Oral microbiome composition changes in mouse models of colitis
Bibliographic record
Abstract
BACKGROUND AND AIM: Oral mucosal pathologies are frequent in inflammatory bowel disease (IBD). Since host-microbiome interactions are implicated in the pathogenesis of IBD, in this study the potential for changes affecting the oral microbiome was evaluated using two complementary mouse models of colitis: either chemically (dextran sulfate sodium) or with Citrobacter rodentium infection. METHODS: After sacrifice, the tongue, buccal mucosa, saliva, colon, and stool samples were collected for analyses. Denaturing gradient gel electrophoresis was performed to assess bacterial 16S rRNA gene profiles. Relative changes were determined using quantitative polymerase chain reaction analysis for the phyla Bacteroidetes, Firmicutes, Spirochetes, and Actinobacteria, classes Gammaproteobacteria and Betaproteobacteria, and the genera Bacillus and Lactobacillus. These groups represent over 99% of the oral microbiota of healthy C57BL/6 mice. RESULTS: Both models of colitis changed the oral microbiome, with the buccal microbiome being the most resistant to alterations in composition (maximum 1.8% change, vs tongue maximum 2.5% change, and saliva which demonstrated up to 7.2% total changes in microbiota composition). Changes in the oral microbiota were greater after dextran sulfate sodium challenge, compared with C. rodentium-induced colitis. Using cluster analysis, tongue and buccal mucosal microbiota composition changed ∼ 5%, saliva ∼ 35%, while stool changed ∼ 10%. CONCLUSION: These findings indicate that dysbiosis observed in murine models of colitis is associated with changes in the composition of bacteria present in the oral cavity and in saliva. Such changes in the oral microbiota could be relevant to the etiology and management of oral mucosal pathologies observed in IBD patients.
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".