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Systems Modeling of the Rho Signaling Network

ABSTRACT: Describing the architecture of robust, nonlinear cell signaling networks is essential to gain a predictive understanding of cellular behavior. The structure of the Drosophila Rho-signaling network, comprised of Rho-family GTPases, RhoGTP Exchange Factors (RhoGEFs), and RhoGTPase Activating Proteins (RhoGAPs), has been particularly difficult to infer due to the highly overlapping function and substrate specificity of network components. We developed a parameterized modeling approach to predict connectivity amongst components of the Rho-signaling network that was driven by hundreds of mRNA expression profiles derived from RNAi-mediated inhibition or overexpression of component genes. Our model incorporated rate kinetics, transcriptional feedback, and noise. We biochemically validated several novel predicted connections, and used this model to predict Rho-signaling response to particular conditions. While functional redundancy is a feature of all signaling systems that often prevents classical genetic methods from elucidating relationships between components, the methods described here provide the basis for describing any complex network architecture. Keywords: Genetic modification, RNAi-mediated gene inhibition

ORGANISM(S): Drosophila melanogaster

PROVIDER: GSE18307 | GEO | 2012/12/31

SECONDARY ACCESSION(S): PRJNA118037

REPOSITORIES: GEO

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Project description:Expression profiles of 22 reference Arabidopsis immunity mutants were collected using the Arabidopsis Pathoarray 464_001 (GPL3638) in order to build a network model predicting the Arabidopsis immune signaling network. Biological signaling processes may be mediated by complex networks in which network components and network sectors interact with each other in complex ways. Studies of complex networks benefit from approaches in which the roles of individual components are considered in the context of the network. The plant immune signaling network, which controls inducible responses to pathogen attack, is such a complex network. Here, we demonstrate that use of mRNA profiling to collect and analyze detailed descriptions of changes in the network state resulting from specific network perturbations is a powerful and economical strategy to elucidate regulatory relationships among the components of a complex signaling network. Specifically, we studied the Arabidopsis immune signaling network upon challenge with a strain of the bacterial pathogen Pseudomonas syringae expressing the effector protein AvrRpt2 (Pto DC3000 AvrRpt2). This bacterial strain feeds multiple inputs into the signaling network, allowing many parts of the network to be activated at once. mRNA profiles of 22 Arabidopsis immunity mutants and wild type were collected 6 hours after inoculation with the Pto DC3000 AvrRpt2 and used as detailed descriptions of the network states resulting from specific genetic perturbations. Regulatory relationships among the genes corresponding to the mutations were inferred by recursively applying a non-linear dimensionality reduction procedure to the mRNA profile data. The resulting network model accurately predicted 22 of 23 regulatory relationships reported in the literature, suggesting that predictions of novel regulatory relationships are also accurate. The network model revealed two striking features: (i) the components of the network are highly interconnected; (ii) negative regulatory relationships are common between signaling sectors. One case of a novel negative regulatory relationship, between the early microbe-associated molecular pattern (MAMP)-activated sector and the salicylic acid (SA)-mediated sector, was further validated. We propose that prevalent negative regulatory relationships among the signaling sectors make the plant immune signaling network a "sector-switching" network, which effectively balances two apparently conflicting demands, robustness against pathogenic perturbations and moderation of negative impacts of immune responses on plant fitness. Keywords: Responses of reference Arabidopsis immunity mutants to Pseudomonas syringae pv. tomato DC3000 carrying avrRpt2 This experiment consists of two (group00) or three (group01-04) biological replicates of each genotype (total 25 genotypes [3 are multiple mutants, which were removed for the network modeling, but were used for normalization]). For each genotype, two leaves per plant were pooled from three pots to prepare total RNA.

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Systems Modeling of the Rho Signaling Network

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