Epstein-Barr virus promotes T cell dysregulation in a humanized mouse model of multiple sclerosis
Bibliographic record
Abstract
ABSTRACT Infection with the human-tropic Epstein-Barr virus (EBV) is a strong risk factor for multiple sclerosis (MS), though the underlying mechanisms remain unclear. To investigate the immunomodulatory effects of latent EBV infection, we induced experimental autoimmune encephalomyelitis (EAE) in immunocompromised mice humanized with peripheral blood mononuclear cells (PBMCs) from individuals with or without a history of EBV infection and/or a diagnosis of relapsing MS. HuPBMC EAE mice generated from EBV seronegative healthy donors were less susceptible to developing severe clinical disease than EBV seropositive healthy donor and RRMS cohorts. Donor EBV seropositivity and RRMS led to a significant incremental increase in the number of brain and spinal cord infiltrating effector T cells, in the absence of viral reactivation, due to enhanced proliferation of donor T cells and reduced regulatory T cell expansion. The data indicate that a history of EBV infection, further compounded by a diagnosis of RRMS, promotes T cell-mediated disease in a novel humanized mouse model of MS. SUMMARY In a novel humanized mouse model of multiple sclerosis (MS), donor history of Epstein-Barr virus (EBV) infection exacerbates disease severity by skewing the balance of effector and regulatory T cells in the brain and spinal cord. These results reveal an immunomodulatory mechanism by which latent EBV infection could predispose to the development of 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".