Systemic and Mucosal Antibody Responses to SARS-CoV-2 Variant-Specific Prime-and-Boost and Prime-and-Spike Vaccination: A Comparison of Intramuscular and Intranasal Bivalent Vaccine Administration in a Murine Model
Bibliographic record
Abstract
Background: The rapid genetic evolution of SARS-CoV-2 has led to the emergence of immune-evading, highly transmissible variants of concern (VOCs). This prompts the need for next-generation vaccines that elicit robust mucosal immunity in the airways to directly curb viral infection. Objective: Here, we investigate the impact of heterologous variant prime–boost regimens on humoral responses, focusing on intramuscular (IM) and intranasal (IN) routes of administration. Using a murine model, we assessed the immunogenicity of unadjuvanted protein boosts with Wu-1, Omicron BA.4/5, or Wu-1 + BA.4/5 spike antigens following monovalent or bivalent IM priming with mRNA-LNP vaccines. Results: IM priming induced strong systemic total and neutralizing antibody responses that were further enhanced by IN boosts with BA.4/5. IN boosting achieved the broadest serum neutralization across all VOCs tested. Notably, bivalent mRNA-LNP IM priming induced robust, cross-variant serum neutralizing antibody production, independent of subsequent IN boost combinations. Conclusions: Our findings highlight the benefit of including distinct antigenic variants in the prime vaccination followed by a variant-tailored IN boost to elicit both systemic and mucosal variant-specific responses that are potentially capable of reducing SARS-CoV-2 transmission.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".