Leaf proteome rebalancing in <i>Nicotiana benthamiana</i> for upstream enrichment of a transiently expressed recombinant protein
Bibliographic record
Abstract
A key factor influencing the yield of biopharmaceuticals in plants is the ratio of recombinant to host proteins in crude extracts. Postextraction procedures have been devised to enrich recombinant proteins before purification. Here, we assessed the potential of methyl jasmonate (MeJA) as a generic trigger of recombinant protein enrichment in Nicotiana benthamiana leaves before harvesting. Previous studies have reported a significant rebalancing of the leaf proteome via the jasmonate signalling pathway, associated with ribulose 1,5-bisphosphate carboxylase oxygenase (RuBisCO) depletion and the up-regulation of stress-related proteins. As expected, leaf proteome alterations were observed 7 days post-MeJA treatment, associated with lowered RuBisCO pools and the induction of stress-inducible proteins such as protease inhibitors, thionins and chitinases. Leaf infiltration with the Agrobacterium tumefaciens bacterial vector 24 h post-MeJA treatment induced a strong accumulation of pathogenesis-related proteins after 6 days, along with a near-complete reversal of MeJA-mediated stress protein up-regulation. RuBisCO pools were partly restored upon infiltration, but most of the depletion effect observed in noninfiltrated plants was maintained over six more days, to give crude protein samples with 50% less RuBisCO than untreated tissue. These changes were associated with net levels reaching 425 μg/g leaf tissue for the blood-typing monoclonal antibody C5-1 expressed in MeJA-treated leaves, compared to less than 200 μg/g in untreated leaves. Our data confirm overall the ability of MeJA to trigger RuBisCO depletion and recombinant protein enrichment in N. benthamiana leaves, estimated here for C5-1 at more than 2-fold relative to host proteins.
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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 | 0.000 | 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".