Insights into the role of Epstein-Barr virus infection in multiple sclerosis using a novel humanized mouse model of disease
Bibliographic record
Abstract
Abstract Multiple sclerosis (MS) is an autoimmune disease of the central nervous system without a clear cause. Epstein-Barr virus (EBV) is proposed to contribute to the pathogenesis of MS through unknown mechanisms. Evidence for a role of EBV infection in MS comes from both epidemiological and experimental studies, however, due to its narrow host tropism, there are currently few suitable animal models of MS that incorporate EBV infection. Recent advancements in humanized mouse modelling has enabled direct infection for the study of EBV-associated malignancies. We therefore chose to evaluate the role of EBV infection in humanized mice with experimental autoimmune encephalomyelitis (EAE), a widely used model of MS. Immunocompromised mice were engrafted with peripheral blood mononuclear cells (PBMC) derived from individuals with relapsing MS (RRMS) or from matched healthy EBV seropositive or seronegative donors. We observed that HuPBMC mice induced with EAE developed ascending paralysis, weight loss and signs of discomfort consistent with classical EAE models. Further, HuPBMC EAE showed significant T cell infiltration of both the brain and spinal cord, notably of IFN gamma-expressing CD4 and CD8 T cells, resulting in spinal cord and cerebellar demyelination. HuPBMC EAE mice derived from EBV seropositive donors developed earlier disease onset with more severe clinical symptoms compared to EBV seronegative donor-derived mice. We also observed increase disease severity among mice derived from RRMS patients compared to healthy controls. With continued improvement and characterization of this novel humanized EAE model, additional environmental and genetic risk factors can be evaluated in a system with human immune-mediated pathology.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".