Protein nanoparticles assemble in plants, display antigenic viral peptides, and produce an epitope‐specific immune response
Bibliographic record
Abstract
Porcine reproductive and respiratory syndrome (PRRS) is one of the major diseases affecting the global swine industry. Current methods to control this viral disease are insufficient or pose safety concerns; therefore, there is a need for a safer and more effective vaccine against PRRS. In this study, we designed a chimeric nanoparticle vaccine through genetic fusion of an epitope composed of portions from two PRRS viral proteins, M and GP5, with lumazine synthase from Aquifex aeolicus . Transient expression in the leaves of Nicotiana benthamiana plants resulted in soluble levels around 0.18 mg·g −1 of plant fresh weight. This fusion protein assembles into nanoparticle structures surface‐displaying the PRRS epitope and is efficiently glycosylated with oligomannose N‐linked glycans. A mouse immunization trial was conducted using this protein as well as a previously described protein consisting of the same epitope displayed on a modified tobacco mosaic virus coat protein, and both vaccine candidates induced epitope‐specific antibodies. This study demonstrates the feasibility of protein nanoparticle‐based vaccines against PRRS produced in plants and lays the foundation for future studies to evaluate vaccine efficacy in pigs.
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) | 0.000 | 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 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".