Gammaherpesvirus infection licenses age-associated B cells for pathogenicity in MS and EAE
Bibliographic record
Abstract
Abstract While age-associated B cells (ABCs) are known to expand and persist following viral infection and during autoimmunity, their interactions are yet to be studied together in these contexts. Epstein-Barr virus (EBV) infection has long been implicated in multiple sclerosis (MS), and it is not known whether ABCs could play a role in mediating viral contribution to autoimmunity. Here, we show that the circulating ABC population is expanded in people with MS and that EBV infection and MS status differentially impact the circulating ABC phenotype. We then directly compared ABCs during viral infection and autoimmunity using mouse models of EBV, gammaherpesvirus 68 ( γ HV68), and MS, experimental autoimmune encephalomyelitis (EAE). We observed that splenic ABCs are expanded in a sex-biased manner during both latent virus infection and EAE, and each event drives the ABC population to opposing phenotypes. We have previously shown that latent γ HV68 infection exacerbates EAE and here we show that mice lacking ABCs fail to display γ HV68-enhanced disease. Collectively, these findings indicate that latent viral infection and central nervous system autoimmunity differentially impact the ABC population and suggests that viral infections such as EBV prime ABCs to contribute pathogenically 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".