Signaling network state predicts twist-mediated effects on breast cell migration across diverse growth factor contexts.

TitleSignaling network state predicts twist-mediated effects on breast cell migration across diverse growth factor contexts.
Publication TypeJournal Article
Year of Publication2011
AuthorsKim, H-D, Meyer, AS, Wagner, JP, Alford, SK, Wells, A, Gertler, FB, Lauffenburger, DA
JournalMol Cell Proteomics
Date Published2011 Nov
KeywordsBreast, Cell Line, Cell Movement, Computer Simulation, Epithelial Cells, Epithelial-Mesenchymal Transition, Female, Humans, Intercellular Signaling Peptides and Proteins, Intracellular Signaling Peptides and Proteins, Least-Squares Analysis, Models, Biological, Multivariate Analysis, Nuclear Proteins, Phenotype, Phosphorylation, Protein Interaction Maps, Receptor, Epidermal Growth Factor, Signal Transduction, Twist Transcription Factor

Epithelial-mesenchymal transition (EMT), whether in developmental morphogenesis or malignant transformation, prominently involves modified cell motility behavior. Although major advances have transpired in understanding the molecular pathways regulating the process of EMT induction per se by certain environmental stimuli, an important outstanding question is how the activities of signaling pathways governing motility yield the diverse movement behaviors characteristic of pre-induction versus postinduction states across a broad landscape of growth factor contexts. For the particular case of EMT induction in human mammary cells by ectopic expression of the transcription factor Twist, we found the migration responses to a panel of growth factors (EGF, HRG, IGF, HGF) dramatically disparate between confluent pre-Twist epithelial cells and sparsely distributed post-Twist mesenchymal cells-but that a computational model quantitatively integrating multiple key signaling node activities could nonetheless account for this full range of behavior. Moreover, motility in both conditions was successfully predicted a priori for an additional growth factor (PDGF) treatment. Although this signaling network state model could comprehend motility behavior globally, modulation of the network interactions underlying the altered pathway activities was identified by ascertaining differences in quantitative topological influences among the nodes between the two conditions.

Alternate JournalMol. Cell Proteomics
PubMed ID21832255
PubMed Central IDPMC3226401
Grant ListI01 BX001017 / BX / BLRD VA / United States
R01 GM081336 / GM / NIGMS NIH HHS / United States
R01-GM081336 / GM / NIGMS NIH HHS / United States
U54-CA112967 / CA / NCI NIH HHS / United States