Inter-domain tagging implicates caveolin-1 in insulin receptor trafficking and Erk signaling bias in pancreatic beta-cells
Bibliographic record
Abstract
OBJECTIVE: The role and mechanisms of insulin receptor internalization remain incompletely understood. Previous trafficking studies of insulin receptors involved fluorescent protein tagging at their termini, manipulations that may be expected to result in dysfunctional receptors. Our objective was to determine the trafficking route and molecular mechanisms of functional tagged insulin receptors and endogenous insulin receptors in pancreatic beta-cells. METHODS: We generated functional insulin receptors tagged with pH-resistant fluorescent proteins between domains. Confocal, TIRF and STED imaging revealed a trafficking pattern of inter-domain tagged insulin receptors and endogenous insulin receptors detected with antibodies. RESULTS: Surprisingly, interdomain-tagged and endogenous insulin receptors in beta-cells bypassed classical Rab5a- or Rab7-mediated endocytic routes. Instead, we found that removal of insulin receptors from the plasma membrane involved tyrosine-phosphorylated caveolin-1, prior to trafficking within flotillin-1-positive structures to lysosomes. Multiple methods of inhibiting caveolin-1 significantly reduced Erk activation in vitro or in vivo, while leaving Akt signaling mostly intact. CONCLUSIONS: We conclude that phosphorylated caveolin-1 plays a role in insulin receptor internalization towards lysosomes through flotillin-1-positive structures and that caveolin-1 helps bias physiological beta-cell insulin signaling towards Erk activation.
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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".