Comparative proteomic and physiological analyses reveal tribenuron‐methyl phytotoxicity and nontarget‐site resistance mechanisms in <i>Brassica napus</i>
Bibliographic record
Abstract
The application of herbicides is the most effective strategy for weed control and the development of herbicide-resistant crops will facilitate the weed management. The acetolactate synthase-inhibiting herbicide, tribenuron-methyl (TBM), is broadly used for weed control. However, its application in rapeseed field is restricted since rapeseed is sensitive to TBM. Herein, an integrated study of cytological, physiological and proteomic analysis of the TBM-resistant rapeseed mutant M342 and its wild-type (WT) plants was conducted. After TBM spraying, M342 showed improved tolerance to TBM, and proteins implicated in non-target-site resistance (NTSR) to herbicides had a significantly higher level in M342 as compared with the WT. Differentially accumulated proteins (DAPs) between these two genotypes were enriched in glutathione metabolism and oxidoreduction coenzyme metabolic process, which protected the mutant from oxidative stress triggered by TBM. Important DAPs related to stress or defence response were up-accumulated in M342 regardless of the TBM treatment, which might serve as the constitutive part of NTSR to TBM. These results provide new clues for further exploration of the NTSR mechanism in plants and establish a theoretical basis for the development of herbicide-resistant crops.
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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) | 0.001 | 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".