Turicibacterales protect mice from severe <i>Citrobacter rodentium</i> infection
Bibliographic record
Abstract
ABSTRACT One of the major contributors to child mortality in the world is diarrheal diseases, with an estimated 800,000 deaths per year. Many pathogens are causative agents of these illnesses, including the enteropathogenic (EPEC) or enterohemorrhagic (EHEC) forms of Escherichia coli . These bacteria are characterized by their ability to cause attaching and effacing lesions in the gut mucosa. Although much has been learned about the pathogenicity of these organisms and the immune response against them, the role of the intestinal microbiota during these infections is not well characterized. Infection of mice with E. coli requires pre-treatment with antibiotics in most mouse models, which hinders the study of the microbiota in an undisturbed environment. Using Citrobacter rodentium as a murine model for attaching and effacing bacteria, we show that C57BL/6 mice deficient in granzyme B expression are highly susceptible to severe disease caused by C. rodentium infection. Although a previous publication from our group shows that granzyme B-deficient CD4 + T cells are partially responsible for this phenotype, in this report we present data demonstrating that the microbiota, in particular members of the order Turicibacterales, have an important role in conferring resistance. Mice deficient in Turicibacter sanguinis have increased susceptibility to severe disease. However, when these mice are co-housed with resistant mice, or colonized with T. sanguinis , susceptibility to severe infection is reduced. These results clearly suggest a critical role for this commensal in the protection against entero-pathogens.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".