Bacterial strain‐specific induction of Foxp3<sup>+</sup> T regulatory cells is protective in murine allergy models
Bibliographic record
Abstract
BACKGROUND: The incidence of atopic disease has increased dramatically during recent decades and the potential immunoregulatory influence of the microbiota in these individuals is under investigation. OBJECTIVE: The aim of our study was to identify a bacterial strain that is protective in murine allergy models and to determine if microbial induction of T regulatory cells was associated with protection from allergic inflammation. METHODS: Three microbes (Bifidobacterium breve AH1205, B. longum AH1206 and Lactobacillus salivarius AH102) of human origin were fed to newborn, adult and germ-free animals. Induction of Foxp3(+) T regulatory cells was assessed by flow cytometry. Gene array analysis was performed on Peyer's patches. Strains were also examined for their protective effects in the ovalbumin (OVA) respiratory allergy model and the OVA-cholera toxin dietary allergy model. RESULTS: Bifidobacterium longum AH1206 consumption resulted in increased numbers of Foxp3(+) T regulatory cells in infant, adult and germ-free animals. B. breve AH1205 induced Foxp3(+) T regulatory cell expansion only in infant mice while L. salivarius AH102 did not alter T regulatory cell numbers in any animal model tested. B. longum AH1206 reduced the Peyer's patch gene expression associated with antigen presentation, TLR signalling and cytokine production while increasing the expression of genes associated with retinoic acid metabolism. B. longum AH1206 protected against airway inflammation in OVA-sensitized animals and B. longum AH1206 blocked the induction of IgE to orally administered OVA. Neither B. breve AH1205 nor L. salivarius AH102 had a protective effect in either model. CONCLUSION: Bacterial strain-specific induction of Foxp3(+) T regulatory cells in vivo is associated with protection from respiratory and oral allergy.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".