Increasing cell permeability of N-acetylglucosamine via 6-acetylation enhances capacity to suppress T-helper 1 (TH1)/TH17 responses and autoimmunity
Bibliographic record
Abstract
N-acetylglucosamine (GlcNAc) branching of Asn (N)-linked glycans inhibits pro-inflammatory T cell responses and models of autoimmune diseases such as Multiple Sclerosis (MS). Metabolism controls N-glycan branching in T cells by regulating de novo hexosamine pathway biosynthesis of UDP-GlcNAc, the donor substrate for the Golgi branching enzymes. Activated T cells switch metabolism from oxidative phosphorylation to aerobic glycolysis and glutaminolysis. This reduces flux of glucose and glutamine into the hexosamine pathway, thereby inhibiting de novo UDP-GlcNAc synthesis and N-glycan branching. Salvage of GlcNAc into the hexosamine pathway overcomes this metabolic suppression to restore UDP-GlcNAc synthesis and N-glycan branching, thereby promoting anti-inflammatory T regulatory (Treg) over pro-inflammatory T helper (TH) 17 and TH1 differentiation to suppress autoimmunity. However, GlcNAc activity is limited by the lack of a cell surface transporter and requires high doses to enter cells via macropinocytosis. Here we report that GlcNAc-6-acetate is a superior pro-drug form of GlcNAc. Acetylation of amino-sugars improves cell membrane permeability, with subsequent de-acetylation by cytoplasmic esterases allowing salvage into the hexosamine pathway. Per- and bi-acetylation of GlcNAc led to toxicity in T cells, whereas mono-acetylation at only the 6 > 3 position raised N-glycan branching greater than GlcNAc without inducing significant toxicity. GlcNAc-6-acetate inhibited T cell activation/proliferation, TH1/TH17 responses and disease progression in Experimental Autoimmune Encephalomyelitis (EAE), a mouse model of MS. Thus, GlcNAc-6-Acetate may provide an improved therapeutic approach to raise N-glycan branching, inhibit pro-inflammatory T cell responses and treat autoimmune diseases such as MS.
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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) | 0.001 | 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".