<i>Nicotiana benthamiana</i> 's Responses to Agroinfiltration, a Treasure Grove of New Avenues to Improve Protein Yields in Plant Molecular Farming
Bibliographic record
Abstract
Transient expression of recombinant proteins in leaves of Nicotiana benthamiana is routinely employed for both basic research and manufacturing of biopharmaceutical products in plants. Relying on disarmed strains of the bacterial plant pathogen Agrobacterium tumefaciens as a transgene vector, this safe, cost-effective and easily scalable 'plant molecular farming' approach offers a reliable alternative to classical protein expression platforms. Commonly referred to as agroinfiltration, scaled-up versions of this manufacturing process have now become helpful in the fight against global health issues, such as those rapidly evolving virus strains causing influenza or coronavirus disease 2019. In the past decades, considerable efforts have been deployed to improve the efficacy of Agrobacterium-mediated expression, including through the development of new binary vectors, the design of strong promoters, and the deployment of approaches to increase levels and stability of transgene mRNAs. By comparison, much less attention has been given to understanding the effects that agroinfiltration unavoidably has on host plants, including the infiltration process itself, the perception of Agrobacterium and the subsequent accumulation of recombinant products throughout the expression phase. Using the upregulation profiles of plant receptor genes during the heterologous expression of virus-like particles in N. benthamiana leaves, I here describe how some of these host responses interact with each other to form an intricate signalling interplay at the molecular level. I also review host plant's responses to agroinfiltration and highlight strategies that have emerged to improve the efficacy of plant cell biofactories based on the better understanding of this transient expression system.
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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 | 0.001 | 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".