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van Karnebeek CDM, O'Donnell-Luria A, Baynam G, Baudot A, Groza T, Jans JJM, Lassmann T, Letinturier MCV, Montgomery SB, Robinson PN, Sansen S, Mehrian-Shai R, Steward C, Kosaki K, Durao P, Sadikovic B. Leaving no patient behind! Expert recommendation in the use of innovative technologies for diagnosing rare diseases. Orphanet J Rare Dis 2024; 19:357. [PMID: 39334316 PMCID: PMC11438178 DOI: 10.1186/s13023-024-03361-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Accepted: 09/11/2024] [Indexed: 09/30/2024] Open
Abstract
Genetic diagnosis plays a crucial role in rare diseases, particularly with the increasing availability of emerging and accessible treatments. The International Rare Diseases Research Consortium (IRDiRC) has set its primary goal as: "Ensuring that all patients who present with a suspected rare disease receive a diagnosis within one year if their disorder is documented in the medical literature". Despite significant advances in genomic sequencing technologies, more than half of the patients with suspected Mendelian disorders remain undiagnosed. In response, IRDiRC proposes the establishment of "a globally coordinated diagnostic and research pipeline". To help facilitate this, IRDiRC formed the Task Force on Integrating New Technologies for Rare Disease Diagnosis. This multi-stakeholder Task Force aims to provide an overview of the current state of innovative diagnostic technologies for clinicians and researchers, focusing on the patient's diagnostic journey. Herein, we provide an overview of a broad spectrum of emerging diagnostic technologies involving genomics, epigenomics and multi-omics, functional testing and model systems, data sharing, bioinformatics, and Artificial Intelligence (AI), highlighting their advantages, limitations, and the current state of clinical adaption. We provide expert recommendations outlining the stepwise application of these innovative technologies in the diagnostic pathways while considering global differences in accessibility. The importance of FAIR (Findability, Accessibility, Interoperability, and Reusability) and CARE (Collective benefit, Authority to control, Responsibility, and Ethics) data management is emphasized, along with the need for enhanced and continuing education in medical genomics. We provide a perspective on future technological developments in genome diagnostics and their integration into clinical practice. Lastly, we summarize the challenges related to genomic diversity and accessibility, highlighting the significance of innovative diagnostic technologies, global collaboration, and equitable access to diagnosis and treatment for people living with rare disease.
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Affiliation(s)
- Clara D M van Karnebeek
- Departments of Pediatrics and Human Genetics, Emma Center for Personalized Medicine, Amsterdam Gastro-Enterology Endocrinology Metabolism, Amsterdam University Medical Centers, Amsterdam, The Netherlands.
| | - Anne O'Donnell-Luria
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, USA
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, USA
| | - Gareth Baynam
- Aix Marseille Univ, INSERM, Marseille Medical Genetics, MMG, Marseille, France
| | - Anaïs Baudot
- Aix Marseille Univ, INSERM, Marseille Medical Genetics, MMG, Marseille, France
| | - Tudor Groza
- Rare Care Centre, Perth Children's Hospital and Western Australian Register of Developmental Anomalies, King Edward Memorial Hospital, Perth, Australia
- European Molecular Biology Laboratory (EMBL-EBI), European Bioinformatics Institute, Hinxton, UK
| | - Judith J M Jans
- Department of Genetics, Section Metabolic Diagnostics, University Medical Center Utrecht, Utrecht, The Netherlands
| | | | | | | | | | | | - Ruty Mehrian-Shai
- Pediatric Brain Cancer Molecular Lab, Sheba Medical Center, Ramat Gan, Israel
| | | | | | - Patricia Durao
- The Cure and Action for Tay-Sachs (CATS) Foundation, Altringham, UK
| | - Bekim Sadikovic
- Verspeeten Clinical Genome Centre, London Health Sciences, London, Canada
- Department of Pathology and Laboratory Medicine, Western University, London, Canada
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O'Neill MJ, Yang T, Laudeman J, Calandranis ME, Harvey ML, Solus JF, Roden DM, Glazer AM. ParSE-seq: a calibrated multiplexed assay to facilitate the clinical classification of putative splice-altering variants. Nat Commun 2024; 15:8320. [PMID: 39333091 PMCID: PMC11437130 DOI: 10.1038/s41467-024-52474-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 09/10/2024] [Indexed: 09/29/2024] Open
Abstract
Interpreting the clinical significance of putative splice-altering variants outside canonical splice sites remains difficult without time-intensive experimental studies. To address this, we introduce Parallel Splice Effect Sequencing (ParSE-seq), a multiplexed assay to quantify variant effects on RNA splicing. We first apply this technique to study hundreds of variants in the arrhythmia-associated gene SCN5A. Variants are studied in 'minigene' plasmids with molecular barcodes to allow pooled variant effect quantification. We perform experiments in two cell types, including disease-relevant induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs). The assay strongly separates known control variants from ClinVar, enabling quantitative calibration of the ParSE-seq assay. Using these evidence strengths and experimental data, we reclassify 29 of 34 variants with conflicting interpretations and 11 of 42 variants of uncertain significance. In addition to intronic variants, we show that many synonymous and missense variants disrupted RNA splicing. Two splice-altering variants in the assay also disrupt splicing and sodium current when introduced into iPSC-CMs by CRISPR-Cas9 editing. ParSE-seq provides high-throughput experimental data for RNA-splicing to support precision medicine efforts and can be readily adopted to study other loss-of-function genotype-phenotype relationships.
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Affiliation(s)
| | - Tao Yang
- Vanderbilt Center for Arrhythmia Research and Therapeutics (VanCART), Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Julie Laudeman
- Vanderbilt Center for Arrhythmia Research and Therapeutics (VanCART), Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Maria E Calandranis
- Vanderbilt Center for Arrhythmia Research and Therapeutics (VanCART), Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - M Lorena Harvey
- Vanderbilt Center for Arrhythmia Research and Therapeutics (VanCART), Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Joseph F Solus
- Vanderbilt Center for Arrhythmia Research and Therapeutics (VanCART), Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Dan M Roden
- Vanderbilt Center for Arrhythmia Research and Therapeutics (VanCART), Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
- Department of Pharmacology, Vanderbilt University Medical Center, Nashville, TN, USA.
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.
| | - Andrew M Glazer
- Vanderbilt Center for Arrhythmia Research and Therapeutics (VanCART), Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
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Engreitz JM, Lawson HA, Singh H, Starita LM, Hon GC, Carter H, Sahni N, Reddy TE, Lin X, Li Y, Munshi NV, Chahrour MH, Boyle AP, Hitz BC, Mortazavi A, Craven M, Mohlke KL, Pinello L, Wang T, Kundaje A, Yue F, Cody S, Farrell NP, Love MI, Muffley LA, Pazin MJ, Reese F, Van Buren E, Dey KK, Kircher M, Ma J, Radivojac P, Balliu B, Williams BA, Huangfu D, Park CY, Quertermous T, Das J, Calderwood MA, Fowler DM, Vidal M, Ferreira L, Mooney SD, Pejaver V, Zhao J, Gazal S, Koch E, Reilly SK, Sunyaev S, Carpenter AE, Buenrostro JD, Leslie CS, Savage RE, Giric S, Luo C, Plath K, Barrera A, Schubach M, Gschwind AR, Moore JE, Ahituv N, Yi SS, Hallgrimsdottir I, Gaulton KJ, Sakaue S, Booeshaghi S, Mattei E, Nair S, Pachter L, Wang AT, Shendure J, Agarwal V, Blair A, Chalkiadakis T, Chardon FM, Dash PM, Deng C, Hamazaki N, Keukeleire P, Kubo C, Lalanne JB, Maass T, Martin B, McDiarmid TA, Nobuhara M, Page NF, Regalado S, Sims J, Ushiki A, Best SM, Boyle G, Camp N, Casadei S, Da EY, Dawood M, Dawson SC, Fayer S, Hamm A, James RG, Jarvik GP, McEwen AE, Moore N, Pendyala S, Popp NA, Post M, Rubin AF, Smith NT, Stone J, Tejura M, Wang ZR, Wheelock MK, Woo I, Zapp BD, Amgalan D, Aradhana A, Arana SM, Bassik MC, Bauman JR, Bhattacharya A, Cai XS, Chen Z, Conley S, Deshpande S, Doughty BR, Du PP, Galante JA, Gifford C, Greenleaf WJ, Guo K, Gupta R, Isobe S, Jagoda E, Jain N, Jones H, Kang HY, Kim SH, Kim Y, Klemm S, Kundu R, Kundu S, Lago-Docampo M, Lee-Yow YC, Levin-Konigsberg R, Li DY, Lindenhofer D, Ma XR, Marinov GK, Martyn GE, McCreery CV, Metzl-Raz E, Monteiro JP, Montgomery MT, Mualim KS, Munger C, Munson G, Nguyen TC, Nguyen T, Palmisano BT, Pampari A, Rabinovitch M, Ramste M, Ray J, Roy KR, Rubio OM, Schaepe JM, Schnitzler G, Schreiber J, Sharma D, Sheth MU, Shi H, Singh V, Sinha R, Steinmetz LM, Tan J, Tan A, Tycko J, Valbuena RC, Amiri VVP, van Kooten MJFM, Vaughan-Jackson A, Venida A, Weldy CS, Worssam MD, Xia F, Yao D, Zeng T, Zhao Q, Zhou R, Chen ZS, Cimini BA, Coppin G, Coté AG, Haghighi M, Hao T, Hill DE, Lacoste J, Laval F, Reno C, Roth FP, Singh S, Spirohn-Fitzgerald K, Taipale M, Teelucksingh T, Tixhon M, Yadav A, Yang Z, Kraus WL, Armendariz DA, Dederich AE, Gogate A, El Hayek L, Goetsch SC, Kaur K, Kim HB, McCoy MK, Nzima MZ, Pinzón-Arteaga CA, Posner BA, Schmitz DA, Sivakumar S, Sundarrajan A, Wang L, Wang Y, Wu J, Xu L, Xu J, Yu L, Zhang Y, Zhao H, Zhou Q, Won H, Bell JL, Broadaway KA, Degner KN, Etheridge AS, Koller BH, Mah W, Mu W, Ritola KD, Rosen JD, Schoenrock SA, Sharp RA, Bauer D, Lettre G, Sherwood R, Becerra B, Blaine LJ, Che E, Francoeur MJ, Gibbs EN, Kim N, King EM, Kleinstiver BP, Lecluze E, Li Z, Patel ZM, Phan QV, Ryu J, Starr ML, Wu T, Gersbach CA, Crawford GE, Allen AS, Majoros WH, Iglesias N, Rai R, Venukuttan R, Li B, Anglen T, Bounds LR, Hamilton MC, Liu S, McCutcheon SR, McRoberts Amador CD, Reisman SJ, ter Weele MA, Bodle JC, Streff HL, Siklenka K, Strouse K, Bernstein BE, Babu J, Corona GB, Dong K, Duarte FM, Durand NC, Epstein CB, Fan K, Gaskell E, Hall AW, Ham AM, Knudson MK, Shoresh N, Wekhande S, White CM, Xi W, Satpathy AT, Corces MR, Chang SH, Chin IM, Gardner JM, Gardell ZA, Gutierrez JC, Johnson AW, Kampman L, Kasowski M, Lareau CA, Liu V, Ludwig LS, McGinnis CS, Menon S, Qualls A, Sandor K, Turner AW, Ye CJ, Yin Y, Zhang W, Wold BJ, Carilli M, Cheong D, Filibam G, Green K, Kawauchi S, Kim C, Liang H, Loving R, Luebbert L, MacGregor G, Merchan AG, Rebboah E, Rezaie N, Sakr J, Sullivan DK, Swarna N, Trout D, Upchurch S, Weber R, Castro CP, Chou E, Feng F, Guerra A, Huang Y, Jiang L, Liu J, Mills RE, Qian W, Qin T, Sartor MA, Sherpa RN, Wang J, Wang Y, Welch JD, Zhang Z, Zhao N, Mukherjee S, Page CD, Clarke S, Doty RW, Duan Y, Gordan R, Ko KY, Li S, Li B, Thomson A, Raychaudhuri S, Price A, Ali TA, Dey KK, Durvasula A, Kellis M, Iakoucheva LM, Kakati T, Chen Y, Benazouz M, Jain S, Zeiberg D, De Paolis Kaluza MC, Velyunskiy M, Gasch A, Huang K, Jin Y, Lu Q, Miao J, Ohtake M, Scopel E, Steiner RD, Sverchkov Y, Weng Z, Garber M, Fu Y, Haas N, Li X, Phalke N, Shan SC, Shedd N, Yu T, Zhang Y, Zhou H, Battle A, Jerby L, Kotler E, Kundu S, Marderstein AR, Montgomery SB, Nigam A, Padhi EM, Patel A, Pritchard J, Raine I, Ramalingam V, Rodrigues KB, Schreiber JM, Singhal A, Sinha R, Wang AT, Abundis M, Bisht D, Chakraborty T, Fan J, Hall DR, Rarani ZH, Jain AK, Kaundal B, Keshari S, McGrail D, Pease NA, Yi VF, Wu H, Kannan S, Song H, Cai J, Gao Z, Kurzion R, Leu JI, Li F, Liang D, Ming GL, Musunuru K, Qiu Q, Shi J, Su Y, Tishkoff S, Xie N, Yang Q, Yang W, Zhang H, Zhang Z, Beer MA, Hadjantonakis AK, Adeniyi S, Cho H, Cutler R, Glenn RA, Godovich D, Hu N, Jovanic S, Luo R, Oh JW, Razavi-Mohseni M, Shigaki D, Sidoli S, Vierbuchen T, Wang X, Williams B, Yan J, Yang D, Yang Y, Sander M, Gaulton KJ, Ren B, Bartosik W, Indralingam HS, Klie A, Mummey H, Okino ML, Wang G, Zemke NR, Zhang K, Zhu H, Zaitlen N, Ernst J, Langerman J, Li T, Sun Y, Rudensky AY, Periyakoil PK, Gao VR, Smith MH, Thomas NM, Donlin LT, Lakhanpal A, Southard KM, Ardy RC, Cherry JM, Gerstein MB, Andreeva K, Assis PR, Borsari B, Douglass E, Dong S, Gabdank I, Graham K, Jolanki O, Jou J, Kagda MS, Lee JW, Li M, Lin K, Miyasato SR, Rozowsky J, Small C, Spragins E, Tanaka FY, Whaling IM, Youngworth IA, Sloan CA, Belter E, Chen X, Chisholm RL, Dickson P, Fan C, Fulton L, Li D, Lindsay T, Luan Y, Luo Y, Lyu H, Ma X, Macias-Velasco J, Miga KH, Quaid K, Stitziel N, Stranger BE, Tomlinson C, Wang J, Zhang W, Zhang B, Zhao G, Zhuo X, Brennand K, Ciccia A, Hayward SB, Huang JW, Leuzzi G, Taglialatela A, Thakar T, Vaitsiankova A, Dey KK, Ali TA, Kim A, Grimes HL, Salomonis N, Gupta R, Fang S, Lee-Kim V, Heinig M, Losert C, Jones TR, Donnard E, Murphy M, Roberts E, Song S, Mostafavi S, Sasse A, Spiro A, Pennacchio LA, Kato M, Kosicki M, Mannion B, Slaven N, Visel A, Pollard KS, Drusinsky S, Whalen S, Ray J, Harten IA, Ho CH, Sanjana NE, Caragine C, Morris JA, Seruggia D, Kutschat AP, Wittibschlager S, Xu H, Fu R, He W, Zhang L, Osorio D, Bly Z, Calluori S, Gilchrist DA, Hutter CM, Morris SA, Samer EK. Deciphering the impact of genomic variation on function. Nature 2024; 633:47-57. [PMID: 39232149 DOI: 10.1038/s41586-024-07510-0] [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: 04/11/2023] [Accepted: 05/02/2024] [Indexed: 09/06/2024]
Abstract
Our genomes influence nearly every aspect of human biology-from molecular and cellular functions to phenotypes in health and disease. Studying the differences in DNA sequence between individuals (genomic variation) could reveal previously unknown mechanisms of human biology, uncover the basis of genetic predispositions to diseases, and guide the development of new diagnostic tools and therapeutic agents. Yet, understanding how genomic variation alters genome function to influence phenotype has proved challenging. To unlock these insights, we need a systematic and comprehensive catalogue of genome function and the molecular and cellular effects of genomic variants. Towards this goal, the Impact of Genomic Variation on Function (IGVF) Consortium will combine approaches in single-cell mapping, genomic perturbations and predictive modelling to investigate the relationships among genomic variation, genome function and phenotypes. IGVF will create maps across hundreds of cell types and states describing how coding variants alter protein activity, how noncoding variants change the regulation of gene expression, and how such effects connect through gene-regulatory and protein-interaction networks. These experimental data, computational predictions and accompanying standards and pipelines will be integrated into an open resource that will catalyse community efforts to explore how our genomes influence biology and disease across populations.
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Padigepati SR, Stafford DA, Tan CA, Silvis MR, Jamieson K, Keyser A, Nunez PAC, Nicoludis JM, Manders T, Fresard L, Kobayashi Y, Araya CL, Aradhya S, Johnson B, Nykamp K, Reuter JA. Scalable approaches for generating, validating and incorporating data from high-throughput functional assays to improve clinical variant classification. Hum Genet 2024; 143:995-1004. [PMID: 39085601 PMCID: PMC11303574 DOI: 10.1007/s00439-024-02691-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Accepted: 07/12/2024] [Indexed: 08/02/2024]
Abstract
As the adoption and scope of genetic testing continue to expand, interpreting the clinical significance of DNA sequence variants at scale remains a formidable challenge, with a high proportion classified as variants of uncertain significance (VUSs). Genetic testing laboratories have historically relied, in part, on functional data from academic literature to support variant classification. High-throughput functional assays or multiplex assays of variant effect (MAVEs), designed to assess the effects of DNA variants on protein stability and function, represent an important and increasingly available source of evidence for variant classification, but their potential is just beginning to be realized in clinical lab settings. Here, we describe a framework for generating, validating and incorporating data from MAVEs into a semi-quantitative variant classification method applied to clinical genetic testing. Using single-cell gene expression measurements, cellular evidence models were built to assess the effects of DNA variation in 44 genes of clinical interest. This framework was also applied to models for an additional 22 genes with previously published MAVE datasets. In total, modeling data was incorporated from 24 genes into our variant classification method. These data contributed evidence for classifying 4043 observed variants in over 57,000 individuals. Genetic testing laboratories are uniquely positioned to generate, analyze, validate, and incorporate evidence from high-throughput functional data and ultimately enable the use of these data to provide definitive clinical variant classifications for more patients.
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Affiliation(s)
| | | | | | - Melanie R Silvis
- Invitae Corporation, San Francisco, CA, 94103, USA
- Epic Bio, South San Francisco, CA, 94080, USA
| | - Kirsty Jamieson
- Invitae Corporation, San Francisco, CA, 94103, USA
- Epic Bio, South San Francisco, CA, 94080, USA
| | - Andrew Keyser
- Invitae Corporation, San Francisco, CA, 94103, USA
- Calico Life Sciences, South San Francisco, CA, 94080, USA
| | | | - John M Nicoludis
- Invitae Corporation, San Francisco, CA, 94103, USA
- Department of Structural Biology, Genentech, South San Francisco, CA, 94080, USA
| | - Toby Manders
- Invitae Corporation, San Francisco, CA, 94103, USA
| | | | | | - Carlos L Araya
- Invitae Corporation, San Francisco, CA, 94103, USA
- Tapanti.org, Santa Barbara, CA, 93108, USA
| | | | - Britt Johnson
- Invitae Corporation, San Francisco, CA, 94103, USA
- GeneDx, Stamford, CT, 06902, USA
| | - Keith Nykamp
- Invitae Corporation, San Francisco, CA, 94103, USA.
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Dawood M, Fayer S, Pendyala S, Post M, Kalra D, Patterson K, Venner E, Muffley LA, Fowler DM, Rubin AF, Posey JE, Plon SE, Lupski JR, Gibbs RA, Starita LM, Robles-Espinoza CD, Coyote-Maestas W, Gallego Romero I. Defining and Reducing Variant Classification Disparities. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.11.24305690. [PMID: 38645101 PMCID: PMC11030469 DOI: 10.1101/2024.04.11.24305690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Background Multiplexed Assays of Variant Effects (MAVEs) can test all possible single variants in a gene of interest. The resulting saturation-style data may help resolve variant classification disparities between populations, especially for variants of uncertain significance (VUS). Methods We analyzed clinical significance classifications in 213,663 individuals of European-like genetic ancestry versus 206,975 individuals of non-European-like genetic ancestry from All of Us and the Genome Aggregation Database. Then, we incorporated clinically calibrated MAVE data into the Clinical Genome Resource's Variant Curation Expert Panel rules to automate VUS reclassification for BRCA1, TP53, and PTEN . Results Using two orthogonal statistical approaches, we show a higher prevalence ( p ≤5.95e-06) of VUS in individuals of non-European-like genetic ancestry across all medical specialties assessed in all three databases. Further, in the non-European-like genetic ancestry group, higher rates of Benign or Likely Benign and variants with no clinical designation ( p ≤2.5e-05) were found across many medical specialties, whereas Pathogenic or Likely Pathogenic assignments were higher in individuals of European-like genetic ancestry ( p ≤2.5e-05). Using MAVE data, we reclassified VUS in individuals of non-European-like genetic ancestry at a significantly higher rate in comparison to reclassified VUS from European-like genetic ancestry ( p =9.1e-03) effectively compensating for the VUS disparity. Further, essential code analysis showed equitable impact of MAVE evidence codes but inequitable impact of allele frequency ( p =7.47e-06) and computational predictor ( p =6.92e-05) evidence codes for individuals of non-European-like genetic ancestry. Conclusions Generation of saturation-style MAVE data should be a priority to reduce VUS disparities and produce equitable training data for future computational predictors.
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Grønbæk-Thygesen M, Hartmann-Petersen R. Cellular and molecular mechanisms of aspartoacylase and its role in Canavan disease. Cell Biosci 2024; 14:45. [PMID: 38582917 PMCID: PMC10998430 DOI: 10.1186/s13578-024-01224-6] [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: 12/15/2023] [Accepted: 03/24/2024] [Indexed: 04/08/2024] Open
Abstract
Canavan disease is an autosomal recessive and lethal neurological disorder, characterized by the spongy degeneration of the white matter in the brain. The disease is caused by a deficiency of the cytosolic aspartoacylase (ASPA) enzyme, which catalyzes the hydrolysis of N-acetyl-aspartate (NAA), an abundant brain metabolite, into aspartate and acetate. On the physiological level, the mechanism of pathogenicity remains somewhat obscure, with multiple, not mutually exclusive, suggested hypotheses. At the molecular level, recent studies have shown that most disease linked ASPA gene variants lead to a structural destabilization and subsequent proteasomal degradation of the ASPA protein variants, and accordingly Canavan disease should in general be considered a protein misfolding disorder. Here, we comprehensively summarize the molecular and cell biology of ASPA, with a particular focus on disease-linked gene variants and the pathophysiology of Canavan disease. We highlight the importance of high-throughput technologies and computational prediction tools for making genotype-phenotype predictions as we await the results of ongoing trials with gene therapy for Canavan disease.
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Affiliation(s)
- Martin Grønbæk-Thygesen
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, 2200N, Copenhagen, Denmark.
| | - Rasmus Hartmann-Petersen
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, 2200N, Copenhagen, Denmark.
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Walsh N, Cooper A, Dockery A, O'Byrne JJ. Variant reclassification and clinical implications. J Med Genet 2024; 61:207-211. [PMID: 38296635 DOI: 10.1136/jmg-2023-109488] [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: 06/30/2023] [Accepted: 12/30/2023] [Indexed: 02/02/2024]
Abstract
Genomic technologies have transformed clinical genetic testing, underlining the importance of accurate molecular genetic diagnoses. Variant classification, ranging from benign to pathogenic, is fundamental to these tests. However, variant reclassification, the process of reassigning the pathogenicity of variants over time, poses challenges to diagnostic legitimacy. This review explores the medical and scientific literature available on variant reclassification, focusing on its clinical implications.Variant reclassification is driven by accruing evidence from diverse sources, leading to variant reclassification frequency ranging from 3.6% to 58.8%. Recent studies have shown that significant changes can occur when reviewing variant classifications within 1 year after initial classification, illustrating the importance of early, accurate variant assignation for clinical care.Variants of uncertain significance (VUS) are particularly problematic. They lack clear categorisation but have influenced patient treatment despite recommendations against it. Addressing VUS reclassification is essential to enhance the credibility of genetic testing and the clinical impact. Factors affecting reclassification include standardised guidelines, clinical phenotype-genotype correlations through deep phenotyping and ancestry studies, large-scale databases and bioinformatics tools. As genomic databases grow and knowledge advances, reclassification rates are expected to change, reducing discordance in future classifications.Variant reclassification affects patient diagnosis, precision therapy and family screening. The exact patient impact is yet unknown. Understanding influencing factors and adopting standardised guidelines are vital for precise molecular genetic diagnoses, ensuring optimal patient care and minimising clinical risk.
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Affiliation(s)
- Nicola Walsh
- Department of Clinical Genetics, Children's Health Ireland, Dublin, Ireland
| | - Aislinn Cooper
- Next Generation Sequencing Lab, Mater Misericordiae University Hospital, Dublin, Ireland
| | - Adrian Dockery
- Next Generation Sequencing Lab, Mater Misericordiae University Hospital, Dublin, Ireland
| | - James J O'Byrne
- National Centre for Inherited Metabolic Disorders, Mater Misericordiae University Hospital, Dublin, Ireland
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Christowitz C, Olivier DW, Schneider JW, Kotze MJ, Engelbrecht AM. Incorporating functional genomics into the pathology-supported genetic testing framework implemented in South Africa: A future view of precision medicine for breast carcinomas. MUTATION RESEARCH. REVIEWS IN MUTATION RESEARCH 2024; 793:108492. [PMID: 38631437 DOI: 10.1016/j.mrrev.2024.108492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 02/25/2024] [Accepted: 04/11/2024] [Indexed: 04/19/2024]
Abstract
A pathology-supported genetic testing (PSGT) framework was established in South Africa to improve access to precision medicine for patients with breast carcinomas. Nevertheless, the frequent identification of variants of uncertain significance (VUSs) with the use of genome-scale next-generation sequencing has created a bottleneck in the return of results to patients. This review highlights the importance of incorporating functional genomics into the PSGT framework as a proposed initiative. Here, we explore various model systems and experimental methods available for conducting functional studies in South Africa to enhance both variant classification and clinical interpretation. We emphasize the distinct advantages of using in vitro, in vivo, and translational ex vivo models to improve the effectiveness of precision oncology. Moreover, we highlight the relevance of methodologies such as protein modelling and structural bioinformatics, multi-omics, metabolic activity assays, flow cytometry, cell migration and invasion assays, tube-formation assays, multiplex assays of variant effect, and database mining and machine learning models. The selection of the appropriate experimental approach largely depends on the molecular mechanism of the gene under investigation and the predicted functional effect of the VUS. However, before making final decisions regarding the pathogenicity of VUSs, it is essential to assess the functional evidence and clinical outcomes under current variant interpretation guidelines. The inclusion of a functional genomics infrastructure within the PSGT framework will significantly advance the reclassification of VUSs and enhance the precision medicine pipeline for patients with breast carcinomas in South Africa.
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Affiliation(s)
- Claudia Christowitz
- Department of Physiological Sciences, Faculty of Science, Stellenbosch University, Stellenbosch 7600, South Africa.
| | - Daniel W Olivier
- Department of Physiological Sciences, Faculty of Science, Stellenbosch University, Stellenbosch 7600, South Africa; Division of Chemical Pathology, Department of Pathology, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town 7505, South Africa
| | - Johann W Schneider
- Division of Anatomical Pathology, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town 7505, South Africa; National Health Laboratory Service, Tygerberg Hospital, Cape Town 7505, South Africa
| | - Maritha J Kotze
- Division of Chemical Pathology, Department of Pathology, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town 7505, South Africa; National Health Laboratory Service, Tygerberg Hospital, Cape Town 7505, South Africa
| | - Anna-Mart Engelbrecht
- Department of Physiological Sciences, Faculty of Science, Stellenbosch University, Stellenbosch 7600, South Africa; Department of Global Health, African Cancer Institute, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town 7505, South Africa
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Maes S, Deploey N, Peelman F, Eyckerman S. Deep mutational scanning of proteins in mammalian cells. CELL REPORTS METHODS 2023; 3:100641. [PMID: 37963462 PMCID: PMC10694495 DOI: 10.1016/j.crmeth.2023.100641] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 07/06/2023] [Accepted: 10/20/2023] [Indexed: 11/16/2023]
Abstract
Protein mutagenesis is essential for unveiling the molecular mechanisms underlying protein function in health, disease, and evolution. In the past decade, deep mutational scanning methods have evolved to support the functional analysis of nearly all possible single-amino acid changes in a protein of interest. While historically these methods were developed in lower organisms such as E. coli and yeast, recent technological advancements have resulted in the increased use of mammalian cells, particularly for studying proteins involved in human disease. These advancements will aid significantly in the classification and interpretation of variants of unknown significance, which are being discovered at large scale due to the current surge in the use of whole-genome sequencing in clinical contexts. Here, we explore the experimental aspects of deep mutational scanning studies in mammalian cells and report the different methods used in each step of the workflow, ultimately providing a useful guide toward the design of such studies.
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Affiliation(s)
- Stefanie Maes
- VIB Center for Medical Biotechnology (CMB), Technologiepark-Zwijnaarde 75, 9052 Ghent, Belgium; Department of Biochemistry and Microbiology, Ghent University, Technologiepark-Zwijnaarde 75, 9052 Ghent, Belgium; Department of Biomolecular Medicine, Ghent University, Technologiepark-Zwijnaarde 75, 9052 Ghent, Belgium
| | - Nick Deploey
- VIB Center for Medical Biotechnology (CMB), Technologiepark-Zwijnaarde 75, 9052 Ghent, Belgium; Department of Biomolecular Medicine, Ghent University, Technologiepark-Zwijnaarde 75, 9052 Ghent, Belgium
| | - Frank Peelman
- VIB Center for Medical Biotechnology (CMB), Technologiepark-Zwijnaarde 75, 9052 Ghent, Belgium; Department of Biomolecular Medicine, Ghent University, Technologiepark-Zwijnaarde 75, 9052 Ghent, Belgium
| | - Sven Eyckerman
- VIB Center for Medical Biotechnology (CMB), Technologiepark-Zwijnaarde 75, 9052 Ghent, Belgium; Department of Biomolecular Medicine, Ghent University, Technologiepark-Zwijnaarde 75, 9052 Ghent, Belgium.
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10
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Walker R, Mahmood K, Como J, Clendenning M, Joo JE, Georgeson P, Joseland S, Preston SG, Pope BJ, Chan JM, Austin R, Bojadzieva J, Campbell A, Edwards E, Gleeson M, Goodwin A, Harris MT, Ip E, Kirk J, Mansour J, Mar Fan H, Nichols C, Pachter N, Ragunathan A, Spigelman A, Susman R, Christie M, Jenkins MA, Pai RK, Rosty C, Macrae FA, Winship IM, Buchanan DD. DNA Mismatch Repair Gene Variant Classification: Evaluating the Utility of Somatic Mutations and Mismatch Repair Deficient Colonic Crypts and Endometrial Glands. Cancers (Basel) 2023; 15:4925. [PMID: 37894291 PMCID: PMC10605939 DOI: 10.3390/cancers15204925] [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: 09/06/2023] [Revised: 10/03/2023] [Accepted: 10/03/2023] [Indexed: 10/29/2023] Open
Abstract
Germline pathogenic variants in the DNA mismatch repair (MMR) genes (Lynch syndrome) predispose to colorectal (CRC) and endometrial (EC) cancer. Lynch syndrome specific tumor features were evaluated for their ability to support the ACMG/InSiGHT framework in classifying variants of uncertain clinical significance (VUS) in the MMR genes. Twenty-eight CRC or EC tumors from 25 VUS carriers (6xMLH1, 9xMSH2, 6xMSH6, 4xPMS2), underwent targeted tumor sequencing for the presence of microsatellite instability/MMR-deficiency (MSI-H/dMMR) status and identification of a somatic MMR mutation (second hit). Immunohistochemical testing for the presence of dMMR crypts/glands in normal tissue was also performed. The ACMG/InSiGHT framework reclassified 7/25 (28%) VUS to likely pathogenic (LP), three (12%) to benign/likely benign, and 15 (60%) VUS remained unchanged. For the seven re-classified LP variants comprising nine tumors, tumor sequencing confirmed MSI-H/dMMR (8/9, 88.9%) and a second hit (7/9, 77.8%). Of these LP reclassified variants where normal tissue was available, the presence of a dMMR crypt/gland was found in 2/4 (50%). Furthermore, a dMMR endometrial gland in a carrier of an MSH2 exon 1-6 duplication provides further support for an upgrade of this VUS to LP. Our study confirmed that identifying these Lynch syndrome features can improve MMR variant classification, enabling optimal clinical care.
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Affiliation(s)
- Romy Walker
- Colorectal Oncogenomics Group, Department of Clinical Pathology, Victorian Comprehensive Cancer Centre, Melbourne Medical School, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, VIC 3000, Australia; (K.M.); (J.C.); (M.C.); (J.E.J.); (P.G.); (S.J.); (S.G.P.); (B.J.P.); (D.D.B.)
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Melbourne Medical School, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, VIC 3000, Australia;
| | - Khalid Mahmood
- Colorectal Oncogenomics Group, Department of Clinical Pathology, Victorian Comprehensive Cancer Centre, Melbourne Medical School, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, VIC 3000, Australia; (K.M.); (J.C.); (M.C.); (J.E.J.); (P.G.); (S.J.); (S.G.P.); (B.J.P.); (D.D.B.)
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Melbourne Medical School, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, VIC 3000, Australia;
- Melbourne Bioinformatics, Melbourne Medical School, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, VIC 3052, Australia
| | - Julia Como
- Colorectal Oncogenomics Group, Department of Clinical Pathology, Victorian Comprehensive Cancer Centre, Melbourne Medical School, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, VIC 3000, Australia; (K.M.); (J.C.); (M.C.); (J.E.J.); (P.G.); (S.J.); (S.G.P.); (B.J.P.); (D.D.B.)
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Melbourne Medical School, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, VIC 3000, Australia;
| | - Mark Clendenning
- Colorectal Oncogenomics Group, Department of Clinical Pathology, Victorian Comprehensive Cancer Centre, Melbourne Medical School, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, VIC 3000, Australia; (K.M.); (J.C.); (M.C.); (J.E.J.); (P.G.); (S.J.); (S.G.P.); (B.J.P.); (D.D.B.)
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Melbourne Medical School, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, VIC 3000, Australia;
| | - Jihoon E. Joo
- Colorectal Oncogenomics Group, Department of Clinical Pathology, Victorian Comprehensive Cancer Centre, Melbourne Medical School, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, VIC 3000, Australia; (K.M.); (J.C.); (M.C.); (J.E.J.); (P.G.); (S.J.); (S.G.P.); (B.J.P.); (D.D.B.)
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Melbourne Medical School, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, VIC 3000, Australia;
| | - Peter Georgeson
- Colorectal Oncogenomics Group, Department of Clinical Pathology, Victorian Comprehensive Cancer Centre, Melbourne Medical School, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, VIC 3000, Australia; (K.M.); (J.C.); (M.C.); (J.E.J.); (P.G.); (S.J.); (S.G.P.); (B.J.P.); (D.D.B.)
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Melbourne Medical School, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, VIC 3000, Australia;
| | - Sharelle Joseland
- Colorectal Oncogenomics Group, Department of Clinical Pathology, Victorian Comprehensive Cancer Centre, Melbourne Medical School, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, VIC 3000, Australia; (K.M.); (J.C.); (M.C.); (J.E.J.); (P.G.); (S.J.); (S.G.P.); (B.J.P.); (D.D.B.)
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Melbourne Medical School, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, VIC 3000, Australia;
| | - Susan G. Preston
- Colorectal Oncogenomics Group, Department of Clinical Pathology, Victorian Comprehensive Cancer Centre, Melbourne Medical School, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, VIC 3000, Australia; (K.M.); (J.C.); (M.C.); (J.E.J.); (P.G.); (S.J.); (S.G.P.); (B.J.P.); (D.D.B.)
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Melbourne Medical School, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, VIC 3000, Australia;
| | - Bernard J. Pope
- Colorectal Oncogenomics Group, Department of Clinical Pathology, Victorian Comprehensive Cancer Centre, Melbourne Medical School, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, VIC 3000, Australia; (K.M.); (J.C.); (M.C.); (J.E.J.); (P.G.); (S.J.); (S.G.P.); (B.J.P.); (D.D.B.)
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Melbourne Medical School, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, VIC 3000, Australia;
- Melbourne Bioinformatics, Melbourne Medical School, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, VIC 3052, Australia
| | - James M. Chan
- Colorectal Oncogenomics Group, Department of Clinical Pathology, Victorian Comprehensive Cancer Centre, Melbourne Medical School, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, VIC 3000, Australia; (K.M.); (J.C.); (M.C.); (J.E.J.); (P.G.); (S.J.); (S.G.P.); (B.J.P.); (D.D.B.)
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Melbourne Medical School, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, VIC 3000, Australia;
| | - Rachel Austin
- Genetic Health Queensland, Royal Brisbane and Women’s Hospital, Brisbane, QLD 4006, Australia; (R.A.); (H.M.F.)
| | - Jasmina Bojadzieva
- Clinical Genetics Unit, Austin Health, Melbourne, VIC 3084, Australia; (J.B.); (A.C.)
| | - Ainsley Campbell
- Clinical Genetics Unit, Austin Health, Melbourne, VIC 3084, Australia; (J.B.); (A.C.)
| | - Emma Edwards
- Familial Cancer Service, Westmead Hospital, Sydney, NSW 2145, Australia;
| | - Margaret Gleeson
- Hunter Family Cancer Service, Newcastle, NSW 2298, Australia; (M.G.); (J.K.); (A.R.)
| | - Annabel Goodwin
- Cancer Genetics Department, Royal Prince Alfred Hospital, Camperdown, NSW 2050, Australia; (A.G.); (A.S.)
- Sydney Medical School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW 2050, Australia
| | - Marion T. Harris
- Monash Health Familial Cancer Centre, Clayton, VIC 3168, Australia;
| | - Emilia Ip
- Cancer Genetics Service, Liverpool Hospital, Liverpool, NSW 2170, Australia;
| | - Judy Kirk
- Hunter Family Cancer Service, Newcastle, NSW 2298, Australia; (M.G.); (J.K.); (A.R.)
| | - Julia Mansour
- Tasmanian Clinical Genetics Service, Royal Hobart Hospital, Hobart, TAS 7000, Australia;
| | - Helen Mar Fan
- Genetic Health Queensland, Royal Brisbane and Women’s Hospital, Brisbane, QLD 4006, Australia; (R.A.); (H.M.F.)
| | - Cassandra Nichols
- Genetic Services of Western Australia, King Edward Memorial Hospital, Perth, WA 6008, Australia; (C.N.); (N.P.)
| | - Nicholas Pachter
- Genetic Services of Western Australia, King Edward Memorial Hospital, Perth, WA 6008, Australia; (C.N.); (N.P.)
- Medical School, Faculty of Health and Medical Sciences, University of Western Australia, Perth, WA 6009, Australia
- School of Medicine, Curtin University, Perth, WA 6102, Australia
| | - Abiramy Ragunathan
- Hunter Family Cancer Service, Newcastle, NSW 2298, Australia; (M.G.); (J.K.); (A.R.)
| | - Allan Spigelman
- Cancer Genetics Department, Royal Prince Alfred Hospital, Camperdown, NSW 2050, Australia; (A.G.); (A.S.)
- St Vincent’s Cancer Genetics Unit, Sydney, NSW 2010, Australia
- Surgical Professorial Unit, UNSW Clinical School of Clinical Medicine, Sydney, NSW 2052, Australia
| | - Rachel Susman
- Genetic Health Queensland, Royal Brisbane and Women’s Hospital, Brisbane, QLD 4006, Australia; (R.A.); (H.M.F.)
| | - Michael Christie
- Department of Medicine, Royal Melbourne Hospital, Melbourne Medical School, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, VIC 3052, Australia;
- Department of Pathology, The Royal Melbourne Hospital, Melbourne, VIC 3052, Australia
| | - Mark A. Jenkins
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Melbourne Medical School, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, VIC 3000, Australia;
- Centre for Epidemiology and Biostatistics, School of Population and Global Health, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, VIC 3052, Australia
| | - Rish K. Pai
- Department of Laboratory Medicine and Pathology, Mayo Clinic Arizona, Scottsdale, AZ 85259, USA;
| | - Christophe Rosty
- Colorectal Oncogenomics Group, Department of Clinical Pathology, Victorian Comprehensive Cancer Centre, Melbourne Medical School, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, VIC 3000, Australia; (K.M.); (J.C.); (M.C.); (J.E.J.); (P.G.); (S.J.); (S.G.P.); (B.J.P.); (D.D.B.)
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Melbourne Medical School, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, VIC 3000, Australia;
- Envoi Specialist Pathologists, Brisbane, QLD 4059, Australia
- School of Biomedical Sciences, Faculty of Medicine, University of Queensland, Brisbane, QLD 4072, Australia
| | - Finlay A. Macrae
- Genomic Medicine and Familial Cancer Centre, Royal Melbourne Hospital, Melbourne, VIC 3052, Australia; (F.A.M.); (I.M.W.)
- Colorectal Medicine and Genetics, The Royal Melbourne Hospital, Melbourne, VIC 3052, Australia
- Department of Medicine, Melbourne Medical School, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, VIC 3052, Australia
| | - Ingrid M. Winship
- Genomic Medicine and Familial Cancer Centre, Royal Melbourne Hospital, Melbourne, VIC 3052, Australia; (F.A.M.); (I.M.W.)
- Department of Medicine, Melbourne Medical School, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, VIC 3052, Australia
| | - Daniel D. Buchanan
- Colorectal Oncogenomics Group, Department of Clinical Pathology, Victorian Comprehensive Cancer Centre, Melbourne Medical School, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, VIC 3000, Australia; (K.M.); (J.C.); (M.C.); (J.E.J.); (P.G.); (S.J.); (S.G.P.); (B.J.P.); (D.D.B.)
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Melbourne Medical School, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, VIC 3000, Australia;
- Genomic Medicine and Familial Cancer Centre, Royal Melbourne Hospital, Melbourne, VIC 3052, Australia; (F.A.M.); (I.M.W.)
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11
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O'Neill MJ, Yang T, Laudeman J, Calandranis M, Solus J, Roden DM, Glazer AM. ParSE-seq: A Calibrated Multiplexed Assay to Facilitate the Clinical Classification of Putative Splice-altering Variants. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.09.04.23295019. [PMID: 37732247 PMCID: PMC10508793 DOI: 10.1101/2023.09.04.23295019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/22/2023]
Abstract
Background Interpreting the clinical significance of putative splice-altering variants outside 2-base pair canonical splice sites remains difficult without functional studies. Methods We developed Parallel Splice Effect Sequencing (ParSE-seq), a multiplexed minigene-based assay, to test variant effects on RNA splicing quantified by high-throughput sequencing. We studied variants in SCN5A, an arrhythmia-associated gene which encodes the major cardiac voltage-gated sodium channel. We used the computational tool SpliceAI to prioritize exonic and intronic candidate splice variants, and ClinVar to select benign and pathogenic control variants. We generated a pool of 284 barcoded minigene plasmids, transfected them into Human Embryonic Kidney (HEK293) cells and induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs), sequenced the resulting pools of splicing products, and calibrated the assay to the American College of Medical Genetics and Genomics scheme. Variants were interpreted using the calibrated functional data, and experimental data were compared to SpliceAI predictions. We further studied some splice-altering missense variants by cDNA-based automated patch clamping (APC) in HEK cells and assessed splicing and sodium channel function in CRISPR-edited iPSC-CMs. Results ParSE-seq revealed the splicing effect of 224 SCN5A variants in iPSC-CMs and 244 variants in HEK293 cells. The scores between the cell types were highly correlated (R2=0.84). In iPSCs, the assay had concordant scores for 21/22 benign/likely benign and 24/25 pathogenic/likely pathogenic control variants from ClinVar. 43/112 exonic variants and 35/70 intronic variants with determinate scores disrupted splicing. 11 of 42 variants of uncertain significance were reclassified, and 29 of 34 variants with conflicting interpretations were reclassified using the functional data. SpliceAI computational predictions correlated well with experimental data (AUC = 0.96). We identified 20 unique SCN5A missense variants that disrupted splicing, and 2 clinically observed splice-altering missense variants of uncertain significance had normal function when tested with the cDNA-based APC assay. A splice-altering intronic variant detected by ParSE-seq, c.1891-5C>G, also disrupted splicing and sodium current when introduced into iPSC-CMs at the endogenous locus by CRISPR editing. Conclusions ParSE-seq is a calibrated, multiplexed, high-throughput assay to facilitate the classification of candidate splice-altering variants.
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Affiliation(s)
| | - Tao Yang
- Vanderbilt Center for Arrhythmia Research and Therapeutics (VanCART), Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Julie Laudeman
- Vanderbilt Center for Arrhythmia Research and Therapeutics (VanCART), Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Maria Calandranis
- Vanderbilt Center for Arrhythmia Research and Therapeutics (VanCART), Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Joseph Solus
- Vanderbilt Center for Arrhythmia Research and Therapeutics (VanCART), Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Dan M Roden
- Vanderbilt Center for Arrhythmia Research and Therapeutics (VanCART), Departments of Medicine, Pharmacology, and Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN
| | - Andrew M Glazer
- Vanderbilt Center for Arrhythmia Research and Therapeutics (VanCART), Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
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12
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The Impact of Genomic Variation on Function (IGVF) Consortium. ARXIV 2023:arXiv:2307.13708v1. [PMID: 37547663 PMCID: PMC10402186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
Our genomes influence nearly every aspect of human biology from molecular and cellular functions to phenotypes in health and disease. Human genetics studies have now associated hundreds of thousands of differences in our DNA sequence ("genomic variation") with disease risk and other phenotypes, many of which could reveal novel mechanisms of human biology and uncover the basis of genetic predispositions to diseases, thereby guiding the development of new diagnostics and therapeutics. Yet, understanding how genomic variation alters genome function to influence phenotype has proven challenging. To unlock these insights, we need a systematic and comprehensive catalog of genome function and the molecular and cellular effects of genomic variants. Toward this goal, the Impact of Genomic Variation on Function (IGVF) Consortium will combine approaches in single-cell mapping, genomic perturbations, and predictive modeling to investigate the relationships among genomic variation, genome function, and phenotypes. Through systematic comparisons and benchmarking of experimental and computational methods, we aim to create maps across hundreds of cell types and states describing how coding variants alter protein activity, how noncoding variants change the regulation of gene expression, and how both coding and noncoding variants may connect through gene regulatory and protein interaction networks. These experimental data, computational predictions, and accompanying standards and pipelines will be integrated into an open resource that will catalyze community efforts to explore genome function and the impact of genetic variation on human biology and disease across populations.
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13
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Fowler DM, Adams DJ, Gloyn AL, Hahn WC, Marks DS, Muffley LA, Neal JT, Roth FP, Rubin AF, Starita LM, Hurles ME. An Atlas of Variant Effects to understand the genome at nucleotide resolution. Genome Biol 2023; 24:147. [PMID: 37394429 PMCID: PMC10316620 DOI: 10.1186/s13059-023-02986-x] [Citation(s) in RCA: 32] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 06/13/2023] [Indexed: 07/04/2023] Open
Abstract
Sequencing has revealed hundreds of millions of human genetic variants, and continued efforts will only add to this variant avalanche. Insufficient information exists to interpret the effects of most variants, limiting opportunities for precision medicine and comprehension of genome function. A solution lies in experimental assessment of the functional effect of variants, which can reveal their biological and clinical impact. However, variant effect assays have generally been undertaken reactively for individual variants only after and, in most cases long after, their first observation. Now, multiplexed assays of variant effect can characterise massive numbers of variants simultaneously, yielding variant effect maps that reveal the function of every possible single nucleotide change in a gene or regulatory element. Generating maps for every protein encoding gene and regulatory element in the human genome would create an 'Atlas' of variant effect maps and transform our understanding of genetics and usher in a new era of nucleotide-resolution functional knowledge of the genome. An Atlas would reveal the fundamental biology of the human genome, inform human evolution, empower the development and use of therapeutics and maximize the utility of genomics for diagnosing and treating disease. The Atlas of Variant Effects Alliance is an international collaborative group comprising hundreds of researchers, technologists and clinicians dedicated to realising an Atlas of Variant Effects to help deliver on the promise of genomics.
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Affiliation(s)
- Douglas M. Fowler
- Department of Genome Sciences, University of Washington, Seattle, WA USA
- Department of Bioengineering, University of Washington, Seattle, WA USA
- Brotman Baty Institute for Precision Medicine, Seattle, WA USA
| | | | - Anna L. Gloyn
- Department of Pediatrics & Department of Genetics, Division of Endocrinology, Stanford School of Medicine, Stanford University, Stanford, CA USA
| | - William C. Hahn
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA USA
- Broad Institute of MIT and Harvard, Cambridge, MA USA
| | - Debora S. Marks
- Broad Institute of MIT and Harvard, Cambridge, MA USA
- Department of Systems Biology, Harvard Medical School, Cambridge, USA
| | - Lara A. Muffley
- Department of Genome Sciences, University of Washington, Seattle, WA USA
| | - James T. Neal
- Broad Institute of MIT and Harvard, Cambridge, MA USA
- Novo Nordisk Foundation Center for Genomic Mechanisms of Disease at Broad Institute, Cambridge, MA USA
| | - Frederick P. Roth
- Donnelly Centre and Departments of Molecular Genetics and Computer Science, University of Toronto, Toronto, ON Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON Canada
| | - Alan F. Rubin
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC Australia
- Department of Medical Biology, University of Melbourne, Melbourne, VIC Australia
| | - Lea M. Starita
- Department of Genome Sciences, University of Washington, Seattle, WA USA
- Department of Bioengineering, University of Washington, Seattle, WA USA
- Brotman Baty Institute for Precision Medicine, Seattle, WA USA
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