Engineering dual-glycan responsive expression systems for tunable production of heterologous proteins in Bacteroides thetaiotaomicron
Bibliographic record
Abstract
Genetically engineering intestinal bacteria, such as Bacteroides thetaiotaomicron (B. theta), holds potential for creating new classes of biological devices, such as diagnostics or therapeutic delivery systems. Here, we have developed a series of B. theta strains that produce functional transgenic enzymes in response to dextran and arabinogalactan, two chemically distinct glycans. Expression systems for single glycan induction, and a novel "dual-glycan" expression system, requiring the presence of both dextran and arabinogalactan, have been developed. In addition, we have created two different chromosomal integration systems and one episomal vector system, compatible with engineered recipient strains, to improve the throughput and flexibility of gene cloning, integration, and expression in B. theta. To monitor activity, we have demonstrated the functionality of two different transgenic enzymes: NanoLuc, a luciferase, and BuGH16C, an agarase from the human intestinal bacterium, Bacteroides uniforms NP1. Together this expression platform provides a new collection of glycan-responsive tools to improve the strength and fidelity of transgene expression in B. theta and provides proof-of-concept for engineering more complex multi-glycan expression systems.
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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".