A Highly Immunogenic, Protective, and Safe Adenovirus-Based Vaccine Expressing Middle East Respiratory Syndrome Coronavirus S1-CD40L Fusion Protein in a Transgenic Human Dipeptidyl Peptidase 4 Mouse Model
Bibliographic record
Abstract
BACKGROUND: Infection control measures have played a major role in limiting human/camel-to-human transmission of Middle East respiratory syndrome coronavirus (MERS-CoV); however, development of effective and safe human or camel vaccines is warranted. METHODS: We extended and optimized our previous recombinant adenovirus 5 (rAd5)-based vaccine platform characterized by in vivo amplified and CD40-mediated specific responses to generate MERS-CoV S1 subunit-based vaccine. We generated rAd5 constructs expressing CD40-targeted S1 fusion protein (rAd5-S1/F/CD40L), untargeted S1 (rAd5-S1), and Green Fluorescent Protein (rAd5-GFP), and evaluated their efficacy and safety in human dipeptidyl peptidase 4 transgenic (hDPP4 Tg+) mice. RESULTS: Immunization of hDPP4 Tg+ mice with a single dose of rAd5-S1/F/CD40L elicited as robust and significant specific immunoglobulin G and neutralizing antibodies as those induced with 2 doses of rAd5-S1. After MERS-CoV challenge, both vaccines conferred complete protection against morbidity and mortality, as evidenced by significantly undetectable/reduced pulmonary viral loads compared to the control group. However, rAd5-S1- but not rAd5-S1/F/CD40L-immunized mice exhibited marked pulmonary perivascular hemorrhage post-MERS-CoV challenge despite the observed protection. CONCLUSIONS: Incorporation of CD40L into rAd5-based MERS-CoV S1 vaccine targeting molecule and molecular adjuvants not only enhances immunogenicity and efficacy but also prevents inadvertent pulmonary pathology after viral challenge, thereby offering a promising strategy to enhance safety and potency of vaccines.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".