B cells with aberrant activation of Notch1 signaling promote Treg and Th2 cell–dominant T-cell responses via IL-33
Bibliographic record
Abstract
Abstract The Notch-signaling pathway in a variety of mature B-cell neoplasms is often activated by gene alterations, but its role remains unclear. Here, we show that B cells harboring dysregulated activation of Notch1 signaling have an immunomodulatory effect on T cells by amplifying regulatory T (Treg) and T helper 2 (Th2) cell responses in an interleukin-33 (IL-33)-dependent manner. A conditional mouse model, in which constitutive expression of an active form of Notch1 is induced in B cells by Aicda gene promoter-driven Cre recombinase, revealed no obvious phenotypic changes in B cells; however, mice demonstrated an expansion of Treg and Th2 cell subsets and a decrease in cytokine production by Th1 and CD8+ T cells. The mice were susceptible to soft tissue sarcoma and defective production of CD8+ T cells specific for inoculated tumor cells, suggesting impaired antitumor T-cell activity. Gene-expression microarray revealed that altered T-cell responses were due to increased IL-33 production by Notch1-activated B cells. Knockout of IL33 or blockade of IL-33 by a receptor-blocking antibody abrogated the Treg and Th2 cell–dominant T-cell response triggered by B cells. Gene-expression data derived from human diffuse large B-cell lymphoma (DLBCL) samples showed that an activated Notch-signaling signature correlates positively with IL33 expression and Treg cell–rich gene-expression signatures. These findings indicate that B cells harboring dysregulated Notch signaling alter T-cell responses via IL-33, and suggest that aberrant activation of Notch signaling plays a role in fostering immune privilege in mature B-cell neoplasms.
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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 | 0.001 |
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".