Single-dose respiratory mucosal delivery of next-generation viral-vectored COVID-19 vaccine provides robust protection against both ancestral and variant strains of SARS-CoV-2
Bibliographic record
Abstract
Summary The emerging SARS-CoV-2 variants of concern (VOC) increasingly threaten the effectiveness of current first-generation COVID-19 vaccines that are administered intramuscularly and are designed to only target the spike protein. There is thus a pressing need to develop next-generation vaccine strategies to provide more broad and long-lasting protection. By using adenoviral vectors (Ad) of human and chimpanzee origin, we developed Ad-vectored trivalent COVID-19 vaccines expressing Spike-1, Nucleocapsid and RdRp antigens and evaluated them following single-dose intramuscular or intranasal immunization in murine models. We show that respiratory mucosal immunization, particularly with chimpanzee Ad-vectored vaccine, is superior to intramuscular immunization in induction of the three-arm immunity, consisting of local and systemic antibody responses, mucosal tissue-resident memory T cells, and mucosal trained innate immunity. We further show that single-dose intranasal immunization provides robust protection against not only the ancestral strain of SARS-CoV-2, but also two emerging VOC, B.1.1.7 and B.1.351. Our findings indicate that single-dose respiratory mucosal delivery of an Ad-vectored multivalent vaccine represents an effective next-generation COVID-19 vaccine strategy against current and future VOC. This strategy has great potential to be used not only to boost first-generation vaccine-induced immunity but also to expand the breadth of protective T cell immunity at the respiratory mucosa.
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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 | 0.001 |
| 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".