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Enregistrement W4206999939 · doi:10.1016/j.ebiom.2022.103831

Do preexisting antibodies against seasonal coronaviruses have a protective role against SARS-CoV-2 infections and impact on COVID-19 severity?

2022· article· en· W4206999939 sur OpenAlexaboutno aff
Gheyath K. Nasrallah

Notice bibliographique

RevueEBioMedicine · 2022
Typearticle
Langueen
DomaineMedicine
ThématiqueSARS-CoV-2 and COVID-19 Research
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésCoronavirus disease 2019 (COVID-19)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakVirologyCoronavirusBetacoronavirusAntibodyMedicineCoronavirus InfectionsSars virusPandemicPneumoniaImmunologyOutbreakInfectious disease (medical specialty)DiseaseInternal medicine

Résumé

récupéré en direct d'OpenAlex

Because of the emergence of coronavirus disease 2019 (COVID-19), caused by the novel severe acute respiratory syndrome coronavirus (SARS-CoV-2), many questions remain unresolved regarding the abundance of cross-reactivity between the SARS-CoV-2 and other human seasonal coronaviruses (sCoVs) antigens and the role of the sCoVs preexisting antibodies in protective immunity to SARS-CoV-2. Four endemic human sCoVs (NL63, 229E, OC43, and HKU1), which cause the common cold and recurrent respiratory disease, are highly prevalent worldwide. While almost everyone has been exposed to at least one of these sCoVs, immune response to each sCoV declines over time.1Edridge A.W.D. Kaczorowska J. Hoste A.C.R. et al.Seasonal coronavirus protective immunity is short-lasting.Nat Med. 2020; 26: 1691-1693https://doi.org/10.1038/s41591-020-1083-1Google Scholar These human sCoVs share striking sequence similarities with the E-envelope (96%), M-membrane (91%), and N-nucleocapsid (91%) proteins of SARS-CoV-2.2Bates T.A. Weinstein J.B. Farley S. Leier H.C. Messer W.B. Tafesse F.G. Cross-reactivity of SARS-CoV structural protein antibodies against SARS-CoV-2.Cell Rep. 2021; 34108737https://doi.org/10.1016/j.celrep.2021.108737Google Scholar However, they only share about 24–30% similarities with the trimeric spike S-protein (S-trimer). The S-protein is considered the major target protein/antigen for the protective humoral and cellular immunity. That is, the S-protein contains the angiotensin-converting enzyme 2 (ACE2) receptor binding domain (known as S-RBD) that is important for viral cell entry.2Bates T.A. Weinstein J.B. Farley S. Leier H.C. Messer W.B. Tafesse F.G. Cross-reactivity of SARS-CoV structural protein antibodies against SARS-CoV-2.Cell Rep. 2021; 34108737https://doi.org/10.1016/j.celrep.2021.108737Google Scholar Due to the apparent similarities between sCoVs, cross-reactivity between antibodies elicited by different sCoVs and cognate antibodies targeting SARS-CoV-2 antigens is expected.2Bates T.A. Weinstein J.B. Farley S. Leier H.C. Messer W.B. Tafesse F.G. Cross-reactivity of SARS-CoV structural protein antibodies against SARS-CoV-2.Cell Rep. 2021; 34108737https://doi.org/10.1016/j.celrep.2021.108737Google Scholar The protective role of preexisting cellular and humoral immunity (cross-reactive antibodies) from exposure to sCoVs against SARS-CoV-2 infections is controversial. Some studies reported that sCoV antibodies are boosted upon SARS-CoV-2 infection but not associated with protection,3Anderson E.M. Goodwin E.C. Verma A. et al.Seasonal human coronavirus antibodies are boosted upon SARS-CoV-2 infection but not associated with protection.Cell. 2021; 184 (e1810. https://doi.org/10.1016/j.cell.2021.02.010): 1858-1864Google Scholar while others provided various lines of evidence for preexisting humoral and cellular cross-neutralization and protection against SARS-CoV-2.4Sagar M. Reifler K. Rossi M. et al.Recent endemic coronavirus infection is associated with less-severe COVID-19.J Clin Invest. 2021; : 131https://doi.org/10.1172/jci143380Google Scholar To investigate the role the humoral immune response in cross-protection between sCoVs and SARS-CoV-2, Galipeau et al., conducted a cross-sectional study to determine the level of cross-reactivity and cross-neutralization to three SARS-CoV-2 antigens (S-RBD, S-trimer, N) in pre-pandemic serum samples.5Galipeau Y. Siragam V. Laroche G. et al.Relative ratios of human seasonal coronavirus antibodies predict the efficiency of cross-neutralization of SARS-CoV-2 spike binding to ACE2.EBioMedicine. 2021; 74https://doi.org/10.1016/j.ebiom.2021.103700Google Scholar The samples were collected from four different study groups, pediatrics and young adults (> 21), adults (21–70 years), elders (> 70 years), and patients infected with HCV or HIV (control group). 580 pre-pandemic samples and 178 post-pandemic samples were collected from diverse sources, including Icahn School of Medicine at Mount Sinai, Eastern Ontario Regional Laboratory Association (EORLA), and Ottawa Hospital (TOH). This study reported a relatively high level of cross-reactivity against various SARS-CoV-2 epitopes, with N being the most frequently detected (11%), followed by S-trimer (5%). These results align with a previous study that reported 16.2% cross-reactivity to N and 4.2% for S-trimer.6Hicks J. Klumpp-Thomas C. Kalish H. et al.Serologic cross-reactivity of SARS-CoV-2 with endemic and seasonal Betacoronaviruses.J Clin Immunol. 2021; 41: 906-913https://doi.org/10.1007/s10875-021-00997-6Google Scholar The authors concluded that although N-antibodies are unlikely to be neutralizing, the Fc region of the N-antibodies may elicit strong effector functions through different protective mechanisms such as antibody-dependent cell mediated cytotoxicity (ADCC), complement activation, and priming the CD8+ T-cell responses, which may indirectly influence other antiviral pathways in the infected patients.7Morgenlander W.R. Henson S.N. Monaco D.R. et al.Antibody responses to endemic coronaviruses modulate COVID-19 convalescent plasma functionality.J Clin Invest. 2021; 131https://doi.org/10.1172/jci146927Google Scholar With the measurement of different antibody isotypes and subclasses against the sCoV antigens, Galipeau and his colleagues demonstrated that pre-pandemic samples exhibit immunoreactivity to SARS-CoV-2 protein/antigens. Although there was no direct association between the titer of sCoV antibodies and neutralization, there was a significant predictive correlation between neutralization of S binding and the relative ratios of the different sCoV antibodies, with NL63 and OC43 being the most weighted for this prediction. These findings provide credence to the hypothesis that latent factors linked with sCoV exposure have a predictive and protective role against SARS-CoV-2 and potentially impact the disease severity. Further, using machine learning procedures, the authors demonstrated that the neutralizing ability of these antibodies to block RBD/ACE2 binding depends on relative ratios of IgGs directed to all four sCoV spike antigens. The strength of this study stems from including a large sample size and extensively characterizing the cross-reactive humoral immune response by covering all SARS-CoV-2 and the four sCoVs structural proteins using different serological assays. The findings of Galipeau et al. study are three-fold; First, it is not the absolute levels of sCoVs antibodies that are predictive of neutralization but rather the relative ratios to all sCoVs. Second, it is the first study to demonstrate a functional relationship between prior exposure to sCoVs and neutralization of SARS-CoV-2 S-RBD by cross-reactive antibodies. Third, the ability to accurately predict which individuals can neutralize SARS-CoV-2 spike-ACE2 interactions using in silico methods such as Machine Learning. It is worth noting that preexisting memory T cells induced by sCoVs can shape susceptibility to and the clinical severity of SARS-CoV-2.8Sette A. Crotty S. Preexisting immunity to SARS-CoV-2: the knowns and unknowns.Nat Rev Immunol. 2020; 20: 457-458https://doi.org/10.1038/s41577-020-0389-zGoogle Scholar Therefore, together with preexisting B-cell and T-cell memory, the relative ratios of all antibodies against sCoVs may substantially reduce viral transmission and mitigate the severity of the symptoms. Nevertheless, it is essential to determine the extent, positive or negative, to which the humoral immune response to SARS-CoV-2 contributes to virus-induced immunopathogenesis. Gheyath K. Nasrallah: Conceptualization, Writing – review & editing. The author declares no conflict of interest. The author would like to thank Ms. Nadin Younes for reviewing and editing of this manuscript. Relative Ratios of Human Seasonal Coronavirus Antibodies Predict the Efficiency of Cross-Neutralization of SARS-CoV-2 Spike Binding to ACE2Our data support the concept that exposure to sCoVs triggers antibody responses that influence the efficiency of SARS-CoV-2 spike binding to ACE2, which may potentially impact COVID-19 disease severity through other latent variables. Full-Text PDF Open Access

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,563
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,001
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,061
Tête enseignante GPT0,396
Écart entre enseignants0,334 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations7
Publié2022
Routes d'admission1
Résumé présentoui

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