Acute Infectious Gastroenteritis Potentiates a Crohn's Disease Pathobiont to Fuel Ongoing Inflammation in the Post-Infectious Period
Bibliographic record
Abstract
Crohn's disease (CD) is a chronic inflammatory condition of diverse etiology. Exposure to foodborne pathogens causing acute gastroenteritis produces a long-term risk of CD well into the post-infectious period but the mechanistic basis for this ongoing relationship to disease onset is unknown. We developed two novel models to study the comorbidity of acute gastroenteritis caused by Salmonella Typhimurium or Citrobacter rodentium in mice colonized with adherent-invasive Escherichia coli (AIEC), a bacterial pathobiont linked to CD. Here, we show that disease activity in the post-infectious period after gastroenteritis is driven by the tissue-associated expansion of the resident AIEC pathobiont, with an attendant increase in immunopathology, barrier defects, and delays in mucosal restitution following pathogen clearance. These features required AIEC resistance to host defense peptides and a fulminant inflammatory response to the enteric pathogen. Our results suggest that individuals colonized by AIEC at the time of acute infectious gastroenteritis may be at greater risk for CD onset. Importantly, our data identify AIEC as a tractable disease modifier, a finding that could be exploited in the development of therapeutic interventions following infectious gastroenteritis in at-risk individuals.
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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".