Mast Cells Down-Regulate CD4+CD25+ T Regulatory Cell Suppressor Function via Histamine H1 Receptor Interaction
Bibliographic record
Abstract
Mast cells promote both innate and acquired immune responses, but little is known about the effect of mast cells on T regulatory (T(reg)) cell function. In this study, we show for the first time that the capacity of murine CD4(+)CD25(+) T(reg) cells to suppress in vitro proliferation by CD4(+)CD25(-) T responder (T(resp)) cells in response to anti-CD3/anti-CD28 mAb-coated beads was reduced in the presence of syngeneic bone marrow-derived mast cells (BMMC) activated by FcepsilonR cross-linking. Activated BMMC culture supernatants or exogenous histamine also inhibited T(reg) cell suppressor function while the histamine H1 receptor-specific antagonist loratadine, but not the H2 receptor-specific antagonist famotidine, restored T(reg) cell suppressor function in the presence of activated BMMC or activated BMMC culture supernatants. Moreover, treatment of T(reg) cells with loratadine, but not famotidine, rescued T(reg) cell suppressor function in the presence of exogenous histamine. In addition, the H1 receptor-specific agonist 2-pyridylethylamine dihydrochloride inhibited T(reg) cell suppressor function to an extent that was comparable to histamine, whereas the H2 receptor-specific agonist amthamine dihydrobromide was without effect. Both T(reg) cells and T(resp) cells expressed H1 receptors. Exposure to histamine caused T(reg) cells to express lower levels of CD25 and the T(reg) cell-specific transcription factor Foxp3. Taken together, these data indicate that BMMC-elaborated histamine inhibited T(reg) cell suppressor function by signaling through the H1 receptor. We suggest that histamine released as a result of mast cell activation by microbial products might cause a transient decrease in T(reg) cell suppressor function, thereby enhancing the development of protective immunity.
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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 | 0.000 |
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".