N-Acetylglucosamine Facilitates Coordinated Myoblast Flow, Forming the Foundation for Efficient Myogenesis
Bibliographic record
Abstract
Abstract Skeletal muscle comprises 30-40% of a mammal’s body mass, maintaining its integrity through efficient muscle fiber regeneration, which involves myoblast differentiation into myotubes. Previously, we reported that N-acetylglucosamine (GlcNAc) promotes myogenesis in C2C12 cells, although the underlying mechanisms were unclear. UDP-GlcNAc, the activated form of GlcNAc, is critical for the biosynthesis of highly branched (N-acetyllactosamine-rich) N-linked oligosaccharides, which are recognized by galectin-3 (Gal-3), facilitating dynamic cell-cell and cell-matrix interactions. In this study, we used primary myoblasts from wild-type and Gal-3 null (Gal-3KO) mice, observing myotube formation through long-term live-cell imaging and single-cell tracking. We found that GlcNAc enhances myoblast fusion in a dose-dependent manner, and the addition of Gal-3 with GlcNAc leads to the formation of larger myotubes. Gal-3KO myoblasts exhibited a reduced capacity for myotube formation, a deficiency that was rectified by supplementing with GlcNAc and Gal-3. Our results highlight the critical role of Gal-3 interaction with oligosaccharides whose synthesis was promoted by GlcNAc in facilitating myotube formation. Single-cell tracking revealed that GlcNAc and Gal-3 increase myoblast motility, creating a faster-coordinated cell flow—a directed movement of myoblasts, along which myotubes form through cell fusion. Interestingly, myoblasts contributing to myotube formation were pre-positioned along the eventual shape of the myotubes before the establishment of the coordinated flow. These myoblasts moved along the flow, paused, and even moved against the flow, suggesting that both flow and initial positioning play roles in aligning myoblasts into the shape of a myotube. Overall, our findings demonstrate that GlcNAc, in conjunction with Gal-3, enhances myotube formation by fostering an environment conducive to myoblast positioning, establishing coordinated flow, and facilitating fusion. This suggests potential therapeutic applications of GlcNAc in muscle repair and muscle disorders.
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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) | 0.001 | 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".