Induced Regulatory T Cells Superimpose Their Suppressive Capacity with Effector T Cells in Lymph Nodes via Antigen-Specific S1p1-Dependent Egress Blockage
Bibliographic record
Abstract
Regulatory T cells (Tregs) restrict over-exuberant lymphocyte activation. While close proximity between Tregs and their suppression targets is important for optimal inhibition, and literature indicates that draining lymph nodes may serve as a prime location for the suppression, signaling details orchestrating this event are not fully characterized. Using a protocol to enable peripheral generation of inducible antigen-specific Tregs (asTregs) to control allergen-induced asthma, we have identified an antigen-specific mechanism that locks asTregs within hilar LNs which in turn suppresses airway inflammation. The suppressive asTregs, upon antigen stimulation in the lymph node, downregulate S1p1 (Sphingosine-1-phosphate receptor 1) egress receptor expression. These asTregs in turn mediate the downregulation of the same receptor on incoming effector T cells. Therefore, asTregs and effector T cells are locked in these draining lymph nodes for prolonged interactions. Disruption of individual steps of this retention sequence abolishes the inflammation controlled by asTregs. Collectively, this study identifies a new requirement of spatial congregation with their suppression targets essential for asTreg functions, and suggests therapeutic programs via Treg traffic control.
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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) | 0.002 | 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".