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Gram-negative and Gram-positive bacterial infections give rise to a different metabolic response in a mouse model.

Hoerr V, Zbytnuik L, Leger C, Tam PP, Kubes P, Vogel HJ - J. Proteome Res. (2012)

Bottom Line: 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.Multivariate statistical analysis revealed correlations between metabolic, cytokine and physiological responses.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.

View Article: PubMed Central - PubMed

Affiliation: Biochemistry Research Group, Department of Biological Sciences, ‡Department of Physiology and Biophysics, Snyder Institute, University of Calgary , Calgary, Alberta T2N 1N4, Canada.

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.

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Scores plots representing 1H NMR spectral data fromserum of C57BL/6 wild-type mice infected with four different bacterialstrains. Mice were infected intraperitoneally with 1 mL of S. aureus (S.a.), S. pneumoniae (S.p.), E. coli (E.c.), and P. aeruginosa (P.a.)suspensions, and serum was analyzed 24 h post administration. (A)2D and (B) 3D PLS-DA scores plots (based on 43 metabolites) showingdifferentiation between the four bacterially infected mouse models(three components; R2 = 0.71, Q2 = 0.56). The 3D plot represents similaritiesalong the third principle component for E. coli and P. aeruginosa infected groups and along the x2–y2 axis for S. aureus and S. pneumoniae infections.(C) OPLS-DA scores plot separating the samples of Gram-positive (S. aureus, S. pneumoniae) and Gram-negative(E. coli, P. aeruginosa) infections(two components; R2 = 0.81, Q2 = 0.59).
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fig3: Scores plots representing 1H NMR spectral data fromserum of C57BL/6 wild-type mice infected with four different bacterialstrains. Mice were infected intraperitoneally with 1 mL of S. aureus (S.a.), S. pneumoniae (S.p.), E. coli (E.c.), and P. aeruginosa (P.a.)suspensions, and serum was analyzed 24 h post administration. (A)2D and (B) 3D PLS-DA scores plots (based on 43 metabolites) showingdifferentiation between the four bacterially infected mouse models(three components; R2 = 0.71, Q2 = 0.56). The 3D plot represents similaritiesalong the third principle component for E. coli and P. aeruginosa infected groups and along the x2–y2 axis for S. aureus and S. pneumoniae infections.(C) OPLS-DA scores plot separating the samples of Gram-positive (S. aureus, S. pneumoniae) and Gram-negative(E. coli, P. aeruginosa) infections(two components; R2 = 0.81, Q2 = 0.59).

Mentions: Using PLS-DA, aclear separation between the four bacterial strains was obtained (Figure 3A). Three components of the PLS-DA analysis encapsulated71% of the interclass variation (R2) with a corresponding cross-validationaccuracy of 56% (Q2). Coefficients of metabolites for different bacterialinfections showed that each infection was correlated to specific changesin certain metabolic intermediates (Table 2A–D, Supporting Information). Mice infected with S. aureus showed extremely enhanced serum levels of metabolitesrelated to fatty acid oxidation such as acetone, 3-hydroxybutyrate,and 2-hydroxybutyrate as well as isobutyrate and creatine. In contrast,in S. pneumoniae infections these metabolites wereidentified as negative contributors, and TCA cycle intermediates (2-oxoglutarate,citrate, and fumarate) as well as glucose and pyruvate showed thehighest serum levels compared to all infections investigated. Hippurate,a carboxylic acid and endogenous conjugate which is not further metabolizedbut actively secreted by tubular cells and excreted in urine,36 became a metabolite of special interest as itsserum concentrations rose by almost 100% during the pneumococcal infection.It is produced by condensation of benzoic acid and glycine in themitochondria of liver and kidney,37,38 and its synthesisis stimulated by metabolic acidosis. It belongs to the group of uremictoxins, and its enhancement in urine is often used as an indicatorof intrahepatic tracer dilution to determine the activity of specifichuman enzymes.39,40 Therefore, unlike the cytokinesand chemokines which simply indicated indiscriminate inflammation,metabolites highlighted striking differences.


Gram-negative and Gram-positive bacterial infections give rise to a different metabolic response in a mouse model.

Hoerr V, Zbytnuik L, Leger C, Tam PP, Kubes P, Vogel HJ - J. Proteome Res. (2012)

Scores plots representing 1H NMR spectral data fromserum of C57BL/6 wild-type mice infected with four different bacterialstrains. Mice were infected intraperitoneally with 1 mL of S. aureus (S.a.), S. pneumoniae (S.p.), E. coli (E.c.), and P. aeruginosa (P.a.)suspensions, and serum was analyzed 24 h post administration. (A)2D and (B) 3D PLS-DA scores plots (based on 43 metabolites) showingdifferentiation between the four bacterially infected mouse models(three components; R2 = 0.71, Q2 = 0.56). The 3D plot represents similaritiesalong the third principle component for E. coli and P. aeruginosa infected groups and along the x2–y2 axis for S. aureus and S. pneumoniae infections.(C) OPLS-DA scores plot separating the samples of Gram-positive (S. aureus, S. pneumoniae) and Gram-negative(E. coli, P. aeruginosa) infections(two components; R2 = 0.81, Q2 = 0.59).
© Copyright Policy - open-access
Related In: Results  -  Collection

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fig3: Scores plots representing 1H NMR spectral data fromserum of C57BL/6 wild-type mice infected with four different bacterialstrains. Mice were infected intraperitoneally with 1 mL of S. aureus (S.a.), S. pneumoniae (S.p.), E. coli (E.c.), and P. aeruginosa (P.a.)suspensions, and serum was analyzed 24 h post administration. (A)2D and (B) 3D PLS-DA scores plots (based on 43 metabolites) showingdifferentiation between the four bacterially infected mouse models(three components; R2 = 0.71, Q2 = 0.56). The 3D plot represents similaritiesalong the third principle component for E. coli and P. aeruginosa infected groups and along the x2–y2 axis for S. aureus and S. pneumoniae infections.(C) OPLS-DA scores plot separating the samples of Gram-positive (S. aureus, S. pneumoniae) and Gram-negative(E. coli, P. aeruginosa) infections(two components; R2 = 0.81, Q2 = 0.59).
Mentions: Using PLS-DA, aclear separation between the four bacterial strains was obtained (Figure 3A). Three components of the PLS-DA analysis encapsulated71% of the interclass variation (R2) with a corresponding cross-validationaccuracy of 56% (Q2). Coefficients of metabolites for different bacterialinfections showed that each infection was correlated to specific changesin certain metabolic intermediates (Table 2A–D, Supporting Information). Mice infected with S. aureus showed extremely enhanced serum levels of metabolitesrelated to fatty acid oxidation such as acetone, 3-hydroxybutyrate,and 2-hydroxybutyrate as well as isobutyrate and creatine. In contrast,in S. pneumoniae infections these metabolites wereidentified as negative contributors, and TCA cycle intermediates (2-oxoglutarate,citrate, and fumarate) as well as glucose and pyruvate showed thehighest serum levels compared to all infections investigated. Hippurate,a carboxylic acid and endogenous conjugate which is not further metabolizedbut actively secreted by tubular cells and excreted in urine,36 became a metabolite of special interest as itsserum concentrations rose by almost 100% during the pneumococcal infection.It is produced by condensation of benzoic acid and glycine in themitochondria of liver and kidney,37,38 and its synthesisis stimulated by metabolic acidosis. It belongs to the group of uremictoxins, and its enhancement in urine is often used as an indicatorof intrahepatic tracer dilution to determine the activity of specifichuman enzymes.39,40 Therefore, unlike the cytokinesand chemokines which simply indicated indiscriminate inflammation,metabolites highlighted striking differences.

Bottom Line: 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.Multivariate statistical analysis revealed correlations between metabolic, cytokine and physiological responses.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.

View Article: PubMed Central - PubMed

Affiliation: Biochemistry Research Group, Department of Biological Sciences, ‡Department of Physiology and Biophysics, Snyder Institute, University of Calgary , Calgary, Alberta T2N 1N4, Canada.

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.

Show MeSH
Related in: MedlinePlus