An adiponectin-S1P autocrine axis protects skeletal muscle cells from palmitate-induced cell death
Bibliographic record
Abstract
BACKGROUND: The prevalence of type 2 diabetes, obesity and their various comorbidities have continued to rise. In skeletal muscle lipotoxicity is well known to be a contributor to the development of insulin resistance. Here it was examined if the small molecule adiponectin receptor agonist AdipoRon mimicked the effect of adiponectin to attenuate palmitate induced reactive oxygen species (ROS) production and cell death in L6 skeletal muscle cells. METHODS: L6 cells were treated ±0.1 mM PA, and ± AdipoRon, then assays analyzing reactive oxygen species (ROS) production and cell death, and intracellular and extracellular levels of sphingosine-1 phosphate (S1P) were conducted. To determine the mechanistic role of S1P gain (using exogenous S1P or using THI) or loss of function (using the SKI-II) were conducted. RESULTS: Using both CellROX and DCFDA assays it was found that AdipoRon reduced palmitate-induced ROS production. Image-IT DEAD, MTT and LDH assays all indicated that AdipoRon reduced palmitate-induced cell death. Palmitate significantly increased intracellular accumulation of S1P, whereas in the presence of AdipoRon there was increased release of S1P from cells to extracellular medium. It was also observed that direct addition of extracellular S1P prevented palmitate-induced ROS production and cell death, indicating that S1P is acting in an autocrine manner. Pharmacological approaches to enhance or decrease S1P levels indicated that accumulation of intracellular S1P correlated with enhanced cell death. CONCLUSION: This data indicates that increased extracellular levels of S1P in response to adiponectin receptor activation can activate S1P receptor-mediated signaling to attenuate lipotoxic cell death. Taken together these findings represent a possible novel mechanism for the protective action of adiponectin.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".