Recombinant protein production in a variety of <i>Nicotiana</i> hosts: a comparative analysis
Bibliographic record
Abstract
Although many different crop species have been used to produce a wide range of vaccines, antibodies, biopharmaceuticals and industrial enzymes, tobacco has the most established history for the production of recombinant proteins. To further improve the heterologous protein yield of tobacco platforms, transient and stable expression of four recombinant proteins (i.e. human erythropoietin and interleukin-10, an antibody against Pseudomonas aeruginosa, and a hyperthermostable α-amylase) was evaluated in numerous species and cultivars of Nicotiana. Whereas the transient level of recombinant protein accumulation varied significantly amongst the different Nicotiana plant hosts, the variety of Nicotiana had little practical impact on the recombinant protein concentration in stable transgenic plants. In addition, this study examined the growth rate, amount of leaf biomass, total soluble protein levels and the alkaloid content of the various Nicotiana varieties to establish the best plant platform for commercial production of recombinant proteins. Of the 52 Nicotiana varieties evaluated, Nicotiana tabacum (cv. I 64) produced the highest transient concentrations of recombinant proteins, in addition to producing a large amount of biomass and a relatively low quantity of alkaloids, probably making it the most effective plant host for recombinant protein production.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".