Transcriptomic and lipidomic profiling reveals distinct bioactive lipid signatures in slow and fast muscles and highlights the role of resolvin‐ <scp>D2</scp> in fiber type determination during myogenesis
Bibliographic record
Abstract
Skeletal muscles are predominantly composed of long, multinucleated muscle fibers, classified according to their metabolic and contractile phenotype. The determination of fiber types is influenced by various factors (e.g., innervation, hormones, physical demand). Our laboratory and others showed that resolvins, lipid mediators derived from omega-3 fatty acids, promote muscle regeneration and function after an injury or in models of muscular dystrophies; however, the effect of resolvins on the determination of muscle phenotype remains unknown. Here, we investigated the impact of lipid mediators on muscle phenotype during myogenesis. Transcriptomics analysis of single-nuclei RNAseq data sets revealed that the enzymes responsible for bioactive lipids biosynthesis are differentially expressed in slow fibers versus fast fibers. Lipidomics analysis of slow-twitch muscle (soleus) versus fast-twitch muscle (tibialis anterior) showed that the levels of lipids derived from arachidonic acid are similar between muscle groups, but lipids derived from alpha-linolenic acid, linoleic acid, eicosapentaenoic acid, and docosahexaenoic acid are enriched in slow-twitch muscle. Screening for different lipids in vitro showed that resolvin-D2 enhances the formation of myotubes expressing the slow myosin heavy chain isoform. In vivo, the administration of resolvin-D2 enhances muscle strength, increases myofiber size, and affects fiber typing in injured muscles but not in uninjured muscles. Resolvin-D2 promoted the transition toward the dominant fiber types in regenerating muscle (i.e., type I in the slow-twitch soleus and type IIB in the fast-twitch tibialis anterior muscle), suggesting its participation in fiber typing in conjunction with other factors. Overall, these findings identified new roles of bioactive lipids in the regulation of fiber typing, which could have therapeutic applicability in muscle injuries or dystrophies.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".