Reconstruction of Escherichia coli transcriptional regulatory networks via regulon-based associations

Abstract

Background: Network reconstruction methods that rely on covariance of expression of transcription regulators and their targets ignore the fact that transcription of regulators and their targets can be controlled differently and/or independently. Such oversight would result in many erroneous predictions. However, accurate prediction of gene regulatory interactions can be made possible through modeling and estimation of transcriptional activity of groups of co-regulated genes.

Results: Incomplete regulatory connectivity and expression data are used here to construct a consensus network of transcriptional regulation in Escherichia coli (E. coli). The network is updated via a covariance model describing the activity of gene sets controlled by common regulators. The proposed model-selection algorithm was used to annotate the likeliest regulatory interactions in E. coli on the basis of two independent sets of expression data, each containing many microarray experiments under a variety of conditions. The key regulatory predictions have been verified by an experiment and literature survey. In addition, the estimated activity profiles of transcription factors were used to describe their responses to environmental and genetic perturbations as well as drug treatments.

Conclusion: Information about transcriptional activity of documented co-regulated genes (a core regulon) should be sufficient for discovering new target genes, whose transcriptional activities significantly co-vary with the activity of the core regulon members. Our ability to derive a highly significant consensus network by applying the regulon-based approach to two very different data sets demonstrated the efficiency of this strategy. We believe that this approach can be used to reconstruct gene regulatory networks of other organisms for which partial sets of known interactions are available.

Figure 1

Activity profile of ArgR, TrpR, Lrp and LexA. Several conditions in our data set were expected to elicit transcriptional responses mediated by the activity of known regulators. We found that in all conditions with well-studied and understood transcriptional responses, the identity of the most active TF matched our expectations. For example, in an experiment which was conducted to measure transcriptional response to addition of the amino acid arginine, transcription factor ArgR appeared to be the most active TF. Similarly, TrpR was the most active TF in the condition when tryptophan was added to the medium, and LexA was the most active TF under conditions of UV and Gamma treatment.

Figure 2

A number of active regulators varies across conditions. The number of transcription factors active in minimal growth medium as compared to rich medium was the highest, followed by the transition from exponential to stationary phase of growth, during which the cells are known to undergo massive regulatory re-programming, and then sodium azide treatment, which results, among other things, in an interruption of the electron flow chain. Among the amino acid effects, addition of isoleucine appeared to stimulate the highest number of TFs, whereas addition of threonine or glutamate appeared to have no or very little effect on the regulators. The smallest number of differentially active transcription factors was observed in the comparison of chemostat cultures grown at different dilution rates ("WildTypeGrowth").

Figure 3

Frequency of condition-specific activity for top regulators. Many TFs were likely to be mediating transcriptional responses in multiple conditions. Given that the set of conditions in our study was enriched by those in which metabolism of various amino acids or nucleotides was directly or indirectly perturbed and by conditions causing DNA damage, it was not surprising that the list of most frequently active regulators included ArgR, GcvA, CysB, MetR/MetJ, DeoR, PurR, LexA.