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. 2013 Feb;23(2):365-76.
doi: 10.1101/gr.138628.112. Epub 2012 Oct 11.

Linking the signaling cascades and dynamic regulatory networks controlling stress responses

Anthony Gitter  1 , Miri Carmi, Naama Barkai, Ziv Bar-Joseph
Affiliations

Linking the signaling cascades and dynamic regulatory networks controlling stress responses

Anthony Gitter et al. Genome Res. 2013 Feb.

Abstract

Accurate models of the cross-talk between signaling pathways and transcriptional regulatory networks within cells are essential to understand complex response programs. We present a new computational method that combines condition-specific time-series expression data with general protein interaction data to reconstruct dynamic and causal stress response networks. These networks characterize the pathways involved in the response, their time of activation, and the affected genes. The signaling and regulatory components of our networks are linked via a set of common transcription factors that serve as targets in the signaling network and as regulators of the transcriptional response network. Detailed case studies of stress responses in budding yeast demonstrate the predictive power of our method. Our method correctly identifies the core signaling proteins and transcription factors of the response programs. It further predicts the involvement of additional transcription factors and other proteins not previously implicated in the response pathways. We experimentally verify several of these predictions for the osmotic stress response network. Our approach requires little condition-specific data: only a partial set of upstream initiators and time-series gene expression data, which are readily available for many conditions and species. Consequently, our method is widely applicable and can be used to derive accurate, dynamic response models in several species.

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Figures

Figure 1.
Figure 1.
Iterative model for integrating signaling and dynamic regulatory networks. The two components of SDREM iteratively refine an end-to-end model of stress response. DREM identifies active transcription factors (TFs) by analyzing divergence points in dynamic gene expression profiles. Protein–protein interaction (PPI) network orientation is used to connect those TFs to proteins that initiate the response by sensing or interacting with the environment.
Figure 2.
Figure 2.
Short osmotic stress model. (A) The regulatory part of the model contains 10 paths in the short time-series data, where each path represents a collection of gene expression profiles. The x-axis displays the time points at which gene expression is measured. The y-axis shows log2 fold change in expression. The nodes following a bifurcation event are annotated with the TFs that are predicted to control the split, providing temporal resolution to the static protein–DNA interaction data. TFs are only shown the first time they are active along a regulatory path. (B) This subset of the oriented interaction network contains three types of nodes: upstream proteins given as sources (red), predicted signaling proteins (blue), and active TFs from DREM (green). The blue nodes consist of all proteins that appear in at least 1% of high-scoring paths and are not sources or targets. Dashed edges are protein–DNA interactions, and solid edges are oriented PPIs. (C) An enlarged view of a subsection of the interaction network identified shows that the core transcriptional unit of the HOG pathway was recovered. These TFs were inferred in the regulatory component of the model, and the network displays SDREM's explanation of how they are activated.
Figure 3.
Figure 3.
Long osmotic stress model. (A) The regulatory model for the long osmotic stress expression data contains nine paths. The initial splits overlap with those in the short model in terms of the TFs predicted to control them. (B) The sources, signaling proteins, and active TFs in the long model are shown. Again, there is a large overlap with the signaling model from the short time-series data set. (C) The primary TFs of the osmotic stress response are recovered in the long model as well. Hog1 and Sko1 are shown a second time along the uppermost regulatory path to emphasize the connection between the signaling and regulatory components.
Figure 4.
Figure 4.
Differential nuclear localization and protein expression after treatment with sorbitol. (A) Each row corresponds to localization of the predicted osmotic stress responder before and after sorbitol treatment. The images were taken 50 min after treatment for Cin5, 21 min for Hog1, and 26 min for Rox1. (B) FACS reveals increased protein levels for Gcn4 and Rox1. The y-axis is the protein level ratio relative to the level before sorbitol treatment. (Error bars) SD of the protein level ratios over all replicates.
Figure 5.
Figure 5.
Knockouts affect downstream expression of genes on the recovered regulatory paths. (A) The six proteins from different regions of the signaling network were selected for deletion. The short network model, reproduced from Figure 2B, is shown here, and the positions of knocked-out genes are highlighted with red boxes. (B) Five knockouts significantly affected the genes assigned to the regulatory paths in the short model. Numbered paths are annotated with the knockouts where we found significant overlap between path members and knockout-affected genes. (C) The subnetwork affected by the ASF1 deletion. Only the relevant subset of the downstream TFs is shown, and the edges connecting Asf1 to the TFs are omitted for clarity. (D) The seven TFs predicted to control path 1's split from path 2 are displayed above path 1. All seven are downstream from Asf1 in the oriented network.
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