A Low-Therapeutic Dose of Lithium Inhibits GSK3 and Enhances Myoblast Fusion in C2C12 Cells
Bibliographic record
Abstract
Glycogen synthase kinase 3 (GSK3) slows myogenic differentiation and myoblast fusion partly by inhibiting the Wnt/β-catenin signaling pathway. Lithium, a common medication for bipolar disorder, inhibits GSK3 via Mg+ competition and increased Ser21 (GSK3α) or Ser9 (GSK3β) phosphorylation, leading to enhanced myoblast fusion and myogenic differentiation. However, previous studies demonstrating the effect of lithium on GSK3 have used concentrations up to 10 mM, which greatly exceeds concentrations measured in the serum of patients being treated for bipolar disorder (0.5–1.2 mM). Here, we determined whether a low-therapeutic (0.5 mM) dose of lithium could promote myoblast fusion and myogenic differentiation in C2C12 cells. C2C12 myotubes differentiated for three days in media containing 0.5 mM lithium chloride (LiCl) had significantly higher GSK3β (ser9) and GSK3α (ser21) phosphorylation compared with control myotubes differentiated in the same media without LiCl (+2–2.5 fold, p < 0.05), a result associated with an increase in total β-catenin. To further demonstrate that 0.5 mM LiCl inhibited GSK3 activity, we also developed a novel GSK3-specific activity assay. Using this enzyme-linked spectrophotometric assay, we showed that 0.5 mM LiCl-treated myotubes had significantly reduced GSK3 activity (−86%, p < 0.001). Correspondingly, 0.5 mM LiCl treated myotubes had a higher myoblast fusion index compared with control (p < 0.001) and significantly higher levels of markers of myogenesis (myogenin, +3-fold, p < 0.001) and myogenic differentiation (myosin heavy chain, +10-fold, p < 0.001). These results indicate that a low-therapeutic dose of LiCl is sufficient to promote myoblast fusion and myogenic differentiation in muscle cells, which has implications for the treatment of several myopathic conditions.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".