Identification of the factors that control synthesis and accumulation of a therapeutic protein, human immune‐regulatory interleukin‐10, in <i><scp>A</scp>rabidopsis thaliana</i>
Bibliographic record
Abstract
Plants are one of the most economical platforms for large-scale production of recombinant proteins for biopharmaceutical and industrial uses. A large number of human recombinant proteins of therapeutic value have been successfully produced in plant systems. One of the main technical challenges of producing recombinant proteins in plants is to obtain sufficient level of protein. This research aims to identify the factors that control synthesis and accumulation of recombinant proteins in stable transgenic plants. A stepwise dissection of human immune-regulatory interleukin-10 (IL-10) protein production was carried out using Arabidopsis thaliana as a model system. EMS-mutagenized transgenic Arabidopsis IL-10 lines, at2762 and at3262, produced significantly higher amount of IL-10 protein than the non-mutagenized IL-10 line (WT-IL-10). The fates of trans-gene in these sets of plants were compared in detail by measuring synthesis and accumulation of IL-10 transcript, transcript stability, protein synthesis and IL-10 protein accumulation. The IL-10 transcripts were more stable in at2762 and at3262 lines than WT-IL-10, which may contribute to higher protein synthesis in these lines. To evaluate whether translational regulation of IL-10 controls its synthesis in non-mutagenized WT-IL-10 and higher IL-10 accumulating mutant lines, we measured the efficiency of the translational machinery. Our results indicate that mutant lines with higher trans-gene expression contain more robust and efficient translational machinery compared with the control line.
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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 | 0.001 |
| 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".