Genetically Engineering Microbes for Detecting and Treating Inflammatory Bowel Disease
Bibliographic record
Abstract
Inflammatory bowel disease (IBD) is a chronic medical condition characterized by inflammation in the intestine. Treating the patients starts by appropriately diagnosing the disease, followed by administering therapeutic drugs to reduce the symptoms and induce tissue repair that can help the patient achieve remission. However, as there are no definitive cures, treatment of IBD requires long-term management that involves periodically monitoring the extent of inflammation within the intestine and several treatment courses. Traditional methods for detecting the disease are highly intrusive, and the therapies are not entirely effective, often leading to relapse and sometimes even requiring surgical interventions. Therefore, there is a need for better diagnostic tools and treatment options to improve the outcomes and quality of life of IBD patients. Small, single-celled organisms, such as bacteria or yeast, have the unique ability to travel through the gastrointestinal tract and into the intestine- a natural habitat for microbes. Once inside this area, they come in close contact to the sites of inflammation, priming them for disease intervention. As these microbes are living organisms, they can also be genetically re-programmed to accomplish desired functions such as sensing molecular or environmental cues, or to deliver molecules with therapeutic properties. With the advances in genetic engineering tools and strategies, microbes can be engineered into diagnostic tools to monitor the inflammation within the patient’s intestine and as therapeutic delivery systems to provide targeted therapies at the source of the disease. The goal of this project was to contribute to the development of engineered microbes for detecting and treating IBD. These contributions include developing and validating genome editing tools, therapeutic protein expression systems, biosensors to detect reactive oxygen species, and a colorimetric yeast-based assay to detect biomarkers of IBD using a probiotic yeast strain Saccharomyces boulardii. I also discovered novel transcriptional markers of colitis within a probiotic bacteria strain, E. coli Nissle, that could be used to monitor intestinal inflammation. Overall, the results of this project should prove valuable for engineering microbial devices for medical applications.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".