Leukemia inhibitory factor increases glucose uptake in mouse skeletal muscle
Bibliographic record
Abstract
Members of the IL-6 family, IL-6 and ciliary neurotrophic factor (CNTF), have been shown to increase glucose uptake and fatty acid oxidation in skeletal muscle. However, the metabolic effects of another family member, leukemia inhibitory factor (LIF), are not well characterized. Effects of LIF on skeletal muscle glucose uptake and palmitate oxidation and signaling were investigated in ex vivo incubated mouse soleus and EDL muscles from muscle-specific AMPKα2 kinase-dead, muscle-specific SOCS3 knockout, and lean and high-fat-fed mice. Inhibitors were used to investigate involvement of specific signaling pathways. LIF increased muscle glucose uptake in dose (50-5,000 pM/l) and time-dependent manners with maximal effects at the 30-min time point. LIF increased Akt Ser(473) phosphorylation (P) in soleus and EDL, whereas AMPK Thr(172) P was unaffected. Incubation with parthenolide abolished LIF-induced glucose uptake and STAT3 Tyr(705) P, whereas incubation with LY-294002 and wortmannin suppressed both basal and LIF-induced glucose uptake and Akt Ser(473) P, indicating that JAK and PI 3-kinase signaling is required for LIF-stimulated glucose uptake. Incubation with rapamycin and AZD8055 indicated that mammalian target of rapamycin complex (mTORC)2, but not mTORC1, also is required for LIF-stimulated glucose uptake. In contrast to CNTF, LIF stimulation did not alter palmitate oxidation. LIF-stimulated glucose uptake was maintained in EDL from obese insulin-resistant mice, whereas soleus developed LIF resistance. Lack of SOCS3 and AMPKα2 did not affect LIF-stimulated glucose uptake. In conclusion, LIF acutely increased muscle glucose uptake by a mechanism potentially involving the PI 3-kinase/mTORC2/Akt pathway and is not impaired in EDL muscle from obese insulin-resistant mice.
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) | 0.001 | 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 | 0.000 | 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".