Structure of, and functional insight into the GLUT family of membrane transporters
Bibliographic record
Abstract
Wentong Long, Chris I Cheeseman Department of Physiology, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB, Canada Abstract: This review examines the development of structure and function of the human GLUT proteins, gene family hSLC2A. These proteins are essential for moving the key metabolites, glucose, galactose, and fructose in and out of cells, as well as a number of other important substrates. Despite over five decades of research, it is still not fully understood how they work at the molecular level, although the recent publication of a crystal structure of GLUT1 suggests this may be resolved soon. The GLUT family is divided into three classes based on their sequence homology. The physiological roles of Class I GLUTs, ie, 1, 2, 3 (14), and 4 have been extensively studied for their contributions to metabolism and development. However, the other two classes have received far less attention. Genetic diseases associated with GLUTs are very rare, emphasizing their critical roles, but it is likely that as our understanding of these transporters increases, there may be more clinical conditions found to be associated with subtle changes in their activity. Another promising area of investigation is the changes in expression levels of GLUTs associated with, and likely in consequence of, disease processes. It has long been known that GLUT1 expression increases significantly in cancer cells, but it is now becoming appreciated that other GLUTs may also be involved. Determination of alterations in expression patterns may prove to be a useful diagnostic tool, and in some cases these are already being taken advantage of for the imaging of cancers. Finally, as we better understand how GLUTs bind and transport their substrates, it may be possible to design drugs that can be delivered into target cells for the treatment of a number of diseases. Keywords: GLUT proteins, Facilitated hexose transporters, protein trafficking, cancer imaging, genetic diseases
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".