Proteomic profiling of bacterial host adaptation : Racing the Red Queen

Abstract: Despite the discovery of antibiotics almost a century ago, infectious diseases continue to be a substantial cause of human mortality and morbidity worldwide, especially in developing countries. The adverse affects of infectious diseases are thought to increase over the coming years as the widespread misuse of antibiotic leads to the emergence of strains for which current therapies are ineffective. The last decades has also seen a large increase of animal pathogens crossing the species barrier to cause disease in humans. To be able to reverse these negative trends we need better knowledge of the events leading to the adaptation of these pathogens to their host. This thesis aspires to increase our understanding of bacterial host adaptation with the hope of finding new targets for diagnostic and therapeutic treatments.In this thesis the development and application of novel mass spectrometry based methods for investigating bacterial host adaptation is studied. The developed methods are based on state of the art mass spectrometry proteomics, which allows the identification and quantification of in principal any expressed protein from a biological sample. The power of this analysis method was used to simultaneously quantify sets of bacterial and host proteins with a specific role in the infection course. These protein measurements are then used as standardization curves to obtain and account for any variation between biological states. The developed methods are combined to construct a quantitative model, depicting host – pathogen interactions and changes during infection progression. The model was used to determine the degree of host adaptation resulting of sequential passaging of the human pathogen Streptococcus pyogenes in a mouse infection model.In summery, this thesis has increased out understanding of the complex interactions leading to host adaptation of bacterial pathogens by the development of a quantitative model for bacterial infections. In addition, this thesis suggests a new approach for biomarker discovery and validation, by using standardization curves of potential biomarkers. The research conducted in this thesis has the potential to lead to increased clinical diagnostic and treatment opportunities of infectious diseases.