Auxin contributes to jasmonate-mediated regulation of abscisic acid signaling during seed germination in Arabidopsis
Bibliographic record
Abstract
Abscisic acid (ABA) represses seed germination and postgerminative growth in Arabidopsis thaliana. Auxin and jasmonic acid (JA) stimulate ABA function; however, the possible synergistic effects of auxin and JA on ABA signaling and the underlying molecular mechanisms remain elusive. Here, we show that exogenous auxin works synergistically with JA to enhance the ABA-induced delay of seed germination. Auxin biosynthesis, perception, and signaling are crucial for JA-promoted ABA responses. The auxin-dependent transcription factors AUXIN RESPONSE FACTOR10 (ARF10) and ARF16 interact with JASMONATE ZIM-DOMAIN (JAZ) repressors of JA signaling. ARF10 and ARF16 positively mediate JA-increased ABA responses, and overaccumulation of ARF16 partially restores the hyposensitive phenotype of JAZ-accumulating plants defective in JA signaling in response to combined ABA and JA treatment. Furthermore, ARF10 and ARF16 physically associate with ABSCISIC ACID INSENSITIVE5 (ABI5), a critical regulator of ABA signaling, and the ability of ARF16 to stimulate JA-mediated ABA responses is mainly dependent on ABI5. ARF10 and ARF16 activate the transcriptional function of ABI5, whereas JAZ repressors antagonize their effects. Collectively, our results demonstrate that auxin contributes to the synergetic modulation of JA on ABA signaling, and explain the mechanism by which ARF10/16 coordinate with JAZ and ABI5 to integrate the auxin, JA, and ABA signaling 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 | 0.000 | 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 | 0.000 |
| 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".