Relative Ratios of Human Seasonal Coronavirus Antibodies Predict the Efficiency of Cross-Neutralization of SARS-CoV-2 Spike Binding to ACE2
Notice bibliographique
Résumé
Abstract Background Antibodies raised against human seasonal coronaviruses (sCoVs), which are responsible for the common cold, are known to cross-react with SARS-CoV-2 antigens. This prompts questions about their protective role against SARS-CoV-2 infections and COVID-19 severity. However, the relationship between sCoV exposure and SARS-CoV-2 correlates of protection are not clearly identified. Methods We performed a cross-sectional analysis of cross-reactivity and cross-neutralization to SARS-CoV-2 antigens (S-RBD, S-trimer, N) using pre-pandemic serum from four different groups: pediatrics and adolescents, persons 21 to 70 years of age, older than 70 years of age, and persons living with HCV or HIV. Findings Antibody cross-reactivity to SARS-CoV-2 antigens varied between 1.6% and 15.3% depending on the cohort and the isotype-antigen pair analyzed. We also show a range of neutralizing activity (0-45%) in serum that interferes with SARS-CoV-2 spike attachment to ACE2. While the abundance of sCoV antibodies did not directly correlate with neutralization, we show that neutralizing activity is rather dependent on relative ratios of IgGs in sera directed to all four sCoV spike proteins. More specifically, we identified antibodies to NL63 and OC43 as being the most important predictors of neutralization. Interpretation Our data support that exposure to sCoVs triggers antibody responses that influence the efficiency of SARS-CoV-2 spike binding to ACE2, and may also impact COVID-19 disease severity through other latent variables. Research in Context Evidence before this study There is a growing body of evidence showing that within the population there are varying levels of pre-existing immunity to SARS-CoV-2 infection and possibly COVID-19 disease severity. This immunity is believed to be attributable to prior infection by four prevalent seasonal coronaviruses (sCoVs) responsible for the common cold. Pre-existing immunity can be assessed in part by antibodies directed to sCoVs that also cross-react to SARS-CoV-2 antigens. The SARS-CoV-2 spike and, more specifically, the receptor binding domain are the primary targets for neutralizing antibodies. It is unclear if cross-reactive antibodies to SARS-CoV-2 are neutralizing and are also responsible for the broad spectrum of COVID-19 disease severity, from asymptomatic to critical, observed in the infected population. Added-value of this study Here we carried out a detailed analysis of sCoV prevalence in samples acquired before the pandemic from individuals of various age groups and in people living with HIV and HCV. We then analyzed the frequency of all the different types of antibodies that cross-react to three SARS-CoV-2 antigens. We found a high level of people with cross-reactive antibodies, surprisingly we also detected that some people have antibodies that block the SARS-CoV-2 spike from binding to its human receptor, ACE2. By using machine learning, we were able to accurate predict which individuals can neutralize SARS-CoV-2 spike-ACE2 interactions based on their relative ratios of antibodies against the four sCoVs. Implications of all the available evidence We demonstrate that it not absolute levels of sCoVs antibodies that are predictive of neutralization but the relative ratios to all four sCoVs, with NL63 being the most weighted for this prediction. Machine learning also highlighted the existence of latent variables that contribute to the neutralization and that may be related to the type of cellular immune response triggered by the infection to certain sCoVs. This study is one of the first to identify a functional relationship between prior-exposure to sCoV and the establishment of a certain degree of immunity to SARS-CoV-2 by way of a cross-reactive antibody response. Graphical Abstract
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».