A Plant‐Produced Candidate Subunit Vaccine Reduces Shedding of Enterohemorrhagic <i>Escherichia coli</i> in Ruminants
Bibliographic record
Abstract
Enterohemorrhagic Escherichia coli (EHEC) are commonly present in the gastrointestinal tract of cattle and cause serious infectious disease in humans. Immunizing cattle against EHEC is a promising strategy to decrease the risk of food contamination; however, veterinary vaccines against EHEC such as Econiche have not been widely adopted by the agricultural industry, and have been discontinued, prompting the need for more cost‐effective EHEC vaccines. The objective of this project is to develop a platform to produce plant‐made antigens for oral vaccination of ruminants against EHEC. Five recombinant proteins were designed as vaccine candidates and expressed transiently in Nicotiana benthamiana and transplastomically in Nicotiana tabacum . Three of these EHEC proteins, NleA, Stx2b, and a fusion of EspA accumulated when transiently expressed. Transient protein accumulation was the highest when EHEC proteins were fused to an elastin‐like polypeptide (ELP) tag. In the transplastomic lines, EspA accumulated up to 479 mg kg −1 in lyophilized leaf material. Sheep that were administered leaf tissue containing recombinant EspA shed less E. coli O157:H7 when challenged, as compared to control animals. These results suggest that plant‐made, transgenic EspA has the potential to reduce EHEC shedding in ruminants.
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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".