Bibliographic record
Abstract
Trypsin inhibitors (TIs) in soybean are known to have antinutritional effects, reducing protein digestibility and limiting the nutritional value of soy products and animal feeds. To address this long-standing challenge, genome editing tools such as CRISPR/Cas9 have emerged as promising strategies for precisely eliminating undesirable traits such as TIs. This study explores the application of CRISPR/Cas9 to targetedly ablate trypsin inhibitor genes in soybean, specifically those encoding Kunitz and Bowman-Birk inhibitors. We discuss the biological functions and limitations of these inhibitors, outline the mechanisms and recent technical improvements of CRISPR/Cas9, and detail methods for identifying TI gene targets using transcriptomic and proteomic analyses. We also review guide RNA design, translational techniques, and gene editing validation. Functional assessments demonstrated that knockout lines exhibited reduced TI activity, improved protein digestibility, and improved nutritional status, with minimal adverse effects on agronomic traits. A case study demonstrating the successful ablation of the Kunitz trypsin inhibitor gene further demonstrates the utility of this approach. We also explore biosafety concerns, regulatory frameworks, and public perception issues surrounding genome-edited crops. Ultimately, this study highlights the transformative potential of CRISPR/Cas9 for improving the nutritional quality of soybeans and supports future efforts to integrate genome editing into breeding programs to develop high-protein, low-antinutrient varieties.
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 | 0.001 |
| 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".