Engagement of glycosylphosphatidylinositol-anchored proteins results in enhanced mouse and human invariant natural killer T cell responses
Bibliographic record
Abstract
Invariant natural killer T (iNKT) cells are a small subset of lymphocytes that recognize glycolipid antigens in the context of CD1d and consequently produce large quantities of pro-inflammatory and/or anti-inflammatory cytokines. Several transmembrane glycoproteins have been implicated in the co-stimulation of iNKT cell responses. However, whether glycosylphosphatidylinositol (GPI)-anchored proteins can function in this capacity is not known. Here, we demonstrate that antibody-mediated cross-linking of the prototype mouse GPI-anchored protein Thy-1 (CD90) on the surface of a double-negative (CD4⁻CD8⁻) iNKT cell line leads to cytokine production at both the mRNA and protein levels. In addition, Thy-1 triggering enhanced cytokine secretion by iNKT cells that were concomitantly stimulated with α-galactosylceramide (αGC), consistent with a co-stimulatory role for Thy-1 in iNKT cell activation. This was also evident when a CD4+ mouse iNKT cell line or primary hepatic NKT cells were stimulated with αGC and/or anti-Thy-1 antibody. Cross-linking Ly-6A/E, another GPI-anchored protein, could also boost cytokine secretion by αGC-stimulated iNKT cells, suggesting that the observed effects reflect a general property of GPI-anchored proteins. To extend these results from mouse to human cells, we focused on CD55, a GPI-anchored protein that, unlike Thy-1, is expressed on human iNKT cells. Cross-linking CD55 augmented αGC-induced iNKT cell responses as judged by more vigorous proliferation and higher CD69 expression. Collectively, these findings demonstrate for the first time that GPI-anchored proteins are able to co-stimulate CD1d-restricted, glycolipid-reactive iNKT cells in both mice and humans.
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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) | 0.002 | 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".