Mitigating Heat Stress Response in CRISPR/Cas‐Mediated Edited Crops by Altering the Expression Pattern of Noncoding DNA
Bibliographic record
Abstract
Heat stress is a major challenge in crop production, becoming more severe with climate change and frequent extreme heat waves. Plants are highly sensitive to temperature changes beyond their optimal growth range. Developing thermotolerant cultivars that can endure high temperatures without compromising yield or agronomic traits is considered the most sustainable way to feed the growing global population in a warming world. However, breeding for thermotolerance is complex due to its polygenic nature and the intricate genetic regulatory networks involved. Gene-editing technologies, especially clustered regularly interspaced palindromic repeats (CRISPRs)/CRISPR-associated protein 9 (Cas9), have been instrumental in creating new thermotolerant plant varieties and understanding their functions. In plant breeding, the common gene-editing method involves targeted mutations in protein-coding genes to generate phenotypic diversity for selection. These mutations often result in gene knockouts, gain-of-function, or neofunctionalization. A new approach in genome editing for crop improvement focuses on noncoding DNA, particularly cis -regulatory elements (CREs), which regulate gene expression through interactions with trans -regulatory elements. Identifying the right noncoding DNA targets for gene editing remains a challenge. Artificial intelligence and machine learning can help analyze genomics, transcriptomics, and phenomics data to find suitable regions for modification. This chapter will explore methods for enhancing thermotolerance by editing CREs and other functional noncoding DNA elements using genome-editing tools like CRISPR/Cas9.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".