Integrated modular model linking metabolism, signaling transduction and gene expression regulation in human skeletal muscle1

Abstract

Exercised-induced adaption of skeletal muscle to aerobic endurance training is ensured by instant activation of signaling transduction pathways in the muscle cells with consequent alteration of both metabolic fluxes and expression for a versatile group of genes. Despite the experimentally based efforts to disentangle the complexity of the muscle adaptation process caused by multiple interactions and intersections on signaling, metabolic and gene expression levels, the quantitative and mechanistic contribution of each component of the signaling cascades on downstream genetic regulation processes has not been fully elucidated. Data-driven mathematical models provide a rigorous way to analyze and understand such intricate biological systems. Herein a novel mathematical model linking anaerobic and aerobic metabolism, Ca2+-dependent signaling pathway and downstream transcription regulation of early and late response genes in human skeletal muscle during and after acute exercise developed in BioUML platform has been presented.

Keywords

• mathematical model
• skeletal muscle
• physical exercise
• Ca2+-dependent signaling pathway
• transcriptome
• RNA sequencing
• regulation of expression
• BioUML

Introduction

hour after aerobic exercise. Both sets of transcription factors modulate muscle metabolism. It means that gene expression on early and late stages of the recovery after the termination of the exercise can be regulated by different ways. Obviously, these molecular mechanisms are pretty complex, but we suppose that expression of early and late response genes may be ensured some general or basic mechanisms of the gene expression regulation.

It is worth to note, although advancement in the development of high-throughput experimental techniques and generation of diverse omics data for human skeletal muscle during endurance exercise enabled to unveil key participants of the cellular response and adaptation [ 4-8 ], the system understanding of signaling-metabolic pathways relationships with downstream genetic regulation in exercising skeletal muscle is still elusive. As a complementary theoretical counterpart to the experimental investigation of molecular mechanisms underlying the skeletal muscle adaptation to the endurance training, a detailed mechanistic mathematical model provides a powerful in silico tool to quantitatively investigate signal transduction pathways and corresponding molecular mechanisms orchestrating gene expression dynamics during an exercise [ 9-11 ]. 2

Results

We have previously developed a multi-compartmental mathematical model describing the dynamics of intracellular species concentrations and fluxes in human muscle at rest and intracellular metabolic rearrangements in exercising skeletal muscles during an aerobic exercise on a cycle ergometer [ 11 ]. As an initial model, we have used a complex model of energy metabolism in the human skeletal muscle developed by Li and coauthors [ 9 ]. We have proposed a modular representation of the complex model using BioUML platform [ 12 ]. The modular representation provides the possibility of rapid expansion and modification of the model compartments to account for the complex organization of muscle cells and the limitations of the rate of diffusion of metabolites between intracellular compartments (Fig. 1). aerobic training on a cycle ergometer demonstrated that concentration levels of ATP and ADP do not significantly change under this condition, while creatine and phosphocreatine concentrations do strongly. The simulation outcome corresponds to predictions of the model published by Li and coauthors.

A physiologically based computational model of the Ca2+-dependent signaling pathway taking into account downstream activation of early (NR4A genes family) and late (PPARGC1A gene) response genes expression in the skeletal muscle (Fig. 2) has been developed based on modular modeling approach too [ 13 ].

Numerical analysis of the model enabled to reveal crucial steps in this signal transduction pathway for the adaptation and demonstrated the necessity of consideration of additional transcription factors modulating transcription of late response genes in order to adequately reproduce gene expression data that were taken in human vastus lateralis muscle during and after acute cycling exercise. Bioinformatics analysis of the original transcriptomics data, in turn, proposed that CREB-like proteins from FOS and JUN families forming heterodimer complexes with transcription factor CREB1 are indeed these intermediate regulators of late response genes [ 13 ]. 3

Conclusion

In the development of the mathematical model describing energy metabolism of the human skeletal muscle [ 11 ] an extended integrated modular model considering Ca2+-dependent signaling pathway and downstream regulatory processes of early and late response genes expression has been built. An activation mechanism which enhances energy metabolism via transport and reaction fluxes due to physical exercise was incorporated in our previous model (represented as «Muscle metabolism» on Fig. 3) as the stress function depending on general work rate parameter. The work rate parameter defines intensity of the physical exercise.

In order to the integrated model represents actual changes in gene expression in exercised human skeletal muscle in more details, we replaced the general work rate parameter on the concentration of Ca2+-Calmodulin complexes and incorporated PPARGC1A–mediated transcription regulation of genes playing an important role in adaptation to regular exercise The integrated modular model provides more precise predictions of adaptation mechanisms of the skeletal muscle cells to exercise on levels of both metabolic pathways and gene expression.

Acknowledgements. The study has been financially supported by RFBR grants (№ 17-00-00308 (K) and № 17-0000296).

References

[1] Pedersen B.K. and Febbraio M. A. Muscles, exercise and obesity: skeletal muscle as a secretory organ// Nature Reviews Endocrinology . 2012 . V. 8 . № 8. P. 457 - 465 .

Hawley J.A. , Hargreaves M. , Joyne , M.J. and Zierath J.R . Integrative biology of exercise// Cell. 2014 . V. 159 .

№4. P. 738 - 749 .

Koulmann N. and Bigard A.X. Interaction between signaling pathways involved in skeletal muscle responses to endurance exercise// Pflügers Archiv. 2006 . V. 452 . № 2. P. 125 .

Neubauer O. , Sabapathy S. , Ashton K.J. , Desbrow B. , Peake J.M. , Lazarus R. , Wessner B. , Cameron-Smith D. , Wagne , K.H. , Haseler L.J. and Bulmer A.C. Time course-dependent changes in the transcriptome of human skeletal muscle during recovery from endurance exercise: from inflammation to adaptive remodeling// Journal of Applied Physiology . 2013 . V. 116 . № 3. P. 274 - 287 .

Vissing K. and Schjerling P. Simplified data access on human skeletal muscle transcriptome responses to differentiated exercise// Scientific data . 2014 . V. 1. P. 140041 .

[6] Popov D.V. , Makhnovskii P.A. , Kurochkina N.S. , Lysenko E.A. , Vepkhvadze T.F. and Vinogradova O.L . Intensity-dependent gene expression after aerobic exercise in endurance-trained skeletal muscle . Biology of sport . 2018 // V. 35. № 3. P. 277 .

Dickinson J.M. , D'Lugos A.C. , Naymik M.A. , Siniard A.L. , Wolfe A.J. , Curtis D.R. , Huentelman M.J. and Carroll C.C. Transcriptome response of human skeletal muscle to divergent exercise stimuli . Journal of Applied Physiology// 2018 . V. 124 . № 6. P. 1529 - 1540 .

[8] Popov D.V. , Makhnovskii P.A. , Shagimardanova E.I. , Gazizova G.R. , Lysenko E.A. , Gusev O.A. and Vinogradova O.L. Contractile activity -specific transcriptome response to acute endurance exercise and training in human skeletal muscle// American Journal of Physiology-Endocrinology and Metabolism. 2019. V. 316 . № 4. P. E605- E614 .

[9] Li Y. , Dash R. K. , Kim J. , Saidel G. M. , Cabrera M. E. Role of NADH/NAD+ transport activity and glycogen store on skeletal muscle energy metabolism during exercise : in silico studies// American Journal of PhysiologyCell Physiology. 2009 . V. 296 . № 1. P. 25 - 46 .

[10] Akberdin I.R. , Kazantsev F.V. , Ermak T.V. , Timonov V.S. , Khlebodarova T.M. , Likhoshvai V . A . In Silico Cell: Challenges and Perspectives// Mathematical Biology and Bioinformatics. 2013 . V. 8 . № 1. P. 295 - 315 .

[11] Kiselev I.N. , Akberdin I.R. , Vertyshev A.Y. , Popov D.V. and Kolpakov F.A. A modular visual model of energy metabolism in human skeletal muscle// Mathematical Biology and Bioinformatics. 2019 . V. 14 . № 2. P. 373 - 392 .

[12] Kolpakov F. , Akberdin I. , Kashapov T. , Kiselev I. , Kolmykov S. , Kondrakhin Y. , Kutumova E. , Mandrik N. , Pintus S. , Ryabova A. , Sharipov R. , Yevshin I. , Kel A. BioUML: an integrated environment for systems biology and collaborative analysis of biomedical data// Nucleic Acids Research . 2019 . V. 47 . № W1. P. W225-W233.

[13] Akberdin I.R. , Vertyshev A.Yu. , Pintus S.S. , Popov D.V. , Kolpakov F.A. A Mathematical model linking Ca2+- dependent signaling pathway and gene expression regulation in human skeletal muscle// Mathematical Biology and Bioinformatics. 2020 . reviewing.