<scp>WRKY6</scp> transcription factor modulates root potassium acquisition through promoting expression of <scp><i>AKT1</i></scp> in Arabidopsis
Bibliographic record
Abstract
SUMMARY Potassium (K+), being an essential macronutrient in plants, plays a central role in many aspects. Root growth is highly plastic and is affected by many different abiotic stresses including nutrient deficiency. The Shaker‐type K+ channel Arabidopsis (Arabidopsis thaliana) K+ Transporter 1 (AKT1) is responsible for K+ uptake under both low and high external K+ conditions. However, the upstream transcription factor of AKT1 is not clear. Here, we demonstrated that the WRKY6 transcription factor modulates root growth to low potassium (LK) stress in Arabidopsis. WRKY6 showed a quick response to LK stress and also to many other abiotic stress treatments. The two wrky6 T‐DNA insertion mutants were highly sensitive to LK treatment, whose primary root lengths were much shorter, less biomass and lower K+ content in roots than those of wild‐type plants, while WRKY6‐overexpression lines showed opposite phenotypes. A further investigation showed that WRKY6 regulated the expression of the AKT1 gene via directly binding to the W‐box elements in its promoter through EMSA and ChIP‐qPCR assays. A dual luciferase reporter analysis further demonstrated that WRKY6 enhanced the transcription of AKT1. Genetic analysis further revealed that the overexpression of AKT1 greatly rescued the short root phenotype of the wrky6 mutant under LK stress, suggesting AKT1 is epistatic to WRKY6 in the control of LK response. Further transcriptome profiling suggested that WRKY6 modulates LK response through a complex regulatory network. Thus, this study unveils a transcription factor that modulates root growth under potassium deficiency conditions by affecting the potassium channel gene AKT1 expression.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".