Extracellular Nucleotides Inhibit Insulin Receptor Signaling, Stimulate Autophagy and Control Lipoprotein Secretion
Bibliographic record
Abstract
Hyperglycemia is associated with abnormal plasma lipoprotein metabolism and with an elevation in circulating nucleotide levels. We evaluated how extracellular nucleotides may act to perturb hepatic lipoprotein secretion. Adenosine diphosphate (ADP) (>10 µM) acts like a proteasomal inhibitor to stimulate apoB100 secretion and inhibit apoA-I secretion from human liver cells at 4 h and 24 h. ADP blocks apoA-I secretion by stimulating autophagy. The nucleotide increases cellular levels of the autophagosome marker, LC3-II, and increases co-localization of LC3 with apoA-I in punctate autophagosomes. ADP affects autophagy and apoA-I secretion through P2Y(13). Overexpression of P2Y(13) increases cellular LC3-II levels by ~50% and blocks induction of apoA-I secretion. Conversely, a siRNA-induced reduction in P2Y(13) protein expression of 50% causes a similar reduction in cellular LC3-II levels and a 3-fold stimulation in apoA-I secretion. P2Y(13) gene silencing blocks the effects of ADP on autophagy and apoA-I secretion. A reduction in P2Y(13) expression suppresses ERK1/2 phosphorylation, increases the phosphorylation of IR-β and protein kinase B (Akt) >3-fold, and blocks the inhibition of Akt phosphorylation by TNFα and ADP. Conversely, increasing P2Y(13) expression significantly inhibits insulin-induced phosphorylation of insulin receptor (IR-β) and Akt, similar to that observed after treatment with ADP. Nucleotides therefore act through P2Y(13), ERK1/2 and insulin receptor signaling to stimulate autophagy and affect hepatic lipoprotein secretion.
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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) | 0.001 | 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".