DROUGHT TOLERANCE IN RICE AND ROLE OF WRKY GENES
Bibliographic record
Abstract
Anthropogenic alteration in climate has resulted in devastating global issue of drought for rice crop. Drought interferes with all the growth stages of crop by delaying its growth mechanisms, metabolic pathways and reducing the spikelet fertility. Rice plant has developed some morphological and physiological mechanisms to cope with the stress which include reduction in photosynthesis and transpiration, increased stomatal conductivity and density, root to shoot ratio, root length and carbon assimilation. Some biochemical modifications like biosynthesis of various hormones (ABA) and proteins (proline) also help in reducing the yield losses. Marker assisted selection and Quantitative Trait Loci (QTL) mapping are the advanced molecular techniques that played a vital role in developing the improved and stress tolerant rice cultivars. Identification of different stress responsive genes and transcriptional factors especially WRKY TFs have provided platform to obtain good crop stand and yield even under stress conditions. Rice possesses more WRKY genes (109 WRKY TFs in rice) as compared to Arabidopsis thaliana. An enormous variation in the expression patterns of WRKY genes and their contribution to the amplification of various signaling pathways and regulatory networks has been observed. These transcription factors work by regulating different mechanisms of drought tolerance and by releasing hormones, proteins, reducing-sugars, solutes and by affecting the stomatal conductance and root architecture. The WRKY genes work by showing up- and down- regulation to proteins, various protein-protein interactions and cross-regulation of WRKY TFs. Advance breeding methods; MAS, GWS, MABC, MARC and biotechnological tools along with different WRKY transcription factors have dynamically contributed in developing abiotic and biotic stress resistant rice varieties/cultivars on large scale. Key words: Drought stress; WRKY TFs; QTLs; Biotechnological approaches; Rice breeding
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".