SARS-CoV-2 neutralizing antibodies decline over one year and patients with severe COVID-19 pneumonia display a unique cytokine profile

Abstract

Objectives: As coronavirus disease 2019 (COVID-19) rages on worldwide, there is an urgent need to characterize immune correlates of protection from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and to identify immune determinants of COVID-19 severity.

Methods: This study examined the longitudinal profiles of neutralizing antibody (NAb) titers in hospitalized COVID-19 patients clinically diagnosed with mild symptoms, pneumonia, or severe pneumonia, up to 12 months after illness onset, using live-virus neutralization. Multiplex, correlation, and network analyses were used to characterize serum-derived inflammatory cytokine profiles in all severity groups.

Results: Peak NAb titers correlated with disease severity, and NAb titers declined over the course of 12 months regardless of severity. Multiplex analyses revealed that IP-10, IL-6, IL-7, and VEGF-α were significantly elevated in severe pneumonia cases compared to those with mild symptoms and pneumonia cases. Correlation and network analyses further suggested that cytokine network formation was distinct in different COVID-19 severity groups.

Conclusions: The study findings inform on the long-term kinetics of naturally acquired serological immunity against SARS-CoV-2 and highlight the importance of identifying key cytokine networks for potential therapeutic immunomodulation.

Keywords: COVID-19 disease severity; Cytokine networks; Cytokine storm; Cytokines; Ig titers; Live virus neutralization; Neutralization kinetics; Neutralizing antibodies; Pneumonia; SARS-CoV-2.

Figure 1

COVID-19 patient antibody profiles based on disease severity. (A) S- and N-specific IgG, (B) S- and N-specific IgM, (C) S1-specific IgG, and (D) S1-specific IgA titers at the time of study enrollment. The fractions and percentages of patients who were seropositive for each Ig subclass are presented. Correlations between neutralizing antibody (NAb) titer and (E) S- and N-specific IgG, (F) S- and N-specific IgM, (G) S1-specific IgG, and (H) S1-specific IgA titers at the time of study enrollment. The correlation coefficient (r) by Spearman rank correlation analysis and P-values are shown for each disease severity subgroup. Colors represent the disease severity group, dot sizes represent the number of days after illness onset, and shapes represent sex. (I) Longitudinal profiling of NAb titers in hospitalized COVID-19 patients with mild symptoms (black), pneumonia (green), and severe pneumonia (red) over 12 months. Days 60, 180, and 365 are approximate dates counting from the reported day of illness onset. (J) NAb titers segregated into time intervals (0–1 month, 2 months, 6 months, and 12 months) after illness onset. The highest titer for each subject within each interval is presented. Mixed-effects models with the Geisser–Greenhouse correction and Tukey's multiple comparisons test were used to calculate statistical significance, with individual variances computed for each comparison. Horizontal bars represent geometric means and error bars denote 95% confidence intervals (CI). Dotted horizontal line represents the NAb lower limit of detection (10). The fractions and percentages of patients retaining NAb positivity (positivity cutoff at ≥20) within each group at different time-points are displayed above the x-axis. Row statistics including the geometric mean and the upper and lower bounds of the 95% CI for each time interval are tabulated below the graph. *P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001, ****P ≤ 0.0001.

Figure 2

Cytokine expression in COVID-19 cases with mild symptoms, pneumonia, and severe pneumonia. (A) Multiplex analysis of target serum-derived inflammatory cytokines in COVID-19 patients with mild symptoms (n = 12), pneumonia (n = 22), and severe pneumonia (n = 14), and in healthy controls (n = 3) at the time of study enrollment. Healthy control blood was collected pre-pandemic. The non-parametric Kruskal–Wallis test with Dunn's multiple comparisons test was used to determine the statistical significance of mediator levels between all three groups at the time of study enrollment. *P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001. Dashed horizontal lines represent the lower limit of detection for each target. Only targets with significantly different levels between groups are shown here; the rest can be found in Supplementary Material Figure S2. (B) Clustered heatmap of 25 inflammatory cytokines from hospitalized COVID-19 patients at the time of study enrollment. Values below the lower limit of detection were imputed for each target, and cytokine data were log-transformed, scaled, and both rows and columns were subjected to the k-means clustering algorithm. Positive z-score values are red and negative values are blue. Columns were annotated with patient characteristics including disease severity (‘Severity’), NAb titer (‘Titer’), number of days from illness onset at the time of study enrollment (‘Onset’), age, and sex.

Figure 3

Correlograms and network analyses of serum-derived inflammatory cytokines across COVID-19 disease severity groups. Correlation matrices of cytokine expression in hospitalized COVID-19 patients with (A) mild symptoms (n = 12), (B) pneumonia (n = 22), and (C) severe pneumonia (n = 14) at the time of study enrollment. Only significantly correlated (P ≤ 0.05) mediator interactions are shown. Positive and negative correlations are shown in blue and red, respectively. The size and color intensity of the dots are proportional to the Spearman correlation coefficients (r_S). Pairwise correlation networks showing positive relationships between cytokines in hospitalized COVID-19 patients with (D) mild symptoms, (E) pneumonia, and (F) severe pneumonia. Nodes represent cytokines/chemokines/growth factors and edges represent positive correlations. Node color represents clusters based on the Markov cluster (MCL) algorithm. Edge color represents the Spearman rank correlation coefficient value between connecting nodes.