Colonic MUC2 mucin regulates the expression and antimicrobial activity of β‐defensin 2
Bibliographic record
Abstract
The MUC2 mucus layer is the first line of host defense in the gut. How MUC2 interacts with antimicrobial peptides in host defense is not well understood. In this study we investigated the interactions between MUC2 and β‐defensin. Of the colonic cells tested, β‐ defensin 2 expressions was the highest in MUC2 producing human LS 174T and the lowest in Caco‐2 cells. Similarly, expression of murine β‐defensin 4 (orthologous to human β‐defensin 2) was low in the colon of Muc2 −/− mice as compared to Wt. Purified MUC2 attenuated the stimulation of β‐defensin as IL‐1β and sodium butyrate induced the expression of β‐defensin in Caco‐2, but not LS 174T cells. The antimicrobial activity of β‐defensin 2 differed among enteric bacteria; Escherichia coli were more susceptible than Bacteroides vulgatus and Clostridium difficile in killing assays. Our studies revealed that MUC2 served as a food source for enteric bacteria, which explains why Muc2 −/− harbored less commensal Bacteroides and Firmicute s spp. Mechanistically, MUC2 N‐ and O‐linked oligosaccharides bound bacteria and protected them against β‐defensin 2 antimicrobial activities. MUC2 protected the microbiota by serving as a food source and by inhibiting the antimicrobial activity of β‐defensin. When the mucus barrier is compromised, β‐defensin kills susceptible bacteria whereas β‐defensin resistant bacteria may colonize and cause disease.
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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".