A2A adenosine receptor mediates suppression in a mouse model of colitis (39.1)
Bibliographic record
Abstract
Abstract Background: Adenosine accumulates in inflamed tissue and can suppress proliferation and cytokine production in helper T cells (Th). Th cells from mice lacking the A2A Adenosine Receptor (A2AAR-/-) do not function properly in the CD45RB transfer model of colitis. Methods: To examine the role of A2AAR in regulating colitis, we tested colonic myeloperoxidase (MPO) activity after adoptive transfer of Th cell susbsets from wildtype or A2AAR-/- mice. Th cells from mesenteric lymph nodes (MLN) of colitic mice were treated with an A2AAR agonist to assess effects on cytokine production. To investigate the role of A2AAR on myeloid cells in the CD45RB model of colitis, we adoptively transferred wildtype Th cells into RAG1/A2AAR double knockout (DKO) mice. Results: Colons of mice that received A2AAR-/- CD45RBhi cells with wildtype or A2AAR-/- CD45RBlo Treg had significantly greater MPO activity than mice that received wildtype CD45RBhi and CD45RBlo cells. Cytokine production by MLN Th cells from colitic mice was suppressed by an A2AAR agonist. Adoptive transfer of CD45RBhi Th cells into either RAG1-/- or DKO mice induced colitis but co-transfer of CD45RBlo Tregs attenuated colitis only in RAG1-/- mice. Conclusions: These data indicate that adenosine signaling through A2AAR plays an important suppressive role in colitis and that this is mediated through effects on both T cells and myeloid cells. CK supported by a CCFA Fellowship.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".