Engineered spermidine-secreting Saccharomyces boulardii ameliorates colitis and colon cancer in mice
Bibliographic record
Abstract
Experimental studies suggest that the probiotic yeast Saccharomyces boulardii can mitigate the symptoms of inflammatory bowel disease. However, these results are equivocal and S. boulardii probiotic therapy has not gained widespread acceptance in clinical practice. To assess whether the therapeutic properties of S. boulardii might be improved upon, we engineered S. boulardii to overproduce and secrete spermidine, a pro-regenerative natural metabolite. We employed CRISPR gene deletion and integration of gene cassettes at the Ty2 locus to achieve high level polyamine synthesis and transport. We tested whether spermidine secreting S. boulardii could reduce disease symptoms in dextran sulfate sodium (DSS) and azoxymethane induced models of intestinal inflammation and cancer. We demonstrate that oral delivery of spermidine-secreting S. boulardii in mice populates the gastrointestinal tract with viable spermidine-secreting S. boulardii cells and raises free spermidine levels in the gastrointestinal tract. Strikingly, spermidine-secreting S. boulardii strains were significantly more effective than wild-type S. boulardii in reducing colitis symptoms as well as colitis-associated carcinogenesis in mice. These results suggest that in situ spermidine secretion by engineered synthetic biotic yeast strains may be an effective and low-cost therapy to mitigate inflammatory bowel disease and associated colon cancer.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".