Metabolic profiling of maize seeds with both insect- and herbicide-resistance genes (cry1Ab and epsps), dual herbicide-resistance genes (epsps and pat), and natural genotypic varieties
Bibliographic record
Abstract
Abstract Background Widely targeted metabolomics was applied to estimate the differences in the metabolite profiles of maize seeds from 3 natural genotypic varieties and 4 genetically modified (GM) lines. Results Pairwise comparison with their isogenic controls revealed 71, 121, 43 and 95 differentially accumulated metabolites (DAMs) in GM maize seeds of C0030.2.4, C0030.3.5, C0010.1.1 and C0010.3.1, respectively. KEGG pathway enrichment analysis showed that most of these DAMs participated in the biosynthesis of secondary metabolites and purine metabolism in GM maize C0030.2.4 and C0030.3.5, but participated in tryptophan metabolism and 2-oxocarboxylic acid metabolism in C0010.3.1 seeds and in metabolic pathways and the biosynthesis of secondary metabolites in C0010.1.1 seeds. The data also showed that the differences in metabolite accumulation, both total DAMs and co-DAMs, among the different natural genotypic varieties (418 DAMs and 39 co-DAMs) were greater than those caused by genetic modification (330 DAMs and 3 co-DAMs). Conclusions None of the DAMs were identified as new or unintended, showing only changes in abundance in the studied maize seeds. The metabolite profile differences among the 3 non-GM lines were more notable than those among GM lines. Different genetic backgrounds affect metabolite profiling more than gene modification itself. Graphic abstract
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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 | 0.000 |
| 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".