Red Fluorescent Protein pH Biosensor to Detect Concentrative Nucleoside Transport
Bibliographic record
Abstract
Human concentrative nucleoside transporter, hCNT3, mediates Na ؉ /nucleoside and H ؉ /nucleoside co-transport.We describe a new approach to monitor H ؉ /uridine co-transport in cultured mammalian cells, using a pH-sensitive monomeric red fluorescent protein variant, mNectarine, whose development and characterization are also reported here.A chimeric protein, mNectarine fused to the N terminus of hCNT3 (mNect.hCNT3),enabled measurement of pH at the intracellular surface of hCNT3.mNectarine fluorescence was monitored in HEK293 cells expressing mNect.hCNT3or mNect.hCNT3-F563C,an inactive hCNT3 mutant.Free cytosolic mNect, mNect.hCNT3,and the traditional pH-sensitive dye, BCECF, reported cytosolic pH similarly in pH-clamped HEK293 cells.Cells were incubated at the permissive pH for H ؉ -coupled nucleoside transport, pH 5.5, under both Na ؉ -free and Na ؉ -containing conditions.In mNect.hCNT3-expressingcells (but not under negative control conditions) the rate of acidification increased in media containing 0.5 mM uridine, providing the first direct evidence for H ؉ -coupled uridine transport.At pH 5.5, there was no significant difference in uridine transport rates (coupled H ؉ flux) in the presence or absence of Na ؉ (1.09 ؎ 0.11 or 1.18 ؎ 0.32 mM min ؊1 , respectively).This suggests that in acidic Na ؉ -containing conditions, 1 Na ؉ and 1 H ؉ are transported per uridine molecule, while in acidic Na ؉ -free conditions, 1 H ؉ alone is transported/uridine.In acid environments, including renal proximal tubule, H ؉ /nucleoside co-transport may drive nucleoside accumulation by hCNT3.Fusion of mNect to hCNT3 provided a simple, self-referencing, and effective way to monitor nucleoside transport, suggesting an approach that may have applications in assays of transport activity of other H ؉ -coupled transport proteins.Nucleosides are hydrophilic molecules that require transport proteins to mediate their movement across the plasma membrane (1).Human (h) 7 nucleoside transport (NT) proteins catalyze the vectorial transport of nucleosides, using either concentrative (C) or equilibrative (E) mechanisms (2).hCNTs use either a Na ϩ or H ϩ gradient to accumulate nucleosides against their concentration gradient, whereas hENTs mediate facilitated diffusion of nucleosides down their concentration gradient (3).Nucleoside transporters also transport anti-cancer and anti-viral drugs, and cellular expression of nucleoside transporters is important in cancer therapy as well as in the treatment of cardiovascular, parasitic, and viral diseases (4, 5).Members of the SLC28 family of concentrative nucleoside transporters (CNTs) divide into two phylogenetic subfamilies: hCNT1/2 belonging to one subfamily, and hCNT3 to the other (6 -8).Cation substitution and charge/flux ratio studies suggest that hCNT1/2 couple the inward movement of nucleoside to the Na ϩ electrochemical gradient with a 1:1 stoichiometry, whereas hCNT3 can couple nucleoside transport to either the Na ϩ gradient (2 Na ϩ :1 nucleoside) or a H ϩ gradient (1 H ϩ :1 nucleoside) in the absence of Na ϩ (9, 10).The 2:1 coupling ratio of hCNT3 allows it to develop a trans-membrane nucleoside concentration gradient up to 10-fold higher than that of hCNT1 or hCNT2 (9, 11).At pH 5.5, hCNT3 also transports uridine in the presence of Na ϩ with a 2 cation:1 nucleoside stoichiometry, which raises the possibility that 1 H ϩ and 1 Na ϩ may be transported per nucleoside molecule in these conditions (9 -12).Up to this point, however, there has been no direct demonstration that hCNT3 can transport H ϩ .Concentrative nucleoside transport has previously been investigated using the Xenopus laevis oocyte expression system and both electrophysiology (two-microelectrode voltage clamp technique) and radioisotope flux measurements (6 -9, 12).Electrophysiological experiments are advantageous in that they
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".