Contracting C<sub>2</sub>C<sub>12</sub> myotubes release CCL2 in an NF-κB-dependent manner to induce monocyte chemoattraction
Bibliographic record
Abstract
Muscle inflammation following exercise is characterized by expression of inflammatory cytokines and chemokines. Exercise also increases muscle macrophages derived from circulating monocytes. However, it is unknown whether muscle cells themselves attract circulating monocytes, or what is the underlying mechanism. We used an in vitro system of electrical stimulation (ES) causing C2C12 myotube contraction to explore whether monocyte chemoattraction ensues and investigated the mediating chemoattractants. Conditioned medium from ES-contracted myotubes caused robust chemoattraction of THP-1 monocytes across Boyden chambers. Following ES, expression of several known monocyte chemokines [C-C motif ligand 2 (CCL2) and C-X-C motif ligand (CXCL)1, -2, and -5] was elevated, but of these, only recombinant CCL2 effectively reproduced monocyte migration. Electrically stimulated myotubes secreted CCL2, and neutralization of CCL2 in conditioned medium or antagonizing the CCL2 receptor (CCR2) in THP-1 monocytes inhibited ES-induced monocyte migration. N-benzyl-p-toluene sulfonamide (BTS), a myosin II-ATPase inhibitor, prevented ES-induced myotube contraction but not CCL2 gene expression and secretion. The membrane-permeant calcium chelator BAPTA-AM reduced ES-induced CCL2 secretion. Hence, electrical depolarization, rather than mechanical contraction, drives the rise in CCL2, with partial calcium input. ES activated the NF-κB pathway; NF-κB inhibitors reduced ES-induced CCL2 gene expression and secretion and repressed ES-induced THP-1 chemoattraction. Thus, electrically stimulated myotubes chemoattract monocytes through NF-κB-regulated CCL2 secretion.
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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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".