Genome-wide inference of regulatory networks in Streptomyces coelicolor

Background: The onset of antibiotics production in Streptomyces species is co-ordinated with differentiation events. An understanding of the genetic circuits that regulate these coupled biological phenomena is essential to discover and engineer the pharmacologically important natural products made by these species. The availability of genomic tools and access to a large warehouse of transcriptome data for the model organism, Streptomyces coelicolor, provides incentive to decipher the intricacies of the regulatory cascades and develop biologically meaningful hypotheses. Results: In this study, more than 500 samples of genome-wide temporal transcriptome data, comprising wild-type and more than 25 regulatory gene mutants of Streptomyces coelicolor probed across multiple stress and medium conditions, were investigated. Information based on transcript and functional similarity was used to update a previously-predicted whole-genome operon map and further applied to predict transcriptional networks constituting modules enriched in diverse functions such as secondary metabolism, and sigma factor. The predicted network displays a scale-free architecture with a small-world property observed in many biological networks. The networks were further investigated to identify functionally-relevant modules that exhibit functional coherence and a consensus motif in the promoter elements indicative of DNA-binding elements. Conclusions: Despite the enormous experimental as well as computational challenges, a systems approach for integrating diverse genome-scale datasets to elucidate complex regulatory networks is beginning to emerge. We present an integrated analysis of transcriptome data and genomic features to refine a whole-genome operon map and to construct regulatory networks at the cistron level in Streptomyces coelicolor. The functionally-relevant modules identified in this study pose as potential targets for further studies and verification.

Statistical reconstruction of transcription factor activity using Michaelis-Menten kinetics.

Summary. The basic building block of a gene regulatory network consists of a gene encoding a transcription factor (TF) and the gene(s) it regulates. Considerable efforts have been directed recently at devising experiments and algorithms to determine TFs and their corresponding target genes using gene expression and other types of data. The underlying problem is that the expression of a gene coding for the TF provides only limited information about the activity of the TF, which can also be controlled posttranscriptionally. In the absence of a reliable technology to routinely measure the activity of regulators, it is of great importance to understand whether this activity can be inferred from gene expression data. We here develop a statistical framework to reconstruct the activity of a TF from gene expression data of the target genes in its regulatory module. The novelty of our approach is that we embed the deterministic Michaelis–Menten model of gene regulation in this statistical framework. The kinetic parameters of the gene regulation model are inferred together with the profile of the TF regulator. We also obtain a goodness-of-fit test to verify the fit of the model. The model is applied to a time series involving the Streptomyces coelicolor bacterium. We focus on the transcriptional activator cdaR, which is partly responsible for the production of a particular type of antibiotic. The aim is to reconstruct the activity profile of this regulator. Our approach can be extended to include more complex regulatory relationships, such as multiple regulatory factors, competition, and cooperativity