CD73 Plays a Protective Role in Collagen-Induced Arthritis
Bibliographic record
Abstract
Rheumatoid arthritis (RA) is a chronic autoimmune disease with significant morbidity and mortality. Recent studies suggest that modulation of adenosine signaling, a potent immunosuppressive pathway, is a promising approach for treatment of RA. Extracellular adenosine can come from two sources: transport of intracellular adenosine and hydrolysis of extracellular adenine nucleotides by CD73. In this study, we investigated the susceptibility of CD73-deficient C57BL/6 mice to collagen-induced arthritis (CIA), a well-established mouse model of RA. Our data demonstrated that CD73-deficient mice are significantly more susceptible to CIA than wild-type mice. CD73 deficiency resulted in an increased production of proinflammatory cytokines in the joints, increased Th1 T cell responses, and increased joint destruction. Surprisingly, this was accompanied by delayed anticollagen IgG responses, suggesting defective isotype class switching in CD73-deficient mice. Using bone marrow chimera mice, we demonstrated that CD73 expression on nonhematopoietic cells, but not on hematopoietic cells, was important for protection from CIA. We further demonstrated that administration of a selective A2A adenosine receptor agonist to CD73-deficient mice resulted in arthritis incidence similar to wild-type mice in support of a protective role for A2A signaling. Taken together, our study identifies CD73 as an important regulator of CIA in mice. It also strengthens the notion that CD73-generated adenosine by nonhematopoietic cells plays a protective role in RA and suggests that strategies able to enhance CD73 activity or expression levels may be a valid therapeutic option.
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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) | 0.002 | 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".