GCNT1-Mediated <i>O</i>-Glycosylation of the Sialomucin CD43 Is a Sensitive Indicator of Notch Signaling in Activated T Cells
Bibliographic record
Abstract
Abstract Notch signaling is emerging as a critical regulator of T cell activation and function. However, there is no reliable cell surface indicator of Notch signaling across activated T cell subsets. In this study, we show that Notch signals induce upregulated expression of the Gcnt1 glycosyltransferase gene in T cells mediating graft-versus-host disease after allogeneic bone marrow transplantation in mice. To determine if Gcnt1-mediated O-glycosylation could be used as a Notch signaling reporter, we quantified the core-2 O-glycoform of CD43 in multiple T cell subsets during graft-versus-host disease. Pharmacological blockade of Delta-like Notch ligands abrogated core-2 O-glycosylation in a dose-dependent manner after allogeneic bone marrow transplantation, both in donor-derived CD4+ and CD8+ effector T cells and in Foxp3+ regulatory T cells. CD43 core-2 O-glycosylation depended on cell-intrinsic canonical Notch signals and identified CD4+ and CD8+ T cells with high cytokine-producing ability. Gcnt1-deficient T cells still drove lethal alloreactivity, showing that core-2 O-glycosylation predicted, but did not cause, Notch-dependent T cell pathogenicity. Using core-2 O-glycosylation as a marker of Notch signaling, we identified Ccl19-Cre+ fibroblastic stromal cells as critical sources of Delta-like ligands in graft-versus-host responses irrespective of conditioning intensity. Core-2 O-glycosylation also reported Notch signaling in CD8+ T cell responses to dendritic cell immunization, Listeria infection, and viral infection. Thus, we uncovered a role for Notch in controlling core-2 O-glycosylation and identified a cell surface marker to quantify Notch signals in multiple immunological contexts. Our findings will help refine our understanding of the regulation, cellular source, and timing of Notch signals in T cell 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) | 0.001 | 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".