Integrin-Dependent Akt1 Activation Regulates PGC-1 Expression and Fatty Acid Oxidation
Bibliographic record
Abstract
BACKGROUND: Poly-N-acetyl glucosamine nanofibers derived from a marine diatom have been used to increase cutaneous wound healing. These nanofibers exert their activity by specifically activating integrins, which makes them a useful tool for dissecting integrin-mediated pathways. We have shown that short-fiber poly-N-acetyl glucosamine nanofiber (sNAG) treatment of endothelial cells results in increased cell motility and metabolic rate in the absence of increased cell proliferation. RESULTS: Using a Seahorse Bioanalyzer to measure oxygen consumption in real time, we show that sNAG treatment increases oxygen consumption rates, correlated with an integrin-dependent activation of Akt1. Akt1 activation leads to an increase in the expression of the transcriptional coactivator, peroxisome proliferator-activated receptor γ coactivator-1α (PGC-1α). This is not due to increased mitochondrial biogenesis, but is associated with an increase in the expression of pyruvate dehydrogenase kinase 4 (PDK4), suggesting regulation of fatty acid oxidation. Blockade of fatty acid oxidation with etomoxir, an O-carnitine palmitoyltransferase-1 inhibitor, blocks the sNAG-dependent increased oxygen consumption. (3)H-palmitate uptake experiments indicate a PDK4-dependent increase in fatty acid oxidation, which is required for nanofiber-induced cell motility. CONCLUSIONS: Our findings imply a linear pathway whereby an integrin-dependent activation of Akt1 leads to increased PGC-1α and PDK4 expression resulting in increased energy production by fatty acid oxidation.
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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) | 0.002 | 0.000 |
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".