Lack of cyclin D3 induces skeletal muscle fiber-type shifting, increased endurance performance and hypermetabolism.
Silvia GiannattasioGiacomo GiacovazzoAgnese BonatoCarla CarusoSiro LuvisettoRoberto CoccurelloMaurizia CarusoPublished in: Scientific reports (2018)
The mitogen-induced D-type cyclins (D1, D2 and D3) are regulatory subunits of the cyclin-dependent kinases CDK4 and CDK6 that drive progression through the G1 phase of the cell cycle. In skeletal muscle, cyclin D3 plays a unique function in controlling the proliferation/differentiation balance of myogenic progenitor cells. Here, we show that cyclin D3 also performs a novel function, regulating muscle fiber type-specific gene expression. Mice lacking cyclin D3 display an increased number of myofibers with higher oxidative capacity in fast-twitch muscle groups, primarily composed of myofibers that utilize glycolytic metabolism. The remodeling of myofibers toward a slower, more oxidative phenotype is accompanied by enhanced running endurance and increased energy expenditure and fatty acid oxidation. In addition, gene expression profiling of cyclin D3-/- muscle reveals the upregulation of genes encoding proteins involved in the regulation of contractile function and metabolic markers specifically expressed in slow-twitch and fast-oxidative myofibers, many of which are targets of MEF2 and/or NFAT transcription factors. Furthermore, cyclin D3 can repress the calcineurin- or MEF2-dependent activation of a slow fiber-specific promoter in cultured muscle cells. These data suggest that cyclin D3 regulates muscle fiber type phenotype, and consequently whole body metabolism, by antagonizing the activity of MEF2 and/or NFAT.
Keyphrases
- cell cycle
- skeletal muscle
- cell proliferation
- insulin resistance
- gene expression
- cell cycle arrest
- transcription factor
- dna methylation
- signaling pathway
- fatty acid
- genome wide
- oxidative stress
- nitric oxide
- big data
- pi k akt
- protein kinase
- induced apoptosis
- endothelial cells
- toll like receptor
- artificial intelligence
- electronic health record
- body composition
- deep learning
- immune response
- copy number
- high glucose
- endoplasmic reticulum stress