Rhizosecretion improves the production of Cyanovirin‐N in <i>Nicotiana tabacum</i> through simplified downstream processing
Bibliographic record
Abstract
Rhizosecretion has many advantages for the production of recombinant pharmaceuticals, notably facile downstream processing from hydroponic medium. The aim of this study was to increase yields of the HIV microbicide candidate, Cyanovirin-N (CV-N), obtained using this production platform and to develop a simplified methodology for its downstream processing from hydroponic medium. Placing hydroponic cultures on an orbital shaker more than doubled the concentration of CV-N in the hydroponic medium compared to plants which remained stationary, reaching a maximum of approximately 20μg/ml in one week, which is more than 3 times higher than previously reported yields. The protein composition of the hydroponic medium, the rhizosecretome, was characterised in plants cultured with or without the plant growth regulator alpha-napthaleneacetic acid by LC-ESI-MS/MS, and CV-N was the most abundant protein. The issue of large volumes in the rhizosecretion system was addressed by using ion exchange chromatography to concentrate CV-N and partially remove impurities. The semi-purified CV-N was demonstrated to bind to HIV gp120 in an ELISA and to neutralise HIVBa-L with an IC50 of 6nM in a cell-based assay. Rhizosecretion is therefore a practicable and inexpensive method for the production of functional CV-N.
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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) | 0.001 | 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".