Colonic microbiota alters host susceptibility to infectious colitis by modulating inflammation, redox status, and ion transporter gene expression
Bibliographic record
Abstract
Individuals vary in their resistance to enteric infections. The role of the intestinal microbiota in altering susceptibility to enteric infection is relatively unknown. Previous studies have identified that C3H/HeOuJ mice suffer 100% mortality during Citrobacter rodentium-induced colitis, whereas C57BL/6 mice recover from infection. The basis for their differences in susceptibility is unclear and has been mainly attributed to differences in host genetics. This study investigated the role of the intestinal microbiota in altering susceptibility to C. rodentium-induced colitis. When the feces of C57BL/6 mice were gavaged into antibiotic treated C3H/HeOuJ mice, the C57BL/6 microflora led to a complete reversal in mortality patterns where 100% of the C3H/HeOuJ mice survived infection. This protection corresponded with reduced colonic pathology and less systemic pathogen load and was associated with increased inflammatory and redox responses with reduced epithelial cell death. C3H/HeOuJ mice are normally susceptible to infection-induced dehydration due to defective expression of colonic ion transporters such as Dra, CA IV, and CA I; expression of these genes was normalized when C3H/HeOuJ mice were colonized with the C57BL/6 microflora. Together, these data reveal that the colonic microbiota play a critical role in protecting against intestinal infection by inducing proinflammatory and prooxidant responses that control pathogen load as well as ion transporter gene expression previously shown to prevent fatal dehydration. Protection of mice from lethal colitis was associated with higher levels of bacteria from Bacteroidetes. This study reveals that the microbiota is sufficient to overcome inherent genetic susceptibility patterns in C3H/HeOuJ mice that cause mortality during C. rodentium infection.
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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 | 0.000 |
| 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".