Genetics and the environment converge to dysregulate N-glycosylation in multiple sclerosis
Bibliographic record
Abstract
How environmental factors combine with genetic risk at the molecular level to promote complex trait diseases such as multiple sclerosis (MS) is largely unknown. In mice, N-glycan branching by the Golgi enzymes Mgat1 and/or Mgat5 prevents T cell hyperactivity, cytotoxic T-lymphocyte antigen 4 (CTLA-4) endocytosis, spontaneous inflammatory demyelination and neurodegeneration, the latter pathologies characteristic of MS. Here we show that MS risk modulators converge to alter N-glycosylation and/or CTLA-4 surface retention conditional on metabolism and vitamin D3, including genetic variants in interleukin-7 receptor-α (IL7RA*C), interleukin-2 receptor-α (IL2RA*T), MGAT1 (IVAVT−T) and CTLA-4 (Thr17Ala). Downregulation of Mgat1 by IL7RA*C and IL2RA*T is opposed by MGAT1 (IVAVT−T) and vitamin D3, optimizing branching and mitigating MS risk when combined with enhanced CTLA-4 N-glycosylation by CTLA-4 Thr17. Our data suggest a molecular mechanism in MS whereby multiple environmental and genetic inputs lead to dysregulation of a final common pathway, namely N-glycosylation. Complex diseases such as multiple sclerosis have both genetic and environmental components. This study demonstrates that variants of genes implicated in multiple sclerosis, and alterations in cellular metabolism and vitamin D3 levels, alterN-glycosylation, a post-translational modification causal of the disease in mice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".