BattleX - Manipulating the fight between human host cells and intracellular pathogens
Infectious diseases are a major cause of morbidity and mortality worldwide. Growing resistance to current antimicrobials and lean pipelines for novel therapeutics increasingly limit treatment options. It is currently unclear how the urgent need for novel strategies to combat infectious diseases can be met. As a result, many pharmaceutical companies have terminated antiinfective discovery programs. On the other hand, there has been vast progress in basic infection biology research over the past two decades. In particular, a large number of pathogen virulence factors and host immune effector mechanism have been elucidated at molecular detail. However, knowledge about a particular mechanism can not be easily translated into new control strategies since infectious diseases are usually the consequence of a fight between two networks of hundreds to thousands of individual factors. Interfering with any single factor has thus often insufficient effects on infection outcome.
In addition to detailed studies on individual mechanisms, an integrated system-level analysis is thus required for rational development of novel control strategies. Such Systems Biology of Infection has not yet been implemented and faces considerable conceptual and technical challenges. Specifically, quantitative genome-scale models are needed for the host, the pathogen, and the interface. Moreover, techniques for efficient experimental perturbation of both host and pathogen, and measurement of large-scale quantitative data sets covering both sides are required. In this project, we will investigate metabolism as one subsystem of the host-pathogen interaction network. Metabolism is highly relevant for pathogen growth and host control, and provides especially attractive targets for antimicrobials. In addition, metabolism is by far the best understood large network in host cells and pathogens.
We will focus on one infection model, intracellular Shigella infection of human cells. This model is medically relevant and offers unique advantages for a systems approach. Our specific goal in this project is to identify novel targets/target combinations in metabolism for control of shigellosis. We have already been able to combine existing genome-scale metabolic models of Shigella and human cells in a super network model that correctly predicts Shigella growth inside infected human cells when certain nutrients are externally supplied.
On the other hand, there are still large knowledge gaps in human metabolism that we will address using a wide range of annotation and curation methods. We will also refine important parameters such as reaction reversibility and maximal reaction rates using combinations of experimental and theoretical approaches. We will use the improving super network model and Flux-Balance Analysis as well as Metabolic Control Analysis to predict phenotypes of perturbations of both host and Shigella metabolic enzymes. Specifically, we will identify targets/target combinations whose inhibition leads to Shigella growth arrest and/or breakdown of infected host cells (thus depriving Shigella of their replicative niche). Experimental validation of predictions with a wide range of methods will identify discrepancies that are valuable for continuous model refinement. With increasing accuracy of model predictions, we expect to identify more and more attractive target candidates that we will prioritize for further development based on additional experimental and modeling data.
This ambitious project will be possible through close collaboration of seven partners with excellent expertise in a wide range of complementary research fields. The super network model written in SBML will be the project core that provides a common language for all partners, incorporates all new data, and facilitates prediction/ validation cycles. Around this model, a data management/integration platform and a central infection platform will ensure high synergy and biosafety.
We hope that this unique project that combines genome-scale models of both host and pathogen will pave the way for an integrated comprehensive understanding of infectious diseases as a rational basis to develop urgently needed control strategies.
