TEV protease‐facilitated stoichiometric delivery of multiple genes using a single expression vector
Why this work is in the frame
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Bibliographic record
Abstract
Delivery and expression of multiple genes is an important requirement in a range of applications such as the engineering of synthetic signaling pathways and the induction of pluripotent stem cells. However, conventional approaches are often inefficient, nonstoichiometric and may limit the maximum number of genes that can be simultaneously expressed. We here describe a versatile approach for multiple gene delivery using a single expression vector by mimicking the protein expression strategy of RNA viruses. This was accomplished by first expressing the genes together with TEV protease as a single fusion protein, then proteolytically self-cleaving the fusion protein into functional components. To demonstrate this method in E. coli cells, we analyzed the translation products using SDS-PAGE and showed that the fusion protein was efficiently cleaved into its components, which can then be purified individually or as a binding complex. To demonstrate this method in mammalian cells, we designed a differential localization scheme and used live cell imaging to observe the distinctive subcellular targeting of the processed products. We also showed that the stoichiometry of the processed products was consistent and corresponded with the frequency of appearance of their genes on the expression vector. In summary, the efficient expression and separation of up to three genes was achieved in both E. coli and mammalian cells using a single TEV protease self-processing vector.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it