Nucleoside/nucleobase transport and metabolism by microvascular endothelial cells isolated from ENT1<sup>−/−</sup>mice
Bibliographic record
Abstract
Nucleoside and nucleobase uptake is integral to mammalian cell function, and its disruption has significant effects on the cardiovasculature. The predominant transporters in this regard are the equilibrative nucleoside transporter subtypes 1 (ENT1) and 2 (ENT2). To examine the role of ENT1 in more detail, we have assessed the mechanisms by which microvascular endothelial cells (MVECs) from ENT1(-/-) mice transport and metabolize nucleosides and nucleobases. Wild-type murine MVECs express mainly the ENT1 subtype with only trace levels of ENT2. These cells also have a Na(+)-independent equilibrative nucleobase transport mechanism for hypoxanthine (ENBT1). In the ENT1(-/-) cells, there is no change in ENT2 or ENBT1, resulting in a very low level of nucleoside uptake in these cells, but a high capacity for nucleobase accumulation. Whereas there were no significant changes in nucleoside transporter subtype expression, there was a dramatic increase in adenosine deaminase and adenosine A(2a) receptors (both transcript and protein) in the ENT1(-/-) tissues compared with WT. These changes in adenosine deaminase and A(2a) receptors likely reflect adaptive cellular mechanisms in response to reduced adenosine flux across the membranes of ENT1(-/-) cells. Our study also revealed that mouse MVECs have a nucleoside/nucleobase transport profile that is more similar to human MVECs than to rat MVECs. Thus mouse MVECs from transgenic animals may prove to be a useful preclinical model for studies of the effects of purine metabolite modifiers on vascular function.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 |
| 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".