CRISPR-Cpfb1-Mediated Manipulation of EPFL9 in Oryza sativa for Increased Drought Tolerance as a Climate Change Adaptation Strategy: A Research Protocol
Bibliographic record
Abstract
Introduction: As a result of climate change, increased drought incidence significantly affects the crop yield of rice, Oryza sativa. Given that rice serves as a staple food, adaptation strategies to combat climate change-induced drought are critical. Water retention is regulated by stomata size, stomata density, and the opening and closing of the stomata central pore. Previous studies have identified relevant developmental genes in the Arabidopsis thaliana model system, encoding for epidermal patterning factor (EPFs) and EPF-like (EPFL) signaling peptides, and their orthologs across various plant species. In barley (Hordeum vulgare), genetic manipulation of EPF1 has been shown to reduce stomatal density, resulting in improved drought tolerance. In rice, overexpression of OsEPF1 yields a similar phenotype. The purpose of our study is to develop a proposal for a method to increase drought tolerance of Oryza sativa in an effort to battle climate change. Methods: It has been shown that CRISPR-mediated editing successfully generated knockouts (KOs) of EPFL9—a positive regulator of stomatal development—in Oryza sativa. As such, we propose to downregulate EPFL9 via CRISPR-Cpfb1 gene editing in Oryza sativa. Our proposal includes the growth of genetically altered and control Oryza sativa under specific conditions, including drought conditions, in order to simulate a natural environment. Following the growth of the plants, we propose conducting tests to determine yield and growth in order to assess drought tolerance. Discussion: We expect to observe reduced stomatal densities and better drought tolerance in the mutant Oryza sativa samples. This should be observed in increased yield and growth from genetically altered samples. Potential implications of our proposal could include improvements in proto-plants developed in the agricultural sector, as well as providing a foundation for future studies to be conducted on drought tolerance. Conclusion: Our proposal uniquely addresses the impact of climate change on rice by potentially providing an opportunity to scale-up, generating a drought-tolerant rice plant for comparison with previous prototypes, and secondarily, the elucidation of stomatal development. Our proposal may open further opportunities to address and alter plant resistance to climate change.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".