Proteome-Level Differences between Auxinic-Herbicide-Susceptible and -Resistant Wild Mustard (<i>Sinapis arvensis </i>L.)
Bibliographic record
Abstract
To identify proteins that may be involved in mediating auxinic herbicide resistance (i.e., resistance to dicamba, picloram, 2,4-D), we compared the proteomes of an auxinic-herbicide-susceptible (S) and -resistant (R) wild mustard (Sinapis arvensis L.) biotype at different developmental stages. Using two-dimensional electrophoresis and mass spectrometry, we identified 11 seedling and leaf proteins that showed reproducible differences in expression between the S and the R wild mustard biotype following application of dicamba. Our proteome-level studies revealed the increased expression of the enzyme peptidylprolyl cis-trans isomerase (PPIase), which has recently been implicated in auxin signal transduction. Juglone, an inhibitor of PPIase, interfered with the normal ability of R seeds to germinate in the presence of dicamba, whereas S seeds did not germinate in the presence of dicamba or dicamba plus juglone. When R and S plants (3-4 leaf stage) were treated with dicamba, S showed typical auxinic herbicide effects (e.g., epinasty) whereas R did not. However, the concomitant application of dicamba and juglone to greenhouse-grown R plants produced morphological changes that were consistent with known auxinic-herbicide-induced symptoms. This is the first report suggesting the potential involvement of differential expression of PPIase in mediating auxinic herbicide resistance.
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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".