title: Gram-negative and Gram-positive bacterial infections give rise to a different metabolic response in a mouse model.
abstract: Metabolomics has become an important tool to study host-pathogen interactions and to discover potential novel therapeutic targets. In an attempt to develop a better understanding of the process of pathogenesis and the associated host response we have used a quantitative (1)H NMR approach to study the metabolic response to different bacterial infections. Here we describe that metabolic changes found in serum of mice that were infected with Staphylococcus aureus, Streptococcus pneumoniae, Escherichia coli and Pseudomonas aeruginosa can distinguish between infections caused by Gram-positive and Gram-negative bacterial strains. By combining the results of the mouse study with those of bacterial footprinting culture experiments, bacterially secreted metabolites could be identified as potential bacterium-specific biomarkers for P. aeruginosa infections but not for the other strains. Multivariate statistical analysis revealed correlations between metabolic, cytokine and physiological responses. In TLR4 and TLR2 knockout mice, host-response pathway correlated metabolites could be identified and allowed us for the first time to distinguish between bacterial- and host-induced metabolic changes. Since Gram-positive and Gram-negative bacteria activate different receptor pathways in the host, our results suggest that it may become possible in the future to use a metabolomics approach to improve on current clinical microbiology diagnostic methods.
authors.affiliation: Biochemistry Research Group, Department of Biological Sciences, ‡Department of Physiology and Biophysics, Snyder Institute, University of Calgary , Calgary, Alberta T2N 1N4, Canada.
authors.forename: Verena
authors.initials: V
authors.lastname: Hoerr
authors.forename: Lori
authors.initials: L
authors.lastname: Zbytnuik
authors.forename: Caroline
authors.initials: C
authors.lastname: Leger
authors.forename: Patrick P C
authors.initials: PP
authors.lastname: Tam
authors.forename: Paul
authors.initials: P
authors.lastname: Kubes
authors.forename: Hans J
authors.initials: HJ
authors.lastname: Vogel
mesh_terms: D000818:Animals; D016207:Cytokines / Q000097:blood; D004195:Disease Models, Animal; D004926:Escherichia coli / Q000502:physiology; D004927:Escherichia coli Infections / Q000276:immunology / Q000378:metabolism*; D045604:Extracellular Fluid / Q000378:metabolism; D054884:Host-Pathogen Interactions; D007424:Intracellular Fluid / Q000378:metabolism; D008070:Lipopolysaccharides / Q000494:pharmacology; D008297:Male; D055442:Metabolome; D051379:Mice; D008810:Mice, Inbred C57BL; D018345:Mice, Knockout; D011008:Pneumococcal Infections / Q000276:immunology / Q000378:metabolism*; D011552:Pseudomonas Infections / Q000276:immunology / Q000378:metabolism*; D011550:Pseudomonas aeruginosa / Q000502:physiology; D015398:Signal Transduction; D013203:Staphylococcal Infections / Q000276:immunology / Q000378:metabolism*; D013211:Staphylococcus aureus / Q000502:physiology; D013296:Streptococcus pneumoniae / Q000502:physiology; D051195:Toll-Like Receptor 2 / Q000235:genetics; D051197:Toll-Like Receptor 4 / Q000235:genetics; D037521:Virulence Factors / Q000502:physiology
chemical_list: D016207:Cytokines; D008070:Lipopolysaccharides; C493486:Tlr2 protein, mouse; C493487:Tlr4 protein, mouse; D051195:Toll-Like Receptor 2; D051197:Toll-Like Receptor 4; D037521:Virulence Factors