Berberine hydrochloride protects against cytokine-induced inflammation through multiple pathways in undifferentiated C2C12 myoblast cells
Bibliographic record
Abstract
Obesity is associated with skeletal muscle insulin resistance and the development of metabolic syndrome. Undifferentiated skeletal muscle cells are sensitive to oxidative stress. Berberine hydrochloride (BBR) improves insulin resistance and exhibits anti-inflammatory properties. However, the underlying mechanism and the cell signaling pathways involved remain largely elusive. We therefore investigated the anti-inflammatory effects of BBR and the signaling pathways using skeletal C2C12 myoblast cells. Undifferentiated C2C12 myoblast cells were treated with interleukin-1β alone or in combination with tumor necrosis factor-α in the presence or absence of BBR. We found that BBR reduced the cytokine-induced expression of inducible nitric oxide synthase and stress-related kinases including p-38 mitogen-activated protein kinase, nuclear factor kappa B (NF-κB), and stress-activated protein kinases/Jun amino-terminal kinases (SAPK/JNK) in C2C12 myoblast cells. Furthermore, BBR reversed cytokine-mediated suppression of AMP-activated protein kinase (AMPKα), sirtuin-1 (SIRT-1), and PPAR-γ coactivator-1α (PGC-1α). In addition, cytokine-induced reduction of mitochondrial marker proteins and function were rescued after BBR treatment. Catalase, an antioxidant enzyme, was elevated after BBR treatment. Our results demonstrate that BBR ameliorates cytokine-induced inflammation. The anti-inflammatory effect of BBR in skeletal progenitor cells is mediated through pathways including activation of the AMPKα-SIRT-1-PGC-1α, inhibition of the mitogen-activated protein kinase 4 (MKK4)-SAPK/JNK-C-JUN, as well as protection of mitochondrial bioenergetics. BBR may be a potential medication for metabolic syndrome.
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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 | 0.000 |
| 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) | 0.001 | 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".