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

Jason Rosado (1) , Charlotte Cockram (2) , Sarah Merkling (3) , Caroline Demeret (4) , Annalisa Meola (5) , Solen Kerneis (6, 7) , Benjamin Terrier (8, 9) , Samira Fafi-Kremer (10) , Jérome De Sèze (11) , Marija Backovic (5) , Ivo Mueller (1, 12) , Michael White (1)

1 Malaria : parasites et hôtes - Malaria : parasites and hosts
2 Régulation spatiale des Génomes - Spatial Regulation of Genomes
3 IVI - Interactions Virus-Insectes - Insect-Virus Interactions
4 GMV-ARN (UMR_3569 / U-Pasteur_2) - Génétique Moléculaire des Virus à ARN - Molecular Genetics of RNA Viruses
5 Virologie Structurale - Structural Virology
6 Equipe mobile d'infectiologie
7 EMAE - Epidémiologie et modélisation de la résistance aux antimicrobiens - Epidemiology and modelling of bacterial escape to antimicrobials
8 CHU Pitié-Salpêtrière [AP-HP]
9 PARCC - UMR-S U970 - Paris-Centre de Recherche Cardiovasculaire
10 Laboratoire de Virologie [Strasbourg]
11 Centre d’Investigation Clinique Plurithématique (CIC - P) - CIC Strasbourg
12 WEHI - The Walter and Eliza Hall Institute of Medical Research

Background The antibody response generated following infection with SARS-CoV-2 is expected to decline over time. This may cause individuals with confirmed SARS-CoV-2 infection to test negative according to serological diagnostic tests in the months and years following symptom onset. Methods A multiplex serological assay was developed to measure IgG and IgM antibody responses to four SARS-CoV-2 Spike (S) antigens: spike trimeric ectodomain (Stri), its receptor-binding domain (RBD), spike subunit 1 (S1), and spike subunit 2 (S2). Antibody responses were measured in serum samples from patients in French hospitals with RT-qPCR confirmed infection (n = 259), and negative control serum samples collected before the start of the SARS-CoV-2 epidemic in 2019 (n = 335). The multiplex antibody data was used to train a random forests algorithm for classifying 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. Results IgG antibody responses to one S antigen identified individuals with previous RT-qPCR confirmed SARS-CoV-2 infection with 90.3% sensitivity (95% confidence interval (CI); 86.1%, 93.4%) and 99.1% specificity (95% CI; 97.4%, 99.7%). Using a serological signature of IgG to four antigens, it was possible to identify infected individuals with 96.1% sensitivity (95% CI; 93.0%, 97.9%) and 99.1% specificity (95% CI; 97.4%, 99.7%). Antibody responses to SARS-CoV-2 increase rapidly 1-2 weeks after symptom onset, with antibody responses predicted to peak within 2-4 weeks. Informed by prior data from other coronaviruses, one year following symptom onset antibody responses are predicted to decay by approximately 60% from the peak response. Depending on the selection of sero-positivity cutoff, we estimate that the sensitivity of serological diagnostics may reduce to 56% – 97% after six months, and to 49% – 93% after one year. Conclusion Serological signatures based on antibody responses to multiple antigens can provide more accurate and robust serological classification of individuals with previous SARS-CoV-2 infection. Changes in antibody levels over time may cause reductions in the sensitivity of serological diagnostics leading to an underestimation of sero-prevalence. It is essential that data continue to be collected to evaluate this potential risk.

Domaines: Sciences du Vivant [q-bio]; Santé publique et épidémiologie; Microbiologie et Parasitologie; Virologie

Jason Rosado, Charlotte Cockram, Sarah Merkling, Caroline Demeret, Annalisa Meola, et al.. Serological signatures of SARS-CoV-2 infection: Implications for antibody-based diagnostics. 2020. ⟨pasteur-02569149v1⟩