Adenosine acts through an A<sub>3</sub> receptor to prevent the induction of murine anti‐CD3‐activated killer T cells
Bibliographic record
Abstract
Adenosine, a purine nucleoside found at high levels in solid tumors, is able to suppress the recognition/adhesion and effector phases of killer lymphocyte-mediated tumor cell destruction. Here, we demonstrate that adenosine, at concentrations that are typically present in the extracellular fluid of solid tumors, exerts a profound inhibitory effect on the induction of mouse cytotoxic T cells, without substantially affecting T-cell viability. T-cell proliferation in response to mitogenic anti-CD3 antibody was impaired in the presence of 10 microM adenosine (plus coformycin to inhibit endogenous adenosine deaminase). Antigen-specific T-cell proliferation was similarly inhibited by adenosine. Anti-CD3-activated killer T (AK-T) cells induced in the presence of adenosine exhibited reduced major histocompatibility complex-unrestricted cytotoxicity against P815 mastocytoma cells in JAM and (51)Cr-release assays. Diminished tumoricidal activity correlated with reduced expression of mRNAs coding for granzyme B, perforin, Fas ligand and tumor necrosis factor (TNF)-related apoptosis-inducing ligand (TRAIL), as well as with diminished Nalpha-CBZ-L-lysine thiobenzylester (BLT) esterase activity. Interleukin-2 and interferon-gamma synthesis by AK-T cells was also inhibited by adenosine. AK-T cells express mRNA coding for A(2A), A(2B) and A(3) receptors, but little or no mRNA coding for A(1) receptors. The inhibitory effect of adenosine on AK-T cell proliferation was blocked by an A(3) receptor antagonist (MRS1191) but not by an A(2) receptor antagonist (3,7-dimethyl-1-propargylxanthine [DMPX]). The A(3) receptor agonists (N(6)-2-(4-aminophenyl)ethyladenosine [APNEA] and N(6)-benzyl-5'-N-ethylcarboxamidoadenosine [N(6)-benzyl-NECA]) also inhibited AK-T cell proliferation. Adenosine, therefore, acts through an A(3) receptor to prevent AK-T cell induction. Tumor-associated adenosine may act through the same mechanism to impair the development of tumor-reactive T cells in cancer patients.
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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) | 0.001 | 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".