CD40-targeted S1 subunit vaccine protects against MERS-CoV and S1-associated pulmonary immunopathology in transgenic human DPP4 mouse model
Bibliographic record
Abstract
Abstract Middle East respiratory syndrome coronavirus (MERS-CoV) is a highly pathogenic respiratory virus that emerged in 2012. While infection control measures have played a major role in limiting human/camel-to-human transmissions, development of safe and effective human or camel vaccines is warranted. Here, we extended and optimized our previous 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 GFP (rAd5-GFP), and evaluated their efficacy and safety in human DPP4 transgenic (hDPP4 Tg+) mice. Immunization of hDPP4 Tg+ mice showed that a single dose of rAd5-S1/F/CD40L elicited robust and significant specific IgG and neutralizing antibody responses as those induced in mice immunized with two-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 as compared to 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. Collectively, these data indicate that incorporating CD40L into this Ad5-based MERS-CoV S1 subunit vaccine as targeting molecule and molecular adjuvant can not only enhance immunogenicity and efficacy but also prevents induction of inadvertent pulmonary pathology in immunized and challenged mice, thereby offering a promising strategy to enhance the safety and potency of antigen-specific immune responses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".