Gram-negative and Gram-Positive Bacterial Infections Give Rise to a Different Metabolic Response in a Mouse Model
Author(s) -
Verena Hoerr,
Lori Zbytnuik,
Caroline Léger,
Patrick Tam,
Paul Kubes,
Hans J. Vogel
Publication year - 2012
Publication title -
journal of proteome research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.644
H-Index - 161
eISSN - 1535-3907
pISSN - 1535-3893
DOI - 10.1021/pr201274r
Subject(s) - biology , microbiology and biotechnology , pseudomonas aeruginosa , metabolomics , escherichia coli , gram negative bacteria , staphylococcus aureus , gram positive bacteria , bacteria , streptococcus pneumoniae , metabolic pathway , gene , antibiotics , bioinformatics , biochemistry , genetics
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.
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