Maize<i>AUXIN-BINDING PROTEIN 1</i>and<i>AUXIN-BINDING PROTEIN 4</i>impact on leaf growth, elongation, and seedling responsiveness to auxin and light
Bibliographic record
Abstract
In maize, at least five auxin-binding proteins (ABPs) have been identified, yet their functions remain unclear. The present study reports the use of maize abp1, abp4, and abp1abp4 mutants to investigate the role of ABPs during maize growth and development. Single and double abp mutant plants grown in a greenhouse differ from the wild type (WT) in their leaf declination and leaf blade growth. The effect of the dark (D), blue light (BL), red light (RL), and exogenous auxin on the development of mutant seedlings was also studied. Relative to WT, etiolated mutant seedlings were shorter and showed a reduced responsiveness to exogenous auxin. In BL or RL, the responsiveness of maize seedlings to auxin was distinctly less than in D. The reducing effect of light on seedling responsiveness to auxin is mediated at least by phytochromes. The suppression of ABP1 and (or) ABP4 led to a distinct accumulation of free indole-3-acetic acid (IAA) in etiolated and light-grown seedling organs. We concluded that ABP1 and ABP4 participate in the growth of maize seedlings, mediate seedling responses to auxin, and interact with light signaling pathway(s). We also deduce a functional interaction between ABP1 and ABP4, which is that the relationship between them is light-, organ- and response-dependent.
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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) | 0.001 | 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 | 0.001 |
| 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".