Transcription Factors Associated with Abiotic and Biotic Stress Tolerance and Their Potential for Crops Improvement
Bibliographic record
Abstract
In field conditions, crops are adversely affected by a wide range of abiotic stresses including drought, cold, salt, and heat, as well as biotic stresses including pests and pathogens. These stresses can have a marked effect on crop yield. The present and future effects of climate change necessitate the improvement of crop stress tolerance. Plants have evolved sophisticated stress response strategies, and genes that encode transcription factors (TFs) that are master regulators of stress-responsive genes are excellent candidates for crop improvement. Related examples in recent studies include TF gene modulation and overexpression approaches in crop species to enhance stress tolerance. However, much remains to be discovered about the diverse plant TFs. Of the >80 TF families, only a few, such as NAC, MYB, WRKY, bZIP, and ERF/DREB, with vital roles in abiotic and biotic stress responses have been intensively studied. Moreover, although significant progress has been made in deciphering the roles of TFs in important cereal crops, fewer TF genes have been elucidated in sorghum. As a model drought-tolerant crop, sorghum research warrants further focus. This review summarizes recent progress on major TF families associated with abiotic and biotic stress tolerance and their potential for crop improvement, particularly in sorghum. Other TF families and non-coding RNAs that regulate gene expression are discussed briefly. Despite the emphasis on sorghum, numerous examples from wheat, rice, maize, and barley are included. Collectively, the aim of this review is to illustrate the potential application of TF genes for stress tolerance improvement and the engineering of resistant crops, with an emphasis on sorghum.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".