The VSL#3 probiotic formula induces mucin gene expression and secretion in colonic epithelial cells
Bibliographic record
Abstract
Several studies have stressed the importance of the microbiota in the maintenance of the gastrointestinal epithelium. Administration of probiotic bacteria, supplements composed of microbiota constituents, was previously shown to diminish symptoms in patients suffering from inflammatory bowel diseases. This raises the possibility that probiotics may play an active role in enhancing the intestinal barrier at the mucosal surface. In this study, we investigated whether the clinically tested VSL#3 probiotic formula and/or its secreted components can augment the protective mucus layer in vivo and in vitro. For in vivo studies, Wistar rats were orally administered the probiotic mixture VSL#3 on a daily basis for seven days. After treatment, basal luminal mucin content increased by 60%. In addition, we exposed isolated rat colonic loops to the VSL#3 probiotic formula, which significantly stimulated colonic mucin (MUC) secretion and MUC2 gene expression; however, MUC1 and MUC3 gene expression were only slightly elevated. The effect of the VSL#3 mucin secretagogue was also tested in vitro by use of LS 174T colonic epithelial cells. In contrast to the animal studies, cultured cells incubated with VSL#3 bacteria did not exhibit increased mucin secretion. However, the bacterial secreted products contained in the conditioned media stimulated a remarkable mucin secretion effect. Among the three bacterial groups (Lactobacilli, Bifidobacteria, and Streptococci) contained in VSL#3, the Lactobacillus species were the strongest potentiator of mucin secretion in vitro. A preliminary characterization of the putative mucin secretagogue suggested that it was a heat-resistant soluble compound, which is not sensitive to protease and DNase treatment. These findings contribute to a better understanding of the complex and beneficial interaction between colonic epithelial cells and intestinal bacteria.
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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 | 0.001 |
| 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".