Distinct Roles of the Mucus Layer and Microbiota in Conferring Innate Host Defense and Susceptibility to Disease
Bibliographic record
Abstract
The human intestine has approximately 100 trillion microorganisms, mainly bacteria that form the microbiota. These microorganisms play a key role in digestive health as well as in the development of a balanced immune system. The intestinal mucosa is covered by MUC2 mucus that acts as a protective barrier. Alteration in microbiota composition (dysbiosis) and the mucus barrier is common in several gastrointestinal pathologies. The distinct contribution of the microbiota and the mucus barrier play in the pathogenesis of colitis is not well understood. The aim of this study was to determine whether shifts in microbiota could alter the onset and progression of colitis in Muc2 mucin deficient and sufficient mice. F2 littermates Wt ( Muc2 +/+ ) and homozygous ( Muc2 ‐/‐ ) mice were gavaged with antibiotics, fecal transplanted with their littermate stool and susceptibility to dextran sulphate sodium (DSS) was quantified. Fecal samples pre‐ and post‐antibiotic was analyzed by MiSeq lll lluminar sequencing. Intestinal permeability was assessed using FITC‐dextran. DNA analysis showed clear phyla differences in microbiota composition between the two groups of mice. Muc2 +/+ mice that received Muc2 ‐/‐ microbiota were highly susceptible to DSS‐induced colitis and showed increased mortality as compared to controls. As predicted, these animals had higher histopathological scores as compared to Muc2 +/+ receiving their own microbiota. Surprisingly, Muc2 ‐/‐ mice showed no increase/decrease in disease susceptibility receiving Muc2 +/+ microbiota. Our results establish that Muc2 ‐/‐ microbiota is highly colitogenic and is capable of exacerbating DSS‐induced colitis in Wt mice with an intact Muc2 layer. Grant: Crohn's and Colitis Canada
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".