Serine protease inhibitors protect better than IL-10 and TGF-β anti-inflammatory cytokines against mouse colitis when delivered by recombinant lactococci
Bibliographic record
Abstract
BACKGROUND: Different studies have described the successful use of recombinant lactic acid bacteria (recLAB) to deliver anti-inflammatory molecules at the mucosal level to treat Inflammatory Bowel Disease (IBD). METHODS: In order to identify the best strategy to treat IBD using recLAB, we compared the efficacy of different recombinant strains of Lactococcus lactis (the model LAB) secreting two types of anti-inflammatory molecules: cytokines (IL-10 and TGF-β1) and serine protease inhibitors (Elafin and Secretory Leukocyte Protease Inhibitor: SLPI), using a dextran sulfate sodium (DSS)-induced mouse model of colitis. RESULTS: Our results show that oral administration of recombinant L. lactis strains expressing either IL-10 or TGF-β1 display moderate anti-inflammatory effects in inflamed mice and only for some clinical parameters. In contrast, delivery of either serine protease inhibitors Elafin or SLPI by recLAB led to a significant reduction of intestinal inflammation for all clinical parameters tested. Since the best results were obtained with Elafin-producing L. lactis strain, we then tried to enhance Elafin expression and hence its delivery rate by producing it in a L. lactis mutant strain inactivated in its major housekeeping protease, HtrA. Strikingly, a higher reduction of intestinal inflammation in DSS-treated mice was observed with the Elafin-overproducing htrA strain suggesting a dose-dependent Elafin effect. CONCLUSIONS: Altogether, these results strongly suggest that serine protease inhibitors are the most efficient anti-inflammatory molecules to be delivered by recLAB at the mucosal level for IBD treatment.
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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 | 0.000 | 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) | 0.001 | 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".