Validation and refinement of gene-regulatory pathways on a network of physical interactions

As genome-scale measurements lead to increasingly complex models of gene regulation, systematic approaches are needed to validate and refine these models. Towards this goal, we describe an automated procedure for prioritizing genetic perturbations in order to discriminate optimally between alternative models of a gene-regulatory network. Using this procedure, we evaluate 38 candidate regulatory networks in yeast and perform four high-priority gene knockout experiments. The refined networks support previously unknown regulatory mechanisms downstream of SOK2 and SWI4. Background Recent advances in genomics and computational biology are enabling construction of large-scale models of gene-regulatory networks. High-throughput technologies such as automated sequencing [1], gene-expression arrays [2], chromatin immunoprecipitation [3], and yeast two-hybrid assays [4], each probe different aspects of the gene-regulatory system through genome-wide datasets. These data have spawned a variety of methods to infer the structure of gene-regulatory networks or to study their high-level properties, as recently reviewed [5]. Regulatory network models generated thus far in Escherichia coli and budding yeast (Saccharomyces cerevisiae) have been most often validated against functional databases or previous literature [6,7]. In contrast, only a few studies have attempted to validate or refine models systematically [8-11]. However, if we are to accurately model large gene networks in complex organisms, including fly, worm, mouse, and human, automated procedures will be essential for analyzing the network, choosing the best new experiments to test the model, conducting the experiments, and integrating the resulting data. The problem of choosing the best experiments to estimate a model, termed 'experimental design' or 'active learning', has been a significant area of research in statistics and machine learning [12-14]. Automating the experimental design process can greatly accelerate data collection and model building, leading to substantial savings in time, materials, and human effort. For these reasons, many industries such as electronic circuit fabrication and airplane manufacturing incorporate Published: 1 July 2005 Genome Biology 2005, 6:R62 (doi:10.1186/gb-2005-6-7-r62) Received: 9 March 2005 Revised: 3 May 2005 Accepted: 3 June 2005 The electronic version of this article is the complete one and can be found online at http://genomebiology.com/2005/6/7/R62 Genome Biology 2005, 6:R62 R62.2 Genome Biology 2005, Volume 6, Issue 7, Article R62 Yeang et al. http://genomebiology.com/2005/6/7/R62 experimental design as an integral step in the design process [15,16]. A promising application of experimental design for biological systems was presented by King et al. [17], who integrated computational modeling and experimental design to reconstruct a small, well studied metabolic pathway. Whether automated experimental design can be useful in a large and poorly characterized biological system with noisy data remains an open question. We recently reported a procedure for inferring gene-regulatory network models by integrating gene-expression profiles with high-throughput measurements of protein interactions [18]. Here we extend this procedure to incorporate automated design of new experiments. First, we use the previously described modeling procedure to generate a library of models corresponding to different gene-regulatory systems in yeast. Many of these models contain transcriptional interactions for which the regulatory effects (inducer versus repressor) are ambiguous and cannot be determined from publicly available expression profiles. Next, to address these ambiguities we implement a score function that ranks possible genetic perturbation experiments on the basis of their projected information content over the models. We perform four of the highest-ranking perturbations experimentally and integrate the data back into the model. The new data support two out of three novel regulatory pathways predicted to mediate expression changes downstream of the yeast transcriptional regulator SWI4. Results Summary of physical regulatory models We applied a previously described network-modeling procedure [18] to integrate three complementary sources of generegulatory information in yeast: 5,558 promoter-binding interactions for 106 transcription factors measured using chromatin immunoprecipitation followed by microarray chip hybridization (ChIP-chip) [3]; the set of all 15,116 pairwise protein-protein interactions recorded in the Database of Interacting Proteins as of April 2004 [19]; and a panel of mRNA expression profiles for 273 individual gene-deletion experiments [20]. Software for performing the network-modeling procedure is available as a plug-in to the Cytoscape package [21,22] on our supplementary website [23]. For each gene-deletion experiment, the modeling procedure identified the most probable paths of protein-protein and promoter-binding interactions that connect the deleted gene (the perturbation) to genes that were differentially expressed in response to the deletion (the effects of perturbation). Thus, a path represented one possible physical explanation by which a deleted gene regulates a second gene downstream. From the expression data, each interaction on a path was annotated with its probable direction of information flow and its probable regulatory effect as an inducer or repressor. For example, the model in Figure 1a (top center) includes a path from GLN3 through GCN4 to a block of downstream affected genes. This model integrates evidence that: Gln3p binds the promoter of GCN4 with high significance in a ChIPchip assay [3] (p ≤ 8 × 10-4); Gcn4p binds the promoters of many genes in the ChIP-chip assay (RIB5, YJL200C, and others in the downstream block); and a significant number of genes in the block are upregulated in a gln3∆ knockout but downregulated in a gcn4∆ knockout [20]. Together, this evidence confirms Gcn4p as an activator of downstream genes [24] and leads to a (novel) annotation that Gln3p is likely to regulate GCN4 via transcriptional repression. In total, the modeling process generated 4,836 paths, each explaining expression changes for a particular gene in one or more knockout experiments. Of the 965 interactions covered by paths, 194 had regulatory effects that were uniquely determined by the data, while regulatory effects of the remaining 771 interactions were ambiguous. For example, Figure 1b includes ambiguous interaction paths through SWI4, SOK2, and MSN4, explaining the observation that many genes for which the promoters are bound by Msn4p are upregulated in a swi4∆ knockout. This observation can be explained by several alternative annotations: one scenario is that SWI4 activates SOK2 and SOK2 represses MSN4 (Figure 1b), whereas another is that SWI4 represses SOK2 and SOK2 activates MSN4 (Figure 1c). These regulatory annotations could be uniquely determined by measuring the expression changes of genes downstream of MSN4 in the model in response to a sok2∆ deletion and an msn4∆ deletion (see below). Paths with ambiguous interactions were partitioned into 37 independent network models (numbered 1-37), where each model contained a distinct region of the physical network (see Materials and methods and Additional data file 1). The remaining non-ambiguous paths were grouped into a single model (Model 0). As shown in Table 1, 21 of the models (55%) contained pathways that are well documented in the literature or are significantly enriched for genes belonging to specific Munich Information Center for Protein Sequences (MIPS) [25] functional categories. Of 132 protein-DNA interactions incorporated into Model 0, we found that 50 had been confirmed in classical (low-throughput) assays as reported in the Proteome BioKnowledge Library [26]. Moreover, the inferred regulatory roles (induction or repression) for 48 out of 50 of these interactions agreed with their experimentally determined roles (96%, binomial p-value < 1.22 × 10-7). Wiring diagrams for Models 0 and 1 are given in Figure 1; diagrams for all other regulatory network models are provided in Additional data file 1 and at [23]. Experiment selection As shown in Figure 2, we implemented an information-theoretic approach to discriminate between ambiguous model annotations using the fewest additional gene-expression experiments. All non-lethal single-gene knockout Genome Biology 2005, 6:R62 http://genomebiology.com/2005/6/7/R62 Genome Biology 2005, Volume 6, Issue 7, Article R62 Yeang et al. R62.3