Abscisic acid‐activated SNRK2 protein kinases function in the gene‐regulation pathway of ABA signal transduction by phosphorylating ABA response element‐binding factors
Bibliographic record
Abstract
The plant hormone abscisic acid (ABA) induces gene expression via the ABA-response element (ABRE) present in the promoters of ABA-regulated genes. A group of bZIP proteins have been identified as ABRE-binding factors (ABFs) that activate transcription through this cis element. A rice ABF, TRAB1, has been shown to be activated via ABA-dependent phosphorylation. While a large number of signalling factors have been identified that are involved in stomatal regulation by ABA, relatively less is known about the ABA-signalling pathway that leads to gene expression. We have shown recently that three members of the rice SnRK2 protein kinase family, SAPK8, SAPK9 and SAPK10, are activated by ABA signal as well as by hyperosmotic stress. Here we show that transient overexpression in cultured cell protoplasts of these ABA-activated SnRK2 protein kinases leads to the activation of an ABRE-regulated promoter, suggesting that these kinases are involved in the gene-regulation pathway of ABA signalling. We further show several lines of evidence that these ABA-activated SnRK2 protein kinases directly phosphorylate TRAB1 in response to ABA. Kinetic analysis of SAPK10 activation and TRAB1 phosphorylation indicated that the latter immediately followed the former. TRAB1 was found to be phosphorylated not only in response to ABA, but also in response to hyperosmotic stress, which was interpreted as the consequence of phosphorylation of TRAB1 by hyperosmotically activated SAPKs. Physical interaction between TRAB1 and SAPK10 in vivo was demonstrated by a co-immunoprecipitation experiment. Finally, TRAB1 was phosphorylated in vitro by the ABA-activated SnRK2 protein kinases at Ser102, which is phosphorylated in vivo in response to ABA and is critical for the activation function.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".