Control of T Cell-mediated Autoimmunity by Metabolite Flux to N-Glycan Biosynthesis
Bibliographic record
Abstract
Autoimmunity is a complex trait disease where the environment influences susceptibility to disease by unclear mechanisms. T cell receptor clustering and signaling at the immune synapse, T cell proliferation, CTLA-4 endocytosis, T(H)1 differentiation, and autoimmunity are negatively regulated by beta1,6GlcNAc-branched N-glycans attached to cell surface glycoproteins. Beta1,6GlcNAc-branched N-glycan expression in T cells is dependent on metabolite supply to UDP-GlcNAc biosynthesis (hexosamine pathway) and in turn to Golgi N-acetylglucosaminyltransferases Mgat1, -2, -4, and -5. In Jurkat T cells, beta1,6GlcNAc-branching in N-glycans is stimulated by metabolites supplying the hexosamine pathway including glucose, GlcNAc, acetoacetate, glutamine, ammonia, or uridine but not by control metabolites mannosamine, galactose, mannose, succinate, or pyruvate. Hexosamine supplementation in vitro and in vivo also increases beta1,6GlcNAc-branched N-glycans in naïve mouse T cells and suppresses T cell receptor signaling, T cell proliferation, CTLA-4 endocytosis, T(H)1 differentiation, experimental autoimmune encephalomyelitis, and autoimmune diabetes in non-obese diabetic mice. Our results indicate that metabolite flux through the hexosamine and N-glycan pathways conditionally regulates autoimmunity by modulating multiple T cell functionalities downstream of beta1,6GlcNAc-branched N-glycans. This suggests metabolic therapy as a potential treatment for autoimmune 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".