Plant‐produced encapsulin displays non‐typhoidal <i>Salmonella enterica</i> antigens and assembles into mosaic nanoparticles
Bibliographic record
Abstract
Foodborne illnesses have major public health and economic impacts. Non‐typhoidal Salmonella enterica serovars ( Salmonella ), associated with the consumption of contaminated poultry products, are among the most common causes of foodborne illness in humans. Vaccination of poultry flocks is one of the main methods to prevent Salmonella infection in poultry; however, the vaccines currently available have limitations. Because of this, there is an urgent need for more effective Salmonella vaccines. Ferric enterobactin receptor (FepA) is a Salmonella outer‐membrane protein that shows promise as a vaccine antigen due to its important role in iron acquisition and its ubiquity on the surface of Salmonella . Here, we describe the production of encapsulin nanoparticles displaying antigenic epitopes from FepA in Nicotiana benthamiana . Five FepA–encapsulin fusion proteins were transiently expressed in N. benthamiana leaves and targeted to either the chloroplast or the cytosol. We found that accumulation was highest in the chloroplast, with levels over 0.7 mg·g −1 of leaf fresh weight. Encapsulin nanoparticles self‐assembled within plant cells and FepA epitopes were displayed on their surface, as confirmed by transmission electron microscopy and immunogold labeling. Differentially tagged encapsulin monomers were also co‐infiltrated into the leaves of N. benthamiana , where they assembled into mosaic nanoparticles displaying each of the different tags. Encapsulin and one FepA–encapsulin fusion protein were also expressed transplastomically in Nicotiana tabacum , where they self‐assembled into nanoparticles and the fusion protein accumulated to 2.4 mg·g −1 of leaf fresh weight, nearly fivefold higher than in N. benthamiana. This study highlights the capability of encapsulin for use in vaccine design.
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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 | 0.000 | 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".