Hypoxia regulates the adenosine transporter, mENT1, in the murine cardiomyocyte cell line, HL-1
Bibliographic record
Abstract
OBJECTIVE: Adenosine is an important paracrine hormone in the cardiovascular system. Adenosine flux across cardiomyocyte membranes occurs mainly via equilibrative nucleoside transporters (ENTs). The role of the ENTs in adenosine physiology is poorly understood, particularly in response to metabolic stress such as hypoxia. Therefore, we investigated the effects of chronic hypoxia on ENT1, the predominant ENT isoform in cardiomyocytes. METHODS: HL-1 cells (immortalized murine cardiomyocytes) were exposed to hypoxia (2% O2) for 0-20 h. Cell viability, lactate dehydrogenase (LDH) release, glucose uptake, GLUT1 and GLUT4 protein, adenosine uptake, PKC activity, translocation profiles of PKCdelta and, nitrobenzylthioinosine (NBTI) binding and mENT1 mRNA levels were measured. The role of PKC in regulating mENT1 was further investigated using phorbol ester (100 nM, 18 h) and a dominant negative PKC construct, pSVK3PKC1-401. RESULTS: HL-1 cells have typical cardiomyocyte responses to hypoxia based on cell viability, LDH release, glucose uptake and GLUT protein levels. Hypoxia (8-20 h) down-regulates mENT1-dependent adenosine uptake, NBTI-binding and PKC but not PKCdelta in HL-1 cells. Abrogation of PKC activity using chronic phorbol ester or a dominant negative PKC mimicked the effect of hypoxia on adenosine uptake suggesting that PKC is involved in regulation of mENT1. Hypoxia (4 h) decreases mENT1 mRNA, which returns to basal levels by 20 h. CONCLUSIONS: Chronic hypoxia down-regulates mENT1 activity possibly via PKC. Hypoxia and PKC also regulate mENT1 RNA levels. Cardiomyocytes may regulate mENT1 (via PKC) to modulate release and/or uptake of adenosine. However, the relationship between mENT1 mRNA levels, protein levels and functional transport is complex.
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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".