Toll-Like Receptors Differentially Regulate CC and CXC Chemokines in Skeletal Muscle via NF-κB and Calcineurin
Bibliographic record
Abstract
Immunologically active molecules such as cytokines and chemokines have been implicated in skeletal muscle weakness during sepsis as well as recovery from muscle injury. In sepsis, Toll-like receptors (TLRs) act as key sentinel molecules of the innate immune system. Here we determined skeletal muscle cell responses of two prototypical CC and CXC chemokine genes (monocyte chemoattractant protein 1 [MCP-1] and KC, respectively), to stimulation with specific TLR ligands. In addition, we examined whether NF-kappaB and calcineurin signaling are involved in these responses. Differentiated myotubes and intact whole muscles expressed TLR2, TLR4, TLR5, and TLR9. Stimulation with ligands for TLR2 (peptidoglycan) or TLR4 (LPS) elicited robust and equivalent levels of MCP-1 and KC mRNA expression, whereas stimulation of TLR5 (by flagellin) required gamma interferon priming to induce similar effects. Although both TLR2 and TLR4 ligands activated the NF-kappaB pathway, NF-kappaB reporter activity was approximately 20-fold greater after TLR4 stimulation than after TLR2 stimulation. Inhibitory effects of NF-kappaB blockade on TLR-mediated chemokine gene expression, by either pharmacological (pyrrolidine dithiocarbamate) or molecular (IKKbeta dominant-negative transfection) methods, were also more pronounced during TLR4 stimulation. In contrast, inhibitory effects on TLR-mediated chemokine expression of calcineurin blockade (by FK506) were greater for TLR2 than for TLR4 stimulation. MCP-1 and KC mRNA levels also demonstrated differential responses to NF-kappaB and calcineurin blockade during stimulation with specific TLR ligands. We conclude that skeletal muscle cells differentially utilize the NF-kappaB and calcineurin pathways in a TLR-specific manner to enable complex regulation of CC and CXC chemokine gene expression.
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".