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Autoantibodies against type I IFNs in patients with life-threatening COVID-19
Paul Bastard, Lindsey B. Rosen, Qian Zhang, Eleftherios Michailidis, Hans-Heinrich Hoffmann, Yu Zhang, Karim Dorgham, Quentin Philippot, Jérémie Rosain, Vivien Béziat, Jérémy Manry, Elana Shaw, Liis Haljasmägi, Pärt Peterson, Lazaro Lorenzo, Lucy Bizien, Sophie Trouillet‐Assant, Kerry Dobbs, Adriana A. de Jesus, Alexandre Bélot, Anne Kallaste, Émilie Catherinot, Yacine Tandjaoui-Lambiotte, Jérémie Le Pen, Gaspard Kerner, Benedetta Bigio, Yoann Seeleuthner, Rui Yang, Alexandre Bolze, András N. Spaan, Ottavia M. Delmonte, Michael S. Abers, Alessandro Aiuti, Giorgio Casari, Vito Lampasona, Lorenzo Piemonti, Fabio Ciceri, Kaya Bilgüvar, Richard P. Lifton, Marc Vasse, David M. Smadja, Mélanie Migaud, Jérôme Hadjadj, Benjamin Terrier, Darragh Duffy, Lluís Quintana‐Murci, Diederik van de Beek, Lucie Roussel, Donald C. Vinh, Stuart G. Tangye, Filomeen Haerynck, David Dalmau, Javier Martínez‐Picado, Petter Brodin, Michel C. Nussenzweig, Stéphanie Boisson‐Dupuis, Guillaume Vogt, Trine H. Mogensen, Andrew J. Oler, Jingwen Gu, Peter D. Burbelo, Jeffrey I. Cohen, Andrea Biondi, Laura Rachele Bettini, Mariella D’Angiò, Paolo Bonfanti, Patrick Rossignol, Julien Mayaux, Frédéric Rieux‐Laucat, Eystein S. Husebye, Francesca Fusco, Matilde Valeria Ursini, Luisa Imberti, Alessandra Sottini, Simone Paghera, Eugenia Quirós-Roldán, Camillo Rossi, Riccardo Castagnoli, Daniela Montagna, Amelia Licari, Gian Luigi Marseglia, Xavier Duval, Jade Ghosn, John S. Tsang, Raphaela Goldbach‐Mansky, Kai Kisand, Michail S. Lionakis, Anne Puel, Shen‐Ying Zhang, Steven M. Holland, Guy Gorochov, Emmanuelle Jouanguy, Charles M. Rice, Aurélie Cobat, Luigi D. Notarangelo, Laurent Abel, Helen C. Su, Jean‐Laurent Casanova, Andrés A. 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Sène, Sevtap Şenoğlu, Mikko Seppänen, Alex Serra Ilovich, Mohammad Shahrooei, Ali Sobh, Jordi Solé‐Violán, Catherine Soler, Pere Soler‐Palacín, Yuri Stepanovskiy, Annabelle Stoclin, Fabio Silvio Taccone, Jean‐Luc Taupin, Simon J. Tavernier, C. Thumerelle, Gabriele Tomasoni, Julie Toubiana, Jesús Troya, Alessandra Tucci, Y. Uzunhan, P. Vabres, Juan Valencia-Ramos, Ana Maria Van Den Rym, Isabelle Vandernoot, Hülya Vatansev, Valentina Vélez-Santamaría, Sébastien Viel, Marie E. Vilaire, Audrey Vincent, Guillaume Voiriot, Fanny Vuotto, Alper Yosunkaya, Barnaby Edward Young, Fatih Yücel, Faı̈ez Zannad, Mayana Zatz, Giuseppe Foti, Giacomo Bellani, Giuseppe Citerio, Ernesto Contro, Alberto Pesci, Maria Grazia Valsecchi, Marina Elena Cazzaniga, Christine Bôle‐Feysot, Stanislas Lyonnet, Cécile Masson, Patrick Nitschké, Aurore Pouliet, Yoann Schmitt, Frédéric Torès, Mohammed Zarhrate, Claire Andréjak, François Angoulvant, Delphine Bachelet, Romain Basmaci, Sylvie Behillil, Marine Beluze, Dehbia Benkerrou, Krishna Bhavsar, François Bompart, Lila Bouadma, Maude Bouscambert, Mireille Caralp, Minerva Cervantes-Gonzalez, Anissa Chair, Alexandra Coelho, Camille Couffignal, Sandrine Couffin-Cadièrgues, Éric D’Ortenzio, Charlène Da Silveira, Marie‐Pierre Debray, Dominique Deplanque, Diane Descamps, Mathilde Desvallées, Alpha Diallo, Alphonsine Diouf, Céline Dorival, François Dubos, Philippine Eloy, Vincent Enouf, Hélène Espérou, Marina Esposito‐Farèse, Manuel Etienne, Nadia Ettalhaoui, Nathalie Gault, Alexandre Gaymard, Tristan Gigante, Isabelle Gorenne, Jérémie Guedj, Alexandre Hoctin, Isabelle Hoffmann, Salma Jaafoura, Ouifiya Kafif, Florentia Kaguelidou, Sabina Kali, Antoine Khalil, Coralie Khan, Cédric Laouenan, Samira Laribi, Minh Quan Lê, Soizic Le Mestre, Hervé Le Nagard, François‐Xavier Lescure, Yves Lévy, Claire Lévy‐Marchal, Bruno Lina, Guillaume Lingas, Jean Christophe Lucet, Denis Malvy, Marina Mambert, France Mentré, Noémie Mercier, Amina Meziane, Jimmy Mullaert, Nadège Néant, Marion Noret, Justine Pages, Aurélie Papadopoulos, Christelle Paul, Nathan Peiffer‐Smadja, Ventzislava Petrov–Sanchez, Gilles Peytavin, Olivier Picone, Oriane Puéchal, Manuel Rosa‐Calatrava, Bénédicte Rossignol, Carine Roy, Marion Schneider, Caroline Semaille, Nassima Si Mohammed, Lysa Tagherset, Coralie Tardivon, Marie-Capucine Tellier, François Téoulé, Olivier Terrier, Jean‐François Timsit, Théo Treoux, Christelle Tual, Sarah Tubiana, Noémie Vanel, Aurélie Veislinger, Aurélie Wiedemann, Yazdan Yazdanpanah, Andrés Alcover, Hugues Aschard, Kalla Astrom, Philippe Bousso, Pierre Bruhns, Ana Cumano, Caroline Demangel, Ludovic Deriano, James P. 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Bibliographic record
VenueScience · 2020
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsMcGill University Health Centre
FundersNHLBI Division of Intramural ResearchInstituto de Salud Carlos IIINational Health and Medical Research CouncilMercatus Center, George Mason UniversityNovo Nordisk FondenUniformed Services University of the Health SciencesAmerican Association for the Advancement of ScienceNational Institutes of HealthNational Center for Advancing Translational SciencesMedical Research CouncilRegione LombardiaFondation pour la Recherche MédicaleEuropean CommissionMinisterio de Ciencia e InnovaciónFondazione TelethonZonMwInstitut National de la Santé et de la Recherche MédicaleU.S. Department of DefenseAgencia Estatal de InvestigaciónUniversity of New South WalesYale UniversityNational Human Genome Research InstituteAmsterdam University Medical CentersInstitut des maladies génétiques ImagineGeorge Mason UniversitySt. Giles FoundationFondation Bettencourt SchuellerNederlandse Organisatie voor Wetenschappelijk OnderzoekNational Institute of Allergy and Infectious DiseasesAgence Nationale de la RechercheGeorgia Clinical and Translational Science AllianceHoward Hughes Medical Institute
KeywordsAutoantibodyImmunologyAsymptomaticMedicinePneumoniaAntibodyPhenocopyCoronavirusDiseaseCoronavirus disease 2019 (COVID-19)Internal medicineBiologyInfectious disease (medical specialty)Gene
Abstract
The genetics underlying severe COVID-19 The immune system is complex and involves many genes, including those that encode cytokines known as interferons (IFNs). Individuals that lack specific IFNs can be more susceptible to infectious diseases. Furthermore, the autoantibody system dampens IFN response to prevent damage from pathogen-induced inflammation. Two studies now examine the likelihood that genetics affects the risk of severe coronavirus disease 2019 (COVID-19) through components of this system (see the Perspective by Beck and Aksentijevich). Q. Zhang et al. used a candidate gene approach and identified patients with severe COVID-19 who have mutations in genes involved in the regulation of type I and III IFN immunity. They found enrichment of these genes in patients and conclude that genetics may determine the clinical course of the infection. Bastard et al. identified individuals with high titers of neutralizing autoantibodies against type I IFN-α2 and IFN-ω in about 10% of patients with severe COVID-19 pneumonia. These autoantibodies were not found either in infected people who were asymptomatic or had milder phenotype or in healthy individuals. Together, these studies identify a means by which individuals at highest risk of life-threatening COVID-19 can be identified. Science , this issue p. eabd4570 , p. eabd4585 ; see also p. 404
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
Opus teacher head0.060
GPT teacher head0.357
Teacher spread0.297 · how far apart the two teachers sit on this one work
Validation status
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".
Quick stats
Citations2,849
Published2020
Admission routes1
Has abstractyes