Nucleoside transporter subtype expression: effects on potency of adenosine kinase inhibitors
Bibliographic record
Abstract
1. Adenosine kinase (AK) inhibitors can enhance adenosine levels and potentiate adenosine receptor activation. As the AK inhibitors 5' iodotubercidin (ITU) and 5-amino-5'-deoxyadenosine (NH(2)dAdo) are nucleoside analogues, we hypothesized that nucleoside transporter subtype expression can affect the potency of these inhibitors in intact cells. 3. Three nucleoside transporter subtypes that mediate adenosine permeation of rat cells have been characterized and cloned: equilibrative transporters rENT1 and rENT2 and concentrative transporter rCNT2. We stably transfected rat C6 glioma cells, which express rENT2 nucleoside transporters, with rENT1 (rENT1-C6 cells) or rCNT2 (rCNT2-C6 cells) nucleoside transporters. 3. We tested the effects of ITU and NH(2)dAdo on [(3)H]-adenosine uptake and conversion to [(3)H]-adenine nucleotides in the three cell types. NH(2)dAdo did not show any cell type selectivity. In contrast, ITU showed significant inhibition of [(3)H]-adenosine uptake and [(3)H]-adenine nucleotide formation at concentrations < or =100 nM in rENT1-C6 cells, while concentrations > or =3 microM were required for C6 or rCNT2-C6 cells. 4. Nitrobenzylthioinosine (NBMPR; 100 nM), a selective inhibitor of rENT1, abolished the effects of nanomolar concentrations of ITU in rENT1-C6 cells. 5. This study demonstrates that the effects of ITU, but not NH(2)dAdo, in whole cell assays are dependent upon nucleoside transporter subtype expression. Thus, cellular and tissue differences in expression of nucleoside transporter subtypes may affect the pharmacological actions of some AK inhibitors.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".