Functional Characteristics of Two Human MATE Transporters: Kinetics of Cimetidine Transport and Profiles of Iinhibition by Various Compounds
Bibliographic record
Abstract
PURPOSE: Human multidrug and toxin extrusion protein 1 (hMATE1) and hMATE2-K are organic cation/H+ antiporters that have recently been identified and suggested to be involved in the renal brush border secretion of various organic cations. Information about functional characteristics of them has been accumulating, but still insufficient to fully understand their functions and respective roles. The present study was conducted to help clarify them. METHODS: The cDNA of hMATE1 was isolated from human brain cDNA by RT-PCR and hMATE2-K cDNA was from human kidney cDNA. HEK293 cells were stably transfected with hMATE1 and hMATE2-K, and the cellular uptakes of [3H]cimetidine and [14C]tetraethylammonium (TEA) were evaluated. RESULTS: It was first found that both hMATE1 and hMATE2-K can transport cimetidine with high affinities, indicated by small Michaelis constants of 8.00 mM and 18.18 mM, respectively. These were much smaller than those for TEA (366 mM and 375 mM, respectively, for hMATE1 and hMATE2-K). Subsequent investigation using cimetidine as a probe substrate into the profiles of inhibition of the two hMATEs by various compounds indicated that they are similar in principle but different to some extent in substrate recognition, reflecting the modest differences in amino acid sequences between them. In fact, cimetidine transport by hMATE1 was correlated to that by hMATE2-K, which is 65% similar to hMATE1, but not as good as to that by rat MATE1, which is 86% similar. CONCLUSIONS: Cimetidine was demonstrated to be a high affinity substrate of both hMATEs. Subsequent evaluation of the inhibition of hMATEs by various compounds indicated no major difference in function or role between hMATE1 and hMATE2-K.
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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 | 0.001 |
| 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 | 0.001 | 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".