Integration of modelling with transcription and gene essentiality profiling to study interaction of MTB bacillus with macrophages and dendritic cells.

Mycobacterium tuberculosis is a major pathogen of man. Drug treatment is available for human disease but it takes six months, which is impractical in developing world settings where TB is most common. Consequent non-compliance with treatment regimes leads to the emergence of drug-resistance. This is now a major world-wide problem with practically incurable 'extreme drug-resistant' strains appearing in many countries, including the UK. In this project we will study the molecular mechanisms of the interaction between Mcyobacterium tuberculosis and human immune system. The knowledge about these mechanisms is necessary for the development of new therapeutic approaches and vaccines which are needed to shorten TB treatment and combat drug resistant strains. We will focus on the interaction of the pathogen with dendritic cells and macrophages, which are cell types active during the immune system response to the infection. The M. tuberculosis is capable of infecting macrophages, but not dendritic cells. Therefore, comparison of the responses of these two cell types to M. tuberculosis will highlight the mechanisms participating in host pathogen interaction. To understand the complex phenomenon of host-pathogen interaction the Systems Biology approach has to be employed, where molecular biology methods are integrated with computational modeling approaches to study cells at the whole genome scale level. We will use state of the art functional genomics techniques to compare interaction of the pathogen with dendritic cells and macrophages and identify human and bacterial genes which are involved in host-pathogen interaction. The voluminous experimental data sets will be analyzed in the context of the literature knowledge about the vast networks of interacting molecules in the living cells. The computer simulation approaches developed in the physical sciences and engineering fields will be used. The computer models will generate hypotheses which will be subjected to experimental verification. At the end of the project we expect to deliver a set models of the molecular interaction networks involved in the interaction of M. tuberculosis with immune system. These models can be used to design therapeutic, diagnostic and vaccination strategies.

Mycobacterium tuberculosis (MTB) remains a global health problem. The interaction of MTB with macrophages and dendritic cells (DCs) is central to the pathogenesis of tuberculosis and to the generation of a protective immune response. Interestingly, MTB is readily phagocytosed by both macrophages and DCs, yet the bacterium is only able to replicate in the macrophage. We will use Systems Biology approach to study host pathogen interactions of human macrophages and dendritic cells (DCs) with the MTB bacillus. We will use high throughput experimental approaches to simultaneously study gene expression and essentiality of both host and pathogen. These data will inform predictive computer models of the molecular mechanisms underpinning observed gene activity changes. We will use available reconstructions of signaling and metabolic networks in both host and pathogen as the starting point of the network analysis. All models will be subjected to the qualitative computer simulations of the relationship between gene activity and the state of proteins and metabolites in the network models. We will identify substances and reactions in the network models which are most affected by gene expression differences between infected macrophages and DCs, as represented by microarray signal ratios. Flux Balance Analysis based approaches will be used for qualitative analysis of metabolic networks. Qualitative analysis of signalling networks will be performed by rule-based approach implemented in the CellNetAnalyzer. Reactions and substances which are most affected by gene expression changes will highlight network modules involved in host pathogen interactions. Detailed modeling of these modules will generate hypotheses about molecular mechanisms underpinning observed gene essentiality and gene activity changes.The hypotheses will be tested by molecular biology teams and observations will inform the models in the iterative cycle of hypothesis generation and experimental validation.

Tuberculosis remains one of the most important infectious diseases of mankind, claiming 33,000 deaths per week in an epidemic presently involving 9 million new cases of disease each year (WHO, 2008). One third of the world's population carry an asymptomatic persistent infection, providing a vast reservoir of infection (Kochi, 1991). Up to 10% of individuals with latent tuberculosis will go on to develop active disease thus contributing substantially to transmission and the overall incidence of disease (Bloom and Murray, 1992). In addition there exists increasing levels of multiple drug resistant TB (MDR-TB) and extensively drug resistant TB (XDR-TB). Present control strategies are clearly insufficient to reduce the global burden of TB. This project will identify molecular interaction networks involved in the interaction of MTB with the host cell and construct predictive computer models of these networks. The major users of these models will be research groups working on tuberculosis both in the academia and in the industry. The models will be used to generate research hypotheses that will direct further experimental research. The models will be also used directly to identify targets for development of new antibiotics. Predictive models of the interaction between MTB and immune system will also inform research on new vaccination strategies. Systems Biology approach adopted by this project will be also used to investigate other human and animal bacterial pathogens. Therefore, the application of the models resulting from this project will help to combat a major human pathogen. Eradication of resurging human and animal infectious diseases will have a major positive impact on the well-being and quality of life. To ensure that academic and industry research groups will benefit from the results of this project we will adopt open data sharing standards accepted by scientific community. Experimental data will be submitted to recognized, publicly available databases. Models will be available in the SBML format which is widely accepted by research community. To further enhance availability of the models we will made them available in the interactive form via set of www-based tools. These tools will help researchers to identify usefulness of our models without the need to install any specialized software.