Early Notch Signals Induce a Pathogenic Molecular Signature during Priming of Alloantigen-Specific Conventional CD4+ T Cells in Graft-versus-Host Disease
Bibliographic record
Abstract
Abstract Graft-versus-host disease (GVHD) is the most serious complication of allogeneic hematopoietic cell transplantation. Notch signals delivered during the first 48 h after transplantation drive proinflammatory cytokine production in conventional T cells (Tconv) and inhibit the expansion of regulatory T cells (Tregs). Short-term Notch inhibition induces long-term GVHD protection. However, it remains unknown whether Notch blockade blunts GVHD through its effects on Tconv, Tregs, or both and what early Notch-regulated molecular events occur in alloantigen-specific T cells. To address these questions, we engineered T cell grafts to achieve selective Notch blockade in Tconv versus Tregs and evaluated their capacity to trigger GVHD in mice. Notch blockade in Tconv was essential for GVHD protection as GVHD severity was similar in the recipients of wild-type Tconv combined with Notch-deprived versus wild-type Tregs. To identify the impact of Notch signaling on the earliest steps of T cell activation in vivo, we established a new acute GVHD model mediated by clonal alloantigen-specific 4C CD4+ Tconv. Notch-deprived 4C T cells had preserved early steps of activation, IL-2 production, proliferation, and Th cell polarization. In contrast, Notch inhibition dampened IFN-γ and IL-17 production, diminished mTORC1 and ERK1/2 activation, and impaired transcription of a subset of Myc-regulated genes. The distinct Notch-regulated signature had minimal overlap with known Notch targets in T cell leukemia and developing T cells, highlighting the specific impact of Notch signaling in mature T cells. Our findings uncover a unique molecular program associated with the pathogenic effects of Notch in T cells at the earliest stages of GVHD.
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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) | 0.001 | 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".