Limits...
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.

Show MeSH

Related in: MedlinePlus

Physiological response and the metabolites and cytokinesactivatedby the TLR2 receptor and associated signaling pathway. (A) + (B) PLS-DAmodeling of metabolite concentrations identified in serum of C57BL/6wild-type (two components, R2 = 0.77, Q2 = 0.65) and in (C) + (D)TLR2 deficient mice (two components, R2 = 99, Q2 = 97) 24 h afterMALP2 treatment and S. aureus infection. (A) and(C) represent scores plots from the PLS-DA analysis and (B) and (D)the corresponding loadings plots. Metabolites which were significantlyenhanced in response to both MALP2 treatment and S. aureus infection are highlighted in blue, those specific for MALP2 or S. aureus are highlighted in green and red, respectively.In addition to metabolic profiles, the subsequent panels show (E)leukocyte counts in blood and peritoneal lavage, MPO activity, weightloss as well as (F) cytokine and chemokine concentrations in the serum.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3368387&req=5

fig7: Physiological response and the metabolites and cytokinesactivatedby the TLR2 receptor and associated signaling pathway. (A) + (B) PLS-DAmodeling of metabolite concentrations identified in serum of C57BL/6wild-type (two components, R2 = 0.77, Q2 = 0.65) and in (C) + (D)TLR2 deficient mice (two components, R2 = 99, Q2 = 97) 24 h afterMALP2 treatment and S. aureus infection. (A) and(C) represent scores plots from the PLS-DA analysis and (B) and (D)the corresponding loadings plots. Metabolites which were significantlyenhanced in response to both MALP2 treatment and S. aureus infection are highlighted in blue, those specific for MALP2 or S. aureus are highlighted in green and red, respectively.In addition to metabolic profiles, the subsequent panels show (E)leukocyte counts in blood and peritoneal lavage, MPO activity, weightloss as well as (F) cytokine and chemokine concentrations in the serum.

Mentions: The putative signaling receptors for LPS and MALP2 are TLR4 andTLR2, respectively. To identify specific metabolites which increasein response to the activation of the TLR4 and TLR2 receptors, we studiedknockout mice. We first compared serum metabolite profiles of noninfected, E. coli infected and LPS treated animals both in C57BL/6wild-type (two components, R2 = 0.76, Q2 = 0.60) and TLR4-deficientmice (two components, R2 = 0.91, Q2 = 0.70) using PLS-DA models (Figure 6). The corresponding loading plots in Figure 6 B and D give an overview of the metabolites causingthe observed clustering. As shown in the previous section, in thewild-type mouse both E. coli infected and LPS treatedanimals showed similar changes in their metabolite profiles comparedto controls, although they also differed from each other along thesecond principal component. In order to distinguish between metaboliteswhich increased in response to an E. coli infectionor to LPS treatment and those which were elevated in both groups,we compared each group with control animals separately via OPLS-DA.Ethanol, acetone, alanine, valine, and threonine (highlighted in red)showed substantially higher serum levels in E. coli infected mice than in controls, whereas the serum concentrationsof creatine, hippurate, histidine, 2-hydroxybutyrate, taurine, andtryptophan (highlighted in green) were significantly elevated in theLPS group. Both disease initiators gave rise to a strong immune responseand severe disease symptoms (Figure 6 E andF) and resulted in similar serum levels of lysine, leucine, isoleucine,ornithine, and phenylalanine in wild-type mice (highlighted in blue).In contrast in TLR4-deficient mice, cytokine responses and diseaseparameters were completely attenuated in the LPS group and stronglysuppressed in infected mice identifying the TLR4 signaling pathwayas major host response for E. coli infections. Inthe LPS group, normal serum concentrations were obtained similar tocontrols. Indeed the PLS-DA scores plot of the serum samples obtainedfrom TLR4 deficient mice revealed only two clusters without any distinctionbetween the control and LPS group (Figure 6C). Encouragingly, wild-type and TLR4 deficient mice respond in asimilar manner to E. coli as evidenced by the factthat several of the induced metabolites are similar. Correspondingexperiments were performed for S. aureus infectedmice. The metabolite profiles in wild-type (Figure 7 A and B) and TLR2-deficient (Figure 7 C and D) mice were compared with those of controls as well as withMALP2 treated mice. In the loadings plot, we identified metabolitesin serum of wild-type mice which were specifically elevated by the S. aureus infection (glycerol, creatine, acetone; highlightedred), compounds which were strongly elevated in response to MALP2treatment (taurine, lactate, valine, isoleucine; highlighted green),and those found in increased concentration in both groups (lysine,isobutyrate; highlighted blue).


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)

Physiological response and the metabolites and cytokinesactivatedby the TLR2 receptor and associated signaling pathway. (A) + (B) PLS-DAmodeling of metabolite concentrations identified in serum of C57BL/6wild-type (two components, R2 = 0.77, Q2 = 0.65) and in (C) + (D)TLR2 deficient mice (two components, R2 = 99, Q2 = 97) 24 h afterMALP2 treatment and S. aureus infection. (A) and(C) represent scores plots from the PLS-DA analysis and (B) and (D)the corresponding loadings plots. Metabolites which were significantlyenhanced in response to both MALP2 treatment and S. aureus infection are highlighted in blue, those specific for MALP2 or S. aureus are highlighted in green and red, respectively.In addition to metabolic profiles, the subsequent panels show (E)leukocyte counts in blood and peritoneal lavage, MPO activity, weightloss as well as (F) cytokine and chemokine concentrations in the serum.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig7: Physiological response and the metabolites and cytokinesactivatedby the TLR2 receptor and associated signaling pathway. (A) + (B) PLS-DAmodeling of metabolite concentrations identified in serum of C57BL/6wild-type (two components, R2 = 0.77, Q2 = 0.65) and in (C) + (D)TLR2 deficient mice (two components, R2 = 99, Q2 = 97) 24 h afterMALP2 treatment and S. aureus infection. (A) and(C) represent scores plots from the PLS-DA analysis and (B) and (D)the corresponding loadings plots. Metabolites which were significantlyenhanced in response to both MALP2 treatment and S. aureus infection are highlighted in blue, those specific for MALP2 or S. aureus are highlighted in green and red, respectively.In addition to metabolic profiles, the subsequent panels show (E)leukocyte counts in blood and peritoneal lavage, MPO activity, weightloss as well as (F) cytokine and chemokine concentrations in the serum.
Mentions: The putative signaling receptors for LPS and MALP2 are TLR4 andTLR2, respectively. To identify specific metabolites which increasein response to the activation of the TLR4 and TLR2 receptors, we studiedknockout mice. We first compared serum metabolite profiles of noninfected, E. coli infected and LPS treated animals both in C57BL/6wild-type (two components, R2 = 0.76, Q2 = 0.60) and TLR4-deficientmice (two components, R2 = 0.91, Q2 = 0.70) using PLS-DA models (Figure 6). The corresponding loading plots in Figure 6 B and D give an overview of the metabolites causingthe observed clustering. As shown in the previous section, in thewild-type mouse both E. coli infected and LPS treatedanimals showed similar changes in their metabolite profiles comparedto controls, although they also differed from each other along thesecond principal component. In order to distinguish between metaboliteswhich increased in response to an E. coli infectionor to LPS treatment and those which were elevated in both groups,we compared each group with control animals separately via OPLS-DA.Ethanol, acetone, alanine, valine, and threonine (highlighted in red)showed substantially higher serum levels in E. coli infected mice than in controls, whereas the serum concentrationsof creatine, hippurate, histidine, 2-hydroxybutyrate, taurine, andtryptophan (highlighted in green) were significantly elevated in theLPS group. Both disease initiators gave rise to a strong immune responseand severe disease symptoms (Figure 6 E andF) and resulted in similar serum levels of lysine, leucine, isoleucine,ornithine, and phenylalanine in wild-type mice (highlighted in blue).In contrast in TLR4-deficient mice, cytokine responses and diseaseparameters were completely attenuated in the LPS group and stronglysuppressed in infected mice identifying the TLR4 signaling pathwayas major host response for E. coli infections. Inthe LPS group, normal serum concentrations were obtained similar tocontrols. Indeed the PLS-DA scores plot of the serum samples obtainedfrom TLR4 deficient mice revealed only two clusters without any distinctionbetween the control and LPS group (Figure 6C). Encouragingly, wild-type and TLR4 deficient mice respond in asimilar manner to E. coli as evidenced by the factthat several of the induced metabolites are similar. Correspondingexperiments were performed for S. aureus infectedmice. The metabolite profiles in wild-type (Figure 7 A and B) and TLR2-deficient (Figure 7 C and D) mice were compared with those of controls as well as withMALP2 treated mice. In the loadings plot, we identified metabolitesin serum of wild-type mice which were specifically elevated by the S. aureus infection (glycerol, creatine, acetone; highlightedred), compounds which were strongly elevated in response to MALP2treatment (taurine, lactate, valine, isoleucine; highlighted green),and those found in increased concentration in both groups (lysine,isobutyrate; highlighted blue).

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