Inhibition or Ablation of p21-activated Kinase (PAK1) Disrupts Glucose Homeostatic Mechanisms in Vivo
Bibliographic record
Abstract
The p21-activated kinase PAK1 is implicated in tumorigenesis, and efforts to inhibit PAK1 signaling as a means to induce tumor cell apoptosis are underway. However, PAK1 has also been implicated as a positive effector of mechanisms in clonal pancreatic beta cells and skeletal myotubes that would be crucial to maintaining glucose homeostasis in vivo. Of relevance, human islets of Type 2 diabetic donors contained ~80% less PAK1 protein compared with non-diabetics, implicating PAK1 in islet signaling/scaffolding functions. Mimicking this, islets from PAK1(-/-) knock-out mice exhibited profound defects in the second/sustained-phase of insulin secretion. Reiteration of this specific defect by human islets treated with the PAK1 signaling inhibitor IPA3 revealed PAK1 signaling to be of primary functional importance. Analyses of human and mouse islet beta cell signaling revealed PAK1 activation to be 1) dependent upon Cdc42 abundance, 2) crucial for signaling downstream to activate ERK1/2, but 3) dispensable for cofilin phosphorylation. Importantly, the PAK1(-/-) knock-out mice were found to exhibit whole body glucose intolerance in vivo. Exacerbating this, the PAK1(-/-) knock-out mice also exhibited peripheral insulin resistance. Insulin resistance was coupled to ablation of insulin-stimulated GLUT4 translocation in skeletal muscle from PAK1(-/-) knock-out mice, and in sharp contrast to islet beta cells, skeletal muscle PAK1 loss was underscored by defective cofilin phosphorylation but normal ERK1/2 activation. Taken together, these data provide the first human islet and mammalian in vivo data unveiling the key and crucial roles for differential PAK1 signaling in the multi-tissue regulation of whole body glucose homeostasis.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".