CRISPRCas9-Mediated Knockout of Drought-Sensitive Genes Improves Maize Tolerance
Bibliographic record
Abstract
Maize ( Zea mays L.) plays a crucial role in ensuring global food security, yet its productivity is severely threatened by recurrent drought stress in many regions. Conventional breeding approaches have achieved limited success in improving drought resilience due to the complex and polygenic nature of drought tolerance. In this study, we explore the application of CRISPR/Cas9 genome editing technology as a precise and efficient strategy for enhancing drought tolerance in maize. By reviewing recent advances, we identified key drought-sensitive genes such as ZmNAC111 , ZmPP2C-A10 , and ZmDREB2A , which were targeted for knockout using various transformation techniques including Agrobacterium-mediated and biolistic methods. Functional validation and field evaluations of CRISPR-edited maize lines demonstrated significant improvements in physiological and agronomic traits under drought conditions, including enhanced root development, reduced stomatal conductance, better water retention, and higher yield stability compared to wild-type plants. The findings highlight that gene knockouts effectively mitigate drought-induced physiological stress and optimize water use efficiency. Although challenges remain regarding off-target effects, regulatory frameworks, and public acceptance, CRISPR/Cas9 offers a transformative platform for integrating molecular precision with traditional breeding. This study underscores the potential of genome editing in developing drought-resilient maize varieties and anticipates future advancements through multiplex editing and next-generation CRISPR technologies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".