Migratory CD103+CD11b+ cDC2s in Peyer’s patches are critical for gut IgA responses following oral immunization
Bibliographic record
Abstract
Induction and regulation of specific intestinal immunoglobulin (Ig)A responses critically depend on dendritic cell (DC) subsets and the T cells they activate in the Peyer's patches (PP). We found that oral immunization with cholera toxin (CT) as an adjuvant resulted in migration-dependent changes in the composition and localization of PP DC subsets with increased numbers of cluster of differentiation (CD)103 − conventional DC (cDC)2s and lysozyme-expressing DC (LysoDCs) in the subepithelial dome and of CD103 + cDC2s that expressed CD101 in the T cell zones, while oral ovalbumin (OVA) tolerization was instead associated with greater accumulation of cDC1s and peripherally induced regulatory T cells (pTregs) in this area. Decreased IgA responses were observed after CT-adjuvanted immunization in huCD207DTA mice lacking CD103 + cDC2s, while oral OVA tolerization was inefficient in cDC1-deficient Batf3 −/− mice. Using OVA transgenic T cell receptor CD4 T cell adoptive transfer models, we found that co-transferred endogenous wildtype CD4 T cells can hinder the induction of OVA-specific IgA responses through secretion of interleukin-10. CT could overcome this blocking effect, apparently through a modulating effect on pTregs while promoting an expansion of follicular helper T cells. The data support a model where cDC1-induced pTreg normally suppresses PP responses for any given antigen and where CT's oral adjuvanticity effect is dependent on promoting follicular helper T cell responses through induction of CD103 + cDC2s.
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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".