γHV68 infection exacerbates arthritis and requires age-associated B cells
Bibliographic record
Abstract
Abstract Epstein-Barr virus (EBV) infection is associated with rheumatoid arthritis (RA), though the mechanism of contribution remains unknown and there does not exist a sufficient in vivo model to examine the relationship. Here, we utilize and expand in vivo models of EBV and RA to examine mechanisms of immune contribution. We find that infection with latent gamma-herpesvirus 68 (γHV68), a murine analogue of EBV, leads to an enhanced clinical and immunological course of collagen-induced arthritis (CIA). γHV68-infected mice display earlier and more severe CIA clinical symptoms and a Th1-skewed immune profile, compared to uninfected CIA mice. Using a latency-free strain of γHV68 we demonstrate that CIA exacerbation is not due to innate immune stimulation during acute infection but, rather, is dependent upon viral latency. Age-associated B cells (ABCs) are increased in RA patients and during viral infection, though if they act as mediators between the infection and disease remains unknown. We find that ABCs (CD19+CD11c+Tbet+) in γHV68-infected CIA mice are increased and display a proinflammatory phenotype compared to uninfected CIA. Using ABC knockout mice, we demonstrate that ABCs are critical for γHV68-enhancement of CIA, though are dispensable in uninfected CIA. This project establishes that latent γHV68 infection enhances CIA and is a viable model for examining mechanisms of EBV’s contribution to RA. Additionally, we demonstrate that ABCs mediate the viral enhancement of disease.
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) | 0.002 | 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".