High‐level production of human interleukin‐10 fusions in tobacco cell suspension cultures
Bibliographic record
Abstract
The production of pharmaceutical proteins in plants has made much progress in recent years with the development of transient expression systems, transplastomic technology and humanizing glycosylation patterns in plants. However, the first therapeutic proteins approved for administration to humans and animals were made in plant cell suspensions for reasons of containment, rapid scale-up and lack of toxic contaminants. In this study, we have investigated the production of human interleukin-10 (IL-10) in tobacco BY-2 cell suspension and evaluated the effect of an elastin-like polypeptide tag (ELP) and a green fluorescent protein (GFP) tag on IL-10 accumulation. We report the highest accumulation levels of hIL-10 obtained with any stable plant expression system using the ELP fusion strategy. Although IL-10-ELP has cytokine activity, its activity is reduced compared to unfused IL-10, likely caused by interference of ELP with folding of IL-10. Green fluorescent protein has no effect on IL-10 accumulation, but examining the trafficking of IL-10-GFP over the cell culture cycle revealed fluorescence in the vacuole during the stationary phase of the culture growth cycle. Analysis of isolated vacuoles indicated that GFP alone is found in vacuoles, while the full-size fusion remains in the whole-cell extract. This indicates that GFP is cleaved off prior to its trafficking to the vacuole. On the other hand, IL-10-GFP-ELP remains mostly in the ER and accumulates to high levels. Protein bodies were observed at the end of the culture cycle and are thought to arise as a consequence of high levels of accumulation in the ER.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".