Muscle cells challenged with saturated fatty acids mount an autonomous inflammatory response that activates macrophages
Why this work is in the frame
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Bibliographic record
Abstract
Obesity is associated with chronic low-grade inflammation. Within adipose tissue of mice fed a high fat diet, resident and infiltrating macrophages assume a pro-inflammatory phenotype characterized by the production of cytokines which in turn impact on the surrounding tissue. However, inflammation is not restricted to adipose tissue and high fat-feeding is responsible for a significant increase in pro-inflammatory cytokine expression in muscle. Although skeletal muscle is the major disposer of dietary glucose and a major determinant of glycemia, the origin and consequence of muscle inflammation in the development of insulin resistance are poorly understood.We used a cell culture approach to investigate the vectorial crosstalk between muscle cells and macrophages upon exposure to physiological, low levels of saturated and unsaturated fatty acids. Inflammatory pathway activation and cytokine expression were analyzed in L6 muscle cells expressing myc-tagged GLUT4 (L6GLUT4myc) exposed to 0.2 mM palmitate or palmitoleate. Conditioned media thereof, free of fatty acids, were then tested for their ability to activate RAW264.7 macrophages.Palmitate -but not palmitoleate- induced IL-6, TNFα and CCL2 expression in muscle cells, through activation of the NF-κB pathway. Palmitate (0.2 mM) alone did not induce insulin resistance in muscle cells, yet conditioned media from palmitate-challenged muscle cells selectively activated macrophages towards a pro-inflammatory phenotype.These results demonstrate that low concentrations of palmitate activate autonomous inflammation in muscle cells to release factors that turn macrophages pro-inflammatory. We hypothesize that saturated fat-induced, low-grade muscle cell inflammation may trigger resident skeletal muscle macrophage polarization, possibly contributing to insulin resistance in vivo.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it