Sialoadhesin Ligand Expression Identifies a Subset of CD4+Foxp3− T Cells with a Distinct Activation and Glycosylation Profile
Bibliographic record
Abstract
Sialoadhesin (Sn) is a sialic acid-binding Ig-like lectin expressed selectively on macrophage subsets. In a model of experimental autoimmune encephalomyelitis, Sn interacted with sialylated ligands expressed selectively on CD4(+)Foxp3(+) regulatory T cells (Tregs) and inhibited their proliferation. In this study, we examined the induction of Sn ligands (SnL) on all splenic CD4(+) T cells following in vitro activation. Most CD4(+) Tregs strongly upregulated SnL, whereas only a small subset of ~20% CD4(+)Foxp3(-) T cells (effector T cells [Teffs]) upregulated SnL. SnL(+) Teffs displayed higher levels of activation markers CD25 and CD69, exhibited increased proliferation, and produced higher amounts of IL-2 and IFN-γ than corresponding SnL(-) Teffs. Coculture of activated Teffs with Sn(+) macrophages or Sn(+) Chinese hamster ovary cells resulted in increased cell death, suggesting a regulatory role for Sn-SnL interactions. The key importance of α2,3-sialylation in SnL expression was demonstrated by increased binding of α2,3-linkage-specific Maackia amurensis lectin, increased expression of α2,3-sialyltransferase ST3GalVI, and loss of SnL following treatment with an α2,3-linkage-specific sialidase. The induction of SnL on activated CD4(+) T cells was dependent on N-glycan rather than O-glycan biosynthesis and independent of the mucin-like molecules CD43 and P-selectin glycoprotein ligand-1, previously implicated in Sn interactions. Induction of ligands on CD4(+)Foxp3(-) Teffs was also observed in vivo using the New Zealand Black × New Zealand White F1 murine model of spontaneous lupus and SnL levels on Teffs correlated strongly with the degree of proteinuria. Collectively, these data indicate that SnL is a novel marker of activated CD4(+) Teffs that are implicated in the pathogenesis of autoimmune diseases.
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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) | 0.002 | 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".