Latent gammaherpesvirus infection licenses age-associated B cells for pathogenicity during EAE and MS
Bibliographic record
Abstract
Abstract Epstein-Barr virus (EBV) has long been implicated in multiple sclerosis (MS), though its mechanism of contribution remains unknown. Age-associated B cells (ABCs) are known to expand and persist following viral infection and are increased in MS patients. We hypothesize that EBV infection expands the number of ABCs and skews the population towards a Th1 inflammatory phenotype, leading them to function pathogenically in MS. To explore how the EBV-primed ABC population contributes to MS we have utilized the in vivo models of EBV, gammaherpesvirus 68 (gHV68), and MS, and MOG35–55 experimental autoimmune encephalomyelitis (EAE). We observe that ABCs display distinct phenotypes during gHV68 infection and EAE: ABCs secrete anti-viral IFNg during gHV68 infection and IL10 during EAE. Intriguingly, latent gHV68 infection prior to EAE results in a heterogeneous ABC phenotype, with IL10 and IFNg-secreting subsets. As IFNg is pathogenic during EAE and MS, and IL10 is protective, the gHV68-EAE ABCs display a more pathogenic phenotype compared to EAE alone, corresponding to the enhanced clinical course during gHV68-EAE. Knocking out ABCs results in an amelioration of disease in gHV68-EAE, but not EAE alone, further substantiating that gHV68 infection drives ABCs towards pathogenicity. In MS patients we observe an increased number and altered inflammatory phenotype of circulating ABCs compared to age and sex-matched healthy controls. These findings indicate that gHV68 and EBV prime ABCs to contribute pathogenically during EAE and MS and suggest that ABCs may be a therapeutic target.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".