Genome-Wide Identification of WRKY Transcription Factors in Chickpea and Their Roles in Stress Responses
Bibliographic record
Abstract
WRKY transcription factors play pivotal roles in regulating plant responses to various biotic and abiotic stresses. In this study, a comprehensive genome-wide identification and analysis of WRKY transcription factors were conducted in Cicer arietinum (chickpea), a key legume crop with growing importance in stress biology. Using advanced bioinformatics tools, we identified and annotated the complete set of WRKY genes in the chickpea genome, classifying them into groups I, II, and III based on conserved domains and structural features. The chromosomal distribution, gene structures, and phylogenetic relationships were analyzed to provide insights into their evolutionary patterns. Expression profiling under drought, salinity, and pathogen stress conditions (e.g., Fusarium and Ascochyta) revealed several stress-responsive WRKY genes with distinct tissue-specific and developmental expression patterns. We further explored their regulatory networks, including interactions with key signaling molecules (ABA, JA, SA) and crosstalk with other transcription factor families. A detailed case study highlighted the role of selected WRKY genes in drought tolerance, supported by phenotypic assessments under water-deficit conditions. Functional validation through overexpression, gene silencing, and omics approaches-such as transcriptomics and CRISPR/Cas-based editing-provided strong evidence for the functional roles of specific WRKY genes. This study underscores the significance of WRKY transcription factors in stress adaptation and sets the foundation for their application in marker-assisted selection and breeding of stress-resilient chickpea varieties.
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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 | 0.001 | 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".