Direct involvement of tumor necrosis factor-α in the regulation of glucose uptake in rainbow trout muscle cells
Bibliographic record
Abstract
The proinflammatory cytokine TNF-α is known to have a direct action on skeletal muscle in mammals. However, little is known regarding the potential effects of cytokines on nonimmune tissues, particularly in skeletal muscle, in fish. The aim of this study was to investigate the effects of recombinant trout TNF-α (rtTNF-α) on skeletal muscle carbohydrate metabolism in rainbow trout (Oncorhynchus mykiss). We used a primary cell culture of muscle cells from rainbow trout to show that rtTNF-α stimulates glucose uptake in myoblasts and myotubes at concentrations that do not affect the viability of the cells, requiring de novo protein synthesis as shown by the impairment of rtTNF-α-stimulated glucose uptake by cycloheximide. With the use of specific inhibitors, we show that rtTNF-α-stimulated glucose uptake is mediated by the p38MAPK, NF-κB, and JNK pathways. Additionally, we provide evidence that the stimulatory effects of rtTNF-α on glucose uptake in trout skeletal muscle cells may be caused, at least in part, by an increase in the amount of GLUT4 at the plasma membrane. Incubation of trout muscle cells with conditioned medium from LPS-stimulated trout macrophages, enriched in TNF-α, increased glucose uptake. Our results indicate that recombinant, as well as native trout TNF-α, directly stimulates glucose uptake in trout muscle cells and provide evidence, for the first time in nonmammalian vertebrates, for a potential regulatory role of TNF-α in skeletal muscle metabolism.
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) | 0.000 | 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) | 0.001 | 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".