Rational Design of Multiclade Coronavirus Spike Immunodominant Domain Nanoparticles Elicit Broad Antibody Responses
Dzuvor, C. K. O.; Moak, S.; McManus, L. R.; Thomas, A.; Dzordzorme, A. E.; Kim, T.; Joseph, J.; Foley, V.; Gordon, I. J.; Novik, L.; Holman, L. A.; Dropulic, L. K.; McNamara, R. P.; Corbett-Helaire, K. S.
Show abstract
Four seasonal endemic human coronaviruses (EhCoVs), HKU1-CoV, OC43-CoV, 229E-CoV, and NL63-CoV, are culprits of mild upper respiratory and periodic severe diseases in vulnerable populations. Despite their prevalence, understanding EhCoVs antigenic and immune signatures remains elusive. SARS-CoV-2 has evolved as the fifth EhCoV, requiring seasonal vaccination in most parts of the world, and currently, no other EhCoV vaccines are available. SARS-CoV-2 co-infection with EhCoVs increases disease severity; thus, combined vaccination may provide increased protection against seasonal EhCoVs overall. Here, we explored Spike (S) receptor binding domain (RBD) vs. N-terminal domain (NTD) B-cell immunodominance in EhCoV-positive convalescent donors and immunogenicity in mice. We found that while antibody and B-cell isotypes were relatively dominant to S NTD, mice immunized with S RBD elicited significantly higher binding and neutralizing antibody (nAb) responses. With that knowledge, we used computational methods to infer that EhCoV S sequences evolve into two main clades and designed chimeric immunodominant domains (IDDs) from both clades for each EhCoV. IDDs were scaffolded onto two-component nanoparticles (NPs) displaying each IDD separately (monovalent IDD NP); three {beta}-EhCoV IDDs (Mosaic-3 IDD NP); or five EhCoVs IDDs (Mosaic-5 IDD NP). Mice immunized with mosaic IDD NPs, but not soluble IDD antigens nor monovalent IDD NPs, elicited potent, broadly cross-reactive binding and neutralizing antibody (Ab) responses against SARS-CoV-2 variants, other EhCoVs, and Sarbecoviruses. System serology revealed that all four IDD immunogens elicited distinct Ab subclasses and Fc-effector functions, with mosaic-5 IDD NPs eliciting the most de novo Ab subclasses, distributions, and broader Fc-mediated immune mechanisms. Dissection of vaccine-immune sera revealed polyclonal Ab responses against multiple non-overlapping cross-reactive S epitopes. Due to elicitation of broad Ab responses with combinatory functionality, IDD NPs open new horizons for developing first-in-class supraseasonal EhCoV vaccine candidates, with potential to decrease frequent SARS-CoV-2 sequence updates and protect against other EhCoVs. Moreover, elicitation of Ab breadth that spans pandemic-threat Sarbecoviruses gives mosaic IDD NPs promise towards pandemic preparedness.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Heterologous Prime-Boost with Immunologically Orthogonal Protein Nanoparticles for Peptide Immunofocusing 97%
- SARS-CoV-2 B Epitope-Guided Neoantigen NanoVaccines Enhance Tumor-Specific CD4/CD8 T Cell Immunity Through B Cell Antigen Presentation 95%
- High-speed AFM reveals fluctuations and dimer splitting of the N-terminal domain of GluA2-γ2 95%
Similar papers in this journal
- Design of the SARS-CoV-2 RBD vaccine antigen improves neutralizing antibody response 97%
- A new universal chimeric-antigen receptor (CAR)- fragment antibody binder (FAB) split system for cancer immunotherapy 97%
- Conserved sites on the influenza H1 and H3 hemagglutinin recognized by human antibodies 97%
Similar papers in this journal
- In vivo affinity maturation of the HIV-1 Env-binding domain of CD4 97%
- Collagen-binding IL-12 expressing STEAP1 CAR-T cells reduce toxicity and eradicate mouse prostate cancer in combination with checkpoint inhibitors 95%
- Macrophage-augmented organoids recapitulate the complex pathophysiology of viral diseases and enable development of multitarget therapeutics 95%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.