Regulation of Notch signaling during T‐ and B‐cell development by <i>O</i>‐fucose glycans
Bibliographic record
Abstract
Notch signaling is required for the development of all T cells and marginal zone (MZ) B cells. Specific roles in T- and B-cell differentiation have been identified for different Notch receptors, the canonical Delta-like (Dll) and Jagged (Jag) Notch ligands, and downstream effectors of Notch signaling. Notch receptors and ligands are post-translationally modified by the addition of glycans to extracellular domain epidermal growth factor-like (EGF) repeats. The O-fucose glycans of Notch cell-autonomously modulate Notch-ligand interactions and the strength of Notch signaling. These glycans are initiated by protein O-fucosyltransferase 1 (Pofut1), and elongated by the transfer of N-acetylglucosamine (GlcNAc) to the fucose by beta1,3GlcNAc-transferases termed lunatic, manic, or radical fringe. This review discusses T- and B-cell development from progenitors deficient in O-fucose glycans. The combined data show that Lfng and Mfng regulate T-cell development by enhancing the interactions of Notch1 in T-cell progenitors with Dll4 on thymic epithelial cells. In the spleen, Lfng and Mfng cooperate to modify Notch2 in MZ B progenitors, enhancing their interaction with Dll1 on endothelial cells and regulating MZ B-cell production. Removal of O-fucose affects Notch signaling in myelopoiesis and lymphopoiesis, and the O-fucose glycan in the Notch1 ligand-binding domain is required for optimal T-cell development.
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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 | 0.000 |
| 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".