Bidirectional transport of 2-chloroadenosine by equilibrative nucleoside transporter 4 (hENT4): Evidence for allosteric kinetics at acidic pH
Bibliographic record
Abstract
Abstract Adenosine has been reported to be transported by equilibrative nucleoside transporter 4 (ENT4), encoded by the SLC29A4 gene, in an acidic pH-dependent manner. This makes hENT4 of interest as a therapeutic target in acidic pathologies where adenosine is protective (e.g. vascular ischaemia). We examined the pH-sensitivity of nucleoside influx and efflux by hENT4 using a recombinant transfection model that lacks the confounding influences of other nucleoside transporters (PK15-NTD). We established that [3H]2-chloroadenosine, which is resistant to metabolism by adenosine deaminase, is a substrate for hENT4. Transport of [3H]2-chloroadenosine at a pH of 6.0 in PK15-NTD cells stably transfected with SLC29A4 was biphasic, with a low capacity (Vmax ~ 30 pmol/mg/min) high-affinity component (Km ~ 50 µM) apparent at low substrate concentrations, which shifted to a high capacity (Vmax ~ 500 pmol/mg/min) low affinity system (Km > 600 µM) displaying positive cooperativity at concentrations above 200 µM. Only the low affinity component was observed at a neutral pH of 7.5 (Km ~ 2 mM). Efflux of [3H]2-chloroadenosine from these cells was also enhanced by more than 4-fold at an acidic pH. Enhanced influx and efflux of nucleosides by hENT4 under acidic conditions supports its potential as a therapeutic target in pathologies such as ischaemia-reperfusion injury.
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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".