Abstract

Infectious diseases represent a major worldwide threat to human health. Host-pathogen interactions are understood at increasing molecular detail but translation of this knowledge into novel strategies to combat infectious disease has remained difficult. Specifically, the dramatic decline in development of novel antimicrobials over the last 20 years is likely to substantially reduce treatment options in the near future. One potential reason why it is so difficult to translate basic research results to effective control strategies, could be the prevailing focus on the action of individual pathogen or host components. Instead, system-level analysis of interacting host/pathogen networks might be required for rational development of novel effective control strategies. We are studying metabolic host-pathogen interactions as a particularly suitable model for establishing approaches for system-level analysis of infectious diseases. Specifically, we are reconstructing Salmonella metabolism in infected mice based on large-scale proteomics and mutant phenotype data. We are interpreting these data using in silico quantitative metabolic modeling. Our results reveal a marked robustness of Salmonella metabolism during infection thus limiting potential targets for novel antimicrobials. Multiple mutations and perturbations of the host-pathogen nutritional interface reveal several factors that contribute to this high robustness, and indicate potential strategies to break redundancy with drug combinations. Our ultimate goal is an integrated quantitative model of metabolic Samonella-host interactions during infection.