Human basophils interact with memory T cells to augment Th17 responses
Bibliographic record
Abstract
Basophils are a rare population of granulocytes that have long been associated with IgE-mediated and Th2-associated allergic diseases. However, the role of basophils in Th17 and/or Th1 diseases has not been reported. In the present study, we report that basophils can be detected in the mucosa of Th17-associated lung and inflammatory bowel disease and accumulate in inflamed colons containing large quantities of IL-33. We also demonstrate that circulating basophils increased memory Th17 responses. Accordingly, IL-3- or IL-33-activated basophils amplified IL-17 release in effector memory T cells (T(EM)), central memory T cells (T(CM)), and CCR6(+) CD4 T cells. More specifically, basophils promoted the emergence of IL-17(+)IFN-γ(-) and IL-17(+)IFN-γ(+), but not IL-17(-)IFN-γ(+) CD4 T cells in T(EM) and T(CM). Mechanistic analysis revealed that the enhancing effect of IL-17 production by basophils in T(EM) involved the ERK1/2 signaling pathway, occurred in a contact-independent manner, and was partially mediated by histamine via H(2) and H(4) histamine receptors. The results of the present study reveal a previously unknown function for basophils in augmenting Th17 and Th17/Th1 cytokine expression in memory CD4 T cells. Because basophils accumulated in inflamed inflammatory bowel disease tissues, we propose that these cells are key players in chronic inflammatory disorders beyond Th2.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".