A pharmacogenetic study implicates <i>NINJ2</i> in the response to Interferon-β in multiple sclerosis
Bibliographic record
Abstract
Background: Multiple sclerosis (MS) is a disease in which biomarker identification is fundamental to predict response to treatments and to deliver the optimal drug to patients. We previously found an association between rs7298096, a polymorphism upstream to the NINJ2 gene, and the 4-year response to interferon-β (IFNβ) treatment in MS patients. Objectives: To analyse the association between rs7298096 and time to first relapse (TTFR) during IFNβ therapy in MS patients and to better investigate its functional role. Methods: Survival analysis was applied in three MS cohorts from different countries ( n = 1004). We also studied the role of the polymorphism on gene expression using GTEx portal and a luciferase assay. We interrogated GEO datasets to explore the relationship between NINJ2 expression, IFNβ and TTFR. Results: Rs7298096 AA patients show a shorter TTFR than rs7298096 G -carriers (P meta-analysis = 3 × 10 −4 , hazard ratio = 1.41). Moreover, rs7298096 AA is associated with a higher NINJ2 expression in blood ( p = 7.0 × 10 −6 ), which was confirmed in vitro ( p = 0.009). Finally, NINJ2 expression is downregulated by IFNβ treatment and related to TTFR. Conclusions: Rs7298096 could influence MS disease activity during IFNβ treatment by modulating NINJ2 expression in blood. The gene encodes for an adhesion molecule involved in inflammation and endothelial cells activation, supporting its role in 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".