Endogenously Produced SARS-CoV-2 Specific IgG Antibodies May Have a Limited Impact on Clearing Nasal Shedding of Virus during Primary Infection in Humans

Abstract

While SARS-CoV-2 specific neutralizing antibodies have been developed for therapeutic purposes, the specific viral triggers that drive the generation of SARS-CoV-2 specific IgG and IgM antibodies remain only partially characterized. Moreover, it is unknown whether endogenously derived antibodies drive viral clearance that might result in mitigation of clinical severity during natural infection. We developed a series of non-linear mathematical models to investigate whether SARS-CoV-2 viral and antibody kinetics are coupled or governed by separate processes. Patients with severe disease had a higher production rate of IgG but not IgM antibodies. Maximal levels of both isotypes were governed by their production rate rather than different saturation levels between people. Our results suggest that an exponential surge in IgG levels occurs approximately 5-10 days after symptom onset with no requirement for continual antigenic stimulation. SARS-CoV-2 specific IgG antibodies appear to have limited to no effect on viral dynamics but may enhance viral clearance late during primary infection resulting from the binding effect of antibody to virus, rather than neutralization. In conclusion, SARS-CoV-2 specific IgG antibodies may play only a limited role in clearing infection from the nasal passages despite providing long-term immunity against infection following vaccination or prior infection.

Keywords: IgG antibodies; IgM antibodies; SARS-CoV-2; mathematical model; severity.

Figure 1

The mathematical model fits SARS-CoV-2 IgG and IgM levels following infection. (A) Schematic representation of the mathematical model reproducing longitudinal IgM and IgG dynamics, where ${\mathrm{r}}_{\mathrm{M}}$ and ${\mathrm{r}}_{\mathrm{G}}$ stand for the antibody production rates, ${\mathrm{k}}_{\mathrm{M}}$ and ${\mathrm{k}}_{\mathrm{G}}$ regulate the saturation of antibody generation, ${\mathrm{d}}_{\mathrm{M}}$ and ${\mathrm{d}}_{\mathrm{G}}$ are the natural clearance rates of IgM and IgG, respectively. The parameters used here correspond to the parameters in Equation (3) from the Materials and Methods section (or, model M3 in Table S1), (B) Simulations (line), and data (markers) of IgM (red) and IgG (black) under the best model (model M3 in Table S1). Severe and non-severe patients are labeled in blue and pink, respectively.

Figure 2

Model parameter comparison between non-severe and severe SARS-CoV-2 cases. (A) Comparison of estimated parameters between severe and non-severe patients using Wilcoxon-rank sum test under the model that best recapitulated the longitudinal IgM and IgG data of 6 severe and 20 non-severe-patients. We compared parameters ${\mathrm{r}}_{\mathrm{M}}$, ${\log }_{10}\left({\mathrm{I}}_{\mathrm{M}0}\right)$, ${\mathrm{r}}_{\mathrm{G}}$, ${\log }_{10}\left({\mathrm{I}}_{\mathrm{G}0}\right)$, ${\log }_{10}\left({\mathsf{\tau }}_{\mathrm{G}}\right)$ and ${\log }_{10}\left({\mathsf{\tau }}_{\mathrm{G}}\right)$ that represent the production rate of IgM, the log-converted concentration of IgM at t = 0, the production rate of IgG, the log-converted concentration of IgG at t = 0, time-delay (since the onset of symptoms) before IgG is produced and time-delay (since the onset of symptoms) before IgM is produced, respectively. p < 0.05 represents a statistically significant difference between severe and non-severe patients. (B) Using partial rank correlation coefficient, the sensitivity of the peak IgM and IgG levels to the initial antibody concentration, the production rate, and the delay before the production is induced.

Figure 3

Mathematical model schematic of SARS-CoV-2 antibody generation and antiviral activity. The SARS-CoV-2 viral replication and antibody generation are displayed as two compartments. The SARS-CoV-2 viral antigen may trigger antibody immune response via one of three mechanisms: one-off stimulation, continuous stimulation, or delayed-continuous stimulation. For each mechanism, the antibody generation rate may proceed in 3 ways: no saturation, saturation by high levels of IgG, or saturation by viral load. The generation of antibodies may affect viral replication through viral binding effects, viral neutralization effects, via both mechanisms, or not at all. Two additional models containing antibody-assisted infected cell clearance are employed. In total, these multiple options give rise to 38 models. The components of the best-performing model are highlighted in orange rectangle boxes.

Figure 4

The mathematical model fits viral load and antibody levels following SARS-CoV-2 infection. Simulations (lines) to observed SARS-CoV-2 (black markers) and IgG (red markers) under the best model (MP-v2 in Table S3).

Figure 5

The slight impact of binding effects of nucleocapsid IgG antibodies on nasal viral dynamics. The solid black line represents the best fits to the observed data (using parameter values in Table S4 estimated under model MP-v2 in Table S3), the dashed blue line representing the case when we assume weak binding effects ($\nu =0.02$), and the dashed-dotted purple line represents strong binding effects ($\nu =0.05$).

Figure 6

(A) Mathematical model fits viral load and antibody levels following SARS-CoV-2 infection. Simulations (lines) to observed SARS-CoV-2 (black markers) and anti-spike IgG (red markers) under the best model (MP-v2 in Table S3). (B) Impact of binding effects of spike IgG antibodies on viral dynamics. The solid black line represents the best fits to the observed data (using parameter values in Table S5 estimated under model MP-v2 in Table S3), the dashed blue line representing the case when we assume weak binding effects ($\nu =0.02$), and the dashed-dotted purple line represents an enhanced strong binding efficacy of IgG antibodies ($\nu =0.05$). The lines all notably overlap.