Identification, Characterization and Down-Regulation of Cysteine Protease Genes in Tobacco for Use in Recombinant Protein Production
Bibliographic record
Abstract
Plants are an attractive host system for pharmaceutical protein production. Many therapeutic proteins have been produced and scaled up in plants at a low cost compared to the conventional microbial and animal-based systems. The main technical challenge during this process is to produce sufficient levels of recombinant proteins in plants. Low yield is generally caused by proteolytic degradation during expression and downstream processing of recombinant proteins. The yield of human therapeutic interleukin (IL)-10 produced in transgenic tobacco leaves was found to be below the critical level, and may be due to degradation by tobacco proteases. Here, we identified a total of 60 putative cysteine protease genes (CysP) in tobacco. Based on their predicted expression in leaf tissue, 10 candidate CysPs (CysP1-CysP10) were selected for further characterization. The effect of CysP gene silencing on IL-10 accumulation was examined in tobacco. It was found that the recombinant protein yield in tobacco could be increased by silencing CysP6. Transient expression of CysP6 silencing construct also showed an increase in IL-10 accumulation in comparison to the control. Moreover, CysP6 localizes to the endoplasmic reticulum (ER), suggesting that ER may be the site of IL-10 degradation. Overall results suggest that CysP6 is important in determining the yield of recombinant IL-10 in tobacco leaves.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".