Polysialylation is a general feature of immune activation
Bibliographic record
Abstract
Abstract Sialic acids are critical regulators of immune responses and the sialic acid-Siglec axis has receive much attention recently for it role as a new immune checkpoint. While α2,3- and α2,6-linked sialosides have been well studied, much less is known about the α2,8-linked polysialic acid (polySia), especially in the adaptive immune system. We demonstrate here that polySia is actually found in all major classes of circulating immune cells, including B and T cells, where it is strongly associated with prior antigen exposure. Cell surface expression of polySia could be observed within 24 h of activation of naïve T cells but did not correlate with expression of the immune-specific polysialyltransferase ST8Sia4. Finally, we assessed the effects of smoking on polySia levels, and noted substantial increased levels of polySia, particularly on effector T cells and preferentially in males. Significance Statement The immune system is central to human health and holds the key to treating many chronic diseases. We show for the first time that the immunomodulatory glycan polysialic acid is widespread in the human immune system and associated with immune cell activation. Moreover, sex-dependent increases in polysialic acid could be observed on key populations of immune effector cells in smokers. This study offers insight into how glycans might contribute to sex differences in development and progression of chronic 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) | 0.003 | 0.001 |
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".