Structural Design and Immunogenicity of a Novel Self‐Adjuvanting Mucosal Vaccine Candidate for <scp>SARS</scp> ‐ <scp>CoV</scp> ‐2 Expressed in Plants
Bibliographic record
Abstract
Mucosal vaccination for COVID-19 to boost preexisting though insufficient systemic and local/mucosal immunity remains an attractive prospect but there are currently no licensed mucosal vaccines against this infection. Here, using a plant expression system, we developed a novel mucosal vaccine platform for respiratory viruses and demonstrated its application in the context of SARS-CoV-2 infection. In addition to the antigen itself, the PCF (Platform CTB-Fc) vaccine candidate incorporates two molecular adjuvants, the IgG-Fc antibody fragment and the nontoxic cholera toxin B subunit (CTB), with the first targeting the vaccine to IgG receptors on antigen-presenting cells, and the second providing local adjuvanticity by targeting cellular gangliosides in the mucosae. We demonstrated that this vaccine candidate is highly immunogenic in mice, inducing virus-neutralising systemic and mucosal antibodies as well as tissue resident memory T cells in the lungs. We also demonstrated that SRBD-PCF is recognised by immune cells from exposed or vaccinated individuals, and that circulating antibodies also bind to the antigen within the vaccine, forming immune complexes (IC). Finally, with a view of respiratory delivery, we demonstrated that the vaccine can be aerosolised without loss of material or biological activity, and that it is noncytotoxic and nonhaemolytic to human cells. Furthermore, we demonstrate that the plant expression system represents a suitable platform to produce these complex, multifunctional macromolecules capable of simultaneously binding to multiple targets. Our data strongly support the case for a safe, self-adjuvanting mucosal COVID-19 vaccine development, as means to boosting both systemic and mucosal immunity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".