CRISPR-Cas9-Mediated Crop Genome Editing: From Basic Research to Breeding Applications
Bibliographic record
Abstract
The advent of CRISPR-Cas9 technology has catalyzed a paradigm shift in plant biology and agricultural biotechnology. This revolutionary genome-editing tool, derived from a prokaryotic adaptive immune system, offers unprecedented precision, efficiency, and versatility in modifying crop genomes. This review systematically charts the journey of CRISPR-Cas9 from a foundational research tool to a powerful driver of modern crop breeding. We begin by elucidating the fundamental mechanisms of the CRISPR-Cas9 system, including the engineering of Cas9 variants and the development of diverse delivery methods such as Agrobacterium-mediated transformation and ribonucleoprotein (RNP) complexes. We then explore its extensive applications in basic plant research, highlighting its role in functional genomics through targeted gene knockouts, transcriptional regulation, and epigenetic modifications. The core of this article focuses on the translational application of CRISPR-Cas9 in crop improvement, presenting landmark cases where the technology has been successfully deployed to enhance yield, nutritional quality, abiotic stress tolerance, and disease resistance in major cereal, vegetable, and fruit crops. We critically discuss the current global regulatory landscape for CRISPR-edited crops, which is pivotal for their commercial trajectory. Finally, we address persistent challenges-including off-target effects, delivery efficiency in recalcitrant species, and societal acceptance-and outline future perspectives, such as the integration of base editing, prime editing, and multiplexed editing strategies. By bridging the gap between laboratory innovation and field application, CRISPR-Cas9 is poised to make an indispensable contribution to global food security and sustainable agricultural systems.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".