Genomically integrated orthogonal translation in <i>Escherichia coli</i> , a new synthetic auxotrophic chassis with altered genetic code, genetic firewall, and enhanced protein expression
Bibliographic record
Abstract
Abstract In the last three decades, genetic code engineering has expanded protein biosynthesis options from the natural set of 20 canonical amino acids to over 250 non-canonical amino acids (ncAAs). This progress involves rewiring of protein translation by establishing Orthogonal Translation Systems (OTS) through orthogonal pairs. Traditionally encoded on plasmid vectors, these systems are often unstable and burdensome in large-scale fermentations. To moving forward from academia to reliable technology, it is crucial to integrate OTS genetic modules stably into a molecular chassis with a defined genome background. Here, we demonstrate genomically integrated OTS in Escherichia coli , creating a synthetic auxotrophic chassis with an altered genetic code. Using CRISPR-associated transposase tool (CASTs), we targeted multiple genome sites, inserting OTS components (enzymes, tRNA genes) non-disruptively. Our OTS system demonstrated site-specific incorporation of m -oNB-Dopa through in-frame amber stop codon readthrough, enabling the expression of smart underwater bioglues. Simple metabolic labelling, introducing fluoroproline analogs enhancing conformational stability during orthogonal translation, further bolstered system robustness. These chassis, equipped also with synthetic auxotrophy for m -oNB-Dopa, serve as a built-in genetic barrier (genetic firewall), ensuring safe bioproduction in genetically isolated settings. Graphical Abstract One-sentence Abstract CRISPR-assisted transposition has enabled the development of a robust and biosafe E scherichia coli -based chassis with genomically integrated orthogonal translation components, enhancing synthetic protein production and laying the foundation for the transition of this research field from academia to reliable technology.
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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 | 0.000 | 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) | 0.000 | 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 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".