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. 2020 Oct 20;1(7):100126.
doi: 10.1016/j.xcrm.2020.100126. Epub 2020 Sep 30.

Cross-Sectional Evaluation of Humoral Responses against SARS-CoV-2 Spike

Affiliations

Cross-Sectional Evaluation of Humoral Responses against SARS-CoV-2 Spike

Jérémie Prévost et al. Cell Rep Med. .

Abstract

SARS-CoV-2 is responsible for the coronavirus disease 2019 (COVID-19) pandemic, infecting millions of people and causing hundreds of thousands of deaths. The Spike glycoproteins of SARS-CoV-2 mediate viral entry and are the main targets for neutralizing antibodies. Understanding the antibody response directed against SARS-CoV-2 is crucial for the development of vaccine, therapeutic, and public health interventions. Here, we perform a cross-sectional study on 106 SARS-CoV-2-infected individuals to evaluate humoral responses against SARS-CoV-2 Spike. Most infected individuals elicit anti-Spike antibodies within 2 weeks of the onset of symptoms. The levels of receptor binding domain (RBD)-specific immunoglobulin G (IgG) persist over time, and the levels of anti-RBD IgM decrease after symptom resolution. Although most individuals develop neutralizing antibodies within 2 weeks of infection, the level of neutralizing activity is significantly decreased over time. Our results highlight the importance of studying the persistence of neutralizing activity upon natural SARS-CoV-2 infection.

Keywords: COVID-19; IgG; IgM; RBD; SARS-CoV-2; Spike glycoproteins; coronavirus; cross-reactivity, IgA; neutralization.

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Conflict of interest statement

The authors declare no competing interests.

Figures

None
Graphical abstract
Figure 1
Figure 1
Detection of SARS-CoV-2 RBD-Specific IgM and IgG over Time Indirect ELISA was performed using recombinant SARS-CoV-2 RBD and incubated with samples from 10 COVID-19-negative or 106 COVID-19-positive patients at different times after symptoms onset (T1, T2, T3, T4, and convalescent). Anti-RBD binding was detected using anti-IgM-HRP (A–C) or anti-IgG-HRP (D–F). Relative light units (RLUs) obtained with BSA (negative control) were subtracted and further normalized to the signal obtained with the anti-RBD CR3022 mAb present in each plate. Data in graphs (A) and (D) represent RLUs performed in quadruplicate. Curves depicted in (B) and (E) represent the mean RLUs detected with all samples from the same group. Undetectable measures are represented as white symbols, and limits of detection are plotted. (C, F) Areas under the curve (AUCs) were calculated based on RLU datasets shown in (A) and (D) using GraphPad Prism software. Statistical significance was tested using Kruskal-Wallis tests with a Dunn’s post-test (∗p < 0.05; ∗∗p < 0.01; ∗∗∗p < 0.001; ∗∗∗∗p < 0.0001).
Figure 2
Figure 2
SARS-CoV-2 Infection Elicits Cross-Reactive Antibodies against Other Human Betacoronavirus Members Cell-surface staining of 293T cells expressing full-length Spike (S) from different HCoVs: SARS-CoV-2 (A), SARS-CoV (B), OC43, NL63, and 229E (C) with samples from 10 COVID-19-negative or 106 COVID-19-positive patients at different stage of infection (T1, T2, T3, T4, and convalescent). The graphs shown represent the median fluorescence intensities (MFIs). Undetectable measures are represented as white symbols, and limits of detection are plotted. Error bars indicate means ± SEM. Statistical significance was tested using Kruskal-Wallis tests with a Dunn’s post-test (∗p < 0.05; ∗∗p < 0.01; ∗∗∗p < 0.001; ∗∗∗∗p < 0.0001).
Figure 3
Figure 3
Anti-S Neutralizing Antibody Titers Decrease over Time Pseudoviral particles coding for the luciferase reporter gene and bearing the glycoproteins SARS-CoV-2 S (A, D, G, and H), SARS-CoV S (B, E, and I), or VSV-G (C and F) were used to infect 293T-ACE2 cells. Pseudoviruses were incubated with serial dilutions of samples from 10 COVID-19-negative or 106 COVID-19-positive patients (T1, T2, T3, T4, and convalescent) at 37°C for 1 h prior to infection of 293T-ACE2 cells. Infectivity at each dilution was assessed in duplicate and is shown as the percentage of infection without sera for each glycoprotein. Neutralization half maximal inhibitory serum dilution (ID50) (G and I) and ID80 (H) values were determined using a normalized non-linear regression using GraphPad Prism software. Undetectable measures are represented as white symbols. Neutralizer represent patients with an ID50 over 100 (G and I) or an ID80 (H). Statistical significance was tested using Mann-Whitney U tests (∗p < 0.05; ∗∗p < 0.01).
Figure 4
Figure 4
Association between Clinical and Serological Parameters in SARS-CoV-2-Infected Patients Chord diagram illustrating the network of linear correlations among nine major serological and clinical factors for all acutely infected individuals (T1, T2, T3, and T4) (A) or at different time points (B–E). Chords are color-coded according to the magnitude of the correlation coefficient (r); chord width inversely corresponds to the p value. Asterisks indicate all statistically significant correlations within chords (∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.005). (A–E) Correlation analysis was done using nonparametric Spearman rank tests. The p values were adjusted for multiple comparisons using Holm-Sidak (α = 0.05). Statistical comparisons of two parameters were done using Mann-Whitney U tests.

Update of

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