Protection of Recombinant Mammalian Antibodies from Development-Dependent Proteolysis in Leaves of Nicotiana benthamiana
Bibliographic record
Abstract
The expression of clinically useful proteins in plants has been bolstered by the development of high-yielding systems for transient protein expression using agroinfiltration. There is a need now to know more about how host plant development and metabolism influence the quantity and quality of recombinant proteins. Endogenous proteolysis is a key determinant of the stability and yield of recombinant proteins in plants. Here we characterised cysteine (C1A) and aspartate (A1) protease profiles in leaves of the widely used expression host Nicotiana benthamiana, in relation with the production of a murine IgG, C5-1, targeted to the cell secretory pathway. Agroinfiltration significantly altered the distribution of C1A and A1 proteases along the leaf age gradient, with a correlation between leaf age and the level of proteolysis in whole-cell and apoplast protein extracts. The co-expression of tomato cystatin SlCYS8, an inhibitor of C1A proteases, alongside C5-1 increased antibody yield by nearly 40% after the usual 6-days incubation period, up to ~3 mg per plant. No positive effect of SlCYS8 was observed in oldest leaves, in line with an increased level of C1A protease activity and a very low expression rate of the inhibitor. By contrast, C5-1 yield was greater by an additional 40% following 8- to 10-days incubations in younger leaves, where high SlCYS8 expression was maintained. These findings confirm that the co-expression of recombinant protease inhibitors is a promising strategy for increasing recombinant protein yields in plants, but that further opportunity exists to improve this approach by addressing the influence of leaf age and proteases of other classes.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".