ResponseNet: revealing signaling and regulatory networks linking genetic and transcriptomic screening data

Abstract

Cellular response to stimuli is typically complex and involves both regulatory and metabolic processes. Large-scale experimental efforts to identify components of these processes often comprise of genetic screening and transcriptomic profiling assays. We previously established that in yeast genetic screens tend to identify response regulators, while transcriptomic profiling assays tend to identify components of metabolic processes. ResponseNet is a network-optimization approach that integrates the results from these assays with data of known molecular interactions. Specifically, ResponseNet identifies a high-probability sub-network, composed of signaling and regulatory molecular interaction paths, through which putative response regulators may lead to the measured transcriptomic changes. Computationally, this is achieved by formulating a minimum-cost flow optimization problem and solving it efficiently using linear programming tools. The ResponseNet web server offers a simple interface for applying ResponseNet. Users can upload weighted lists of proteins and genes and obtain a sparse, weighted, molecular interaction sub-network connecting their data. The predicted sub-network and its gene ontology enrichment analysis are presented graphically or as text. Consequently, the ResponseNet web server enables researchers that were previously limited to separate analysis of their distinct, large-scale experiments, to meaningfully integrate their data and substantially expand their understanding of the underlying cellular response. ResponseNet is available at http://bioinfo.bgu.ac.il/respnet.

Figure 1.

The signaling and regulatory sub-network, by which stimulus-related proteins detected by genetic screens may lead to the measured transcriptomic response. ResponseNet integrates the identified stimulus-related proteins and genes with known molecular interactions to find molecular interaction paths, through which a subset of the proteins may regulate the transcription of a subset of the genes. The regulation may be direct, e.g. when the stimulus-related protein is the transcriptional regulator of a stimulus-related gene, or indirect via intermediate proteins and transcriptional regulators. Stimulus-related proteins appear as orange nodes; stimulus-related genes appear as blue nodes; intermediary proteins appear as white nodes. Known protein–protein and protein–DNA interactions appear as gray edges. Transcriptional regulators appear as triangles.

Figure 2.

The ResponseNet web server output. In this example the ResponseNet web server was applied to a protein set containing Ste2 and Ste3 and to a gene set containing Fus1 and Fus3 using default parameters. The middle panel presents the regulatory and signaling output sub-network identified by ResponseNet. The left panel presents a visual summary of the coverage of the output sub-network, as well as a link to the GO enrichment analysis of the connected input sets and the output sub-network. The right panel presents a link to a text version of the output sub-network in a Cytoscape-compatible format, and a legend for the sub-network appearing in the middle panel.