Recent trends and advances in chloroplast engineering and transformation methods
Bibliographic record
Abstract
Chloroplast transformation technology has become a powerful platform for generating plants that express foreign proteins of pharmaceutical and agricultural importance at high levels. Chloroplasts are often chosen as attractive targets for the introduction of new agronomic traits because they have their own genome and protein synthesis machinery. Certain valuable traits have been genetically engineered into plastid genomes to improve crop yield, nutritional quality, resistance to abiotic and biotic stresses, and the production of industrial enzymes and therapeutic proteins. Synthetic biology approaches aim to play an important role in expressing multiple genes through plastid engineering, without the risk of pleiotropic effects in transplastomic plants. Despite many promising laboratory-level successes, no transplastomic crop has been commercialized to date. This technology is mostly confined to model species in academic laboratories and needs to be expanded to other agronomically important crop species to capitalize on its significant commercial potential. However, in recent years, some transplastomic lines are progressing in field trials, offering hope that they will pass regulatory approval and enter the marketplace. This review provides a comprehensive summary of new and emerging technologies employed for plastid transformation and discusses key synthetic biology elements that are necessary for the construction of modern transformation vectors. It also focuses on various novel insights and challenges to overcome in chloroplast transformation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".