Immunogenicity of Candidate MERS-CoV DNA Vaccines Based on the Spike Protein
Bibliographic record
Abstract
MERS-coronavirus is a novel zoonotic pathogen which spread rapidly to >25 countries since 2012. Its apparent endemicity and the wide spread of its reservoir host (dromedary camels) in the Arabian Peninsula highlight the ongoing public health threat of this virus. Therefore, development of effective prophylactic vaccine needs to be urgently explored given that there are no approved prophylactics or therapeutics for humans or animals to date. Different vaccine candidates have been investigated but serious safety concerns remain over protein or full-length spike (S) protein-based vaccines. Here, we investigated the immunogenicity of naked DNA vaccines expressing different fragments of MERS-CoV S protein in mice. We found that plasmids expressing full-length (pS) or S1-subunit (pS1) could induce significant levels of S1-specific antibodies (Abs) but with distinct IgG isotype patterns. Specifically, pS1 immunization elicited a balanced Th1/Th2 response and generally higher levels of all IgG isotypes compared to pS vaccination. Interestingly, only mice immunized with pS1 demonstrated significant S1-specific cellular immune response. Importantly, both constructs induced cross-neutralizing Abs against multiple strains of human and camel origins. These results indicate that vaccines expressing S1-subunit of the MERS-CoV S protein could represent a potential vaccine candidate without the possible safety concerns associated with full-length protein-based 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) | 0.000 | 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".