Production of Biopharmaceuticals in Nicotiana benthamiana—Axillary Stem Growth as a Key Determinant of Total Protein Yield
Bibliographic record
Abstract
Data are scarce about the influence of basic cultural conditions on growth patterns and overall performance of plants used as heterologous production hosts for protein pharmaceuticals. Higher plants are complex organisms with young, mature and senescing organs that show distinct metabolic backgrounds and differ in their ability to sustain foreign protein expression and accumulation. Here, we used the transient protein expression host Nicotiana benthamiana as a model to map the accumulation profile of influenza virus hemagglutinin H1, a clinically promising vaccine antigen, at the whole plant scale. Greenhouse-grown plants submitted to different light regimes, submitted to apical bud pruning or treated with the axillary growth-promoting cytokinin 6-benzylaminopurine were vacuum-infiltrated with agrobacteria harboring a DNA sequence for H1 and allowed to express the viral antigen for 7 d in growth chamber under similar environmental conditions. Our data highlight the importance of young leaves on H1 yield per plant, unlike older leaves which account for a significant part of the plant biomass but contribute little to total antigen titer. Our data also highlight the key contribution of axillary stem leaves, which contribute more than 50% of total yield under certain conditions despite representing only one third of the total biomass. These findings underline the relevance of both considering main stem leaves and axillary stem leaves while modelling heterologous protein production in N. benthamiana. They also demonstrate the potential of exogenous applied growth-promoting hormones to modulate host plant architecture for improvement of protein yields.
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.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".