Distribution and Functional Characterization of Equilibrative Nucleoside Transporter-4, a Novel Cardiac Adenosine Transporter Activated at Acidic pH
Bibliographic record
Abstract
Adenosine plays multiple roles in the efficient functioning of the heart by regulating coronary blood flow, cardiac pacemaking, and contractility. Previous studies have implicated the equilibrative nucleoside transporter family member equilibrative nucleoside transporter-1 (ENT1) in the regulation of cardiac adenosine levels. We report here that a second member of this family, ENT4, is also abundant in the heart, in particular in the plasma membranes of ventricular myocytes and vascular endothelial cells but, unlike ENT1, is virtually absent from the sinoatrial and atrioventricular nodes. Originally described as a monoamine/organic cation transporter, we found that both human and mouse ENT4 exhibited a novel, pH-dependent adenosine transport activity optimal at acidic pH (apparent K(m) values 0.78 and 0.13 mmol/L, respectively, at pH 5.5) and absent at pH 7.4. In contrast, serotonin transport by ENT4 was relatively insensitive to pH. ENT4-mediated nucleoside transport was adenosine selective, sodium independent and only weakly inhibited by the classical inhibitors of equilibrative nucleoside transport, dipyridamole, dilazep, and nitrobenzylthioinosine. We hypothesize that ENT4, in addition to playing roles in cardiac serotonin transport, contributes to the regulation of extracellular adenosine concentrations, in particular under the acidotic conditions associated with ischemia.
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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 | 0.000 |
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".