DOCK8 and STAT3 cooperate to restrain IgE-inducing T follicular helper cells.
Bibliographic record
Abstract
Abstract DOCK8 deficient patients have elevated food specific IgE and severe food allergy. In Dock8-/- mice, we showed that T cell intrinsic DOCK8 restrains the development of IL-13 expressing T follicular helper (Tfh13) cells that drive anaphylactic IgE production. Oral exposure to food results in tolerance, yet T cell intrinsic loss of DOCK8 (T-Dock8-/-) is sufficient to drive Tfh13 differentiation and peanut specific IgE. How DOCK8 prevents Tfh13 differentiation is unknown. In human T cells, DOCK8 promotes STAT3 phosphorylation, an inhibitor of GATA3. We show that mouse DOCK8 promotes STAT3 activity in T cells and STAT3-/- T cells differentiate into Tfh13-like cells in vitro. Yet, T cell specific loss of STAT3 (T-Stat3-/-) did not drive peanut specific IgE or Tfh13 differentiation in vivo after oral peanut. STAT3 patients have high IgE, but not severe food allergies and we found elevated levels of Tfh13s in donors with DOCK8 but not STAT3 mutations, suggesting additional mechanisms to inhibit Tfh13 differentiation. DOCK8 promotes Tregs and we show that the analogous regulatory population for Tfh cells, T follicular regulatory (Tfr) cells were reduced in T-Dock8-/- but not T-Stat3-/- mice. To overcome tolerance, we used oral peanut with cholera toxin; levels of Tfh13s and peanut specific IgE where higher in T-Stat3-/- mice than controls. We propose that DOCK8 and STAT3 cooperate in CD4+ T cells to prevent Tfh13 differentiation.
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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) | 0.003 | 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".