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Gargano MA, Matentzoglu N, Coleman B, Addo-Lartey EB, Anagnostopoulos A, Anderton J, Avillach P, Bagley AM, Bakštein E, Balhoff JP, Baynam G, Bello SM, Berk M, Bertram H, Bishop S, Blau H, Bodenstein DF, Botas P, Boztug K, Čady J, Callahan TJ, Cameron R, Carbon S, Castellanos F, Caufield JH, Chan LE, Chute C, Cruz-Rojo J, Dahan-Oliel N, Davids JR, de Dieuleveult M, de Souza V, de Vries BBA, de Vries E, DePaulo JR, Derfalvi B, Dhombres F, Diaz-Byrd C, Dingemans AJM, Donadille B, Duyzend M, Elfeky R, Essaid S, Fabrizzi C, Fico G, Firth HV, Freudenberg-Hua Y, Fullerton JM, Gabriel DL, Gilmour K, Giordano J, Goes FS, Moses RG, Green I, Griese M, Groza T, Gu W, Guthrie J, Gyori B, Hamosh A, Hanauer M, Hanušová K, He Y(O, Hegde H, Helbig I, Holasová K, Hoyt CT, Huang S, Hurwitz E, Jacobsen JOB, Jiang X, Joseph L, Keramatian K, King B, Knoflach K, Koolen DA, Kraus M, Kroll C, Kusters M, Ladewig MS, Lagorce D, Lai MC, Lapunzina P, Laraway B, Lewis-Smith D, Li X, Lucano C, Majd M, Marazita ML, Martinez-Glez V, McHenry TH, McInnis MG, McMurry JA, Mihulová M, Millett CE, Mitchell PB, Moslerová V, Narutomi K, Nematollahi S, Nevado J, Nierenberg AA, Čajbiková NN, Nurnberger JI, Ogishima S, Olson D, Ortiz A, Pachajoa H, Perez de Nanclares G, Peters A, Putman T, Rapp CK, Rath A, Reese J, Rekerle L, Roberts A, Roy S, Sanders SJ, Schuetz C, Schulte EC, Schulze TG, Schwarz M, Scott K, Seelow D, Seitz B, Shen Y, Similuk MN, Simon ES, Singh B, Smedley D, Smith CL, Smolinsky JT, Sperry S, Stafford E, Stefancsik R, Steinhaus R, Strawbridge R, Sundaramurthi JC, Talapova P, Tenorio Castano JA, Tesner P, Thomas RH, Thurm A, Turnovec M, van Gijn ME, Vasilevsky NA, Vlčková M, Walden A, Wang K, Wapner R, Ware JS, Wiafe AA, Wiafe SA, Wiggins LD, Williams AE, Wu C, Wyrwoll MJ, Xiong H, Yalin N, Yamamoto Y, Yatham LN, Yocum AK, Young AH, Yüksel Z, Zandi PP, Zankl A, Zarante I, Zvolský M, Toro S, Carmody LC, Harris NL, Munoz-Torres MC, Danis D, Mungall CJ, Köhler S, Haendel MA, Robinson PN. The Human Phenotype Ontology in 2024: phenotypes around the world. Nucleic Acids Res 2024; 52:D1333-D1346. [PMID: 37953324 PMCID: PMC10767975 DOI: 10.1093/nar/gkad1005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 10/12/2023] [Accepted: 10/19/2023] [Indexed: 11/14/2023] Open
Abstract
The Human Phenotype Ontology (HPO) is a widely used resource that comprehensively organizes and defines the phenotypic features of human disease, enabling computational inference and supporting genomic and phenotypic analyses through semantic similarity and machine learning algorithms. The HPO has widespread applications in clinical diagnostics and translational research, including genomic diagnostics, gene-disease discovery, and cohort analytics. In recent years, groups around the world have developed translations of the HPO from English to other languages, and the HPO browser has been internationalized, allowing users to view HPO term labels and in many cases synonyms and definitions in ten languages in addition to English. Since our last report, a total of 2239 new HPO terms and 49235 new HPO annotations were developed, many in collaboration with external groups in the fields of psychiatry, arthrogryposis, immunology and cardiology. The Medical Action Ontology (MAxO) is a new effort to model treatments and other measures taken for clinical management. Finally, the HPO consortium is contributing to efforts to integrate the HPO and the GA4GH Phenopacket Schema into electronic health records (EHRs) with the goal of more standardized and computable integration of rare disease data in EHRs.
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Affiliation(s)
| | | | - Ben Coleman
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | | | | | - Joel Anderton
- Center for Craniofacial and Dental Genetics, Department of Oral and Craniofacial Sciences, School of Dental Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | | | - Anita M Bagley
- Shriners Children's Northern California, Sacramento, CA, USA
| | - Eduard Bakštein
- National Institute of Mental Health, Klecany, Czech Republic
| | - James P Balhoff
- Renaissance Computing Institute, University of North Carolina, Chapel Hill, NC 27517, USA
| | - Gareth Baynam
- Rare Care Centre, Perth Children's Hospital, Perth, Australia
| | | | - Michael Berk
- Deakin University, IMPACT - the Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Barwon Health, Geelong, Australia
| | - Holli Bertram
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - Somer Bishop
- Department of Psychiatry and Behavioral Sciences, UCSF Weil Institute for Neuroscience, San Francisco, CA, USA
| | - Hannah Blau
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - David F Bodenstein
- Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON, Canada
| | | | - Kaan Boztug
- St. Anna Children's Cancer Research Institute (CCRI), Vienna, Austria
| | - Jolana Čady
- Institute of Health Information and Statistics of the Czech Republic, Prague, Czech Republic
| | - Tiffany J Callahan
- Department of Biomedical Informatics, Columbia University Irving Medical Center, NY, NY, USA
| | | | - Seth J Carbon
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | | | - J Harry Caufield
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Lauren E Chan
- College of Public Health and Human Sciences, Oregon State University, Corvallis, OR 97331, USA
| | - Christopher G Chute
- Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, MD 21287, USA
| | - Jaime Cruz-Rojo
- UDISGEN (Dysmorphology and Genetics Unit), 12 de Octubre Hospital, Madrid, Spain
| | - Noémi Dahan-Oliel
- Department of Clinical Research, Shriners Hospitals for Children, Montreal, Quebec, Canada
| | - Jon R Davids
- Shriners Children's Northern California, Sacramento, CA, USA
| | - Maud de Dieuleveult
- Département I&D, AP-HP, Banque Nationale de Données Maladies Rares, Paris, France
| | - Vinicius de Souza
- European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, UK
| | - Bert B A de Vries
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, Netherlands
| | | | - J Raymond DePaulo
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Beata Derfalvi
- Department of Pediatrics, Dalhousie University, Halifax, NS, Canada
| | - Ferdinand Dhombres
- Fetal Medicine Department, Armand Trousseau Hospital, Sorbonne University, GRC26, INSERM, Limics, Paris, France
| | - Claudia Diaz-Byrd
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - Alexander J M Dingemans
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, Netherlands
| | - Bruno Donadille
- St Antoine Hospital, Reference Center for Rare Growth Endocrine Disorders, Sorbonne University, AP-HP, INSERM, US14 - Orphanet, Plateforme Maladies Rares, Paris, France
| | | | - Reem Elfeky
- Department of Immunology, GOS Hospital for Children NHS Foundation Trust, University College London, London, UK
| | - Shahim Essaid
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | | | - Giovanna Fico
- Bipolar and Depressive Disorders Unit, Institute of Neuroscience, Hospital Clinic, University of Barcelona, IDIBAPS, CIBERSAM, Barcelona, Catalonia, Spain
| | - Helen V Firth
- Addenbrooke's Hospital, Cambridge University Hospitals, Cambridge, UK
| | - Yun Freudenberg-Hua
- Department of Psychiatry, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
| | | | - Davera L Gabriel
- School of Medicine, Johns Hopkins University, Baltimore, MD 21287, USA
| | | | - Jessica Giordano
- Department of Obstetrics and Gynecology, Columbia University Irving Medical Center, New York, NY, USA
| | - Fernando S Goes
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Rachel Gore Moses
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Ian Green
- SNOMED International, London W2 6BD, UK
| | - Matthias Griese
- Department of Pediatrics, Dr. von Hauner Children's Hospital, University Hospital, LMU Munich, German center for Lung research (DZL), Munich, Germany
| | - Tudor Groza
- Rare Care Centre, Perth Children's Hospital, Perth, Australia
| | | | - Julia Guthrie
- Department of Structural and Computational Biology, University of Vienna; Max Perutz Labs, Vienna, Austria
| | - Benjamin Gyori
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Ada Hamosh
- Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Marc Hanauer
- INSERM, US14 - Orphanet, Plateforme Maladies Rares, Paris, France
| | - Kateřina Hanušová
- Institute of Health Information and Statistics of the Czech Republic, Prague, Czech Republic
| | | | - Harshad Hegde
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Ingo Helbig
- Neurology, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Kateřina Holasová
- Institute of Health Information and Statistics of the Czech Republic, Prague, Czech Republic
| | - Charles Tapley Hoyt
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | | | - Eric Hurwitz
- University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Julius O B Jacobsen
- William Harvey Research Institute, Queen Mary University of London, London, UK
| | | | - Lisa Joseph
- Neurodevelopmental and Behavioral Phenotyping Service, National Institute of Mental Health, Bethesda, MD, USA
| | - Kamyar Keramatian
- Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
| | - Bryan King
- Department of Psychiatry and Behavioral Sciences, UCSF Weil Institute for Neuroscience, San Francisco, CA, USA
| | - Katrin Knoflach
- Department of Pediatrics, Dr. von Hauner Children's Hospital, University Hospital, LMU Munich, German center for Lung research (DZL), Munich, Germany
| | - David A Koolen
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, Netherlands
| | - Megan L Kraus
- University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Carlo Kroll
- William Harvey Research Institute, Queen Mary University of London, London, UK
| | - Maaike Kusters
- Immunology, NIHR Great Ormond Street Hospital BRC, London, UK
| | - Markus S Ladewig
- Department of Ophthalmology, University Clinic Marburg - Campus Fulda, Fulda, Germany
| | - David Lagorce
- INSERM, US14 - Orphanet, Plateforme Maladies Rares, Paris, France
| | - Meng-Chuan Lai
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Pablo Lapunzina
- Institute of Medical and Molecular Genetics, Hospital Univ. La Paz, Madrid, Spain
| | - Bryan Laraway
- University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - David Lewis-Smith
- Translational and Clinical Research Institute, Henry Wellcome Building, Framlington Place, Newcastle University, Newcastle-Upon-Tyne NE14LP, UK
| | | | - Caterina Lucano
- INSERM, US14 - Orphanet, Plateforme Maladies Rares, Paris, France
| | - Marzieh Majd
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Mary L Marazita
- Center for Craniofacial and Dental Genetics, Department of Oral and Craniofacial Sciences, School of Dental Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Victor Martinez-Glez
- Center for Genomic Medicine, Parc Taulí Hospital Universitari, Institut d’Investigació i Innovació Parc Taulí (I3PT-CERCA), Sabadell, Spain
| | - Toby H McHenry
- Center for Craniofacial and Dental Genetics, Department of Oral and Craniofacial Sciences, School of Dental Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Melvin G McInnis
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - Julie A McMurry
- University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Michaela Mihulová
- Department of Biology and Medical Genetics, 2nd Medical Faculty of Charles University and University Hospital Motol, Prague, Czech Republic
| | - Caitlin E Millett
- Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
| | - Philip B Mitchell
- Discipline of Psychiatry & Mental Health, School of Clinical Medicine, Faculty of Medicine & Health, University of New South Wales, Sydney, NSW, Australia
| | - Veronika Moslerová
- Department of Biology and Medical Genetics, 2nd Medical Faculty of Charles University and University Hospital Motol, Prague, Czech Republic
| | - Kenji Narutomi
- Okinawa Prefectural Nanbu Medical Center & Children's Medical Center
| | - Shahrzad Nematollahi
- School of Physical and Occupational Therapy, McGill University, Montreal, Quebec, Canada
| | - Julian Nevado
- Institute of Medical and Molecular Genetics, Hospital Univ. La Paz, Madrid, Spain
| | - Andrew A Nierenberg
- Dauten Family Center for Bipolar Treatment Innovation, Massachusetts General Hospital, Boston, MA, USA
| | - Nikola Novák Čajbiková
- Department of Biology and Medical Genetics, 2nd Medical Faculty of Charles University and University Hospital Motol, Prague, Czech Republic
| | - John I Nurnberger
- Stark Neurosciences Research Institute, Departments of Psychiatry and Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | | | - Daniel Olson
- Data Collaboration Center, Data Science, Critical Path Institute, Tucson, AZ, USA
| | - Abigail Ortiz
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Harry Pachajoa
- Centro de Investigaciones en Anomalías Congénitas y Enfermedades Raras (CIACER), Universidad Icesi, Cali, Colombia
| | - Guiomar Perez de Nanclares
- Molecular (epi) genetics lab, Bioaraba Health Research Institute, Araba University Hospital, Vitoria-Gasteiz, Spain
| | - Amy Peters
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - Tim Putman
- University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Christina K Rapp
- Department of Pediatrics, Dr. von Hauner Children's Hospital, University Hospital, LMU Munich, German center for Lung research (DZL), Munich, Germany
| | - Ana Rath
- INSERM, US14 - Orphanet, Plateforme Maladies Rares, Paris, France
| | - Justin Reese
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Lauren Rekerle
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Angharad M Roberts
- National Heart & Lung Institute & MRC London Institute of Medical Sciences, Imperial College London, London W12 0HS, UK
| | - Suzy Roy
- SNOMED International, London W2 6BD, UK
| | - Stephan J Sanders
- Department of Paediatrics, Institute of Developmental and Regenerative Medicine, University of Oxford, Oxford, UK
| | - Catharina Schuetz
- Universitätsklinikum Carl Gustav Carus, Medizinische Fakultät, TU, Dresden, Germany
| | - Eva C Schulte
- Institute of Psychiatric Phenomics and Genomics (IPPG), LMU University Hospital, LMU Munich, Munich, Germany
| | - Thomas G Schulze
- Department of Psychiatry and Behavioral Sciences, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Martin Schwarz
- Department of Biology and Medical Genetics, 2nd Medical Faculty of Charles University and University Hospital Motol, Prague, Czech Republic
| | - Katie Scott
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
| | - Dominik Seelow
- Exploratory Diagnostic Sciences, Berliner Institut für Gesundheitsforschung - Charité, Berlin, Germany
| | - Berthold Seitz
- Department of Ophthalmology, Saarland University Medical Center UKS, Homburg/Saar, Germany
| | | | - Morgan N Similuk
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Eric S Simon
- Eisenberg Family Depression Center, University of Michigan, Ann Arbor, MI, USA
| | - Balwinder Singh
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
| | - Damian Smedley
- William Harvey Research Institute, Queen Mary University of London, London, UK
| | | | - Jake T Smolinsky
- Human Genetics Institute of New Jersey, Rutgers University, Piscataway, NJ, USA
| | - Sarah Sperry
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | | | - Ray Stefancsik
- European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, UK
| | - Robin Steinhaus
- Exploratory Diagnostic Sciences, Berliner Institut für Gesundheitsforschung - Charité, Berlin, Germany
| | - Rebecca Strawbridge
- Department of Psychological Medicine, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | | | - Polina Talapova
- Institute for Research and Health Policy Studies, Tufts Medicine, Boston, MA 2111, USA
| | | | - Pavel Tesner
- Department of Biology and Medical Genetics, 2nd Medical Faculty of Charles University and University Hospital Motol, Prague, Czech Republic
| | - Rhys H Thomas
- Translational and Clinical Research Institute, Henry Wellcome Building, Framlington Place, Newcastle University, Newcastle-Upon-Tyne NE14LP, UK
| | - Audrey Thurm
- Neurodevelopmental and Behavioral Phenotyping Service, National Institute of Mental Health, Bethesda, MD, USA
| | - Marek Turnovec
- Department of Biology and Medical Genetics, 2nd Medical Faculty of Charles University and University Hospital Motol, Prague, Czech Republic
| | - Marielle E van Gijn
- Department of Genetics, University Medical Center Groningen, Groningen, Netherlands
| | | | - Markéta Vlčková
- Department of Biology and Medical Genetics, 2nd Medical Faculty of Charles University and University Hospital Motol, Prague, Czech Republic
| | - Anita Walden
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Kai Wang
- Chinese HPO Consortium, Beijing, China
| | - Ron Wapner
- Department of Obstetrics and Gynecology, Columbia University Irving Medical Center, New York, NY, USA
| | - James S Ware
- National Heart & Lung Institute & MRC London Institute of Medical Sciences, Imperial College London, London W12 0HS, UK
| | | | | | - Lisa D Wiggins
- National Center on Birth Defects and Developmental Disabilities, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Andrew E Williams
- Institute for Research and Health Policy Studies, Tufts Medicine, Boston, MA 2111, USA
| | - Chen Wu
- Chinese HPO Consortium, Beijing, China
| | - Margot J Wyrwoll
- Centre for Regenerative Medicine, Institute for Regeneration and Repair, Institute for Stem Cell Research, University of Edinburgh, Edinburgh, UK
| | - Hui Xiong
- Chinese HPO Consortium, Beijing, China
| | - Nefize Yalin
- Department of Psychological Medicine, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Yasunori Yamamoto
- Database Center for Life Science, Joint Support-Center for Data Science Research, Research Organization of Information and Systems, Japan
| | - Lakshmi N Yatham
- Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
| | - Anastasia K Yocum
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - Allan H Young
- Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London & South London and Maudsley NHS Foundation Trust, Bethlem Royal Hospital, Monks Orchard Road, Beckenham, Kent, London SE5 8AF, UK
| | - Zafer Yüksel
- Department of Human Genetics, Bioscientia Healthcare GmbH, Ingelheim, Germany
| | - Peter P Zandi
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Andreas Zankl
- Faculty of Medicine and Health, The University of Sydney, Camperdown, Australia
| | - Ignacio Zarante
- Institute of Human Genetics, Pontificia Universidad Javeriana, Bogotá, Colombia
| | - Miroslav Zvolský
- Institute of Health Information and Statistics of the Czech Republic, Prague, Czech Republic
| | - Sabrina Toro
- University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Leigh C Carmody
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Nomi L Harris
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Monica C Munoz-Torres
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Daniel Danis
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Christopher J Mungall
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | | | - Melissa A Haendel
- University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Peter N Robinson
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
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Casiraghi E, Wong R, Hall M, Coleman B, Notaro M, Evans MD, Tronieri JS, Blau H, Laraway B, Callahan TJ, Chan LE, Bramante CT, Buse JB, Moffitt RA, Stürmer T, Johnson SG, Raymond Shao Y, Reese J, Robinson PN, Paccanaro A, Valentini G, Huling JD, Wilkins KJ. A method for comparing multiple imputation techniques: A case study on the U.S. national COVID cohort collaborative. J Biomed Inform 2023; 139:104295. [PMID: 36716983 PMCID: PMC10683778 DOI: 10.1016/j.jbi.2023.104295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 01/16/2023] [Accepted: 01/21/2023] [Indexed: 02/01/2023]
Abstract
Healthcare datasets obtained from Electronic Health Records have proven to be extremely useful for assessing associations between patients' predictors and outcomes of interest. However, these datasets often suffer from missing values in a high proportion of cases, whose removal may introduce severe bias. Several multiple imputation algorithms have been proposed to attempt to recover the missing information under an assumed missingness mechanism. Each algorithm presents strengths and weaknesses, and there is currently no consensus on which multiple imputation algorithm works best in a given scenario. Furthermore, the selection of each algorithm's parameters and data-related modeling choices are also both crucial and challenging. In this paper we propose a novel framework to numerically evaluate strategies for handling missing data in the context of statistical analysis, with a particular focus on multiple imputation techniques. We demonstrate the feasibility of our approach on a large cohort of type-2 diabetes patients provided by the National COVID Cohort Collaborative (N3C) Enclave, where we explored the influence of various patient characteristics on outcomes related to COVID-19. Our analysis included classic multiple imputation techniques as well as simple complete-case Inverse Probability Weighted models. Extensive experiments show that our approach can effectively highlight the most promising and performant missing-data handling strategy for our case study. Moreover, our methodology allowed a better understanding of the behavior of the different models and of how it changed as we modified their parameters. Our method is general and can be applied to different research fields and on datasets containing heterogeneous types.
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Affiliation(s)
- Elena Casiraghi
- AnacletoLab, Department of Computer Science "Giovanni degli Antoni", Università degli Studi di Milano, Milan, Italy; CINI, Infolife National Laboratory, Roma, Italy; Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Rachel Wong
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Margaret Hall
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Ben Coleman
- The Jackson Laboratory for Genomic Medicine, Farmington, USA; Institute for Systems Genomics, University of Connecticut, Farmington, CT, USA
| | - Marco Notaro
- AnacletoLab, Department of Computer Science "Giovanni degli Antoni", Università degli Studi di Milano, Milan, Italy; CINI, Infolife National Laboratory, Roma, Italy
| | - Michael D Evans
- Biostatistical Design and Analysis Center, Clinical and Translational Science Institute, University of Minnesota, Minneapolis, MN, USA
| | - Jena S Tronieri
- Department of Psychiatry, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Hannah Blau
- The Jackson Laboratory for Genomic Medicine, Farmington, USA
| | - Bryan Laraway
- University of Colorado, Anschutz Medical Campus, Aurora, CO, USA
| | | | - Lauren E Chan
- College of Public Health and Human Sciences, Oregon State University, Corvallis, USA
| | - Carolyn T Bramante
- Division of General Internal Medicine, University of Minnesota, Minneapolis, MN, USA
| | - John B Buse
- NC Translational and Clinical Sciences Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Division of Endocrinology, Department of Medicine, University of North Carolina School of Medicine, USA
| | - Richard A Moffitt
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Til Stürmer
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Steven G Johnson
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA
| | - Yu Raymond Shao
- Harvard-MIT Division of Health Sciences and Technology (HST), 260 Longwood Ave, Boston, USA; Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, USA
| | - Justin Reese
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Peter N Robinson
- The Jackson Laboratory for Genomic Medicine, Farmington, USA; Institute for Systems Genomics, University of Connecticut, Farmington, CT, USA
| | - Alberto Paccanaro
- School of Applied Mathematics (EMAp), Fundação Getúlio Vargas, Rio de Janeiro, Brazil; Department of Computer Science, Royal Holloway, University of London, Egham, UK
| | - Giorgio Valentini
- AnacletoLab, Department of Computer Science "Giovanni degli Antoni", Università degli Studi di Milano, Milan, Italy; CINI, Infolife National Laboratory, Roma, Italy
| | - Jared D Huling
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Kenneth J Wilkins
- Biostatistics Program, Office of the Director, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA
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3
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Hadley E, Yoo YJ, Patel S, Zhou A, Laraway B, Wong R, Preiss A, Chew R, Davis H, Chute CG, Pfaff ER, Loomba J, Haendel M, Hill E, Moffitt R. SARS-CoV-2 Reinfection is Preceded by Unique Biomarkers and Related to Initial Infection Timing and Severity: an N3C RECOVER EHR-Based Cohort Study. medRxiv 2023:2023.01.03.22284042. [PMID: 36656776 PMCID: PMC9844020 DOI: 10.1101/2023.01.03.22284042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Although the COVID-19 pandemic has persisted for over 2 years, reinfections with SARS-CoV-2 are not well understood. We use the electronic health record (EHR)-based study cohort from the National COVID Cohort Collaborative (N3C) as part of the NIH Researching COVID to Enhance Recovery (RECOVER) Initiative to characterize reinfection, understand development of Long COVID after reinfection, and compare severity of reinfection with initial infection. We validate previous findings of reinfection incidence (5.9%), the occurrence of most reinfections during the Omicron epoch, and evidence of multiple reinfections. We present novel findings that Long COVID diagnoses occur closer to the index date for infection or reinfection in the Omicron BA epoch. We report lower albumin levels leading up to reinfection and a statistically significant association of severity between first infection and reinfection (chi-squared value: 9446.2, p-value: 0) with a medium effect size (Cramer's V: 0.18, DoF = 4).
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Affiliation(s)
| | | | | | - Andrea Zhou
- University of Virginia, Charlottesville, VA, US
| | | | | | | | - Rob Chew
- RTI International, Durham, NC, US
| | - Hannah Davis
- RECOVER Patient Led Research Collaborative (PLRC), US
| | | | | | | | - Melissa Haendel
- University of Colorado Anschutz Medical Campus, Denver, CO, US
| | - Elaine Hill
- University of Rochester Medical Center, Rochester, NY, US
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Reese JT, Blau H, Casiraghi E, Bergquist T, Loomba JJ, Callahan TJ, Laraway B, Antonescu C, Coleman B, Gargano M, Wilkins KJ, Cappelletti L, Fontana T, Ammar N, Antony B, Murali TM, Caufield JH, Karlebach G, McMurry JA, Williams A, Moffitt R, Banerjee J, Solomonides AE, Davis H, Kostka K, Valentini G, Sahner D, Chute CG, Madlock-Brown C, Haendel MA, Robinson PN. Generalisable long COVID subtypes: findings from the NIH N3C and RECOVER programmes. EBioMedicine 2023; 87:104413. [PMID: 36563487 PMCID: PMC9769411 DOI: 10.1016/j.ebiom.2022.104413] [Citation(s) in RCA: 32] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Revised: 11/23/2022] [Accepted: 11/29/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Stratification of patients with post-acute sequelae of SARS-CoV-2 infection (PASC, or long COVID) would allow precision clinical management strategies. However, long COVID is incompletely understood and characterised by a wide range of manifestations that are difficult to analyse computationally. Additionally, the generalisability of machine learning classification of COVID-19 clinical outcomes has rarely been tested. METHODS We present a method for computationally modelling PASC phenotype data based on electronic healthcare records (EHRs) and for assessing pairwise phenotypic similarity between patients using semantic similarity. Our approach defines a nonlinear similarity function that maps from a feature space of phenotypic abnormalities to a matrix of pairwise patient similarity that can be clustered using unsupervised machine learning. FINDINGS We found six clusters of PASC patients, each with distinct profiles of phenotypic abnormalities, including clusters with distinct pulmonary, neuropsychiatric, and cardiovascular abnormalities, and a cluster associated with broad, severe manifestations and increased mortality. There was significant association of cluster membership with a range of pre-existing conditions and measures of severity during acute COVID-19. We assigned new patients from other healthcare centres to clusters by maximum semantic similarity to the original patients, and showed that the clusters were generalisable across different hospital systems. The increased mortality rate originally identified in one cluster was consistently observed in patients assigned to that cluster in other hospital systems. INTERPRETATION Semantic phenotypic clustering provides a foundation for assigning patients to stratified subgroups for natural history or therapy studies on PASC. FUNDING NIH (TR002306/OT2HL161847-01/OD011883/HG010860), U.S.D.O.E. (DE-AC02-05CH11231), Donald A. Roux Family Fund at Jackson Laboratory, Marsico Family at CU Anschutz.
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Affiliation(s)
- Justin T Reese
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Hannah Blau
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT, USA
| | - Elena Casiraghi
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA; AnacletoLab, Dipartimento di Informatica, Università Degli Studi di Milano, Milan, Italy
| | | | - Johanna J Loomba
- The Integrated Translational Health Research Institute of Virginia (iTHRIV), University of Virginia, Charlottesville, VA, USA
| | - Tiffany J Callahan
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA
| | - Bryan Laraway
- Departments of Biomedical Informatics and Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | | | - Ben Coleman
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT, USA
| | - Michael Gargano
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT, USA
| | - Kenneth J Wilkins
- Biostatistics Program, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Luca Cappelletti
- AnacletoLab, Dipartimento di Informatica, Università Degli Studi di Milano, Milan, Italy
| | - Tommaso Fontana
- AnacletoLab, Dipartimento di Informatica, Università Degli Studi di Milano, Milan, Italy
| | - Nariman Ammar
- Health Science Center, University of Tennessee, Memphis, TN, USA
| | - Blessy Antony
- Department of Computer Science, Virginia Tech, Blacksburg, VA, USA
| | - T M Murali
- Department of Computer Science, Virginia Tech, Blacksburg, VA, USA
| | - J Harry Caufield
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Guy Karlebach
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT, USA
| | - Julie A McMurry
- Departments of Biomedical Informatics and Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Andrew Williams
- Tufts Medical Center Clinical and Translational Science Institute, Tufts Medical Center, Boston, MA, USA; Tufts University School of Medicine, Institute for Clinical Research and Health Policy Studies, Boston, MA, USA; Northeastern University, OHDSI Center at the Roux Institute, Boston, MA, USA
| | - Richard Moffitt
- Department of Biomedical Informatics and Stony Brook Cancer Center, Stony Brook University, Stony Brook, NY, USA
| | | | | | | | - Kristin Kostka
- Northeastern University, OHDSI Center at the Roux Institute, Boston, MA, USA
| | - Giorgio Valentini
- AnacletoLab, Dipartimento di Informatica, Università Degli Studi di Milano, Milan, Italy
| | | | - Christopher G Chute
- Schools of Medicine, Public Health and Nursing, Johns Hopkins University, Baltimore, MD, USA
| | | | - Melissa A Haendel
- Departments of Biomedical Informatics and Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Peter N Robinson
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT, USA; Institute for Systems Genomics, University of Connecticut, Farmington, CT, USA.
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5
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Chan LE, Casiraghi E, Laraway B, Coleman B, Blau H, Zaman A, Harris NL, Wilkins K, Antony B, Gargano M, Valentini G, Sahner D, Haendel M, Robinson PN, Bramante C, Reese J. Metformin is associated with reduced COVID-19 severity in patients with prediabetes. Diabetes Res Clin Pract 2022; 194:110157. [PMID: 36400170 PMCID: PMC9663381 DOI: 10.1016/j.diabres.2022.110157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 11/03/2022] [Accepted: 11/09/2022] [Indexed: 11/17/2022]
Abstract
AIMS Studies suggest that metformin is associated with reduced COVID-19 severity in individuals with diabetes compared to other antihyperglycemics. We assessed if metformin is associated with reduced incidence of severe COVID-19 for patients with prediabetes or polycystic ovary syndrome (PCOS), common diseases that increase the risk of severe COVID-19. METHODS This observational, retrospective study utilized EHR data from 52 hospitals for COVID-19 patients with PCOS or prediabetes treated with metformin or levothyroxine/ondansetron (controls). After balancing via inverse probability score weighting, associations with COVID-19 severity were assessed by logistic regression. RESULTS In the prediabetes cohort, when compared to levothyroxine, metformin was associated with a significantly lower incidence of COVID-19 with "mild-ED" or worse (OR [95% CI]: 0.636, [0.455-0.888]) and "moderate" or worse severity (0.493 [0.339-0.718]). Compared to ondansetron, metformin was associated with lower incidence of "mild-ED" or worse severity (0.039 [0.026-0.057]), "moderate" or worse (0.045 [0.03-0.069]), "severe" or worse (0.183 [0.077-0.431]), and "mortality/hospice" (0.223 [0.071-0.694]). For PCOS, metformin showed no significant differences in severity compared to levothyroxine, but was associated with a significantly lower incidence of "mild-ED" or worse (0.101 [0.061-0.166]), and "moderate" or worse (0.094 [0.049-0.18]) COVID-19 outcome compared to ondansetron. CONCLUSIONS Metformin use is associated with less severe COVID-19 in patients with prediabetes or PCOS.
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Affiliation(s)
- Lauren E Chan
- College of Public Health and Human Sciences, Oregon State University, Corvallis, OR, USA
| | - Elena Casiraghi
- AnacletoLab, Dipartimento di Informatica, Università degli Studi di Milano, Italy; Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Bryan Laraway
- University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Ben Coleman
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT, USA
| | - Hannah Blau
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT, USA
| | - Adnin Zaman
- Department of Medicine, Division of Endocrinology, Metabolism and Diabetes, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Nomi L Harris
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Kenneth Wilkins
- Biostatistics Program, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Blessy Antony
- Department of Computer Science, Virginia Tech, Blacksburg, VA, USA
| | - Michael Gargano
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT, USA
| | - Giorgio Valentini
- AnacletoLab, Dipartimento di Informatica, Università degli Studi di Milano, Italy
| | | | - Melissa Haendel
- University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Peter N Robinson
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT, USA; Institute for Systems Genomics, University of Connecticut, Farmington, CT, USA
| | - Carolyn Bramante
- Division of General Internal Medicine, University of Minnesota, Minneapolis, MN, USA
| | - Justin Reese
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
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6
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Coleman B, Casiraghi E, Callahan TJ, Blau H, Chan L, Laraway B, Clark KB, Reâ Em Y, Gersing KR, Wilkins K, Harris NL, Valentini G, Haendel MA, Reese J, Robinson PN. Post-COVID Phenotypic Manifestations are Associated with New-Onset Psychiatric Disease: Findings from the NIH N3C and RECOVER Studies. medRxiv 2022:2022.07.08.22277388. [PMID: 36380762 PMCID: PMC9645424 DOI: 10.1101/2022.07.08.22277388] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
UNLABELLED Acute COVID-19 infection can be followed by diverse clinical manifestations referred to as Post Acute Sequelae of SARS-CoV2 Infection (PASC). Studies have shown an increased risk of being diagnosed with new-onset psychiatric disease following a diagnosis of acute COVID-19. However, it was unclear whether non-psychiatric PASC-associated manifestations (PASC-AMs) are associated with an increased risk of new-onset psychiatric disease following COVID-19. A retrospective EHR cohort study of 1,603,767 individuals with acute COVID-19 was performed to evaluate whether non-psychiatric PASC-AMs are associated with new-onset psychiatric disease. Data were obtained from the National COVID Cohort Collaborative (N3C), which has EHR data from 65 clinical organizations. EHR codes were mapped to 151 non-psychiatric PASC-AMs recorded 28-120 days following SARS-CoV-2 diagnosis and before diagnosis of new-onset psychiatric disease. Association of newly diagnosed psychiatric disease with age, sex, race, pre-existing comorbidities, and PASC-AMs in seven categories was assessed by logistic regression. There was a significant association between six categories and newly diagnosed anxiety, mood, and psychotic disorders, with odds ratios highest for cardiovascular (1.35, 1.27-1.42) PASC-AMs. Secondary analysis revealed that the proportions of 95 individual clinical features significantly differed between patients diagnosed with different psychiatric disorders. Our study provides evidence for association between non-psychiatric PASC-AMs and the incidence of newly diagnosed psychiatric disease. Significant associations were found for features related to multiple organ systems. This information could prove useful in understanding risk stratification for new-onset psychiatric disease following COVID-19. Prospective studies are needed to corroborate these findings. FUNDING NCATS U24 TR002306.
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7
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Chan LE, Casiraghi E, Laraway B, Coleman B, Blau H, Zaman A, Harris N, Wilkins K, Gargano M, Valentini G, Sahner D, Haendel M, Robinson PN, Bramante C, Reese J. Metformin is Associated with Reduced COVID-19 Severity in Patients with Prediabetes. medRxiv 2022:2022.08.29.22279355. [PMID: 36093353 PMCID: PMC9460973 DOI: 10.1101/2022.08.29.22279355] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Background With the continuing COVID-19 pandemic, identifying medications that improve COVID-19 outcomes is crucial. Studies suggest that use of metformin, an oral antihyperglycemic, is associated with reduced COVID-19 severity in individuals with diabetes compared to other antihyperglycemic medications. Some patients without diabetes, including those with polycystic ovary syndrome (PCOS) and prediabetes, are prescribed metformin for off-label use, which provides an opportunity to further investigate the effect of metformin on COVID-19. Participants In this observational, retrospective analysis, we leveraged the harmonized electronic health record data from 53 hospitals to construct cohorts of COVID-19 positive, metformin users without diabetes and propensity-weighted control users of levothyroxine (a medication for hypothyroidism that is not known to affect COVID-19 outcome) who had either PCOS (n = 282) or prediabetes (n = 3136). The primary outcome of interest was COVID-19 severity, which was classified as: mild, mild ED (emergency department), moderate, severe, or mortality/hospice. Results In the prediabetes cohort, metformin use was associated with a lower rate of COVID-19 with severity of mild ED or worse (OR: 0.630, 95% CI 0.450 - 0.882, p < 0.05) and a lower rate of COVID-19 with severity of moderate or worse (OR: 0.490, 95% CI 0.336 - 0.715, p < 0.001). In patients with PCOS, we found no significant association between metformin use and COVID-19 severity, although the number of patients was relatively small. Conclusions Metformin was associated with less severe COVID-19 in patients with prediabetes, as seen in previous studies of patients with diabetes. This is an important finding, since prediabetes affects between 19 and 38% of the US population, and COVID-19 is an ongoing public health emergency. Further observational and prospective studies will clarify the relationship between metformin and COVID-19 severity in patients with prediabetes, and whether metformin usage may reduce COVID-19 severity.
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Affiliation(s)
- Lauren E. Chan
- College of Public Health and Human Sciences, Oregon State University, Corvallis, OR, USA
| | - Elena Casiraghi
- AnacletoLab, Dipartimento di Informatica, Università degli Studi di Milano, Italy
| | - Bryan Laraway
- University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Ben Coleman
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT, USA
| | - Hannah Blau
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT, USA
| | - Adnin Zaman
- Department of Medicine, Division of Endocrinology, Metabolism and Diabetes, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Nomi Harris
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Kenneth Wilkins
- Biostatistics Program, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Michael Gargano
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT, USA
| | - Giorgio Valentini
- AnacletoLab, Dipartimento di Informatica, Università degli Studi di Milano, Italy
| | | | - Melissa Haendel
- University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Peter N. Robinson
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT, USA
- Institute for Systems Genomics, University of Connecticut, Farmington, CT, USA
| | - Carolyn Bramante
- Division of General Internal Medicine, University of Minnesota, Minneapolis, MN, USA
| | - Justin Reese
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
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8
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Reese JT, Blau H, Bergquist T, Loomba JJ, Callahan T, Laraway B, Antonescu C, Casiraghi E, Coleman B, Gargano M, Wilkins KJ, Cappelletti L, Fontana T, Ammar N, Antony B, Murali TM, Karlebach G, McMurry JA, Williams A, Moffitt R, Banerjee J, Solomonides AE, Davis H, Kostka K, Valentini G, Sahner D, Chute CG, Madlock-Brown C, Haendel MA, Robinson PN. Generalizable Long COVID Subtypes: Findings from the NIH N3C and RECOVER Programs. medRxiv 2022:2022.05.24.22275398. [PMID: 35665012 PMCID: PMC9164456 DOI: 10.1101/2022.05.24.22275398] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/11/2023]
Abstract
Accurate stratification of patients with post-acute sequelae of SARS-CoV-2 infection (PASC, or long COVID) would allow precision clinical management strategies. However, the natural history of long COVID is incompletely understood and characterized by an extremely wide range of manifestations that are difficult to analyze computationally. In addition, the generalizability of machine learning classification of COVID-19 clinical outcomes has rarely been tested. We present a method for computationally modeling PASC phenotype data based on electronic healthcare records (EHRs) and for assessing pairwise phenotypic similarity between patients using semantic similarity. Our approach defines a nonlinear similarity function that maps from a feature space of phenotypic abnormalities to a matrix of pairwise patient similarity that can be clustered using unsupervised machine learning procedures. Using k-means clustering of this similarity matrix, we found six distinct clusters of PASC patients, each with distinct profiles of phenotypic abnormalities. There was a significant association of cluster membership with a range of pre-existing conditions and with measures of severity during acute COVID-19. Two of the clusters were associated with severe manifestations and displayed increased mortality. We assigned new patients from other healthcare centers to one of the six clusters on the basis of maximum semantic similarity to the original patients. We show that the identified clusters were generalizable across different hospital systems and that the increased mortality rate was consistently observed in two of the clusters. Semantic phenotypic clustering can provide a foundation for assigning patients to stratified subgroups for natural history or therapy studies on PASC.
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Coleman B, Casiraghi E, Blau H, Chan L, Haendel MA, Laraway B, Callahan TJ, Deer RR, Wilkins KJ, Reese J, Robinson PN. Risk of new-onset psychiatric sequelae of COVID-19 in the early and late post-acute phase. World Psychiatry 2022; 21:319-320. [PMID: 35524622 PMCID: PMC9077621 DOI: 10.1002/wps.20992] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Affiliation(s)
- Ben Coleman
- Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
- Institute for Systems Genomics, University of Connecticut, Farmington, CT, USA
| | - Elena Casiraghi
- AnacletoLab, Dipartimento di Informatica "Giovanni degli Antoni", Università di Milano, Milan, Italy
- CINI, Infolife National Laboratory, Rome, Italy
| | - Hannah Blau
- Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Lauren Chan
- College of Public Health and Human Sciences, Oregon State University, Corvallis, OR, USA
| | - Melissa A Haendel
- Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Bryan Laraway
- Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Tiffany J Callahan
- Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Rachel R Deer
- University of Texas Medical Branch, Galveston, TX, USA
| | - Kenneth J Wilkins
- Biostatistics Program, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Justin Reese
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Peter N Robinson
- Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
- Institute for Systems Genomics, University of Connecticut, Farmington, CT, USA
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Tobias J, Olyaei A, Laraway B, Jordan BK, Dickinson SL, Golzarri-Arroyo L, Fialkowski E, Owora A, Scottoline B. Bifidobacteriumlongum subsp. infantis EVC001 Administration Is Associated with a Significant Reduction in the Incidence of Necrotizing Enterocolitis in Very Low Birth Weight Infants. J Pediatr 2022; 244:64-71.e2. [PMID: 35032555 DOI: 10.1016/j.jpeds.2021.12.070] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Revised: 12/05/2021] [Accepted: 12/15/2021] [Indexed: 12/14/2022]
Abstract
OBJECTIVE To assess the effects of Bifidobacteriumlongum subsp. infantis EVC001 (Binfantis EVC001) administration on the incidence of necrotizing enterocolitis (NEC) in preterm infants in a single level IV neonatal intensive care unit (NICU). STUDY DESIGN Nonconcurrent retrospective analysis of 2 cohorts of very low birth weight (VLBW) infants not exposed and exposed to Binfantis EVC001 probiotic at Oregon Health & Science University from 2014 to 2020. Outcomes included NEC incidence and NEC-associated mortality, including subgroup analysis of extremely low birth weight (ELBW) infants. Log-binomial regression models were used to compare the incidence and risk of NEC-associated outcomes between the unexposed and exposed cohorts. RESULTS The cumulative incidence of NEC diagnoses decreased from 11.0% (n = 301) in the no EVC001 (unexposed) cohort to 2.7% (n = 182) in the EVC001 (exposed) cohort (P < .01). The EVC001 cohort had a 73% risk reduction of NEC compared with the no EVC001 cohort (adjusted risk ratio, 0.27; 95% CI, 0.094-0.614; P < .01) resulting in an adjusted number needed to treat of 13 (95% CI, 10.0-23.5) for Binfantis EVC001. NEC-associated mortality decreased from 2.7% in the no EVC001 cohort to 0% in the EVC001 cohort (P = .03). There were similar reductions in NEC incidence and risk for ELBW infants (19.2% vs 5.3% [P < .01]; adjusted risk ratio, 0.28; 95% CI, 0.085-0.698 [P = .02]) and mortality (5.6% vs 0%; P < .05) in the 2 cohorts. CONCLUSIONS In this observational study of 483 VLBW infants, Binfantis EVC001 administration was associated with significant reductions in the risk of NEC and NEC-related mortality. Binfantis EVC001 supplementation may be considered safe and effective for reducing morbidity and mortality in the NICU.
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Affiliation(s)
- Joseph Tobias
- Department of Surgery, Oregon Health & Science University, Portland, OR
| | - Amy Olyaei
- Division of Neonatology, Department of Pediatrics, Oregon Health & Science University, Portland, OR
| | - Bryan Laraway
- Oregon Clinical and Translational Research Institute, Oregon Health & Science University, Portland, OR
| | - Brian K Jordan
- Division of Neonatology, Department of Pediatrics, Oregon Health & Science University, Portland, OR
| | | | | | | | - Arthur Owora
- School of Public Health, Indiana University, Bloomington, IN
| | - Brian Scottoline
- Division of Neonatology, Department of Pediatrics, Oregon Health & Science University, Portland, OR.
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11
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Coleman B, Casiraghi E, Blau H, Chan L, Haendel M, Laraway B, Callahan TJ, Deer RR, Wilkins K, Reese J, Robinson PN. Increased risk of psychiatric sequelae of COVID-19 is highest early in the clinical course. medRxiv 2021:2021.11.30.21267071. [PMID: 34909790 PMCID: PMC8669857 DOI: 10.1101/2021.11.30.21267071] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Background COVID-19 has been shown to increase the risk of adverse mental health consequences. A recent electronic health record (EHR)-based observational study showed an almost two-fold increased risk of new-onset mental illness in the first 90 days following a diagnosis of acute COVID-19. Methods We used the National COVID Cohort Collaborative, a harmonized EHR repository with 2,965,506 COVID-19 positive patients, and compared cohorts of COVID-19 patients with comparable controls. Patients were propensity score-matched to control for confounding factors. We estimated the hazard ratio (COVID-19:control) for new-onset of mental illness for the first year following diagnosis. We additionally estimated the change in risk for new-onset mental illness between the periods of 21-120 and 121-365 days following infection. Findings We find a significant increase in incidence of new-onset mental disorders in the period of 21-120 days following COVID-19 (3.8%, 3.6-4.0) compared to patients with respiratory tract infections (3%, 2.8-3.2). We further show that the risk for new-onset mental illness decreases over the first year following COVID-19 diagnosis compared to other respiratory tract infections and demonstrate a reduced (non-significant) hazard ratio over the period of 121-365 days following diagnosis. Similar findings are seen for new-onset anxiety disorders but not for mood disorders. Interpretation Patients who have recovered from COVID-19 are at an increased risk for developing new-onset mental illness, especially anxiety disorders. This risk is most prominent in the first 120 days following infection. Funding National Center for Advancing Translational Sciences (NCATS).
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Affiliation(s)
- Ben Coleman
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
- Institute for Systems Genomics, University of Connecticut, Farmington, CT 06032, USA
| | - Elena Casiraghi
- AnacletoLab, Dipartimento di Informatica, Università degli Studi di Milano, Italy
| | - Hannah Blau
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Lauren Chan
- College of Public Health and Human Sciences, Oregon State University, Corvallis, OR, USA
| | - Melissa Haendel
- Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Bryan Laraway
- Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Tiffany J Callahan
- University of Colorado Anschutz Medical Campus, Center for Health AI, Aurora 80045, CO, USA
| | - Rachel R Deer
- University of Texas Medical Branch, Galveston, TX, 77550 USA
| | - Ken Wilkins
- Biostatistics Program, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland
| | - Justin Reese
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Peter N Robinson
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
- Institute for Systems Genomics, University of Connecticut, Farmington, CT 06032, USA
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12
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Mungall CJ, McMurry JA, Köhler S, Balhoff JP, Borromeo C, Brush M, Carbon S, Conlin T, Dunn N, Engelstad M, Foster E, Gourdine JP, Jacobsen JOB, Keith D, Laraway B, Lewis SE, NguyenXuan J, Shefchek K, Vasilevsky N, Yuan Z, Washington N, Hochheiser H, Groza T, Smedley D, Robinson PN, Haendel MA. The Monarch Initiative: an integrative data and analytic platform connecting phenotypes to genotypes across species. Nucleic Acids Res 2016; 45:D712-D722. [PMID: 27899636 PMCID: PMC5210586 DOI: 10.1093/nar/gkw1128] [Citation(s) in RCA: 189] [Impact Index Per Article: 23.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2016] [Revised: 10/26/2016] [Accepted: 11/02/2016] [Indexed: 02/04/2023] Open
Abstract
The correlation of phenotypic outcomes with genetic variation and environmental factors is a core pursuit in biology and biomedicine. Numerous challenges impede our progress: patient phenotypes may not match known diseases, candidate variants may be in genes that have not been characterized, model organisms may not recapitulate human or veterinary diseases, filling evolutionary gaps is difficult, and many resources must be queried to find potentially significant genotype–phenotype associations. Non-human organisms have proven instrumental in revealing biological mechanisms. Advanced informatics tools can identify phenotypically relevant disease models in research and diagnostic contexts. Large-scale integration of model organism and clinical research data can provide a breadth of knowledge not available from individual sources and can provide contextualization of data back to these sources. The Monarch Initiative (monarchinitiative.org) is a collaborative, open science effort that aims to semantically integrate genotype–phenotype data from many species and sources in order to support precision medicine, disease modeling, and mechanistic exploration. Our integrated knowledge graph, analytic tools, and web services enable diverse users to explore relationships between phenotypes and genotypes across species.
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Affiliation(s)
- Christopher J Mungall
- Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Julie A McMurry
- Department of Medical Informatics and Clinical Epidemiology and OHSU Library, Oregon Health & Science University, Portland, OR, 97239, USA
| | - Sebastian Köhler
- Institute for Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany
| | | | - Charles Borromeo
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, 15260, USA
| | - Matthew Brush
- Department of Medical Informatics and Clinical Epidemiology and OHSU Library, Oregon Health & Science University, Portland, OR, 97239, USA
| | - Seth Carbon
- Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Tom Conlin
- Department of Medical Informatics and Clinical Epidemiology and OHSU Library, Oregon Health & Science University, Portland, OR, 97239, USA
| | - Nathan Dunn
- Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Mark Engelstad
- Department of Medical Informatics and Clinical Epidemiology and OHSU Library, Oregon Health & Science University, Portland, OR, 97239, USA
| | - Erin Foster
- Department of Medical Informatics and Clinical Epidemiology and OHSU Library, Oregon Health & Science University, Portland, OR, 97239, USA
| | - J P Gourdine
- Department of Medical Informatics and Clinical Epidemiology and OHSU Library, Oregon Health & Science University, Portland, OR, 97239, USA
| | - Julius O B Jacobsen
- William Harvey Research Institute, Barts & The London School of Medicine & Dentistry, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK
| | - Dan Keith
- Department of Medical Informatics and Clinical Epidemiology and OHSU Library, Oregon Health & Science University, Portland, OR, 97239, USA
| | - Bryan Laraway
- Department of Medical Informatics and Clinical Epidemiology and OHSU Library, Oregon Health & Science University, Portland, OR, 97239, USA
| | - Suzanna E Lewis
- Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Jeremy NguyenXuan
- Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Kent Shefchek
- Department of Medical Informatics and Clinical Epidemiology and OHSU Library, Oregon Health & Science University, Portland, OR, 97239, USA
| | - Nicole Vasilevsky
- Department of Medical Informatics and Clinical Epidemiology and OHSU Library, Oregon Health & Science University, Portland, OR, 97239, USA
| | - Zhou Yuan
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, 15260, USA
| | - Nicole Washington
- Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Harry Hochheiser
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, 15260, USA
| | - Tudor Groza
- Kinghorn Centre for Clinical Genomics, Garvan Institute of Medical Research, Darlinghurst, NSW 2010, Australia
| | - Damian Smedley
- William Harvey Research Institute, Barts & The London School of Medicine & Dentistry, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK
| | - Peter N Robinson
- Institute for Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany.,The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032mUSA
| | - Melissa A Haendel
- Department of Medical Informatics and Clinical Epidemiology and OHSU Library, Oregon Health & Science University, Portland, OR, 97239, USA
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13
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Rutten MJ, Laraway B, Gregory CR, Xie H, Renken C, Keese C, Gregory KW. Rapid assay of stem cell functionality and potency using electric cell-substrate impedance sensing. Stem Cell Res Ther 2015; 6:192. [PMID: 26438432 PMCID: PMC4594964 DOI: 10.1186/s13287-015-0182-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2015] [Revised: 07/30/2015] [Accepted: 09/10/2015] [Indexed: 01/09/2023] Open
Abstract
Regenerative medicine studies using autologous bone marrow mononuclear cells (BM-MNCs) have shown improved clinical outcomes that correlate to in vitro BM-MNC invasive capacity. The current Boyden-chamber assay for testing invasive capacity is labor-intensive, provides only a single time point, and takes 36 hours to collect data and results, which is not practical from a clinical cell delivery perspective. To develop a rapid, sensitive and reproducible invasion assay, we employed Electric Cell-substrate Impedance Sensing (ECIS) technology. Chemokine-directed BM-MNC cell invasion across a Matrigel-coated Transwell filter was measurable within minutes using the ECIS system we developed. This ECIS-Transwell chamber system provides a rapid and sensitive test of stem and progenitor cell invasive capacity for evaluation of stem cell functionality to provide timely clinical data for selection of patients likely to realize clinical benefit in regenerative medicine treatments. This device could also supply robust unambiguous, reproducible and cost effective data as a potency assay for cell product release and regulatory strategies.
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Affiliation(s)
- Michael J Rutten
- Center for Regenerative Medicine, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, 97239, Portland, OR, USA.
| | - Bryan Laraway
- Center for Regenerative Medicine, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, 97239, Portland, OR, USA.
| | - Cynthia R Gregory
- Center for Regenerative Medicine, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, 97239, Portland, OR, USA. .,VA Portland Health Care System, 3710 SW US Veterans Hospital Road, 97239, Portland, OR, USA. .,Department of Molecular Microbiology and Immunology, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, 97239, Portland, OR, USA.
| | - Hua Xie
- Center for Regenerative Medicine, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, 97239, Portland, OR, USA.
| | - Christian Renken
- Applied BioPhysics, Inc., 185 Jordan Road, 12180, Troy, NY, USA.
| | - Charles Keese
- Applied BioPhysics, Inc., 185 Jordan Road, 12180, Troy, NY, USA.
| | - Kenton W Gregory
- Center for Regenerative Medicine, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, 97239, Portland, OR, USA. .,Department of Biomedical Engineering, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, 97239, Portland, OR, USA.
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14
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Wang X, Cunningham M, Zhang X, Tokarz S, Laraway B, Troxell M, Sears RC. Phosphorylation regulates c-Myc's oncogenic activity in the mammary gland. Cancer Res 2011; 71:925-36. [PMID: 21266350 DOI: 10.1158/0008-5472.can-10-1032] [Citation(s) in RCA: 124] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Expression of the c-Myc oncoprotein is affected by conserved threonine 58 (T58) and serine 62 (S62) phosphorylation sites that help to regulate c-Myc protein stability, and altered ratios of T58 and S62 phosphorylation have been observed in human cancer. Here, we report the development of 3 unique c-myc knock-in mice that conditionally express either c-Myc(WT) or the c-Myc(T58A) or c-Myc(S62A) phosphorylation mutant from the constitutively active ROSA26 locus in response to Cre recombinase to study the role of these phosphorylation sites in vivo. Using a mammary-specific Cre model, we found that expression of c-Myc(WT) resulted in increased mammary gland density, but normal morphology and no tumors at the level expressed from the ROSA promoter. In contrast, c-Myc(T58A) expression yielded enhanced mammary gland density, hyperplastic foci, cellular dysplasia, and mammary carcinoma, associated with increased genomic instability and suppressed apoptosis relative to c-Myc(WT). Alternatively, c-Myc(S62A) expression reduced mammary gland density relative to control glands, and this was associated with increased genomic instability and normal apoptotic function. Our results indicate that specific activities of c-Myc are differentially affected by T58 and S62 phosphorylation. This model provides a robust platform to interrogate the role that these phosphorylation sites play in c-Myc function during development and tumorigenesis.
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Affiliation(s)
- Xiaoyan Wang
- Molecular and Medical Genetics Department, Oregon Health and Sciences University, Portland, Oregon, USA
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