Selective enrichment of commensal gut bacteria protects against<i>Citrobacter rodentium</i>-induced colitis
Bibliographic record
Abstract
The intestinal microbiota plays a key role in shaping the host immune system. Perturbation of gut microbial composition, termed dysbiosis, is associated with an increased susceptibility to intestinal pathogens and is a hallmark of a number of inflammatory, metabolic, and infectious diseases. The prospect of mining the commensal gut microbiota for bacterial strains that can impact immune function represents an attractive strategy to counteract dysbiosis and resulting disease. In this study, we show that selective enrichment of commensal gut lactobacilli protects against the murine pathogen Citrobacter rodentium, a well-characterized model of enteropathogenic and enterohemorrhagic Escherichia coli infection. The lactobacilli-enriched bacterial culture prevented the expansion of Gammaproteobacteria and Actinobacteria and was associated with improved indexes of epithelial barrier function (dextran flux), transmissible crypt hyperplasia, and tissue inflammatory cytokine levels. Moreover, cultivation of gut bacteria from Citrobacter rodentium-infected mice reveals the differential capacity of bacterial subsets to mobilize neutrophil oxidative burst and initiate the formation of weblike neutrophil extracellular traps. Our findings highlight the beneficial effects of a lactobacilli-enriched commensal gut microenvironment and, in the context of an intestinal barrier breach, the ability of neutrophils to immobilize both commensal and pathogenic 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".