Discovery of TBC1D1 as an Insulin-, AICAR-, and Contraction-stimulated Signaling Nexus in Mouse Skeletal Muscle
Bibliographic record
Abstract
The Akt substrate of 160 kDa (AS160) is phosphorylated on Akt substrate (PAS) motifs in response to insulin and contraction in skeletal muscle, regulating glucose uptake. Here we discovered a dissociation between AS160 protein expression and apparent AS160 PAS phosphorylation among soleus, tibialis anterior, and extensor digitorum longus muscles. Immunodepletion of AS160 in tibialis anterior muscle lysates resulted in minimal depletion of the PAS band at 160 kDa, suggesting the presence of an additional PAS immunoreactive protein. By immunoprecipitation and mass spectrometry, we identified this protein as the AS160 paralog TBC1D1, an obesity candidate gene regulating GLUT4 translocation in adipocytes. TBC1D1 expression was severalfold higher in skeletal muscles compared with all other tissues and was the dominant protein detected by the anti-PAS antibody at 160 kDa in tibialis anterior and extensor digitorum longus but not soleus muscles. In vivo stimulation by insulin, contraction, and the AMP-activated protein kinase (AMPK) activator AICAR increased TBC1D1 PAS phosphorylation. Using mass spectrometry on TBC1D1 from mouse skeletal muscle, we identified several novel phosphorylation sites on TBC1D1 and found the majority were consensus or near consensus sites for AMPK. Semiquantitative analysis of spectra suggested that AICAR caused greater overall phosphorylation of TBC1D1 sites compared with insulin. Purified Akt and AMPK phosphorylated TBC1D1 in vitro, and AMPK, but not Akt, reduced TBC1D1 electrophoretic mobility. TBC1D1 is a major PAS immunoreactive protein in skeletal muscle that is phosphorylated in vivo by insulin, AICAR, and contraction. Both Akt and AMPK phosphorylate TBC1D1, but AMPK may be the more robust regulator.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".