Cellular and Molecular Bioengineering

, Volume 10, Issue 5, pp 433–450 | Cite as

Data-Modeling Identifies Conflicting Signaling Axes Governing Myoblast Proliferation and Differentiation Responses to Diverse Ligand Stimuli

  • Alexander M. Loiben
  • Sharon Soueid-Baumgarten
  • Ruth F. Kopyto
  • Debadrita Bhattacharya
  • Joseph C. Kim
  • Benjamin D. CosgroveEmail author



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.


Cue-signal-response modeling Cytokines Growth factors Partial least-squares regression Skeletal muscle Systems biology 







Dual specificity phosphatase


Epidermal growth factor


Fibroblast growth factor 2


Insulin-like growth factor 1






Leukemia inhibitor factor


Myosin heavy chain




Principal component


Partial-least squares


Tumor necrosis factor-α



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.

Animal Studies

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.

Human Studies

No human studies were carried out by the authors for this article.

Supplementary material

12195_2017_508_MOESM1_ESM.pdf (3.8 mb)
Supplementary material 1 (PDF 3868 kb)


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Copyright information

© Biomedical Engineering Society 2017

Authors and Affiliations

  1. 1.Meinig School of Biomedical EngineeringCornell UniversityIthacaUSA
  2. 2.Biological Sciences, College of Agriculture and Life SciencesCornell UniversityIthacaUSA
  3. 3.Graduate Field of Biochemistry, Molecular and Cell Biology, Department of Molecular Biology and GeneticsCornell UniversityIthacaUSA

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