Memory B cells are critical in gamma herpes virus driven disease enhancement of a mouse model for MS (BA2P.124)
Bibliographic record
Abstract
Abstract Genetic and environmental factors are known to be involved in the development of Multiple Sclerosis. However, the mechanism describing how these factors are responsible for the onset of MS has not been elucidated. One of the environmental factors believed to play an important role in the development of MS is previous infection with the Epstein Barr Virus (EBV). The study of the relationship between MS and EBV remains elusive since EBV only infects humans. We hypothesized that latent EBV infection alters the immune system’s response in genetically predisposed individuals, thereby exacerbating autoimmunity. To address this question our lab previously developed a model where latent infection with murine gamma herpesvirus 68 (γHV-68), the murine homolog to EBV, enhances the symptoms of experimental autoimmune encephalomyelitis (EAE), which closely resembles MS pathogenesis. These enhanced symptoms are driven by the upregulation of CD40 on dendritic cells (DCs) of latently infected mice which drives the priming of a strong TH1 response and a decrease in Tregs frequencies. Latency of γHV-68 mainly occurs in memory B cells and the virus is not found in the DCs driving the TH1 response. Here we demonstrate that latently infected memory B cells are the cells responsible for the upregulation of CD40 in DCs, most likely through the upregulation of type I interferons in DCs. This represents a novel mechanism by which infection with EBV can influence MS and other autoimmune diseases.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".