Impact of protein supplementation during endurance training on changes in skeletal muscle transcriptome

Pim Knuiman, Roland Hangelbroek, Mark Boekschoten, Maria Hopman, Marco Mensink, Pim Knuiman, Roland Hangelbroek, Mark Boekschoten, Maria Hopman, Marco Mensink

Abstract

Background: Protein supplementation improves physiological adaptations to endurance training, but the impact on adaptive changes in the skeletal muscle transcriptome remains elusive. The present analysis was executed to determine the impact of protein supplementation on changes in the skeletal muscle transcriptome following 5-weeks of endurance training.

Results: Skeletal muscle tissue samples from the vastus lateralis were taken before and after 5-weeks of endurance training to assess changes in the skeletal muscle transcriptome. One hundred and 63 genes were differentially expressed after 5-weeks of endurance training in both groups (q-value< 0.05). In addition, the number of genes differentially expressed was higher in the protein group (PRO) (892, q-value< 0.05) when compared with the control group (CON) (440, q-value< 0.05), with no time-by-treatment interaction effect (q-value> 0.05). Endurance training primarily affected expression levels of genes related to extracellular matrix and these changes tended to be greater in PRO than in CON.

Conclusions: Protein supplementation subtly impacts endurance training-induced changes in the skeletal muscle transcriptome. In addition, our transcriptomic analysis revealed that the extracellular matrix may be an important factor for skeletal muscle adaptation in response to endurance training. This trial was registered at clinicaltrials.gov as NCT03462381, March 12, 2018.

Trial registration: This trial was registered at clinicaltrials.gov as NCT03462381.

Conflict of interest statement

The authors have no conflicts of interest to declare that are directly relevant to the contents of this manuscript.

Figures

Fig. 1
Fig. 1
Venn diagram showing the number of differentially expressed genes per group. Selected genes (F-test q-valuep-value< 0.0001)
Fig. 2
Fig. 2
Scatterplots with line of identity to visualize the magnitude of change in muscle transcriptome per group. Figs. A & B are based on the total number of genes changed per group (184 for CON (a) and 384 for PRO (b), F-test q-value< 0.05). Figs. C & D are based on the top 20 significant genes changes in the CON (c) and PRO (d) group
Fig. 3
Fig. 3
Heatmap of changes in gene expression per group. (F-test q-value

Fig. 4

Schematic overview of the study…

Fig. 4

Schematic overview of the study protocol. Forty subjects completed 10 wk. of progressive…

Fig. 4
Schematic overview of the study protocol. Forty subjects completed 10 wk. of progressive endurance training while consuming either 25 g carbohydrates or 25 g protein post-exercise and daily before sleep. All measurements were assessed before, midterm (week 6) and after (week 12). Strongest effect of protein supplementation was observed following 5 weeks of endurance training. To gain more insight into mechanisms underlying greater physiological adaptation as a result of protein supplementation we analyzed skeletal muscle transcriptome data from baseline to midterm. Black dots: measurement points, bleu dots: exercise sessions. Grey part: contains physiological and microarray data analyzed for this manuscript
Fig. 4
Fig. 4
Schematic overview of the study protocol. Forty subjects completed 10 wk. of progressive endurance training while consuming either 25 g carbohydrates or 25 g protein post-exercise and daily before sleep. All measurements were assessed before, midterm (week 6) and after (week 12). Strongest effect of protein supplementation was observed following 5 weeks of endurance training. To gain more insight into mechanisms underlying greater physiological adaptation as a result of protein supplementation we analyzed skeletal muscle transcriptome data from baseline to midterm. Black dots: measurement points, bleu dots: exercise sessions. Grey part: contains physiological and microarray data analyzed for this manuscript

References

    1. Charge SB, Rudnicki MA. Cellular and molecular regulation of muscle regeneration. Physiol Rev. 2004;84(1):209–238. doi: 10.1152/physrev.00019.2003.
    1. Hawley JA, Hargreaves M, Joyner MJ, Zierath JR. Integrative biology of exercise. Cell. 2014;159(4):738–749. doi: 10.1016/j.cell.2014.10.029.
    1. Keller P, Vollaard NB, Gustafsson T, Gallagher IJ, Sundberg CJ, Rankinen T, Britton SL, Bouchard C, Koch LG, Timmons JA. A transcriptional map of the impact of endurance exercise training on skeletal muscle phenotype. J Appl Physiol. 2011;110(1):46–59. doi: 10.1152/japplphysiol.00634.2010.
    1. Timmons JA, Jansson E, Fischer H, Gustafsson T, Greenhaff PL, Ridden J, Rachman J, Sundberg CJ. Modulation of extracellular matrix genes reflects the magnitude of physiological adaptation to aerobic exercise training in humans. BMC Biol. 2005;3:19. doi: 10.1186/1741-7007-3-19.
    1. Pilegaard H, Ordway GA, Saltin B, Neufer PD. Transcriptional regulation of gene expression in human skeletal muscle during recovery from exercise. Am J Phys Endocrinol Metab. 2000;279(4):E806–E814. doi: 10.1152/ajpendo.2000.279.4.E806.
    1. Hildebrandt AL, Pilegaard H, Neufer PD. Differential transcriptional activation of select metabolic genes in response to variations in exercise intensity and duration. Am J Phys Endocrinol Metab. 2003;285(5):E1021–E1027. doi: 10.1152/ajpendo.00234.2003.
    1. Mahoney DJ, Parise G, Melov S, Safdar A, Tarnopolsky MA. Analysis of global mRNA expression in human skeletal muscle during recovery from endurance exercise. FASEB J. 2005;19(11):1498–1500. doi: 10.1096/fj.04-3149fje.
    1. Egan B, Zierath JR. Exercise metabolism and the molecular regulation of skeletal muscle adaptation. Cell Metab. 2013;17(2):162–184. doi: 10.1016/j.cmet.2012.12.012.
    1. Hjorth M, Norheim F, Meen AJ, Pourteymour S, Lee S, Holen T, Jensen J, Birkeland KI, Martinov VN, Langleite TM, et al. The effect of acute and long-term physical activity on extracellular matrix and serglycin in human skeletal muscle. Physiol Rep. 2015;3(8).
    1. Johnson ML, Lanza IR, Short DK, Asmann YW, Nair KS. Chronically endurance-trained individuals preserve skeletal muscle mitochondrial gene expression with age but differences within age groups remain. Physiol Rep. 2014;2(12).
    1. Popov DV, Makhnovskii PA, Shagimardanova EI, Gazizova GR, Lysenko EA, Gusev OA, Vinogradova OL. Contractile activity-specific transcriptome response to acute endurance exercise and training in human skeletal muscle. Am J Phys Endocrinol Metab. 2019;316(4):E605–e614. doi: 10.1152/ajpendo.00449.2018.
    1. Vissing K, Schjerling P. Simplified data access on human skeletal muscle transcriptome responses to differentiated exercise. Scientific Data. 2014;1:140041. doi: 10.1038/sdata.2014.41.
    1. Nishida Y, Tanaka H, Tobina T, Murakami K, Shono N, Shindo M, Ogawa W, Yoshioka M, St-Amand J. Regulation of muscle genes by moderate exercise. Int J Sports Med. 2010;31(9):656–670. doi: 10.1055/s-0030-1255065.
    1. Radom-Aizik S, Hayek S, Shahar I, Rechavi G, Kaminski N, Ben-Dov I. Effects of aerobic training on gene expression in skeletal muscle of elderly men. Med Sci Sports Exerc. 2005;37(10):1680–1696. doi: 10.1249/01.mss.0000181838.96815.4d.
    1. Rowlands DS, Thomson JS, Timmons BW, Raymond F, Fuerholz A, Mansourian R, Zwahlen MC, Metairon S, Glover E, Stellingwerff T, et al. Transcriptome and translational signaling following endurance exercise in trained skeletal muscle: impact of dietary protein. Physiol Genomics. 2011;43(17):1004–1020. doi: 10.1152/physiolgenomics.00073.2011.
    1. Knuiman P, van Loon LJC, Wouters J, Hopman M, Mensink M. Protein supplementation elicits greater gains in maximal oxygen uptake capacity and stimulates lean mass accretion during prolonged endurance training: a double-blind randomized controlled trial. Am J Clin Nutr. 2019;110(2):508–518. doi: 10.1093/ajcn/nqz093.
    1. Sanes JR. The basement membrane/basal lamina of skeletal muscle. J Biol Chem. 2003;278(15):12601–12604. doi: 10.1074/jbc.R200027200.
    1. Katz BZ, Yamada KM. Integrins in morphogenesis and signaling. Biochimie. 1997;79(8):467–476. doi: 10.1016/S0300-9084(97)82738-1.
    1. Jensen L, Pilegaard H, Neufer PD, Hellsten Y. Effect of acute exercise and exercise training on VEGF splice variants in human skeletal muscle. Am J Physiol Regul Integr Comp Physiol. 2004;287(2):R397–R402. doi: 10.1152/ajpregu.00071.2004.
    1. Hoppeler H, Baum O, Lurman G, Mueller M. Molecular mechanisms of muscle plasticity with exercise. Comp Physiol. 2011;1(3):1383–1412.
    1. Saltin B, PDJHoPSM G. Skeletal muscle adaptability: significance for metabolism and performance. 1983;10:555–631.
    1. Sundberg CJ. Exercise and training during graded leg ischaemia in healthy man with special reference to effects on skeletal muscle. Acta Physiol Scand Suppl. 1994;615:1–50.
    1. Blomqvist CG, Saltin B. Cardiovascular adaptations to physical training. Annu Rev Physiol. 1983;45:169–189. doi: 10.1146/annurev.ph.45.030183.001125.
    1. Haas TL, Milkiewicz M, Davis SJ, Zhou AL, Egginton S, Brown MD, Madri JA, Hudlicka O. Matrix metalloproteinase activity is required for activity-induced angiogenesis in rat skeletal muscle. Am J Physiol Heart Circ Physiol. 2000;279(4):H1540–H1547. doi: 10.1152/ajpheart.2000.279.4.H1540.
    1. Areta JL, Burke LM, Ross ML, Camera DM, West DW, Broad EM, Jeacocke NA, Moore DR, Stellingwerff T, Phillips SM, et al. Timing and distribution of protein ingestion during prolonged recovery from resistance exercise alters myofibrillar protein synthesis. J Physiol. 2013;591(Pt 9):2319–2331. doi: 10.1113/jphysiol.2012.244897.
    1. Yan Z, Okutsu M, Akhtar YN, Lira VA. Regulation of exercise-induced fiber type transformation, mitochondrial biogenesis, and angiogenesis in skeletal muscle. J Appl Physiol. 2011;110(1):264–274. doi: 10.1152/japplphysiol.00993.2010.
    1. Goody MF, Sher RB, Henry CA. Hanging on for the ride: adhesion to the extracellular matrix mediates cellular responses in skeletal muscle morphogenesis and disease. Dev Biol. 2015;401(1):75–91. doi: 10.1016/j.ydbio.2015.01.002.
    1. Perry CG, Lally J, Holloway GP, Heigenhauser GJ, Bonen A, Spriet LL. Repeated transient mRNA bursts precede increases in transcriptional and mitochondrial proteins during training in human skeletal muscle. J Physiol. 2010;588(Pt 23):4795–4810. doi: 10.1113/jphysiol.2010.199448.
    1. Akimoto T, Ribar TJ, Williams RS, Yan Z. Skeletal muscle adaptation in response to voluntary running in Ca2+/calmodulin-dependent protein kinase IV-deficient mice. Am J Physiol Cell Physiol. 2004;287(5):C1311–C1319. doi: 10.1152/ajpcell.00248.2004.
    1. Handschin C, Spiegelman BM. The role of exercise and PGC1alpha in inflammation and chronic disease. Nature. 2008;454(7203):463–469. doi: 10.1038/nature07206.
    1. Jorgensen SB, Jensen TE, Richter EA. Role of AMPK in skeletal muscle gene adaptation in relation to exercise. Appl Physiol Nutr Metab. 2007;32(5):904–911. doi: 10.1139/H07-079.
    1. Leick L, Wojtaszewski JF, Johansen ST, Kiilerich K, Comes G, Hellsten Y, Hidalgo J, Pilegaard H. PGC-1alpha is not mandatory for exercise- and training-induced adaptive gene responses in mouse skeletal muscle. Am J Phys Endocrinol Metab. 2008;294(2):E463–E474. doi: 10.1152/ajpendo.00666.2007.
    1. Bergstrom J. Percutaneous needle biopsy of skeletal muscle in physiological and clinical research. Scand J Clin Lab Invest. 1975;35(7):609–616. doi: 10.3109/00365517509095787.
    1. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7):e47. doi: 10.1093/nar/gkv007.
    1. Sartor MA, Tomlinson CR, Wesselkamper SC, Sivaganesan S, Leikauf GD, Medvedovic M. Intensity-based hierarchical Bayes method improves testing for differentially expressed genes in microarray experiments. BMC Bioinformatics. 2006;7:538. doi: 10.1186/1471-2105-7-538.
    1. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005;102(43):15545–15550. doi: 10.1073/pnas.0506580102.
    1. Mootha VK, Lindgren CM, Eriksson K-F, Subramanian A, Sihag S, Lehar J, Puigserver P, Carlsson E, Ridderstråle M, Laurila E, et al. PGC-1α-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet. 2003;34:267. doi: 10.1038/ng1180.
    1. Gu Z, Eils R, Schlesner M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics (Oxford, England) 2016;32(18):2847–2849. doi: 10.1093/bioinformatics/btw313.
    1. Chen EY, Tan CM, Kou Y, Duan Q, Wang Z, Meirelles GV, Clark NR, Ma'ayan A. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics. 2013;14:128. doi: 10.1186/1471-2105-14-128.
    1. Kuleshov MV, Jones MR, Rouillard AD, Fernandez NF, Duan Q, Wang Z, Koplev S, Jenkins SL, Jagodnik KM, Lachmann A, et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 2016;44(W1):W90–W97. doi: 10.1093/nar/gkw377.

Source: PubMed

Sponsorit ja yhteistyökumppanit

Sairaudet

Huumeiden interventiot

3
Tilaa