Skeletal muscle tissue development and regeneration relies on the proliferation, maturation and fusion of muscle progenitor cells (myoblasts), which arise transiently from muscle stem cells (satellite cells). Following muscle damage, myoblasts proliferate and differentiate in response to temporally-varying inflammatory cytokines, growth factors, and extracellular matrix cues, which stimulate a shared network of intracellular signaling pathways. Here we present an integrated data-modeling approach to elucidate synergies and antagonisms among proliferation and differentiation signaling axes in myoblasts stimulated by regeneration-associated ligands.
We treated mouse primary myoblasts in culture with combinations of eight regeneration-associated growth factors and cytokines in mixtures that induced additive, synergistic, and antagonistic effects on myoblast proliferation and differentiation responses. For these combinatorial stimuli, we measured the activation dynamics of seven signal transduction pathways using multiplexed phosphoprotein assays and scored proliferation and differentiation responses based on expression of myogenic commitment factors to assemble a cue-signaling-response data compendium. We interrogated the relationship between these signals and responses by partial least-squares (PLS) regression modeling.
Partial least-squares data-modeling accurately predicted response outcomes in cross-validation on the training compendium (cumulative R 2 = 0.96). The PLS model highlighted signaling axes that distinctly govern myoblast proliferation (MEK–ERK, Stat3) and differentiation (JNK) in response to these combinatorial cues, and we confirmed these signal-response associations with small molecule perturbations. Unexpectedly, we observed that a negative feedback circuit involving the phosphatase DUSP6/MKP-3 auto-regulates MEK–ERK signaling in myoblasts.
This data-modeling approach identified conflicting signaling axes that underlie muscle progenitor cell proliferation and differentiation.
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Dual specificity phosphatase
Epidermal growth factor
Fibroblast growth factor 2
Insulin-like growth factor 1
Leukemia inhibitor factor
Myosin heavy chain
Tumor necrosis factor-α
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This work was financially supported by the National Institute on Aging of the National Institutes of Health under Award R00AG042491 (to B.D.C), a US Department of Education Graduate Assistantship in Areas of National Need under Award P200A150273 (to A.M.L), a Roberta G. and John B. DeVries Graduate Fellowship (to A.M.L.), and Hunter R. Rawlings III Cornell Presidential Research Scholarship (to R.F.K. and J.K.). This work made use of the Nanobiotechnology Center (NBTC) shared research facilities at Cornell University. The authors acknowledge technical assistance from Teresa Porri, Penny Burke, Andrea De Micheli, Hilarie Sit, Muhammad Safwan Jalal, Nancy Mejia, Isabella Mercado, Ryan Ausmus, and Paula Fraczek. The authors thank the anonymous reviewers for their constructive reviews.
All institutional and national guidelines for the care and use of laboratory animals were followed in a protocol approved by Cornell University’s Institutional Animal Care and Use Committee (IACUC).
Conflicts of interest
A. M. Loiben, S. Soueld-Baumgarten, D. Bhattacharya, R. F. Kopyto, J. C. Kim and B. D. Cosgrove declare that they have no conflicts of interest.
No human studies were carried out by the authors for this article.
Benjamin D. Cosgrove
is an Assistant Professor in the Meinig School of Biomedical Engineering at Cornell University in Ithaca, NY, where he directs the Laboratory of Regenerative Systems Biology. His research group, which is currently supported by a NIH R00 Pathway-to-Independence Award, develops and implements systems biology and biomaterials engineering approaches to study how cell–cell communication and intracellular signaling networks regulate stem and progenitor cell function in skeletal muscle homeostasis and regeneration, and how these processes become dysfunctional in aging and muscular dystrophies. Dr. Cosgrove earned a Bachelor’s in Biomedical Engineering at the University of Minnesota and a Ph.D. in Bioengineering at the Massachusetts Institute of Technology. His Ph.D. thesis research, under the joint supervision of Dr. Douglas Lauffenburger and Dr. Linda Griffith, which established on experimental and computational systems biology tools to elucidate signaling network mechanisms regulating liver hepatocyte cell-fate decisions, was supported by a Whitaker Foundation Graduate Research Fellowship and a Biomedical Engineering Society Graduate Research Award. His postdoctoral research with Dr. Helen Blau at Stanford University was supported by a Stanford Molecular Imaging Scholars Fellowship and NIH K99 Pathway-to-Independence Award and was recognized by the Cellular and Molecular Bioengineering Special Interest Group of the Biomedical Engineering Society with a Rising Star award in 2015.
This article is part of the 2017 CMBE Young Innovators special issue.
Associate Editor Richard Waugh oversaw the review of this article.
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Loiben, A.M., Soueid-Baumgarten, S., Kopyto, R.F. et al. Data-Modeling Identifies Conflicting Signaling Axes Governing Myoblast Proliferation and Differentiation Responses to Diverse Ligand Stimuli. Cel. Mol. Bioeng. 10, 433–450 (2017). https://doi.org/10.1007/s12195-017-0508-5
- Cue-signal-response modeling
- Growth factors
- Partial least-squares regression
- Skeletal muscle
- Systems biology