Critical Roles of Two Hydrophobic Residues within Human Glucose Transporter 9 (hSLC2A9) in Substrate Selectivity and Urate Transport
Bibliographic record
Abstract
High blood urate levels (hyperuricemia) have been found to be a significant risk factor for cardiovascular diseases and inflammatory arthritis, such as hypertension and gout. Human glucose transporter 9 (hSLC2A9) is an essential protein that mainly regulates urate/hexose homeostasis in human kidney and liver. hSLC2A9 is a high affinity-low capacity hexose transporter and a high capacity urate transporter. Our previous studies identified a single hydrophobic residue in trans-membrane domain 7 of class II glucose transporters as a determinant of fructose transport. A mutation of isoleucine 335 to valine (I355V) in hSLC2A9 can reduce fructose transport while not affecting glucose fluxes. This current study demonstrates that the I335V mutant transports urate similarly to the wild type hSLC2A9; however, Ile-335 is necessary for urate/fructose trans-acceleration exchange to occur. Furthermore, Trp-110 is a critical site for urate transport. Two structural models of the class II glucose transporters, hSLC2A9 and hSLC2A5, based on the crystal structure of hSLC2A1 (GLUT1), reveal that Ile-335 (or the homologous Ile-296 in hSLC2A5) is a key component for protein conformational changes when the protein translocates substrates. The hSLC2A9 model also predicted that Trp-110 is a crucial site that could directly interact with urate during transport. Together, these studies confirm that hSLC2A9 transports both urate and fructose, but it interacts with them in different ways. Therefore, this study advances our understanding of how hSLC2A9 mediates urate and fructose transport, providing further information for developing pharmacological agents to treat hyperuricemia and related diseases, such as gout, hypertension, and diabetes.
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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 | 0.001 | 0.000 |
| 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".