Distinct subclades of <scp><i>Aux/IAA</i></scp> genes are direct targets of <scp>ARF</scp>5/<scp>MP</scp> transcriptional regulation
Bibliographic record
Abstract
The regulatory interactions between AUXIN RESPONSE FACTORS (ARFs) and Aux/IAA repressors play a central role in auxin signal transduction. Yet, the systems properties of this regulatory network are not well established. We generated a steroid-inducible ARF5/MONOPTEROS (MP) transgenic background to survey the involvement of this factor in the transcriptional regulation of the entire Aux/IAA family in Arabidopsis thaliana. Target genes of ARF5/MP identified by this approach were confirmed by chromatin immunoprecipitation, in vitro gel retardation assays and gene expression analyses. Our study shows that ARF5/MP is indispensable for the correct regulation of nearly one-half of all Aux/IAA genes, and that these targets coincide with distinct subclades. Further, genetic analyses demonstrate that the protein products of multiple Aux/IAA targets negatively feed back onto ARF5/MP activity. This work indicates that ARF5/MP broadly influences the expression of the Aux/IAA gene family, and suggests that such regulation involves the activation of specific subsets of redundantly functioning factors. These groups of factors may then act together to control various processes within the plant through negative feedback on ARF5. Similar detailed analyses of other Aux/IAA-ARF regulatory modules will be required to fully understand how auxin signal transduction influences virtually every aspect of plant growth and development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".