Genetic Regulation of Myofiber Hypertrophy?

Introduction. Progressive, resistance exercise training (RT) induces skeletal muscle hypertrophy, increases strength, power, and quality of muscle, and is potentially the most promising method to regenerate and re-grow muscle in populations suffering from involuntary atrophy. However, we have previously shown that there is a large degree of intersubject variability for myofiber hypertrophy in response to RT with adults having no response [-16μm2 (mean myofiber growth), Non], a modest response (1111μm2, Mod), or an extreme hypertrophic response (2475μm2, Xtr). Underlying mechanisms for this differential growth response are largely unknown. Therefore, the purpose of this study was to determine whether differences in the skeletal muscle transcriptome exist among the three response clusters, prior to 16 weeks of RT. Methods. mRNA was isolated from muscle biopsies taken from the vastus lateralis of 44 previously clustered men and women (aged 19-75y). Agilent 4X44K single color genechips were used to determine differences in skeletal muscle gene expression among the three response clusters. Ingenuity Pathways Analysis (IPA) and available Gene Ontology were used for functional annotation of differentially expressed genes and identification of informative genes that may instigate the observed myofiber growth phenotypes. Results. After removing genes with low signal intensities and normalizing the data, we identified substantial differences in the transcript profile among the response clusters with the most notable differences between the Xtr- and Non-responders. 8026 differentially expressed genes were identified between Xtr vs. Non, 2463 between Xtr vs. Mod, and 1294 between Mod vs. Non. There were 1632 genes with expression specific to the Xtr (i.e. differences existed between Xtr vs. Non and Mod, but not between the Non vs. Mod) and 617 genes with expression specific to the Non. Functional classification, with IPA, identified Skeletal Muscle System Development and Function (SMSDF) as a top functional category containing a significant number of differentially expressed genes (p\u3c0.05) in all three comparisons. SMSDF was also a top five functional category for the genes specific to both Xtr and Non (p\u3c0.05). Within the broad SMSDF category, IPA defined sub-categories of functional annotation, which allowed us to further interpret the differentially expressed genes. We have highlighted several genes that primarily had expression specific to the Xtr or had increased expression from Non to Mod to Xtr. Highlighted genes are involved with satellite cell activation and function (SOX8, HGF, PAX7), differentiation (MYOD1, MYOG, APOE, TRIO, MSTN), skeletal muscle growth (DGKZ, ESR1, OXT, OXTR, UCN2, GREB1), modulation of inflammation and fuel utilization (PYY), and improved function (TFAM, UCN2, CRHR1, CRHR2). Additionally, there was a decrease in expression (Xtr vs. Non) for several genes involved with modulation of inflammation and fuel utilization (AEBP1, NFKB1, CD36, AIF1). Discussion. These results indicate that differences in gene expression do exist among the response clusters prior to mechanically induced hypertrophy and that the Xtr-responders were “primed” to respond. We identified several genes and signaling pathways that may promote or inhibit muscle growth and thus, initiate the three observed hypertrophic response phenotypes. Results from this study enabled us to identify distinctive molecular pathways, particularly between the Xtr- and Non-responders, for development of targeted interventions. Further research is necessary to determine which of these genes or networks of genes truly distinguish load mediated hypertrophy potential

Potential Benefits of Combined Statin and Metformin Therapy on Resistance Training Response in Older Individuals

Metformin and statins are currently the focus of large clinical trials testing their ability to counter age-associated declines in health, but recent reports suggest that both may negatively affect skeletal muscle response to exercise. However, it has also been suggested that metformin may act as a possible protectant of statin-related muscle symptoms. The potential impact of combined drug use on the hypertrophic response to resistance exercise in healthy older adults has not been described. We present secondary statin analyses of data from the MASTERS trial where metformin blunted the hypertrophy response in healthy participants (\u3e65 years) following 14 weeks of progressive resistance training (PRT) when compared to identical placebo treatment (n = 94). Approximately one-third of MASTERS participants were taking prescribed statins. Combined metformin and statin resulted in rescue of the metformin-mediated impaired growth response to PRT but did not significantly affect strength. Improved muscle fiber growth may be associated with medication-induced increased abundance of CD11b+/CD206+ M2-like macrophages. Sarcopenia is a significant problem with aging and this study identifies a potential interaction between these commonly used drugs which may help prevent metformin-related blunting of the beneficial effects of PRT

A Guide for Using NIH Image J for Single Slice Cross-Sectional Area and Composition Analysis of the Thigh from Computed Tomography

Reports using computed tomography (CT) to estimate thigh skeletal muscle cross-sectional area and mean muscle attenuation are often difficult to evaluate due to inconsistent methods of quantification and/or poorly described analysis methods. This CT tutorial provides step-by-step instructions in using free, NIH Image J software to quantify both muscle size and composition in the mid-thigh, which was validated against a robust commercially available software, SliceOmatic. CT scans of the mid-thigh were analyzed from 101 healthy individuals aged 65 and older. Mean cross-sectional area and mean attenuation values are presented across seven defined Hounsfield unit (HU) ranges along with the percent contribution of each region to the total mid-thigh area. Inter-software correlation coefficients ranged from R2 = 0.92–0.99 for all specific area comparisons measured using the Image J method compared to SliceOmatic. We recommend reporting individual HU ranges for all areas measured. Although HU range 0–100 includes the majority of skeletal muscle area, HU range -29 to 150 appears to be the most inclusive for quantifying total thigh muscle. Reporting all HU ranges is necessary to determine the relative contribution of each, as they may be differentially affected by age, obesity, disease, and exercise. This standardized operating procedure will facilitate consistency among investigators reporting computed tomography characteristics of the thigh on single slice images. Trial Registration: ClinicalTrials.gov NCT02308228

Effects of exercise on sleep spindles in Parkinson's disease

BackgroundIn a randomized, controlled trial, we showed that high-intensity rehabilitation, combining resistance training and body-weight interval training, improves sleep efficiency in Parkinson's disease (PD). Quantitative sleep EEG (sleep qEEG) features, including sleep spindles, are altered in aging and in neurodegenerative disease.ObjectiveThe objective of this post-hoc analysis was to determine the effects of exercise, in comparison to a sleep hygiene, no-exercise control group, on the quantitative characteristics of sleep spindle morphology in PD.MethodsWe conducted an exploratory post-hoc analysis of 24 PD participants who were randomized to exercise (supervised 3 times/week for 16 weeks) versus 26 PD participants who were assigned to a sleep hygiene, no-exercise control group. At baseline and post-intervention, all participants completed memory testing and underwent polysomnography (PSG). PSG-derived sleep EEG central leads (C3 and C4) were manually inspected, with rejection of movement and electrical artifacts. Sleep spindle events were detected based on the following parameters: (1) frequency filter = 11–16 Hz, (2) event duration = 0.5–3 s, and (3) amplitude threshold 75% percentile. We then calculated spindle morphological features, including density and amplitude. These characteristics were computed and averaged over non-rapid eye movement (NREM) sleep stages N2 and N3 for the full night and separately for the first and second halves of the recording. Intervention effects on these features were analyzed using general linear models with group x time interaction. Significant interaction effects were evaluated for correlations with changes in performance in the memory domain.ResultsA significant group x time interaction effect was observed for changes in sleep spindle density due to exercise compared to sleep hygiene control during N2 and N3 during the first half of the night, with a moderate effect size. This change in spindle density was positively correlated with changes in performance on memory testing in the exercise group.ConclusionsThis study is the first to demonstrate that high-intensity exercise rehabilitation has a potential role in improving sleep spindle density in PD and leading to better cognitive performance in the memory domain. These findings represent a promising advance in the search for non-pharmacological treatments for this common and debilitating non-motor symptom

A Muscle Cell-Macrophage Axis Involving Matrix Metalloproteinase 14 Facilitates Extracellular Matrix Remodeling with Mechanical Loading

The extracellular matrix (ECM) in skeletal muscle plays an integral role in tissue development, structural support, and force transmission. For successful adaptation to mechanical loading, remodeling processes must occur. In a large cohort of older adults, transcriptomics revealed that genes involved in ECM remodeling, including matrix metalloproteinase 14 (MMP14), were the most upregulated following 14 weeks of progressive resistance exercise training (PRT). Using single-cell RNA-seq, we identified macrophages as a source of Mmp14 in muscle following a hypertrophic exercise stimulus in mice. In vitro contractile activity in myotubes revealed that the gene encoding cytokine leukemia inhibitory factor (LIF) is robustly upregulated and can stimulate Mmp14 expression in macrophages. Functional experiments confirmed that modulation of this muscle cell-macrophage axis facilitated Type I collagen turnover. Finally, changes in LIF expression were significantly correlated with MMP14 expression in humans following 14 weeks of PRT. Our experiments reveal a mechanism whereby muscle fibers influence macrophage behavior to promote ECM remodeling in response to mechanical loading

State of knowledge on molecular adaptations to exercise in humans: historical perspectives and future directions

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Immunohistochemical Identification of Human Skeletal Muscle Macrophages

Macrophages have well-characterized roles in skeletal muscle repair and regeneration. Relatively little is known regarding the role of resident macrophages in skeletal muscle homeostasis, extracellular matrix remodeling, growth, metabolism and adaptation to various stimuli including exercise and training. Despite speculation into macrophage contributions during these processes, studies characterizing macrophages in non-injured muscle are limited and methods used to identify macrophages vary. A standardized method for the identification of human resident skeletal muscle macrophages will aide in the characterization of these immune cells and allow for the comparison of results across studies. Here, we present an immunohistochemistry (IHC) protocol, validated by flow cytometry, to distinctly identify resident human skeletal muscle macrophage populations. We show that CD11b and CD206 double IHC effectively identifies macrophages in human skeletal muscle. Furthermore, the majority of macrophages in non-injured human skeletal muscle show a ‘mixed’ M1/M2 phenotype, expressing CD11b, CD14, CD68, CD86 and CD206. A relatively small population of CD11b+/CD206- macrophages are present in resting skeletal muscle. Changes in the relative abundance of this population may reflect important changes in the skeletal muscle environment. CD11b and CD206 IHC in muscle also reveals distinct morphological features of macrophages that may be related to the functional status of these cells