1
|
Ogloblinsky MSC, Bocher O, Aloui C, Leutenegger AL, Ozisik O, Baudot A, Tournier-Lasserve E, Castillo-Madeen H, Lewinsohn D, Conrad DF, Génin E, Marenne G. PSAP-Genomic-Regions: A Method Leveraging Population Data to Prioritize Coding and Non-Coding Variants in Whole Genome Sequencing for Rare Disease Diagnosis. Genet Epidemiol 2024. [PMID: 39318036 DOI: 10.1002/gepi.22593] [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: 06/05/2024] [Revised: 07/30/2024] [Accepted: 09/03/2024] [Indexed: 09/26/2024]
Abstract
The introduction of Next-Generation Sequencing technologies in the clinics has improved rare disease diagnosis. Nonetheless, for very heterogeneous or very rare diseases, more than half of cases still lack molecular diagnosis. Novel strategies are needed to prioritize variants within a single individual. The Population Sampling Probability (PSAP) method was developed to meet this aim but only for coding variants in exome data. Here, we propose an extension of the PSAP method to the non-coding genome called PSAP-genomic-regions. In this extension, instead of considering genes as testing units (PSAP-genes strategy), we use genomic regions defined over the whole genome that pinpoint potential functional constraints. We conceived an evaluation protocol for our method using artificially generated disease exomes and genomes, by inserting coding and non-coding pathogenic ClinVar variants in large data sets of exomes and genomes from the general population. PSAP-genomic-regions significantly improves the ranking of these variants compared to using a pathogenicity score alone. Using PSAP-genomic-regions, more than 50% of non-coding ClinVar variants were among the top 10 variants of the genome. On real sequencing data from six patients with Cerebral Small Vessel Disease and nine patients with male infertility, all causal variants were ranked in the top 100 variants with PSAP-genomic-regions. By revisiting the testing units used in the PSAP method to include non-coding variants, we have developed PSAP-genomic-regions, an efficient whole-genome prioritization tool which offers promising results for the diagnosis of unresolved rare diseases.
Collapse
Affiliation(s)
| | - Ozvan Bocher
- Univ Brest, Inserm, EFS, UMR 1078, GGB, Brest, France
- Institute of Translational Genomics, Helmholtz Zentrum München, Munich, Germany
| | - Chaker Aloui
- Inserm, NeuroDiderot, Unité Mixte de Recherche, Université Paris Cité, Paris, France
| | | | - Ozan Ozisik
- INSERM, Marseille Medical Genetics (MMG), Aix Marseille University, Marseille, France
| | - Anaïs Baudot
- INSERM, Marseille Medical Genetics (MMG), Aix Marseille University, Marseille, France
| | - Elisabeth Tournier-Lasserve
- Inserm, NeuroDiderot, Unité Mixte de Recherche, Université Paris Cité, Paris, France
- Assistance Publique-Hôpitaux de Paris, Service de Génétique Moléculaire Neurovasculaire, Hôpital Saint-Louis, Paris, France
| | - Helen Castillo-Madeen
- Division of Genetics, Oregon National Primate Research Center, Oregon Health and Science University, Portland, Oregon, USA
| | - Daniel Lewinsohn
- Division of Genetics, Oregon National Primate Research Center, Oregon Health and Science University, Portland, Oregon, USA
| | - Donald F Conrad
- Division of Genetics, Oregon National Primate Research Center, Oregon Health and Science University, Portland, Oregon, USA
| | - Emmanuelle Génin
- Univ Brest, Inserm, EFS, UMR 1078, GGB, Brest, France
- Centre Hospitalier Régional Universitaire de Brest, Brest, France
| | | |
Collapse
|
2
|
Woo BJ, Moussavi-Baygi R, Karner H, Karimzadeh M, Yousefi H, Lee S, Garcia K, Joshi T, Yin K, Navickas A, Gilbert LA, Wang B, Asgharian H, Feng FY, Goodarzi H. Integrative identification of non-coding regulatory regions driving metastatic prostate cancer. Cell Rep 2024; 43:114764. [PMID: 39276353 PMCID: PMC11466230 DOI: 10.1016/j.celrep.2024.114764] [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: 06/13/2023] [Revised: 07/08/2024] [Accepted: 08/29/2024] [Indexed: 09/17/2024] Open
Abstract
Large-scale sequencing efforts have been undertaken to understand the mutational landscape of the coding genome. However, the vast majority of variants occur within non-coding genomic regions. We designed an integrative computational and experimental framework to identify recurrently mutated non-coding regulatory regions that drive tumor progression. Applying this framework to sequencing data from a large prostate cancer patient cohort revealed a large set of candidate drivers. We used (1) in silico analyses, (2) massively parallel reporter assays, and (3) in vivo CRISPR interference screens to systematically validate metastatic castration-resistant prostate cancer (mCRPC) drivers. One identified enhancer region, GH22I030351, acts on a bidirectional promoter to simultaneously modulate expression of the U2-associated splicing factor SF3A1 and chromosomal protein CCDC157. SF3A1 and CCDC157 promote tumor growth in vivo. We nominated a number of transcription factors, notably SOX6, to regulate expression of SF3A1 and CCDC157. Our integrative approach enables the systematic detection of non-coding regulatory regions that drive human cancers.
Collapse
Affiliation(s)
- Brian J Woo
- Department of Biochemistry & Biophysics, University of California, San Francisco, San Francisco, CA, USA; Department of Urology, University of California, San Francisco, San Francisco, CA, USA; Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA; Arc Institute, Palo Alto, CA 94305, USA
| | - Ruhollah Moussavi-Baygi
- Department of Biochemistry & Biophysics, University of California, San Francisco, San Francisco, CA, USA; Department of Urology, University of California, San Francisco, San Francisco, CA, USA; Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
| | - Heather Karner
- Department of Biochemistry & Biophysics, University of California, San Francisco, San Francisco, CA, USA; Department of Urology, University of California, San Francisco, San Francisco, CA, USA; Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA; Arc Institute, Palo Alto, CA 94305, USA
| | - Mehran Karimzadeh
- Department of Biochemistry & Biophysics, University of California, San Francisco, San Francisco, CA, USA; Department of Urology, University of California, San Francisco, San Francisco, CA, USA; Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA; Vector Institute, Toronto, ON, Canada; Peter Munk Cardiac Centre, University Health Network, Toronto, ON, Canada; Arc Institute, Palo Alto, CA 94305, USA
| | - Hassan Yousefi
- Department of Biochemistry & Biophysics, University of California, San Francisco, San Francisco, CA, USA; Department of Urology, University of California, San Francisco, San Francisco, CA, USA; Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA; Arc Institute, Palo Alto, CA 94305, USA
| | - Sean Lee
- Department of Biochemistry & Biophysics, University of California, San Francisco, San Francisco, CA, USA; Department of Urology, University of California, San Francisco, San Francisco, CA, USA; Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA; Arc Institute, Palo Alto, CA 94305, USA
| | - Kristle Garcia
- Department of Biochemistry & Biophysics, University of California, San Francisco, San Francisco, CA, USA; Department of Urology, University of California, San Francisco, San Francisco, CA, USA; Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
| | - Tanvi Joshi
- Department of Biochemistry & Biophysics, University of California, San Francisco, San Francisco, CA, USA; Department of Urology, University of California, San Francisco, San Francisco, CA, USA; Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
| | - Keyi Yin
- Department of Biochemistry & Biophysics, University of California, San Francisco, San Francisco, CA, USA; Department of Urology, University of California, San Francisco, San Francisco, CA, USA; Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
| | - Albertas Navickas
- Department of Biochemistry & Biophysics, University of California, San Francisco, San Francisco, CA, USA; Department of Urology, University of California, San Francisco, San Francisco, CA, USA; Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
| | - Luke A Gilbert
- Department of Biochemistry & Biophysics, University of California, San Francisco, San Francisco, CA, USA; Department of Urology, University of California, San Francisco, San Francisco, CA, USA; Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA; Arc Institute, Palo Alto, CA 94305, USA
| | - Bo Wang
- Vector Institute, Toronto, ON, Canada; Peter Munk Cardiac Centre, University Health Network, Toronto, ON, Canada
| | - Hosseinali Asgharian
- Department of Biochemistry & Biophysics, University of California, San Francisco, San Francisco, CA, USA; Department of Urology, University of California, San Francisco, San Francisco, CA, USA; Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA; Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.
| | - Felix Y Feng
- Department of Urology, University of California, San Francisco, San Francisco, CA, USA; Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA; Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA, USA.
| | - Hani Goodarzi
- Department of Biochemistry & Biophysics, University of California, San Francisco, San Francisco, CA, USA; Department of Urology, University of California, San Francisco, San Francisco, CA, USA; Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA; Arc Institute, Palo Alto, CA 94305, USA; Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.
| |
Collapse
|
3
|
Kerkhof J, Rastin C, Levy MA, Relator R, McConkey H, Demain L, Dominguez-Garrido E, Kaat LD, Houge SD, DuPont BR, Fee T, Fletcher RS, Gokhale D, Haukanes BI, Henneman P, Hilton S, Hilton BA, Jenkinson S, Lee JA, Louie RJ, Motazacker MM, Rzasa J, Stevenson RE, Plomp A, van der Laan L, van der Smagt J, Walden KK, Banka S, Mannens M, Skinner SA, Friez MJ, Campbell C, Tedder ML, Alders M, Sadikovic B. Diagnostic utility and reporting recommendations for clinical DNA methylation episignature testing in genetically undiagnosed rare diseases. Genet Med 2024; 26:101075. [PMID: 38251460 DOI: 10.1016/j.gim.2024.101075] [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: 08/29/2023] [Revised: 01/09/2024] [Accepted: 01/12/2024] [Indexed: 01/23/2024] Open
Abstract
PURPOSE This study aims to assess the diagnostic utility and provide reporting recommendations for clinical DNA methylation episignature testing based on the cohort of patients tested through the EpiSign Clinical Testing Network. METHODS The EpiSign assay utilized unsupervised clustering techniques and a support vector machine-based classification algorithm to compare each patient's genome-wide DNA methylation profile with the EpiSign Knowledge Database, yielding the result that was reported. An international working group, representing distinct EpiSign Clinical Testing Network health jurisdictions, collaborated to establish recommendations for interpretation and reporting of episignature testing. RESULTS Among 2399 cases analyzed, 1667 cases underwent a comprehensive screen of validated episignatures, imprinting, and promoter regions, resulting in 18.7% (312/1667) positive reports. The remaining 732 referrals underwent targeted episignature analysis for assessment of sequence or copy-number variants (CNVs) of uncertain significance or for assessment of clinical diagnoses without confirmed molecular findings, and 32.4% (237/732) were positive. Cases with detailed clinical information were highlighted to describe various utility scenarios for episignature testing. CONCLUSION Clinical DNA methylation testing including episignatures, imprinting, and promoter analysis provided by an integrated network of clinical laboratories enables test standardization and demonstrates significant diagnostic yield and clinical utility beyond DNA sequence analysis in rare diseases.
Collapse
Affiliation(s)
- Jennifer Kerkhof
- Verspeeten Clinical Genome Centre, London Health Sciences Centre, London, ON, Canada
| | - Cassandra Rastin
- Verspeeten Clinical Genome Centre, London Health Sciences Centre, London, ON, Canada
| | - Michael A Levy
- Verspeeten Clinical Genome Centre, London Health Sciences Centre, London, ON, Canada
| | - Raissa Relator
- Verspeeten Clinical Genome Centre, London Health Sciences Centre, London, ON, Canada
| | - Haley McConkey
- Verspeeten Clinical Genome Centre, London Health Sciences Centre, London, ON, Canada
| | - Leigh Demain
- Manchester Centre for Genomic Medicine, Manchester University NHS Foundation Trust, Manchester, United Kingdom
| | | | - Laura Donker Kaat
- Department of Clinical Genetics, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Sofia Douzgou Houge
- Haukeland University Hospital, Centre for Medical Genetics and Molecular Medicine, Bergen, Norway
| | | | | | | | - David Gokhale
- Manchester Centre for Genomic Medicine, Manchester University NHS Foundation Trust, Manchester, United Kingdom
| | - Bjørn Ivar Haukanes
- Haukeland University Hospital, Centre for Medical Genetics and Molecular Medicine, Bergen, Norway
| | - Peter Henneman
- Amsterdam University Medical Center, University of Amsterdam, Department of Human Genetics, Amsterdam Reproduction and Development Research Institute, Amsterdam, The Netherlands
| | - Sarah Hilton
- Manchester Centre for Genomic Medicine, Manchester University NHS Foundation Trust, Manchester, United Kingdom
| | | | - Sarah Jenkinson
- Manchester Centre for Genomic Medicine, Manchester University NHS Foundation Trust, Manchester, United Kingdom
| | | | | | - M Mahdi Motazacker
- Amsterdam University Medical Center, University of Amsterdam, Department of Human Genetics, Amsterdam Reproduction and Development Research Institute, Amsterdam, The Netherlands
| | - Jessica Rzasa
- Verspeeten Clinical Genome Centre, London Health Sciences Centre, London, ON, Canada
| | | | - Astrid Plomp
- Department of Clinical Genetics, AMC, Amsterdam, The Netherlands
| | - Liselot van der Laan
- Amsterdam University Medical Center, University of Amsterdam, Department of Human Genetics, Amsterdam Reproduction and Development Research Institute, Amsterdam, The Netherlands
| | - Jasper van der Smagt
- Department of Genetics, Utrecht University Medical Center, Utrecht, The Netherlands
| | | | - Siddharth Banka
- Manchester Centre for Genomic Medicine, Manchester University NHS Foundation Trust, Manchester, United Kingdom; Division of Evolution, Infection and Genomic Sciences, School of Biological Sciences, University of Manchester, Manchester, United Kingdom
| | - Marcel Mannens
- Amsterdam University Medical Center, University of Amsterdam, Department of Human Genetics, Amsterdam Reproduction and Development Research Institute, Amsterdam, The Netherlands
| | | | | | - Christopher Campbell
- Manchester Centre for Genomic Medicine, Manchester University NHS Foundation Trust, Manchester, United Kingdom
| | | | - Marielle Alders
- Amsterdam University Medical Center, University of Amsterdam, Department of Human Genetics, Amsterdam Reproduction and Development Research Institute, Amsterdam, The Netherlands
| | - Bekim Sadikovic
- Verspeeten Clinical Genome Centre, London Health Sciences Centre, London, ON, Canada; Department of Pathology and Laboratory Medicine, Western University, London, ON, Canada.
| |
Collapse
|
4
|
Baudic M, Murata H, Bosada FM, Melo US, Aizawa T, Lindenbaum P, van der Maarel LE, Guedon A, Baron E, Fremy E, Foucal A, Ishikawa T, Ushinohama H, Jurgens SJ, Choi SH, Kyndt F, Le Scouarnec S, Wakker V, Thollet A, Rajalu A, Takaki T, Ohno S, Shimizu W, Horie M, Kimura T, Ellinor PT, Petit F, Dulac Y, Bru P, Boland A, Deleuze JF, Redon R, Le Marec H, Le Tourneau T, Gourraud JB, Yoshida Y, Makita N, Vieyres C, Makiyama T, Mundlos S, Christoffels VM, Probst V, Schott JJ, Barc J. TAD boundary deletion causes PITX2-related cardiac electrical and structural defects. Nat Commun 2024; 15:3380. [PMID: 38643172 PMCID: PMC11032321 DOI: 10.1038/s41467-024-47739-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: 02/17/2023] [Accepted: 04/08/2024] [Indexed: 04/22/2024] Open
Abstract
While 3D chromatin organization in topologically associating domains (TADs) and loops mediating regulatory element-promoter interactions is crucial for tissue-specific gene regulation, the extent of their involvement in human Mendelian disease is largely unknown. Here, we identify 7 families presenting a new cardiac entity associated with a heterozygous deletion of 2 CTCF binding sites on 4q25, inducing TAD fusion and chromatin conformation remodeling. The CTCF binding sites are located in a gene desert at 1 Mb from the Paired-like homeodomain transcription factor 2 gene (PITX2). By introducing the ortholog of the human deletion in the mouse genome, we recapitulate the patient phenotype and characterize an opposite dysregulation of PITX2 expression in the sinoatrial node (ectopic activation) and ventricle (reduction), respectively. Chromatin conformation assay performed in human induced pluripotent stem cell-derived cardiomyocytes harboring the minimal deletion identified in family#1 reveals a conformation remodeling and fusion of TADs. We conclude that TAD remodeling mediated by deletion of CTCF binding sites causes a new autosomal dominant Mendelian cardiac disorder.
Collapse
Affiliation(s)
- Manon Baudic
- Nantes Université, CHU Nantes, CNRS, INSERM, l'institut du Thorax, F-44000, Nantes, France
| | - Hiroshige Murata
- The Department of Cardiovascular Medicine, Nippon Medical School Hospital, Tokyo, Japan
| | - Fernanda M Bosada
- Department of Medical Biology, Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centers, University of Amsterdam, 1105 AZ, Amsterdam, The Netherlands
| | - Uirá Souto Melo
- Max Planck Institute for Molecular Genetics, RG Development and Disease, 13353, Berlin, Germany
| | - Takanori Aizawa
- Department of Cardiovascular Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Pierre Lindenbaum
- Nantes Université, CHU Nantes, CNRS, INSERM, l'institut du Thorax, F-44000, Nantes, France
| | - Lieve E van der Maarel
- Department of Medical Biology, Amsterdam Cardiovascular Sciences, Amsterdam Reproduction and Development, Amsterdam University Medical Centers, University of Amsterdam, 1105 AZ, Amsterdam, The Netherlands
| | - Amaury Guedon
- Nantes Université, CHU Nantes, CNRS, INSERM, l'institut du Thorax, F-44000, Nantes, France
| | - Estelle Baron
- Nantes Université, CHU Nantes, CNRS, INSERM, l'institut du Thorax, F-44000, Nantes, France
| | - Enora Fremy
- Nantes Université, CHU Nantes, CNRS, INSERM, l'institut du Thorax, F-44000, Nantes, France
| | - Adrien Foucal
- Nantes Université, CHU Nantes, CNRS, INSERM, l'institut du Thorax, F-44000, Nantes, France
| | - Taisuke Ishikawa
- Omics Research Center, National Cerebral and Cardiovascular Center, Suita, Japan
| | - Hiroya Ushinohama
- Department of Cardiology, Fukuoka Children's Hospital, Fukuoka, Japan
| | - Sean J Jurgens
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Experimental Cardiology, Heart Center, Amsterdam Cardiovascular Sciences, Amsterdam UMC Location University of Amsterdam, Amsterdam, The Netherlands
| | - Seung Hoan Choi
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Florence Kyndt
- Nantes Université, CHU Nantes, CNRS, INSERM, l'institut du Thorax, F-44000, Nantes, France
| | - Solena Le Scouarnec
- Nantes Université, CHU Nantes, CNRS, INSERM, l'institut du Thorax, F-44000, Nantes, France
| | - Vincent Wakker
- Department of Medical Biology, Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centers, University of Amsterdam, 1105 AZ, Amsterdam, The Netherlands
| | - Aurélie Thollet
- Nantes Université, CHU Nantes, CNRS, INSERM, l'institut du Thorax, F-44000, Nantes, France
| | - Annabelle Rajalu
- Nantes Université, CHU Nantes, CNRS, INSERM, l'institut du Thorax, F-44000, Nantes, France
| | - Tadashi Takaki
- Department of Cell Growth and Differentiation, Center for iPS Cell Research and Application, Kyoto University, Kyoto, Japan
- Takeda-CiRA Joint Program for iPS Cell Applications, Fujisawa, Japan
- Department of Pancreatic Islet Cell Transplantation, National Center for Global Health and Medicine, Tokyo, Japan
| | - Seiko Ohno
- Department of Bioscience and Genetics, National Cerebral and Cardiovascular Center Research Institute, Suita, Japan
| | - Wataru Shimizu
- The Department of Cardiovascular Medicine, Nippon Medical School Hospital, Tokyo, Japan
| | - Minoru Horie
- Department of Cardiovascular Medicine, Shiga University of Medical Science, Ohtsu, Japan
| | - Takeshi Kimura
- Department of Cardiovascular Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Patrick T Ellinor
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital, Boston, MA, USA
| | - Florence Petit
- Service de Génétique Clinique, CHU Lille, Hôpital Jeanne de Flandre, F-59000, Lille, France
- University of Lille, EA 7364-RADEME, F-59000, Lille, France
| | - Yves Dulac
- Unité de Cardiologie Pédiatrique, Hôpital des Enfants, F-31000, Toulouse, France
| | - Paul Bru
- Service de Cardiologie, GH La Rochelle, F-17019, La Rochelle, France
| | - Anne Boland
- Université Paris-Saclay, CEA, Centre National de Recherche en Génomique Humaine (CNRGH), 91057, Evry, France
| | - Jean-François Deleuze
- Université Paris-Saclay, CEA, Centre National de Recherche en Génomique Humaine (CNRGH), 91057, Evry, France
| | - Richard Redon
- Nantes Université, CHU Nantes, CNRS, INSERM, l'institut du Thorax, F-44000, Nantes, France
| | - Hervé Le Marec
- Nantes Université, CHU Nantes, CNRS, INSERM, l'institut du Thorax, F-44000, Nantes, France
| | - Thierry Le Tourneau
- Nantes Université, CHU Nantes, CNRS, INSERM, l'institut du Thorax, F-44000, Nantes, France
| | - Jean-Baptiste Gourraud
- Nantes Université, CHU Nantes, CNRS, INSERM, l'institut du Thorax, F-44000, Nantes, France
- European Reference Network for Rare and Low Prevalence Complex Diseases of the Heart: ERN GUARD-Heart, Amsterdam, The Netherlands
| | - Yoshinori Yoshida
- Department of Cell Growth and Differentiation, Center for iPS Cell Research and Application, Kyoto University, Kyoto, Japan
| | - Naomasa Makita
- Omics Research Center, National Cerebral and Cardiovascular Center, Suita, Japan
- Department of Cardiology, Sapporo Teishinkai Hospital, Sapporo, Japan
| | - Claude Vieyres
- Cabinet Cardiologique, Clinique St. Joseph, F-16000, Angoulême, France
| | - Takeru Makiyama
- Department of Cardiovascular Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
- Department of Community Medicine Supporting System, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Stephan Mundlos
- Max Planck Institute for Molecular Genetics, RG Development and Disease, 13353, Berlin, Germany
| | - Vincent M Christoffels
- Department of Medical Biology, Amsterdam Cardiovascular Sciences, Amsterdam Reproduction and Development, Amsterdam University Medical Centers, University of Amsterdam, 1105 AZ, Amsterdam, The Netherlands
| | - Vincent Probst
- Nantes Université, CHU Nantes, CNRS, INSERM, l'institut du Thorax, F-44000, Nantes, France
- European Reference Network for Rare and Low Prevalence Complex Diseases of the Heart: ERN GUARD-Heart, Amsterdam, The Netherlands
| | - Jean-Jacques Schott
- Nantes Université, CHU Nantes, CNRS, INSERM, l'institut du Thorax, F-44000, Nantes, France.
- European Reference Network for Rare and Low Prevalence Complex Diseases of the Heart: ERN GUARD-Heart, Amsterdam, The Netherlands.
| | - Julien Barc
- Nantes Université, CHU Nantes, CNRS, INSERM, l'institut du Thorax, F-44000, Nantes, France.
- European Reference Network for Rare and Low Prevalence Complex Diseases of the Heart: ERN GUARD-Heart, Amsterdam, The Netherlands.
| |
Collapse
|
5
|
Brlek P, Bulić L, Bračić M, Projić P, Škaro V, Shah N, Shah P, Primorac D. Implementing Whole Genome Sequencing (WGS) in Clinical Practice: Advantages, Challenges, and Future Perspectives. Cells 2024; 13:504. [PMID: 38534348 DOI: 10.3390/cells13060504] [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: 02/06/2024] [Revised: 03/04/2024] [Accepted: 03/11/2024] [Indexed: 03/28/2024] Open
Abstract
The integration of whole genome sequencing (WGS) into all aspects of modern medicine represents the next step in the evolution of healthcare. Using this technology, scientists and physicians can observe the entire human genome comprehensively, generating a plethora of new sequencing data. Modern computational analysis entails advanced algorithms for variant detection, as well as complex models for classification. Data science and machine learning play a crucial role in the processing and interpretation of results, using enormous databases and statistics to discover new and support current genotype-phenotype correlations. In clinical practice, this technology has greatly enabled the development of personalized medicine, approaching each patient individually and in accordance with their genetic and biochemical profile. The most propulsive areas include rare disease genomics, oncogenomics, pharmacogenomics, neonatal screening, and infectious disease genomics. Another crucial application of WGS lies in the field of multi-omics, working towards the complete integration of human biomolecular data. Further technological development of sequencing technologies has led to the birth of third and fourth-generation sequencing, which include long-read sequencing, single-cell genomics, and nanopore sequencing. These technologies, alongside their continued implementation into medical research and practice, show great promise for the future of the field of medicine.
Collapse
Affiliation(s)
- Petar Brlek
- St. Catherine Specialty Hospital, 10000 Zagreb, Croatia
- International Center for Applied Biological Research, 10000 Zagreb, Croatia
- School of Medicine, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia
| | - Luka Bulić
- St. Catherine Specialty Hospital, 10000 Zagreb, Croatia
| | - Matea Bračić
- St. Catherine Specialty Hospital, 10000 Zagreb, Croatia
| | - Petar Projić
- International Center for Applied Biological Research, 10000 Zagreb, Croatia
| | | | - Nidhi Shah
- Dartmouth Hitchcock Medical Center, Lebannon, NH 03766, USA
| | - Parth Shah
- Dartmouth Hitchcock Medical Center, Lebannon, NH 03766, USA
| | - Dragan Primorac
- St. Catherine Specialty Hospital, 10000 Zagreb, Croatia
- International Center for Applied Biological Research, 10000 Zagreb, Croatia
- School of Medicine, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia
- Medical School, University of Split, 21000 Split, Croatia
- Eberly College of Science, The Pennsylvania State University, State College, PA 16802, USA
- The Henry C. Lee College of Criminal Justice and Forensic Sciences, University of New Haven, West Haven, CT 06516, USA
- REGIOMED Kliniken, 96450 Coburg, Germany
- Medical School, University of Rijeka, 51000 Rijeka, Croatia
- Faculty of Dental Medicine and Health, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia
- Medical School, University of Mostar, 88000 Mostar, Bosnia and Herzegovina
- National Forensic Sciences University, Gujarat 382007, India
| |
Collapse
|
6
|
Zalusky MPG, Gustafson JA, Bohaczuk SC, Mallory B, Reed P, Wenger T, Beckman E, Chang IJ, Paschal CR, Buchan JG, Lockwood CM, Puia-Dumitrescu M, Garalde DR, Guillory J, Markham AJ, Bamshad MJ, Eichler EE, Stergachis AB, Miller DE. 3-hour genome sequencing and targeted analysis to rapidly assess genetic risk. GENETICS IN MEDICINE OPEN 2024; 2:101833. [PMID: 39421454 PMCID: PMC11484281 DOI: 10.1016/j.gimo.2024.101833] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/19/2024]
Abstract
Purpose Rapid genetic testing in the critical care setting may guide diagnostic evaluation, direct therapies, and help families and care providers make informed decisions about goals of care. We tested whether a simplified DNA extraction and library preparation process would enable us to perform ultra-rapid assessment of genetic risk for a Mendelian condition, based on information from an affected sibling, using long-read genome sequencing and targeted analysis. Methods Following extraction of DNA from cord blood and rapid library preparation, genome sequencing was performed on an Oxford Nanopore PromethION. FASTQ files were generated from original sequencing data in near real-time and aligned to a reference genome. Variant calling and analysis were performed at timed intervals. Results We optimized the DNA extraction and library preparation methods to create sufficient library for sequencing from 500 μL of blood. Real-time, targeted analysis was performed to determine that the newborn was neither affected nor a heterozygote for variants underlying a Mendelian condition. Phasing of the target region and prior knowledge of the affected haplotypes supported our interpretation despite a low level of coverage at 3 hours of life. Conclusion This proof-of-concept experiment demonstrates how prior knowledge of haplotype structure or familial variants can be used to rapidly evaluate an individual at risk for a genetic disease. While ultra-rapid sequencing remains both complex and cost prohibitive, our method is more easily automated than prior approaches and uses smaller volumes of blood, thus may be more easily adopted for future studies of ultra-rapid genome sequencing in the clinical setting.
Collapse
Affiliation(s)
- Miranda PG Zalusky
- Division of Genetic Medicine, Department of Pediatrics, University of Washington and Seattle Children’s Hospital, Seattle, WA, USA
| | - Jonas A Gustafson
- Division of Genetic Medicine, Department of Pediatrics, University of Washington and Seattle Children’s Hospital, Seattle, WA, USA
- Molecular and Cellular Biology Program, University of Washington School of Medicine, Seattle, WA, USA
| | - Stephanie C Bohaczuk
- Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA
| | - Ben Mallory
- Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Paxton Reed
- Division of Genetic Medicine, Department of Pediatrics, University of Washington and Seattle Children’s Hospital, Seattle, WA, USA
| | - Tara Wenger
- Division of Genetic Medicine, Department of Pediatrics, University of Washington and Seattle Children’s Hospital, Seattle, WA, USA
| | - Erika Beckman
- Division of Genetic Medicine, Department of Pediatrics, University of Washington and Seattle Children’s Hospital, Seattle, WA, USA
| | - Irene J. Chang
- Division of Genetic Medicine, Department of Pediatrics, University of Washington and Seattle Children’s Hospital, Seattle, WA, USA
| | - Cate R. Paschal
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
- Department of Laboratories, Seattle Children’s Hospital, Seattle, WA, USA
| | - Jillian G. Buchan
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Christina M. Lockwood
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA, USA
| | - Mihai Puia-Dumitrescu
- Division of Neonatology, Department of Pediatrics, University of Washington and Seattle Children’s Hospital, Seattle, WA, USA
| | | | | | | | - Michael J. Bamshad
- Division of Genetic Medicine, Department of Pediatrics, University of Washington and Seattle Children’s Hospital, Seattle, WA, USA
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA, USA
| | - Evan E. Eichler
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA
| | - Andrew B. Stergachis
- Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA, USA
| | - Danny E. Miller
- Division of Genetic Medicine, Department of Pediatrics, University of Washington and Seattle Children’s Hospital, Seattle, WA, USA
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA, USA
| |
Collapse
|
7
|
Ali S, Abrar M, Hussain I, Batool F, Raza RZ, Khatoon H, Zoia M, Visel A, Shubin NH, Osterwalder M, Abbasi AA. Identification of ancestral gnathostome Gli3 enhancers with activity in mammals. Dev Growth Differ 2024; 66:75-88. [PMID: 37925606 PMCID: PMC10841732 DOI: 10.1111/dgd.12901] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 09/01/2023] [Accepted: 10/23/2023] [Indexed: 11/06/2023]
Abstract
Abnormal expression of the transcriptional regulator and hedgehog (Hh) signaling pathway effector Gli3 is known to trigger congenital disease, most frequently affecting the central nervous system (CNS) and the limbs. Accurate delineation of the genomic cis-regulatory landscape controlling Gli3 transcription during embryonic development is critical for the interpretation of noncoding variants associated with congenital defects. Here, we employed a comparative genomic analysis on fish species with a slow rate of molecular evolution to identify seven previously unknown conserved noncoding elements (CNEs) in Gli3 intronic intervals (CNE15-21). Transgenic assays in zebrafish revealed that most of these elements drive activities in Gli3 expressing tissues, predominantly the fins, CNS, and the heart. Intersection of these CNEs with human disease associated SNPs identified CNE15 as a putative mammalian craniofacial enhancer, with conserved activity in vertebrates and potentially affected by mutation associated with human craniofacial morphology. Finally, comparative functional dissection of an appendage-specific CNE conserved in slowly evolving fish (elephant shark), but not in teleost (CNE14/hs1586) indicates co-option of limb specificity from other tissues prior to the divergence of amniotes and lobe-finned fish. These results uncover a novel subset of intronic Gli3 enhancers that arose in the common ancestor of gnathostomes and whose sequence components were likely gradually modified in other species during the process of evolutionary diversification.
Collapse
Affiliation(s)
- Shahid Ali
- National Center for Bioinformatics, Program of Comparative and Evolutionary Genomics, Faculty of Biological Sciences, Quaid-i-Azam University, 45320, Islamabad Pakistan
- Department of Organismal Biology and Anatomy, The University of Chicago, Chicago, IL 60637, USA
| | - Muhammad Abrar
- National Center for Bioinformatics, Program of Comparative and Evolutionary Genomics, Faculty of Biological Sciences, Quaid-i-Azam University, 45320, Islamabad Pakistan
| | - Irfan Hussain
- National Center for Bioinformatics, Program of Comparative and Evolutionary Genomics, Faculty of Biological Sciences, Quaid-i-Azam University, 45320, Islamabad Pakistan
| | - Fatima Batool
- National Center for Bioinformatics, Program of Comparative and Evolutionary Genomics, Faculty of Biological Sciences, Quaid-i-Azam University, 45320, Islamabad Pakistan
| | - Rabail Zehra Raza
- Department of Biological Sciences, Faculty of Multidisciplinary Studies, National University of Medical Sciences Rawalpindi, Pakistan
| | - Hizran Khatoon
- National Center for Bioinformatics, Program of Comparative and Evolutionary Genomics, Faculty of Biological Sciences, Quaid-i-Azam University, 45320, Islamabad Pakistan
| | - Matteo Zoia
- Department for Biomedical Research (DBMR), University of Bern, Bern, Switzerland
| | - Axel Visel
- Environmental Genomics and System Biology Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA
- U.S. Department of Energy Joint Genome Institute, 1 Cyclotron Road, Berkeley, CA 94720, USA
- School of Natural Sciences, University of California, Merced, Merced, CA 95343, USA
| | - Neil H. Shubin
- Department of Organismal Biology and Anatomy, The University of Chicago, Chicago, IL 60637, USA
| | - Marco Osterwalder
- Department for Biomedical Research (DBMR), University of Bern, Bern, Switzerland
- Department of Cardiology, Bern University Hospital, Bern, Switzerland
| | - Amir Ali Abbasi
- National Center for Bioinformatics, Program of Comparative and Evolutionary Genomics, Faculty of Biological Sciences, Quaid-i-Azam University, 45320, Islamabad Pakistan
| |
Collapse
|
8
|
Chen Y, Paramo MI, Zhang Y, Yao L, Shah SR, Jin Y, Zhang J, Pan X, Yu H. Finding Needles in the Haystack: Strategies for Uncovering Noncoding Regulatory Variants. Annu Rev Genet 2023; 57:201-222. [PMID: 37562413 DOI: 10.1146/annurev-genet-030723-120717] [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] [Indexed: 08/12/2023]
Abstract
Despite accumulating evidence implicating noncoding variants in human diseases, unraveling their functionality remains a significant challenge. Systematic annotations of the regulatory landscape and the growth of sequence variant data sets have fueled the development of tools and methods to identify causal noncoding variants and evaluate their regulatory effects. Here, we review the latest advances in the field and discuss potential future research avenues to gain a more in-depth understanding of noncoding regulatory variants.
Collapse
Affiliation(s)
- You Chen
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA;
| | - Mauricio I Paramo
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA;
| | - Yingying Zhang
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA;
| | - Li Yao
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA;
- Department of Computational Biology, Cornell University, Ithaca, New York, USA
| | - Sagar R Shah
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA;
| | - Yiyang Jin
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA;
| | - Junke Zhang
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA;
- Department of Computational Biology, Cornell University, Ithaca, New York, USA
| | - Xiuqi Pan
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA;
| | - Haiyuan Yu
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA;
- Department of Computational Biology, Cornell University, Ithaca, New York, USA
| |
Collapse
|
9
|
Larrea-Sebal A, Jebari-Benslaiman S, Galicia-Garcia U, Jose-Urteaga AS, Uribe KB, Benito-Vicente A, Martín C. Predictive Modeling and Structure Analysis of Genetic Variants in Familial Hypercholesterolemia: Implications for Diagnosis and Protein Interaction Studies. Curr Atheroscler Rep 2023; 25:839-859. [PMID: 37847331 PMCID: PMC10618353 DOI: 10.1007/s11883-023-01154-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/15/2023] [Indexed: 10/18/2023]
Abstract
PURPOSE OF REVIEW Familial hypercholesterolemia (FH) is a hereditary condition characterized by elevated levels of low-density lipoprotein cholesterol (LDL-C), which increases the risk of cardiovascular disease if left untreated. This review aims to discuss the role of bioinformatics tools in evaluating the pathogenicity of missense variants associated with FH. Specifically, it highlights the use of predictive models based on protein sequence, structure, evolutionary conservation, and other relevant features in identifying genetic variants within LDLR, APOB, and PCSK9 genes that contribute to FH. RECENT FINDINGS In recent years, various bioinformatics tools have emerged as valuable resources for analyzing missense variants in FH-related genes. Tools such as REVEL, Varity, and CADD use diverse computational approaches to predict the impact of genetic variants on protein function. These tools consider factors such as sequence conservation, structural alterations, and receptor binding to aid in interpreting the pathogenicity of identified missense variants. While these predictive models offer valuable insights, the accuracy of predictions can vary, especially for proteins with unique characteristics that might not be well represented in the databases used for training. This review emphasizes the significance of utilizing bioinformatics tools for assessing the pathogenicity of FH-associated missense variants. Despite their contributions, a definitive diagnosis of a genetic variant necessitates functional validation through in vitro characterization or cascade screening. This step ensures the precise identification of FH-related variants, leading to more accurate diagnoses. Integrating genetic data with reliable bioinformatics predictions and functional validation can enhance our understanding of the genetic basis of FH, enabling improved diagnosis, risk stratification, and personalized treatment for affected individuals. The comprehensive approach outlined in this review promises to advance the management of this inherited disorder, potentially leading to better health outcomes for those affected by FH.
Collapse
Affiliation(s)
- Asier Larrea-Sebal
- Department of Biochemistry and Molecular Biology, Universidad del País Vasco UPV/EHU, 48080, Bilbao, Spain
- Department of Molecular Biophysics, Biofisika Institute, University of Basque Country and Consejo Superior de Investigaciones Científicas (UPV/EHU, CSIC), 48940, Leioa, Spain
- Fundación Biofisika Bizkaia, 48940, Leioa, Spain
| | - Shifa Jebari-Benslaiman
- Department of Biochemistry and Molecular Biology, Universidad del País Vasco UPV/EHU, 48080, Bilbao, Spain
- Department of Molecular Biophysics, Biofisika Institute, University of Basque Country and Consejo Superior de Investigaciones Científicas (UPV/EHU, CSIC), 48940, Leioa, Spain
| | - Unai Galicia-Garcia
- Department of Biochemistry and Molecular Biology, Universidad del País Vasco UPV/EHU, 48080, Bilbao, Spain
- Department of Molecular Biophysics, Biofisika Institute, University of Basque Country and Consejo Superior de Investigaciones Científicas (UPV/EHU, CSIC), 48940, Leioa, Spain
| | - Ane San Jose-Urteaga
- Department of Biochemistry and Molecular Biology, Universidad del País Vasco UPV/EHU, 48080, Bilbao, Spain
| | - Kepa B Uribe
- Department of Biochemistry and Molecular Biology, Universidad del País Vasco UPV/EHU, 48080, Bilbao, Spain
| | - Asier Benito-Vicente
- Department of Biochemistry and Molecular Biology, Universidad del País Vasco UPV/EHU, 48080, Bilbao, Spain
- Department of Molecular Biophysics, Biofisika Institute, University of Basque Country and Consejo Superior de Investigaciones Científicas (UPV/EHU, CSIC), 48940, Leioa, Spain
| | - César Martín
- Department of Biochemistry and Molecular Biology, Universidad del País Vasco UPV/EHU, 48080, Bilbao, Spain.
- Department of Molecular Biophysics, Biofisika Institute, University of Basque Country and Consejo Superior de Investigaciones Científicas (UPV/EHU, CSIC), 48940, Leioa, Spain.
| |
Collapse
|
10
|
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.
Collapse
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
| |
Collapse
|
11
|
Woo BJ, Moussavi-Baygi R, Karner H, Karimzadeh M, Garcia K, Joshi T, Yin K, Navickas A, Gilbert LA, Wang B, Asgharian H, Feng FY, Goodarzi H. Integrative identification of non-coding regulatory regions driving metastatic prostate cancer. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.14.535921. [PMID: 37398273 PMCID: PMC10312451 DOI: 10.1101/2023.04.14.535921] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Large-scale sequencing efforts of thousands of tumor samples have been undertaken to understand the mutational landscape of the coding genome. However, the vast majority of germline and somatic variants occur within non-coding portions of the genome. These genomic regions do not directly encode for specific proteins, but can play key roles in cancer progression, for example by driving aberrant gene expression control. Here, we designed an integrative computational and experimental framework to identify recurrently mutated non-coding regulatory regions that drive tumor progression. Application of this approach to whole-genome sequencing (WGS) data from a large cohort of metastatic castration-resistant prostate cancer (mCRPC) revealed a large set of recurrently mutated regions. We used (i) in silico prioritization of functional non-coding mutations, (ii) massively parallel reporter assays, and (iii) in vivo CRISPR-interference (CRISPRi) screens in xenografted mice to systematically identify and validate driver regulatory regions that drive mCRPC. We discovered that one of these enhancer regions, GH22I030351, acts on a bidirectional promoter to simultaneously modulate expression of U2-associated splicing factor SF3A1 and chromosomal protein CCDC157. We found that both SF3A1 and CCDC157 are promoters of tumor growth in xenograft models of prostate cancer. We nominated a number of transcription factors, including SOX6, to be responsible for higher expression of SF3A1 and CCDC157. Collectively, we have established and confirmed an integrative computational and experimental approach that enables the systematic detection of non-coding regulatory regions that drive the progression of human cancers.
Collapse
Affiliation(s)
- Brian J Woo
- Department of Biochemistry & Biophysics, University of California, San Francisco, San Francisco, California, USA
- Department of Urology, University of California, San Francisco, San Francisco, California, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, California, USA
| | - Ruhollah Moussavi-Baygi
- Department of Biochemistry & Biophysics, University of California, San Francisco, San Francisco, California, USA
- Department of Urology, University of California, San Francisco, San Francisco, California, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, California, USA
| | - Heather Karner
- Department of Biochemistry & Biophysics, University of California, San Francisco, San Francisco, California, USA
- Department of Urology, University of California, San Francisco, San Francisco, California, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, California, USA
| | - Mehran Karimzadeh
- Department of Biochemistry & Biophysics, University of California, San Francisco, San Francisco, California, USA
- Department of Urology, University of California, San Francisco, San Francisco, California, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, California, USA
- Vector Institute, Toronto, ON, Canada
- Peter Munk Cardiac Centre, University Health Network, Toronto, ON, Canada
- Arc Institute, Palo Alto 94305, USA
| | - Kristle Garcia
- Department of Biochemistry & Biophysics, University of California, San Francisco, San Francisco, California, USA
- Department of Urology, University of California, San Francisco, San Francisco, California, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, California, USA
| | - Tanvi Joshi
- Department of Biochemistry & Biophysics, University of California, San Francisco, San Francisco, California, USA
- Department of Urology, University of California, San Francisco, San Francisco, California, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, California, USA
| | - Keyi Yin
- Department of Biochemistry & Biophysics, University of California, San Francisco, San Francisco, California, USA
- Department of Urology, University of California, San Francisco, San Francisco, California, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, California, USA
| | - Albertas Navickas
- Department of Biochemistry & Biophysics, University of California, San Francisco, San Francisco, California, USA
- Department of Urology, University of California, San Francisco, San Francisco, California, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, California, USA
| | - Luke A. Gilbert
- Department of Biochemistry & Biophysics, University of California, San Francisco, San Francisco, California, USA
- Department of Urology, University of California, San Francisco, San Francisco, California, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, California, USA
- Arc Institute, Palo Alto 94305, USA
| | - Bo Wang
- Vector Institute, Toronto, ON, Canada
- Peter Munk Cardiac Centre, University Health Network, Toronto, ON, Canada
| | - Hosseinali Asgharian
- Department of Biochemistry & Biophysics, University of California, San Francisco, San Francisco, California, USA
- Department of Urology, University of California, San Francisco, San Francisco, California, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, California, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, US
| | - Felix Y. Feng
- Department of Urology, University of California, San Francisco, San Francisco, California, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, California, USA
- Department of Radiation Oncology, University of California, San Francisco, San Francisco, California, USA
| | - Hani Goodarzi
- Department of Biochemistry & Biophysics, University of California, San Francisco, San Francisco, California, USA
- Department of Urology, University of California, San Francisco, San Francisco, California, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, California, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, US
| |
Collapse
|
12
|
Shea A, Bartz J, Zhang L, Dong X. Predicting mutational function using machine learning. MUTATION RESEARCH. REVIEWS IN MUTATION RESEARCH 2023; 791:108457. [PMID: 36965820 PMCID: PMC10239318 DOI: 10.1016/j.mrrev.2023.108457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 03/11/2023] [Accepted: 03/20/2023] [Indexed: 03/27/2023]
Abstract
Genetic variations are one of the major causes of phenotypic variations between human individuals. Although beneficial as being the substrate of evolution, germline mutations may cause diseases, including Mendelian diseases and complex diseases such as diabetes and heart diseases. Mutations occurring in somatic cells are a main cause of cancer and likely cause age-related phenotypes and other age-related diseases. Because of the high abundance of genetic variations in the human genome, i.e., millions of germline variations per human subject and thousands of additional somatic mutations per cell, it is technically challenging to experimentally verify the function of every possible mutation and their interactions. Significant progress has been made to solve this problem using computational approaches, especially machine learning (ML). Here, we review the progress and achievements made in recent years in this field of research. We classify the computational models in two ways: one according to their prediction goals including protein structural alterations, gene expression changes, and disease risks, and the other according to their methodologies, including non-machine learning methods, classical machine learning methods, and deep neural network methods. For models in each category, we discuss their architecture, prediction accuracy, and potential limitations. This review provides new insights into the applications and future directions of computational approaches in understanding the role of mutations in aging and disease.
Collapse
Affiliation(s)
- Anthony Shea
- Institute on the Biology of Aging and Metabolism, University of Minnesota, Minneapolis, MN 55455, USA; Department of Genetics, Cell Biology and Development, University of Minnesota, Minneapolis, MN 55455, USA
| | - Josh Bartz
- Institute on the Biology of Aging and Metabolism, University of Minnesota, Minneapolis, MN 55455, USA; Department of Genetics, Cell Biology and Development, University of Minnesota, Minneapolis, MN 55455, USA; Bioinformatics and Computational Biology Program, University of Minnesota, Minneapolis, MN 55455, USA
| | - Lei Zhang
- Institute on the Biology of Aging and Metabolism, University of Minnesota, Minneapolis, MN 55455, USA; Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA
| | - Xiao Dong
- Institute on the Biology of Aging and Metabolism, University of Minnesota, Minneapolis, MN 55455, USA; Department of Genetics, Cell Biology and Development, University of Minnesota, Minneapolis, MN 55455, USA.
| |
Collapse
|
13
|
Mew M, Caldwell KA, Caldwell GA. From bugs to bedside: functional annotation of human genetic variation for neurological disorders using invertebrate models. Hum Mol Genet 2022; 31:R37-R46. [PMID: 35994032 PMCID: PMC9585664 DOI: 10.1093/hmg/ddac203] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 08/11/2022] [Accepted: 08/17/2022] [Indexed: 02/02/2023] Open
Abstract
The exponential accumulation of DNA sequencing data has opened new avenues for discovering the causative roles of single-nucleotide polymorphisms (SNPs) in neurological diseases. The opportunities emerging from this are staggering, yet only as good as our abilities to glean insights from this surplus of information. Whereas computational biology continues to improve with respect to predictions and molecular modeling, the differences between in silico and in vivo analysis remain substantial. Invertebrate in vivo model systems represent technically advanced, experimentally mature, high-throughput, efficient and cost-effective resources for investigating a disease. With a decades-long track record of enabling investigators to discern function from DNA, fly (Drosophila) and worm (Caenorhabditis elegans) models have never been better poised to serve as living engines of discovery. Both of these animals have already proven useful in the classification of genetic variants as either pathogenic or benign across a range of neurodevelopmental and neurodegenerative disorders-including autism spectrum disorders, ciliopathies, amyotrophic lateral sclerosis, Alzheimer's and Parkinson's disease. Pathogenic SNPs typically display distinctive phenotypes in functional assays when compared with null alleles and frequently lead to protein products with gain-of-function or partial loss-of-function properties that contribute to neurological disease pathogenesis. The utility of invertebrates is logically limited by overt differences in anatomical and physiological characteristics, and also the evolutionary distance in genome structure. Nevertheless, functional annotation of disease-SNPs using invertebrate models can expedite the process of assigning cellular and organismal consequences to mutations, ascertain insights into mechanisms of action, and accelerate therapeutic target discovery and drug development for neurological conditions.
Collapse
Affiliation(s)
- Melanie Mew
- Department of Biological Sciences, The University of Alabama, Tuscaloosa, AL 35487, USA
| | - Kim A Caldwell
- Department of Biological Sciences, The University of Alabama, Tuscaloosa, AL 35487, USA
- Alabama Research Institute on Aging, The University of Alabama, Tuscaloosa, AL 35487, USA
- Center for Convergent Bioscience and Medicine, The University of Alabama, Tuscaloosa, AL 35487, USA
- Departments of Neurobiology and Neurology, Center for Neurodegeneration and Experimental Therapeutics, Nathan Shock Center of Excellence for Research in the Basic Biology of Aging, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Guy A Caldwell
- Department of Biological Sciences, The University of Alabama, Tuscaloosa, AL 35487, USA
- Center for Convergent Bioscience and Medicine, The University of Alabama, Tuscaloosa, AL 35487, USA
- Departments of Neurobiology and Neurology, Center for Neurodegeneration and Experimental Therapeutics, Nathan Shock Center of Excellence for Research in the Basic Biology of Aging, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| |
Collapse
|