iTRAQ-based quantitative tissue proteomic analysis of differentially expressed proteins (DEPs) in non-transgenic and transgenic soybean seeds
Bibliographic record
Abstract
The unintended effects of transgenesis have increased food safety concerns, meriting comprehensive evaluation. Proteomic profiling provides an approach to directly assess the unintended effects. Herein, the isobaric tags for relative and absolute quantitation (iTRAQ) comparative proteomic approach was employed to evaluate proteomic profile differences in seed cotyledons from 4 genetically modified (GM) and 3 natural genotypic soybean lines. Compared with their non-GM parents, there were 67, 61, 13 and 22 differentially expressed proteins (DEPs) in MON87705, MON87701 × MON89788, MON87708, and FG72. Overall, 170 DEPs were identified in the 3 GM soybean lines with the same parents, but 232 DEPs were identified in the 3 natural soybean lines. Thus, the differences in protein expression among the genotypic varieties were greater than those caused by GM. When considering ≥2 replicates, 4 common DEPs (cDEPs) were identified in the 3 different GM soybean lines with the same parents and 6 cDEPs were identified in the 3 natural varieties. However, when considering 3 replicates, no cDEPs were identified. Regardless of whether ≥2 or 3 replicates were considered, no cDEPs were identified among the 4 GM soybean lines. Therefore, no feedback due to GM was observed at the common protein level in this study.
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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 | 0.001 | 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 | 0.000 |
| 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".