4.7 Article

Gram-negative and Gram-Positive Bacterial Infections Give Rise to a Different Metabolic Response in a Mouse Model

期刊

JOURNAL OF PROTEOME RESEARCH
卷 11, 期 6, 页码 3231-3245

出版社

AMER CHEMICAL SOC
DOI: 10.1021/pr201274r

关键词

metabolomics; H-1 NMR spectroscopy; Gram-negative and Gram-positive bacterial infections; bacteria-secreted metabolites; knockout mice; host response; metabolic responses; physiological responses; cytokine responses; multivariate data analysis

资金

  1. Canadian Institutes of Health Research
  2. Alberta Innovates Health Solutions (AIHS)
  3. Alberta Hertiage Foundation for Medical Research (AHFMR)
  4. AIHS/AHFMR

向作者/读者索取更多资源

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 H-1 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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