Tubers from potato lines expressing a tomato Kunitz protease inhibitor are substantially equivalent to parental and transgenic controls
Bibliographic record
Abstract
Recombinant protease inhibitors represent useful tools for the development of insect-resistant transgenic crops, but questions have been raised in recent years about the impact of these proteins on endogenous proteases and chemical composition of derived food products. In this study, we performed a detailed compositional analysis of tubers from potato lines expressing the broad-spectrum inhibitor of Ser and Asp proteases, tomato cathepsin D inhibitor (SlCDI), to detect possible unintended effects on tuber composition. A compositional analysis of key nutrients and toxic chemicals was carried out with tubers of SlCDI-expressing and control (comparator) lines, followed by a two-dimensional gel electrophoresis (2-DE) proteomic profiling of total and allergenic proteins to detect eventual effects at the proteome level. No significant differences were observed among control and SlCDI-expressing lines for most chemicals assayed, in line with the very low abundance of SlCDI in tubers. Likewise, proteins detected after 2-DE showed no quantitative variation among the lines, except for a few proteins in some control and test lines, independent of slcdi transgene expression. Components of the patatin storage protein complex and Kunitz protease inhibitors immunodetected after 2-DE showed unaltered deposition patterns in SlCDI-expressing lines, clearly suggesting a null impact of slcdi on the intrinsic allergenic potential of potato tubers. These data suggest, overall, a null impact of slcdi expression on tuber composition and substantial equivalence between comparator and SlCDI-expressing tubers despite reported effects on leaf protein catabolism. They also illustrate the usefulness of proteomics as a tool to assess the authenticity of foods derived from novel-generation transgenic plants.
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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) | 0.002 | 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".