Serological signatures of SARS-CoV-2 infection: Implications for antibody-based diagnostics

Autor: François Dejardin, Caroline Demeret, Narimane Nekkab, Charlotte Cockram, Michael T. White, Rhea J. Longley, Annalisa Meola, Marija Backovic, Sarah H. Merkling, Stéphane Petres, Jérôme De Seze, Ivo Mueller, Solen Kernéis, Samira Fafi-Kremer, Benjamin Terrier, Stéphane Pelleau, Jason Rosado
Přispěvatelé: Malaria : parasites et hôtes - Malaria : parasites and hosts, Institut Pasteur [Paris], Régulation spatiale des Génomes - Spatial Regulation of Genomes, Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Interactions Virus-Insectes - Insect-Virus Interactions (IVI), Génétique Moléculaire des Virus à ARN - Molecular Genetics of RNA Viruses (GMV-ARN (UMR_3569 / U-Pasteur_2)), Institut Pasteur [Paris]-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Virologie Structurale, Equipe mobile d'infectiologie, CHU Necker - Enfants Malades [AP-HP], Epidémiologie et modélisation de la résistance aux antimicrobiens - Epidemiology and modelling of bacterial escape to antimicrobials, Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut Pasteur [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre National de Référence Maladies auto-immunes Systémiques Rares [CHU Pitié-Salpêtrière], Assistance publique - Hôpitaux de Paris (AP-HP) (APHP)-CHU Pitié-Salpêtrière [APHP], Paris-Centre de Recherche Cardiovasculaire (PARCC - UMR-S U970), Hôpital Européen Georges Pompidou [APHP] (HEGP), Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Assistance publique - Hôpitaux de Paris (AP-HP) (APHP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Assistance publique - Hôpitaux de Paris (AP-HP) (APHP)-Université Paris Descartes - Paris 5 (UPD5)-Institut National de la Santé et de la Recherche Médicale (INSERM), Laboratoire de Virologie [Strasbourg], CIC Strasbourg (Centre d’Investigation Clinique Plurithématique (CIC - P) ), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Strasbourg (UNISTRA)-Hôpital de Hautepierre [Strasbourg]-Nouvel Hôpital Civil de Strasbourg, The Walter and Eliza Hall Institute of Medical Research (WEHI), Santé publique : épidémiologie & sciences de l'information biomédicale (ED 393), Sorbonne Université (SU), Centre National de la Recherche Scientifique (CNRS)-Institut Pasteur [Paris], Virologie Structurale - Structural Virology, Epidémiologie et modélisation de la résistance aux antimicrobiens - Epidemiology and modelling of bacterial escape to antimicrobials (EMAE), Institut Pasteur [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Université Paris Descartes - Paris 5 (UPD5)-Institut National de la Santé et de la Recherche Médicale (INSERM), University of Melbourne, This work was supported by the European Research Council (MultiSeroSurv ERC Starting Grant, MW), l’Agence Nationale de la Recherche and Fondation pour la Recherche Médicale (CorPopImm, MW), and the Institut Pasteur International Network (CoronaSeroSurv, MW). JR was supported by the Pasteur Paris University (PPU) International PhD Program, Institut Pasteur [Paris] (IP), Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), Institut Pasteur [Paris] (IP)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Institut Pasteur [Paris] (IP)-Institut National de la Santé et de la Recherche Médicale (INSERM), CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Université de Strasbourg (UNISTRA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Nouvel Hôpital Civil de Strasbourg-Hôpital de Hautepierre [Strasbourg], Michael, White, Service de Département de médecine interne et immunologie clinique [CHU Pitié-Salpêtrière] (DMIIC), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Assistance publique - Hôpitaux de Paris (AP-HP) (APHP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Assistance publique - Hôpitaux de Paris (AP-HP) (APHP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Université Paris Descartes - Paris 5 (UPD5)-Institut National de la Santé et de la Recherche Médicale (INSERM)
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
[SDV]Life Sciences [q-bio]
Serology
Peak response
03 medical and health sciences
0302 clinical medicine
Antigen
Medicine
Multiplex
030212 general & internal medicine
[SDV.MP] Life Sciences [q-bio]/Microbiology and Parasitology
030304 developmental biology
[SDV.MP.VIR] Life Sciences [q-bio]/Microbiology and Parasitology/Virology
0303 health sciences
biology
business.industry
Serum samples
Confidence interval
3. Good health
[SDV] Life Sciences [q-bio]
[SDV.MP]Life Sciences [q-bio]/Microbiology and Parasitology
[SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie
Immunology
[SDV.MP.VIR]Life Sciences [q-bio]/Microbiology and Parasitology/Virology
biology.protein
[SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie
Antibody
business
Popis: BackgroundInfection with SARS-CoV-2 induces an antibody response targeting multiple antigens that changes over time. This complexity presents challenges and opportunities for serological diagnostics.MethodsA multiplex serological assay was developed to measure IgG and IgM antibody responses to seven SARS-CoV-2 spike or nucleoprotein antigens, two antigens for the nucleoproteins of the 229E and NL63 seasonal coronaviruses, and three non-coronavirus antigens. Antibodies were measured in serum samples from patients in French hospitals with RT-qPCR confirmed SARS-CoV-2 infection (n= 259), and negative control serum samples collected before the start of the SARS-CoV-2 epidemic (n= 335). A random forests algorithm was trained with the multiplex data to classify individuals with previous SARS-CoV-2 infection. A mathematical model of antibody kinetics informed by prior information from other coronaviruses was used to estimate time-varying antibody responses and assess the potential sensitivity and classification performance of serological diagnostics during the first year following symptom onset. A statistical estimator is presented that can provide estimates of seroprevalence in very low transmission settings.ResultsIgG antibody responses to trimeric Spike protein identified individuals with previous RT-qPCR confirmed SARS-CoV-2 infection with 91.6% sensitivity (95% confidence interval (CI); 87.5%, 94.5%) and 99.1% specificity (95% CI; 97.4%, 99.7%). Using a serological signature of IgG and IgM to multiple antigens, it was possible to identify infected individuals with 98.8% sensitivity (95% CI; 96.5%, 99.6%) and 99.3% specificity (95% CI; 97.6%, 99.8%). Informed by prior data from other coronaviruses, we estimate that one year following infection a monoplex assay with optimal anti-StriIgG cutoff has 88.7% sensitivity (95% CI: 63.4%, 97.4%), and that a multiplex assay can increase sensitivity to 96.4% (95% CI: 80.9%, 100.0%). When applied to population-level serological surveys, statistical analysis of multiplex data allows estimation of seroprevalence levels less than 1%, below the false positivity rate of many other assays.ConclusionSerological signatures based on antibody responses to multiple antigens can provide accurate and robust serological classification of individuals with previous SARS-CoV-2 infection. This provides potential solutions to two pressing challenges for SARS-CoV-2 serological surveillance: classifying individuals who were infected greater than six months ago, and measuring seroprevalence in serological surveys in very low transmission settings.
Databáze: OpenAIRE
Externí odkaz: