Bioengineered Probiotics: Synthetic Biology Can Provide Live Cell Therapeutics for the Treatment of Foodborne Diseases
Bibliographic record
Abstract
The rising prevalence of antibiotic resistant microbial pathogens presents an ominous health and economic challenge to modern society. The discovery and large-scale development of antibiotic drugs in previous decades was transformational, providing cheap, effective treatment for what would previously have been a lethal infection. As microbial strains resistant to many or even all antibiotic drug treatments have evolved, there is an urgent need for new drugs or antimicrobial treatments to control these pathogens. The ability to sequence and mine the genomes of an increasing number of microbial strains from previously unexplored environments has the potential to identify new natural product antibiotic biosynthesis pathways. This coupled with the power of synthetic biology to generate new production chassis, biosensors and “weaponized” live cell therapeutics may provide new means to combat the rapidly evolving threat of drug resistant microbial pathogens. This review focuses on the application of synthetic biology to construct probiotic strains that have been endowed with functionalities allowing them to identify, compete with and in some cases kill microbial pathogens as well as stimulate host immunity. Weaponized probiotics may have the greatest potential for use against pathogens that infect the gastrointestinal tract: Vibrio cholerae , Staphylococcus aureus , Clostridium perfringens and Clostridioides difficile . The potential benefits of engineered probiotics are highlighted along with the challenges that must still be met before these intriguing and exciting new therapeutic tools can be widely deployed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".