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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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Similaritiesbetween TLR2 and TLR4 agonists and Gram-positive andGram-negative bacterial infections. PLS-DA scores plot of serum 1H NMR data of (A) P. aeruginosa (three components,R2 = 0.92, Q2 = 0.81) or (C) E. coli (two components,R2 = 0.77, Q2 = 0.65) infected mice vs LPS treated animals and controlsand (B) S. pneumoniae (two components, R2 = 0.92,Q2 = 0.81) or (D) S. aureus (two components, R2 =0.76, Q2 = 0.60) infected mice vs MALP2 treatment and controls.
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fig5: Similaritiesbetween TLR2 and TLR4 agonists and Gram-positive andGram-negative bacterial infections. PLS-DA scores plot of serum 1H NMR data of (A) P. aeruginosa (three components,R2 = 0.92, Q2 = 0.81) or (C) E. coli (two components,R2 = 0.77, Q2 = 0.65) infected mice vs LPS treated animals and controlsand (B) S. pneumoniae (two components, R2 = 0.92,Q2 = 0.81) or (D) S. aureus (two components, R2 =0.76, Q2 = 0.60) infected mice vs MALP2 treatment and controls.

Mentions: In order to distinguish between bacteria and host responseswe injected mice with LPS or MALP2 to activate different TLRs. Theadvantage of using LPS over intact bacteria is that it should elicita host response for a Gram-negative organism but does not contributebacterial metabolites. Likewise, MALP2 emulates a host response forGram-positive bacteria. Separate PLS-DA models were constructed comparinginfected, noninfected and either LPS or MALP2 treated mice. The correspondingscores plots showed well separated clusters of differently treatedmice but also demonstrated similar trends for groups exposed to similarcell constituents (Figure 5). Gram-negativeinfected mice and animals having received LPS clustered together andwere clearly separated from controls along the first component. Howeverthe E. coli and P. aeruginosa inducedinfections gave rise to an additional shift in the second principalcomponent, compared to LPS treatment. In multivariate statisticalmodeling, the first component explains the largest source of variancein the data set, while subsequent components are orthogonal to eachother and explain lower levels of data variance. Clearly, the TLR-mediatedresponse provides the major source of variance in the case of Gram-negativebacteria. Very similar results were obtained for the comparison ofthe S. pneumoniae infected mice and those treatedwith the synthetic TLR2 agonist MALP2. In contrast, the S.aureus infected and MALP2 treated groups displayed significantdifferences along the first component. Compared to controls, S. aureus infected mice showed clear discrimination bothalong the first and second component while the MALP2 treated animalsdiffered from the controls only along the second component. Whileactivation by LPS is obviously the most important virulence factorfor E. coli and P. aeruginosa infections,for S. aureus it appears that most of the metabolicchanges are not related to a MALP2-induced host response.


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)

Similaritiesbetween TLR2 and TLR4 agonists and Gram-positive andGram-negative bacterial infections. PLS-DA scores plot of serum 1H NMR data of (A) P. aeruginosa (three components,R2 = 0.92, Q2 = 0.81) or (C) E. coli (two components,R2 = 0.77, Q2 = 0.65) infected mice vs LPS treated animals and controlsand (B) S. pneumoniae (two components, R2 = 0.92,Q2 = 0.81) or (D) S. aureus (two components, R2 =0.76, Q2 = 0.60) infected mice vs MALP2 treatment and controls.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3368387&req=5

fig5: Similaritiesbetween TLR2 and TLR4 agonists and Gram-positive andGram-negative bacterial infections. PLS-DA scores plot of serum 1H NMR data of (A) P. aeruginosa (three components,R2 = 0.92, Q2 = 0.81) or (C) E. coli (two components,R2 = 0.77, Q2 = 0.65) infected mice vs LPS treated animals and controlsand (B) S. pneumoniae (two components, R2 = 0.92,Q2 = 0.81) or (D) S. aureus (two components, R2 =0.76, Q2 = 0.60) infected mice vs MALP2 treatment and controls.
Mentions: In order to distinguish between bacteria and host responseswe injected mice with LPS or MALP2 to activate different TLRs. Theadvantage of using LPS over intact bacteria is that it should elicita host response for a Gram-negative organism but does not contributebacterial metabolites. Likewise, MALP2 emulates a host response forGram-positive bacteria. Separate PLS-DA models were constructed comparinginfected, noninfected and either LPS or MALP2 treated mice. The correspondingscores plots showed well separated clusters of differently treatedmice but also demonstrated similar trends for groups exposed to similarcell constituents (Figure 5). Gram-negativeinfected mice and animals having received LPS clustered together andwere clearly separated from controls along the first component. Howeverthe E. coli and P. aeruginosa inducedinfections gave rise to an additional shift in the second principalcomponent, compared to LPS treatment. In multivariate statisticalmodeling, the first component explains the largest source of variancein the data set, while subsequent components are orthogonal to eachother and explain lower levels of data variance. Clearly, the TLR-mediatedresponse provides the major source of variance in the case of Gram-negativebacteria. Very similar results were obtained for the comparison ofthe S. pneumoniae infected mice and those treatedwith the synthetic TLR2 agonist MALP2. In contrast, the S.aureus infected and MALP2 treated groups displayed significantdifferences along the first component. Compared to controls, S. aureus infected mice showed clear discrimination bothalong the first and second component while the MALP2 treated animalsdiffered from the controls only along the second component. Whileactivation by LPS is obviously the most important virulence factorfor E. coli and P. aeruginosa infections,for S. aureus it appears that most of the metabolicchanges are not related to a MALP2-induced host response.

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