Is 14-3-3 the Combination to Unlock New Pathways to Improve Metabolic Homeostasis and β-Cell Function?
Bibliographic record
Abstract
Since their discovery nearly five decades ago, molecular scaffolds belonging to the 14-3-3 protein family have been recognized as pleiotropic regulators of diverse cellular and physiological functions. With their ability to bind to proteins harboring specific serine and threonine phosphorylation motifs, 14-3-3 proteins can interact with and influence the function of docking proteins, enzymes, transcription factors, and transporters that have essential roles in metabolism and glucose homeostasis. Here, we will discuss the regulatory functions of 14-3-3 proteins that will be of great interest to the fields of metabolism, pancreatic β-cell biology, and diabetes. We first describe how 14-3-3 proteins play a central role in glucose and lipid homeostasis by modulating key pathways of glucose uptake, glycolysis, oxidative phosphorylation, and adipogenesis. This is followed by a discussion of the contributions of 14-3-3 proteins to calcium-dependent exocytosis and how this relates to insulin secretion from β-cells. As 14-3-3 proteins are major modulators of apoptosis and cell cycle progression, we will explore if 14-3-3 proteins represent a viable target for promoting β-cell regeneration and discuss the feasibility of targeting 14-3-3 proteins to treat metabolic diseases such as diabetes. ARTICLE HIGHLIGHTS: 14-3-3 proteins are ubiquitously expressed scaffolds with multiple roles in glucose homeostasis and metabolism. 14-3-3ζ regulates adipogenesis via distinct mechanisms and is required for postnatal adiposity and adipocyte function. 14-3-3ζ controls glucose-stimulated insulin secretion from pancreatic β-cells by regulating mitochondrial function and ATP synthesis as well as facilitating cross talk between β-cells and α-cells.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".