NR4A1‐dependent Ly6C<sup>low</sup> monocytes contribute to reducing joint inflammation in arthritic mice through Treg cells
Bibliographic record
Abstract
Monocytes are central to the physiopathology of arthritis, but their roles in progression and resolution of the disease remain to be clarified. Using NR4A1−/− mice, which lack patrolling lymphocyte antigen 6C (Ly6Clow) monocytes, we found that inflammatory Ly6Chigh monocytes contribute to rapid development of arthritis in a serum transfer‐induced arthritis (STIA) model. Our experiments suggest that patrolling monocytes do not promote the initiation and progression of arthritis in mice, as severity of symptoms was amplified in NR4A1−/− mice. Moreover, we show that treatment of arthritic wild type (WT) mice with cytosporone B (Csn‐B), a NR4A1‐specific agonist, significantly reduces severity of disease. Effects of Csn‐B were absent in monocyte‐depleted mice treated with clodronate until Ly6Clow monocytes were restored. Adoptive transfer of Ly6Clow monocytes in arthritic NR4A1−/− mice treated with Csn‐B reduces joint inflammation, supporting the regulatory role of Ly6Clow subset on disease development. Our results also reveal that administration of Csn‐B to arthritic mice enhances levels of circulating CD4+CD25+FoxP3+ Treg cells, a process requiring the presence of Ly6Clow monocytes. Together, these data indicate that Ly6Chigh monocytes are involved in the initiation and progression of arthritis and Ly6Clow monocytes contribute to reduce joint inflammation through the mobilization of Treg cells.
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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) | 0.000 | 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".