Central Insulin Signaling Is Attenuated by Long-Term Insulin Exposure via Insulin Receptor Substrate-1 Serine Phosphorylation, Proteasomal Degradation, and Lysosomal Insulin Receptor Degradation
Bibliographic record
Abstract
Central insulin signaling is critical for the prevention of insulin resistance. Hyperinsulinemia contributes to insulin resistance, but it is not yet clear whether neurons are subject to cellular insulin resistance. We used an immortalized, hypothalamic, clonal cell line, mHypoE-46, which exemplifies neuronal function and expresses the components of the insulin signaling pathway, to determine how hyperinsulinemia modifies neuronal function. Western blot analysis indicated that prolonged insulin treatment of mHypoE-46 cells attenuated insulin signaling through phospho-Akt. To understand the mechanisms involved, time-course analysis was performed. Insulin exposure for 4 and 8 h phosphorylated Akt and p70-S6 kinase (S6K1), whereas 8 and 24 h treatment decreased insulin receptor (IR) and IR substrate 1 (IRS-1) protein levels. Insulin phosphorylation of S6K1 correlated with IRS-1 ser1101 phosphorylation and the mTOR-S6K1 pathway inhibitor rapamycin prevented IRS-1 serine phosphorylation. The proteasomal inhibitor epoxomicin and the lysosomal pathway inhibitor 3-methyladenine prevented the degradation of IRS-1 and IR by insulin, respectively, and pretreatment with rapamycin, epoxomicin, or 3-methyladenine prevented attenuation of insulin signaling by long-term insulin exposure. Thus, a sustained elevation of insulin levels diminishes neuronal insulin signaling through mTOR-S6K1-mediated IRS-1 serine phosphorylation, proteasomal degradation of IRS-1 and lysosomal degradation of the IR.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".