Mixed lipopeptide-based mucosal vaccine candidate induces cross-variant immunity and protects against SARS-CoV-2 infection in hamsters
Bibliographic record
Abstract
The global dissemination of SARS-CoV-2 led to a worldwide pandemic in March 2020. Even after the official downgrading of the COVID-19 pandemic, infection with SARS-CoV-2 variants continues. The rapid development and deployment of SARS-CoV-2 vaccines helped to mitigate the pandemic to a great extent. However, the current vaccines are suboptimal; they elicit incomplete and short-lived protection and are ineffective against evolving virus variants. Updating the spike antigen according to the prevailing variant and repeated boosters is not the long-term solution. We have designed a lipopeptide-based, mucosal, pan-coronavirus vaccine candidate, derived from highly conserved and/or functional regions of the SARS-CoV-2 spike, nucleocapsid, and membrane proteins. Our studies demonstrate that the designed lipopeptides (LPMix) induced both cellular and humoral (mucosal and systemic) immune responses upon intranasal immunization in mice. Furthermore, the antibodies bound to the wild-type and mutated S proteins of SARS-CoV-2 variants of concern, including Alpha, Beta, Delta and Omicron, and also led to efficient neutralization in a surrogate viral neutralization assay. Our sequence alignment and 3-dimensional molecular modeling studies demonstrated that spike-derived epitopes, P1 and P2, are sequentially and/or structurally conserved among the SARS-CoV-2 variants. The addition of a novel mucosal adjuvant, heat-killed Caulobacter crescentus, to the lipopeptide vaccine significantly bolstered mucosal antibody responses. Finally, the lipopeptide-based intranasal vaccine demonstrated significant improvement in lung pathologies in a hamster model of SARS-CoV-2 infection. These studies are fundamentally important and open new avenues in the investigation of an innovative, broadly protective intranasal vaccine platform for SARS-CoV-2 and its variants.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".