Understanding auxinic herbicide resistance in wild mustard: physiological, biochemical, and molecular genetic approaches
Bibliographic record
Abstract
The incidence of auxinic herbicide resistance in plants has increased worldwide. Auxinic herbicides were the first selective organic herbicides developed and have been used in agriculture for over 50 yr, primarily for the selective control of broadleaf weeds in cereal crops. However, the mode of action of auxinic herbicides and the molecular basis of auxinic herbicide resistance remain unknown, although an auxin-binding protein (ABP) is proposed to be the primary target site. Using auxinic herbicide-resistant (R) and -susceptible (S) biotypes of wild mustard as a model system, we have extensively studied the mode of action of auxinic herbicides and the resistance mechanisms at the physiological, biochemical, and molecular genetic levels. There are no differences in uptake, transport, and metabolism of auxinic herbicides between the R and S biotypes. Based on these results, as well as the studies on the role of auxin-enhanced ethylene biosynthesis and calcium in mediating the auxinic herbicide resistance, we hypothesize that resistance of the R biotype to auxinic herbicides is due to an altered target site, possibly an auxin receptor. We have identified and characterized a small ABP gene family as well as their cDNAs from both R and S of wild mustard. Amino acid changes were found in the ABP of the R biotype. Functional and mutational analyses of these genes are underway to determine the role of ABP in mediating auxinic herbicide resistance. In this review, we focus on the mode of action of auxinic herbicides and the molecular basis of auxinic herbicide resistance in wild mustard.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".