Mechanisms by which smoothelin-like protein 1 reverses insulin resistance in myotubules and mice
Bibliographic record
Abstract
Insulin resistance (InR) is manifested in skeletal muscle by decreased insulin-stimulated glucose uptake due to impaired insulin signaling and multiple post-receptor intracellular defects. Chronic glucose-induced insulin resistance leads to the activation of Ser/Thr kinases and elevated phosphorylation of insulin receptor substrate 1 (IRS1) on Ser residues. Phosphorylation of IRS1 triggers the dissociation of IRS1 and its downstream effector, phosphatidylinositol 3-kinase. In the present study, we provide evidence for the insulin-sensitizing role of smoothelin-like protein 1 (SMTNL1) that is a ligand-dependent co-regulator of steroid receptors, predominantly the progesterone receptor. SMTNL1 was transiently overexpressed in insulin-resistant C2C12 myotubes. A proteome profiler array revealed that mTOR and Ser/Thr kinases were SMTNL1-dependent signaling pathways. In the presence of progesterone, overexpression was coupled to decreased Ser phosphorylation of IRS1 at Ser307, Ser318, and Ser612 residues. SMTNL1 also induced the expression and activity of the p85 subunit of PI3K. SMTNL1 regulated the expression of PKCε, which phosphorylates IRS1 at Ser318 residue. SMTNL1 also regulated ERK1/2 and JNK, which phosphorylate IRS1 at Ser612 and Ser307, respectively. Real-time metabolic measurements of oxygen consumption rate and extracellular acidification rate revealed that SMTNL1 improved glycolysis and promoted the utilization of alternative carbon fuels. SMTNL1 also rescued the mitochondrial respiration defect induced by chronic insulin exposure. Collectively, SMTNL1 plays a crucial role in maintaining the physiological ratio of Tyr/Ser IRS1 phosphorylation and attenuates the insulin-signaling cascade that contributes to impaired glucose disposal, which makes it a potential therapeutic target for improving InR.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".