CD8α<sup>+</sup> and CD8α<sup>−</sup> DC subsets from BCG‐infected mice inhibit allergic Th2‐cell responses by enhancing Th1‐cell and Treg‐cell activity respectively
Bibliographic record
Abstract
The hygiene hypothesis has suggested an inhibitory effect of infections on allergic diseases, but the related mechanism remains unclear. We recently reported that DCs played a critical role in Mycobacterium bovis Bacille Calmette-Guérin (BCG)-mediated inhibition of allergy, which depended on IL-12 and IL-10-related mechanisms. Here, we tested the hypothesis that BCG infection could modulate the function of DC subsets, which might in turn inhibit allergic responses through different mechanisms. We sorted CD8α(+) and CD8α(-) DCs from BCG-infected mice and tested their ability to modulate Th2-cell responses to ovalbumin (OVA) using in vitro and in vivo approaches. We found that both DC subsets could inhibit the allergic Th2-cell response in both a DC:T-cell co-culture system and after adoptive transfer. These subsets exhibited different co-stimulatory marker expression and cytokine production patterns and were different in inducing Th1 and Treg cells. Specifically, we found that CD8α(+) DCs produced higher IL-12, inducing higher Th1 cell response, while CD8α(-) DCs expressed higher ICOS-L and produced higher IL-10, inducing CD4(+) CD25(+) FoxP3(+) Treg cells with IL-10 production and membrane-bound TGF-β expression. The finding suggests that one infection may inhibit allergy by both immune deviation and regulation mechanisms through modulation of DC subsets.
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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 | 0.000 | 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) | 0.002 | 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".