Unintended molecular interactions in transgenic plants expressing clinically useful proteins: The case of bovine aprotinin traveling the potato leaf cell secretory pathway
Bibliographic record
Abstract
We assessed the impact of subcellular targeting on the heterologous expression of a clinically useful protease inhibitor, bovine aprotinin, in leaves of potato, Solanum tuberosum. Transgenic potato lines targeting aprotinin to the cytosol, the ER or the apoplast were first generated, and then assessed for their ability to accumulate the recombinant protein. On-chip detection and quantitation of aprotinin variants by SELDI TOF MS showed the inhibitor to be absent in the cytosol, but present under different forms in the ER and the apoplast. No visible phenotypic effects of aprotinin were observed for the transgenic lines, but aprotinin retention in the ER was associated with a significant decrease of leaf soluble protein content. A 2-D gel assessment of control and transgenic lines revealed a possible link between this altered protein content and the down-regulation of proteins implicated in protein synthesis and maturation. These observations, supported by complementary 2-DE analyses with potato lines targeting aprotinin to the apoplast, suggest an aprotinin-mediated feedback in planta negatively altering protein anabolism. From a practical viewpoint, these data illustrate the importance of taking into account not only the characteristics of recombinant proteins expressed in heterologous environments, but also their possible effects on protein accumulation in the host plant factory.
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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) | 0.000 | 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".