya Hospital, Central South University, Changsha 410008, China; Centre for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha 410008, China.
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21
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Rozowsky J, Gao J, Borsari B, Yang YT, Galeev T, Gürsoy G, Epstein CB, Xiong K, Xu J, Li T, Liu J, Yu K, Berthel A, Chen Z, Navarro F, Sun MS, Wright J, Chang J, Cameron CJF, Shoresh N, Gaskell E, Drenkow J, Adrian J, Aganezov S, Aguet F, Balderrama-Gutierrez G, Banskota S, Corona GB, Chee S, Chhetri SB, Cortez Martins GC, Danyko C, Davis CA, Farid D, Farrell NP, Gabdank I, Gofin Y, Gorkin DU, Gu M, Hecht V, Hitz BC, Issner R, Jiang Y, Kirsche M, Kong X, Lam BR, Li S, Li B, Li X, Lin KZ, Luo R, Mackiewicz M, Meng R, Moore JE, Mudge J, Nelson N, Nusbaum C, Popov I, Pratt HE, Qiu Y, Ramakrishnan S, Raymond J, Salichos L, Scavelli A, Schreiber JM, Sedlazeck FJ, See LH, Sherman RM, Shi X, Shi M, Sloan CA, Strattan JS, Tan Z, Tanaka FY, Vlasova A, Wang J, Werner J, Williams B, Xu M, Yan C, Yu L, Zaleski C, Zhang J, Ardlie K, Cherry JM, Mendenhall EM, Noble WS, Weng Z, Levine ME, Dobin A, Wold B, Mortazavi A, Ren B, Gillis J, Myers RM, Snyder MP, Choudhary J, Milosavljevic A, Schatz MC, Bernstein BE, Guigó R, Gingeras TR, Gerstein M. The EN-TEx resource of multi-tissue personal epigenomes & variant-impact models. Cell 2023; 186:1493-1511.e40. [PMID: 37001506 PMCID: PMC10074325 DOI: 10.1016/j.cell.2023.02.018] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Revised: 10/16/2022] [Accepted: 02/10/2023] [Indexed: 04/03/2023]
Abstract
Understanding how genetic variants impact molecular phenotypes is a key goal of functional genomics, currently hindered by reliance on a single haploid reference genome. Here, we present the EN-TEx resource of 1,635 open-access datasets from four donors (∼30 tissues × ∼15 assays). The datasets are mapped to matched, diploid genomes with long-read phasing and structural variants, instantiating a catalog of >1 million allele-specific loci. These loci exhibit coordinated activity along haplotypes and are less conserved than corresponding, non-allele-specific ones. Surprisingly, a deep-learning transformer model can predict the allele-specific activity based only on local nucleotide-sequence context, highlighting the importance of transcription-factor-binding motifs particularly sensitive to variants. Furthermore, combining EN-TEx with existing genome annotations reveals strong associations between allele-specific and GWAS loci. It also enables models for transferring known eQTLs to difficult-to-profile tissues (e.g., from skin to heart). Overall, EN-TEx provides rich data and generalizable models for more accurate personal functional genomics.
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Affiliation(s)
- Joel Rozowsky
- Section on Biomedical Informatics and Data Science, Yale University, New Haven, CT, USA; Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Jiahao Gao
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Beatrice Borsari
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA; Centre for Genomic Regulation, The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
| | - Yucheng T Yang
- Institute of Science and Technology for Brain-Inspired Intelligence; MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence; MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200433, China; Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Timur Galeev
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Gamze Gürsoy
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | | | - Kun Xiong
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Jinrui Xu
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Tianxiao Li
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Jason Liu
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Keyang Yu
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Ana Berthel
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Zhanlin Chen
- Department of Statistics and Data Science, Yale University, New Haven, CT, USA
| | - Fabio Navarro
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Maxwell S Sun
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | | | - Justin Chang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Christopher J F Cameron
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Noam Shoresh
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Jorg Drenkow
- Functional Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Jessika Adrian
- Department of Genetics, School of Medicine, Stanford University, Palo Alto, CA, USA
| | - Sergey Aganezov
- Departments of Computer Science and Biology, Johns Hopkins University, Baltimore, MD, USA
| | | | | | | | | | - Sora Chee
- Ludwig Institute for Cancer Research, University of California, San Diego, La Jolla, CA, USA
| | - Surya B Chhetri
- HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA
| | - Gabriel Conte Cortez Martins
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Cassidy Danyko
- Functional Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Carrie A Davis
- Functional Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Daniel Farid
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | | | - Idan Gabdank
- Department of Genetics, School of Medicine, Stanford University, Palo Alto, CA, USA
| | - Yoel Gofin
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - David U Gorkin
- Ludwig Institute for Cancer Research, University of California, San Diego, La Jolla, CA, USA
| | - Mengting Gu
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Vivian Hecht
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Benjamin C Hitz
- Department of Genetics, School of Medicine, Stanford University, Palo Alto, CA, USA
| | - Robbyn Issner
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Yunzhe Jiang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Melanie Kirsche
- Departments of Computer Science and Biology, Johns Hopkins University, Baltimore, MD, USA
| | - Xiangmeng Kong
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Bonita R Lam
- Department of Genetics, School of Medicine, Stanford University, Palo Alto, CA, USA
| | - Shantao Li
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Bian Li
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Xiqi Li
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Khine Zin Lin
- Department of Genetics, School of Medicine, Stanford University, Palo Alto, CA, USA
| | - Ruibang Luo
- Department of Computer Science, The University of Hong Kong, Hong Kong, CHN
| | - Mark Mackiewicz
- HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA
| | - Ran Meng
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Jill E Moore
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA, USA
| | - Jonathan Mudge
- European Bioinformatics Institute, Cambridge, Cambridgeshire, GB
| | | | - Chad Nusbaum
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ioann Popov
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Henry E Pratt
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA, USA
| | - Yunjiang Qiu
- Ludwig Institute for Cancer Research, University of California, San Diego, La Jolla, CA, USA
| | - Srividya Ramakrishnan
- Departments of Computer Science and Biology, Johns Hopkins University, Baltimore, MD, USA
| | - Joe Raymond
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Leonidas Salichos
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA; Department of Biological and Chemical Sciences, New York Institute of Technology, Old Westbury, NY, USA
| | - Alexandra Scavelli
- Functional Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Jacob M Schreiber
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Fritz J Sedlazeck
- Departments of Computer Science and Biology, Johns Hopkins University, Baltimore, MD, USA; Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA; Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Lei Hoon See
- Functional Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Rachel M Sherman
- Departments of Computer Science and Biology, Johns Hopkins University, Baltimore, MD, USA
| | - Xu Shi
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Minyi Shi
- Department of Genetics, School of Medicine, Stanford University, Palo Alto, CA, USA
| | - Cricket Alicia Sloan
- Department of Genetics, School of Medicine, Stanford University, Palo Alto, CA, USA
| | - J Seth Strattan
- Department of Genetics, School of Medicine, Stanford University, Palo Alto, CA, USA
| | - Zhen Tan
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Forrest Y Tanaka
- Department of Genetics, School of Medicine, Stanford University, Palo Alto, CA, USA
| | - Anna Vlasova
- Centre for Genomic Regulation, The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain; Comparative Genomics Group, Life Science Programme, Barcelona Supercomputing Centre, Barcelona, Spain; Institute of Research in Biomedicine, Barcelona, Spain
| | - Jun Wang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Jonathan Werner
- Functional Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Brian Williams
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Min Xu
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Chengfei Yan
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Lu Yu
- Institute of Cancer Research, London, UK
| | - Christopher Zaleski
- Functional Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Jing Zhang
- Department of Computer Science, University of California, Irvine, Irvine, CA, USA
| | | | - J Michael Cherry
- Department of Genetics, School of Medicine, Stanford University, Palo Alto, CA, USA
| | | | - William S Noble
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Zhiping Weng
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA, USA
| | - Morgan E Levine
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Pathology, Yale University School of Medicine, New Haven, CT, USA
| | - Alexander Dobin
- Functional Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Barbara Wold
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Ali Mortazavi
- Department of Developmental and Cell Biology, University of California, Irvine, Irvine, CA, USA
| | - Bing Ren
- Ludwig Institute for Cancer Research, University of California, San Diego, La Jolla, CA, USA
| | - Jesse Gillis
- Functional Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA; Department of Physiology, University of Toronto, Toronto, ON, Canada
| | - Richard M Myers
- HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA
| | - Michael P Snyder
- Department of Genetics, School of Medicine, Stanford University, Palo Alto, CA, USA
| | | | | | - Michael C Schatz
- Departments of Computer Science and Biology, Johns Hopkins University, Baltimore, MD, USA; Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
| | - Bradley E Bernstein
- Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA.
| | - Roderic Guigó
- Centre for Genomic Regulation, The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain; Universitat Pompeu Fabra, Barcelona, Catalonia, Spain.
| | - Thomas R Gingeras
- Functional Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
| | - Mark Gerstein
- Section on Biomedical Informatics and Data Science, Yale University, New Haven, CT, USA; Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA; Department of Statistics and Data Science, Yale University, New Haven, CT, USA; Department of Computer Science, Yale University, New Haven, CT, USA.
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22
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Li Y, Huang D, Pei Y, Wu Y, Xu R, Quan F, Gao H, Zhang J, Hou H, Zhang K, Li J. CasSABER for Programmable In Situ Visualization of Low and Nonrepetitive Gene Loci. Anal Chem 2023; 95:2992-3001. [PMID: 36703533 DOI: 10.1021/acs.analchem.2c04867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
Site-specific imaging of target genes using CRISPR probes is essential for understanding the molecular mechanisms of gene function and engineering tools to modulate its downstream pathways. Herein, we develop CRISPR/Cas9-mediated signal amplification by exchange reaction (CasSABER) for programmable in situ imaging of low and nonrepetitive regions of the target gene in the cell nucleus. The presynthesized primer-exchange reaction (PER) probe is able to hybridize multiple fluorophore-bearing imager strands to specifically light up dCas9/sgRNA target-bound gene loci, enabling in situ imaging of fixed cellular gene loci with high specificity and signal-to-noise ratio. In combination with a multiround branching strategy, we successfully detected nonrepetitive gene regions using a single sgRNA. As an intensity-codable and orthogonal probe system, CasSABER enables the adjustable amplification of local signals in fixed cells, resulting in the simultaneous visualization of multicopy and single-copy gene loci with similar fluorescence intensity. Owing to avoiding the complexity of controlling in situ mutistep enzymatic reactions, CasSABER shows good reliability, sensitivity, and ease of implementation, providing a rapid and cost-effective molecular toolkit for studying multigene interaction in fundamental research and gene diagnosis.
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Affiliation(s)
- Yanan Li
- School of Pharmaceutical Sciences, Key Laboratory of Targeting Therapy and Diagnosis for Critical Diseases, Collaborative Innovation Center of New Drug Research and Safety Evaluation, State Key Laboratory of Esophageal Cancer Prevention & Treatment, Zhengzhou University, Zhengzhou450001, China
| | - Di Huang
- School of Pharmaceutical Sciences, Key Laboratory of Targeting Therapy and Diagnosis for Critical Diseases, Collaborative Innovation Center of New Drug Research and Safety Evaluation, State Key Laboratory of Esophageal Cancer Prevention & Treatment, Zhengzhou University, Zhengzhou450001, China
| | - Yiran Pei
- School of Pharmaceutical Sciences, Key Laboratory of Targeting Therapy and Diagnosis for Critical Diseases, Collaborative Innovation Center of New Drug Research and Safety Evaluation, State Key Laboratory of Esophageal Cancer Prevention & Treatment, Zhengzhou University, Zhengzhou450001, China
| | - Yonghua Wu
- School of Pharmaceutical Sciences, Key Laboratory of Targeting Therapy and Diagnosis for Critical Diseases, Collaborative Innovation Center of New Drug Research and Safety Evaluation, State Key Laboratory of Esophageal Cancer Prevention & Treatment, Zhengzhou University, Zhengzhou450001, China
| | - Ru Xu
- School of Pharmaceutical Sciences, Key Laboratory of Targeting Therapy and Diagnosis for Critical Diseases, Collaborative Innovation Center of New Drug Research and Safety Evaluation, State Key Laboratory of Esophageal Cancer Prevention & Treatment, Zhengzhou University, Zhengzhou450001, China
| | - Fenglei Quan
- School of Pharmaceutical Sciences, Key Laboratory of Targeting Therapy and Diagnosis for Critical Diseases, Collaborative Innovation Center of New Drug Research and Safety Evaluation, State Key Laboratory of Esophageal Cancer Prevention & Treatment, Zhengzhou University, Zhengzhou450001, China
| | - Hua Gao
- School of Pharmaceutical Sciences, Key Laboratory of Targeting Therapy and Diagnosis for Critical Diseases, Collaborative Innovation Center of New Drug Research and Safety Evaluation, State Key Laboratory of Esophageal Cancer Prevention & Treatment, Zhengzhou University, Zhengzhou450001, China
| | - Junli Zhang
- School of Pharmaceutical Sciences, Key Laboratory of Targeting Therapy and Diagnosis for Critical Diseases, Collaborative Innovation Center of New Drug Research and Safety Evaluation, State Key Laboratory of Esophageal Cancer Prevention & Treatment, Zhengzhou University, Zhengzhou450001, China
| | - Hongwei Hou
- China National Tobacco Quality Supervision & Test Center, Zhengzhou450001, China
- Beijing Institute of Life Science and Technology, Beijing100083, China
| | - Kaixiang Zhang
- School of Pharmaceutical Sciences, Key Laboratory of Targeting Therapy and Diagnosis for Critical Diseases, Collaborative Innovation Center of New Drug Research and Safety Evaluation, State Key Laboratory of Esophageal Cancer Prevention & Treatment, Zhengzhou University, Zhengzhou450001, China
| | - Jinghong Li
- Department of Chemistry, Key Laboratory of Bioorganic Phosphorus Chemistry and Chemical Biology, Tsinghua University, Beijing100084, China
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23
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Virolainen SJ, VonHandorf A, Viel KCMF, Weirauch MT, Kottyan LC. Gene-environment interactions and their impact on human health. Genes Immun 2023; 24:1-11. [PMID: 36585519 PMCID: PMC9801363 DOI: 10.1038/s41435-022-00192-6] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 12/09/2022] [Accepted: 12/13/2022] [Indexed: 12/31/2022]
Abstract
The molecular processes underlying human health and disease are highly complex. Often, genetic and environmental factors contribute to a given disease or phenotype in a non-additive manner, yielding a gene-environment (G × E) interaction. In this work, we broadly review current knowledge on the impact of gene-environment interactions on human health. We first explain the independent impact of genetic variation and the environment. We next detail well-established G × E interactions that impact human health involving environmental toxicants, pollution, viruses, and sex chromosome composition. We conclude with possibilities and challenges for studying G × E interactions.
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Affiliation(s)
- Samuel J Virolainen
- Division of Human Genetics, Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave., Cincinnati, OH, 45229, USA
- Immunology Graduate Program, University of Cincinnati College of Medicine, 3230 Eden Ave, Cincinnati, OH, 45229, USA
| | - Andrew VonHandorf
- Division of Human Genetics, Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave., Cincinnati, OH, 45229, USA
| | - Kenyatta C M F Viel
- Division of Human Genetics, Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave., Cincinnati, OH, 45229, USA
| | - Matthew T Weirauch
- Division of Human Genetics, Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave., Cincinnati, OH, 45229, USA.
- Immunology Graduate Program, University of Cincinnati College of Medicine, 3230 Eden Ave, Cincinnati, OH, 45229, USA.
- Divisions of Biomedical Informatics and Developmental Biology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave., Cincinnati, OH, 45229, USA.
- Department of Pediatrics, University of Cincinnati College of Medicine, 3230 Eden Ave, Cincinnati, OH, 45229, USA.
| | - Leah C Kottyan
- Division of Human Genetics, Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave., Cincinnati, OH, 45229, USA.
- Immunology Graduate Program, University of Cincinnati College of Medicine, 3230 Eden Ave, Cincinnati, OH, 45229, USA.
- Department of Pediatrics, University of Cincinnati College of Medicine, 3230 Eden Ave, Cincinnati, OH, 45229, USA.
- Division of Allergy and Immunology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave., MLC 15012, Cincinnati, OH, 45229, USA.
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24
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dos Santos CG, Sousa MF, Vieira JIG, de Morais LR, Fernandes AAS, de Oliveira Littiere T, Itajara Otto P, Machado MA, Silva MVGB, Bonafé CM, Braga Magalhães AF, Verardo LL. Candidate genes for tick resistance in cattle: a systematic review combining post-GWAS analyses with sequencing data. JOURNAL OF APPLIED ANIMAL RESEARCH 2022. [DOI: 10.1080/09712119.2022.2096035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Affiliation(s)
- Cassiane Gomes dos Santos
- Department of Animal Science, Universidade Federal dos Vales do Jequitinhonha e Mucuri, Diamantina, Brazil
| | - Mariele Freitas Sousa
- Department of Animal Science, Universidade Federal dos Vales do Jequitinhonha e Mucuri, Diamantina, Brazil
| | - João Inácio Gomes Vieira
- Department of Animal Science, Universidade Federal dos Vales do Jequitinhonha e Mucuri, Diamantina, Brazil
| | - Luana Rafaela de Morais
- Department of Animal Science, Universidade Federal dos Vales do Jequitinhonha e Mucuri, Diamantina, Brazil
| | | | | | - Pamela Itajara Otto
- Department of Animal Science, Universidade Federal de Santa Maria, Santa Maria, Brazil
| | | | | | - Cristina Moreira Bonafé
- Department of Animal Science, Universidade Federal dos Vales do Jequitinhonha e Mucuri, Diamantina, Brazil
| | | | - Lucas Lima Verardo
- Department of Animal Science, Universidade Federal dos Vales do Jequitinhonha e Mucuri, Diamantina, Brazil
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25
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Exploration of Tools for the Interpretation of Human Non-Coding Variants. Int J Mol Sci 2022; 23:ijms232112977. [PMID: 36361767 PMCID: PMC9654743 DOI: 10.3390/ijms232112977] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 10/17/2022] [Accepted: 10/23/2022] [Indexed: 02/01/2023] Open
Abstract
The advent of Whole Genome Sequencing (WGS) broadened the genetic variation detection range, revealing the presence of variants even in non-coding regions of the genome, which would have been missed using targeted approaches. One of the most challenging issues in WGS analysis regards the interpretation of annotated variants. This review focuses on tools suitable for the functional annotation of variants falling into non-coding regions. It couples the description of non-coding genomic areas with the results and performance of existing tools for a functional interpretation of the effect of variants in these regions. Tools were tested in a controlled genomic scenario, representing the ground-truth and allowing us to determine software performance.
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26
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Sun S, Miller M, Wang Y, Tyc KM, Cao X, Scott RT, Tao X, Bromberg Y, Schindler K, Xing J. Predicting embryonic aneuploidy rate in IVF patients using whole-exome sequencing. Hum Genet 2022; 141:1615-1627. [PMID: 35347416 PMCID: PMC10095970 DOI: 10.1007/s00439-022-02450-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Accepted: 03/16/2022] [Indexed: 01/13/2023]
Abstract
Infertility is a major reproductive health issue that affects about 12% of women of reproductive age in the United States. Aneuploidy in eggs accounts for a significant proportion of early miscarriage and in vitro fertilization failure. Recent studies have shown that genetic variants in several genes affect chromosome segregation fidelity and predispose women to a higher incidence of egg aneuploidy. However, the exact genetic causes of aneuploid egg production remain unclear, making it difficult to diagnose infertility based on individual genetic variants in mother's genome. In this study, we evaluated machine learning-based classifiers for predicting the embryonic aneuploidy risk in female IVF patients using whole-exome sequencing data. Using two exome datasets, we obtained an area under the receiver operating curve of 0.77 and 0.68, respectively. High precision could be traded off for high specificity in classifying patients by selecting different prediction score cutoffs. For example, a strict prediction score cutoff of 0.7 identified 29% of patients as high-risk with 94% precision. In addition, we identified MCM5, FGGY, and DDX60L as potential aneuploidy risk genes that contribute the most to the predictive power of the model. These candidate genes and their molecular interaction partners are enriched for meiotic-related gene ontology categories and pathways, such as microtubule organizing center and DNA recombination. In summary, we demonstrate that sequencing data can be mined to predict patients' aneuploidy risk thus improving clinical diagnosis. The candidate genes and pathways we identified are promising targets for future aneuploidy studies.
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Affiliation(s)
- Siqi Sun
- Department of Genetics, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA
| | - Maximilian Miller
- Department of Biochemistry and Microbiology, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA
| | - Yanran Wang
- Department of Biochemistry and Microbiology, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA
| | - Katarzyna M Tyc
- Department of Genetics, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA
- Current address: Department of Pharmacology and Toxicology, Virginia Commonwealth University, Richmond, VA, USA
| | - Xiaolong Cao
- Department of Genetics, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA
| | - Richard T Scott
- Reproductive Medicine Associates of New Jersey, Basking Ridge, NJ, USA
| | - Xin Tao
- Foundation for Embryonic Competence, Basking Ridge, NJ, USA
| | - Yana Bromberg
- Department of Genetics, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA
- Department of Biochemistry and Microbiology, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA
- Human Genetics Institute of New Jersey, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Karen Schindler
- Department of Genetics, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA
- Human Genetics Institute of New Jersey, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Jinchuan Xing
- Department of Genetics, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA.
- Human Genetics Institute of New Jersey, Rutgers, The State University of New Jersey, Piscataway, NJ, USA.
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27
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LaPierre MP, Lawler K, Godbersen S, Farooqi IS, Stoffel M. MicroRNA-7 regulates melanocortin circuits involved in mammalian energy homeostasis. Nat Commun 2022; 13:5733. [PMID: 36175420 PMCID: PMC9522793 DOI: 10.1038/s41467-022-33367-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 09/14/2022] [Indexed: 11/09/2022] Open
Abstract
MicroRNAs (miRNAs) modulate physiological responses by repressing the expression of gene networks. We found that global deletion of microRNA-7 (miR-7), the most enriched miRNA in the hypothalamus, causes obesity in mice. Targeted deletion of miR-7 in Single-minded homolog 1 (Sim1) neurons, a critical component of the hypothalamic melanocortin pathway, causes hyperphagia, obesity and increased linear growth, mirroring Sim1 and Melanocortin-4 receptor (MC4R) haplo-insufficiency in mice and humans. We identified Snca (α-Synuclein) and Igsf8 (Immunoglobulin Superfamily Member 8) as miR-7 target genes that act in Sim1 neurons to regulate body weight and endocrine axes. In humans, MIR-7-1 is located in the last intron of HNRNPK, whose promoter drives the expression of both genes. Genetic variants at the HNRNPK locus that reduce its expression are associated with increased height and truncal fat mass. These findings demonstrate that miR-7 suppresses gene networks involved in the hypothalamic melanocortin pathway to regulate mammalian energy homeostasis.
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Affiliation(s)
- Mary P LaPierre
- Institute of Molecular Health Sciences, ETH Zürich, 8093, Zürich, Switzerland
| | - Katherine Lawler
- University of Cambridge Metabolic Research Laboratories and NIHR Cambridge Biomedical Research Centre, Wellcome Trust-MRC Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, CB2 0QQ, UK
| | - Svenja Godbersen
- Institute of Molecular Health Sciences, ETH Zürich, 8093, Zürich, Switzerland
| | - I Sadaf Farooqi
- University of Cambridge Metabolic Research Laboratories and NIHR Cambridge Biomedical Research Centre, Wellcome Trust-MRC Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, CB2 0QQ, UK
| | - Markus Stoffel
- Institute of Molecular Health Sciences, ETH Zürich, 8093, Zürich, Switzerland. .,Medical Faculty, University of Zürich, 8091, Zürich, Switzerland.
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28
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Nayara Góes de Araújo J, Fernandes de Oliveira V, Bassani Borges J, Dagli-Hernandez C, da Silva Rodrigues Marçal E, Caroline Costa de Freitas R, Medeiros Bastos G, Marques Gonçalves R, Arpad Faludi A, Elim Jannes C, da Costa Pereira A, Dominguez Crespo Hirata R, Hiroyuki Hirata M, Ducati Luchessi A, Nogueira Silbiger V. In silico analysis of upstream variants in Brazilian patients with Familial Hypercholesterolemia. Gene X 2022; 849:146908. [PMID: 36167182 DOI: 10.1016/j.gene.2022.146908] [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: 02/21/2022] [Revised: 08/16/2022] [Accepted: 09/19/2022] [Indexed: 10/14/2022] Open
Abstract
Familial hypercholesterolemia (FH) is a prevalent autosomal genetic disease associated with increased risk of early cardiovascular events and death due to chronic exposure to very high levels of low-density lipoprotein cholesterol (LDL-c). Pathogenic variants in the coding regions of LDLR, APOB and PCSK9 account for most FH cases, and variants in non-coding regions maybe involved in FH as well. Variants in the upstream region of LDLR, APOB and PCSK9 were screened by targeted next-generation sequencing and their effects were explored using in silico tools. Twenty-five patients without pathogenic variants in FH-related genes were selected. 3 kb upstream regions of LDLR, APOB and PCSK9 were sequenced using the AmpliSeq (Illumina) and Miseq Reagent Nano Kit v2 (Illumina). Sequencing data were analyzed using variant discovery and functional annotation tools. Potentially regulatory variants were selected by integrating data from public databases, published data and context-dependent regulatory prediction score. Thirty-four single nucleotide variants (SNVs) in upstream regions were identified (6 in LDLR, 15 in APOB, and 13 in PCSK9). Five SNVs were prioritized as potentially regulatory variants (rs934197, rs9282606, rs36218923, rs538300761, g.55038486A>G). APOB rs934197 was previously associated with increased rate of transcription, which in silico analysis suggests that could be due to reducing binding affinity of a transcriptional repressor. Our findings highlight the importance of variant screening outside of coding regions of all relevant genes. Further functional studies are necessary to confirm that prioritized variants could impact gene regulation and contribute to the FH phenotype.
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Affiliation(s)
- Jéssica Nayara Góes de Araújo
- Northeast Biotechnology Network (RENORBIO), Graduate Program in Biotechnology, Federal University of Rio Grande do Norte, Natal 59078-900, Brazil
| | - Victor Fernandes de Oliveira
- Department of Clinical and Toxicological Analyses, School of Pharmaceutical Sciences, University of Sao Paulo, Sao Paulo 05508-000, Brazil
| | - Jéssica Bassani Borges
- Department of Clinical and Toxicological Analyses, School of Pharmaceutical Sciences, University of Sao Paulo, Sao Paulo 05508-000, Brazil; Laboratory of Molecular Research in Cardiology, Institute Dante Pazzanese of Cardiology, Sao Paulo, 04012-909, Brazil
| | - Carolina Dagli-Hernandez
- Department of Clinical and Toxicological Analyses, School of Pharmaceutical Sciences, University of Sao Paulo, Sao Paulo 05508-000, Brazil
| | | | - Renata Caroline Costa de Freitas
- Department of Clinical and Toxicological Analyses, School of Pharmaceutical Sciences, University of Sao Paulo, Sao Paulo 05508-000, Brazil
| | - Gisele Medeiros Bastos
- Laboratory of Molecular Research in Cardiology, Institute Dante Pazzanese of Cardiology, Sao Paulo, 04012-909, Brazil; Medical Clinic Division, Institute Dante Pazzanese of Cardiology, Sao Paulo 04012-909, Brazil
| | | | - André Arpad Faludi
- Medical Clinic Division, Institute Dante Pazzanese of Cardiology, Sao Paulo 04012-909, Brazil
| | - Cinthia Elim Jannes
- Laboratory of Genetics and Molecular Cardiology, Heart Institute, University of Sao Paulo 05403-900, Brazil
| | - Alexandre da Costa Pereira
- Laboratory of Genetics and Molecular Cardiology, Heart Institute, University of Sao Paulo 05403-900, Brazil
| | - Rosario Dominguez Crespo Hirata
- Department of Clinical and Toxicological Analyses, School of Pharmaceutical Sciences, University of Sao Paulo, Sao Paulo 05508-000, Brazil
| | - Mario Hiroyuki Hirata
- Department of Clinical and Toxicological Analyses, School of Pharmaceutical Sciences, University of Sao Paulo, Sao Paulo 05508-000, Brazil
| | - André Ducati Luchessi
- Northeast Biotechnology Network (RENORBIO), Graduate Program in Biotechnology, Federal University of Rio Grande do Norte, Natal 59078-900, Brazil; Department of Clinical and Toxicological Analyses, Federal University of Rio Grande do Norte, Natal 59012-570, Brazil
| | - Vivian Nogueira Silbiger
- Northeast Biotechnology Network (RENORBIO), Graduate Program in Biotechnology, Federal University of Rio Grande do Norte, Natal 59078-900, Brazil; Department of Clinical and Toxicological Analyses, Federal University of Rio Grande do Norte, Natal 59012-570, Brazil.
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Two-layer design protects genes from mutations in their enhancers. Nature 2022; 609:477-478. [PMID: 36064783 DOI: 10.1038/d41586-022-02341-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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30
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Pilz RA, Skowronek D, Hamed M, Weise A, Mangold E, Radbruch A, Pietsch T, Felbor U, Rath M. Using CRISPR/Cas9 genome editing in human iPSCs for deciphering the pathogenicity of a novel CCM1 transcription start site deletion. Front Mol Biosci 2022; 9:953048. [PMID: 36090026 PMCID: PMC9453596 DOI: 10.3389/fmolb.2022.953048] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 08/09/2022] [Indexed: 11/17/2022] Open
Abstract
Cerebral cavernous malformations are clusters of aberrant vessels that can lead to severe neurological complications. Pathogenic loss-of-function variants in the CCM1, CCM2, or CCM3 gene are associated with the autosomal dominant form of the disease. While interpretation of variants in protein-coding regions of the genes is relatively straightforward, functional analyses are often required to evaluate the impact of non-coding variants. Because of multiple alternatively spliced transcripts and different transcription start points, interpretation of variants in the 5′ untranslated and upstream regions of CCM1 is particularly challenging. Here, we identified a novel deletion of the non-coding exon 1 of CCM1 in a proband with multiple CCMs which was initially classified as a variant of unknown clinical significance. Using CRISPR/Cas9 genome editing in human iPSCs, we show that the deletion leads to loss of CCM1 protein and deregulation of KLF2, THBS1, NOS3, and HEY2 expression in iPSC-derived endothelial cells. Based on these results, the variant could be reclassified as likely pathogenic. Taken together, variants in regulatory regions need to be considered in genetic CCM analyses. Our study also demonstrates that modeling variants of unknown clinical significance in an iPSC-based system can help to come to a final diagnosis.
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Affiliation(s)
- Robin A. Pilz
- Department of Human Genetics, University Medicine Greifswald, and Interfaculty Institute of Genetics and Functional Genomics, University of Greifswald, Greifswald, Germany
| | - Dariush Skowronek
- Department of Human Genetics, University Medicine Greifswald, and Interfaculty Institute of Genetics and Functional Genomics, University of Greifswald, Greifswald, Germany
| | - Motaz Hamed
- Department of Neurosurgery, University Hospital Bonn, Bonn, Germany
| | - Anja Weise
- Institute of Human Genetics, Jena University Hospital, Friedrich Schiller University, Jena, Germany
| | - Elisabeth Mangold
- Institute of Human Genetics, Medical Faculty and University Hospital Bonn, University of Bonn, Bonn, Germany
| | | | - Torsten Pietsch
- Institute of Neuropathology, DGNN Brain Tumor Reference Center, University of Bonn, Bonn, Germany
| | - Ute Felbor
- Department of Human Genetics, University Medicine Greifswald, and Interfaculty Institute of Genetics and Functional Genomics, University of Greifswald, Greifswald, Germany
| | - Matthias Rath
- Department of Human Genetics, University Medicine Greifswald, and Interfaculty Institute of Genetics and Functional Genomics, University of Greifswald, Greifswald, Germany
- *Correspondence: Matthias Rath,
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31
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Farrow SL, Schierding W, Gokuladhas S, Golovina E, Fadason T, Cooper AA, O’Sullivan JM. Establishing gene regulatory networks from Parkinson's disease risk loci. Brain 2022; 145:2422-2435. [PMID: 35094046 PMCID: PMC9373962 DOI: 10.1093/brain/awac022] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 12/02/2021] [Accepted: 12/20/2021] [Indexed: 11/25/2022] Open
Abstract
The latest meta-analysis of genome-wide association studies identified 90 independent variants across 78 genomic regions associated with Parkinson's disease, yet the mechanisms by which these variants influence the development of the disease remains largely elusive. To establish the functional gene regulatory networks associated with Parkinson's disease risk variants, we utilized an approach combining spatial (chromosomal conformation capture) and functional (expression quantitative trait loci) data. We identified 518 genes subject to regulation by 76 Parkinson's variants across 49 tissues, whicih encompass 36 peripheral and 13 CNS tissues. Notably, one-third of these genes were regulated via trans-acting mechanisms (distal; risk locus-gene separated by >1 Mb, or on different chromosomes). Of particular interest is the identification of a novel trans-expression quantitative trait loci-gene connection between rs10847864 and SYNJ1 in the adult brain cortex, highlighting a convergence between familial studies and Parkinson's disease genome-wide association studies loci for SYNJ1 (PARK20) for the first time. Furthermore, we identified 16 neurodevelopment-specific expression quantitative trait loci-gene regulatory connections within the foetal cortex, consistent with hypotheses suggesting a neurodevelopmental involvement in the pathogenesis of Parkinson's disease. Through utilizing Louvain clustering we extracted nine significant and highly intraconnected clusters within the entire gene regulatory network. The nine clusters are enriched for specific biological processes and pathways, some of which have not previously been associated with Parkinson's disease. Together, our results not only contribute to an overall understanding of the mechanisms and impact of specific combinations of Parkinson's disease variants, but also highlight the potential impact gene regulatory networks may have when elucidating aetiological subtypes of Parkinson's disease.
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Affiliation(s)
- Sophie L Farrow
- Liggins Institute, The University of Auckland, Auckland, New Zealand
- The Maurice Wilkins Centre, The University of Auckland, Auckland, New Zealand
| | - William Schierding
- Liggins Institute, The University of Auckland, Auckland, New Zealand
- The Maurice Wilkins Centre, The University of Auckland, Auckland, New Zealand
| | | | - Evgeniia Golovina
- Liggins Institute, The University of Auckland, Auckland, New Zealand
| | - Tayaza Fadason
- Liggins Institute, The University of Auckland, Auckland, New Zealand
- The Maurice Wilkins Centre, The University of Auckland, Auckland, New Zealand
| | - Antony A Cooper
- Australian Parkinson’s Mission, Garvan Institute of Medical Research, Sydney, New South Wales, Australia
- St Vincent’s Clinical School, UNSW Sydney, Sydney, New South Wales, Australia
| | - Justin M O’Sullivan
- Liggins Institute, The University of Auckland, Auckland, New Zealand
- The Maurice Wilkins Centre, The University of Auckland, Auckland, New Zealand
- Australian Parkinson’s Mission, Garvan Institute of Medical Research, Sydney, New South Wales, Australia
- Brain Research New Zealand, The University of Auckland, Auckland, New Zealand
- MRC Lifecourse Epidemiology Unit, University of Southampton, UK
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Koczwara KE, Lake NJ, DeSimone AM, Lek M. Neuromuscular disorders: finding the missing genetic diagnoses. Trends Genet 2022; 38:956-971. [PMID: 35908999 DOI: 10.1016/j.tig.2022.07.001] [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: 04/15/2022] [Revised: 07/01/2022] [Accepted: 07/04/2022] [Indexed: 11/24/2022]
Abstract
Neuromuscular disorders (NMDs) are a wide-ranging group of diseases that seriously affect the quality of life of affected individuals. The development of next-generation sequencing revolutionized the diagnosis of NMD, enabling the discovery of hundreds of NMD genes and many more pathogenic variants. However, the diagnostic yield of genetic testing in NMD cohorts remains incomplete, indicating a large number of genetic diagnoses are not identified through current methods. Fortunately, recent advancements in sequencing technologies, analytical tools, and high-throughput functional screening provide an opportunity to circumvent current challenges. Here, we discuss reasons for missing genetic diagnoses in NMD, how emerging technologies and tools can overcome these hurdles, and examine future approaches to improving diagnostic yields in NMD.
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Affiliation(s)
- Katherine E Koczwara
- Department of Genetics, Yale University School of Medicine, New Haven, CT 06510, USA
| | - Nicole J Lake
- Department of Genetics, Yale University School of Medicine, New Haven, CT 06510, USA
| | - Alec M DeSimone
- Department of Genetics, Yale University School of Medicine, New Haven, CT 06510, USA
| | - Monkol Lek
- Department of Genetics, Yale University School of Medicine, New Haven, CT 06510, USA.
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The Emerging Roles of Long Non-Coding RNAs in Intellectual Disability and Related Neurodevelopmental Disorders. Int J Mol Sci 2022; 23:ijms23116118. [PMID: 35682796 PMCID: PMC9181295 DOI: 10.3390/ijms23116118] [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: 04/30/2022] [Revised: 05/23/2022] [Accepted: 05/27/2022] [Indexed: 02/05/2023] Open
Abstract
In the human brain, long non-coding RNAs (lncRNAs) are widely expressed in an exquisitely temporally and spatially regulated manner, thus suggesting their contribution to normal brain development and their probable involvement in the molecular pathology of neurodevelopmental disorders (NDD). Bypassing the classic protein-centric conception of disease mechanisms, some studies have been conducted to identify and characterize the putative roles of non-coding sequences in the genetic pathogenesis and diagnosis of complex diseases. However, their involvement in NDD, and more specifically in intellectual disability (ID), is still poorly documented and only a few genomic alterations affecting the lncRNAs function and/or expression have been causally linked to the disease endophenotype. Considering that a significant fraction of patients still lacks a genetic or molecular explanation, we expect that a deeper investigation of the non-coding genome will unravel novel pathogenic mechanisms, opening new translational opportunities. Here, we present evidence of the possible involvement of many lncRNAs in the etiology of different forms of ID and NDD, grouping the candidate disease-genes in the most frequently affected cellular processes in which ID-risk genes were previously collected. We also illustrate new approaches for the identification and prioritization of NDD-risk lncRNAs, together with the current strategies to exploit them in diagnosis.
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Katsonis P, Wilhelm K, Williams A, Lichtarge O. Genome interpretation using in silico predictors of variant impact. Hum Genet 2022; 141:1549-1577. [PMID: 35488922 PMCID: PMC9055222 DOI: 10.1007/s00439-022-02457-6] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 04/17/2022] [Indexed: 02/06/2023]
Abstract
Estimating the effects of variants found in disease driver genes opens the door to personalized therapeutic opportunities. Clinical associations and laboratory experiments can only characterize a tiny fraction of all the available variants, leaving the majority as variants of unknown significance (VUS). In silico methods bridge this gap by providing instant estimates on a large scale, most often based on the numerous genetic differences between species. Despite concerns that these methods may lack reliability in individual subjects, their numerous practical applications over cohorts suggest they are already helpful and have a role to play in genome interpretation when used at the proper scale and context. In this review, we aim to gain insights into the training and validation of these variant effect predicting methods and illustrate representative types of experimental and clinical applications. Objective performance assessments using various datasets that are not yet published indicate the strengths and limitations of each method. These show that cautious use of in silico variant impact predictors is essential for addressing genome interpretation challenges.
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Affiliation(s)
- Panagiotis Katsonis
- Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA.
| | - Kevin Wilhelm
- Graduate School of Biomedical Sciences, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA
| | - Amanda Williams
- Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA
| | - Olivier Lichtarge
- Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA. .,Department of Biochemistry, Human Genetics and Molecular Biology, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA. .,Department of Pharmacology, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA. .,Computational and Integrative Biomedical Research Center, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA.
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35
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Li C, Chen Q, Wu J, Ren J, Zhang M, Wang H, Li J, Tang Y. Identification and characterization of two novel noncoding tyrosinase (TYR) gene variants leading to oculocutaneous albinism type 1. J Biol Chem 2022; 298:101922. [PMID: 35413289 PMCID: PMC9108984 DOI: 10.1016/j.jbc.2022.101922] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2022] [Revised: 04/01/2022] [Accepted: 04/03/2022] [Indexed: 11/25/2022] Open
Abstract
Oculocutaneous albinism type 1 (OCA1), resulting from pathogenic variants in the tyrosinase (TYR) gene, refers to a group of phenotypically heterogeneous autosomal recessive disorders characterized by a partial or a complete absence of pigment in the skin/hair and is also associated with common developmental eye defects. In this study, we identified two novel compound heterozygous TYR variants from a Chinese hypopigmentary patient by whole-exome sequencing. Specifically, the two variants were c.-89T>G, located at the core of the initiator E-box (Inr E-box) of the TYR promoter, and p.S16Y (c.47C>A), located within the signal sequence. We performed both in silico analysis and experimental validation and verified these mutations as OCA1 variants that caused either impaired or complete loss of function of TYR. Mechanistically, the Inr E-box variant dampened TYR binding to microphthalmia-associated transcription factor, a master transcriptional regulator of the melanocyte development, whereas the S16Y variant contributed to endoplasmic reticulum retention, a common and principal cause of impaired TYR activity. Interestingly, we found that the Inr E-box variant creates novel protospacer adjacent motif sites, recognized by nucleases SpCas9 and SaCas9-KKH, respectively, without compromising the functional TYR coding sequence. We further used allele-specific genomic editing by CRISPR activation to specifically target the variant promoter and successfully activated its downstream gene expression, which could lead to potential therapeutic benefits. In conclusion, this study expands the spectrum of TYR variants, especially those within the promoter and noncoding regions, which can facilitate genetic counseling and clinical diagnosis of OCA1.
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Affiliation(s)
- Chaoyi Li
- Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, China; Aging Research Center, Xiangya Hospital, Central South University, Changsha, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Qian Chen
- Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, China; Aging Research Center, Xiangya Hospital, Central South University, Changsha, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Junjiao Wu
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China; Department of Rheumatology and Immunology, Xiangya Hospital, Central South University, Changsha, China; Provincial Clinical Research Center for Rheumatic and Immunologic Diseases, Xiangya Hospital, Central South University, Changsha, China
| | - Jie Ren
- Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, China; Aging Research Center, Xiangya Hospital, Central South University, Changsha, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Mengfei Zhang
- Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, China; Aging Research Center, Xiangya Hospital, Central South University, Changsha, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Huakun Wang
- Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, China; Aging Research Center, Xiangya Hospital, Central South University, Changsha, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Jinchen Li
- Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, China; Aging Research Center, Xiangya Hospital, Central South University, Changsha, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Yu Tang
- Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, China; Aging Research Center, Xiangya Hospital, Central South University, Changsha, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China; The Biobank of Xiangya Hospital, Central South University, Changsha, China.
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36
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Alsheikh AJ, Wollenhaupt S, King EA, Reeb J, Ghosh S, Stolzenburg LR, Tamim S, Lazar J, Davis JW, Jacob HJ. The landscape of GWAS validation; systematic review identifying 309 validated non-coding variants across 130 human diseases. BMC Med Genomics 2022; 15:74. [PMID: 35365203 PMCID: PMC8973751 DOI: 10.1186/s12920-022-01216-w] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Accepted: 03/17/2022] [Indexed: 02/08/2023] Open
Abstract
Background The remarkable growth of genome-wide association studies (GWAS) has created a critical need to experimentally validate the disease-associated variants, 90% of which involve non-coding variants. Methods To determine how the field is addressing this urgent need, we performed a comprehensive literature review identifying 36,676 articles. These were reduced to 1454 articles through a set of filters using natural language processing and ontology-based text-mining. This was followed by manual curation and cross-referencing against the GWAS catalog, yielding a final set of 286 articles. Results We identified 309 experimentally validated non-coding GWAS variants, regulating 252 genes across 130 human disease traits. These variants covered a variety of regulatory mechanisms. Interestingly, 70% (215/309) acted through cis-regulatory elements, with the remaining through promoters (22%, 70/309) or non-coding RNAs (8%, 24/309). Several validation approaches were utilized in these studies, including gene expression (n = 272), transcription factor binding (n = 175), reporter assays (n = 171), in vivo models (n = 104), genome editing (n = 96) and chromatin interaction (n = 33). Conclusions This review of the literature is the first to systematically evaluate the status and the landscape of experimentation being used to validate non-coding GWAS-identified variants. Our results clearly underscore the multifaceted approach needed for experimental validation, have practical implications on variant prioritization and considerations of target gene nomination. While the field has a long way to go to validate the thousands of GWAS associations, we show that progress is being made and provide exemplars of validation studies covering a wide variety of mechanisms, target genes, and disease areas. Supplementary Information The online version contains supplementary material available at 10.1186/s12920-022-01216-w.
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Affiliation(s)
- Ammar J Alsheikh
- Genomics Research Center, AbbVie Inc, North Chicago, Illinois, 60064, USA.
| | - Sabrina Wollenhaupt
- Information Research, AbbVie Deutschland GmbH & Co. KG, 67061, Knollstrasse, Ludwigshafen, Germany
| | - Emily A King
- Genomics Research Center, AbbVie Inc, North Chicago, Illinois, 60064, USA
| | - Jonas Reeb
- Information Research, AbbVie Deutschland GmbH & Co. KG, 67061, Knollstrasse, Ludwigshafen, Germany
| | - Sujana Ghosh
- Genomics Research Center, AbbVie Inc, North Chicago, Illinois, 60064, USA
| | | | - Saleh Tamim
- Genomics Research Center, AbbVie Inc, North Chicago, Illinois, 60064, USA
| | - Jozef Lazar
- Genomics Research Center, AbbVie Inc, North Chicago, Illinois, 60064, USA
| | - J Wade Davis
- Genomics Research Center, AbbVie Inc, North Chicago, Illinois, 60064, USA
| | - Howard J Jacob
- Genomics Research Center, AbbVie Inc, North Chicago, Illinois, 60064, USA
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VandenBosch LS, Luu K, Timms AE, Challam S, Wu Y, Lee AY, Cherry TJ. Machine Learning Prediction of Non-Coding Variant Impact in Human Retinal cis-Regulatory Elements. Transl Vis Sci Technol 2022; 11:16. [PMID: 35435921 PMCID: PMC9034719 DOI: 10.1167/tvst.11.4.16] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 03/25/2022] [Indexed: 11/24/2022] Open
Abstract
Purpose Prior studies have demonstrated the significance of specific cis-regulatory variants in retinal disease; however, determining the functional impact of regulatory variants remains a major challenge. In this study, we utilized a machine learning approach, trained on epigenomic data from the adult human retina, to systematically quantify the predicted impact of cis-regulatory variants. Methods We used human retinal DNA accessibility data (ATAC-seq) to determine a set of 18.9k high-confidence, putative cis-regulatory elements. Eighty percent of these elements were used to train a machine learning model utilizing a gapped k-mer support vector machine-based approach. In silico saturation mutagenesis and variant scoring was applied to predict the functional impact of all potential single nucleotide variants within cis-regulatory elements. Impact scores were tested in a 20% hold-out dataset and compared to allele population frequency, phylogenetic conservation, transcription factor (TF) binding motifs, and existing massively parallel reporter assay data. Results We generated a model that distinguishes between human retinal regulatory elements and negative test sequences with 95% accuracy. Among a hold-out test set of 3.7k human retinal CREs, all possible single nucleotide variants were scored. Variants with negative impact scores correlated with higher phylogenetic conservation of the reference allele, disruption of predicted TF binding motifs, and massively parallel reporter expression. Conclusions We demonstrated the utility of human retinal epigenomic data to train a machine learning model for the purpose of predicting the impact of non-coding regulatory sequence variants. Our model accurately scored sequences and predicted putative transcription factor binding motifs. This approach has the potential to expedite the characterization of pathogenic non-coding sequence variants in the context of unexplained retinal disease. Translational Relevance This workflow and resulting dataset serve as a promising genomic tool to facilitate the clinical prioritization of functionally disruptive non-coding mutations in the retina.
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Affiliation(s)
- Leah S. VandenBosch
- Center for Developmental Biology and Regenerative Medicine, Seattle Children's Research Institute, Seattle, WA, USA
| | - Kelsey Luu
- Center for Developmental Biology and Regenerative Medicine, Seattle Children's Research Institute, Seattle, WA, USA
| | - Andrew E. Timms
- Center for Developmental Biology and Regenerative Medicine, Seattle Children's Research Institute, Seattle, WA, USA
| | - Shriya Challam
- Center for Developmental Biology and Regenerative Medicine, Seattle Children's Research Institute, Seattle, WA, USA
| | - Yue Wu
- University of Washington Department of Ophthalmology, Seattle, WA, USA
| | - Aaron Y. Lee
- University of Washington Department of Ophthalmology, Seattle, WA, USA
- Brotman Baty Institute for Precision Medicine, Seattle, WA, USA
| | - Timothy J. Cherry
- Center for Developmental Biology and Regenerative Medicine, Seattle Children's Research Institute, Seattle, WA, USA
- Brotman Baty Institute for Precision Medicine, Seattle, WA, USA
- University of Washington Department of Pediatrics, Seattle, WA, USA
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38
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Alade A, Awotoye W, Butali A. Genetic and Epigenetic Studies in Nonsyndromic Oral Clefts. Oral Dis 2022; 28:1339-1350. [PMID: 35122708 DOI: 10.1111/odi.14146] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 01/11/2022] [Accepted: 01/20/2022] [Indexed: 11/28/2022]
Abstract
The etiology of non-syndromic oral clefts (NSOFC) is complex with genetics, genomics, epigenetics and stochastics factors playing a role. Several approaches have been applied to understand the etiology of non-syndromic oral clefts. These include linkage, candidate gene association studies, genome-wide association studies, whole genome sequencing, copy number variations and epigenetics. In this review we shared these approaches, genes and loci reported in some studies.
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Affiliation(s)
- Azeez Alade
- Department of Oral Pathology, Radiology and Medicine, College of Dentistry, University of Iowa, Iowa City, IA, USA.,Iowa Institute for Oral Health Research, University of Iowa, Iowa City, IA, USA.,Department of Epidemiology, College of Public Health, University of Iowa, Iowa City, IA, USA
| | - Waheed Awotoye
- Department of Oral Pathology, Radiology and Medicine, College of Dentistry, University of Iowa, Iowa City, IA, USA.,Iowa Institute for Oral Health Research, University of Iowa, Iowa City, IA, USA
| | - Azeez Butali
- Department of Oral Pathology, Radiology and Medicine, College of Dentistry, University of Iowa, Iowa City, IA, USA.,Iowa Institute for Oral Health Research, University of Iowa, Iowa City, IA, USA
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Romero R, de la Fuente L, Del Pozo-Valero M, Riveiro-Álvarez R, Trujillo-Tiebas MJ, Martín-Mérida I, Ávila-Fernández A, Iancu IF, Perea-Romero I, Núñez-Moreno G, Damián A, Rodilla C, Almoguera B, Cortón M, Ayuso C, Mínguez P. An evaluation of pipelines for DNA variant detection can guide a reanalysis protocol to increase the diagnostic ratio of genetic diseases. NPJ Genom Med 2022; 7:7. [PMID: 35087072 PMCID: PMC8795168 DOI: 10.1038/s41525-021-00278-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 12/09/2021] [Indexed: 01/01/2023] Open
Abstract
Clinical exome (CE) sequencing has become a first-tier diagnostic test for hereditary diseases; however, its diagnostic rate is around 30-50%. In this study, we aimed to increase the diagnostic yield of CE using a custom reanalysis algorithm. Sequencing data were available for three cohorts using two commercial protocols applied as part of the diagnostic process. Using these cohorts, we compared the performance of general and clinically relevant variant calling and the efficacy of an in-house bioinformatic protocol (FJD-pipeline) in detecting causal variants as compared to commercial protocols. On the whole, the FJD-pipeline detected 99.74% of the causal variants identified by the commercial protocol in previously solved cases. In the unsolved cases, FJD-pipeline detects more INDELs and non-exonic variants, and is able to increase the diagnostic yield in 2.5% and 3.2% in the re-analysis of 78 cancer and 62 cardiovascular cases. These results were considered to design a reanalysis, filtering and prioritization algorithm that was tested by reassessing 68 inconclusive cases of monoallelic autosomal recessive retinal dystrophies increasing the diagnosis by 4.4%. In conclusion, a guided NGS reanalysis of unsolved cases increases the diagnostic yield in genetic disorders, making it a useful diagnostic tool in medical genetics.
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Affiliation(s)
- Raquel Romero
- Department of Genetics, Health Research Institute-Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain
- Center for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III, Madrid, Spain
| | - Lorena de la Fuente
- Department of Genetics, Health Research Institute-Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain
- Bioinformatics Unit, Health Research Institute-Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain
| | - Marta Del Pozo-Valero
- Department of Genetics, Health Research Institute-Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain
- Center for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III, Madrid, Spain
| | - Rosa Riveiro-Álvarez
- Department of Genetics, Health Research Institute-Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain
- Center for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III, Madrid, Spain
| | - María José Trujillo-Tiebas
- Department of Genetics, Health Research Institute-Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain
- Center for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III, Madrid, Spain
| | - Inmaculada Martín-Mérida
- Department of Genetics, Health Research Institute-Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain
- Center for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III, Madrid, Spain
| | - Almudena Ávila-Fernández
- Department of Genetics, Health Research Institute-Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain
- Center for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III, Madrid, Spain
| | - Ionut-Florin Iancu
- Department of Genetics, Health Research Institute-Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain
- Center for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III, Madrid, Spain
| | - Irene Perea-Romero
- Department of Genetics, Health Research Institute-Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain
- Center for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III, Madrid, Spain
| | - Gonzalo Núñez-Moreno
- Department of Genetics, Health Research Institute-Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain
- Bioinformatics Unit, Health Research Institute-Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain
| | - Alejandra Damián
- Department of Genetics, Health Research Institute-Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain
- Center for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III, Madrid, Spain
| | - Cristina Rodilla
- Department of Genetics, Health Research Institute-Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain
| | - Berta Almoguera
- Department of Genetics, Health Research Institute-Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain
- Center for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III, Madrid, Spain
| | - Marta Cortón
- Department of Genetics, Health Research Institute-Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain
- Center for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III, Madrid, Spain
| | - Carmen Ayuso
- Department of Genetics, Health Research Institute-Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain.
- Center for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III, Madrid, Spain.
| | - Pablo Mínguez
- Department of Genetics, Health Research Institute-Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain.
- Center for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III, Madrid, Spain.
- Bioinformatics Unit, Health Research Institute-Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain.
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40
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Orozco G. Fine mapping with epigenetic information and 3D structure. Semin Immunopathol 2022; 44:115-125. [PMID: 35022890 PMCID: PMC8837508 DOI: 10.1007/s00281-021-00906-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Accepted: 12/13/2021] [Indexed: 12/12/2022]
Abstract
Since 2005, thousands of genome-wide association studies (GWAS) have been published, identifying hundreds of thousands of genetic variants that increase risk of complex traits such as autoimmune diseases. This wealth of data has the potential to improve patient care, through personalized medicine and the identification of novel drug targets. However, the potential of GWAS for clinical translation has not been fully achieved yet, due to the fact that the functional interpretation of risk variants and the identification of causal variants and genes are challenging. The past decade has seen the development of great advances that are facilitating the overcoming of these limitations, by utilizing a plethora of genomics and epigenomics tools to map and characterize regulatory elements and chromatin interactions, which can be used to fine map GWAS loci, and advance our understanding of the biological mechanisms that cause disease.
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Affiliation(s)
- Gisela Orozco
- Centre for Genetics and Genomics Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, AV Hill Building, Oxford Road, Manchester, M13 9LJ, UK. .,NIHR Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK.
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Sbihi Z, Tanita K, Bachelet C, Bole C, Jabot-Hanin F, Tores F, Le Loch M, Khodr R, Hoshino A, Lenoir C, Oleastro M, Villa M, Spossito L, Prieto E, Danielian S, Brunet E, Picard C, Taga T, Abdrabou SSMA, Isoda T, Yamada M, Palma A, Kanegane H, Latour S. Identification of Germline Non-coding Deletions in XIAP Gene Causing XIAP Deficiency Reveals a Key Promoter Sequence. J Clin Immunol 2022; 42:559-571. [PMID: 35000057 DOI: 10.1007/s10875-021-01188-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 11/21/2021] [Indexed: 11/24/2022]
Abstract
PURPOSE X-linked inhibitor of apoptosis protein (XIAP) deficiency, also known as the X-linked lymphoproliferative syndrome of type 2 (XLP-2), is a rare immunodeficiency characterized by recurrent hemophagocytic lymphohistiocytosis, splenomegaly, and inflammatory bowel disease. Variants in XIAP including missense, non-sense, frameshift, and deletions of coding exons have been reported to cause XIAP deficiency. We studied three young boys with immunodeficiency displaying XLP-2-like clinical features. No genetic variation in the coding exons of XIAP was identified by whole-exome sequencing (WES), although the patients exhibited a complete loss of XIAP expression. METHODS Targeted next-generation sequencing (NGS) of the entire locus of XIAP was performed on DNA samples from the three patients. Molecular investigations were assessed by gene reporter expression assays in HEK cells and CRISPR-Cas9 genome editing in primary T cells. RESULTS NGS of XIAP identified three distinct non-coding deletions in the patients that were predicted to be driven by repetitive DNA sequences. These deletions share a common region of 839 bp that encompassed the first non-coding exon of XIAP and contained regulatory elements and marks specific of an active promoter. Moreover, we showed that among the 839 bp, the exon was transcriptionally active. Finally, deletion of the exon by CRISPR-Cas9 in primary cells reduced XIAP protein expression. CONCLUSIONS These results identify a key promoter sequence contained in the first non-coding exon of XIAP. Importantly, this study highlights that sequencing of the non-coding exons that are not currently captured by WES should be considered in the genetic diagnosis when no variation is found in coding exons.
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Affiliation(s)
- Zineb Sbihi
- Laboratory of Lymphocyte Activation and Susceptibility to EBV Infection, INSERM UMR 1163, Imagine Institute, Paris, France
| | - Kay Tanita
- Department of Pediatrics and Developmental Biology, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), Tokyo, Japan
| | - Camille Bachelet
- Laboratory of Lymphocyte Activation and Susceptibility to EBV Infection, INSERM UMR 1163, Imagine Institute, Paris, France.,Université de Paris, Paris, France
| | - Christine Bole
- Genomics Core Facility, Institut Imagine-Structure Fédérative de Recherche Necker, INSERM UMR 1163, INSERM US24/CNRS UMS3633, Université de Paris, Paris, France
| | - Fabienne Jabot-Hanin
- Genomics Core Facility, Institut Imagine-Structure Fédérative de Recherche Necker, INSERM UMR 1163, INSERM US24/CNRS UMS3633, Université de Paris, Paris, France.,Bioinformatic Platform, INSERM UMR 1163, Institut Imagine, Paris, France
| | - Frederic Tores
- Genomics Core Facility, Institut Imagine-Structure Fédérative de Recherche Necker, INSERM UMR 1163, INSERM US24/CNRS UMS3633, Université de Paris, Paris, France.,Bioinformatic Platform, INSERM UMR 1163, Institut Imagine, Paris, France
| | - Marc Le Loch
- Service d'Histologie-Embryologie-Cytogénétique, Hôpital Necker-Enfants Malades, Paris, France
| | - Radi Khodr
- Laboratory of Lymphocyte Activation and Susceptibility to EBV Infection, INSERM UMR 1163, Imagine Institute, Paris, France
| | - Akihiro Hoshino
- Laboratory of Lymphocyte Activation and Susceptibility to EBV Infection, INSERM UMR 1163, Imagine Institute, Paris, France
| | - Christelle Lenoir
- Laboratory of Lymphocyte Activation and Susceptibility to EBV Infection, INSERM UMR 1163, Imagine Institute, Paris, France
| | - Matias Oleastro
- Immunology and Rheumatology Division, Hospital de Pediatria S.A.M.I.C. Prof. Dr. Juan P. Garrahan, Buenos Aires, Argentina
| | - Mariana Villa
- Immunology and Rheumatology Division, Hospital de Pediatria S.A.M.I.C. Prof. Dr. Juan P. Garrahan, Buenos Aires, Argentina
| | - Lucia Spossito
- Immunology and Rheumatology Division, Hospital de Pediatria S.A.M.I.C. Prof. Dr. Juan P. Garrahan, Buenos Aires, Argentina
| | - Emma Prieto
- Immunology and Rheumatology Division, Hospital de Pediatria S.A.M.I.C. Prof. Dr. Juan P. Garrahan, Buenos Aires, Argentina
| | - Silvia Danielian
- Immunology and Rheumatology Division, Hospital de Pediatria S.A.M.I.C. Prof. Dr. Juan P. Garrahan, Buenos Aires, Argentina
| | - Erika Brunet
- Laboratory of Dynamic of Genome and Immune System, INSERM UMR 1163, Imagine Institute, Paris, France
| | - Capucine Picard
- Laboratory of Lymphocyte Activation and Susceptibility to EBV Infection, INSERM UMR 1163, Imagine Institute, Paris, France.,Université de Paris, Paris, France.,Study Center for Primary Immunodeficiencies, Necker-Enfants Malades Hospital, APHP, Paris, France
| | - Takashi Taga
- Department of Pediatrics, Shiga University of Medical Science, Otsu, Japan
| | | | - Takeshi Isoda
- Department of Pediatrics and Developmental Biology, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), Tokyo, Japan
| | - Masafumi Yamada
- Department of Pediatrics, Faculty of Medicine, Graduate School of Medicine, Hokkaido University, Sapporo, Japan
| | - Alejandro Palma
- Immunology and Rheumatology Division, Hospital de Pediatria S.A.M.I.C. Prof. Dr. Juan P. Garrahan, Buenos Aires, Argentina
| | - Hirokazu Kanegane
- Department of Child Health and Development, Graduate School of Medical and Dental Sciences, TMDU, Tokyo, Japan
| | - Sylvain Latour
- Laboratory of Lymphocyte Activation and Susceptibility to EBV Infection, INSERM UMR 1163, Imagine Institute, Paris, France. .,Université de Paris, Paris, France.
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42
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Mortlock S, McKinnon B, Montgomery GW. Genetic Regulation of Transcription in the Endometrium in Health and Disease. FRONTIERS IN REPRODUCTIVE HEALTH 2022; 3:795464. [PMID: 36304015 PMCID: PMC9580733 DOI: 10.3389/frph.2021.795464] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Accepted: 12/06/2021] [Indexed: 11/25/2023] Open
Abstract
The endometrium is a complex and dynamic tissue essential for fertility and implicated in many reproductive disorders. The tissue consists of glandular epithelium and vascularised stroma and is unique because it is constantly shed and regrown with each menstrual cycle, generating up to 10 mm of new mucosa. Consequently, there are marked changes in cell composition and gene expression across the menstrual cycle. Recent evidence shows expression of many genes is influenced by genetic variation between individuals. We and others have reported evidence for genetic effects on hundreds of genes in endometrium. The genetic factors influencing endometrial gene expression are highly correlated with the genetic effects on expression in other reproductive (e.g., in uterus and ovary) and digestive tissues (e.g., salivary gland and stomach), supporting a shared genetic regulation of gene expression in biologically similar tissues. There is also increasing evidence for cell specific genetic effects for some genes. Sample size for studies in endometrium are modest and results from the larger studies of gene expression in blood report genetic effects for a much higher proportion of genes than currently reported for endometrium. There is also emerging evidence for the importance of genetic variation on RNA splicing. Gene mapping studies for common disease, including diseases associated with endometrium, show most variation maps to intergenic regulatory regions. It is likely that genetic risk factors for disease function through modifying the program of cell specific gene expression. The emerging evidence from our gene mapping studies coupled with tissue specific studies, and the GTEx, eQTLGen and EpiMap projects, show we need to expand our understanding of the complex regulation of gene expression. These data also help to link disease genetic risk factors to specific target genes. Combining our data on genetic regulation of gene expression in endometrium, and cell types within the endometrium with gene mapping data for endometriosis and related diseases is beginning to uncover the specific genes and pathways responsible for increased risk of these diseases.
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Affiliation(s)
| | | | - Grant W. Montgomery
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
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43
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Zhang CY, Xiao X, Zhang Z, Hu Z, Li M. An alternative splicing hypothesis for neuropathology of schizophrenia: evidence from studies on historical candidate genes and multi-omics data. Mol Psychiatry 2022; 27:95-112. [PMID: 33686213 DOI: 10.1038/s41380-021-01037-w] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/04/2020] [Revised: 01/08/2021] [Accepted: 01/22/2021] [Indexed: 01/31/2023]
Abstract
Alternative splicing of schizophrenia risk genes, such as DRD2, GRM3, and DISC1, has been extensively described. Nevertheless, the alternative splicing characteristics of the growing number of schizophrenia risk genes identified through genetic analyses remain relatively opaque. Recently, transcriptomic analyses in human brains based on short-read RNA-sequencing have discovered many "local splicing" events (e.g., exon skipping junctions) associated with genetic risk of schizophrenia, and further molecular characterizations have identified novel spliced isoforms, such as AS3MTd2d3 and ZNF804AE3E4. In addition, long-read sequencing analyses of schizophrenia risk genes (e.g., CACNA1C and NRXN1) have revealed multiple previously unannotated brain-abundant isoforms with therapeutic potentials, and functional analyses of KCNH2-3.1 and Ube3a1 have provided examples for investigating such spliced isoforms in vitro and in vivo. These findings suggest that alternative splicing may be an essential molecular mechanism underlying genetic risk of schizophrenia, however, the incomplete annotations of human brain transcriptomes might have limited our understanding of schizophrenia pathogenesis, and further efforts to elucidate these transcriptional characteristics are urgently needed to gain insights into the illness-correlated brain physiology and pathology as well as to translate genetic discoveries into novel therapeutic targets.
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Affiliation(s)
- Chu-Yi Zhang
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences and Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China.,Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, Yunnan, China
| | - Xiao Xiao
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences and Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China.,KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
| | - Zhuohua Zhang
- Institute of Molecular Precision Medicine and Hunan Key Laboratory of Molecular Precision Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, China.,Center for Medical Genetics and Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan, China
| | - Zhonghua Hu
- Institute of Molecular Precision Medicine and Hunan Key Laboratory of Molecular Precision Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, China. .,Center for Medical Genetics and Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan, China. .,Department of Critical Care Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, China. .,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China. .,Hunan Key Laboratory of Animal Models for Human Diseases, School of Life Sciences, Central South University, Changsha, Hunan, China. .,Eye Center of Xiangya Hospital and Hunan Key Laboratory of Ophthalmology, Central South University, Changsha, Hunan, China. .,National Clinical Research Center on Mental Disorders, Changsha, Hunan, China.
| | - Ming Li
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences and Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China. .,Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, Yunnan, China. .,KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China.
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Orozco G, Schoenfelder S, Walker N, Eyre S, Fraser P. 3D genome organization links non-coding disease-associated variants to genes. Front Cell Dev Biol 2022; 10:995388. [PMID: 36340032 PMCID: PMC9631826 DOI: 10.3389/fcell.2022.995388] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 09/27/2022] [Indexed: 11/13/2022] Open
Abstract
Genome sequencing has revealed over 300 million genetic variations in human populations. Over 90% of variants are single nucleotide polymorphisms (SNPs), the remainder include short deletions or insertions, and small numbers of structural variants. Hundreds of thousands of these variants have been associated with specific phenotypic traits and diseases through genome wide association studies which link significant differences in variant frequencies with specific phenotypes among large groups of individuals. Only 5% of disease-associated SNPs are located in gene coding sequences, with the potential to disrupt gene expression or alter of the function of encoded proteins. The remaining 95% of disease-associated SNPs are located in non-coding DNA sequences which make up 98% of the genome. The role of non-coding, disease-associated SNPs, many of which are located at considerable distances from any gene, was at first a mystery until the discovery that gene promoters regularly interact with distal regulatory elements to control gene expression. Disease-associated SNPs are enriched at the millions of gene regulatory elements that are dispersed throughout the non-coding sequences of the genome, suggesting they function as gene regulation variants. Assigning specific regulatory elements to the genes they control is not straightforward since they can be millions of base pairs apart. In this review we describe how understanding 3D genome organization can identify specific interactions between gene promoters and distal regulatory elements and how 3D genomics can link disease-associated SNPs to their target genes. Understanding which gene or genes contribute to a specific disease is the first step in designing rational therapeutic interventions.
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Affiliation(s)
- Gisela Orozco
- Centre for Genetics and Genomics Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester Academic Health Science Centre, Manchester, United Kingdom.,NIHR Manchester Biomedical Research Centre, Manchester University Foundation Trust, Manchester, United Kingdom
| | - Stefan Schoenfelder
- Enhanc3D Genomics Ltd., Cambridge, United Kingdom.,Epigenetics Programme, The Babraham Institute, Babraham Research Campus, CB22 3AT Cambridge, Cambridge, United Kingdom
| | | | - Stephan Eyre
- Centre for Genetics and Genomics Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester Academic Health Science Centre, Manchester, United Kingdom.,NIHR Manchester Biomedical Research Centre, Manchester University Foundation Trust, Manchester, United Kingdom
| | - Peter Fraser
- Enhanc3D Genomics Ltd., Cambridge, United Kingdom.,Department of Biological Science, Florida State University, Tallahassee, FL, United States
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Xiao X, Zhang CY, Zhang Z, Hu Z, Li M, Li T. Revisiting tandem repeats in psychiatric disorders from perspectives of genetics, physiology, and brain evolution. Mol Psychiatry 2022; 27:466-475. [PMID: 34650204 DOI: 10.1038/s41380-021-01329-1] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Revised: 09/16/2021] [Accepted: 09/28/2021] [Indexed: 01/28/2023]
Abstract
Genome-wide association studies (GWASs) have revealed substantial genetic components comprised of single nucleotide polymorphisms (SNPs) in the heritable risk of psychiatric disorders. However, genetic risk factors not covered by GWAS also play pivotal roles in these illnesses. Tandem repeats, which are likely functional but frequently overlooked by GWAS, may account for an important proportion in the "missing heritability" of psychiatric disorders. Despite difficulties in characterizing and quantifying tandem repeats in the genome, studies have been carried out in an attempt to describe impact of tandem repeats on gene regulation and human phenotypes. In this review, we have introduced recent research progress regarding the genomic distribution and regulatory mechanisms of tandem repeats. We have also summarized the current knowledge of the genetic architecture and biological underpinnings of psychiatric disorders brought by studies of tandem repeats. These findings suggest that tandem repeats, in candidate psychiatric risk genes or in different levels of linkage disequilibrium (LD) with psychiatric GWAS SNPs and haplotypes, may modulate biological phenotypes related to psychiatric disorders (e.g., cognitive function and brain physiology) through regulating alternative splicing, promoter activity, enhancer activity and so on. In addition, many tandem repeats undergo tight natural selection in the human lineage, and likely exert crucial roles in human brain evolution. Taken together, the putative roles of tandem repeats in the pathogenesis of psychiatric disorders is strongly implicated, and using examples from previous literatures, we wish to call for further attention to tandem repeats in the post-GWAS era of psychiatric disorders.
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Affiliation(s)
- Xiao Xiao
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences and Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
| | - Chu-Yi Zhang
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences and Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China.,Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, Yunnan, China
| | - Zhuohua Zhang
- Institute of Molecular Precision Medicine and Hunan Key Laboratory of Molecular Precision Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, China.,Center for Medical Genetics and Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan, China
| | - Zhonghua Hu
- Institute of Molecular Precision Medicine and Hunan Key Laboratory of Molecular Precision Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, China. .,Center for Medical Genetics and Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan, China. .,Department of Critical Care Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, China. .,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China. .,Hunan Key Laboratory of Animal Models for Human Diseases, School of Life Sciences, Central South University, Changsha, Hunan, China. .,Eye Center of Xiangya Hospital and Hunan Key Laboratory of Ophthalmology, Central South University, Changsha, Hunan, China. .,National Clinical Research Center on Mental Disorders, Changsha, Hunan, China.
| | - Ming Li
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences and Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China. .,CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China. .,KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China.
| | - Tao Li
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China. .,Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence, Guangzhou, China.
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46
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Giacopuzzi E, Popitsch N, Taylor JC. OUP accepted manuscript. Nucleic Acids Res 2022; 50:2522-2535. [PMID: 35234913 PMCID: PMC8934622 DOI: 10.1093/nar/gkac130] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Revised: 02/02/2022] [Accepted: 02/14/2022] [Indexed: 11/25/2022] Open
Abstract
Non-coding variants have long been recognized as important contributors to common disease risks, but with the expansion of clinical whole genome sequencing, examples of rare, high-impact non-coding variants are also accumulating. Despite recent advances in the study of regulatory elements and the availability of specialized data collections, the systematic annotation of non-coding variants from genome sequencing remains challenging. Here, we propose a new framework for the prioritization of non-coding regulatory variants that integrates information about regulatory regions with prediction scores and HPO-based prioritization. Firstly, we created a comprehensive collection of annotations for regulatory regions including a database of 2.4 million regulatory elements (GREEN-DB) annotated with controlled gene(s), tissue(s) and associated phenotype(s) where available. Secondly, we calculated a variation constraint metric and showed that constrained regulatory regions associate with disease-associated genes and essential genes from mouse knock-outs. Thirdly, we compared 19 non-coding impact prediction scores providing suggestions for variant prioritization. Finally, we developed a VCF annotation tool (GREEN-VARAN) that can integrate all these elements to annotate variants for their potential regulatory impact. In our evaluation, we show that GREEN-DB can capture previously published disease-associated non-coding variants as well as identify additional candidate disease genes in trio analyses.
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Affiliation(s)
- Edoardo Giacopuzzi
- Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
- National Institute for Health Research Oxford Biomedical Research Centre, Oxford OX4 2PG, UK
| | - Niko Popitsch
- Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
- Max Perutz Labs, University of Vienna, Dr. Bohr-Gasse 9, 1030 Vienna, Austria
| | - Jenny C Taylor
- To whom correspondence should be addressed. Tel: +44 01865 287631;
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47
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Mohajer N, Joloya EM, Seo J, Shioda T, Blumberg B. Epigenetic Transgenerational Inheritance of the Effects of Obesogen Exposure. Front Endocrinol (Lausanne) 2021; 12:787580. [PMID: 34975759 PMCID: PMC8716683 DOI: 10.3389/fendo.2021.787580] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 11/25/2021] [Indexed: 12/13/2022] Open
Abstract
Obesity and metabolic disorders have become a worldwide pandemic affecting millions of people. Although obesity is a multifaceted disease, there is growing evidence supporting the obesogen hypothesis, which proposes that exposure to a subset of endocrine disrupting chemicals (EDCs), known as obesogens, promotes obesity. While these effects can be observed in vitro using cell models, in vivo and human epidemiological studies have strengthened this hypothesis. Evidence from animal models showed that the effects of obesogen exposure can be inherited transgenerationally through at least the F4 generation. Transgenerational effects of EDC exposure predispose future generations to undesirable phenotypic traits and diseases, including obesity and related metabolic disorders. The exact mechanisms through which phenotypic traits are passed from an exposed organism to their offspring, without altering the primary DNA sequence, remain largely unknown. Recent research has provided strong evidence suggesting that a variety of epigenetic mechanisms may underlie transgenerational inheritance. These include differential DNA methylation, histone methylation, histone retention, the expression and/or deposition of non-coding RNAs and large-scale alterations in chromatin structure and organization. This review highlights the most recent advances in the field of epigenetics with respect to the transgenerational effects of environmental obesogens. We highlight throughout the paper the strengths and weaknesses of the evidence for proposed mechanisms underlying transgenerational inheritance and why none of these is sufficient to fully explain the phenomenon. We propose that changes in higher order chromatin organization and structure may be a plausible explanation for how some disease predispositions are heritable through multiple generations, including those that were not exposed. A solid understanding of these possible mechanisms is essential to fully understanding how environmental exposures can lead to inherited susceptibility to diseases such as obesity.
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Affiliation(s)
- Nicole Mohajer
- Department of Pharmaceutical Sciences, University of California, Irvine, CA, United States
| | - Erika M. Joloya
- Department of Developmental and Cell Biology, University of California, Irvine, CA, United States
| | - Jeongbin Seo
- Department of Developmental and Cell Biology, University of California, Irvine, CA, United States
| | - Toshi Shioda
- Center for Cancer Research, Massachusetts General Hospital, Charlestown, MA, United States
| | - Bruce Blumberg
- Department of Pharmaceutical Sciences, University of California, Irvine, CA, United States
- Department of Developmental and Cell Biology, University of California, Irvine, CA, United States
- Department of Biomedical Engineering, University of California, Irvine, CA, United States
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48
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Tian P, Zhong M, Wei GH. Mechanistic insights into genetic susceptibility to prostate cancer. Cancer Lett 2021; 522:155-163. [PMID: 34560228 DOI: 10.1016/j.canlet.2021.09.025] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 09/11/2021] [Accepted: 09/14/2021] [Indexed: 12/24/2022]
Abstract
Prostate cancer (PCa) is the second most common cancer in men and is a highly heritable disease that affects millions of individuals worldwide. Genome-wide association studies have to date discovered nearly 270 genetic loci harboring hundreds of single nucleotide polymorphisms (SNPs) that are associated with PCa susceptibility. In contrast, the functional characterization of the mechanisms underlying PCa risk association is still growing. Given that PCa risk-associated SNPs are highly enriched in noncoding cis-regulatory genomic regions, accumulating evidence suggests a widespread modulation of transcription factor chromatin binding and allelic enhancer activity by these noncoding SNPs, thereby dysregulating gene expression. Emerging studies have shown that a proportion of noncoding variants can modulate the formation of transcription factor complexes at enhancers and CTCF-mediated 3D genome architecture. Interestingly, DNA methylation-regulated CTCF binding could orchestrate a long-range chromatin interaction between PCa risk enhancer and causative genes. Additionally, one-causal-variant-two-risk genes or multiple-risk-variant-multiple-genes are prevalent in some PCa risk-associated loci. In this review, we will discuss the current understanding of the general principles of SNP-mediated gene regulation, experimental advances, and functional evidence supporting the mechanistic roles of several PCa genetic loci with potential clinical impact on disease prevention and treatment.
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Affiliation(s)
- Pan Tian
- Fudan University Shanghai Cancer Center; Key Laboratory of Metabolism and Molecular Medicine of the Ministry of Education, Department of Biochemistry and Molecular Biology of School of Basic Medical Sciences, Shanghai Medical College of Fudan University, Shanghai, 200032, China
| | - Mengjie Zhong
- Fudan University Shanghai Cancer Center; Key Laboratory of Metabolism and Molecular Medicine of the Ministry of Education, Department of Biochemistry and Molecular Biology of School of Basic Medical Sciences, Shanghai Medical College of Fudan University, Shanghai, 200032, China
| | - Gong-Hong Wei
- Fudan University Shanghai Cancer Center; Key Laboratory of Metabolism and Molecular Medicine of the Ministry of Education, Department of Biochemistry and Molecular Biology of School of Basic Medical Sciences, Shanghai Medical College of Fudan University, Shanghai, 200032, China.
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49
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Strunz T, Kellner M, Kiel C, Weber BHF. Assigning Co-Regulated Human Genes and Regulatory Gene Clusters. Cells 2021; 10:2395. [PMID: 34572044 PMCID: PMC8470523 DOI: 10.3390/cells10092395] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 08/30/2021] [Accepted: 09/10/2021] [Indexed: 12/12/2022] Open
Abstract
Elucidating the role of genetic variation in the regulation of gene expression is key to understanding the pathobiology of complex diseases which, in consequence, is crucial in devising targeted treatment options. Expression quantitative trait locus (eQTL) analysis correlates a genetic variant with the strength of gene expression, thus defining thousands of regulated genes in a multitude of human cell types and tissues. Some eQTL may not act independently of each other but instead may be regulated in a coordinated fashion by seemingly independent genetic variants. To address this issue, we combined the approaches of eQTL analysis and colocalization studies. Gene expression was determined in datasets comprising 49 tissues from the Genotype-Tissue Expression (GTEx) project. From about 33,000 regulated genes, over 14,000 were found to be co-regulated in pairs and were assembled across all tissues to almost 15,000 unique clusters containing up to nine regulated genes affected by the same eQTL signal. The distance of co-regulated eGenes was, on average, 112 kilobase pairs. Of 713 genes known to express clinical symptoms upon haploinsufficiency, 231 (32.4%) are part of at least one of the identified clusters. This calls for caution should treatment approaches aim at an upregulation of a haploinsufficient gene. In conclusion, we present an unbiased approach to identifying co-regulated genes in and across multiple tissues. Knowledge of such common effects is crucial to appreciate implications on biological pathways involved, specifically when a treatment option targets a co-regulated disease gene.
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Affiliation(s)
- Tobias Strunz
- Institute of Human Genetics, University of Regensburg, 93053 Regensburg, Germany; (T.S.); (M.K.); (C.K.)
| | - Martin Kellner
- Institute of Human Genetics, University of Regensburg, 93053 Regensburg, Germany; (T.S.); (M.K.); (C.K.)
| | - Christina Kiel
- Institute of Human Genetics, University of Regensburg, 93053 Regensburg, Germany; (T.S.); (M.K.); (C.K.)
| | - Bernhard H. F. Weber
- Institute of Human Genetics, University of Regensburg, 93053 Regensburg, Germany; (T.S.); (M.K.); (C.K.)
- Institute of Clinical Human Genetics, University Hospital Regensburg, 93053 Regensburg, Germany
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50
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Shen Y, Verboon JM, Zhang Y, Liu N, Kim YJ, Marglous S, Nandakumar SK, Voit RA, Fiorini C, Ejaz A, Basak A, Orkin SH, Xu J, Sankaran VG. A unified model of human hemoglobin switching through single-cell genome editing. Nat Commun 2021; 12:4991. [PMID: 34404810 PMCID: PMC8371164 DOI: 10.1038/s41467-021-25298-9] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Accepted: 08/02/2021] [Indexed: 12/17/2022] Open
Abstract
Key mechanisms of fetal hemoglobin (HbF) regulation and switching have been elucidated through studies of human genetic variation, including mutations in the HBG1/2 promoters, deletions in the β-globin locus, and variation impacting BCL11A. While this has led to substantial insights, there has not been a unified understanding of how these distinct genetically-nominated elements, as well as other key transcription factors such as ZBTB7A, collectively interact to regulate HbF. A key limitation has been the inability to model specific genetic changes in primary isogenic human hematopoietic cells to uncover how each of these act individually and in aggregate. Here, we describe a single-cell genome editing functional assay that enables specific mutations to be recapitulated individually and in combination, providing insights into how multiple mutation-harboring functional elements collectively contribute to HbF expression. In conjunction with quantitative modeling and chromatin capture analyses, we illustrate how these genetic findings enable a comprehensive understanding of how distinct regulatory mechanisms can synergistically modulate HbF expression. Genetic mechanisms underlying fetal hemoglobin (HbF) regulation and switching are not fully understood. Here, the authors develop a single-cell genome editing functional assay to model how effects of mutation-harbouring functional elements contribute to HbF expression.
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Affiliation(s)
- Yong Shen
- Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.,Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.,Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jeffrey M Verboon
- Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.,Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.,Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Yuannyu Zhang
- Children's Medical Center Research Institute, Department of Pediatrics, Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Nan Liu
- Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.,Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Yoon Jung Kim
- Children's Medical Center Research Institute, Department of Pediatrics, Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Samantha Marglous
- Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.,Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.,Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Harvard Stem Cell Institute, Cambridge, MA, USA
| | - Satish K Nandakumar
- Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.,Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.,Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Richard A Voit
- Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.,Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.,Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Claudia Fiorini
- Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.,Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.,Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ayesha Ejaz
- Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.,Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.,Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Anindita Basak
- Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.,Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.,Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Stuart H Orkin
- Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.,Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.,Harvard Stem Cell Institute, Cambridge, MA, USA.,Howard Hughes Medical Institute, Boston, MA, USA
| | - Jian Xu
- Children's Medical Center Research Institute, Department of Pediatrics, Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Vijay G Sankaran
- Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA. .,Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA. .,Broad Institute of MIT and Harvard, Cambridge, MA, USA. .,Harvard Stem Cell Institute, Cambridge, MA, USA.
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