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Crosstalk and competition in signaling networks.

ABSTRACT: Signaling networks have evolved to transduce external and internal information into critical cellular decisions such as growth, differentiation, and apoptosis. These networks form highly interconnected systems within cells due to network crosstalk, where an enzyme from one canonical pathway acts on targets from other pathways. It is currently unclear what types of effects these interconnections can have on the response of networks to incoming signals. In this work, we employ mathematical models to characterize the influence that multiple substrates have on one another. These models build off of the atomistic motif of a kinase/phosphatase pair acting on a single substrate. We find that the ultrasensitive, switch-like response these motifs can exhibit becomes transitive: if one substrate saturates the enzymes and responds ultrasensitively, then all substrates will do so regardless of their degree of saturation. We also demonstrate that the phosphatases themselves can induce crosstalk even when the kinases are independent. These findings have strong implications for how we understand and classify crosstalk, as well as for the rational development of kinase inhibitors aimed at pharmaceutically modulating network behavior.

SUBMITTER: Rowland MA

PROVIDER: S-EPMC3514525 | biostudies-literature | 2012 Dec

REPOSITORIES: biostudies-literature

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Crosstalk and competition in signaling networks.

Rowland Michael A MA Fontana Walter W Deeds Eric J EJ

Biophysical journal 20121201 11

Signaling networks have evolved to transduce external and internal information into critical cellular decisions such as growth, differentiation, and apoptosis. These networks form highly interconnected systems within cells due to network crosstalk, where an enzyme from one canonical pathway acts on targets from other pathways. It is currently unclear what types of effects these interconnections can have on the response of networks to incoming signals. In this work, we employ mathematical models ...[more]

PMID: 23283238

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Project description:BACKGROUND:Bulk tumor tissue samples are used for generating gene expression profiles in most research studies, making it difficult to decipher the stroma-cancer crosstalk networks. In the present study, we describe the use of microdissected transcriptome profiles for the identification of cancer-stroma crosstalk networks with prognostic value, which presents a unique opportunity for developing new treatment strategies for ovarian cancer. METHODS:Transcriptome profiles from microdissected ovarian cancer-associated fibroblasts (CAFs) and ovarian cancer cells from patients with high-grade serous ovarian cancer (n = 70) were used as input data for the computational systems biology program CCCExplorer to uncover crosstalk networks between various cell types within the tumor microenvironment. The crosstalk analysis results were subsequently used for discovery of new indications for old drugs in ovarian cancer by computational ranking of candidate agents. Survival analysis was performed on ovarian tumor-bearing Dicer/Pten double-knockout mice treated with calcitriol, a US Food and Drug Administration-approved agent that suppresses the Smad signaling cascade, or vehicle control (9-11 mice per group). All statistical tests were two-sided. RESULTS:Activation of TGF-β-dependent and TGF-β-independent Smad signaling was identified in a particular subtype of CAFs and was associated with poor patient survival (patients with higher levels of Smad-regulated gene expression by CAFs: median overall survival = 15 months, 95% confidence interval [CI] = 12.7 to 17.3 months; vs patients with lower levels of Smad-regulated gene expression: median overall survival = 26 months, 95% CI = 15.9 to 36.1 months, P = .02). In addition, the activated Smad signaling identified in CAFs was found to be targeted by repositioning calcitriol. Calcitriol suppressed Smad signaling in CAFs, inhibited tumor progression in mice, and prolonged the median survival duration of ovarian cancer-bearing mice from 36 to 48 weeks (P = .04). CONCLUSIONS:Our findings suggest the feasibility of using novel multicellular systems biology modeling to identify and repurpose known drugs targeting cancer-stroma crosstalk networks, potentially leading to faster and more effective cures for cancers.

Dataset Information

Crosstalk and competition in signaling networks.

Publications

Crosstalk and competition in signaling networks.

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