Cross-talk in Plant Hormone Signalling: What Arabidopsis Mutants Are Telling Us
Bibliographic record
Abstract
Genetic screens have been extremely useful in identifying genes involved in hormone signal transduction. However, although these screens were originally designed to identify specific components involved in early hormone signalling, mutations in these genes often confer changes in sensitivity to more than one hormone at the whole-plant level. Moreover, a variety of hormone response genes has been identified through screens that were originally designed to uncover regulators of sugar metabolism. Together, these facts indicate that the linear representation of the hormone signalling pathways controlling a specific aspect of plant growth and development is not sufficient, and that hormones interact with each other and with a variety of developmental and metabolic signals. Following the advent of arabidopsis molecular genetics we are beginning to understand some of the mechanisms by which a hormone is transduced into a cellular response. In this Botanical Briefing we review a subset of genes in arabidopsis that influence hormonal cross-talk, with emphasis on the gibberellin, abscisic acid and ethylene pathways. In some cases it appears that modulation of hormone sensitivity can cause changes in the synthesis of an unrelated hormone, while in other cases a hormone response gene defines a node of interaction between two response pathways. It also appears that a variety of hormones may converge to regulate the turnover of important regulators involved in growth and development. Examples are cited of the recent use of suppressor and enhancer analysis to identify new nodes of interaction between these pathways.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".