Regulation of Mucosal Dendritic Cell Function by Receptor Activator of NF-κB (RANK)/RANK Ligand Interactions: Impact on Tolerance Induction
Bibliographic record
Abstract
The mucosal immune system is uniquely equipped to discriminate between potentially invasive pathogens and innocuous food proteins. While the mechanisms responsible for induction of mucosal immunity vs tolerance are not yet fully delineated, recent studies have highlighted mucosal dendritic cells (DC) as being important in determining the fate of orally administered Ag. To further investigate the DC:T cell signals involved in regulating the homeostatic balance between mucosal immunity and tolerance, we have examined the expression and function of the TNFR family member receptor activator of NF-kappaB (RANK) and its cognate ligand, RANKL, in vitro and in vivo. Our data show that although DC isolated from mucosal lymphoid tissues expressed similar levels of surface RANK compared with DC isolated from peripheral lymphoid tissues, DC from the distinct anatomical sites displayed differential responsiveness to RANK engagement with soluble RANKL. Whereas splenic DC responded to RANKL stimulation with elevated IL-12 p40 mRNA expression, Peyer's patch DC instead preferentially displayed increased IL-10 mRNA expression. Our data also show that the in vivo functional capacity of mucosal DC can be modulated by RANKL. Treatment with RANKL in vivo at the time of oral administration of soluble OVA enhanced the induction of tolerance in two different mouse models. These studies underscore the functional differences between mucosal and peripheral DC and highlight a novel role for RANK/RANKL interactions during the induction of mucosal immune responses.
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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 | 0.001 | 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".