Disease-Modifying Treatments for Multiple Sclerosis Affect Measures of Cellular Immune Responses to EBNA-1 Peptides
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Epstein-Barr virus (EBV) has been strongly implicated in the pathogenesis of multiple sclerosis (MS). Despite this, there are no routinely used tests to measure cellular response to EBV. In this study, we analyzed the cellular response to EBV nuclear antigen-1 (EBNA-1) in people with MS (pwMS) using a whole blood assay. METHODS: This cross-sectional study took place in a dedicated MS clinic in a university hospital. We recruited healthy controls, people with epilepsy (PWE), and pwMS taking a range of disease-modifying treatments (DMTs) including natalizumab, anti-CD20 monoclonal antibodies (mAbs), dimethyl fumarate (DMF), and also treatment naïve. Whole blood samples were stimulated with commercially available PepTivator EBNA1 peptides and a control virus-cytomegalovirus (CMV) peptide. We recorded the cellular response to stimulation with both interferon gamma (IFN-γ) and interleukin-2 (IL-2). We also compared the cellular responses to EBNA1 with IgG responses to EBNA1, viral capsid antigen (VCA), and EBV viral load. RESULTS: = 0.26). The results of recording IL-2 response were concordant with IFN-γ: with suppression also seen with anti-CD20 mAbs and DMF. By contrast, we did not record any differential effect of DMTs on the levels of IgG to either EBNA-1 or VCA. Nor did we observe differences in cellular response to cytomegalovirus between groups. DISCUSSION: This study demonstrates how testing and recording the cellular response to EBNA-1 in pwMS may be beneficial. EBNA-1 stimulation of whole blood samples produced higher levels of IFN-γ and IL-2 in pwMS compared with controls and PWE. In addition, we show a differential effect of currently available DMTs on this response. The functional assay deployed uses whole blood samples with minimal preprocessing suggesting that employment as a treatment response measure in clinical trials targeting EBV may be possible.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".