Nucleoside and nucleobase transporters of primary human cardiac microvascular endothelial cells: characterization of a novel nucleobase transporter
Bibliographic record
Abstract
Levels of cardiovascular active metabolites, like adenosine, are regulated by nucleoside transporters of endothelial cells. We characterized the nucleoside and nucleobase transport capabilities of primary human cardiac microvascular endothelial cells (hMVECs). hMVECs accumulated 2-[3H]chloroadenosine via the nitrobenzylmercaptopurine riboside-sensitive equilibrative nucleoside transporter 1 (ENT1) at a V(max) of 3.4 +/- 1 pmol.microl(-1).s(-1), with no contribution from the nitrobenzylmercaptopurine riboside-insensitive ENT2. Inhibition of 2-chloroadenosine uptake by ENT1 blockers produced monophasic inhibition curves, which are also compatible with minimal ENT2 expression. The nucleobase [3H]hypoxanthine was accumulated within hMVECs (K(m) = 96 +/- 37 microM; V(max) = 1.6 +/- 0.3 pmol.microl(-1).s(-1)) despite the lack of a known nucleobase transport system. This novel transporter was dipyridamole-insensitive but could be inhibited by adenine (K(i) = 19 +/- 7 microM) and other purine nucleobases, including chemotherapeutic analogs. A variety of other cell types also expressed the nucleobase transporter, including the nucleoside transporter-deficient PK(15) cell line (PK15NTD). Further characterization of [3H]hypoxanthine uptake in the PK15NTD cells showed no dependence on Na(+) or H(+). PK15NTD cells expressing human ENT2 accumulated 4.5-fold more [3H]hypoxanthine in the presence of the ENT2 inhibitor dipyridamole than did PK15NTD cells or hMVECs, suggesting trapping of ENT2-permeable metabolites. Understanding the nucleoside and nucleobase transporter profiles in the vasculature will allow for further study into their roles in pathophysiological conditions such as hypoxia or 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".