Metabolomics Differences of Glycine max QTLs Resistant to Soybean Looper
Bibliographic record
Abstract
Quantitative trait loci (QTLs) E and M are major soybean alleles that confer resistance to leaf-chewing insects, and are particularly effective in combination. Flavonoids and/or isoflavonoids are classes of plant secondary metabolites that previous studies agree are the causative agents of resistance of these QTLs. However, all previous studies have compared soybean genotypes that are of dissimilar genetic backgrounds, leaving it questionable what metabolites are a result of the QTL rather than the genetic background. Here, we conducted a non-targeted mass spectrometry approach without liquid chromatography to identify differences in metabolite levels among QTLs E, M, and both (EM) that were introgressed into the background of the susceptible variety Benning. Our results found that E and M mainly confer low-level, global differences in distinct sets of metabolites. The isoflavonoid daidzein was the only metabolite that demonstrated major increases, specifically in insect-treated M and EM. Interestingly, M confers increased daidzein levels in response to insect, whereas E restores M's depleted daidzein levels in the absence of insect. Since daidzein levels do not parallel levels of resistance, our data suggest a novel mechanism that the QTLs confer resistance to insects by mediating changes in hundreds of metabolites, which would be difficult for the insect to evolve tolerance. Collective global metabolite differences conferred by E and M might explain the increased resistance of EM.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Metabolomics of soybean insect resistance QTLs; a plant science question.
The study investigates soybean metabolites and insect resistance.
Plant metabolomics of insect-resistance QTLs in soybean is agricultural biology domain research.
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) | 0.001 | 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".