Increased mucin‐degrading bacteria by high protein diet leads to thinner mucus layer and aggravates experimental colitis
Bibliographic record
Abstract
BACKGROUND AND AIM: Westernized high-fat diet increases the risk for inflammatory bowel diseases (IBDs), yet with insufficient understanding of the role of high-protein diet. We aimed to identify the effect of high-protein diets from different dietary proteins (casein, whey protein, soy protein) on experimental colitis and its impact on microbiota, structure and function of colonic mucus layer. METHODS: Female BALB/c mice were fed by standard diet, high-casein diet (HCD), high whey protein diet or high soy protein diet for 4 weeks. The susceptibility of dextran sulfate sodium (DSS)-induced colitis in mice and thickness of colonic mucus layer were compared after different dietary interventions, associated with the identification of the reversal effect of broad-spectrum antibiotic intervention (0.5 g/L of vancomycin and 1 g/L of neomycin sulfate, metronidazole and ampicillin in drinking water). Further analysis was performed on the synthesis of mucin, microbiota and sialidase involved in degradation of mucus layer. RESULTS: High-protein diets aggravated acute DSS-induced colitis independent of protein composition, while broad-spectrum antibiotics reversed this effect. HCD significantly altered the composition of bacteria in the colonic mucus layer, especially Bacteroides thetaiotaomicron and total mucin-degrading bacteria; besides, it increased sialidase concentration and reduced the thickness of mucus layer. However, it exhibited no significant effect on the synthesis of Muc2. Broad-spectrum antibiotics decreased the abundance of mucin-degrading bacteria and sialidase concentration while increased the thickness of mucus layer. CONCLUSION: High-protein diet shifts microbial composition and thickness of colonic mucus layer, leading to the aggravation of acute DSS-induced colitis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".