Expression of risk genes linked to vitamin D receptor super-enhancer regions and their association with phenotype severity in multiple sclerosis
Bibliographic record
Abstract
Multiple sclerosis (MS) is a chronic debilitating neurological condition with a wide range of phenotype variability. A complex interplay of genetic and environmental factors contributes to disease onset and progression in MS patients. Vitamin D deficiency is a known susceptibility factor for MS, however the underlying mechanism of vitamin D-gene interactions in MS etiology is still poorly understood. Vitamin D receptor super-enhancers (VSEs) are enriched in MS risk variants and may modulate these environment-gene interactions. mRNA expression in total of 64 patients with contrasting MS severity was quantified in select genes. First, RNA-seq was performed on a discovery cohort (10 mild, 10 severe MS phenotype) and ten genes regulated by VSEs that have been linked to MS risk were analyzed. Four candidates showed a significant positive association (GRINA, PLEC, PARP10, and LRG1) in the discovery cohort and were then quantified using digital droplet PCR (ddPCR) in a validation cohort (33 mild, 11 severe MS phenotype). A significant differential expression persisted in the validation cohort for three of the VSE-MS genes: GRINA ( p = 0.0138), LRG1 ( p = 0.0157), and PLEC ( p = 0.0391). In summary, genes regulated by VSE regions that contain known MS risk variants were shown to have differential expression based on disease severity (p<0.05). The findings implicate a role for vitamin D super-enhancers in modulating disease activity. In addition, expression levels may have some utility as prognostic biomarkers in the future.
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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".