Insulin-dependent and -independent dynamics of insulin receptor trafficking in muscle cells
Bibliographic record
Abstract
Insulin resistance contributes to type 2 diabetes and can be driven by hyperinsulinemia. Insulin receptor (INSR) internalization and cell-surface dynamics at rest and during insulin exposure are incompletely understood in muscle cells. Using surfacing labelling and live-cell imaging, we observed robust basal internalization of INSR in C2C12 myoblasts, without an effect of added insulin. Mass-spectrometry using INSR knockout cells as controls, identified high-confidence binding partners, including proteins associated with internalization. We confirmed known interactors, including IGF1R, but also identified underappreciated INSR-binding factors such as ANXA2. AlphaFold-Multimer analysis of these INSR-binding proteins predicted potential INSR binding sites of these proteins. Protein-protein interaction network mapping suggested links between INSR and caveolin-mediated endocytosis. INSR interacted with both caveolin and clathrin heavy chain (CLTC) in mouse skeletal muscle and C2C12 myoblasts. Whole cell 2D super-resolution imaging revealed that high levels of insulin (20 nM) increased INSR colocalization with CAV1 but decreased its colocalization with CLTC. Single particle tracking confirmed the colocalization of cell-surface INSR with both over-expressed CAV1-mRFP and CLTC-mRFP. INSR tracks that colocalized with CAV1 exhibited longer radii and lifetimes, regardless of insulin exposure, compared to non-colocalized tracks, whereas insulin further increased the lifetime of INSR/CLTC colocalized tracks. Overall, these data suggest that muscle cells utilize both CAV1 and CLTC-dependent pathways for INSR dynamics and internalization.
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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 | 0.001 | 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".