Development of Modular Geminivirus‐Based Vectors for High Cargo Expression and Gene Targeting in Plants
Bibliographic record
Abstract
Viral vectors can be useful tools for expressing recombinant proteins as well as delivering gene-editing machinery. Despite their utility, the development and subsequent optimisation of these tools is often a difficult and tedious process. Thus, although considerable work has been done to create useful viral vectors for gene editing and protein expression, there is a lack of understanding of how best to design these vectors for specific applications. For instance, it is often unclear whether the inclusion of heterologous promoter sequences or different viral components will improve cargo expression or replicon accumulation. To address some of these hurdles, we designed a GoldenBraid (GB)-compatible viral vector system based on the geminivirus-beet curly top virus (BCTV). This system allows for simple, modular cloning of a variety of reporter constructs. Making use of this modular cloning strategy, we compared a variety of alternative viral vector architectures. Interestingly, native BCTV promoters outperformed the constitutive 35S promoter, while the removal of the BCTV virion-sense genes promoted reporter expression. Intriguingly, these modifications had no effect on total replicon accumulation. These results show the utility of the new modular BCTV-based vectors for protein expression and gene targeting applications, as well as uncover design principles that may inform future geminivirus-based viral vector architectures. We anticipate that the availability of this new modular system will spark the broad adoption of replicon-based strategies in protein expression and gene editing experiments in plants.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".