Insulin increases plasma membrane content and reduces phosphorylation of Na<sup>+</sup>-K<sup>+</sup> pump α<sub>1</sub>-subunit in HEK-293 cells
Bibliographic record
Abstract
Insulin stimulates K(+) uptake and Na(+) efflux via the Na(+)-K(+) pump in kidney, skeletal muscle, and brain. The mechanism of insulin action in these tissues differs, in part, because of differences in the isoform complement of the catalytic alpha-subunit of the Na(+)-K(+) pump. To analyze specifically the effect of insulin on the alpha(1)-isoform of the pump, we have studied human embryonic kidney (HEK)-293 cells stably transfected with the rat Na(+)-K(+) pump alpha(1)-isoform tagged on its first exofacial loop with a hemagglutinin (HA) epitope. The plasma membrane content of alpha(1)-subunits was quantitated by binding a specific HA antibody to intact cells. Insulin rapidly increased the number of alpha(1)-subunits at the cell surface. This gain was sensitive to the phosphatidylinositol (PI) 3-kinase inhibitor wortmannin and to the protein kinase C (PKC) inhibitor bisindolylmaleimide. Furthermore, the insulin-stimulated gain in surface alpha-subunits correlated with an increase in the binding of an antibody that recognizes only the nonphosphorylated form of alpha(1) (at serine-18). These results suggest that insulin regulates the Na(+)-K(+) pump in HEK-293 cells, at least in part, by decreasing serine phosphorylation and increasing plasma membrane content of alpha(1)-subunits via a signaling pathway involving PI 3-kinase and PKC.
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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 | 0.001 |
| 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".