The SLC28 (CNT) and SLC29 (ENT) nucleoside transporter families: a 30-year collaborative odyssey
Bibliographic record
Abstract
Specialized nucleoside transporter (NT) proteins are required for passage of nucleosides and hydrophilic nucleoside analogues across biological membranes. Physiologic nucleosides serve as central salvage metabolites in nucleotide biosynthesis, and nucleoside analogues are used as chemotherapeutic agents in the treatment of cancer and antiviral diseases. The nucleoside adenosine modulates numerous cellular events via purino-receptor cell signalling pathways. Human NTs are divided into two structurally unrelated protein families: the SLC28 concentrative nucleoside transporter (CNT) family and the SLC29 equilibrative nucleoside transporter (ENT) family. Human CNTs are inwardly directed Na(+)-dependent nucleoside transporters found predominantly in intestinal and renal epithelial and other specialized cell types. Human ENTs mediate bidirectional fluxes of purine and pyrimidine nucleosides down their concentration gradients and are ubiquitously found in most, possibly all, cell types. Both protein families are evolutionarily old: CNTs are present in both eukaryotes and prokaryotes; ENTs are widely distributed in mammalian, lower vertebrate and other eukaryote species. This mini-review describes a 30-year collaboration with Professor Stephen Baldwin to identify and understand the structures and functions of these physiologically and clinically important transport proteins.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".