Physiological Roles of Glycogen Synthase Kinase-3: Potential as a Therapeutic Target for Diabetes and Other Disorders
Bibliographic record
Abstract
Glycogen synthase kinase-3 (GSK-3) has perplexed signal transduction researchers since its detection in skeletal muscle 25 years ago. The enzyme confounds most of the rules normally associated with protein kinases in that it exhibits significant activity, even in resting, unstimulated cells. However, the protein is highly regulated and potently inactivated in response to signals such as insulin and polypeptide growth factors. The enzyme also displays a distinct and unusual preference for substrates that have been previously phosphorylated by other protein kinases which provides obvious opportunities for cross-talk. Its substrates are diverse and are predominantly regulatory molecules. The molecular cloning of the kinase revealed it to be encoded by two related but distinct genes. Moreover, the mammalian proteins showed remarkable similarity to a fruitfly protein isolated on the basis of its role in cell fate determination. From these humble beginnings, study of the enzyme has accrued further surprises such as its inhibition by lithium, its regulation by serine and tyrosine phosphorylation and its implication in several human disorders including Alzheimers disease, bipolar disorder, cancer and diabetes. Most recently, small molecule inhibitors of GSK-3 have been developed and assessed for therapeutic potential in several of models of pathophysiology. The question is whether modulation of such an "involved" enzyme could lead to selective restoration of defects without multiple unwanted side effects. This review summarizes current knowledge of GSK-3 with respect to its known functions, together with an assessment of its real-life potential as a drug target for chronic conditions such as type 2 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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".