WRKY42 transcription factor positively regulates leaf senescence through modulating SA and ROS synthesis in<i>Arabidopsis thaliana</i>
Bibliographic record
Abstract
Leaf senescence represents the final stage of leaf growth and development, and its onset and progression are strictly regulated; however, the underlying regulatory mechanisms remain largely unknown. In this study we found that WRKY42 was highly induced during leaf senescence. Loss-of-function wrky42 mutants showed delayed leaf senescence whereas the overexpression of WRKY42 accelerated senescence. Transcriptome analysis revealed 2721 differentially expressed genes between wild-type and WRKY42-overexpressing plants, including genes involved in salicylic acid (SA) and reactive oxygen species (ROS) synthesis as well as several senescence-associated genes (SAGs). Moreover, WRKY42 activated the transcription of isochorismate synthase 1 (ICS1), respiratory burst oxidase homolog F (RbohF) and a few SAG genes. Consistently, the expression of these genes was reduced in wrky42 mutants but was markedly increased in transgenic Arabidopsis overexpressing WRKY42. Both in vitro electrophoretic mobility shift assays (EMSAs) and in vivo chromatin immunoprecipitation and dual luciferase assays demonstrated that WRKY42 directly bound to the promoters of ICS1 and RbohF, as well as a few SAGs, to activate their expression. Genetic analysis further showed that mutations of ICS1 and RbohF suppressed the early senescence phenotype evoked by WRKY42 overexpression. Thus, we have identified WRKY42 as a novel transcription factor positively regulating leaf senescence by directly activating the transcription of ICS1, RbohF and SAGs, without any seed yield penalty.
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 | 0.000 |
| 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".