Identification, Characterization and Down-Regulation of Cysteine Protease Genes in Tobacco for Use in Recombinant Protein Production
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 it