Environmental factors drive bacterial degradation of gastrointestinal mucus
Bibliographic record
Abstract
ABSTRACT The mucus layer lining the gastrointestinal tract is essential for gut health, providing a protective barrier against pathogens while maintaining symbiosis with the microbiome. Its disruption is a hallmark of gastrointestinal diseases like ulcerative colitis. While glycan foraging by gut bacteria is thought to initiate mucus disruption, its impact on mucus structural properties remains poorly understood, largely due to the lack of physiologically relevant models. To address this gap, we developed a method to collect human-cell-derived mucus that closely mimics the mechanical properties of human colonic mucus. Using this system, we investigated mucus utilization and degradation by a panel of commensal bacteria with distinct metabolic profiles. Glycan utilization by species such as Bacteroides thetaiotaomicron and Bacteroides fragilis showed no correlation with changes in mucus rheology. Instead, secreted proteases were identified as the primary driver of mucus degradation. Protease activity by B. fragilis and Bifidobacterium longum was influenced by nutrient availability, whereas in Enterococcus faecalis, it was additionally affected by oxygen exposure . E. faecalis also adapted to oxidative stress by enhancing carbohydrate metabolism and upregulating several virulence genes. Together, our findings reveal that bacterial mucus degradation is context-dependent and shaped by environmental factors. This study provides key insights into the mechanisms underlying mucus degradation and underscores the value of human cell-derived mucus models for understanding bacteria-mucus interactions in health and 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".