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Glitsch MD. Recent advances in acid sensing by G protein coupled receptors. Pflugers Arch 2024; 476:445-455. [PMID: 38340167 PMCID: PMC11006784 DOI: 10.1007/s00424-024-02919-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 01/30/2024] [Accepted: 01/30/2024] [Indexed: 02/12/2024]
Abstract
Changes in extracellular proton concentrations occur in a variety of tissues over a range of timescales under physiological conditions and also accompany virtually all pathologies, notably cancers, stroke, inflammation and trauma. Proton-activated, G protein coupled receptors are already partially active at physiological extracellular proton concentrations and their activity increases with rising proton concentrations. Their ability to monitor and report changes in extracellular proton concentrations and hence extracellular pH appears to be involved in a variety of processes, and it is likely to mirror and in some cases promote disease progression. Unsurprisingly, therefore, these pH-sensing receptors (pHR) receive increasing attention from researchers working in an expanding range of research areas, from cellular neurophysiology to systemic inflammatory processes. This review is looking at progress made in the field of pHRs over the past few years and also highlights outstanding issues.
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Affiliation(s)
- Maike D Glitsch
- Medical School Hamburg, Am Sandtorkai 1, 20457, Hamburg, Germany.
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2
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Trajanoska K, Bhérer C, Taliun D, Zhou S, Richards JB, Mooser V. From target discovery to clinical drug development with human genetics. Nature 2023; 620:737-745. [PMID: 37612393 DOI: 10.1038/s41586-023-06388-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 06/29/2023] [Indexed: 08/25/2023]
Abstract
The substantial investments in human genetics and genomics made over the past three decades were anticipated to result in many innovative therapies. Here we investigate the extent to which these expectations have been met, excluding cancer treatments. In our search, we identified 40 germline genetic observations that led directly to new targets and subsequently to novel approved therapies for 36 rare and 4 common conditions. The median time between genetic target discovery and drug approval was 25 years. Most of the genetically driven therapies for rare diseases compensate for disease-causing loss-of-function mutations. The therapies approved for common conditions are all inhibitors designed to pharmacologically mimic the natural, disease-protective effects of rare loss-of-function variants. Large biobank-based genetic studies have the power to identify and validate a large number of new drug targets. Genetics can also assist in the clinical development phase of drugs-for example, by selecting individuals who are most likely to respond to investigational therapies. This approach to drug development requires investments into large, diverse cohorts of deeply phenotyped individuals with appropriate consent for genetically assisted trials. A robust framework that facilitates responsible, sustainable benefit sharing will be required to capture the full potential of human genetics and genomics and bring effective and safe innovative therapies to patients quickly.
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Affiliation(s)
- Katerina Trajanoska
- Canada Excellence Research Chair in Genomic Medicine, Department of Human Genetics, Faculty of Medicine and Health Sciences, Victor Phillip Dahdaleh Institute of Genomic Medicine, McGill University, Montreal, Quebec, Canada
| | - Claude Bhérer
- Canada Excellence Research Chair in Genomic Medicine, Department of Human Genetics, Faculty of Medicine and Health Sciences, Victor Phillip Dahdaleh Institute of Genomic Medicine, McGill University, Montreal, Quebec, Canada
| | - Daniel Taliun
- Canada Excellence Research Chair in Genomic Medicine, Department of Human Genetics, Faculty of Medicine and Health Sciences, Victor Phillip Dahdaleh Institute of Genomic Medicine, McGill University, Montreal, Quebec, Canada
| | - Sirui Zhou
- Canada Excellence Research Chair in Genomic Medicine, Department of Human Genetics, Faculty of Medicine and Health Sciences, Victor Phillip Dahdaleh Institute of Genomic Medicine, McGill University, Montreal, Quebec, Canada
| | - J Brent Richards
- Lady Davis Institute for Medical Research, Jewish General Hospital, McGill University, Montreal, Quebec, Canada
- Department of Epidemiology and Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada
| | - Vincent Mooser
- Canada Excellence Research Chair in Genomic Medicine, Department of Human Genetics, Faculty of Medicine and Health Sciences, Victor Phillip Dahdaleh Institute of Genomic Medicine, McGill University, Montreal, Quebec, Canada.
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3
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Callahan TJ, Stefanski AL, Wyrwa JM, Zeng C, Ostropolets A, Banda JM, Baumgartner WA, Boyce RD, Casiraghi E, Coleman BD, Collins JH, Deakyne Davies SJ, Feinstein JA, Lin AY, Martin B, Matentzoglu NA, Meeker D, Reese J, Sinclair J, Taneja SB, Trinkley KE, Vasilevsky NA, Williams AE, Zhang XA, Denny JC, Ryan PB, Hripcsak G, Bennett TD, Haendel MA, Robinson PN, Hunter LE, Kahn MG. Ontologizing health systems data at scale: making translational discovery a reality. NPJ Digit Med 2023; 6:89. [PMID: 37208468 PMCID: PMC10196319 DOI: 10.1038/s41746-023-00830-x] [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: 09/09/2022] [Accepted: 04/28/2023] [Indexed: 05/21/2023] Open
Abstract
Common data models solve many challenges of standardizing electronic health record (EHR) data but are unable to semantically integrate all of the resources needed for deep phenotyping. Open Biological and Biomedical Ontology (OBO) Foundry ontologies provide computable representations of biological knowledge and enable the integration of heterogeneous data. However, mapping EHR data to OBO ontologies requires significant manual curation and domain expertise. We introduce OMOP2OBO, an algorithm for mapping Observational Medical Outcomes Partnership (OMOP) vocabularies to OBO ontologies. Using OMOP2OBO, we produced mappings for 92,367 conditions, 8611 drug ingredients, and 10,673 measurement results, which covered 68-99% of concepts used in clinical practice when examined across 24 hospitals. When used to phenotype rare disease patients, the mappings helped systematically identify undiagnosed patients who might benefit from genetic testing. By aligning OMOP vocabularies to OBO ontologies our algorithm presents new opportunities to advance EHR-based deep phenotyping.
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Affiliation(s)
- Tiffany J Callahan
- Computational Bioscience Program, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA.
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10032, USA.
| | - Adrianne L Stefanski
- Computational Bioscience Program, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - Jordan M Wyrwa
- Department of Physical Medicine and Rehabilitation, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - Chenjie Zeng
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Anna Ostropolets
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10032, USA
| | - Juan M Banda
- Department of Computer Science, Georgia State University, Atlanta, GA, 30303, USA
| | - William A Baumgartner
- Computational Bioscience Program, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - Richard D Boyce
- Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15260, USA
| | - Elena Casiraghi
- Computer Science, Università degli Studi di Milano, Milan, Italy
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA
| | - Ben D Coleman
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA
| | - Janine H Collins
- Department of Haematology, University of Cambridge, Cambridge, UK
| | - Sara J Deakyne Davies
- Department of Research Informatics & Data Science, Analytics Resource Center, Children's Hospital Colorado, Aurora, CO, 80045, USA
| | - James A Feinstein
- Adult and Child Center for Health Outcomes Research and Delivery Science (ACCORDS), University of Colorado Anschutz School of Medicine, Aurora, CO, 80045, USA
| | - Asiyah Y Lin
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Blake Martin
- Departments of Biomedical Informatics and Pediatrics, University of Colorado School of Medicine, Aurora, CO, 80045, USA
| | | | | | - Justin Reese
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | | | - Sanya B Taneja
- Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, 15260, USA
| | - Katy E Trinkley
- Department of Family Medicine, University of Colorado Anschutz School of Medicine, Aurora, CO, 80045, USA
| | - Nicole A Vasilevsky
- Translational and Integrative Sciences Lab, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - Andrew E Williams
- Tufts Institute for Clinical Research and Health Policy Studies, Tufts University, Boston, MA, 02155, USA
| | - Xingmin A Zhang
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA
| | - Joshua C Denny
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Patrick B Ryan
- Janssen Research and Development, Raritan, NJ, 08869, USA
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10032, USA
| | - Tellen D Bennett
- Departments of Biomedical Informatics and Pediatrics, University of Colorado School of Medicine, Aurora, CO, 80045, USA
| | - Melissa A Haendel
- Departments of Biomedical Informatics and Pediatrics, University of Colorado School of Medicine, Aurora, CO, 80045, USA
| | - Peter N Robinson
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA
| | - Lawrence E Hunter
- Computational Bioscience Program, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, 80045, USA
| | - Michael G Kahn
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, 80045, USA
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Bloch-Zupan A, Rey T, Jimenez-Armijo A, Kawczynski M, Kharouf N, Dure-Molla MDL, Noirrit E, Hernandez M, Joseph-Beaudin C, Lopez S, Tardieu C, Thivichon-Prince B, Dostalova T, Macek M, Alloussi ME, Qebibo L, Morkmued S, Pungchanchaikul P, Orellana BU, Manière MC, Gérard B, Bugueno IM, Laugel-Haushalter V. Amelogenesis imperfecta: Next-generation sequencing sheds light on Witkop's classification. Front Physiol 2023; 14:1130175. [PMID: 37228816 PMCID: PMC10205041 DOI: 10.3389/fphys.2023.1130175] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 03/06/2023] [Indexed: 05/27/2023] Open
Abstract
Amelogenesis imperfecta (AI) is a heterogeneous group of genetic rare diseases disrupting enamel development (Smith et al., Front Physiol, 2017a, 8, 333). The clinical enamel phenotypes can be described as hypoplastic, hypomineralized or hypomature and serve as a basis, together with the mode of inheritance, to Witkop's classification (Witkop, J Oral Pathol, 1988, 17, 547-553). AI can be described in isolation or associated with others symptoms in syndromes. Its occurrence was estimated to range from 1/700 to 1/14,000. More than 70 genes have currently been identified as causative. Objectives: We analyzed using next-generation sequencing (NGS) a heterogeneous cohort of AI patients in order to determine the molecular etiology of AI and to improve diagnosis and disease management. Methods: Individuals presenting with so called "isolated" or syndromic AI were enrolled and examined at the Reference Centre for Rare Oral and Dental Diseases (O-Rares) using D4/phenodent protocol (www.phenodent.org). Families gave written informed consents for both phenotyping and molecular analysis and diagnosis using a dedicated NGS panel named GenoDENT. This panel explores currently simultaneously 567 genes. The study is registered under NCT01746121 and NCT02397824 (https://clinicaltrials.gov/). Results: GenoDENT obtained a 60% diagnostic rate. We reported genetics results for 221 persons divided between 115 AI index cases and their 106 associated relatives from a total of 111 families. From this index cohort, 73% were diagnosed with non-syndromic amelogenesis imperfecta and 27% with syndromic amelogenesis imperfecta. Each individual was classified according to the AI phenotype. Type I hypoplastic AI represented 61 individuals (53%), Type II hypomature AI affected 31 individuals (27%), Type III hypomineralized AI was diagnosed in 18 individuals (16%) and Type IV hypoplastic-hypomature AI with taurodontism concerned 5 individuals (4%). We validated the genetic diagnosis, with class 4 (likely pathogenic) or class 5 (pathogenic) variants, for 81% of the cohort, and identified candidate variants (variant of uncertain significance or VUS) for 19% of index cases. Among the 151 sequenced variants, 47 are newly reported and classified as class 4 or 5. The most frequently discovered genotypes were associated with MMP20 and FAM83H for isolated AI. FAM20A and LTBP3 genes were the most frequent genes identified for syndromic AI. Patients negative to the panel were resolved with exome sequencing elucidating for example the gene involved ie ACP4 or digenic inheritance. Conclusion: NGS GenoDENT panel is a validated and cost-efficient technique offering new perspectives to understand underlying molecular mechanisms of AI. Discovering variants in genes involved in syndromic AI (CNNM4, WDR72, FAM20A … ) transformed patient overall care. Unravelling the genetic basis of AI sheds light on Witkop's AI classification.
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Affiliation(s)
- Agnes Bloch-Zupan
- Université de Strasbourg, Faculté de Chirurgie Dentaire, Strasbourg, France
- Université de Strasbourg, Institut d’études avancées (USIAS), Strasbourg, France
- Hôpitaux Universitaires de Strasbourg (HUS), Pôle de Médecine et Chirurgie Bucco-dentaires, Hôpital Civil, Centre de référence des maladies rares orales et dentaires, O-Rares, Filiére Santé Maladies rares TETE COU, European Reference Network ERN CRANIO, Strasbourg, France
- Université de Strasbourg, Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), IN-SERM U1258, CNRS- UMR7104, Illkirch, France
- Eastman Dental Institute, University College London, London, United Kingdom
| | - Tristan Rey
- Université de Strasbourg, Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), IN-SERM U1258, CNRS- UMR7104, Illkirch, France
- Hôpitaux Universitaires de Strasbourg, Laboratoires de diagnostic génétique, Institut de Génétique Médicale d’Alsace, Strasbourg, France
| | - Alexandra Jimenez-Armijo
- Université de Strasbourg, Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), IN-SERM U1258, CNRS- UMR7104, Illkirch, France
| | - Marzena Kawczynski
- Hôpitaux Universitaires de Strasbourg (HUS), Pôle de Médecine et Chirurgie Bucco-dentaires, Hôpital Civil, Centre de référence des maladies rares orales et dentaires, O-Rares, Filiére Santé Maladies rares TETE COU, European Reference Network ERN CRANIO, Strasbourg, France
| | - Naji Kharouf
- Université de Strasbourg, Laboratoire de Biomatériaux et Bioingénierie, Inserm UMR_S 1121, Strasbourg, France
| | | | - Muriel de La Dure-Molla
- Rothschild Hospital, Public Assistance-Paris Hospitals (AP-HP), Reference Center for Rare Oral and Den-tal Diseases (O-Rares), Paris, France
| | - Emmanuelle Noirrit
- Centre Hospitalier Universitaire (CHU) Rangueil, Toulouse, Competence Center for Rare Oral and Den-tal Diseases, Toulouse, France
| | - Magali Hernandez
- Centre Hospitalier Régional Universitaire de Nancy, Université de Lorraine, Competence Center for Rare Oral and Dental Diseases, Nancy, France
| | - Clara Joseph-Beaudin
- Centre Hospitalier Universitaire de Nice, Competence Center for Rare Oral and Dental Diseases, Nice, France
| | - Serena Lopez
- Centre Hospitalier Universitaire de Nantes, Competence Center for Rare Oral and Dental Diseases, Nantes, France
| | - Corinne Tardieu
- APHM, Hôpitaux Universitaires de Marseille, Hôpital Timone, Competence Center for Rare Oral and Dental Diseases, Marseille, France
| | - Béatrice Thivichon-Prince
- Centre Hospitalier Universitaire de Lyon, Competence Center for Rare Oral and Dental Diseases, Lyon, France
| | | | - Tatjana Dostalova
- Department of Stomatology (TD) and Department of Biology and Medical Genetics (MM) Charles University 2nd Faculty of Medicine and Motol University Hospital, Prague, Czechia
| | - Milan Macek
- Department of Stomatology (TD) and Department of Biology and Medical Genetics (MM) Charles University 2nd Faculty of Medicine and Motol University Hospital, Prague, Czechia
| | | | - Mustapha El Alloussi
- Faculty of Dentistry, International University of Rabat, CReSS Centre de recherche en Sciences de la Santé, Rabat, Morocco
| | - Leila Qebibo
- Unité de génétique médicale et d’oncogénétique, CHU Hassan II, Fes, Morocco
| | | | | | - Blanca Urzúa Orellana
- Instituto de Investigación en Ciencias Odontológicas, Facultad de Odontología, Universidad de Chile, Santiago, Chile
| | - Marie-Cécile Manière
- Université de Strasbourg, Faculté de Chirurgie Dentaire, Strasbourg, France
- Hôpitaux Universitaires de Strasbourg (HUS), Pôle de Médecine et Chirurgie Bucco-dentaires, Hôpital Civil, Centre de référence des maladies rares orales et dentaires, O-Rares, Filiére Santé Maladies rares TETE COU, European Reference Network ERN CRANIO, Strasbourg, France
| | - Bénédicte Gérard
- Hôpitaux Universitaires de Strasbourg, Laboratoires de diagnostic génétique, Institut de Génétique Médicale d’Alsace, Strasbourg, France
| | - Isaac Maximiliano Bugueno
- Université de Strasbourg, Faculté de Chirurgie Dentaire, Strasbourg, France
- Hôpitaux Universitaires de Strasbourg (HUS), Pôle de Médecine et Chirurgie Bucco-dentaires, Hôpital Civil, Centre de référence des maladies rares orales et dentaires, O-Rares, Filiére Santé Maladies rares TETE COU, European Reference Network ERN CRANIO, Strasbourg, France
- Université de Strasbourg, Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), IN-SERM U1258, CNRS- UMR7104, Illkirch, France
| | - Virginie Laugel-Haushalter
- Université de Strasbourg, Faculté de Chirurgie Dentaire, Strasbourg, France
- Université de Strasbourg, Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), IN-SERM U1258, CNRS- UMR7104, Illkirch, France
- Hôpitaux Universitaires de Strasbourg, Laboratoires de diagnostic génétique, Institut de Génétique Médicale d’Alsace, Strasbourg, France
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The Enigmatic Genetic Landscape of Hereditary Hearing Loss: A Multistep Diagnostic Strategy in the Italian Population. Biomedicines 2023; 11:biomedicines11030703. [PMID: 36979683 PMCID: PMC10045163 DOI: 10.3390/biomedicines11030703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 02/21/2023] [Accepted: 02/22/2023] [Indexed: 03/03/2023] Open
Abstract
Hearing loss is the most frequent sensorineural disorder, affecting approximately 1:1000 newborns. Hereditary forms (HHL) represent 50–60% of cases, highlighting the relevance of genetic testing in deaf patients. HHL is classified as non-syndromic (NSHL—70% of cases) or syndromic (SHL—30% of cases). In this study, a multistep and integrative approach aimed at identifying the molecular cause of HHL in 102 patients, whose GJB2 analysis already showed a negative result, is described. In NSHL patients, multiplex ligation probe amplification and long-range PCR analyses of the STRC gene solved 13 cases, while whole exome sequencing (WES) identified the genetic diagnosis in 26 additional ones, with a total detection rate of 47.6%. Concerning SHL, WES detected the molecular cause in 55% of cases. Peculiar findings are represented by the identification of four subjects displaying a dual molecular diagnosis and eight affected by non-syndromic mimics, five of them presenting Usher syndrome type 2. Overall, this study provides a detailed characterisation of the genetic causes of HHL in the Italian population. Furthermore, we highlighted the frequency of Usher syndrome type 2 carriers in the Italian population to pave the way for a more effective implementation of diagnostic and follow-up strategies for this disease.
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A new impact factor for European Journal of Human Genetics. Eur J Hum Genet 2021; 29:1165. [PMID: 34429525 PMCID: PMC8384554 DOI: 10.1038/s41431-021-00941-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
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