Antibiotic Treatment Alters the Colonic Mucus Layer and Predisposes the Host to Exacerbated <i>Citrobacter rodentium</i> -Induced Colitis
Bibliographic record
Abstract
Antibiotics are often used in the clinic to treat bacterial infections, but the effects of these drugs on microbiota composition and on intestinal immunity are poorly understood. Citrobacter rodentium was used as a model enteric pathogen to investigate the effect of microbial perturbation on intestinal barriers and susceptibility to colitis. Streptomycin and metronidazole were used to induce alterations in the composition of the microbiota prior to infection with C. rodentium. Metronidazole pretreatment increased susceptibility to C. rodentium-induced colitis over that of untreated and streptomycin-pretreated mice, 6 days postinfection. Both antibiotic treatments altered microbial composition, without affecting total numbers, but metronidazole treatment resulted in a more dramatic change, including a reduced population of Porphyromonadaceae and increased numbers of lactobacilli. Disruption of the microbiota with metronidazole, but not streptomycin treatment, resulted in an increased inflammatory tone of the intestine characterized by increased bacterial stimulation of the epithelium, altered goblet cell function, and thinning of the inner mucus layer, suggesting a weakened mucosal barrier. This reduction in mucus thickness correlates with increased attachment of C. rodentium to the intestinal epithelium, contributing to the exacerbated severity of C. rodentium-induced colitis in metronidazole-pretreated mice. These results suggest that antibiotic perturbation of the microbiota can disrupt intestinal homeostasis and the integrity of intestinal defenses, which protect against invading pathogens and intestinal inflammation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".