Survey of the GLUT2 translocation pore for substrate selectivity determinants
Bibliographic record
Abstract
GLUT2 plays a major role in the absorption of nutrient hexoses, being expressed constitutively in the BLM and transiently in the apical membrane. This study's objective was to characterize motifs within TM 7 of GLUT2 which determine substrate specificity. When expressed in Xenopus oocytes, hGLUT1 transports fructose (FRU) at a rate of only 0.1% relative to glucose (GLU), while in GLUT2 they are equivalent. Mutating the NAV motif to NAI in GLUT1 increased FRU transport to approximately 0.9% (2.5 pmoles/oocyte/30 min with 100 μM substrate) relative to that of GLU. Mutating the molecular filter QLS in hGLUT1 to HVA also increased the relative rate of FRU transport (up to 0.5%) but to a lesser extent than the NAI mutation. Further, the combined hGLUT1 HVA‐NAI mutant had a relative FRU transport rate of 1.1 % (6 pmoles/oocyte/30 min at 100 μM substrate). Kinetic analysis for the hGLUT1 HVA‐NAI revealed that the Km for GLU was comparable to that of hGLUT1wt (1.5mM), while the Km for FRU was comparable to that for hGLUT2 (~70 mM). Thus, we have successfully introduced hGLUT2‐like FRU kinetics into hGLUT1, without affecting the GLU transport by this isoform. These results point to the presence of, at least two, selectivity filters within class I hGLUTs. NAV/NAI is the exofacial access filter, while QLS/HVA seems to allow FRU translocation beyond the substrate binding site without influencing GLU binding and/or translocation.
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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 | 0.001 |
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".