DISTILLER: a data integration framework to reveal condition dependency of complex regulons in Escherichia coli

Abstract

We present DISTILLER, a data integration framework for the inference of transcriptional module networks. Experimental validation of predicted targets for the well-studied fumarate nitrate reductase regulator showed the effectiveness of our approach in Escherichia coli. In addition, the condition dependency and modularity of the inferred transcriptional network was studied. Surprisingly, the level of regulatory complexity seemed lower than that which would be expected from RegulonDB, indicating that complex regulatory programs tend to decrease the degree of modularity.

Figure 1

Condition dependency of the regulatory modules. Columns are conditional categories, and rows are regulators for which modules were detected by DISTILLER. Each entry indicates to what extent the conditions of the modules of a particular regulator are enriched (log P-value) for a specific category. Dark blue entries correspond to the most significant enrichments.

Figure 2

Types of combinatorial regulation. Type 1 shows combinatorial regulation at the module level. The genes cdd, nupG, udp and deoC have two motifs in common (corresponding to the regulators CytR and CRP) and are co-expressed in condition set 1. This kind of control often seems to occur as a combination of a global regulator and a more specific one. Type 2 shows combinatorial regulation at the level of a connector gene. All genes of module 1 share two motifs, MarA and SoxS, and are co-expressed in a subset of conditions. For module 2 all genes are regulated by Fur. SodA, a connector gene, is shared by both modules and is thus regulated by the regulators of module 1 and module 2, but under a different set of conditions (as shown by the heatmap image), indicating that the corresponding regulators of both modules act independently of each other. Both types of interactions mentioned above can be identified by DISTILLER. Cases where condition-specific complex interactions between regulators result in such highly gene-specific expression patterns that genes are no longer found co-expressed in modules (type 3) cannot be detected by DISTILLER.

Figure 3

Venn Diagram showing the number of overlapping interactions between the networks of RegulonDB, CLR, SEREND and DISTILLER. CLR, SEREND and DISTILLER were applied to our data sets. As the overlap between SEREND and RegulonDB is algorithmically defined to be 100%, we show only the predictions of SEREND that were not reported in RegulonDB and do not explicitly visualize the overlap with RegulonDB for SEREND.