Advanced and Safe Synthetic Microbial Chassis with Orthogonal Translation System Integration
Bibliographic record
Abstract
Through the use of CRISPR-assisted transposition, we have engineered a safe Escherichia coli chassis that integrates an orthogonal translation system (OTS) directly into the chromosome. This approach circumvents the limitations and genetic instability associated with conventional plasmid vectors. Precision in genome modification is crucial for the top–down creation of synthetic cells, especially in the orthogonalization of vital cellular processes, such as metabolism and protein translation. Here, we targeted multiple loci in the E. coli chromosome to integrate the OTS simultaneously, creating a synthetic auxotrophic chassis with an altered genetic code to establish a reliable, robust, and safe synthetic protein producer. Our OTS-integrated chassis enabled the site-specific incorporation of m -oNB-Dopa through in-frame amber stop codon readthrough. This allowed for the expression of advanced underwater bioglues containing Dopa-Lysine motifs, which are crucial for wound healing and tissue regeneration. Additionally, we have enhanced the expression process by incorporating scaffold-stabilizing fluoroprolines into bioglues, utilizing our chassis, which has been modified through metabolic engineering (i.e., by introducing proline auxotrophy). We also engineered a synthetic auxotroph reliant on caged Dopa, creating a genetic barrier (genetic firewall) between the synthetic cells and their surroundings, thereby boosting their stability and safety.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".