Characterizing the immunomodulatory effect of a microbiome-based therapy for the treatment of ulcerative colitis 3457
Bibliographic record
Abstract
Abstract Description Microbiome-based therapies are being developed to treat complex diseases like ulcerative colitis (UC). The premise of these therapies is that they target both, the host and the gut microbiome. Our lab has developed BioPersistTM, a bio-engineered version of the probiotic E. coli Nissle 1917 (EcN) introduced with a persistence feature. BioPersist has shown protective effects in acute and spontaneous UC models. To further characterize how BioPersist impacts the gut microenvironment, we treated Muc2-/- mice (a spontaneous UC model) p.o. with vehicle, EcN or BioPersist. Although the gut microbiome remained unchanged, BioPersist-treated mice had reduced fecal calprotectin and mucosal infiltrating macrophages and neutrophils, key drivers of UC. Further analysis of the colon’s lamina propria cells showed that BioPersist treatment decreased % of eosinophils, macrophages and neutrophils; moreover, BioPersist-treated mice had populations of CD103+ cells and macrophages with significantly lower secretion of TNF-α, a known therapeutic target for UC, along with increased % of Foxp3+ Treg cells. Altogether, our results indicate the immunomodulatory effect of BioPersist in the gut environment that ultimately protects Muc2-/- mice from severe colitis. Future studies will focus on characterizing how the immunomodulatory effect protects against metabolic dysfunction associated with UC Funding Sources Consejo Nacional de Humanidades, Ciencias y Tecnologías (CONAHCyT), Crohn’s and Colitis Canada (CCC) and Michael Smith Health Research Innovation 2 Commercialization phase 1 and phase 2 (partnered with Melius MicroBiomics Inc.). Topic Categories Mucosal and Regional Immunology (MUC)
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".