Sialoglycans on human T cells attenuate death programs executed through the Fas pathway
Bibliographic record
Abstract
Abstract T cells are critical executors of adaptive immune responses and their persistence is tightly regulated. Part of this regulation relies on programmed cell death driven by the Tumor Necrosis Factor (TNF) receptor superfamily. The addition of glycans that terminate in the monosaccharide sialic acid (sialoglycans) to these cell death receptors has been shown to attenuate their apoptotic functions. While this is now understood to be a pro-survival mechanism in settings of cancer pathophysiology, the specific roles of sialoglycans in regulating cell death receptor activity on human T cells remains unexplored. This is of particular importance given the rising interest in T cell glycan editing for therapeutic benefit. Here, we address this gap using both immortalized (Jurkat) and primary human T cells deficient in sialoglycans. We found that T cell sialoglycans suppressed apoptosis induced by the Fas receptor (FasR) but not other TNF receptor superfamily members such as TNFR1 and TRAIL-R1. Dynamic reorganization of FasR was increased on sialoglycan-deficient Jurkat cells, suggesting that these glycans limit receptor clustering. This model was further supported by phosphoproteomics results, which confirmed that loss of sialoglycans negatively regulated the pro-survival MAPK/ERK signalling pathway. Finally, we used a recombinant sialic acid cleaving enzyme (sialidase) to confirm that sialoglycans on primary human T cells are bona fide immunophysiological regulators of FasR-driven programmed cell death. Combined, our results demonstrate that sialoglycan remodelling on T cells influences cell fate driven by the Fas pathway and provide motivation to further characterize the immunoregulatory roles of the glycocalyx in health and disease.
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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".