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

Peptide YY (PYY) Is Expressed in Human Skeletal Muscle Tissue and Expanding Human Muscle Progenitor Cells

Peptide YY (PYY) is considered a gut peptide with roles in post-prandial appetite and glucose regulation. Circulating PYY protein levels increase during aerobic exercise. Furthermore, people who have greater increases in muscle progenitor cells (hMPCs), the adult stem cell population responsible for skeletal muscle (SkM) repair, after resistance training have higher PYY transcript levels in SkM prior to training. Currently, examination of PYY expression patterns in SkM and/or hMPCs is lacking. Our objective was to identify the expression patterns of PYY in SkM and hMPCs. PYY and the associated Y receptors were analyzed in SkM biopsy tissue and cultured hMPCs from young and old human participants. Additional experiments to assess the role and regulation of PYY in hMPCs were performed. In SkM, PYY and one of the three Y receptors (Y1r) were detectable, but expression patterns were not affected by age. In expanding hMPCs, PYY and all three Y receptor (Y1r, Y2r, and Y5r) proteins were expressed in a temporal fashion with young hMPCs having greater levels of Y receptors at various time points. Exogenous PYY did not affect hMPC population expansion. hMPC PYY levels increased following the metabolic stimulus, 5-Aminoimidazole-4-carboxamide ribonucleotide (AICAR), but were not affected by the inflammatory stimulus, tumor necrosis factor alpha (TNFα). In conclusion, PYY and Y receptor expression are not impacted by age in SkM tissue but are reduced in old vs. young expanding hMPCs. Furthermore, endogenous PYY production is stimulated by low energy states and thus may be integral for skeletal muscle and hMPC responses to metabolic stimuli

When a calorie is not just a calorie : Diet quality and timing as mediators of metabolism and healthy aging

Funding Information: We thank Dr. Yih-Woei Fridell of the National Institute on Aging for organizing the meeting, as well as the NIA Division of Aging Biology for their support. We thank Dr. Gino Cortopassi for his edits and suggestions. The figures were created with BioRender.com. The Mihaylova lab is supported in part by the NIA (R00AG054760), Office of the NIH Director (DP2CA271361), the American Federation for Aging Research, the V Foundation, Pew Biomedical Scholar award, and startup funds from the Ohio State University. The Delibegovic lab is funded by the British Heart Foundation, Diabetes UK, BBSRC, NHS Grampian, Tenovus Scotland, and the Development Trust (University of Aberdeen). J.J.R. is supported by NIA PO1AG062817, R21AG064290, and R21AG071156. Research support for J.B. was from NIH National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) grants R01DK127800, R01DK113011, R01DK090625, and R01DK050203 and the National Institute on Aging (NIA) grants R01AG065988 and P01AG011412, as well as the University of Chicago Diabetes Research and Training Center grant P30DK020595. This work was supported by NIH grants AG065992 to G.M. and AG068550 to G.M. and S.P. as well as UAB Startup funds 3123226 and 3123227 to G.M. R.S. is supported by NIH grants RF1AG043517, R01AG065985, R01DK123327, R56AG074568, and P01AG031782. Z.C. is primarily funded by The Welch Foundation (AU-1731-20190330) and NIH/NIA (R01AG065984, R56AG063746, RF1AG061901, and R56AG076144). A.C. is supported by NIA grant R01AG065993. W.W.J. is supported by the NIH (R01DC020031). M.S.-H. is supported by NIH R01 R35GM127049, R01 AG045842, and R21 NS122366. The research in the Dixit lab was supported in part by NIH grants AG031797, AG045712, P01AG051459, AR070811, AG076782, AG073969, and AG068863 and Cure Alzheimer's Fund (CAF). A.E.T.-M. is supported by the NIH/NIA (AG075059 and AG058630), NIAMS (AR071133), NHLBI (HL153460), pilot and feasibility funds from the NIDDK-funded UAB Nutrition Obesity Research Center (DK056336) and the NIA-funded UAB Nathan Shock Center (AG050886), and startup funds from UAB. J.A.M. is supported by the Intramural Research Program, NIA, NIH. The Panda lab is supported by the NIH (R01CA236352, R01CA258221, RF1AG068550, and P30CA014195), the Wu Tsai Human Performance Alliance, and the Joe and Clara Tsai Foundation. The Lamming lab is supported in part by the NIA (AG056771, AG062328, AG061635, and AG081482), the NIDDK (DK125859), startup funds from UW-Madison, and the U.S. Department of Veterans Affairs (I01-BX004031), and this work was supported using facilities and resources from the William S. Middleton Memorial Veterans Hospital. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. This work does not represent the views of the Department of Veterans Affairs or the United States Government. D.W.L. has received funding from, and is a scientific advisory board member of, Aeovian Pharmaceuticals, which seeks to develop novel, selective mTOR inhibitors for the treatment of various diseases. S.P. is the author of the books The Circadian Code and The Circadian Diabetes Code. Funding Information: We thank Dr. Yih-Woei Fridell of the National Institute on Aging for organizing the meeting, as well as the NIA Division of Aging Biology for their support. We thank Dr. Gino Cortopassi for his edits and suggestions. The figures were created with BioRender.com . The Mihaylova lab is supported in part by the NIA ( R00AG054760 ), Office of the NIH Director ( DP2CA271361 ), the American Federation for Aging Research , the V Foundation , Pew Biomedical Scholar award, and startup funds from the Ohio State University . The Delibegovic lab is funded by the British Heart Foundation , Diabetes UK , BBSRC , NHS Grampian , Tenovus Scotland , and the Development Trust ( University of Aberdeen ). J.J.R. is supported by NIA PO1AG062817 , R21AG064290 , and R21AG071156 . Research support for J.B. was from NIH National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) grants R01DK127800 , R01DK113011 , R01DK090625 , and R01DK050203 and the National Institute on Aging (NIA) grants R01AG065988 and P01AG011412 , as well as the University of Chicago Diabetes Research and Training Center grant P30DK020595 . This work was supported by NIH grants AG065992 to G.M. and AG068550 to G.M. and S.P., as well as UAB Startup funds 3123226 and 3123227 to G.M. R.S. is supported by NIH grants RF1AG043517 , R01AG065985 , R01DK123327 , R56AG074568 , and P01AG031782 . Z.C. is primarily funded by The Welch Foundation ( AU-1731-20190330 ) and NIH/NIA ( R01AG065984 , R56AG063746 , RF1AG061901 , and R56AG076144 ). A.C. is supported by NIA grant R01AG065993 . W.W.J. is supported by the NIH ( R01DC020031 ). M.S.-H. is supported by NIH R01 R35GM127049 , R01 AG045842 , and R21 NS122366 . The research in the Dixit lab was supported in part by NIH grants AG031797 , AG045712 , P01AG051459 , AR070811 , AG076782 , AG073969 , and AG068863 and Cure Alzheimer's Fund (CAF). A.E.T.-M. is supported by the NIH/NIA ( AG075059 and AG058630 ), NIAMS ( AR071133 ), NHLBI ( HL153460 ), pilot and feasibility funds from the NIDDK -funded UAB Nutrition Obesity Research Center ( DK056336 ) and the NIA -funded UAB Nathan Shock Center ( AG050886 ), and startup funds from UAB . J.A.M. is supported by the Intramural Research Program, NIA, NIH . The Panda lab is supported by the NIH ( R01CA236352 , R01CA258221 , RF1AG068550 , and P30CA014195 ), the Wu Tsai Human Performance Alliance , and the Joe and Clara Tsai Foundation . The Lamming lab is supported in part by the NIA ( AG056771 , AG062328 , AG061635 , and AG081482 ), the NIDDK ( DK125859 ), startup funds from UW-Madison , and the U.S. Department of Veterans Affairs ( I01-BX004031 ), and this work was supported using facilities and resources from the William S. Middleton Memorial Veterans Hospital. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. This work does not represent the views of the Department of Veterans Affairs or the United States Government.Peer reviewedPostprin

Genetic variation in genes regulating skeletal muscle regeneration and tissue remodelling associated with weight loss in chronic obstructive pulmonary disease

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is the third leading cause of death globally. COPD patients with cachexia or weight loss have increased risk of death independent of body mass index (BMI) and lung function. We tested the hypothesis genetic variation is associated with weight loss in COPD using a genome-wide association study approach. METHODS: Participants with COPD (N = 4308) from three studies (COPDGene, ECLIPSE, and SPIROMICS) were analysed. Discovery analyses were performed in COPDGene with replication in SPIROMICS and ECLIPSE. In COPDGene, weight loss was defined as self-reported unintentional weight loss > 5% in the past year or low BMI (BMI < 20 kg/m2). In ECLIPSE and SPIROMICS, weight loss was calculated using available longitudinal visits. Stratified analyses were performed among African American (AA) and Non-Hispanic White (NHW) participants with COPD. Single variant and gene-based analyses were performed adjusting for confounders. Fine mapping was performed using a Bayesian approach integrating genetic association results with linkage disequilibrium and functional annotation. Significant gene networks were identified by integrating genetic regions associated with weight loss with skeletal muscle protein–protein interaction (PPI) data. RESULTS: At the single variant level, only the rs35368512 variant, intergenic to GRXCR1 and LINC02383, was associated with weight loss (odds ratio = 3.6, 95% confidence interval = 2.3–5.6, P = 3.2 × 10−8) among AA COPD participants in COPDGene. At the gene level in COPDGene, EFNA2 and BAIAP2 were significantly associated with weight loss in AA and NHW COPD participants, respectively. The EFNA2 association replicated among AA from SPIROMICS (P = 0.0014), whereas the BAIAP2 association replicated in NHW from ECLIPSE (P = 0.025). The EFNA2 gene encodes the membrane-bound protein ephrin-A2 involved in the regulation of developmental processes and adult tissue homeostasis such as skeletal muscle. The BAIAP2 gene encodes the insulin-responsive protein of mass 53 kD (IRSp53), a negative regulator of myogenic differentiation. Integration of the gene-based findings participants with PPI data revealed networks of genes involved in pathways such as Rho and synapse signalling. CONCLUSIONS: The EFNA2 and BAIAP2 genes were significantly associated with weight loss in COPD participants. Collectively, the integrative network analyses indicated genetic variation associated with weight loss in COPD may influence skeletal muscle regeneration and tissue remodelling

Concurrent exercise training: do opposites distract?

Specificity is a core principle of exercise training to promote the desired adaptations for maximising athletic performance. The principle of specificity of adaptation is underpinned by the volume, intensity, frequency and mode of contractile activity and is most evident when contrasting the divergent phenotypes that result after undertaking either prolonged endurance or resistance training. The molecular profiles that generate the adaptive response to different exercise modes have undergone intense scientific scrutiny. Given divergent exercise induces similar signalling and gene expression profiles in skeletal muscle of untrained or recreationally active individuals, what is currently unclear is how the specificity of the molecular response is modified by prior training history. The time course of adaptation and when ‘phenotype specificity’ occurs has important implications for exercise prescription. This context is essential when attempting to concomitantly develop resistance to fatigue (through endurance-based exercise) and increased muscle mass (through resistance-based exercise), typically termed ‘concurrent training’. Chronic training studies provide robust evidence that endurance exercise can attenuate muscle hypertrophy and strength but the mechanistic underpinning of this ‘interference’ effect with concurrent training is unknown. Moreover, despite the potential for several key regulators of muscle metabolism to explain an incompatibility in adaptation between endurance and resistance exercise, it now seems likely that multiple integrated, rather than isolated, effectors or processes generate the interference effect. Here we review studies of the molecular responses in skeletal muscle and evidence for the interference effect with concurrent training within the context of the specificity of training adaptation

Power training and postmenopausal hormone therapy affect transcriptional control of specific co-regulated gene clusters in skeletal muscle

At the moment, there is no clear molecular explanation for the steeper decline in muscle performance after menopause or the mechanisms of counteractive treatments. The goal of this genome-wide study was to identify the genes and gene clusters through which power training (PT) comprising jumping activities or estrogen containing hormone replacement therapy (HRT) may affect skeletal muscle properties after menopause. We used musculus vastus lateralis samples from early stage postmenopausal (50–57 years old) women participating in a yearlong randomized double-blind placebo-controlled trial with PT and HRT interventions. Using microarray platform with over 24,000 probes, we identified 665 differentially expressed genes. The hierarchical clustering method was used to assort the genes. Additionally, enrichment analysis of gene ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways was carried out to clarify whether assorted gene clusters are enriched with particular functional categories. The analysis revealed transcriptional regulation of 49 GO/KEGG categories. PT upregulated transcription in “response to contraction”—category revealing novel candidate genes for contraction-related regulation of muscle function while HRT upregulated gene expression related to functionality of mitochondria. Moreover, several functional categories tightly related to muscle energy metabolism, development, and function were affected regardless of the treatment. Our results emphasize that during the early stages of the postmenopause, muscle properties are under transcriptional modulation, which both PT and HRT partially counteract leading to preservation of muscle power and potentially reducing the risk for aging-related muscle weakness. More specifically, PT and HRT may function through improving energy metabolism, response to contraction as well as by preserving functionality of the mitochondria