The novel coronavirus named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused a global pandemic of the coronavirus disease 2019 (COVID-19), which elicits a wide variety of symptoms, ranging from mild to severe, with the potential to lead to death. Although used as the standard method to screen patients for SARS-CoV-2 infection, real-time PCR has challenges in dealing with asymptomatic patients and those with an undetectable viral load. Serological tests are therefore considered potent diagnostic tools to complement real-time PCR-based diagnosis and are used for surveillance of seroprevalence in populations. However, the dynamics of the antibody response against SARS-CoV-2 currently remain to be investigated. Here, through analysis of plasma samples from 84 patients with COVID-19, we observed that the response of virus-specific antibodies against three important antigens, RBD, N and S, dynamically changed over time and reached a peak 5-8 weeks after the onset of symptoms. The antibody responses were irrespective of sex. Severe cases were found to have higher levels of antibody response, larger numbers of inflammatory cells and C-reactive protein levels. Within the mild/moderate cases, pairwise comparison indicated moderate association between anti-RBD vs. anti-N, anti-RBD vs. anti-S1S2, and anti-N vs. anti-S1S2. Furthermore, the majority of cases could achieve IgM and IgG seroconversion at 2 weeks since the disease onset. Analysis of neutralizing antibodies indicated that these responses were able to last for more than 112 days but decline significantly after the peak. In summary, our findings demonstrate the longitudinally dynamic changes in antibody responses against SARS-CoV-2, which can contribute to the knowledge of humoral immune response after SARS-CoV-2 infection and are informative for future development of vaccine and antibody-based therapies.
Keywords: COVID-19; RBD; S antigen; SARS-CoV-2; neutralizing antibodies; spike.
Copyright © 2021 Feng, Yin, Zhang, Hu, Ouyang, Qiao, Zhao, Zhang, Li, Zhang, Zhang, Jin, Feng and Su.