Characterization of RBD-specific cross-neutralizing antibodies responses against SARS-CoV-2 variants from COVID-19 convalescents.

Introduction: The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has been posing a severe threat to global public health. Although broadly neutralizing antibodies have been used to prevent or treat corona virus disease 2019 (COVID-19), new emerging variants have been proven resistant to these antibodies. Methods: In this study, we isolated receptor binding domain (RBD)-specific memory B cells using single-cell sorting method from two COVID-19 convalescents and expressed the antibody to test their neutralizing activity against diverse SARS-CoV-2 variants. Then, we resolved antibody-RBD complex structures of potent RBD-specific neutralizing antibodies by X-ray diffraction method. Finally, we analyzed the whole antibody repertoires of the two donors and studied the evolutionary pathway of potent neutralizing antibodies. Results and discussion: We identified three potent RBD-specific neutralizing antibodies (1D7, 3G10 and 3C11) from two COVID-19 convalescents that neutralized authentic SARS-CoV-2 WH-1 and Delta variant, and one of them, 1D7, presented broadly neutralizing activity against WH-1, Beta, Gamma, Delta and Omicron authentic viruses. The resolved antibody-RBD complex structures of two antibodies, 3G10 and 3C11, indicate that both of them interact with the external subdomain of the RBD and that they belong to the RBD-1 and RBD-4 communities, respectively. From the antibody repertoire analysis, we found that the CDR3 frequencies of the light chain, which shared high degrees of amino acid identity with these three antibodies, were higher than those of the heavy chain. This research will contribute to the development of RBD-specific antibody-based drugs and immunogens against multiple variants.

Identifying key genes related to inflammasome in severe COVID-19 patients based on a joint model with random forest and artificial neural network.

Background: The coronavirus disease 2019 (COVID-19) has been spreading astonishingly and caused catastrophic losses worldwide. The high mortality of severe COVID-19 patients is an serious problem that needs to be solved urgently. However, the biomarkers and fundamental pathological mechanisms of severe COVID-19 are poorly understood. The aims of this study was to explore key genes related to inflammasome in severe COVID-19 and their potential molecular mechanisms using random forest and artificial neural network modeling. Methods: Differentially expressed genes (DEGs) in severe COVID-19 were screened from GSE151764 and GSE183533 via comprehensive transcriptome Meta-analysis. Protein-protein interaction (PPI) networks and functional analyses were conducted to identify molecular mechanisms related to DEGs or DEGs associated with inflammasome (IADEGs), respectively. Five the most important IADEGs in severe COVID-19 were explored using random forest. Then, we put these five IADEGs into an artificial neural network to construct a novel diagnostic model for severe COVID-19 and verified its diagnostic efficacy in GSE205099. Results: Using combining P value < 0.05, we obtained 192 DEGs, 40 of which are IADEGs. The GO enrichment analysis results indicated that 192 DEGs were mainly involved in T cell activation, MHC protein complex and immune receptor activity. The KEGG enrichment analysis results indicated that 192 GEGs were mainly involved in Th17 cell differentiation, IL-17 signaling pathway, mTOR signaling pathway and NOD-like receptor signaling pathway. In addition, the top GO terms of 40 IADEGs were involved in T cell activation, immune response-activating signal transduction, external side of plasma membrane and phosphatase binding. The KEGG enrichment analysis results indicated that IADEGs were mainly involved in FoxO signaling pathway, Toll-like receptor, JAK-STAT signaling pathway and Apoptosis. Then, five important IADEGs (AXL, MKI67, CDKN3, BCL2 and PTGS2) for severe COVID-19 were screened by random forest analysis. By building an artificial neural network model, we found that the AUC values of 5 important IADEGs were 0.972 and 0.844 in the train group (GSE151764 and GSE183533) and test group (GSE205099), respectively. Conclusion: The five genes related to inflammasome, including AXL, MKI67, CDKN3, BCL2 and PTGS2, are important for severe COVID-19 patients, and these molecules are related to the activation of NLRP3 inflammasome. Furthermore, AXL, MKI67, CDKN3, BCL2 and PTGS2 as a marker combination could be used as potential markers to identify severe COVID-19 patients.

Pre-existing anti-HCoV-OC43 immunity influences the durability and cross-reactivity of humoral response to SARS-CoV-2 vaccination.

Purpose: This study was conducted in order to properly understand whether prior seasonal human coronavirus (HCoV) immunity could impact the potential cross-reactivity of humoral responses induced by SARS-CoV-2 vaccine, thereby devising universal coronavirus vaccines for future outbreaks. Methods: We performed enzyme-linked immunosorbent assay (ELISA) to quantify the immunoglobulin G (IgG) antibody levels to spike (S) protein and S1 subunit of HCoVs (HCoV-OC43, HCoV-HKU1, HCoV-NL63, and HCoV-229E), and ELISA [anti-RBD and anti-nucleoprotein (N)], chemiluminescence immunoassay assays (anti-RBD), pseudovirus neutralization test, and authentic viral neutralization test to detect the binding and neutralizing antibodies to SARS-CoV-2 in the vaccinees. Results: We found that the antibody of seasonal HCoVs did exist before vaccination and could be boosted by SARS-CoV-2 vaccine. A further analysis demonstrated that the prior S and S1 IgG antibodies of HCoV-OC43 were positively correlated with anti-RBD and neutralization antibodies to SARS-CoV-2 at 12 and 24 weeks after the second vaccination, and the correlation is more statistically significant at 24 weeks. The persistent antibody levels of SARS-CoV-2 were observed in vaccinees with higher pre-existing HCoV-OC43 antibodies. Conclusion: Our data indicate that inactivated SARS-CoV-2 vaccination may confer cross-protection against seasonal coronaviruses in most individuals, and more importantly, the pre-existing HCoV-OC43 antibody was associated with protective immunity to SARS-CoV-2, supporting the development of a pan-coronavirus vaccine.

Pre-existing anti-HCoV-OC43 immunity influences the durability and cross-reactivity of humoral response to SARS-CoV-2 vaccination

Purpose This study was conducted in order to properly understand whether prior seasonal human coronavirus (HCoV) immunity could impact the potential cross-reactivity of humoral responses induced by SARS-CoV-2 vaccine, thereby devising universal coronavirus vaccines for future outbreaks. Methods We performed enzyme-linked immunosorbent assay (ELISA) to quantify the immunoglobulin G (IgG) antibody levels to spike (S) protein and S1 subunit of HCoVs (HCoV-OC43, HCoV-HKU1, HCoV-NL63, and HCoV-229E), and ELISA [anti-RBD and anti-nucleoprotein (N)], chemiluminescence immunoassay assays (anti-RBD), pseudovirus neutralization test, and authentic viral neutralization test to detect the binding and neutralizing antibodies to SARS-CoV-2 in the vaccinees. Results We found that the antibody of seasonal HCoVs did exist before vaccination and could be boosted by SARS-CoV-2 vaccine. A further analysis demonstrated that the prior S and S1 IgG antibodies of HCoV-OC43 were positively correlated with anti-RBD and neutralization antibodies to SARS-CoV-2 at 12 and 24 weeks after the second vaccination, and the correlation is more statistically significant at 24 weeks. The persistent antibody levels of SARS-CoV-2 were observed in vaccinees with higher pre-existing HCoV-OC43 antibodies. Conclusion Our data indicate that inactivated SARS-CoV-2 vaccination may confer cross-protection against seasonal coronaviruses in most individuals, and more importantly, the pre-existing HCoV-OC43 antibody was associated with protective immunity to SARS-CoV-2, supporting the development of a pan-coronavirus vaccine.

Exploring Rapid and Effective Screening Methods for Anti-SARS-CoV-2 Neutralizing Antibodies in COVID-19 Convalescent Patients and Longitudinal Vaccinated Populations.

Assessing the duration of neutralizing antibodies (nAbs) following SARS-CoV-2 infection or vaccination is critical to evaluate the protective immunity and formulate public health strategies. In this study, SARS-CoV-2 Ab ELISA (enzyme-linked immunosorbent assay), chemiluminescent microparticle immunoassay (CMIA), as well as pseudovirus neutralization test (PVNT) were performed in two cohorts, convalescent patients (CP) from coronavirus disease 2019 (COVID-19) and BBIBP-CorV vaccinated population. It was found that nAbs and binding antibodies emerged at 14 days post the 1st dose of vaccination, reached peaks at 28 days after 2nd dose vaccination and then gradually declined over time. CP-6M (convalescent patients up to 6 months) from COVID-19 presented stronger nAbs or binding antibodies responses than vaccinees 90 days or 180 days after 2nd dose vaccination. CMIA or SARS-CoV-2 Ab ELISA correlated well with PVNT with high consistency in the two cohorts. It shown that nAbs and binding antibodies can keep 6 months both in CP and vaccinees. Most importantly, our data show the application of using CMIA and SARS-CoV-2 Ab ELISA as rapid screening tests for nAb titer and could be used as alternative strategies for quickly evaluating SARS-CoV-2 nAbs responses in vaccine research.