OsWRKY70 Plays Opposite Roles in Blast Resistance and Cold Stress Tolerance in Rice
Bibliographic record
Abstract
The transcription factor WRKYs play pivotal roles in the adapting to adverse environments in plants. Prior research has demonstrated the involvement of OsWRKY70 in resistance against herbivores and its response to abiotic stress. Here, we reported the functional analysis of OsWRKY70 in immunity against fungal diseases and cold tolerance. The results revealed that OsWRKY70 was induced by various Magnaporthe oryzae strains. Knock out mutants of OsWRKY70, which were generated by the CRISPR/Cas9 system, exhibited enhanced resistance to M. oryzae. This was consistent with fortifying the reactive oxygen species (ROS) burst after inoculation in the mutants, elevated transcript levels of defense-responsive genes (OsPR1b, OsPBZ1, OsPOX8.1 and OsPOX22.3) and the observation of the sluggish growth of invasive hyphae under fluorescence microscope. RNA sequencing (RNA-seq) and quantitative real-time PCR (qRT-PCR) validations demonstrated that differentially expressed genes were related to plant-pathogen interactions, hormone transduction and MAPK cascades. Notably, OsbHLH6, a key component of the JA signaling pathway, was down-regulated in the mutants compared to wild type plants. Further investigation confirmed that OsWRKY70 bound to the promoter of OsbHLH6 by semi-in vivo chromatin immunoprecipitation (ChIP). Additionally, the loss-function of OsWRKY70 impaired cold tolerance in rice. The enhanced susceptibility in the mutants characterized by excessive ROS production, elevated ion leakage rate and increased malondialdehyde content, as well as decreased activity of catalase (CAT) and peroxidase (POD) under low temperature stress was, which might be attributed to down-regulation of cold-responsive genes (OsLti6b and OsICE1). In conclusion, our findings indicate that OsWRKY70 negatively contributes to blast resistance but positively regulates cold tolerance in rice, providing a strategy for crop breeding with tolerance to stress.
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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) | 0.000 | 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".