A Novel WRKY Transcription Factor, MuWRKY3 (Macrotyloma uniflorum Lam. Verdc.) Enhances Drought Stress Tolerance in Transgenic Groundnut (Arachis hypogaea L.) Plants
Bibliographic record
Abstract
Drought stress has adverse effects on growth, water relations, photosynthesis and yield of groundnut. WRKY transcription factors (TFs) are the plant-specific TFs which regulate several down-stream stress-responsive genes and play an essential role in plant biotic and abiotic stress responses. We found that WRKY3 gene is highly up-regulated under drought stress conditions and therefore isolated a new WRKY3TF gene from a drought-adapted horsegram (Macrotyloma uniflorum Lam. Verdc.). Conserved domain studies revealed that protein encoded by this gene contains highly conserved regions of two WRKY domains and two C2H2 zinc-finger motifs. The fusion protein localization studies of transient MuWRKY3-YFP revealed its nuclear localization. Overexpression of MuWRKY3 TF gene in groundnut (Arachis hypogaea L.) showed increased tolerance to drought stress compared to wild-type (WT) plants. MuWRKY3 groundnut transgenics displayed lesser and delayed wilting symptoms than WT plants after 10-days of drought stress imposition. The transgenic groundnut plants expressing MuWRKY3 showed less accumulation of malondialdehyde, hydrogen peroxide (H2O2), and superoxide anion (O2●− ), accompanied by more free proline, total soluble sugar content, and activities of antioxidant enzymes than WT plants under drought stress. Moreover, a series of stress-related LEA, HSP, MIPS, APX, SOD and CAT genes found up-regulated in the transgenic groundnut plants. The study demonstrates that nuclear-localized MuWRKY3 TF regulates the expression of stress-responsive genes and the activity of ROS scavenging enzymes which results in improved drought tolerance in groundnut. We conclude that MuWRKY3 may serve as a new putative candidate gene for the improvement of stress resistance in plants.
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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 | 0.000 |
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".