The interplay between two-component signal transduction systems and the genome scale metabolic network of Streptomyces coelicolor.

Streptomyces coelicolor is a bacterium that lives in the soil. It competes and communicates with other bacteria and fungi living in the same environment. In order to compete streptomycetes produce many antibiotics that can destroy their neighbours. They are of great societal importance because many of these natural antibiotics are used as medicines to treat infections, and also kill off 'superbugs' such as MRSA. Some streptomycete antibiotics are also used for treatment of cancer and for transplant surgery. For these reasons streptomycetes are widely used in the pharmaceutical industry to produce drugs. In fact, most of antibiotics that we use everyday are produced by streptomyces. The complete DNA sequence of Streptomyces coelicolor has been determined by a group of research laboratories funded by a BBSRC grant. Substantial financial support is also given to follow-up studies that apply recent advances in scientific instrumentation to collect even more data about the gene activities, protein products and small molecules in the Streptomyces cell. The knowledge of how streptomycetes co-ordinate all their cellular processes will help genetic and metabolic engineers to redesign it. This will allow us to construct microbial strains that synthesise commercially valuable substances more efficiently or produce chemical compounds that are not synthesised at all in naturally occurring microbial strains. Engineers would like to be able to use Streptomyces as a 'cell factory' that could be programmed to synthesise any organic substances of commercial value. However, the major obstacle to achieving this level of control over Streptomyces is the complexity of the molecular machinery of the cell, in which thousands of genes, proteins and chemical compounds interact. To be able to predict how the engineered change in the DNA of the cell would alter its biosynthetic capabilities we need to understand this complex network of interactions. To do this biologists try to use mathematical and computer simulation methods similar to those that are used to describe other complex systems such as integrated circuits, communication networks and weather. This new approach to biology is frequently referred to as Systems Biology. In this project we will study two important regulatory switches of the cell, called PhoRP and AbsA1A2. Both systems detect changes in the cellular environment and respond by activating large numbers of genes encoding enzymes taking part in synthesis of organic substances. We plan to perform quantitative measurements of messenger RNA levels, protein amounts and metabolic fluxes (a measure of the flow of molecules into and out of the cell). State-of-the art technologies will be used: DNA microarrays will make possible simultaneous detection of all transcripts within the cell; robots will be used to set up chemical reactions for accurate determination of transcript levels with 'QRT-PCR'. The cells will be cultured under experimental conditions where temperature, gases, pressure and media composition are strictly controlled by fermentors. Experimental results will be used to build a mathematical model describing the relationship between the action of regulatory switches and biosynthetic capabilities of the cell. The model will be validated, i.e. we will check whether it is able to predict results of the experiments which were not used for model construction. Finally, we will use the model to understand how natural regulatory switches in the Streptomyces cell could be modified to enhance biosynthesis of commercially valuable compounds.

The streptomycetes are filamentous soil-dwelling bacteria that display remarkable metabolic diversity. They produce most of the known natural antibiotics and other bioactive secondary metabolites used in human and veterinary medicine. The genome of Streptomyces coelicolor A3(2) has been sequenced by a BBSRC-funded consortium and substantial funds have been also committed to studies of its functional genomics. Computational data analysis and mathematical modelling are essential to explore the genome sequence and high-throughput experimental data that is collected in these projects, for the benefit of the pharmaceutical industry and basic research. In particular, our knowledge of the component parts of the complete system should be expressed in the form of interaction networks. Mathematical modelling and computer simulations of these networks must be performed to unravel how the physiological phenotype of the streptomycete organism emerges from the interaction between the molecular components of the cell. A Systems Biology approach is required to use genome sequence and high throughput experimental data so that we can predict the effects of engineered changes and rationally design novel, commercially valuable, Streptomyces strains. Antibiotic production in streptomycetes is highly regulated. Expression of genes encoding enzymes of secondary metabolite biosynthesis pathways is regulated by complex gene regulatory networks and depends on environmental conditions and developmental stage (low growth rate, stationary phase, sporulation, aerial hyphae formation). Therefore, one cannot understand the true organisation and in vivo behaviour of secondary metabolism in streptomyces without taking gene regulation into account. Moreover, research on other systems provides strong evidence that knowledge on gene regulatory networks increases predictive power of theoretical models used in metabolic engineering, and that under some circumstances engineering of the transcriptional regulatory network itself may be advantageous over traditional strategies based on inactivation/overexpression of genes encoding enzymes. The objective of this project is to understand the mechanisms by which two key two-component signal transduction systems, specifically PhoRP and AbsA1A2, regulate global flux distribution in the genome scale metabolic network (GSMN) of Streptomyces coelicolor. We will first examine the influence of the inactivation of the PhoP and AbsA2 regulators on the global expression profiles determined by DNA microarrays. We will relate expression changes to the organization of the GSMN and the metabolic flux distribution in this network. We will identify the enzymes regulated by the PhoRP and AbsA1A2 systems by integrating data from ChIP-on-chip experiments and from analysis of transcriptome data supported by analysis of overrepresented sequence motifs in gene regulatory regions. Subsequently, we will build a kinetic model of the transcriptional regulatory network, determined from the previous experiments, which will be embedded into the GSMN. We will take advantage of time-scale separation between transcriptional regulation and metabolism and consider regulation as a perturbation of metabolic flux distribution in quasi-stationary state. Modelling will be supported and validated by quantitative measurements of biomass growth, metabolite levels and uptake/secretion rates and quantitative time resolved measurements of mRNA levels, mRNA half-lives and reporter protein amounts. Finally, the model will allow us to understand the relationship between different levels of organization of biochemical reaction networks and provide a predictive model for the benefit of metabolic engineering in streptomycetes. Numerous quantitative parameters established in the course of our project will be valuable for future whole-cell modelling efforts of streptomycetes and other microorganisms, including pathogens.